PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

b8db2c52c5c18cdfa22b37a6b538f27da2efa22c

127,159

py

Python

mrcnn/model.py

ashvegeta/Mask-R-CNN

46921555869675e2518cff44d3861ad98f34cb37

[ "MIT" ]

null

mrcnn/model.py

ashvegeta/Mask-R-CNN

46921555869675e2518cff44d3861ad98f34cb37

[ "MIT" ]

null

mrcnn/model.py

ashvegeta/Mask-R-CNN

46921555869675e2518cff44d3861ad98f34cb37

[ "MIT" ]

null

""" Mask R-CNN The main Mask R-CNN model implementation. Copyright (c) 2017 Matterport, Inc. Licensed under the MIT License (see LICENSE for details) Written by Waleed Abdulla """ import os os.environ['CUDA_VISIBLE_DEVICES'] = '-1' import datetime import re import math from collections import OrderedDict import multiprocessing import numpy as np import tensorflow as tf import tensorflow.keras as keras import tensorflow.keras.backend as K import tensorflow.keras.layers as KL import tensorflow.keras.layers as KE import tensorflow.keras.utils as KU from tensorflow.python.eager import context import tensorflow.keras.models as KM from mrcnn import utils # Requires TensorFlow 2.0+ from distutils.version import LooseVersion assert LooseVersion(tf.__version__) >= LooseVersion("2.0") tf.compat.v1.disable_eager_execution() ############################################################ # Utility Functions ############################################################ def log(text, array=None): """Prints a text message. And, optionally, if a Numpy array is provided it prints it's shape, min, and max values. """ if array is not None: text = text.ljust(25) text += ("shape: {:20} ".format(str(array.shape))) if array.size: text += ("min: {:10.5f} max: {:10.5f}".format(array.min(),array.max())) else: text += ("min: {:10} max: {:10}".format("","")) text += " {}".format(array.dtype) print(text) class BatchNorm(KL.BatchNormalization): """Extends the Keras BatchNormalization class to allow a central place to make changes if needed. Batch normalization has a negative effect on training if batches are small so this layer is often frozen (via setting in Config class) and functions as linear layer. """ def call(self, inputs, training=None): """ Note about training values: None: Train BN layers. This is the normal mode False: Freeze BN layers. Good when batch size is small True: (don't use). Set layer in training mode even when making inferences """ return super(self.__class__, self).call(inputs, training=training) def compute_backbone_shapes(config, image_shape): """Computes the width and height of each stage of the backbone network. Returns: [N, (height, width)]. Where N is the number of stages """ if callable(config.BACKBONE): return config.COMPUTE_BACKBONE_SHAPE(image_shape) # Currently supports ResNet only assert config.BACKBONE in ["resnet50", "resnet101"] return np.array( [[int(math.ceil(image_shape[0] / stride)), int(math.ceil(image_shape[1] / stride))] for stride in config.BACKBONE_STRIDES]) ############################################################ # Resnet Graph ############################################################ # Code adopted from: # https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py def identity_block(input_tensor, kernel_size, filters, stage, block, use_bias=True, train_bn=True): """The identity_block is the block that has no conv layer at shortcut # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names use_bias: Boolean. To use or not use a bias in conv layers. train_bn: Boolean. Train or freeze Batch Norm layers """ nb_filter1, nb_filter2, nb_filter3 = filters conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a', use_bias=use_bias)(input_tensor) x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', use_bias=use_bias)(x) x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=use_bias)(x) x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn) x = KL.Add()([x, input_tensor]) x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x) return x def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), use_bias=True, train_bn=True): """conv_block is the block that has a conv layer at shortcut # Arguments input_tensor: input tensor kernel_size: default 3, the kernel size of middle conv layer at main path filters: list of integers, the nb_filters of 3 conv layer at main path stage: integer, current stage label, used for generating layer names block: 'a','b'..., current block label, used for generating layer names use_bias: Boolean. To use or not use a bias in conv layers. train_bn: Boolean. Train or freeze Batch Norm layers Note that from stage 3, the first conv layer at main path is with subsample=(2,2) And the shortcut should have subsample=(2,2) as well """ nb_filter1, nb_filter2, nb_filter3 = filters conv_name_base = 'res' + str(stage) + block + '_branch' bn_name_base = 'bn' + str(stage) + block + '_branch' x = KL.Conv2D(nb_filter1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=use_bias)(input_tensor) x = BatchNorm(name=bn_name_base + '2a')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', name=conv_name_base + '2b', use_bias=use_bias)(x) x = BatchNorm(name=bn_name_base + '2b')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c', use_bias=use_bias)(x) x = BatchNorm(name=bn_name_base + '2c')(x, training=train_bn) shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides, name=conv_name_base + '1', use_bias=use_bias)(input_tensor) shortcut = BatchNorm(name=bn_name_base + '1')(shortcut, training=train_bn) x = KL.Add()([x, shortcut]) x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x) return x def resnet_graph(input_image, architecture, stage5=False, train_bn=True): """Build a ResNet graph. architecture: Can be resnet50 or resnet101 stage5: Boolean. If False, stage5 of the network is not created train_bn: Boolean. Train or freeze Batch Norm layers """ assert architecture in ["resnet50", "resnet101"] # Stage 1 x = KL.ZeroPadding2D((3, 3))(input_image) x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x) x = BatchNorm(name='bn_conv1')(x, training=train_bn) x = KL.Activation('relu')(x) C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x) # Stage 2 x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1), train_bn=train_bn) x = identity_block(x, 3, [64, 64, 256], stage=2, block='b', train_bn=train_bn) C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c', train_bn=train_bn) # Stage 3 x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='b', train_bn=train_bn) x = identity_block(x, 3, [128, 128, 512], stage=3, block='c', train_bn=train_bn) C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d', train_bn=train_bn) # Stage 4 x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', train_bn=train_bn) block_count = {"resnet50": 5, "resnet101": 22}[architecture] for i in range(block_count): x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i), train_bn=train_bn) C4 = x # Stage 5 if stage5: x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', train_bn=train_bn) x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', train_bn=train_bn) C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', train_bn=train_bn) else: C5 = None return [C1, C2, C3, C4, C5] ############################################################ # Proposal Layer ############################################################ def apply_box_deltas_graph(boxes, deltas): """Applies the given deltas to the given boxes. boxes: [N, (y1, x1, y2, x2)] boxes to update deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply """ # Convert to y, x, h, w height = boxes[:, 2] - boxes[:, 0] width = boxes[:, 3] - boxes[:, 1] center_y = boxes[:, 0] + 0.5 * height center_x = boxes[:, 1] + 0.5 * width # Apply deltas center_y += deltas[:, 0] * height center_x += deltas[:, 1] * width height *= tf.exp(deltas[:, 2]) width *= tf.exp(deltas[:, 3]) # Convert back to y1, x1, y2, x2 y1 = center_y - 0.5 * height x1 = center_x - 0.5 * width y2 = y1 + height x2 = x1 + width result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out") return result def clip_boxes_graph(boxes, window): """ boxes: [N, (y1, x1, y2, x2)] window: [4] in the form y1, x1, y2, x2 """ # Split wy1, wx1, wy2, wx2 = tf.split(window, 4) y1, x1, y2, x2 = tf.split(boxes, 4, axis=1) # Clip y1 = tf.maximum(tf.minimum(y1, wy2), wy1) x1 = tf.maximum(tf.minimum(x1, wx2), wx1) y2 = tf.maximum(tf.minimum(y2, wy2), wy1) x2 = tf.maximum(tf.minimum(x2, wx2), wx1) clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes") clipped.set_shape((clipped.shape[0], 4)) return clipped class ProposalLayer(KE.Layer): """Receives anchor scores and selects a subset to pass as proposals to the second stage. Filtering is done based on anchor scores and non-max suppression to remove overlaps. It also applies bounding box refinement deltas to anchors. Inputs: rpn_probs: [batch, num_anchors, (bg prob, fg prob)] rpn_bbox: [batch, num_anchors, (dy, dx, log(dh), log(dw))] anchors: [batch, num_anchors, (y1, x1, y2, x2)] anchors in normalized coordinates Returns: Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)] """ def __init__(self, proposal_count, nms_threshold, config=None, **kwargs): super(ProposalLayer, self).__init__(**kwargs) self.config = config self.proposal_count = proposal_count self.nms_threshold = nms_threshold def get_config(self): config = super(ProposalLayer, self).get_config() config["config"] = self.config.to_dict() config["proposal_count"] = self.proposal_count config["nms_threshold"] = self.nms_threshold return config def call(self, inputs): # Box Scores. Use the foreground class confidence. [Batch, num_rois, 1] scores = inputs[0][:, :, 1] # Box deltas [batch, num_rois, 4] deltas = inputs[1] deltas = deltas * np.reshape(self.config.RPN_BBOX_STD_DEV, [1, 1, 4]) # Anchors anchors = inputs[2] # Improve performance by trimming to top anchors by score # and doing the rest on the smaller subset. pre_nms_limit = tf.minimum(self.config.PRE_NMS_LIMIT, tf.shape(input=anchors)[1]) ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True, name="top_anchors").indices scores = utils.batch_slice([scores, ix], lambda x, y: tf.gather(x, y), self.config.IMAGES_PER_GPU) deltas = utils.batch_slice([deltas, ix], lambda x, y: tf.gather(x, y), self.config.IMAGES_PER_GPU) pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x), self.config.IMAGES_PER_GPU, names=["pre_nms_anchors"]) # Apply deltas to anchors to get refined anchors. # [batch, N, (y1, x1, y2, x2)] boxes = utils.batch_slice([pre_nms_anchors, deltas], lambda x, y: apply_box_deltas_graph(x, y), self.config.IMAGES_PER_GPU, names=["refined_anchors"]) # Clip to image boundaries. Since we're in normalized coordinates, # clip to 0..1 range. [batch, N, (y1, x1, y2, x2)] window = np.array([0, 0, 1, 1], dtype=np.float32) boxes = utils.batch_slice(boxes, lambda x: clip_boxes_graph(x, window), self.config.IMAGES_PER_GPU, names=["refined_anchors_clipped"]) # Filter out small boxes # According to Xinlei Chen's paper, this reduces detection accuracy # for small objects, so we're skipping it. # Non-max suppression def nms(boxes, scores): indices = tf.image.non_max_suppression( boxes, scores, self.proposal_count, self.nms_threshold, name="rpn_non_max_suppression") proposals = tf.gather(boxes, indices) # Pad if needed padding = tf.maximum(self.proposal_count - tf.shape(input=proposals)[0], 0) proposals = tf.pad(tensor=proposals, paddings=[(0, padding), (0, 0)]) return proposals proposals = utils.batch_slice([boxes, scores], nms, self.config.IMAGES_PER_GPU) if not context.executing_eagerly(): # Infer the static output shape: out_shape = self.compute_output_shape(None) proposals.set_shape(out_shape) return proposals def compute_output_shape(self, input_shape): return None, self.proposal_count, 4 ############################################################ # ROIAlign Layer ############################################################ def log2_graph(x): """Implementation of Log2. TF doesn't have a native implementation.""" return tf.math.log(x) / tf.math.log(2.0) class PyramidROIAlign(KE.Layer): """Implements ROI Pooling on multiple levels of the feature pyramid. Params: - pool_shape: [pool_height, pool_width] of the output pooled regions. Usually [7, 7] Inputs: - boxes: [batch, num_boxes, (y1, x1, y2, x2)] in normalized coordinates. Possibly padded with zeros if not enough boxes to fill the array. - image_meta: [batch, (meta data)] Image details. See compose_image_meta() - feature_maps: List of feature maps from different levels of the pyramid. Each is [batch, height, width, channels] Output: Pooled regions in the shape: [batch, num_boxes, pool_height, pool_width, channels]. The width and height are those specific in the pool_shape in the layer constructor. """ def __init__(self, pool_shape, **kwargs): super(PyramidROIAlign, self).__init__(**kwargs) self.pool_shape = tuple(pool_shape) def get_config(self): config = super(PyramidROIAlign, self).get_config() config['pool_shape'] = self.pool_shape return config def call(self, inputs): # Crop boxes [batch, num_boxes, (y1, x1, y2, x2)] in normalized coords boxes = inputs[0] # Image meta # Holds details about the image. See compose_image_meta() image_meta = inputs[1] # Feature Maps. List of feature maps from different level of the # feature pyramid. Each is [batch, height, width, channels] feature_maps = inputs[2:] # Assign each ROI to a level in the pyramid based on the ROI area. y1, x1, y2, x2 = tf.split(boxes, 4, axis=2) h = y2 - y1 w = x2 - x1 # Use shape of first image. Images in a batch must have the same size. image_shape = parse_image_meta_graph(image_meta)['image_shape'][0] # Equation 1 in the Feature Pyramid Networks paper. Account for # the fact that our coordinates are normalized here. # e.g. a 224x224 ROI (in pixels) maps to P4 image_area = tf.cast(image_shape[0] * image_shape[1], tf.float32) roi_level = log2_graph(tf.sqrt(h * w) / (224.0 / tf.sqrt(image_area))) roi_level = tf.minimum(5, tf.maximum( 2, 4 + tf.cast(tf.round(roi_level), tf.int32))) roi_level = tf.squeeze(roi_level, 2) # Loop through levels and apply ROI pooling to each. P2 to P5. pooled = [] box_to_level = [] for i, level in enumerate(range(2, 6)): ix = tf.compat.v1.where(tf.equal(roi_level, level)) level_boxes = tf.gather_nd(boxes, ix) # Box indices for crop_and_resize. box_indices = tf.cast(ix[:, 0], tf.int32) # Keep track of which box is mapped to which level box_to_level.append(ix) # Stop gradient propogation to ROI proposals level_boxes = tf.stop_gradient(level_boxes) box_indices = tf.stop_gradient(box_indices) # Crop and Resize # From Mask R-CNN paper: "We sample four regular locations, so # that we can evaluate either max or average pooling. In fact, # interpolating only a single value at each bin center (without # pooling) is nearly as effective." # # Here we use the simplified approach of a single value per bin, # which is how it's done in tf.crop_and_resize() # Result: [batch * num_boxes, pool_height, pool_width, channels] pooled.append(tf.image.crop_and_resize( feature_maps[i], level_boxes, box_indices, self.pool_shape, method="bilinear")) # Pack pooled features into one tensor pooled = tf.concat(pooled, axis=0) # Pack box_to_level mapping into one array and add another # column representing the order of pooled boxes box_to_level = tf.concat(box_to_level, axis=0) box_range = tf.expand_dims(tf.range(tf.shape(input=box_to_level)[0]), 1) box_to_level = tf.concat([tf.cast(box_to_level, tf.int32), box_range], axis=1) # Rearrange pooled features to match the order of the original boxes # Sort box_to_level by batch then box index # TF doesn't have a way to sort by two columns, so merge them and sort. sorting_tensor = box_to_level[:, 0] * 100000 + box_to_level[:, 1] ix = tf.nn.top_k(sorting_tensor, k=tf.shape( input=box_to_level)[0]).indices[::-1] ix = tf.gather(box_to_level[:, 2], ix) pooled = tf.gather(pooled, ix) # Re-add the batch dimension shape = tf.concat([tf.shape(input=boxes)[:2], tf.shape(input=pooled)[1:]], axis=0) pooled = tf.reshape(pooled, shape) return pooled def compute_output_shape(self, input_shape): return input_shape[0][:2] + self.pool_shape + (input_shape[2][-1], ) ############################################################ # Detection Target Layer ############################################################ def overlaps_graph(boxes1, boxes2): """Computes IoU overlaps between two sets of boxes. boxes1, boxes2: [N, (y1, x1, y2, x2)]. """ # 1. Tile boxes2 and repeat boxes1. This allows us to compare # every boxes1 against every boxes2 without loops. # TF doesn't have an equivalent to np.repeat() so simulate it # using tf.tile() and tf.reshape. b1 = tf.reshape(tf.tile(tf.expand_dims(boxes1, 1), [1, 1, tf.shape(input=boxes2)[0]]), [-1, 4]) b2 = tf.tile(boxes2, [tf.shape(input=boxes1)[0], 1]) # 2. Compute intersections b1_y1, b1_x1, b1_y2, b1_x2 = tf.split(b1, 4, axis=1) b2_y1, b2_x1, b2_y2, b2_x2 = tf.split(b2, 4, axis=1) y1 = tf.maximum(b1_y1, b2_y1) x1 = tf.maximum(b1_x1, b2_x1) y2 = tf.minimum(b1_y2, b2_y2) x2 = tf.minimum(b1_x2, b2_x2) intersection = tf.maximum(x2 - x1, 0) * tf.maximum(y2 - y1, 0) # 3. Compute unions b1_area = (b1_y2 - b1_y1) * (b1_x2 - b1_x1) b2_area = (b2_y2 - b2_y1) * (b2_x2 - b2_x1) union = b1_area + b2_area - intersection # 4. Compute IoU and reshape to [boxes1, boxes2] iou = intersection / union overlaps = tf.reshape(iou, [tf.shape(input=boxes1)[0], tf.shape(input=boxes2)[0]]) return overlaps def detection_targets_graph(proposals, gt_class_ids, gt_boxes, gt_masks, config): """Generates detection targets for one image. Subsamples proposals and generates target class IDs, bounding box deltas, and masks for each. Inputs: proposals: [POST_NMS_ROIS_TRAINING, (y1, x1, y2, x2)] in normalized coordinates. Might be zero padded if there are not enough proposals. gt_class_ids: [MAX_GT_INSTANCES] int class IDs gt_boxes: [MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized coordinates. gt_masks: [height, width, MAX_GT_INSTANCES] of boolean type. Returns: Target ROIs and corresponding class IDs, bounding box shifts, and masks. rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized coordinates class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. Zero padded. deltas: [TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw))] masks: [TRAIN_ROIS_PER_IMAGE, height, width]. Masks cropped to bbox boundaries and resized to neural network output size. Note: Returned arrays might be zero padded if not enough target ROIs. """ # Assertions asserts = [ tf.Assert(tf.greater(tf.shape(input=proposals)[0], 0), [proposals], name="roi_assertion"), ] with tf.control_dependencies(asserts): proposals = tf.identity(proposals) # Remove zero padding proposals, _ = trim_zeros_graph(proposals, name="trim_proposals") gt_boxes, non_zeros = trim_zeros_graph(gt_boxes, name="trim_gt_boxes") gt_class_ids = tf.boolean_mask(tensor=gt_class_ids, mask=non_zeros, name="trim_gt_class_ids") gt_masks = tf.gather(gt_masks, tf.compat.v1.where(non_zeros)[:, 0], axis=2, name="trim_gt_masks") # Handle COCO crowds # A crowd box in COCO is a bounding box around several instances. Exclude # them from training. A crowd box is given a negative class ID. crowd_ix = tf.compat.v1.where(gt_class_ids < 0)[:, 0] non_crowd_ix = tf.compat.v1.where(gt_class_ids > 0)[:, 0] crowd_boxes = tf.gather(gt_boxes, crowd_ix) gt_class_ids = tf.gather(gt_class_ids, non_crowd_ix) gt_boxes = tf.gather(gt_boxes, non_crowd_ix) gt_masks = tf.gather(gt_masks, non_crowd_ix, axis=2) # Compute overlaps matrix [proposals, gt_boxes] overlaps = overlaps_graph(proposals, gt_boxes) # Compute overlaps with crowd boxes [proposals, crowd_boxes] crowd_overlaps = overlaps_graph(proposals, crowd_boxes) crowd_iou_max = tf.reduce_max(input_tensor=crowd_overlaps, axis=1) no_crowd_bool = (crowd_iou_max < 0.001) # Determine positive and negative ROIs roi_iou_max = tf.reduce_max(input_tensor=overlaps, axis=1) # 1. Positive ROIs are those with >= 0.5 IoU with a GT box positive_roi_bool = (roi_iou_max >= 0.5) positive_indices = tf.compat.v1.where(positive_roi_bool)[:, 0] # 2. Negative ROIs are those with < 0.5 with every GT box. Skip crowds. negative_indices = tf.compat.v1.where(tf.logical_and(roi_iou_max < 0.5, no_crowd_bool))[:, 0] # Subsample ROIs. Aim for 33% positive # Positive ROIs positive_count = int(config.TRAIN_ROIS_PER_IMAGE * config.ROI_POSITIVE_RATIO) positive_indices = tf.random.shuffle(positive_indices)[:positive_count] positive_count = tf.shape(input=positive_indices)[0] # Negative ROIs. Add enough to maintain positive:negative ratio. r = 1.0 / config.ROI_POSITIVE_RATIO negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count negative_indices = tf.random.shuffle(negative_indices)[:negative_count] # Gather selected ROIs positive_rois = tf.gather(proposals, positive_indices) negative_rois = tf.gather(proposals, negative_indices) # Assign positive ROIs to GT boxes. positive_overlaps = tf.gather(overlaps, positive_indices) roi_gt_box_assignment = tf.cond( pred=tf.greater(tf.shape(input=positive_overlaps)[1], 0), true_fn=lambda: tf.argmax(input=positive_overlaps, axis=1), false_fn=lambda: tf.cast(tf.constant([]), tf.int64) ) roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment) roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment) # Compute bbox refinement for positive ROIs deltas = utils.box_refinement_graph(positive_rois, roi_gt_boxes) deltas /= config.BBOX_STD_DEV # Assign positive ROIs to GT masks # Permute masks to [N, height, width, 1] transposed_masks = tf.expand_dims(tf.transpose(a=gt_masks, perm=[2, 0, 1]), -1) # Pick the right mask for each ROI roi_masks = tf.gather(transposed_masks, roi_gt_box_assignment) # Compute mask targets boxes = positive_rois if config.USE_MINI_MASK: # Transform ROI coordinates from normalized image space # to normalized mini-mask space. y1, x1, y2, x2 = tf.split(positive_rois, 4, axis=1) gt_y1, gt_x1, gt_y2, gt_x2 = tf.split(roi_gt_boxes, 4, axis=1) gt_h = gt_y2 - gt_y1 gt_w = gt_x2 - gt_x1 y1 = (y1 - gt_y1) / gt_h x1 = (x1 - gt_x1) / gt_w y2 = (y2 - gt_y1) / gt_h x2 = (x2 - gt_x1) / gt_w boxes = tf.concat([y1, x1, y2, x2], 1) box_ids = tf.range(0, tf.shape(input=roi_masks)[0]) masks = tf.image.crop_and_resize(tf.cast(roi_masks, tf.float32), boxes, box_ids, config.MASK_SHAPE) # Remove the extra dimension from masks. masks = tf.squeeze(masks, axis=3) # Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with # binary cross entropy loss. masks = tf.round(masks) # Append negative ROIs and pad bbox deltas and masks that # are not used for negative ROIs with zeros. rois = tf.concat([positive_rois, negative_rois], axis=0) N = tf.shape(input=negative_rois)[0] P = tf.maximum(config.TRAIN_ROIS_PER_IMAGE - tf.shape(input=rois)[0], 0) rois = tf.pad(tensor=rois, paddings=[(0, P), (0, 0)]) roi_gt_boxes = tf.pad(tensor=roi_gt_boxes, paddings=[(0, N + P), (0, 0)]) roi_gt_class_ids = tf.pad(tensor=roi_gt_class_ids, paddings=[(0, N + P)]) deltas = tf.pad(tensor=deltas, paddings=[(0, N + P), (0, 0)]) masks = tf.pad(tensor=masks, paddings=[[0, N + P], (0, 0), (0, 0)]) return rois, roi_gt_class_ids, deltas, masks class DetectionTargetLayer(KE.Layer): """Subsamples proposals and generates target box refinement, class_ids, and masks for each. Inputs: proposals: [batch, N, (y1, x1, y2, x2)] in normalized coordinates. Might be zero padded if there are not enough proposals. gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs. gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in normalized coordinates. gt_masks: [batch, height, width, MAX_GT_INSTANCES] of boolean type Returns: Target ROIs and corresponding class IDs, bounding box shifts, and masks. rois: [batch, TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] in normalized coordinates target_class_ids: [batch, TRAIN_ROIS_PER_IMAGE]. Integer class IDs. target_deltas: [batch, TRAIN_ROIS_PER_IMAGE, (dy, dx, log(dh), log(dw)] target_mask: [batch, TRAIN_ROIS_PER_IMAGE, height, width] Masks cropped to bbox boundaries and resized to neural network output size. Note: Returned arrays might be zero padded if not enough target ROIs. """ def __init__(self, config, **kwargs): super(DetectionTargetLayer, self).__init__(**kwargs) self.config = config def get_config(self): config = super(DetectionTargetLayer, self).get_config() config["config"] = self.config.to_dict() return config def call(self, inputs): proposals = inputs[0] gt_class_ids = inputs[1] gt_boxes = inputs[2] gt_masks = inputs[3] # Slice the batch and run a graph for each slice # TODO: Rename target_bbox to target_deltas for clarity names = ["rois", "target_class_ids", "target_bbox", "target_mask"] outputs = utils.batch_slice( [proposals, gt_class_ids, gt_boxes, gt_masks], lambda w, x, y, z: detection_targets_graph( w, x, y, z, self.config), self.config.IMAGES_PER_GPU, names=names) return outputs def compute_output_shape(self, input_shape): return [ (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # rois (None, self.config.TRAIN_ROIS_PER_IMAGE), # class_ids (None, self.config.TRAIN_ROIS_PER_IMAGE, 4), # deltas (None, self.config.TRAIN_ROIS_PER_IMAGE, self.config.MASK_SHAPE[0], self.config.MASK_SHAPE[1]) # masks ] def compute_mask(self, inputs, mask=None): return [None, None, None, None] ############################################################ # Detection Layer ############################################################ def refine_detections_graph(rois, probs, deltas, window, config): """Refine classified proposals and filter overlaps and return final detections. Inputs: rois: [N, (y1, x1, y2, x2)] in normalized coordinates probs: [N, num_classes]. Class probabilities. deltas: [N, num_classes, (dy, dx, log(dh), log(dw))]. Class-specific bounding box deltas. window: (y1, x1, y2, x2) in normalized coordinates. The part of the image that contains the image excluding the padding. Returns detections shaped: [num_detections, (y1, x1, y2, x2, class_id, score)] where coordinates are normalized. """ # Class IDs per ROI class_ids = tf.argmax(input=probs, axis=1, output_type=tf.int32) # Class probability of the top class of each ROI indices = tf.stack([tf.range(probs.shape[0]), class_ids], axis=1) class_scores = tf.gather_nd(probs, indices) # Class-specific bounding box deltas deltas_specific = tf.gather_nd(deltas, indices) # Apply bounding box deltas # Shape: [boxes, (y1, x1, y2, x2)] in normalized coordinates refined_rois = apply_box_deltas_graph( rois, deltas_specific * config.BBOX_STD_DEV) # Clip boxes to image window refined_rois = clip_boxes_graph(refined_rois, window) # TODO: Filter out boxes with zero area # Filter out background boxes keep = tf.compat.v1.where(class_ids > 0)[:, 0] # Filter out low confidence boxes if config.DETECTION_MIN_CONFIDENCE: conf_keep = tf.compat.v1.where(class_scores >= config.DETECTION_MIN_CONFIDENCE)[:, 0] keep = tf.sets.intersection(tf.expand_dims(keep, 0), tf.expand_dims(conf_keep, 0)) keep = tf.sparse.to_dense(keep)[0] # Apply per-class NMS # 1. Prepare variables pre_nms_class_ids = tf.gather(class_ids, keep) pre_nms_scores = tf.gather(class_scores, keep) pre_nms_rois = tf.gather(refined_rois, keep) unique_pre_nms_class_ids = tf.unique(pre_nms_class_ids)[0] def nms_keep_map(class_id): """Apply Non-Maximum Suppression on ROIs of the given class.""" # Indices of ROIs of the given class ixs = tf.compat.v1.where(tf.equal(pre_nms_class_ids, class_id))[:, 0] # Apply NMS class_keep = tf.image.non_max_suppression( tf.gather(pre_nms_rois, ixs), tf.gather(pre_nms_scores, ixs), max_output_size=config.DETECTION_MAX_INSTANCES, iou_threshold=config.DETECTION_NMS_THRESHOLD) # Map indices class_keep = tf.gather(keep, tf.gather(ixs, class_keep)) # Pad with -1 so returned tensors have the same shape gap = config.DETECTION_MAX_INSTANCES - tf.shape(input=class_keep)[0] class_keep = tf.pad(tensor=class_keep, paddings=[(0, gap)], mode='CONSTANT', constant_values=-1) # Set shape so map_fn() can infer result shape class_keep.set_shape([config.DETECTION_MAX_INSTANCES]) return class_keep # 2. Map over class IDs nms_keep = tf.map_fn(nms_keep_map, unique_pre_nms_class_ids, dtype=tf.int64) # 3. Merge results into one list, and remove -1 padding nms_keep = tf.reshape(nms_keep, [-1]) nms_keep = tf.gather(nms_keep, tf.compat.v1.where(nms_keep > -1)[:, 0]) # 4. Compute intersection between keep and nms_keep keep = tf.sets.intersection(tf.expand_dims(keep, 0), tf.expand_dims(nms_keep, 0)) keep = tf.sparse.to_dense(keep)[0] # Keep top detections roi_count = config.DETECTION_MAX_INSTANCES class_scores_keep = tf.gather(class_scores, keep) num_keep = tf.minimum(tf.shape(input=class_scores_keep)[0], roi_count) top_ids = tf.nn.top_k(class_scores_keep, k=num_keep, sorted=True)[1] keep = tf.gather(keep, top_ids) # Arrange output as [N, (y1, x1, y2, x2, class_id, score)] # Coordinates are normalized. detections = tf.concat([ tf.gather(refined_rois, keep), tf.dtypes.cast(tf.gather(class_ids, keep), tf.float32)[..., tf.newaxis], tf.gather(class_scores, keep)[..., tf.newaxis] ], axis=1) # Pad with zeros if detections < DETECTION_MAX_INSTANCES gap = config.DETECTION_MAX_INSTANCES - tf.shape(input=detections)[0] detections = tf.pad(tensor=detections, paddings=[(0, gap), (0, 0)], mode="CONSTANT") return detections class DetectionLayer(KE.Layer): """Takes classified proposal boxes and their bounding box deltas and returns the final detection boxes. Returns: [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] where coordinates are normalized. """ def __init__(self, config=None, **kwargs): super(DetectionLayer, self).__init__(**kwargs) self.config = config def get_config(self): config = super(DetectionLayer, self).get_config() config["config"] = self.config.to_dict() return config def call(self, inputs): rois = inputs[0] mrcnn_class = inputs[1] mrcnn_bbox = inputs[2] image_meta = inputs[3] # Get windows of images in normalized coordinates. Windows are the area # in the image that excludes the padding. # Use the shape of the first image in the batch to normalize the window # because we know that all images get resized to the same size. m = parse_image_meta_graph(image_meta) image_shape = m['image_shape'][0] window = norm_boxes_graph(m['window'], image_shape[:2]) # Run detection refinement graph on each item in the batch detections_batch = utils.batch_slice( [rois, mrcnn_class, mrcnn_bbox, window], lambda x, y, w, z: refine_detections_graph(x, y, w, z, self.config), self.config.IMAGES_PER_GPU) # Reshape output # [batch, num_detections, (y1, x1, y2, x2, class_id, class_score)] in # normalized coordinates return tf.reshape( detections_batch, [self.config.BATCH_SIZE, self.config.DETECTION_MAX_INSTANCES, 6]) def compute_output_shape(self, input_shape): return (None, self.config.DETECTION_MAX_INSTANCES, 6) ############################################################ # Region Proposal Network (RPN) ############################################################ def rpn_graph(feature_map, anchors_per_location, anchor_stride): """Builds the computation graph of Region Proposal Network. feature_map: backbone features [batch, height, width, depth] anchors_per_location: number of anchors per pixel in the feature map anchor_stride: Controls the density of anchors. Typically 1 (anchors for every pixel in the feature map), or 2 (every other pixel). Returns: rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax) rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities. rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be applied to anchors. """ # TODO: check if stride of 2 causes alignment issues if the feature map # is not even. # Shared convolutional base of the RPN shared = KL.Conv2D(512, (3, 3), padding='same', activation='relu', strides=anchor_stride, name='rpn_conv_shared')(feature_map) # Anchor Score. [batch, height, width, anchors per location * 2]. x = KL.Conv2D(2 * anchors_per_location, (1, 1), padding='valid', activation='linear', name='rpn_class_raw')(shared) # Reshape to [batch, anchors, 2] rpn_class_logits = KL.Lambda( lambda t: tf.reshape(t, [tf.shape(input=t)[0], -1, 2]))(x) # Softmax on last dimension of BG/FG. rpn_probs = KL.Activation( "softmax", name="rpn_class_xxx")(rpn_class_logits) # Bounding box refinement. [batch, H, W, anchors per location * depth] # where depth is [x, y, log(w), log(h)] x = KL.Conv2D(anchors_per_location * 4, (1, 1), padding="valid", activation='linear', name='rpn_bbox_pred')(shared) # Reshape to [batch, anchors, 4] rpn_bbox = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(input=t)[0], -1, 4]))(x) return [rpn_class_logits, rpn_probs, rpn_bbox] def build_rpn_model(anchor_stride, anchors_per_location, depth): """Builds a Keras model of the Region Proposal Network. It wraps the RPN graph so it can be used multiple times with shared weights. anchors_per_location: number of anchors per pixel in the feature map anchor_stride: Controls the density of anchors. Typically 1 (anchors for every pixel in the feature map), or 2 (every other pixel). depth: Depth of the backbone feature map. Returns a Keras Model object. The model outputs, when called, are: rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax) rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities. rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be applied to anchors. """ input_feature_map = KL.Input(shape=[None, None, depth], name="input_rpn_feature_map") outputs = rpn_graph(input_feature_map, anchors_per_location, anchor_stride) return KM.Model([input_feature_map], outputs, name="rpn_model") ############################################################ # Feature Pyramid Network Heads ############################################################ def fpn_classifier_graph(rois, feature_maps, image_meta, pool_size, num_classes, train_bn=True, fc_layers_size=1024): """Builds the computation graph of the feature pyramid network classifier and regressor heads. rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized coordinates. feature_maps: List of feature maps from different layers of the pyramid, [P2, P3, P4, P5]. Each has a different resolution. image_meta: [batch, (meta data)] Image details. See compose_image_meta() pool_size: The width of the square feature map generated from ROI Pooling. num_classes: number of classes, which determines the depth of the results train_bn: Boolean. Train or freeze Batch Norm layers fc_layers_size: Size of the 2 FC layers Returns: logits: [batch, num_rois, NUM_CLASSES] classifier logits (before softmax) probs: [batch, num_rois, NUM_CLASSES] classifier probabilities bbox_deltas: [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] Deltas to apply to proposal boxes """ # ROI Pooling # Shape: [batch, num_rois, POOL_SIZE, POOL_SIZE, channels] x = PyramidROIAlign([pool_size, pool_size], name="roi_align_classifier")([rois, image_meta] + feature_maps) # Two 1024 FC layers (implemented with Conv2D for consistency) x = KL.TimeDistributed(KL.Conv2D(fc_layers_size, (pool_size, pool_size), padding="valid"), name="mrcnn_class_conv1")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_class_bn1')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.TimeDistributed(KL.Conv2D(fc_layers_size, (1, 1)), name="mrcnn_class_conv2")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_class_bn2')(x, training=train_bn) x = KL.Activation('relu')(x) shared = KL.Lambda(lambda x: K.squeeze(K.squeeze(x, 3), 2), name="pool_squeeze")(x) # Classifier head mrcnn_class_logits = KL.TimeDistributed(KL.Dense(num_classes), name='mrcnn_class_logits')(shared) mrcnn_probs = KL.TimeDistributed(KL.Activation("softmax"), name="mrcnn_class")(mrcnn_class_logits) # BBox head # [batch, num_rois, NUM_CLASSES * (dy, dx, log(dh), log(dw))] x = KL.TimeDistributed(KL.Dense(num_classes * 4, activation='linear'), name='mrcnn_bbox_fc')(shared) # Reshape to [batch, num_rois, NUM_CLASSES, (dy, dx, log(dh), log(dw))] s = K.int_shape(x) if s[1] is None: mrcnn_bbox = KL.Reshape((-1, num_classes, 4), name="mrcnn_bbox")(x) else: mrcnn_bbox = KL.Reshape((s[1], num_classes, 4), name="mrcnn_bbox")(x) return mrcnn_class_logits, mrcnn_probs, mrcnn_bbox def build_fpn_mask_graph(rois, feature_maps, image_meta, pool_size, num_classes, train_bn=True): """Builds the computation graph of the mask head of Feature Pyramid Network. rois: [batch, num_rois, (y1, x1, y2, x2)] Proposal boxes in normalized coordinates. feature_maps: List of feature maps from different layers of the pyramid, [P2, P3, P4, P5]. Each has a different resolution. image_meta: [batch, (meta data)] Image details. See compose_image_meta() pool_size: The width of the square feature map generated from ROI Pooling. num_classes: number of classes, which determines the depth of the results train_bn: Boolean. Train or freeze Batch Norm layers Returns: Masks [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, NUM_CLASSES] """ # ROI Pooling # Shape: [batch, num_rois, MASK_POOL_SIZE, MASK_POOL_SIZE, channels] x = PyramidROIAlign([pool_size, pool_size], name="roi_align_mask")([rois, image_meta] + feature_maps) # Conv layers x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv1")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_mask_bn1')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv2")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_mask_bn2')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv3")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_mask_bn3')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"), name="mrcnn_mask_conv4")(x) x = KL.TimeDistributed(BatchNorm(), name='mrcnn_mask_bn4')(x, training=train_bn) x = KL.Activation('relu')(x) x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2), strides=2, activation="relu"), name="mrcnn_mask_deconv")(x) x = KL.TimeDistributed(KL.Conv2D(num_classes, (1, 1), strides=1, activation="sigmoid"), name="mrcnn_mask")(x) return x ############################################################ # Loss Functions ############################################################ def smooth_l1_loss(y_true, y_pred): """Implements Smooth-L1 loss. y_true and y_pred are typically: [N, 4], but could be any shape. """ diff = K.abs(y_true - y_pred) less_than_one = K.cast(K.less(diff, 1.0), "float32") loss = (less_than_one * 0.5 * diff**2) + (1 - less_than_one) * (diff - 0.5) return loss def rpn_class_loss_graph(rpn_match, rpn_class_logits): """RPN anchor classifier loss. rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive, -1=negative, 0=neutral anchor. rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for BG/FG. """ # Squeeze last dim to simplify rpn_match = tf.squeeze(rpn_match, -1) # Get anchor classes. Convert the -1/+1 match to 0/1 values. anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32) # Positive and Negative anchors contribute to the loss, # but neutral anchors (match value = 0) don't. indices = tf.compat.v1.where(K.not_equal(rpn_match, 0)) # Pick rows that contribute to the loss and filter out the rest. rpn_class_logits = tf.gather_nd(rpn_class_logits, indices) anchor_class = tf.gather_nd(anchor_class, indices) # Cross entropy loss loss = K.sparse_categorical_crossentropy(target=anchor_class, output=rpn_class_logits, from_logits=True) loss = K.switch(tf.size(input=loss) > 0, K.mean(loss), tf.constant(0.0)) return loss def rpn_bbox_loss_graph(config, target_bbox, rpn_match, rpn_bbox): """Return the RPN bounding box loss graph. config: the model config object. target_bbox: [batch, max positive anchors, (dy, dx, log(dh), log(dw))]. Uses 0 padding to fill in unsed bbox deltas. rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive, -1=negative, 0=neutral anchor. rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))] """ # Positive anchors contribute to the loss, but negative and # neutral anchors (match value of 0 or -1) don't. rpn_match = K.squeeze(rpn_match, -1) indices = tf.compat.v1.where(K.equal(rpn_match, 1)) # Pick bbox deltas that contribute to the loss rpn_bbox = tf.gather_nd(rpn_bbox, indices) # Trim target bounding box deltas to the same length as rpn_bbox. batch_counts = K.sum(K.cast(K.equal(rpn_match, 1), tf.int32), axis=1) target_bbox = batch_pack_graph(target_bbox, batch_counts, config.IMAGES_PER_GPU) loss = smooth_l1_loss(target_bbox, rpn_bbox) loss = K.switch(tf.size(input=loss) > 0, K.mean(loss), tf.constant(0.0)) return loss def mrcnn_class_loss_graph(target_class_ids, pred_class_logits, active_class_ids): """Loss for the classifier head of Mask RCNN. target_class_ids: [batch, num_rois]. Integer class IDs. Uses zero padding to fill in the array. pred_class_logits: [batch, num_rois, num_classes] active_class_ids: [batch, num_classes]. Has a value of 1 for classes that are in the dataset of the image, and 0 for classes that are not in the dataset. """ # During model building, Keras calls this function with # target_class_ids of type float32. Unclear why. Cast it # to int to get around it. target_class_ids = tf.cast(target_class_ids, 'int64') # Find predictions of classes that are not in the dataset. pred_class_ids = tf.argmax(input=pred_class_logits, axis=2) # TODO: Update this line to work with batch > 1. Right now it assumes all # images in a batch have the same active_class_ids pred_active = tf.gather(active_class_ids[0], pred_class_ids) # Loss loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=target_class_ids, logits=pred_class_logits) # Erase losses of predictions of classes that are not in the active # classes of the image. loss = loss * pred_active # Computer loss mean. Use only predictions that contribute # to the loss to get a correct mean. loss = tf.reduce_sum(input_tensor=loss) / tf.reduce_sum(input_tensor=pred_active) return loss def mrcnn_bbox_loss_graph(target_bbox, target_class_ids, pred_bbox): """Loss for Mask R-CNN bounding box refinement. target_bbox: [batch, num_rois, (dy, dx, log(dh), log(dw))] target_class_ids: [batch, num_rois]. Integer class IDs. pred_bbox: [batch, num_rois, num_classes, (dy, dx, log(dh), log(dw))] """ # Reshape to merge batch and roi dimensions for simplicity. target_class_ids = K.reshape(target_class_ids, (-1,)) target_bbox = K.reshape(target_bbox, (-1, 4)) pred_bbox = K.reshape(pred_bbox, (-1, K.int_shape(pred_bbox)[2], 4)) # Only positive ROIs contribute to the loss. And only # the right class_id of each ROI. Get their indices. positive_roi_ix = tf.compat.v1.where(target_class_ids > 0)[:, 0] positive_roi_class_ids = tf.cast( tf.gather(target_class_ids, positive_roi_ix), tf.int64) indices = tf.stack([positive_roi_ix, positive_roi_class_ids], axis=1) # Gather the deltas (predicted and true) that contribute to loss target_bbox = tf.gather(target_bbox, positive_roi_ix) pred_bbox = tf.gather_nd(pred_bbox, indices) # Smooth-L1 Loss loss = K.switch(tf.size(input=target_bbox) > 0, smooth_l1_loss(y_true=target_bbox, y_pred=pred_bbox), tf.constant(0.0)) loss = K.mean(loss) return loss def mrcnn_mask_loss_graph(target_masks, target_class_ids, pred_masks): """Mask binary cross-entropy loss for the masks head. target_masks: [batch, num_rois, height, width]. A float32 tensor of values 0 or 1. Uses zero padding to fill array. target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded. pred_masks: [batch, proposals, height, width, num_classes] float32 tensor with values from 0 to 1. """ # Reshape for simplicity. Merge first two dimensions into one. target_class_ids = K.reshape(target_class_ids, (-1,)) mask_shape = tf.shape(input=target_masks) target_masks = K.reshape(target_masks, (-1, mask_shape[2], mask_shape[3])) pred_shape = tf.shape(input=pred_masks) pred_masks = K.reshape(pred_masks, (-1, pred_shape[2], pred_shape[3], pred_shape[4])) # Permute predicted masks to [N, num_classes, height, width] pred_masks = tf.transpose(a=pred_masks, perm=[0, 3, 1, 2]) # Only positive ROIs contribute to the loss. And only # the class specific mask of each ROI. positive_ix = tf.compat.v1.where(target_class_ids > 0)[:, 0] positive_class_ids = tf.cast( tf.gather(target_class_ids, positive_ix), tf.int64) indices = tf.stack([positive_ix, positive_class_ids], axis=1) # Gather the masks (predicted and true) that contribute to loss y_true = tf.gather(target_masks, positive_ix) y_pred = tf.gather_nd(pred_masks, indices) # Compute binary cross entropy. If no positive ROIs, then return 0. # shape: [batch, roi, num_classes] loss = K.switch(tf.size(input=y_true) > 0, K.binary_crossentropy(target=y_true, output=y_pred), tf.constant(0.0)) loss = K.mean(loss) return loss ############################################################ # Data Generator ############################################################ def load_image_gt(dataset, config, image_id, augmentation=None): """Load and return ground truth data for an image (image, mask, bounding boxes). augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation. For example, passing imgaug.augmenters.Fliplr(0.5) flips images right/left 50% of the time. Returns: image: [height, width, 3] shape: the original shape of the image before resizing and cropping. class_ids: [instance_count] Integer class IDs bbox: [instance_count, (y1, x1, y2, x2)] mask: [height, width, instance_count]. The height and width are those of the image unless use_mini_mask is True, in which case they are defined in MINI_MASK_SHAPE. """ # Load image and mask image = dataset.load_image(image_id) mask, class_ids = dataset.load_mask(image_id) original_shape = image.shape image, window, scale, padding, crop = utils.resize_image( image, min_dim=config.IMAGE_MIN_DIM, min_scale=config.IMAGE_MIN_SCALE, max_dim=config.IMAGE_MAX_DIM, mode=config.IMAGE_RESIZE_MODE) mask = utils.resize_mask(mask, scale, padding, crop) # Augmentation # This requires the imgaug lib (https://github.com/aleju/imgaug) if augmentation: import imgaug # Augmenters that are safe to apply to masks # Some, such as Affine, have settings that make them unsafe, so always # test your augmentation on masks MASK_AUGMENTERS = ["Sequential", "SomeOf", "OneOf", "Sometimes", "Fliplr", "Flipud", "CropAndPad", "Affine", "PiecewiseAffine"] def hook(images, augmenter, parents, default): """Determines which augmenters to apply to masks.""" return augmenter.__class__.__name__ in MASK_AUGMENTERS # Store shapes before augmentation to compare image_shape = image.shape mask_shape = mask.shape # Make augmenters deterministic to apply similarly to images and masks det = augmentation.to_deterministic() image = det.augment_image(image) # Change mask to np.uint8 because imgaug doesn't support np.bool mask = det.augment_image(mask.astype(np.uint8), hooks=imgaug.HooksImages(activator=hook)) # Verify that shapes didn't change assert image.shape == image_shape, "Augmentation shouldn't change image size" assert mask.shape == mask_shape, "Augmentation shouldn't change mask size" # Change mask back to bool mask = mask.astype(np.bool) # Note that some boxes might be all zeros if the corresponding mask got cropped out. # and here is to filter them out _idx = np.sum(mask, axis=(0, 1)) > 0 mask = mask[:, :, _idx] class_ids = class_ids[_idx] # Bounding boxes. Note that some boxes might be all zeros # if the corresponding mask got cropped out. # bbox: [num_instances, (y1, x1, y2, x2)] bbox = utils.extract_bboxes(mask) # Active classes # Different datasets have different classes, so track the # classes supported in the dataset of this image. active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32) source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]["source"]] active_class_ids[source_class_ids] = 1 # Resize masks to smaller size to reduce memory usage if config.USE_MINI_MASK: mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE) # Image meta data image_meta = compose_image_meta(image_id, original_shape, image.shape, window, scale, active_class_ids) return image, image_meta, class_ids, bbox, mask def build_detection_targets(rpn_rois, gt_class_ids, gt_boxes, gt_masks, config): """Generate targets for training Stage 2 classifier and mask heads. This is not used in normal training. It's useful for debugging or to train the Mask RCNN heads without using the RPN head. Inputs: rpn_rois: [N, (y1, x1, y2, x2)] proposal boxes. gt_class_ids: [instance count] Integer class IDs gt_boxes: [instance count, (y1, x1, y2, x2)] gt_masks: [height, width, instance count] Ground truth masks. Can be full size or mini-masks. Returns: rois: [TRAIN_ROIS_PER_IMAGE, (y1, x1, y2, x2)] class_ids: [TRAIN_ROIS_PER_IMAGE]. Integer class IDs. bboxes: [TRAIN_ROIS_PER_IMAGE, NUM_CLASSES, (y, x, log(h), log(w))]. Class-specific bbox refinements. masks: [TRAIN_ROIS_PER_IMAGE, height, width, NUM_CLASSES). Class specific masks cropped to bbox boundaries and resized to neural network output size. """ assert rpn_rois.shape[0] > 0 assert gt_class_ids.dtype == np.int32, "Expected int but got {}".format( gt_class_ids.dtype) assert gt_boxes.dtype == np.int32, "Expected int but got {}".format( gt_boxes.dtype) assert gt_masks.dtype == np.bool_, "Expected bool but got {}".format( gt_masks.dtype) # It's common to add GT Boxes to ROIs but we don't do that here because # according to XinLei Chen's paper, it doesn't help. # Trim empty padding in gt_boxes and gt_masks parts instance_ids = np.where(gt_class_ids > 0)[0] assert instance_ids.shape[0] > 0, "Image must contain instances." gt_class_ids = gt_class_ids[instance_ids] gt_boxes = gt_boxes[instance_ids] gt_masks = gt_masks[:, :, instance_ids] # Compute areas of ROIs and ground truth boxes. rpn_roi_area = (rpn_rois[:, 2] - rpn_rois[:, 0]) * \ (rpn_rois[:, 3] - rpn_rois[:, 1]) gt_box_area = (gt_boxes[:, 2] - gt_boxes[:, 0]) * \ (gt_boxes[:, 3] - gt_boxes[:, 1]) # Compute overlaps [rpn_rois, gt_boxes] overlaps = np.zeros((rpn_rois.shape[0], gt_boxes.shape[0])) for i in range(overlaps.shape[1]): gt = gt_boxes[i] overlaps[:, i] = utils.compute_iou( gt, rpn_rois, gt_box_area[i], rpn_roi_area) # Assign ROIs to GT boxes rpn_roi_iou_argmax = np.argmax(overlaps, axis=1) rpn_roi_iou_max = overlaps[np.arange( overlaps.shape[0]), rpn_roi_iou_argmax] # GT box assigned to each ROI rpn_roi_gt_boxes = gt_boxes[rpn_roi_iou_argmax] rpn_roi_gt_class_ids = gt_class_ids[rpn_roi_iou_argmax] # Positive ROIs are those with >= 0.5 IoU with a GT box. fg_ids = np.where(rpn_roi_iou_max > 0.5)[0] # Negative ROIs are those with max IoU 0.1-0.5 (hard example mining) # TODO: To hard example mine or not to hard example mine, that's the question # bg_ids = np.where((rpn_roi_iou_max >= 0.1) & (rpn_roi_iou_max < 0.5))[0] bg_ids = np.where(rpn_roi_iou_max < 0.5)[0] # Subsample ROIs. Aim for 33% foreground. # FG fg_roi_count = int(config.TRAIN_ROIS_PER_IMAGE * config.ROI_POSITIVE_RATIO) if fg_ids.shape[0] > fg_roi_count: keep_fg_ids = np.random.choice(fg_ids, fg_roi_count, replace=False) else: keep_fg_ids = fg_ids # BG remaining = config.TRAIN_ROIS_PER_IMAGE - keep_fg_ids.shape[0] if bg_ids.shape[0] > remaining: keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False) else: keep_bg_ids = bg_ids # Combine indices of ROIs to keep keep = np.concatenate([keep_fg_ids, keep_bg_ids]) # Need more? remaining = config.TRAIN_ROIS_PER_IMAGE - keep.shape[0] if remaining > 0: # Looks like we don't have enough samples to maintain the desired # balance. Reduce requirements and fill in the rest. This is # likely different from the Mask RCNN paper. # There is a small chance we have neither fg nor bg samples. if keep.shape[0] == 0: # Pick bg regions with easier IoU threshold bg_ids = np.where(rpn_roi_iou_max < 0.5)[0] assert bg_ids.shape[0] >= remaining keep_bg_ids = np.random.choice(bg_ids, remaining, replace=False) assert keep_bg_ids.shape[0] == remaining keep = np.concatenate([keep, keep_bg_ids]) else: # Fill the rest with repeated bg rois. keep_extra_ids = np.random.choice( keep_bg_ids, remaining, replace=True) keep = np.concatenate([keep, keep_extra_ids]) assert keep.shape[0] == config.TRAIN_ROIS_PER_IMAGE, \ "keep doesn't match ROI batch size {}, {}".format( keep.shape[0], config.TRAIN_ROIS_PER_IMAGE) # Reset the gt boxes assigned to BG ROIs. rpn_roi_gt_boxes[keep_bg_ids, :] = 0 rpn_roi_gt_class_ids[keep_bg_ids] = 0 # For each kept ROI, assign a class_id, and for FG ROIs also add bbox refinement. rois = rpn_rois[keep] roi_gt_boxes = rpn_roi_gt_boxes[keep] roi_gt_class_ids = rpn_roi_gt_class_ids[keep] roi_gt_assignment = rpn_roi_iou_argmax[keep] # Class-aware bbox deltas. [y, x, log(h), log(w)] bboxes = np.zeros((config.TRAIN_ROIS_PER_IMAGE, config.NUM_CLASSES, 4), dtype=np.float32) pos_ids = np.where(roi_gt_class_ids > 0)[0] bboxes[pos_ids, roi_gt_class_ids[pos_ids]] = utils.box_refinement( rois[pos_ids], roi_gt_boxes[pos_ids, :4]) # Normalize bbox refinements bboxes /= config.BBOX_STD_DEV # Generate class-specific target masks masks = np.zeros((config.TRAIN_ROIS_PER_IMAGE, config.MASK_SHAPE[0], config.MASK_SHAPE[1], config.NUM_CLASSES), dtype=np.float32) for i in pos_ids: class_id = roi_gt_class_ids[i] assert class_id > 0, "class id must be greater than 0" gt_id = roi_gt_assignment[i] class_mask = gt_masks[:, :, gt_id] if config.USE_MINI_MASK: # Create a mask placeholder, the size of the image placeholder = np.zeros(config.IMAGE_SHAPE[:2], dtype=bool) # GT box gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[gt_id] gt_w = gt_x2 - gt_x1 gt_h = gt_y2 - gt_y1 # Resize mini mask to size of GT box placeholder[gt_y1:gt_y2, gt_x1:gt_x2] = \ np.round(utils.resize(class_mask, (gt_h, gt_w))).astype(bool) # Place the mini batch in the placeholder class_mask = placeholder # Pick part of the mask and resize it y1, x1, y2, x2 = rois[i].astype(np.int32) m = class_mask[y1:y2, x1:x2] mask = utils.resize(m, config.MASK_SHAPE) masks[i, :, :, class_id] = mask return rois, roi_gt_class_ids, bboxes, masks def build_rpn_targets(image_shape, anchors, gt_class_ids, gt_boxes, config): """Given the anchors and GT boxes, compute overlaps and identify positive anchors and deltas to refine them to match their corresponding GT boxes. anchors: [num_anchors, (y1, x1, y2, x2)] gt_class_ids: [num_gt_boxes] Integer class IDs. gt_boxes: [num_gt_boxes, (y1, x1, y2, x2)] Returns: rpn_match: [N] (int32) matches between anchors and GT boxes. 1 = positive anchor, -1 = negative anchor, 0 = neutral rpn_bbox: [N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas. """ # RPN Match: 1 = positive anchor, -1 = negative anchor, 0 = neutral rpn_match = np.zeros([anchors.shape[0]], dtype=np.int32) # RPN bounding boxes: [max anchors per image, (dy, dx, log(dh), log(dw))] rpn_bbox = np.zeros((config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4)) # Handle COCO crowds # A crowd box in COCO is a bounding box around several instances. Exclude # them from training. A crowd box is given a negative class ID. crowd_ix = np.where(gt_class_ids < 0)[0] if crowd_ix.shape[0] > 0: # Filter out crowds from ground truth class IDs and boxes non_crowd_ix = np.where(gt_class_ids > 0)[0] crowd_boxes = gt_boxes[crowd_ix] gt_class_ids = gt_class_ids[non_crowd_ix] gt_boxes = gt_boxes[non_crowd_ix] # Compute overlaps with crowd boxes [anchors, crowds] crowd_overlaps = utils.compute_overlaps(anchors, crowd_boxes) crowd_iou_max = np.amax(crowd_overlaps, axis=1) no_crowd_bool = (crowd_iou_max < 0.001) else: # All anchors don't intersect a crowd no_crowd_bool = np.ones([anchors.shape[0]], dtype=bool) # Compute overlaps [num_anchors, num_gt_boxes] overlaps = utils.compute_overlaps(anchors, gt_boxes) # Match anchors to GT Boxes # If an anchor overlaps a GT box with IoU >= 0.7 then it's positive. # If an anchor overlaps a GT box with IoU < 0.3 then it's negative. # Neutral anchors are those that don't match the conditions above, # and they don't influence the loss function. # However, don't keep any GT box unmatched (rare, but happens). Instead, # match it to the closest anchor (even if its max IoU is < 0.3). # # 1. Set negative anchors first. They get overwritten below if a GT box is # matched to them. Skip boxes in crowd areas. anchor_iou_argmax = np.argmax(overlaps, axis=1) anchor_iou_max = overlaps[np.arange(overlaps.shape[0]), anchor_iou_argmax] rpn_match[(anchor_iou_max < 0.3) & (no_crowd_bool)] = -1 # 2. Set an anchor for each GT box (regardless of IoU value). # If multiple anchors have the same IoU match all of them gt_iou_argmax = np.argwhere(overlaps == np.max(overlaps, axis=0))[:,0] rpn_match[gt_iou_argmax] = 1 # 3. Set anchors with high overlap as positive. rpn_match[anchor_iou_max >= 0.7] = 1 # Subsample to balance positive and negative anchors # Don't let positives be more than half the anchors ids = np.where(rpn_match == 1)[0] extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE // 2) if extra > 0: # Reset the extra ones to neutral ids = np.random.choice(ids, extra, replace=False) rpn_match[ids] = 0 # Same for negative proposals ids = np.where(rpn_match == -1)[0] extra = len(ids) - (config.RPN_TRAIN_ANCHORS_PER_IMAGE - np.sum(rpn_match == 1)) if extra > 0: # Rest the extra ones to neutral ids = np.random.choice(ids, extra, replace=False) rpn_match[ids] = 0 # For positive anchors, compute shift and scale needed to transform them # to match the corresponding GT boxes. ids = np.where(rpn_match == 1)[0] ix = 0 # index into rpn_bbox # TODO: use box_refinement() rather than duplicating the code here for i, a in zip(ids, anchors[ids]): # Closest gt box (it might have IoU < 0.7) gt = gt_boxes[anchor_iou_argmax[i]] # Convert coordinates to center plus width/height. # GT Box gt_h = gt[2] - gt[0] gt_w = gt[3] - gt[1] gt_center_y = gt[0] + 0.5 * gt_h gt_center_x = gt[1] + 0.5 * gt_w # Anchor a_h = a[2] - a[0] a_w = a[3] - a[1] a_center_y = a[0] + 0.5 * a_h a_center_x = a[1] + 0.5 * a_w # Compute the bbox refinement that the RPN should predict. rpn_bbox[ix] = [ (gt_center_y - a_center_y) / a_h, (gt_center_x - a_center_x) / a_w, np.log(gt_h / a_h), np.log(gt_w / a_w), ] # Normalize rpn_bbox[ix] /= config.RPN_BBOX_STD_DEV ix += 1 return rpn_match, rpn_bbox def generate_random_rois(image_shape, count, gt_class_ids, gt_boxes): """Generates ROI proposals similar to what a region proposal network would generate. image_shape: [Height, Width, Depth] count: Number of ROIs to generate gt_class_ids: [N] Integer ground truth class IDs gt_boxes: [N, (y1, x1, y2, x2)] Ground truth boxes in pixels. Returns: [count, (y1, x1, y2, x2)] ROI boxes in pixels. """ # placeholder rois = np.zeros((count, 4), dtype=np.int32) # Generate random ROIs around GT boxes (90% of count) rois_per_box = int(0.9 * count / gt_boxes.shape[0]) for i in range(gt_boxes.shape[0]): gt_y1, gt_x1, gt_y2, gt_x2 = gt_boxes[i] h = gt_y2 - gt_y1 w = gt_x2 - gt_x1 # random boundaries r_y1 = max(gt_y1 - h, 0) r_y2 = min(gt_y2 + h, image_shape[0]) r_x1 = max(gt_x1 - w, 0) r_x2 = min(gt_x2 + w, image_shape[1]) # To avoid generating boxes with zero area, we generate double what # we need and filter out the extra. If we get fewer valid boxes # than we need, we loop and try again. while True: y1y2 = np.random.randint(r_y1, r_y2, (rois_per_box * 2, 2)) x1x2 = np.random.randint(r_x1, r_x2, (rois_per_box * 2, 2)) # Filter out zero area boxes threshold = 1 y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >= threshold][:rois_per_box] x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >= threshold][:rois_per_box] if y1y2.shape[0] == rois_per_box and x1x2.shape[0] == rois_per_box: break # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape # into x1, y1, x2, y2 order x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1) y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1) box_rois = np.hstack([y1, x1, y2, x2]) rois[rois_per_box * i:rois_per_box * (i + 1)] = box_rois # Generate random ROIs anywhere in the image (10% of count) remaining_count = count - (rois_per_box * gt_boxes.shape[0]) # To avoid generating boxes with zero area, we generate double what # we need and filter out the extra. If we get fewer valid boxes # than we need, we loop and try again. while True: y1y2 = np.random.randint(0, image_shape[0], (remaining_count * 2, 2)) x1x2 = np.random.randint(0, image_shape[1], (remaining_count * 2, 2)) # Filter out zero area boxes threshold = 1 y1y2 = y1y2[np.abs(y1y2[:, 0] - y1y2[:, 1]) >= threshold][:remaining_count] x1x2 = x1x2[np.abs(x1x2[:, 0] - x1x2[:, 1]) >= threshold][:remaining_count] if y1y2.shape[0] == remaining_count and x1x2.shape[0] == remaining_count: break # Sort on axis 1 to ensure x1 <= x2 and y1 <= y2 and then reshape # into x1, y1, x2, y2 order x1, x2 = np.split(np.sort(x1x2, axis=1), 2, axis=1) y1, y2 = np.split(np.sort(y1y2, axis=1), 2, axis=1) global_rois = np.hstack([y1, x1, y2, x2]) rois[-remaining_count:] = global_rois return rois class DataGenerator(KU.Sequence): """An iterable that returns images and corresponding target class ids, bounding box deltas, and masks. It inherits from keras.utils.Sequence to avoid data redundancy when multiprocessing=True. dataset: The Dataset object to pick data from config: The model config object shuffle: If True, shuffles the samples before every epoch augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation. For example, passing imgaug.augmenters.Fliplr(0.5) flips images right/left 50% of the time. random_rois: If > 0 then generate proposals to be used to train the network classifier and mask heads. Useful if training the Mask RCNN part without the RPN. detection_targets: If True, generate detection targets (class IDs, bbox deltas, and masks). Typically for debugging or visualizations because in trainig detection targets are generated by DetectionTargetLayer. Returns a Python iterable. Upon calling __getitem__() on it, the iterable returns two lists, inputs and outputs. The contents of the lists differ depending on the received arguments: inputs list: - images: [batch, H, W, C] - image_meta: [batch, (meta data)] Image details. See compose_image_meta() - rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral) - rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas. - gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs - gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] - gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width are those of the image unless use_mini_mask is True, in which case they are defined in MINI_MASK_SHAPE. outputs list: Usually empty in regular training. But if detection_targets is True then the outputs list contains target class_ids, bbox deltas, and masks. """ def __init__(self, dataset, config, shuffle=True, augmentation=None, random_rois=0, detection_targets=False): self.image_ids = np.copy(dataset.image_ids) self.dataset = dataset self.config = config # Anchors # [anchor_count, (y1, x1, y2, x2)] self.backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE) self.anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES, config.RPN_ANCHOR_RATIOS, self.backbone_shapes, config.BACKBONE_STRIDES, config.RPN_ANCHOR_STRIDE) self.shuffle = shuffle self.augmentation = augmentation self.random_rois = random_rois self.batch_size = self.config.BATCH_SIZE self.detection_targets = detection_targets def __len__(self): return int(np.ceil(len(self.image_ids) / float(self.batch_size))) def __getitem__(self, idx): b = 0 image_index = -1 while b < self.batch_size: # Increment index to pick next image. Shuffle if at the start of an epoch. image_index = (image_index + 1) % len(self.image_ids) if self.shuffle and image_index == 0: np.random.shuffle(self.image_ids) # Get GT bounding boxes and masks for image. image_id = self.image_ids[image_index] image, image_meta, gt_class_ids, gt_boxes, gt_masks = \ load_image_gt(self.dataset, self.config, image_id, augmentation=self.augmentation) # Skip images that have no instances. This can happen in cases # where we train on a subset of classes and the image doesn't # have any of the classes we care about. if not np.any(gt_class_ids > 0): continue # RPN Targets rpn_match, rpn_bbox = build_rpn_targets(image.shape, self.anchors, gt_class_ids, gt_boxes, self.config) # Mask R-CNN Targets if self.random_rois: rpn_rois = generate_random_rois( image.shape, self.random_rois, gt_class_ids, gt_boxes) if self.detection_targets: rois, mrcnn_class_ids, mrcnn_bbox, mrcnn_mask = \ build_detection_targets( rpn_rois, gt_class_ids, gt_boxes, gt_masks, self.config) # Init batch arrays if b == 0: batch_image_meta = np.zeros( (self.batch_size,) + image_meta.shape, dtype=image_meta.dtype) batch_rpn_match = np.zeros( [self.batch_size, self.anchors.shape[0], 1], dtype=rpn_match.dtype) batch_rpn_bbox = np.zeros( [self.batch_size, self.config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], dtype=rpn_bbox.dtype) batch_images = np.zeros( (self.batch_size,) + image.shape, dtype=np.float32) batch_gt_class_ids = np.zeros( (self.batch_size, self.config.MAX_GT_INSTANCES), dtype=np.int32) batch_gt_boxes = np.zeros( (self.batch_size, self.config.MAX_GT_INSTANCES, 4), dtype=np.int32) batch_gt_masks = np.zeros( (self.batch_size, gt_masks.shape[0], gt_masks.shape[1], self.config.MAX_GT_INSTANCES), dtype=gt_masks.dtype) if self.random_rois: batch_rpn_rois = np.zeros( (self.batch_size, rpn_rois.shape[0], 4), dtype=rpn_rois.dtype) if self.detection_targets: batch_rois = np.zeros( (self.batch_size,) + rois.shape, dtype=rois.dtype) batch_mrcnn_class_ids = np.zeros( (self.batch_size,) + mrcnn_class_ids.shape, dtype=mrcnn_class_ids.dtype) batch_mrcnn_bbox = np.zeros( (self.batch_size,) + mrcnn_bbox.shape, dtype=mrcnn_bbox.dtype) batch_mrcnn_mask = np.zeros( (self.batch_size,) + mrcnn_mask.shape, dtype=mrcnn_mask.dtype) # If more instances than fits in the array, sub-sample from them. if gt_boxes.shape[0] > self.config.MAX_GT_INSTANCES: ids = np.random.choice( np.arange(gt_boxes.shape[0]), self.config.MAX_GT_INSTANCES, replace=False) gt_class_ids = gt_class_ids[ids] gt_boxes = gt_boxes[ids] gt_masks = gt_masks[:, :, ids] # Add to batch batch_image_meta[b] = image_meta batch_rpn_match[b] = rpn_match[:, np.newaxis] batch_rpn_bbox[b] = rpn_bbox batch_images[b] = mold_image(image.astype(np.float32), self.config) batch_gt_class_ids[b, :gt_class_ids.shape[0]] = gt_class_ids batch_gt_boxes[b, :gt_boxes.shape[0]] = gt_boxes batch_gt_masks[b, :, :, :gt_masks.shape[-1]] = gt_masks if self.random_rois: batch_rpn_rois[b] = rpn_rois if self.detection_targets: batch_rois[b] = rois batch_mrcnn_class_ids[b] = mrcnn_class_ids batch_mrcnn_bbox[b] = mrcnn_bbox batch_mrcnn_mask[b] = mrcnn_mask b += 1 inputs = [batch_images, batch_image_meta, batch_rpn_match, batch_rpn_bbox, batch_gt_class_ids, batch_gt_boxes, batch_gt_masks] outputs = [] if self.random_rois: inputs.extend([batch_rpn_rois]) if self.detection_targets: inputs.extend([batch_rois]) # Keras requires that output and targets have the same number of dimensions batch_mrcnn_class_ids = np.expand_dims( batch_mrcnn_class_ids, -1) outputs.extend( [batch_mrcnn_class_ids, batch_mrcnn_bbox, batch_mrcnn_mask]) return inputs, outputs ############################################################ # MaskRCNN Class ############################################################ class MaskRCNN(object): """Encapsulates the Mask RCNN model functionality. The actual Keras model is in the keras_model property. """ def __init__(self, mode, config, model_dir): """ mode: Either "training" or "inference" config: A Sub-class of the Config class model_dir: Directory to save training logs and trained weights """ assert mode in ['training', 'inference'] self.mode = mode self.config = config self.model_dir = model_dir self.set_log_dir() self.keras_model = self.build(mode=mode, config=config) def build(self, mode, config): """Build Mask R-CNN architecture. input_shape: The shape of the input image. mode: Either "training" or "inference". The inputs and outputs of the model differ accordingly. """ assert mode in ['training', 'inference'] # Image size must be dividable by 2 multiple times h, w = config.IMAGE_SHAPE[:2] if h / 2**6 != int(h / 2**6) or w / 2**6 != int(w / 2**6): raise Exception("Image size must be dividable by 2 at least 6 times " "to avoid fractions when downscaling and upscaling." "For example, use 256, 320, 384, 448, 512, ... etc. ") # Inputs input_image = KL.Input( shape=[None, None, config.IMAGE_SHAPE[2]], name="input_image") input_image_meta = KL.Input(shape=[config.IMAGE_META_SIZE], name="input_image_meta") if mode == "training": # RPN GT input_rpn_match = KL.Input( shape=[None, 1], name="input_rpn_match", dtype=tf.int32) input_rpn_bbox = KL.Input( shape=[None, 4], name="input_rpn_bbox", dtype=tf.float32) # Detection GT (class IDs, bounding boxes, and masks) # 1. GT Class IDs (zero padded) input_gt_class_ids = KL.Input( shape=[None], name="input_gt_class_ids", dtype=tf.int32) # 2. GT Boxes in pixels (zero padded) # [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)] in image coordinates input_gt_boxes = KL.Input( shape=[None, 4], name="input_gt_boxes", dtype=tf.float32) # Normalize coordinates gt_boxes = KL.Lambda(lambda x: norm_boxes_graph( x, K.shape(input_image)[1:3]))(input_gt_boxes) # 3. GT Masks (zero padded) # [batch, height, width, MAX_GT_INSTANCES] if config.USE_MINI_MASK: input_gt_masks = KL.Input( shape=[config.MINI_MASK_SHAPE[0], config.MINI_MASK_SHAPE[1], None], name="input_gt_masks", dtype=bool) else: input_gt_masks = KL.Input( shape=[config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1], None], name="input_gt_masks", dtype=bool) elif mode == "inference": # Anchors in normalized coordinates input_anchors = KL.Input(shape=[None, 4], name="input_anchors") # Build the shared convolutional layers. # Bottom-up Layers # Returns a list of the last layers of each stage, 5 in total. # Don't create the thead (stage 5), so we pick the 4th item in the list. if callable(config.BACKBONE): _, C2, C3, C4, C5 = config.BACKBONE(input_image, stage5=True, train_bn=config.TRAIN_BN) else: _, C2, C3, C4, C5 = resnet_graph(input_image, config.BACKBONE, stage5=True, train_bn=config.TRAIN_BN) # Top-down Layers # TODO: add assert to varify feature map sizes match what's in config P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c5p5')(C5) P4 = KL.Add(name="fpn_p4add")([ KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5), KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c4p4')(C4)]) P3 = KL.Add(name="fpn_p3add")([ KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4), KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c3p3')(C3)]) P2 = KL.Add(name="fpn_p2add")([ KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3), KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (1, 1), name='fpn_c2p2')(C2)]) # Attach 3x3 conv to all P layers to get the final feature maps. P2 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p2")(P2) P3 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p3")(P3) P4 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p4")(P4) P5 = KL.Conv2D(config.TOP_DOWN_PYRAMID_SIZE, (3, 3), padding="SAME", name="fpn_p5")(P5) # P6 is used for the 5th anchor scale in RPN. Generated by # subsampling from P5 with stride of 2. P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5) # Note that P6 is used in RPN, but not in the classifier heads. rpn_feature_maps = [P2, P3, P4, P5, P6] mrcnn_feature_maps = [P2, P3, P4, P5] # Anchors if mode == "training": anchors = self.get_anchors(config.IMAGE_SHAPE) # Duplicate across the batch dimension because Keras requires it # TODO: can this be optimized to avoid duplicating the anchors? anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape) # A hack to get around Keras's bad support for constants # 원본 tf1 버전 코드 # anchors = tf.keras.layers.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image) """케라스에서 상수 레이어에 해당하는 레이어가 없어서 람다 레이어를 사용하는 편법이 있었는데 이제 통하지 않는다! 그건 그렇고 인풋 함수가 있는데 왜 저렇게 짠거지??? 아무튼 커스텀 레이어 작성해서 상수 텐서를 리턴해주기로 함. """ class ConstLayer(tf.keras.layers.Layer): def __init__(self, x, name=None): super(ConstLayer, self).__init__(name=name) self.x = tf.Variable(x) def call(self, input): return self.x anchors = ConstLayer(anchors, name="anchors")(input_image) else: anchors = input_anchors # RPN Model rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE, len(config.RPN_ANCHOR_RATIOS), config.TOP_DOWN_PYRAMID_SIZE) # Loop through pyramid layers layer_outputs = [] # list of lists for p in rpn_feature_maps: layer_outputs.append(rpn([p])) # Concatenate layer outputs # Convert from list of lists of level outputs to list of lists # of outputs across levels. # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]] output_names = ["rpn_class_logits", "rpn_class", "rpn_bbox"] outputs = list(zip(*layer_outputs)) outputs = [KL.Concatenate(axis=1, name=n)(list(o)) for o, n in zip(outputs, output_names)] rpn_class_logits, rpn_class, rpn_bbox = outputs # Generate proposals # Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates # and zero padded. proposal_count = config.POST_NMS_ROIS_TRAINING if mode == "training"\ else config.POST_NMS_ROIS_INFERENCE rpn_rois = ProposalLayer( proposal_count=proposal_count, nms_threshold=config.RPN_NMS_THRESHOLD, name="ROI", config=config)([rpn_class, rpn_bbox, anchors]) if mode == "training": # Class ID mask to mark class IDs supported by the dataset the image # came from. active_class_ids = KL.Lambda( lambda x: parse_image_meta_graph(x)["active_class_ids"] )(input_image_meta) if not config.USE_RPN_ROIS: # Ignore predicted ROIs and use ROIs provided as an input. input_rois = KL.Input(shape=[config.POST_NMS_ROIS_TRAINING, 4], name="input_roi", dtype=np.int32) # Normalize coordinates target_rois = KL.Lambda(lambda x: norm_boxes_graph( x, K.shape(input_image)[1:3]))(input_rois) else: target_rois = rpn_rois # Generate detection targets # Subsamples proposals and generates target outputs for training # Note that proposal class IDs, gt_boxes, and gt_masks are zero # padded. Equally, returned rois and targets are zero padded. rois, target_class_ids, target_bbox, target_mask =\ DetectionTargetLayer(config, name="proposal_targets")([ target_rois, input_gt_class_ids, gt_boxes, input_gt_masks]) # Network Heads # TODO: verify that this handles zero padded ROIs mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\ fpn_classifier_graph(rois, mrcnn_feature_maps, input_image_meta, config.POOL_SIZE, config.NUM_CLASSES, train_bn=config.TRAIN_BN, fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE) mrcnn_mask = build_fpn_mask_graph(rois, mrcnn_feature_maps, input_image_meta, config.MASK_POOL_SIZE, config.NUM_CLASSES, train_bn=config.TRAIN_BN) # TODO: clean up (use tf.identify if necessary) output_rois = KL.Lambda(lambda x: x * 1, name="output_rois")(rois) # Losses rpn_class_loss = KL.Lambda(lambda x: rpn_class_loss_graph(*x), name="rpn_class_loss")( [input_rpn_match, rpn_class_logits]) rpn_bbox_loss = KL.Lambda(lambda x: rpn_bbox_loss_graph(config, *x), name="rpn_bbox_loss")( [input_rpn_bbox, input_rpn_match, rpn_bbox]) class_loss = KL.Lambda(lambda x: mrcnn_class_loss_graph(*x), name="mrcnn_class_loss")( [target_class_ids, mrcnn_class_logits, active_class_ids]) bbox_loss = KL.Lambda(lambda x: mrcnn_bbox_loss_graph(*x), name="mrcnn_bbox_loss")( [target_bbox, target_class_ids, mrcnn_bbox]) mask_loss = KL.Lambda(lambda x: mrcnn_mask_loss_graph(*x), name="mrcnn_mask_loss")( [target_mask, target_class_ids, mrcnn_mask]) # Model inputs = [input_image, input_image_meta, input_rpn_match, input_rpn_bbox, input_gt_class_ids, input_gt_boxes, input_gt_masks] if not config.USE_RPN_ROIS: inputs.append(input_rois) outputs = [rpn_class_logits, rpn_class, rpn_bbox, mrcnn_class_logits, mrcnn_class, mrcnn_bbox, mrcnn_mask, rpn_rois, output_rois, rpn_class_loss, rpn_bbox_loss, class_loss, bbox_loss, mask_loss] model = KM.Model(inputs, outputs, name='mask_rcnn') else: # Network Heads # Proposal classifier and BBox regressor heads mrcnn_class_logits, mrcnn_class, mrcnn_bbox =\ fpn_classifier_graph(rpn_rois, mrcnn_feature_maps, input_image_meta, config.POOL_SIZE, config.NUM_CLASSES, train_bn=config.TRAIN_BN, fc_layers_size=config.FPN_CLASSIF_FC_LAYERS_SIZE) # Detections # output is [batch, num_detections, (y1, x1, y2, x2, class_id, score)] in # normalized coordinates detections = DetectionLayer(config, name="mrcnn_detection")( [rpn_rois, mrcnn_class, mrcnn_bbox, input_image_meta]) # Create masks for detections detection_boxes = KL.Lambda(lambda x: x[..., :4])(detections) mrcnn_mask = build_fpn_mask_graph(detection_boxes, mrcnn_feature_maps, input_image_meta, config.MASK_POOL_SIZE, config.NUM_CLASSES, train_bn=config.TRAIN_BN) model = KM.Model([input_image, input_image_meta, input_anchors], [detections, mrcnn_class, mrcnn_bbox, mrcnn_mask, rpn_rois, rpn_class, rpn_bbox], name='mask_rcnn') # Add multi-GPU support. if config.GPU_COUNT > 1: from mrcnn.parallel_model import ParallelModel model = ParallelModel(model, config.GPU_COUNT) return model def find_last(self): """Finds the last checkpoint file of the last trained model in the model directory. Returns: The path of the last checkpoint file """ # Get directory names. Each directory corresponds to a model dir_names = next(os.walk(self.model_dir))[1] key = self.config.NAME.lower() dir_names = filter(lambda f: f.startswith(key), dir_names) dir_names = sorted(dir_names) if not dir_names: import errno raise FileNotFoundError( errno.ENOENT, "Could not find model directory under {}".format(self.model_dir)) # Pick last directory dir_name = os.path.join(self.model_dir, dir_names[-1]) # Find the last checkpoint checkpoints = next(os.walk(dir_name))[2] checkpoints = filter(lambda f: f.startswith("mask_rcnn"), checkpoints) checkpoints = sorted(checkpoints) if not checkpoints: import errno raise FileNotFoundError( errno.ENOENT, "Could not find weight files in {}".format(dir_name)) checkpoint = os.path.join(dir_name, checkpoints[-1]) return checkpoint def load_weights(self, filepath, by_name=False, exclude=None): """Modified version of the corresponding Keras function with the addition of multi-GPU support and the ability to exclude some layers from loading. exclude: list of layer names to exclude """ import h5py from tensorflow.python.keras.saving import hdf5_format if exclude: by_name = True if h5py is None: raise ImportError('`load_weights` requires h5py.') with h5py.File(filepath, mode='r') as f: if 'layer_names' not in f.attrs and 'model_weights' in f: f = f['model_weights'] # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. keras_model = self.keras_model layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers # Exclude some layers if exclude: layers = filter(lambda l: l.name not in exclude, layers) if by_name: hdf5_format.load_weights_from_hdf5_group_by_name(f, layers) else: hdf5_format.load_weights_from_hdf5_group(f, layers) # Update the log directory self.set_log_dir(filepath) def get_imagenet_weights(self): """Downloads ImageNet trained weights from Keras. Returns path to weights file. """ from keras.utils.data_utils import get_file TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/'\ 'releases/download/v0.2/'\ 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5' weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af') return weights_path def compile(self, learning_rate, momentum): """Gets the model ready for training. Adds losses, regularization, and metrics. Then calls the Keras compile() function. """ # Optimizer object optimizer = keras.optimizers.SGD( lr=learning_rate, momentum=momentum, clipnorm=self.config.GRADIENT_CLIP_NORM) # Add Losses loss_names = [ "rpn_class_loss", "rpn_bbox_loss", "mrcnn_class_loss", "mrcnn_bbox_loss", "mrcnn_mask_loss"] for name in loss_names: layer = self.keras_model.get_layer(name) if layer.output in self.keras_model.losses: continue loss = ( tf.reduce_mean(input_tensor=layer.output, keepdims=True) * self.config.LOSS_WEIGHTS.get(name, 1.)) self.keras_model.add_loss(loss) # Add L2 Regularization # Skip gamma and beta weights of batch normalization layers. reg_losses = [ keras.regularizers.l2(self.config.WEIGHT_DECAY)(w) / tf.cast(tf.size(input=w), tf.float32) for w in self.keras_model.trainable_weights if 'gamma' not in w.name and 'beta' not in w.name] self.keras_model.add_loss(tf.add_n(reg_losses)) # Compile self.keras_model.compile( optimizer=optimizer, loss=[None] * len(self.keras_model.outputs)) # Add metrics for losses for name in loss_names: if name in self.keras_model.metrics_names: continue layer = self.keras_model.get_layer(name) self.keras_model.metrics_names.append(name) loss = ( tf.reduce_mean(input_tensor=layer.output, keepdims=True) * self.config.LOSS_WEIGHTS.get(name, 1.)) self.keras_model.add_metric(loss, name=name, aggregation='mean') def set_trainable(self, layer_regex, keras_model=None, indent=0, verbose=1): """Sets model layers as trainable if their names match the given regular expression. """ # Print message on the first call (but not on recursive calls) if verbose > 0 and keras_model is None: log("Selecting layers to train") keras_model = keras_model or self.keras_model # In multi-GPU training, we wrap the model. Get layers # of the inner model because they have the weights. layers = keras_model.inner_model.layers if hasattr(keras_model, "inner_model")\ else keras_model.layers for layer in layers: # Is the layer a model? if layer.__class__.__name__ == 'Model': print("In model: ", layer.name) self.set_trainable( layer_regex, keras_model=layer, indent=indent + 4) continue if not layer.weights: continue # Is it trainable? trainable = bool(re.fullmatch(layer_regex, layer.name)) # Update layer. If layer is a container, update inner layer. if layer.__class__.__name__ == 'TimeDistributed': layer.layer.trainable = trainable else: layer.trainable = trainable # Print trainable layer names if trainable and verbose > 0: log("{}{:20} ({})".format(" " * indent, layer.name, layer.__class__.__name__)) def set_log_dir(self, model_path=None): """Sets the model log directory and epoch counter. model_path: If None, or a format different from what this code uses then set a new log directory and start epochs from 0. Otherwise, extract the log directory and the epoch counter from the file name. """ # Set date and epoch counter as if starting a new model self.epoch = 0 now = datetime.datetime.now() # If we have a model path with date and epochs use them if model_path: # Continue from we left of. Get epoch and date from the file name # A sample model path might look like: # \path\to\logs\coco20171029T2315\mask_rcnn_coco_0001.h5 (Windows) # /path/to/logs/coco20171029T2315/mask_rcnn_coco_0001.h5 (Linux) regex = r".*[/\\][\w-]+(\d{4})(\d{2})(\d{2})T(\d{2})(\d{2})[/\\]mask\_rcnn\_[\w-]+(\d{4})\.h5" # Use string for regex since we might want to use pathlib.Path as model_path m = re.match(regex, str(model_path)) if m: now = datetime.datetime(int(m.group(1)), int(m.group(2)), int(m.group(3)), int(m.group(4)), int(m.group(5))) # Epoch number in file is 1-based, and in Keras code it's 0-based. # So, adjust for that then increment by one to start from the next epoch self.epoch = int(m.group(6)) - 1 + 1 print('Re-starting from epoch %d' % self.epoch) # Directory for training logs self.log_dir = os.path.join(self.model_dir, "{}{:%Y%m%dT%H%M}".format( self.config.NAME.lower(), now)) # Path to save after each epoch. Include placeholders that get filled by Keras. self.checkpoint_path = os.path.join(self.log_dir, "mask_rcnn_{}_*epoch*.h5".format( self.config.NAME.lower())) self.checkpoint_path = self.checkpoint_path.replace( "*epoch*", "{epoch:04d}") def train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation=None, custom_callbacks=None, no_augmentation_sources=None): """Train the model. train_dataset, val_dataset: Training and validation Dataset objects. learning_rate: The learning rate to train with epochs: Number of training epochs. Note that previous training epochs are considered to be done alreay, so this actually determines the epochs to train in total rather than in this particaular call. layers: Allows selecting wich layers to train. It can be: - A regular expression to match layer names to train - One of these predefined values: heads: The RPN, classifier and mask heads of the network all: All the layers 3+: Train Resnet stage 3 and up 4+: Train Resnet stage 4 and up 5+: Train Resnet stage 5 and up augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation. For example, passing imgaug.augmenters.Fliplr(0.5) flips images right/left 50% of the time. You can pass complex augmentations as well. This augmentation applies 50% of the time, and when it does it flips images right/left half the time and adds a Gaussian blur with a random sigma in range 0 to 5. augmentation = imgaug.augmenters.Sometimes(0.5, [ imgaug.augmenters.Fliplr(0.5), imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0)) ]) custom_callbacks: Optional. Add custom callbacks to be called with the keras fit_generator method. Must be list of type keras.callbacks. no_augmentation_sources: Optional. List of sources to exclude for augmentation. A source is string that identifies a dataset and is defined in the Dataset class. """ assert self.mode == "training", "Create model in training mode." # Pre-defined layer regular expressions layer_regex = { # all layers but the backbone "heads": r"(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", # From a specific Resnet stage and up "3+": r"(res3.*)|(bn3.*)|(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", "4+": r"(res4.*)|(bn4.*)|(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", "5+": r"(res5.*)|(bn5.*)|(mrcnn\_.*)|(rpn\_.*)|(fpn\_.*)", # All layers "all": ".*", } if layers in layer_regex.keys(): layers = layer_regex[layers] # Data generators train_generator = DataGenerator(train_dataset, self.config, shuffle=True, augmentation=augmentation) val_generator = DataGenerator(val_dataset, self.config, shuffle=True) # Create log_dir if it does not exist if not os.path.exists(self.log_dir): os.makedirs(self.log_dir) # Callbacks callbacks = [ keras.callbacks.TensorBoard(log_dir=self.log_dir, histogram_freq=0, write_graph=True, write_images=False), keras.callbacks.ModelCheckpoint(self.checkpoint_path, verbose=0, save_weights_only=True), ] # Add custom callbacks to the list if custom_callbacks: callbacks += custom_callbacks # Train log("\nStarting at epoch {}. LR={}\n".format(self.epoch, learning_rate)) log("Checkpoint Path: {}".format(self.checkpoint_path)) self.set_trainable(layers) self.compile(learning_rate, self.config.LEARNING_MOMENTUM) # Work-around for Windows: Keras fails on Windows when using # multiprocessing workers. See discussion here: # https://github.com/matterport/Mask_RCNN/issues/13#issuecomment-353124009 if os.name == 'nt': workers = 0 else: workers = multiprocessing.cpu_count() self.keras_model.fit( train_generator, initial_epoch=self.epoch, epochs=epochs, steps_per_epoch=self.config.STEPS_PER_EPOCH, callbacks=callbacks, validation_data=val_generator, validation_steps=self.config.VALIDATION_STEPS, max_queue_size=100, workers=workers, use_multiprocessing=workers > 1, ) self.epoch = max(self.epoch, epochs) def mold_inputs(self, images): """Takes a list of images and modifies them to the format expected as an input to the neural network. images: List of image matrices [height,width,depth]. Images can have different sizes. Returns 3 Numpy matrices: molded_images: [N, h, w, 3]. Images resized and normalized. image_metas: [N, length of meta data]. Details about each image. windows: [N, (y1, x1, y2, x2)]. The portion of the image that has the original image (padding excluded). """ molded_images = [] image_metas = [] windows = [] for image in images: # Resize image # TODO: move resizing to mold_image() molded_image, window, scale, padding, crop = utils.resize_image( image, min_dim=self.config.IMAGE_MIN_DIM, min_scale=self.config.IMAGE_MIN_SCALE, max_dim=self.config.IMAGE_MAX_DIM, mode=self.config.IMAGE_RESIZE_MODE) molded_image = mold_image(molded_image, self.config) # Build image_meta image_meta = compose_image_meta( 0, image.shape, molded_image.shape, window, scale, np.zeros([self.config.NUM_CLASSES], dtype=np.int32)) # Append molded_images.append(molded_image) windows.append(window) image_metas.append(image_meta) # Pack into arrays molded_images = np.stack(molded_images) image_metas = np.stack(image_metas) windows = np.stack(windows) return molded_images, image_metas, windows def unmold_detections(self, detections, mrcnn_mask, original_image_shape, image_shape, window): """Reformats the detections of one image from the format of the neural network output to a format suitable for use in the rest of the application. detections: [N, (y1, x1, y2, x2, class_id, score)] in normalized coordinates mrcnn_mask: [N, height, width, num_classes] original_image_shape: [H, W, C] Original image shape before resizing image_shape: [H, W, C] Shape of the image after resizing and padding window: [y1, x1, y2, x2] Pixel coordinates of box in the image where the real image is excluding the padding. Returns: boxes: [N, (y1, x1, y2, x2)] Bounding boxes in pixels class_ids: [N] Integer class IDs for each bounding box scores: [N] Float probability scores of the class_id masks: [height, width, num_instances] Instance masks """ # How many detections do we have? # Detections array is padded with zeros. Find the first class_id == 0. zero_ix = np.where(detections[:, 4] == 0)[0] N = zero_ix[0] if zero_ix.shape[0] > 0 else detections.shape[0] # Extract boxes, class_ids, scores, and class-specific masks boxes = detections[:N, :4] class_ids = detections[:N, 4].astype(np.int32) scores = detections[:N, 5] masks = mrcnn_mask[np.arange(N), :, :, class_ids] # Translate normalized coordinates in the resized image to pixel # coordinates in the original image before resizing window = utils.norm_boxes(window, image_shape[:2]) wy1, wx1, wy2, wx2 = window shift = np.array([wy1, wx1, wy1, wx1]) wh = wy2 - wy1 # window height ww = wx2 - wx1 # window width scale = np.array([wh, ww, wh, ww]) # Convert boxes to normalized coordinates on the window boxes = np.divide(boxes - shift, scale) # Convert boxes to pixel coordinates on the original image boxes = utils.denorm_boxes(boxes, original_image_shape[:2]) # Filter out detections with zero area. Happens in early training when # network weights are still random exclude_ix = np.where( (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0] if exclude_ix.shape[0] > 0: boxes = np.delete(boxes, exclude_ix, axis=0) class_ids = np.delete(class_ids, exclude_ix, axis=0) scores = np.delete(scores, exclude_ix, axis=0) masks = np.delete(masks, exclude_ix, axis=0) N = class_ids.shape[0] # Resize masks to original image size and set boundary threshold. full_masks = [] for i in range(N): # Convert neural network mask to full size mask full_mask = utils.unmold_mask(masks[i], boxes[i], original_image_shape) full_masks.append(full_mask) full_masks = np.stack(full_masks, axis=-1)\ if full_masks else np.empty(original_image_shape[:2] + (0,)) return boxes, class_ids, scores, full_masks def detect(self, images, verbose=0): """Runs the detection pipeline. images: List of images, potentially of different sizes. Returns a list of dicts, one dict per image. The dict contains: rois: [N, (y1, x1, y2, x2)] detection bounding boxes class_ids: [N] int class IDs scores: [N] float probability scores for the class IDs masks: [H, W, N] instance binary masks """ assert self.mode == "inference", "Create model in inference mode." assert len( images) == self.config.BATCH_SIZE, "len(images) must be equal to BATCH_SIZE" if verbose: log("Processing {} images".format(len(images))) for image in images: log("image", image) # Mold inputs to format expected by the neural network molded_images, image_metas, windows = self.mold_inputs(images) # Validate image sizes # All images in a batch MUST be of the same size image_shape = molded_images[0].shape for g in molded_images[1:]: assert g.shape == image_shape,\ "After resizing, all images must have the same size. Check IMAGE_RESIZE_MODE and image sizes." # Anchors anchors = self.get_anchors(image_shape) # Duplicate across the batch dimension because Keras requires it # TODO: can this be optimized to avoid duplicating the anchors? anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) if verbose: log("molded_images", molded_images) log("image_metas", image_metas) log("anchors", anchors) # Run object detection detections, _, _, mrcnn_mask, _, _, _ =\ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0) # Process detections results = [] for i, image in enumerate(images): final_rois, final_class_ids, final_scores, final_masks =\ self.unmold_detections(detections[i], mrcnn_mask[i], image.shape, molded_images[i].shape, windows[i]) results.append({ "rois": final_rois, "class_ids": final_class_ids, "scores": final_scores, "masks": final_masks, }) return results def detect_molded(self, molded_images, image_metas, verbose=0): """Runs the detection pipeline, but expect inputs that are molded already. Used mostly for debugging and inspecting the model. molded_images: List of images loaded using load_image_gt() image_metas: image meta data, also returned by load_image_gt() Returns a list of dicts, one dict per image. The dict contains: rois: [N, (y1, x1, y2, x2)] detection bounding boxes class_ids: [N] int class IDs scores: [N] float probability scores for the class IDs masks: [H, W, N] instance binary masks """ assert self.mode == "inference", "Create model in inference mode." assert len(molded_images) == self.config.BATCH_SIZE,\ "Number of images must be equal to BATCH_SIZE" if verbose: log("Processing {} images".format(len(molded_images))) for image in molded_images: log("image", image) # Validate image sizes # All images in a batch MUST be of the same size image_shape = molded_images[0].shape for g in molded_images[1:]: assert g.shape == image_shape, "Images must have the same size" # Anchors anchors = self.get_anchors(image_shape) # Duplicate across the batch dimension because Keras requires it # TODO: can this be optimized to avoid duplicating the anchors? anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) if verbose: log("molded_images", molded_images) log("image_metas", image_metas) log("anchors", anchors) # Run object detection detections, _, _, mrcnn_mask, _, _, _ =\ self.keras_model.predict([molded_images, image_metas, anchors], verbose=0) # Process detections results = [] for i, image in enumerate(molded_images): window = [0, 0, image.shape[0], image.shape[1]] final_rois, final_class_ids, final_scores, final_masks =\ self.unmold_detections(detections[i], mrcnn_mask[i], image.shape, molded_images[i].shape, window) results.append({ "rois": final_rois, "class_ids": final_class_ids, "scores": final_scores, "masks": final_masks, }) return results def get_anchors(self, image_shape): """Returns anchor pyramid for the given image size.""" backbone_shapes = compute_backbone_shapes(self.config, image_shape) # Cache anchors and reuse if image shape is the same if not hasattr(self, "_anchor_cache"): self._anchor_cache = {} if not tuple(image_shape) in self._anchor_cache: # Generate Anchors a = utils.generate_pyramid_anchors( self.config.RPN_ANCHOR_SCALES, self.config.RPN_ANCHOR_RATIOS, backbone_shapes, self.config.BACKBONE_STRIDES, self.config.RPN_ANCHOR_STRIDE) # Keep a copy of the latest anchors in pixel coordinates because # it's used in inspect_model notebooks. # TODO: Remove this after the notebook are refactored to not use it self.anchors = a # Normalize coordinates self._anchor_cache[tuple(image_shape)] = utils.norm_boxes(a, image_shape[:2]) return self._anchor_cache[tuple(image_shape)] def ancestor(self, tensor, name, checked=None): """Finds the ancestor of a TF tensor in the computation graph. tensor: TensorFlow symbolic tensor. name: Name of ancestor tensor to find checked: For internal use. A list of tensors that were already searched to avoid loops in traversing the graph. """ checked = checked if checked is not None else [] # Put a limit on how deep we go to avoid very long loops if len(checked) > 500: return None # Convert name to a regex and allow matching a number prefix # because Keras adds them automatically if isinstance(name, str): name = re.compile(name.replace("/", r"(\_\d+)*/")) parents = tensor.op.inputs for p in parents: if p in checked: continue if bool(re.fullmatch(name, p.name)): return p checked.append(p) a = self.ancestor(p, name, checked) if a is not None: return a return None def find_trainable_layer(self, layer): """If a layer is encapsulated by another layer, this function digs through the encapsulation and returns the layer that holds the weights. """ if layer.__class__.__name__ == 'TimeDistributed': return self.find_trainable_layer(layer.layer) return layer def get_trainable_layers(self): """Returns a list of layers that have weights.""" layers = [] # Loop through all layers for l in self.keras_model.layers: # If layer is a wrapper, find inner trainable layer l = self.find_trainable_layer(l) # Include layer if it has weights if l.get_weights(): layers.append(l) return layers def run_graph(self, images, outputs, image_metas=None): """Runs a sub-set of the computation graph that computes the given outputs. image_metas: If provided, the images are assumed to be already molded (i.e. resized, padded, and normalized) outputs: List of tuples (name, tensor) to compute. The tensors are symbolic TensorFlow tensors and the names are for easy tracking. Returns an ordered dict of results. Keys are the names received in the input and values are Numpy arrays. """ model = self.keras_model # Organize desired outputs into an ordered dict outputs = OrderedDict(outputs) for o in outputs.values(): assert o is not None # Build a Keras function to run parts of the computation graph inputs = model.inputs # if model.uses_learning_phase and not isinstance(K.learning_phase(), int): # inputs += [K.learning_phase()] kf = K.function(model.inputs, list(outputs.values())) # Prepare inputs if image_metas is None: molded_images, image_metas, _ = self.mold_inputs(images) else: molded_images = images image_shape = molded_images[0].shape # Anchors anchors = self.get_anchors(image_shape) # Duplicate across the batch dimension because Keras requires it # TODO: can this be optimized to avoid duplicating the anchors? anchors = np.broadcast_to(anchors, (self.config.BATCH_SIZE,) + anchors.shape) model_in = [molded_images, image_metas, anchors] # Run inference # if model.uses_learning_phase and not isinstance(K.learning_phase(), int): # model_in.append(0.) outputs_np = kf(model_in) # Pack the generated Numpy arrays into a a dict and log the results. outputs_np = OrderedDict([(k, v) for k, v in zip(outputs.keys(), outputs_np)]) for k, v in outputs_np.items(): log(k, v) return outputs_np ############################################################ # Data Formatting ############################################################ def compose_image_meta(image_id, original_image_shape, image_shape, window, scale, active_class_ids): """Takes attributes of an image and puts them in one 1D array. image_id: An int ID of the image. Useful for debugging. original_image_shape: [H, W, C] before resizing or padding. image_shape: [H, W, C] after resizing and padding window: (y1, x1, y2, x2) in pixels. The area of the image where the real image is (excluding the padding) scale: The scaling factor applied to the original image (float32) active_class_ids: List of class_ids available in the dataset from which the image came. Useful if training on images from multiple datasets where not all classes are present in all datasets. """ meta = np.array( [image_id] + # size=1 list(original_image_shape) + # size=3 list(image_shape) + # size=3 list(window) + # size=4 (y1, x1, y2, x2) in image cooredinates [scale] + # size=1 list(active_class_ids) # size=num_classes ) return meta def parse_image_meta(meta): """Parses an array that contains image attributes to its components. See compose_image_meta() for more details. meta: [batch, meta length] where meta length depends on NUM_CLASSES Returns a dict of the parsed values. """ image_id = meta[:, 0] original_image_shape = meta[:, 1:4] image_shape = meta[:, 4:7] window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels scale = meta[:, 11] active_class_ids = meta[:, 12:] return { "image_id": image_id.astype(np.int32), "original_image_shape": original_image_shape.astype(np.int32), "image_shape": image_shape.astype(np.int32), "window": window.astype(np.int32), "scale": scale.astype(np.float32), "active_class_ids": active_class_ids.astype(np.int32), } def parse_image_meta_graph(meta): """Parses a tensor that contains image attributes to its components. See compose_image_meta() for more details. meta: [batch, meta length] where meta length depends on NUM_CLASSES Returns a dict of the parsed tensors. """ image_id = meta[:, 0] original_image_shape = meta[:, 1:4] image_shape = meta[:, 4:7] window = meta[:, 7:11] # (y1, x1, y2, x2) window of image in in pixels scale = meta[:, 11] active_class_ids = meta[:, 12:] return { "image_id": image_id, "original_image_shape": original_image_shape, "image_shape": image_shape, "window": window, "scale": scale, "active_class_ids": active_class_ids, } def mold_image(images, config): """Expects an RGB image (or array of images) and subtracts the mean pixel and converts it to float. Expects image colors in RGB order. """ return images.astype(np.float32) - config.MEAN_PIXEL def unmold_image(normalized_images, config): """Takes a image normalized with mold() and returns the original.""" return (normalized_images + config.MEAN_PIXEL).astype(np.uint8) ############################################################ # Miscellenous Graph Functions ############################################################ def trim_zeros_graph(boxes, name='trim_zeros'): """Often boxes are represented with matrices of shape [N, 4] and are padded with zeros. This removes zero boxes. boxes: [N, 4] matrix of boxes. non_zeros: [N] a 1D boolean mask identifying the rows to keep """ non_zeros = tf.cast(tf.reduce_sum(input_tensor=tf.abs(boxes), axis=1), tf.bool) boxes = tf.boolean_mask(tensor=boxes, mask=non_zeros, name=name) return boxes, non_zeros def batch_pack_graph(x, counts, num_rows): """Picks different number of values from each row in x depending on the values in counts. """ outputs = [] for i in range(num_rows): outputs.append(x[i, :counts[i]]) return tf.concat(outputs, axis=0) def norm_boxes_graph(boxes, shape): """Converts boxes from pixel coordinates to normalized coordinates. boxes: [..., (y1, x1, y2, x2)] in pixel coordinates shape: [..., (height, width)] in pixels Note: In pixel coordinates (y2, x2) is outside the box. But in normalized coordinates it's inside the box. Returns: [..., (y1, x1, y2, x2)] in normalized coordinates """ h, w = tf.split(tf.cast(shape, tf.float32), 2) scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0) shift = tf.constant([0., 0., 1., 1.]) return tf.divide(boxes - shift, scale) def denorm_boxes_graph(boxes, shape): """Converts boxes from normalized coordinates to pixel coordinates. boxes: [..., (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height, width)] in pixels Note: In pixel coordinates (y2, x2) is outside the box. But in normalized coordinates it's inside the box. Returns: [..., (y1, x1, y2, x2)] in pixel coordinates """ h, w = tf.split(tf.cast(shape, tf.float32), 2) scale = tf.concat([h, w, h, w], axis=-1) - tf.constant(1.0) shift = tf.constant([0., 0., 1., 1.]) return tf.cast(tf.round(tf.multiply(boxes, scale) + shift), tf.int32)

44.290839

115

0.613106

707383a933ad226a97f1a88b93f375b0163066db

3,203

py

Python

dedupsqlfs/db/mysql/table/hash_sizes.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

22

2015-04-09T09:00:00.000Z

2022-03-23T00:16:04.000Z

dedupsqlfs/db/mysql/table/hash_sizes.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

119

2015-02-11T21:39:27.000Z

2021-07-27T23:04:49.000Z

dedupsqlfs/db/mysql/table/hash_sizes.py

tabulon-ext/dedupsqlfs

9dfbed17450e7f2a499a7381e0368d08ae3c700d

[ "MIT" ]

7

2016-03-16T11:53:45.000Z

2022-02-24T13:47:31.000Z

# -*- coding: utf8 -*- __author__ = 'sergey' from dedupsqlfs.db.mysql.table import Table class TableHashSizes( Table ): _table_name = "hash_sizes" def create( self ): cur = self.getCursor() # Create table cur.execute( "CREATE TABLE IF NOT EXISTS `%s` (" % self.getName()+ "`hash_id` BIGINT UNSIGNED PRIMARY KEY, "+ "`writed_size` INT UNSIGNED NOT NULL, "+ "`compressed_size` INT UNSIGNED NOT NULL "+ ")"+ self._getCreationAppendString() ) return def insert( self, hash_id, writed_size, compressed_size): """ :return: int """ self.startTimer() cur = self.getCursor() cur.execute( "INSERT INTO `%s` " % self.getName()+ " (`hash_id`, `writed_size`, `compressed_size`) VALUES (%(id)s, %(ws)s, %(cs)s)", { "id": hash_id, "ws": writed_size, "cs": compressed_size } ) item = cur.lastrowid self.stopTimer('insert') return item def update( self, hash_id, writed_size, compressed_size): """ :return: int """ self.startTimer() cur = self.getCursor() cur.execute( "UPDATE `%s` " % self.getName() + " SET `compressed_size`=%(cs)s, `writed_size`=%(ws)s WHERE `hash_id`=%(id)s", { "cs": compressed_size, "ws": writed_size, "id": hash_id } ) count = cur.rowcount self.stopTimer('update') return count def get( self, hash_id): """ :param hash_id: int :return: Row """ self.startTimer() cur = self.getCursor() cur.execute( "SELECT * FROM `%s` " % self.getName()+ " WHERE `hash_id`=%(id)s", { "id": hash_id } ) item = cur.fetchone() self.stopTimer('get') return item def remove_by_ids(self, id_str): self.startTimer() count = 0 if id_str: cur = self.getCursor() cur.execute("DELETE FROM `%s` " % self.getName()+ " WHERE `hash_id` IN (%s)" % (id_str,)) count = cur.rowcount self.stopTimer('remove_by_ids') return count def get_sizes_by_hash_ids(self, id_str): self.startTimer() items = {} if id_str: cur = self.getCursor() cur.execute("SELECT * FROM `%s` " % self.getName()+ " WHERE `hash_id` IN (%s)" % (id_str,)) for _i in cur: items[ _i["hash_id"] ] = (_i["writed_size"], _i["compressed_size"],) self.stopTimer('get_sizes_by_hash_ids') return items def get_median_compressed_size(self): self.startTimer() self.stopTimer('get_median_compressed_size') return 0 def get_mean_compressed_size(self): self.startTimer() self.stopTimer('get_mean_compressed_size') return 0 pass

26.915966

93

0.491727

8686a23127283b6e792f75fef2a5d762de36176d

2,966

py

Python

MerakiGuestPW.py

donthor/MerakiGuestPW

1ecfd89a4e7681c11b4fe05dfdb4548635a97086

[ "BSD-3-Clause" ]

1

2021-06-14T14:37:52.000Z

MerakiGuestPW.py

donthor/MerakiGuestPW

1ecfd89a4e7681c11b4fe05dfdb4548635a97086

[ "BSD-3-Clause" ]

null

MerakiGuestPW.py

donthor/MerakiGuestPW

1ecfd89a4e7681c11b4fe05dfdb4548635a97086

[ "BSD-3-Clause" ]

null

#!/usr/local/bin/python3 import requests import json import os import click import random import string import sys meraki_key = os.environ.get('MERAKI_API_KEY') merakinetwork = os.environ.get('MY_MERAKI_NETWORK') webexBearerToken = os.environ.get('WEBEX_BEARER_TOKEN') roomID = os.environ.get('WEBEX_PAROCKHO_BOT') baseurl = "https://dashboard.meraki.com/api/v1/networks/" def randomize_pw(): pw_chars = string.ascii_letters + string.digits pw = ''.join(random.choice(pw_chars) for i in range(8)) return pw @click.command() @click.option('--pw', type=str, default='', help="Password for SSID - use 'random' to generate 8 character password") @click.option('--ssid', type=int, default=14, help="SSID number (0-14)") @click.option('--force/--no-force', default=False, help="Continue without y/n verification") @click.option('--webex/--no-webex', default=False, help="Send new password to Webex room of your choice") def changePass(pw, ssid, force, webex): if pw == 'random': pw = randomize_pw() url = baseurl + str(merakinetwork) + '/wireless/ssids/' payload = json.dumps( {"psk": pw }) headers = { 'X-Cisco-Meraki-API-Key': meraki_key, 'Content-Type': 'application/json' } response_get = requests.get(url + str(ssid), headers=headers) data = response_get.json() if force: response = requests.request("PUT", url + str(ssid), headers=headers, data=payload) else: print(f'Please confirm change to SSID: {data["name"]} with password: {pw}') while (answer := input("Do you want to continue? (Enter y/n)").lower() ) not in {"y", "n"}: pass if answer == 'y': response = requests.request("PUT", url + str(ssid), headers=headers, data=payload) # print(response.status_code) if response.status_code == 200: click.echo('Password Modified') if answer == 'n': sys.exit(0) #Post in Webex using '--webex' option if webex: #get SSID and password and set it to the 'message' variable message = f'The new password for SSID {data["name"]} is {pw}' #set the 'url' variable to the webex url url = "https://api.ciscospark.com/v1/messages" #define body and header data payload="{\r\n \"roomId\" : \"" + roomID + "\",\r\n \"text\" : \"" + message + "\"\r\n}" headers = { 'Authorization': 'Bearer ' + webexBearerToken, 'Content-Type': 'application/json' } #make the API call and save the response into the 'response' variable response = requests.request("POST", url, headers=headers, data=payload) #evaluate if the response was successful # if response.status_code == 200: # print("Success!") # else: # print("Message failed. Code: ", response.status_code) # print(response.text) if __name__ == '__main__': changePass()

38.519481

117

0.624073

bfbff30ac88dd01af6ec157d3740fa88f964783a

2,766

py

Python

examples/laserScanningMicroscope.py

gregsadetsky/RayTracing

3d11ed91014a47bddc797495ca2af059005e810d

[ "MIT" ]

null

examples/laserScanningMicroscope.py

gregsadetsky/RayTracing

3d11ed91014a47bddc797495ca2af059005e810d

[ "MIT" ]

null

examples/laserScanningMicroscope.py

gregsadetsky/RayTracing

3d11ed91014a47bddc797495ca2af059005e810d

[ "MIT" ]

null

import envexamples from raytracing import * import matplotlib.pyplot as plt import numpy as np """ In a laser scanning system, the scanning components define the covered field-of-view (FOV) at the sample plane. Here, we show a one-dimensional example with a polygonal mirror of 36 facets that rotates rapidly to scan the beam along the horizontal direction. It produces a meachnical sweep of 10 degrees, or 0.1750 rad, between each facets. Therefore, the laser beam covers a total optical scan angle of 20 degrees. In the following example, the object is considered to be the laser beam at the polygonal mirror plane. The output profile shows on its x-axis the width of the FOV under the objective. """ # List of the scan angle of the ray making it throught the system. thetas = [] # List of 1 corresponding to the number of elements in heights # so that plt.plot() doesn't freak out. positions = [] # Radius of the laser beam at the scanning element. objectHalfHeight = 0.000250 # Angle produced by the scanning element. scanAngle = 10*np.pi/180 # Number of total rays considered in these calculations. nRays = 10000 # Production of rays in the angle range of the scanning element. scanRays = UniformRays(yMax=0, thetaMax=scanAngle, M=1, N=nRays) class UISUPLAPO60XW(Objective): def __init__(self): super(UISUPLAPO60XW, self).__init__(f=180/60, NA=1.2, focusToFocusLength=40, backAperture=7, workingDistance=0.28, magnification=60, fieldNumber=22, label='UISUPLAPO60XW Objective') def illuminationPath(): illumination = ImagingPath() # The object in this situation is the laser beam at the scanning element. illumination.objectHeight = objectHalfHeight*2 illumination.rayNumber = 3 illumination.fanNumber = 3 illumination.fanAngle = 0 illumination.append(System4f(f1=40, f2=75, diameter1=24.5, diameter2=24.5)) illumination.append(System4f(f1=100, f2=100, diameter1=24.5, diameter2=24.5)) illumination.append(Space(d=180/40)) illumination.append(UISUPLAPO60XW()) illumination.append(Space(d=180/40)) return illumination path = illuminationPath() outputRays = path.traceManyThrough(scanRays) for i in range(len(outputRays)): thetas.append(scanRays[i].theta*180/np.pi) positions.append(outputRays[i].y*1000) scanRays.displayProgress() plt.plot(positions, thetas) plt.ylabel('Scan angle (degrees)') plt.xlabel('Scanning position of the focal spot (µm)') plt.show()

28.8125

81

0.670644

d548bc779d8e42b407e3f34d848185eec318cd18

6,143

py

Python

comptox_ai/scripts/make_tabular_dataset.py

van-truong/comptox_ai

393b05c617822e30f54c967ef07ec53ba4b09688

[ "MIT" ]

6

2020-03-09T17:27:34.000Z

2022-03-15T15:37:15.000Z

comptox_ai/scripts/make_tabular_dataset.py

van-truong/comptox_ai

393b05c617822e30f54c967ef07ec53ba4b09688

[ "MIT" ]

11

2021-05-04T18:46:51.000Z

2022-03-01T01:06:49.000Z

comptox_ai/scripts/make_tabular_dataset.py

van-truong/comptox_ai

393b05c617822e30f54c967ef07ec53ba4b09688

[ "MIT" ]

2

2020-02-23T14:11:10.000Z

2021-10-04T18:27:58.000Z

#!/usr/bin/env python3 """ make_tabular_dataset.py Standalone Python job to generate a tabular dataset for predictive toxicology. Briefly, the user specifies a node type and node features, as well as a target feature or relationship. This returns a file that is suitable for QSAR or similar analyses. The algorithm for extracting a tabular dataset is roughly as follows: First, the user creates a new 'in memory' graph in the Neo4j GDS library's graph catalog. This is basically a subgraph of the complete graph database, originally meant for providing an environment in which users can efficiently run graph algorithms on relevant portions of the database. However, we co-opt this functionality to allow rapid exporting of those same subgraphs for use in external data analysis frameworks (like scikit-learn, networkx, DGL, etc.). Specifically, we perform a *native projection*, where we supply the entire set of node types and relationship types in the desired subgraph, along with either specific node features or all defined node features for those node types. We then call a routine to stream the graph into local Python data structures. """ from comptox_ai.db.graph_db import Graph, GraphDB from yaml import load, Loader from pathlib import Path import pandas as pd import ipdb import os import datetime as dt def _get_default_config_file(): root_dir = Path(__file__).resolve().parents[2] if os.path.exists(os.path.join(root_dir, 'CONFIG.yaml')): default_config_file = os.path.join(root_dir, 'CONFIG.yaml') else: default_config_file = os.path.join(root_dir, 'CONFIG-default.yaml') return default_config_file def _make_timestamped_output_directory(parent_dir, prefixes=['subgraph', 'tsv'], suffixes=None): ts = dt.datetime.now().strftime('%Y%m%d_%H%M%S') pre = f"{'-'.join(prefixes)}_" if prefixes else "" post = f"_{'-'.join(suffixes)}" if suffixes else "" dirname = f"{pre}{ts}{post}" full_path = os.path.join(parent_dir, dirname) os.makedirs(full_path) return full_path def make_node_table(db, node_label, node_properties = None): if node_properties is None: # No filters on properties, so we will just fetch all of them res = db.fetch_nodes(node_label) res_df = pd.DataFrame(res) del(res) else: raise NotImplementedError return res_df def make_relationship_table(db, relationship_type, from_label, to_label, relationship_properties = None): if relationship_properties is not None: # TODO: Figure out how/if to handle relationship properties if it ever # looks like it would be useful. raise NotImplementedError res = db.fetch_relationships(relationship_type, from_label, to_label) res_df = pd.DataFrame(res, columns=['s', 'r', 'o']) del(res) return res_df # connect to running Neo4j instance db = GraphDB() # Ideally, we'd be able to tap into GDS' subgraph projection functionality, but # it unfortunately isn't compatible with non-numeric node or relationship # properties. E.g., we store MACCS bitstrings as a string, because Neo4j # doesn't have a better datatype. Therefore, we couldn't export them using GDS # alone. We'll be keeping an eye on this, because it should drastically speed # up the subgraph dataset generation procedure if they can get this to work. # # Build the graph using a native projection # TEST_NODE_PROJ = ['Chemical', 'Gene', 'Assay'] # TEST_REL_PROJ = ['CHEMICALBINDSGENE', 'CHEMICALDECREASESEXPRESSION', 'CHEMICALHASACTIVEASSAY'] # db.build_graph_native_projection( # 'testgraph', # TEST_NODE_PROJ, # TEST_REL_PROJ # ) NODE_LABELS = [ 'Gene', 'Pathway', 'Assay' ] OUTPUT_TYPE = 'tsv' # Instead of what we described above, we will instead let Python do much of the # heavy lifting. First we get the meta structure of the graph using APOC, we # then determine the minimum spanning subtree over the metagraph, and then # export all of the node types and relationship types contained in that tree. metagraph = db.get_metagraph() node_tables = dict() rel_tables = dict() # get node data for nl in metagraph.node_labels: if nl in NODE_LABELS: # print(f"Loading {nl} nodes...") node_table = make_node_table(db, nl) print(f" Adding node table: {nl}") node_tables[nl] = node_table # get relationship data for rt in metagraph.relationship_types: this_rel_table = pd.DataFrame(columns=['s', 'r', 'o']) for rt_s in metagraph.relationship_path_schema[rt]: from_label = rt_s['from'] to_label = rt_s['to'] # The induced subgraph only includes edges where both the subject and the # object of the relationship are members of NODE_LABELS if (from_label in NODE_LABELS) and (to_label in NODE_LABELS): print(f" Loading relationships for rel type {rt}...") rel_table = make_relationship_table(db, rt, from_label, to_label) # Note that we need to make a copy of the data and then reassign. Pandas # doesn't have decent support for large-scale in-place operations. A # possible solution is to somehow use HDFStore tables during the process. this_rel_table = this_rel_table.append(rel_table, ignore_index=True) # Only hold onto the dataframe if it contains rows if this_rel_table.shape[0] > 0: print("Adding relationship table: {from_label}-[{rt}]->{to_label}") rel_tables[rt] = this_rel_table else: del(this_rel_table) print() print("Finished parsing nodes and relationships; now writing to disk...") # export the results config_file_path = _get_default_config_file() with open(config_file_path, 'r') as fp: cnf = load(fp, Loader=Loader) output_directory = cnf['data']['output_dir'] if OUTPUT_TYPE == 'tsv': output_dir = _make_timestamped_output_directory(output_directory) for k_node, v_node in node_tables.items(): node_fname = os.path.join(output_dir, f"node_{k_node}.tsv") v_node.to_csv(node_fname, sep="\t", index=None) print(f"Wrote node file: {node_fname}") for k_rel, v_rel in rel_tables.items(): rel_fname = os.path.join(output_dir, f"edge_{k_rel}.tsv") v_rel.to_csv(rel_fname, sep="\t", index=None) print(f"Wrote edge file: {rel_fname}")

35.304598

105

0.740843

a6955237a979d7b679ae37164f1e054c247c6940

3,775

py

Python

indico/modules/events/persons/util_test.py

salevajo/indico

6f9cbabc20d1641caea907099388ae2b04965cf8

[ "MIT" ]

1

2021-12-27T17:51:27.000Z

indico/modules/events/persons/util_test.py

salevajo/indico

6f9cbabc20d1641caea907099388ae2b04965cf8

[ "MIT" ]

5

2021-04-08T19:26:47.000Z

2022-01-24T16:30:18.000Z

indico/modules/events/persons/util_test.py

salevajo/indico

6f9cbabc20d1641caea907099388ae2b04965cf8

[ "MIT" ]

2

2019-02-24T17:29:10.000Z

2021-04-08T19:23:27.000Z

# This file is part of Indico. # Copyright (C) 2002 - 2021 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. import pytest from indico.modules.events.persons.util import create_event_person, get_event_person, get_event_person_for_user def test_get_person_for_user(db, dummy_event, dummy_user): person = get_event_person_for_user(dummy_event, dummy_user) # EventPerson created from scratch assert person.id is None assert person.user == dummy_user db.session.add(person) db.session.flush() person = get_event_person_for_user(dummy_event, dummy_user) # Already existing EventPerson assert person.id is not None assert person.user == dummy_user def test_create_event_person(db, dummy_event): data = { 'email': 'test@acme.com', 'firstName': 'John', 'familyName': 'Doe', 'affiliation': 'ACME Inc.' } person = create_event_person(dummy_event, **data) assert person.event == dummy_event assert person.email == 'test@acme.com' assert person.full_name == 'John Doe' assert person.affiliation == 'ACME Inc.' def test_get_event_person(db, dummy_event, dummy_user): data = { 'email': 'test@acme.com', 'firstName': 'John', 'familyName': 'Doe', 'affiliation': 'ACME Inc.' } person_1 = get_event_person(dummy_event, data) # Person doesn't exist in the DB assert person_1.id is None # User neither assert person_1.user is None # save in the DB for later db.session.add(person_1) db.session.flush() data = { 'email': '1337@example.com', 'firstName': 'Sea', 'familyName': 'Pig', 'affiliation': 'ACME Inc.' } person_2 = get_event_person(dummy_event, data) # Person not in the DB either assert person_2.id is None # User exists, however (matched by e-mail) assert person_2.user == dummy_user assert person_2.full_name == 'Guinea Pig' db.session.add(person_2) db.session.flush() person = get_event_person(dummy_event, data) # Retrieved person should now be in the DB assert person.id == person_2.id # User for whom there is already an EventPerson in this event data = { 'email': 'test@acme.com', 'firstName': 'JOHN', 'familyName': 'DOE', 'affiliation': 'ACME' } person_3 = get_event_person(dummy_event, data) # We should get the first person assert person_3.id == person_1.id assert person_3.user is None assert person_3.full_name == 'John Doe' data = { 'firstName': 'Foo', 'familyName': 'Bar' } person_4 = get_event_person(dummy_event, data) # We should get a new person assert person_4.id is None assert person_4.user is None assert person_4.email == '' assert person_4.full_name == 'Foo Bar' def test_get_event_person_edit(db, dummy_event, dummy_user): data = { 'email': 'test@acme.com', 'firstName': 'John', 'familyName': 'Doe', 'affiliation': 'ACME Inc.' } person_1 = get_event_person(dummy_event, dict(data, _type='Avatar', identifier=f'User:{dummy_user.id}')) assert person_1.id is None assert person_1.user == dummy_user db.session.add(person_1) db.session.flush() person_2 = get_event_person(dummy_event, dict(data, _type='EventPerson', id=person_1.id)) assert person_2 == person_1 person_3 = get_event_person(dummy_event, dict(data, _type='PersonLink', personId=person_1.id)) assert person_3 == person_1 with pytest.raises(ValueError): get_event_person(dummy_event, dict(data, _type='UnsupportedPersonType'))

31.198347

111

0.664901

7a4eaa2838241d97035cb71f36eee3cc992ca115

2,698

py

Python

vision.py

cemakd/makeathon-autonomous-vehicle

480e46d5e351c02ead1b5980f8c421b57c2f39fd

[ "MIT" ]

null

vision.py

cemakd/makeathon-autonomous-vehicle

480e46d5e351c02ead1b5980f8c421b57c2f39fd

[ "MIT" ]

null

vision.py

cemakd/makeathon-autonomous-vehicle

480e46d5e351c02ead1b5980f8c421b57c2f39fd

[ "MIT" ]

null

''' Makeathon National Instruments Autonomous Vehicle Challenge Visual Traffic Signal and Sign Recognition ''' #!/usr/bin/env python import sys import time from array import array import numpy as np import argparse import imutils import sys sys.path.append('/usr/local/lib/python2.7/site-packages') import cv2 import argparse import time import glob class Vision(object): """docstring for Vision.""" cap = None show_mode = False current_ret = None current_frame = None blob_detector = None blob_params = None prototypeImg = cv2.imread("./stopPrototype.png") def __init__(self): super(Vision, self).__init__() # Blob Detector Parameters: self.blob_params = cv2.SimpleBlobDetector_Params() # Change thresholds self.blob_params.minThreshold = 10 self.blob_params.maxThreshold = 200 # Filter by Area. self.blob_params.filterByArea = True self.blob_params.minArea = 500 # Filter by Circularity self.blob_params.filterByCircularity = True self.blob_params.minCircularity = 0.80 # Filter by Convexity self.blob_params.filterByConvexity = True self.blob_params.minConvexity = 0.80 # Filter by Inertia self.blob_params.filterByInertia = True self.blob_params.minInertiaRatio = 0.01 # Create a detector with the parameters ver = (cv2.__version__).split('.') if int(ver[0]) < 3 : self.blob_detector = cv2.SimpleBlobDetector(self.blob_params) else : self.blob_detector = cv2.SimpleBlobDetector_create(self.blob_params) def __del__(self): # When everything done, release the capture self.cap.release() cv2.destroyAllWindows() def connect(self): # Connect to PS3 Eye: print("Connection to camera") self.cap = cv2.VideoCapture(1) print("Connection Established") def check_sign(self): print("Checking Sign") if self.current_ret == False: self.connect() # Read: self.current_ret, self.current_frame = self.cap.read(1) self.current_frame = cv2.medianBlur(self.current_frame, 5) # Detect blobs. keypoints = self.blob_detector.detect(self.current_frame) # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures # the size of the circle corresponds to the size of blob print(keypoints) im_with_keypoints = cv2.drawKeypoints(self.current_frame, keypoints, np.array([]), (0,255,0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # Show blobs cv2.imshow("Keypoints", im_with_keypoints) cv2.waitKey(0) # Show: if self.show_mode: cv2.imshow('current_frame', self.current_frame) waitKey(0) def check_light(self): # i = 1 def set_show(self, arg): show_mode = arg v = Vision() v.connect() v.set_show(True) v.check_sign() destroyAllWindows()

22.483333

139

0.742031

bf2d6dd9bbfcdc36dc39e0b3f451cb3333a04eb5

100

py

Python

tweet_display/apps.py

aashaka/twitter-analyser

313ea4e342d100dac78b0fd07822cac7457450f9

[ "MIT" ]

null

tweet_display/apps.py

aashaka/twitter-analyser

313ea4e342d100dac78b0fd07822cac7457450f9

[ "MIT" ]

null

tweet_display/apps.py

aashaka/twitter-analyser

313ea4e342d100dac78b0fd07822cac7457450f9

[ "MIT" ]

null

from django.apps import AppConfig class TweetDisplayConfig(AppConfig): name = 'tweet_display'

16.666667

36

0.78

f6c4edf923f89dd360215125bc4ed2b2bb20d9bc

9,731

py

Python

src/pandas_transformers/transformers.py

NedimB911/pandas-transformers

9f5f29c9aebe103b9ca81889d51b144ea21ee180

[ "MIT" ]

null

src/pandas_transformers/transformers.py

NedimB911/pandas-transformers

9f5f29c9aebe103b9ca81889d51b144ea21ee180

[ "MIT" ]

null

src/pandas_transformers/transformers.py

NedimB911/pandas-transformers

9f5f29c9aebe103b9ca81889d51b144ea21ee180

[ "MIT" ]

null

# pylint: disable=missing-function-docstring # pylint: disable=arguments-differ # pylint: disable=unused-argument from typing import List, Iterable from sklearn.utils.validation import check_is_fitted from sklearn.base import TransformerMixin from sklearn.feature_extraction.text import TfidfVectorizer import pandas as pd import numpy as np from .utils import check_columns_exist, check_if_dataframe def test(): print("hello world") class PandasOneHotEncoder(TransformerMixin): """ This one-hot encoder preserves the dataframe structure. It works the same as pd.get_dummies(), however pd.get_dummies() cannot deal with unseen categories during transformation (e.g. when a category appears in the test set but not in the train set) https://github.com/pandas-dev/pandas/issues/8918 https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html Parameters ---------- min_frequency : Any category of a categorical column that appears less than 'min_frequency', will be ignored (no extra dummy column for that category) columns : The list of columns to one-hot-encode. The default is None, in which case all columns of type 'object' or 'category' will be one-hot-encoded. dummy_na : Add a column to indicate NaNs, if False NaNs are ignored (default) drop_first: Whether to get k-1 dummies out of k categorical levels by removing the first level. """ def __init__( self, min_frequency: int = -1, columns: List[str] = None, dummy_na: bool = False, drop_first: bool = False, handle_unknown: str = "ignore", ): super().__init__() self.min_frequency = min_frequency self.dummy_na = dummy_na self.drop_first = drop_first if handle_unknown not in {"ignore", "error"}: raise ValueError( "handle_unknown must be either 'ignore' or 'error'." f" Got {handle_unknown}." ) self.handle_unknown = handle_unknown if (columns is not None) & (not isinstance(columns, list)): raise ValueError( f"'columns' must be a list (of strings). Got {type(columns)}" ) self.columns = columns def fit(self, X: pd.DataFrame, y: pd.Series = None) -> "PandasOneHotEncoder": dtypes_to_encode = ["object", "category"] check_if_dataframe(X) if self.columns is None: self.columns = X.select_dtypes(include=dtypes_to_encode).columns.tolist() else: check_columns_exist(X, self.columns) self.categories_ = {} self.categories_unfiltered_ = {} for col in self.columns: counts = X[col].value_counts(dropna=True) self.categories_[col] = list( set(counts[counts >= self.min_frequency].index.tolist()) ) self.categories_unfiltered_[col] = set(counts.index.tolist()) return self def transform(self, X: pd.DataFrame) -> pd.DataFrame: check_is_fitted(self) check_if_dataframe(X) check_columns_exist(X, self.columns) if self.handle_unknown == "error": self._check_unknown_categories_all_columns(X) # pylint: disable=cell-var-from-loop for col in self.columns: cat = pd.CategoricalDtype(self.categories_[col], ordered=True) X = X.assign(**{f"{col}": lambda _df: _df[col].astype(cat)}) return pd.get_dummies( X, columns=self.columns, dummy_na=self.dummy_na, drop_first=self.drop_first ) def _find_unseen_categories(self, X, col): """ We check whether X has any categories that were not seen during training for a single column. NOTE: We also consider categories that we have ignored during training due to 'min_frequency'. """ seen_categories = set(self.categories_unfiltered_[col]) new_categories = set(X[col].value_counts(dropna=False).index) unseen_categories = new_categories - seen_categories return unseen_categories def _check_unknown_categories_all_columns(self, X): """ We check whether X has any categories that were not seen during training for *ALL* columns. NOTE: We also consider categories that we have ignored during training due to 'min_frequency'. """ unseen_categories_dict = {} for col in self.columns: unseen_categories = self._find_unseen_categories(X, col) if unseen_categories: unseen_categories_dict[col] = unseen_categories if unseen_categories_dict: raise ValueError( f"Encountered categories not seen during fitting:" f"{unseen_categories_dict}" ) class PandasTfidfVectorizer(TfidfVectorizer): """ PandasTfidfVectorizer A pandas version for sklearn's tf-idf vectorizer. The PandasTfidfVectorizer converts the sparse array returned by sklearn's tf-idf vectorizer into a dense array and uses the feature names to convert it into a dataframe. # https://www.datacamp.com/community/tutorials # /super-multiple-inheritance-diamond-problem # https://stackoverflow.com/questions/3277367 # /how-does-pythons-super-work-with-multiple-inheritance Parameters ---------- column: str (optional) Column which we wish to apply the tf-idf vectorizer to. If no column is given, then the input dataframe should be a pd.Series or a np.ndarray. **kwargs Keyword arguments for sklearn.feature_extraction.text.TfidfVectorizer """ def __init__(self, column: str = None, **kwargs): super().__init__(**kwargs) self.column = column # pylint: disable = arguments-differ def fit(self, X: pd.DataFrame, y=None): """ Fits the tf-idf vectorizer. Parameters ---------- X : pd.DataFrame or 1-d Iterable[str] y : optional """ if self.column is not None: # In this case the input must be a dataframe self._init_input_validation() check_if_dataframe(X) check_columns_exist(X, [self.column]) raw_documents = X[self.column] else: # if no column is given, the input must be a 1-d iterable # (pd.Series or np array) self._check_if_1d_series_or_np_array(X) raw_documents = X self._check_missing(raw_documents) return super().fit(raw_documents, y) # pylint: disable = arguments-differ def transform(self, X: pd.DataFrame): """ Transforms the input dataframe using the fitted tf-idf vectorizer. Parameters ---------- X : pd.DataFrame Returns ------- pd.DataFrame """ if self.column is not None: # In this case the input must be a dataframe check_if_dataframe(X) check_columns_exist(X, [self.column]) raw_documents = X[self.column] else: # if no column is given, the input must be a 1-d iterable # (pd.Series or np array) self._check_if_1d_series_or_np_array(X) raw_documents = X self._check_missing(raw_documents) transformed = super().transform(raw_documents) transformed_df = pd.DataFrame( transformed.toarray(), columns=self.get_feature_names(), index=X.index ) if self.column is not None: transformed_df = pd.concat( [X.drop(columns=[self.column]), transformed_df], axis=1 ) return transformed_df def fit_transform(self, X: pd.DataFrame, y=None): """ fit_transform Parameters ---------- X : pd.DataFrame y : optional Returns ------- pd.DataFrame """ self.fit(X, y) return self.transform(X) def _init_input_validation(self): """ Validates the __init__() inputs. """ if not isinstance(self.column, str): raise TypeError( f"'column' argument should be a string. Got {type(self.column)}" ) def _check_missing(self, raw_documents: Iterable[str]): """ Checks whether the raw_documents have any missing values. If so, return a ValueError. Parameters ---------- raw_documents : Iterable[str] Raises ------ ValueError """ if raw_documents.isnull().any(): raise ValueError( f"The {self.column} column contains None's. TfidfVectorizer requires" " the column to not have any None's." ) def _check_if_1d_series_or_np_array(self, obj): if isinstance(obj, (pd.Series, np.ndarray)): if obj.ndim == 1: return True raise TypeError( "Input is of the correct type (pd.Series or np.ndarray)" " However, not of the correct dimension. It should be 1d." ) raise TypeError( f"If no column is given, the input should be either a pd.Series or a " f"np.ndarray. Got {type(obj)} instead." ) def _get_param_names(self): tfidf_param_names = TfidfVectorizer._get_param_names() current_param_names = super()._get_param_names() param_names = tfidf_param_names + current_param_names return param_names

31.089457

87

0.611653

4f7febed329bdfd42b8e3b29ac1840a4a010dc3f

351

py

Python

src/airfly/_vendor/airflow/providers/apache/hive/transfers/vertica_to_hive.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

7

2021-09-27T11:38:48.000Z

2022-02-01T06:06:24.000Z

src/airfly/_vendor/airflow/providers/apache/hive/transfers/vertica_to_hive.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

src/airfly/_vendor/airflow/providers/apache/hive/transfers/vertica_to_hive.py

ryanchao2012/airfly

230ddd88885defc67485fa0c51f66c4a67ae98a9

[ "MIT" ]

null

# Auto generated by 'inv collect-airflow' from airfly._vendor.airflow.models.baseoperator import BaseOperator class VerticaToHiveOperator(BaseOperator): sql: "_empty" hive_table: "_empty" create: "_empty" recreate: "_empty" partition: "_empty" delimiter: "_empty" vertica_conn_id: "_empty" hive_cli_conn_id: "_empty"

25.071429

67

0.723647

50fae1f0b12d2448f5766a0c02738d794ad928e9

16,638

py

Python

dataset/tiny_vid.py

pengzhiliang/object-localization

a1472b69e33af5be3aea0476f9f467c3994be4d4

[ "MIT" ]

3

2019-03-29T07:41:00.000Z

2021-08-15T06:48:57.000Z

dataset/tiny_vid.py

pengzhiliang/object-localization

a1472b69e33af5be3aea0476f9f467c3994be4d4

[ "MIT" ]

null

dataset/tiny_vid.py

pengzhiliang/object-localization

a1472b69e33af5be3aea0476f9f467c3994be4d4

[ "MIT" ]

3

2019-09-20T03:17:26.000Z

2021-08-15T06:48:58.000Z

#-*-coding:utf-8-*- ''' Created on Oct 29,2018 @author: pengzhiliang ''' from __future__ import print_function import torch,os,sys,random,cv2 import numpy as np from PIL import Image,ImageFont,ImageDraw from matplotlib import pyplot as plt from encoder import DataEncoder from torch.utils import data from torchvision import transforms from os.path import join as pjoin import sys sys.path.insert(0,'../') #============================================================================= # # Create train_images.txt and test_images.txt # #============================================================================= def train_test_txt(defualt_path='/home/pzl/Data/tiny_vid'): """ 将如下格式存入文件： /home/pzl/Data/tiny_vid/turtle/000151.JPEG 1 29 38 108 84 2 其中参数： /home/pzl/Data/tiny_vid/turtle/000151.JPEG：图片路径 1 ：一个物体（方便使用ssd架构） 29 38 108 84：分别为 xmin, ymin, xmax, ymax，表示bounding box的位置 2 ：种类 """ classes = {'car':0, 'bird':1, 'turtle':2, 'dog':3, 'lizard':4} for dirname in classes.keys(): bbox_dic = {} with open(pjoin(defualt_path,dirname+'_gt.txt'),'r') as f: for n,line in enumerate(f.readlines()): line = line.strip().split() bbox_dic[line[0]] = line[1:] if n == 179: break with open(pjoin(defualt_path,'train_images.txt'),'a') as f: for i in range(1,151): imgname = '000000' pad0 = 6 - len(str(i)) imgname = imgname[:pad0]+str(i)+'.JPEG' imgpath = pjoin(pjoin(defualt_path,dirname),imgname) imageclass = str(classes[dirname]) imgbbox = ' '.join(bbox_dic[str(i)]) f.write('\t'.join([imgpath,'1',imgbbox,imageclass])+'\n') with open(pjoin(defualt_path,'test_images.txt'),'a') as f: for i in range(151,181): imgname = '000000' pad0 = 6 - len(str(i)) imgname = imgname[:pad0]+str(i)+'.JPEG' imgpath = pjoin(pjoin(defualt_path,dirname),imgname) imageclass = str(classes[dirname]) imgbbox = ' '.join(bbox_dic[str(i)]) f.write('\t'.join([imgpath,'1',imgbbox,imageclass])+'\n') #============================================================================= # # Create a data loader for two-output net(not ssd),don't need to encode # #============================================================================= class tiny_vid_loader(data.Dataset): """ 功能：构造一个用于tiny_vid数据集的迭代器参数： """ img_size =128 def __init__(self,defualt_path='/home/pzl/Data/tiny_vid',mode='train',transform='some augmentation'): """ defualt_path: 如'/home/pzl/Data/tiny_vid' mode : 'train' or 'test' """ if not (os.path.exists(pjoin(defualt_path,'train_images.txt')) and os.path.exists(pjoin(defualt_path,'test_images.txt'))): train_test_txt(defualt_path) self.filelist=[] self.class_coor = [] self.mode = True if mode =='train' else False # /home/pzl/Data/tiny_vid/turtle/000151.JPEG 1 29 38 108 84 2 with open(pjoin(defualt_path,mode+'_images.txt')) as f: for line in f.readlines(): line = line.strip().split() self.filelist.append(line[0]) self.class_coor.append([int(i) for i in line[2:]]) self.ToTensor = transforms.ToTensor() self.Normalize = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) self.data_encoder = DataEncoder() self.transform = transform def random_distort( self, img, brightness_delta=32/255., contrast_delta=0.5, saturation_delta=0.5, hue_delta=0.1): '''A color related data augmentation used in SSD. Args: img: (PIL.Image) image to be color augmented. brightness_delta: (float) shift of brightness, range from [1-delta,1+delta]. contrast_delta: (float) shift of contrast, range from [1-delta,1+delta]. saturation_delta: (float) shift of saturation, range from [1-delta,1+delta]. hue_delta: (float) shift of hue, range from [-delta,delta]. Returns: img: (PIL.Image) color augmented image. ''' def brightness(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(brightness=delta)(img) return img def contrast(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(contrast=delta)(img) return img def saturation(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(saturation=delta)(img) return img def hue(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(hue=delta)(img) return img img = brightness(img, brightness_delta) if random.random() < 0.5: img = contrast(img, contrast_delta) img = saturation(img, saturation_delta) img = hue(img, hue_delta) else: img = saturation(img, saturation_delta) img = hue(img, hue_delta) img = contrast(img, contrast_delta) return img def random_flip(self, img, boxes): '''Randomly flip the image and adjust the bbox locations. 只在水平方向翻转 For bbox (xmin, ymin, xmax, ymax), the flipped bbox is: (w-xmax, ymin, w-xmin, ymax). Args: img: (PIL.Image) image. boxes: (tensor) bbox locations, sized [#obj, 4]. Returns: img: (PIL.Image) randomly flipped image. boxes: (tensor) randomly flipped bbox locations, sized [#obj, 4]. ''' if random.random() < 0.5: img = img.transpose(Image.FLIP_LEFT_RIGHT) w = img.width xmin = w - boxes[2] xmax = w - boxes[0] boxes[0] = xmin boxes[2] = xmax return img, boxes def random_crop(self, img, boxes, labels): '''Randomly crop the image and adjust the bbox locations. For more details, see 'Chapter2.2: Data augmentation' of the paper. Args: img: (PIL.Image) image. boxes: (tensor) bbox locations, sized [4,]. labels: (tensor) bbox labels, sized [1,]. Returns: img: (PIL.Image) cropped image. selected_boxes: (tensor) selected bbox locations. labels: (tensor) selected bbox labels. ''' imw, imh = img.size boxes = torch.unsqueeze(boxes,dim=0) # expand [1,4] # print(boxes) while True: min_iou = random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9]) if min_iou is None: return img, boxes, labels for _ in range(100): # random.randrange(min,max)包含min 不包含max w = random.randrange(int(0.1*imw), imw) h = random.randrange(int(0.1*imh), imh) if h > 2*w or w > 2*h: continue x = random.randrange(imw - w) y = random.randrange(imh - h) roi = torch.Tensor([[x, y, x+w, y+h]]) center = (boxes[:,:2] + boxes[:,2:]) / 2 # [N,2] roi2 = roi.expand(len(center), 4) # [N,4] mask = (center > roi2[:,:2]) & (center < roi2[:,2:]) # [N,2] mask = mask[:,0] & mask[:,1] #[N,] if not mask.any(): continue selected_boxes = boxes.index_select(0, mask.nonzero().squeeze(1)) iou = self.data_encoder.iou(selected_boxes, roi) if iou.min() < min_iou: continue img = img.crop((x, y, x+w, y+h)) selected_boxes[:,0].add_(-x).clamp_(min=0, max=w) selected_boxes[:,1].add_(-y).clamp_(min=0, max=h) selected_boxes[:,2].add_(-x).clamp_(min=0, max=w) selected_boxes[:,3].add_(-y).clamp_(min=0, max=h) # print(selected_boxes, mask) return img, selected_boxes, labels#labels[mask] def __getitem__(self, index): imgpath = self.filelist[index] gt_class = np.array(self.class_coor[index][-1],dtype = np.float32) gt_bbox = np.array(self.class_coor[index][:-1],dtype = np.float32) # print('1:',gt_bbox) img = Image.open(imgpath).convert('RGB') if self.transform is not None: gt_class , gt_bbox = torch.Tensor(gt_class),torch.Tensor(gt_bbox) # print('2:',gt_class) if self.mode: img = self.random_distort(img) img , gt_bbox = self.random_flip(img,gt_bbox) img, gt_bbox, gt_class = self.random_crop(img, gt_bbox, gt_class) w,h = img.size gt_bbox /= torch.Tensor([w,h,w,h]).expand_as(gt_bbox) # print('3:',gt_bbox*128) img = transforms.Resize((128,128))(img) img = self.ToTensor(img) img = self.Normalize(img) else: img,gt_class , gt_bbox = self.ToTensor(img),torch.Tensor(gt_class),torch.Tensor(gt_bbox/128.) img = self.Normalize(img) return img,gt_class.long(),(gt_bbox*128).squeeze() def __len__(self): return len(self.filelist) class ListDataset(data.Dataset): img_size = 300 def __init__(self, root, list_file, train, transform): ''' Args: root: (str) ditectory to images. list_file: (str) path to index file. train: (boolean) train or test. transform: ([transforms]) image transforms. ''' self.root = root self.train = train self.transform = transform self.fnames = [] self.boxes = [] self.labels = [] self.data_encoder = DataEncoder() with open(list_file) as f: lines = f.readlines() self.num_samples = len(lines) for line in lines: splited = line.strip().split() self.fnames.append(splited[0]) num_objs = int(splited[1]) box = [] label = [] for i in range(num_objs): xmin = splited[2+5*i] ymin = splited[3+5*i] xmax = splited[4+5*i] ymax = splited[5+5*i] c = splited[6+5*i] box.append([float(xmin),float(ymin),float(xmax),float(ymax)]) label.append(int(c)) self.boxes.append(torch.Tensor(box)) self.labels.append(torch.LongTensor(label)) def __getitem__(self, idx): '''Load a image, and encode its bbox locations and class labels. Args: idx: (int) image index. Returns: img: (tensor) image tensor. loc_target: (tensor) location targets, sized [8732,4]. conf_target: (tensor) label targets, sized [8732,]. ''' # Load image and bbox locations. fname = self.fnames[idx] img = Image.open(fname) boxes = self.boxes[idx].clone() labels = self.labels[idx] # Data augmentation while training. if self.train: img =self.random_distort(img) img, boxes = self.random_flip(img, boxes) img, boxes, labels = self.random_crop(img, boxes, labels) # Scale bbox locaitons to [0,1]. w,h = img.size boxes /= torch.Tensor([w,h,w,h]).expand_as(boxes) img = img.resize((self.img_size,self.img_size)) img = self.transform(img) # Encode loc & conf targets. loc_target, conf_target = self.data_encoder.encode(boxes, labels) return img, loc_target, conf_target,boxes,labels def random_distort(self, img, brightness_delta=32/255., contrast_delta=0.5, saturation_delta=0.5, hue_delta=0.1): '''A color related data augmentation used in SSD. Args: img: (PIL.Image) image to be color augmented. brightness_delta: (float) shift of brightness, range from [1-delta,1+delta]. contrast_delta: (float) shift of contrast, range from [1-delta,1+delta]. saturation_delta: (float) shift of saturation, range from [1-delta,1+delta]. hue_delta: (float) shift of hue, range from [-delta,delta]. Returns: img: (PIL.Image) color augmented image. ''' def brightness(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(brightness=delta)(img) return img def contrast(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(contrast=delta)(img) return img def saturation(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(saturation=delta)(img) return img def hue(img, delta): if random.random() < 0.5: img = transforms.ColorJitter(hue=delta)(img) return img img = brightness(img, brightness_delta) if random.random() < 0.5: img = contrast(img, contrast_delta) img = saturation(img, saturation_delta) img = hue(img, hue_delta) else: img = saturation(img, saturation_delta) img = hue(img, hue_delta) img = contrast(img, contrast_delta) return img def random_flip(self, img, boxes): '''Randomly flip the image and adjust the bbox locations. For bbox (xmin, ymin, xmax, ymax), the flipped bbox is: (w-xmax, ymin, w-xmin, ymax). Args: img: (PIL.Image) image. boxes: (tensor) bbox locations, sized [#obj, 4]. Returns: img: (PIL.Image) randomly flipped image. boxes: (tensor) randomly flipped bbox locations, sized [#obj, 4]. ''' if random.random() < 0.5: img = img.transpose(Image.FLIP_LEFT_RIGHT) w = img.width xmin = w - boxes[:,2] xmax = w - boxes[:,0] boxes[:,0] = xmin boxes[:,2] = xmax return img, boxes def random_crop(self, img, boxes, labels): '''Randomly crop the image and adjust the bbox locations. For more details, see 'Chapter2.2: Data augmentation' of the paper. Args: img: (PIL.Image) image. boxes: (tensor) bbox locations, sized [#obj, 4]. labels: (tensor) bbox labels, sized [#obj,]. Returns: img: (PIL.Image) cropped image. selected_boxes: (tensor) selected bbox locations. labels: (tensor) selected bbox labels. ''' imw, imh = img.size while True: min_iou = random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9]) if min_iou is None: return img, boxes, labels for _ in range(100): # random.randrange(min,max)包含min 不包含max w = random.randrange(int(0.1*imw), imw) h = random.randrange(int(0.1*imh), imh) if h > 2*w or w > 2*h: continue x = random.randrange(imw - w) y = random.randrange(imh - h) roi = torch.Tensor([[x, y, x+w, y+h]]) center = (boxes[:,:2] + boxes[:,2:]) / 2 # [N,2] roi2 = roi.expand(len(center), 4) # [N,4] mask = (center > roi2[:,:2]) & (center < roi2[:,2:]) # [N,2] mask = mask[:,0] & mask[:,1] #[N,] if not mask.any(): continue selected_boxes = boxes.index_select(0, mask.nonzero().squeeze(1)) iou = self.data_encoder.iou(selected_boxes, roi) if iou.min() < min_iou: continue img = img.crop((x, y, x+w, y+h)) selected_boxes[:,0].add_(-x).clamp_(min=0, max=w) selected_boxes[:,1].add_(-y).clamp_(min=0, max=h) selected_boxes[:,2].add_(-x).clamp_(min=0, max=w) selected_boxes[:,3].add_(-y).clamp_(min=0, max=h) return img, selected_boxes, labels[mask] def __len__(self): return self.num_samples

36.169565

130

0.527888

8d4d8b72d469e693a8dc373e1611aa8109966b9c

3,652

py

Python

dispatcher/qube.py

plinecom/JobManager

ce2c85fa740d5dce2d582e694bb3adc9176101d7

[ "MIT" ]

null

dispatcher/qube.py

plinecom/JobManager

ce2c85fa740d5dce2d582e694bb3adc9176101d7

[ "MIT" ]

null

dispatcher/qube.py

plinecom/JobManager

ce2c85fa740d5dce2d582e694bb3adc9176101d7

[ "MIT" ]

null

import abstract import subprocess import xml.etree.ElementTree class Qube(abstract.DispatcherBase): def __init__(self): abstract.DispatcherBase.__init__(self) class Qube6(Qube): def __init__(self, configdict): Qube.__init__(self) print configdict self._configdict = configdict self.setvalue("executable", self._configdict["submitter"]) self.setvalue("server", "172.29.115.99") self.setvalue("dispatch_software", "Qube6") cmd = self._configdict["qbhosts"]+" --xml" p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout_data, stderr_data = p.communicate() qb_xml_document = xml.etree.ElementTree.fromstring("<doc>"+stdout_data+"</doc>") # print stdout_data group_dic = {} for e in qb_xml_document.findall("object/item/object/groups"): if e.text is not None: group_dic[e.text.encode("utf-8")] = "1" self.setvalue("groups", sorted(group_dic.keys())) # print group_dic.keys() cluster_dic = {} for e in qb_xml_document.findall("object/item/object/cluster"): cluster_dic[e.text.encode("utf-8")] = "1" cluster_dic2 = {} for cluster in cluster_dic.keys(): cluster_level = cluster.split("/") for i in range(0,len(cluster_level)+1): cluster_dic2["/".join(cluster_level[0:i])] = 1 # print cluster_dic2 # group_dic[e.text]="1" self.setvalue("pools", sorted(cluster_dic2.keys())) # print cluster_dic.keys() # self.setvalue("dispatherObj", self) def addJob(self, job): self._job.append(job) def submmision(self): self._buildCmd() def getDispatcherName(self): return "Qube 6" def submit(self, jobObj): print jobObj.getparam() # job = jobObj.getparam(); cmd = self.getvalue("executable") + ' --name ' + jobObj.getvalue("jobName")\ + ' --priority '+jobObj.getvalue("priority") \ + ' --range '+jobObj.getvalue("startFrame") + '-' + jobObj.getvalue("endFrame")\ + ' --chunk 10' if isinstance(jobObj.getvalue("selected_groups"), list): if len(jobObj.getvalue("selected_groups"))>0: cmd += ' --groups '+','.join(jobObj.getvalue("selected_groups")) cmd += ' /usr/autodesk/maya2014-x64/bin/Render -s QB_FRAME_START -e QB_FRAME_END ' \ + jobObj.getvalue("filePath") # --groups string # --cluster string # --processors int # --supervisor # --range 1-100 --chunk 10 print cmd p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout_data, stderr_data = p.communicate() print stdout_data print stderr_data def _buildCmd(self): for job in self._job: # if job.__class__.__name__ == "JobMayaSw": cmd = self.getvalue("executable") + ' -s ' + self.getvalue("server") + ' -u admin -b -department ' + getpass.getuser() + ' -e 566 -n ' + job.getvalue("job") + ' -f ' + job.getvalue("filePath") + ' -proj ' + job.getvalue("proj") + ' -sf ' + job.getvalue("startFrame") + " -ef " + job.getvalue("endFrame") + " -bf " + job.getvalue("byFrameSteps") + " -se 0 -st 1 -attr MAYADIGITS 10 1 -max 1" print cmd p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) (stdout_data, stderr_data) = p.communicate() print stdout_data print stderr_data

37.265306

407

0.588719

576044306c647959738d6a9eab632407e88a3740

1,251

py

Python

src/health/admin.py

kensonman/health

d2b54faa2f6b6c7ce1a21ca64622737739797c66

[ "Apache-2.0" ]

null

src/health/admin.py

kensonman/health

d2b54faa2f6b6c7ce1a21ca64622737739797c66

[ "Apache-2.0" ]

null

src/health/admin.py

kensonman/health

d2b54faa2f6b6c7ce1a21ca64622737739797c66

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # File: health/admin.py # Date: 2019-03-04 20:52 # Author: Kenson Man <kenson@kenson.idv.hk> # Desc: Define the admin-tools for the health project; from django.contrib import admin from django.utils.translation import gettext_lazy as _ from .models import * @admin.register(Category) class CategoryAdmin(admin.ModelAdmin): list_display=('id', 'name', 'desc', 'fmt', 'minimum', 'maximum', 'user', 'lmb', 'lmd') @admin.register(Tag) class TagAdmin(admin.ModelAdmin): list_display=('id', 'category_user', 'category_name', 'name', 'lmb', 'lmd') def category_user(self, record): return record.category.user category_user.short_description=_('Category.user') def category_name(self, record): return record.category.name category_name.short_description=_('Category.name') @admin.register(Index) class IndexAdmin(admin.ModelAdmin): list_display=('id', 'category_user', 'category_name', 'value', 'lmb', 'lmd') def category_user(self, record): return record.category.user category_user.short_description=_('Category.user') def category_name(self, record): return record.category.name category_name.short_description=_('Category.name')

32.076923

89

0.709832

126d2f097f5d88423eb0290e061608d2dc0ad56f

21,850

py

Python

heat/tests/openstack/aodh/test_gnocchi_alarm.py

grebennikov/heat1

6a11bd0b5984c8f880d1a24ed324620020032b5a

[ "Apache-2.0" ]

null

heat/tests/openstack/aodh/test_gnocchi_alarm.py

grebennikov/heat1

6a11bd0b5984c8f880d1a24ed324620020032b5a

[ "Apache-2.0" ]

null

heat/tests/openstack/aodh/test_gnocchi_alarm.py

grebennikov/heat1

6a11bd0b5984c8f880d1a24ed324620020032b5a

[ "Apache-2.0" ]

null

# # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock import mox from heat.common import exception from heat.common import template_format from heat.engine.clients.os import aodh from heat.engine.resources.openstack.aodh.gnocchi import ( alarm as gnocchi) from heat.engine import scheduler from heat.tests import common from heat.tests import utils gnocchi_resources_alarm_template = ''' heat_template_version: 2013-05-23 description: Gnocchi Resources Alarm Test resources: GnoResAlarm: type: OS::Aodh::GnocchiResourcesAlarm properties: description: Do stuff with gnocchi metric: cpu_util aggregation_method: mean granularity: 60 evaluation_periods: 1 threshold: 50 alarm_actions: [] resource_type: instance resource_id: 5a517ceb-b068-4aca-9eb9-3e4eb9b90d9a comparison_operator: gt ''' gnocchi_aggregation_by_metrics_alarm_template = ''' heat_template_version: 2013-05-23 description: Gnocchi Aggregation by Metrics Alarm Test resources: GnoAggregationByMetricsAlarm: type: OS::Aodh::GnocchiAggregationByMetricsAlarm properties: description: Do stuff with gnocchi metrics metrics: ["911fce07-e0d7-4210-8c8c-4a9d811fcabc", "2543d435-fe93-4443-9351-fb0156930f94"] aggregation_method: mean granularity: 60 evaluation_periods: 1 threshold: 50 alarm_actions: [] comparison_operator: gt ''' gnocchi_aggregation_by_resources_alarm_template = ''' heat_template_version: 2013-05-23 description: Gnocchi Aggregation by Resources Alarm Test resources: GnoAggregationByResourcesAlarm: type: OS::Aodh::GnocchiAggregationByResourcesAlarm properties: description: Do stuff with gnocchi aggregation by resource aggregation_method: mean granularity: 60 evaluation_periods: 1 threshold: 50 metric: cpu_util alarm_actions: [] resource_type: instance query: '{"=": {"server_group": "my_autoscaling_group"}}' comparison_operator: gt ''' FakeAodhAlarm = {'other_attrs': 'val', 'alarm_id': 'foo'} class GnocchiResourcesAlarmTest(common.HeatTestCase): def setUp(self): super(GnocchiResourcesAlarmTest, self).setUp() self.fc = mock.Mock() def create_alarm(self): self.patchobject(aodh.AodhClientPlugin, '_create').return_value = self.fc self.m.StubOutWithMock(self.fc.alarm, 'create') self.fc.alarm.create( { 'alarm_actions': [], 'description': u'Do stuff with gnocchi', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'name': mox.IgnoreArg(), 'type': 'gnocchi_resources_threshold', 'repeat_actions': True, 'gnocchi_resources_threshold_rule': { "metric": "cpu_util", "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "resource_type": "instance", "resource_id": "5a517ceb-b068-4aca-9eb9-3e4eb9b90d9a", "comparison_operator": "gt", }, 'time_constraints': [], 'severity': 'low' }).AndReturn(FakeAodhAlarm) self.tmpl = template_format.parse(gnocchi_resources_alarm_template) self.stack = utils.parse_stack(self.tmpl) resource_defns = self.stack.t.resource_definitions(self.stack) return gnocchi.AodhGnocchiResourcesAlarm( 'GnoResAlarm', resource_defns['GnoResAlarm'], self.stack) def test_update(self): rsrc = self.create_alarm() self.m.StubOutWithMock(self.fc.alarm, 'update') self.fc.alarm.update( 'foo', { 'alarm_actions': [], 'description': u'Do stuff with gnocchi', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'repeat_actions': True, 'gnocchi_resources_threshold_rule': { "metric": "cpu_util", "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "resource_type": "instance", "resource_id": "d3d6c642-921e-4fc2-9c5f-15d9a5afb598", "comparison_operator": "gt", }, 'time_constraints': [], 'severity': 'low' } ) self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() props = self.tmpl['resources']['GnoResAlarm']['properties'] props['resource_id'] = 'd3d6c642-921e-4fc2-9c5f-15d9a5afb598' update_template = rsrc.t.freeze(properties=props) scheduler.TaskRunner(rsrc.update, update_template)() self.assertEqual((rsrc.UPDATE, rsrc.COMPLETE), rsrc.state) self.m.VerifyAll() def _prepare_check_resource(self): snippet = template_format.parse(gnocchi_resources_alarm_template) self.stack = utils.parse_stack(snippet) res = self.stack['GnoResAlarm'] res.client = mock.Mock() mock_alarm = mock.Mock(enabled=True, state='ok') res.client().alarm.get.return_value = mock_alarm return res def test_create(self): rsrc = self.create_alarm() self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() self.assertEqual((rsrc.CREATE, rsrc.COMPLETE), rsrc.state) self.assertEqual('foo', rsrc.resource_id) self.m.VerifyAll() def test_suspend(self): rsrc = self.create_alarm() self.m.StubOutWithMock(self.fc.alarm, 'update') self.fc.alarm.update('foo', {'enabled': False}) self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() scheduler.TaskRunner(rsrc.suspend)() self.assertEqual((rsrc.SUSPEND, rsrc.COMPLETE), rsrc.state) self.m.VerifyAll() def test_resume(self): rsrc = self.create_alarm() self.m.StubOutWithMock(self.fc.alarm, 'update') self.fc.alarm.update('foo', {'enabled': True}) self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() rsrc.state_set(rsrc.SUSPEND, rsrc.COMPLETE) scheduler.TaskRunner(rsrc.resume)() self.assertEqual((rsrc.RESUME, rsrc.COMPLETE), rsrc.state) self.m.VerifyAll() def test_check(self): res = self._prepare_check_resource() scheduler.TaskRunner(res.check)() self.assertEqual((res.CHECK, res.COMPLETE), res.state) def test_check_failure(self): res = self._prepare_check_resource() res.client().alarm.get.side_effect = Exception('Boom') self.assertRaises(exception.ResourceFailure, scheduler.TaskRunner(res.check)) self.assertEqual((res.CHECK, res.FAILED), res.state) self.assertIn('Boom', res.status_reason) def test_show_resource(self): res = self._prepare_check_resource() res.client().alarm.create.return_value = FakeAodhAlarm res.client().alarm.get.return_value = FakeAodhAlarm scheduler.TaskRunner(res.create)() self.assertEqual(FakeAodhAlarm, res.FnGetAtt('show')) def test_gnocchi_alarm_live_state(self): snippet = template_format.parse(gnocchi_resources_alarm_template) self.stack = utils.parse_stack(snippet) resource_defns = self.stack.t.resource_definitions(self.stack) self.rsrc_defn = resource_defns['GnoResAlarm'] self.client = mock.Mock() self.patchobject(gnocchi.AodhGnocchiResourcesAlarm, 'client', return_value=self.client) alarm_res = gnocchi.AodhGnocchiResourcesAlarm( 'alarm', self.rsrc_defn, self.stack) alarm_res.create() value = { 'description': 'Do stuff with gnocchi', 'alarm_actions': [], 'time_constraints': [], 'gnocchi_resources_threshold_rule': { 'resource_id': '5a517ceb-b068-4aca-9eb9-3e4eb9b90d9a', 'metric': 'cpu_util', 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt', 'resource_type': 'instance' } } self.client.alarm.get.return_value = value expected_data = { 'description': 'Do stuff with gnocchi', 'alarm_actions': [], 'resource_id': '5a517ceb-b068-4aca-9eb9-3e4eb9b90d9a', 'metric': 'cpu_util', 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt', 'resource_type': 'instance', 'insufficient_data_actions': None, 'enabled': None, 'ok_actions': None, 'repeat_actions': None, 'severity': None } reality = alarm_res.get_live_state(alarm_res.properties) self.assertEqual(expected_data, reality) class GnocchiAggregationByMetricsAlarmTest(GnocchiResourcesAlarmTest): def create_alarm(self): self.patchobject(aodh.AodhClientPlugin, '_create').return_value = self.fc self.m.StubOutWithMock(self.fc.alarm, 'create') self.fc.alarm.create( { 'alarm_actions': [], 'description': u'Do stuff with gnocchi metrics', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'name': mox.IgnoreArg(), 'type': 'gnocchi_aggregation_by_metrics_threshold', 'repeat_actions': True, 'gnocchi_aggregation_by_metrics_threshold_rule': { "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "comparison_operator": "gt", "metrics": ["911fce07-e0d7-4210-8c8c-4a9d811fcabc", "2543d435-fe93-4443-9351-fb0156930f94"], }, 'time_constraints': [], 'severity': 'low'} ).AndReturn(FakeAodhAlarm) self.tmpl = template_format.parse( gnocchi_aggregation_by_metrics_alarm_template) self.stack = utils.parse_stack(self.tmpl) resource_defns = self.stack.t.resource_definitions(self.stack) return gnocchi.AodhGnocchiAggregationByMetricsAlarm( 'GnoAggregationByMetricsAlarm', resource_defns['GnoAggregationByMetricsAlarm'], self.stack) def test_update(self): rsrc = self.create_alarm() self.m.StubOutWithMock(self.fc.alarm, 'update') self.fc.alarm.update( 'foo', { 'alarm_actions': [], 'description': u'Do stuff with gnocchi metrics', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'repeat_actions': True, 'gnocchi_aggregation_by_metrics_threshold_rule': { "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "comparison_operator": "gt", 'metrics': ['d3d6c642-921e-4fc2-9c5f-15d9a5afb598', 'bc60f822-18a0-4a0c-94e7-94c554b00901'] }, 'time_constraints': [], 'severity': 'low' } ) self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() snippet = self.tmpl['resources']['GnoAggregationByMetricsAlarm'] props = snippet['properties'].copy() props['metrics'] = ['d3d6c642-921e-4fc2-9c5f-15d9a5afb598', 'bc60f822-18a0-4a0c-94e7-94c554b00901'] update_template = rsrc.t.freeze(properties=props) scheduler.TaskRunner(rsrc.update, update_template)() self.assertEqual((rsrc.UPDATE, rsrc.COMPLETE), rsrc.state) self.m.VerifyAll() def _prepare_check_resource(self): snippet = template_format.parse( gnocchi_aggregation_by_metrics_alarm_template) self.stack = utils.parse_stack(snippet) res = self.stack['GnoAggregationByMetricsAlarm'] res.client = mock.Mock() mock_alarm = mock.Mock(enabled=True, state='ok') res.client().alarm.get.return_value = mock_alarm return res def test_show_resource(self): res = self._prepare_check_resource() res.client().alarm.create.return_value = FakeAodhAlarm res.client().alarm.get.return_value = FakeAodhAlarm scheduler.TaskRunner(res.create)() self.assertEqual(FakeAodhAlarm, res.FnGetAtt('show')) def test_gnocchi_alarm_aggr_by_metrics_live_state(self): snippet = template_format.parse( gnocchi_aggregation_by_metrics_alarm_template) self.stack = utils.parse_stack(snippet) resource_defns = self.stack.t.resource_definitions(self.stack) self.rsrc_defn = resource_defns['GnoAggregationByMetricsAlarm'] self.client = mock.Mock() self.patchobject(gnocchi.AodhGnocchiAggregationByMetricsAlarm, 'client', return_value=self.client) alarm_res = gnocchi.AodhGnocchiAggregationByMetricsAlarm( 'alarm', self.rsrc_defn, self.stack) alarm_res.create() value = { 'description': 'Do stuff with gnocchi metrics', 'alarm_actions': [], 'time_constraints': [], 'gnocchi_aggregation_by_metrics_threshold_rule': { 'metrics': ['911fce07-e0d7-4210-8c8c-4a9d811fcabc', '2543d435-fe93-4443-9351-fb0156930f94'], 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt' } } self.client.alarm.get.return_value = value expected_data = { 'description': 'Do stuff with gnocchi metrics', 'alarm_actions': [], 'metrics': ['911fce07-e0d7-4210-8c8c-4a9d811fcabc', '2543d435-fe93-4443-9351-fb0156930f94'], 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt', 'insufficient_data_actions': None, 'enabled': None, 'ok_actions': None, 'repeat_actions': None, 'severity': None } reality = alarm_res.get_live_state(alarm_res.properties) self.assertEqual(expected_data, reality) class GnocchiAggregationByResourcesAlarmTest(GnocchiResourcesAlarmTest): def create_alarm(self): self.patchobject(aodh.AodhClientPlugin, '_create').return_value = self.fc self.m.StubOutWithMock(self.fc.alarm, 'create') self.fc.alarm.create( { 'alarm_actions': [], 'description': 'Do stuff with gnocchi aggregation by resource', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'name': mox.IgnoreArg(), 'type': 'gnocchi_aggregation_by_resources_threshold', 'repeat_actions': True, 'gnocchi_aggregation_by_resources_threshold_rule': { "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "comparison_operator": "gt", "metric": "cpu_util", "resource_type": "instance", "query": '{"=": {"server_group": "my_autoscaling_group"}}', }, 'time_constraints': [], 'severity': 'low'} ).AndReturn(FakeAodhAlarm) self.tmpl = template_format.parse( gnocchi_aggregation_by_resources_alarm_template) self.stack = utils.parse_stack(self.tmpl) resource_defns = self.stack.t.resource_definitions(self.stack) return gnocchi.AodhGnocchiAggregationByResourcesAlarm( 'GnoAggregationByResourcesAlarm', resource_defns['GnoAggregationByResourcesAlarm'], self.stack) def test_update(self): rsrc = self.create_alarm() self.m.StubOutWithMock(self.fc.alarm, 'update') self.fc.alarm.update( 'foo', { 'alarm_actions': [], 'description': 'Do stuff with gnocchi aggregation by resource', 'enabled': True, 'insufficient_data_actions': [], 'ok_actions': [], 'repeat_actions': True, 'gnocchi_aggregation_by_resources_threshold_rule': { "aggregation_method": "mean", "granularity": 60, "evaluation_periods": 1, "threshold": 50, "comparison_operator": "gt", "metric": "cpu_util", "resource_type": "instance", "query": '{"=": {"server_group": "my_new_group"}}', }, 'time_constraints': [], 'severity': 'low' } ) self.m.ReplayAll() scheduler.TaskRunner(rsrc.create)() snippet = self.tmpl['resources']['GnoAggregationByResourcesAlarm'] props = snippet['properties'].copy() props['query'] = '{"=": {"server_group": "my_new_group"}}' update_template = rsrc.t.freeze(properties=props) scheduler.TaskRunner(rsrc.update, update_template)() self.assertEqual((rsrc.UPDATE, rsrc.COMPLETE), rsrc.state) self.m.VerifyAll() def _prepare_check_resource(self): snippet = template_format.parse( gnocchi_aggregation_by_resources_alarm_template) self.stack = utils.parse_stack(snippet) res = self.stack['GnoAggregationByResourcesAlarm'] res.client = mock.Mock() mock_alarm = mock.Mock(enabled=True, state='ok') res.client().alarm.get.return_value = mock_alarm return res def test_show_resource(self): res = self._prepare_check_resource() res.client().alarm.create.return_value = FakeAodhAlarm res.client().alarm.get.return_value = FakeAodhAlarm scheduler.TaskRunner(res.create)() self.assertEqual(FakeAodhAlarm, res.FnGetAtt('show')) def test_gnocchi_alarm_aggr_by_resources_live_state(self): snippet = template_format.parse( gnocchi_aggregation_by_resources_alarm_template) self.stack = utils.parse_stack(snippet) resource_defns = self.stack.t.resource_definitions(self.stack) self.rsrc_defn = resource_defns['GnoAggregationByResourcesAlarm'] self.client = mock.Mock() self.patchobject(gnocchi.AodhGnocchiAggregationByResourcesAlarm, 'client', return_value=self.client) alarm_res = gnocchi.AodhGnocchiAggregationByResourcesAlarm( 'alarm', self.rsrc_defn, self.stack) alarm_res.create() value = { 'description': 'Do stuff with gnocchi aggregation by resource', 'alarm_actions': [], 'time_constraints': [], 'gnocchi_aggregation_by_resources_threshold_rule': { 'metric': 'cpu_util', 'resource_type': 'instance', 'query': "{'=': {'server_group': 'my_autoscaling_group'}}", 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt' } } self.client.alarm.get.return_value = value expected_data = { 'description': 'Do stuff with gnocchi aggregation by resource', 'alarm_actions': [], 'metric': 'cpu_util', 'resource_type': 'instance', 'query': "{'=': {'server_group': 'my_autoscaling_group'}}", 'evaluation_periods': 1, 'aggregation_method': 'mean', 'granularity': 60, 'threshold': 50, 'comparison_operator': 'gt', 'insufficient_data_actions': None, 'enabled': None, 'ok_actions': None, 'repeat_actions': None, 'severity': None } reality = alarm_res.get_live_state(alarm_res.properties) self.assertEqual(expected_data, reality)

39.017857

79

0.585538

e8631ada3867bf61e8ad20aa120af6ab2af76242

659

py

Python

modules/common/age_filter.py

BurraAbhishek/VirtualElections_v2

db95f58d09ee5ed9755a3910aebcbfb48302b04e

[ "Apache-2.0" ]

1

2022-01-30T19:55:47.000Z

modules/common/age_filter.py

BurraAbhishek/VirtualElections_v2

db95f58d09ee5ed9755a3910aebcbfb48302b04e

[ "Apache-2.0" ]

null

modules/common/age_filter.py

BurraAbhishek/VirtualElections_v2

db95f58d09ee5ed9755a3910aebcbfb48302b04e

[ "Apache-2.0" ]

null

def user_age_filter(age: int, setting: dict) -> bool: if not setting["boolRequired"]: return True elif setting["minAge"] <= 0 and setting["maxAge"] >= 100: return True else: if setting["minAge"] <= 0: if age <= setting["maxAge"]: return True else: return False elif setting["maxAge"] >= 100: if age >= setting["minAge"]: return True else: return False else: if setting["minAge"] <= age <= setting["maxAge"]: return True else: return False

29.954545

61

0.46434

eca389721a0b959b621e2bb557f742f17f735794

3,764

py

Python

devlib/utils/serial_port.py

valschneider/devlib

cafc0a4bc0de956bc80a97bbf478aa79221e5257

[ "Apache-2.0" ]

null

devlib/utils/serial_port.py

valschneider/devlib

cafc0a4bc0de956bc80a97bbf478aa79221e5257

[ "Apache-2.0" ]

null

devlib/utils/serial_port.py

valschneider/devlib

cafc0a4bc0de956bc80a97bbf478aa79221e5257

[ "Apache-2.0" ]

null

# Copyright 2013-2015 ARM Limited # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import time from contextlib import contextmanager from logging import Logger import serial import pexpect from distutils.version import StrictVersion as V if V(pexpect.__version__) < V('4.0.0'): import fdpexpect else: from pexpect import fdpexpect # Adding pexpect exceptions into this module's namespace from pexpect import EOF, TIMEOUT # NOQA pylint: disable=W0611 from devlib.exception import HostError class SerialLogger(Logger): write = Logger.debug def flush(self): pass def pulse_dtr(conn, state=True, duration=0.1): """Set the DTR line of the specified serial connection to the specified state for the specified duration (note: the initial state of the line is *not* checked.""" conn.setDTR(state) time.sleep(duration) conn.setDTR(not state) def get_connection(timeout, init_dtr=None, logcls=SerialLogger, *args, **kwargs): if init_dtr is not None: kwargs['dsrdtr'] = True try: conn = serial.Serial(*args, **kwargs) except serial.SerialException as e: raise HostError(e.message) if init_dtr is not None: conn.setDTR(init_dtr) conn.nonblocking() conn.flushOutput() target = fdpexpect.fdspawn(conn.fileno(), timeout=timeout) target.logfile_read = logcls('read') target.logfile_send = logcls('send') # Monkey-patching sendline to introduce a short delay after # chacters are sent to the serial. If two sendline s are issued # one after another the second one might start putting characters # into the serial device before the first one has finished, causing # corruption. The delay prevents that. tsln = target.sendline def sendline(x): tsln(x) time.sleep(0.1) target.sendline = sendline return target, conn def write_characters(conn, line, delay=0.05): """Write a single line out to serial charcter-by-character. This will ensure that nothing will be dropped for longer lines.""" line = line.rstrip('\r\n') for c in line: conn.send(c) time.sleep(delay) conn.sendline('') @contextmanager def open_serial_connection(timeout, get_conn=False, init_dtr=None, logcls=SerialLogger, *args, **kwargs): """ Opens a serial connection to a device. :param timeout: timeout for the fdpexpect spawn object. :param conn: ``bool`` that specfies whether the underlying connection object should be yielded as well. :param init_dtr: specifies the initial DTR state stat should be set. All arguments are passed into the __init__ of serial.Serial. See pyserial documentation for details: http://pyserial.sourceforge.net/pyserial_api.html#serial.Serial :returns: a pexpect spawn object connected to the device. See: http://pexpect.sourceforge.net/pexpect.html """ target, conn = get_connection(timeout, init_dtr=init_dtr, logcls=logcls, *args, **kwargs) if get_conn: yield target, conn else: yield target target.close() # Closes the file descriptor used by the conn. del conn

30.852459

98

0.691552

27424bf8c8a7b8fd48477c1dc460d36c51e03513

20,604

py

Python

tests/test_cloudformation/test_cloudformation_stack_crud.py

harveywi/moto

3a5d857a60c3a2d140ed2c8adfe8dcaf71a4cac8

[ "Apache-2.0" ]

1

2019-10-23T02:58:29.000Z

tests/test_cloudformation/test_cloudformation_stack_crud.py

harveywi/moto

3a5d857a60c3a2d140ed2c8adfe8dcaf71a4cac8

[ "Apache-2.0" ]

2

2021-03-31T20:15:51.000Z

2021-12-13T20:50:52.000Z

tests/test_cloudformation/test_cloudformation_stack_crud.py

harveywi/moto

3a5d857a60c3a2d140ed2c8adfe8dcaf71a4cac8

[ "Apache-2.0" ]

null

from __future__ import unicode_literals import os import json import boto import boto.s3 import boto.s3.key import boto.cloudformation from boto.exception import BotoServerError import sure # noqa # Ensure 'assert_raises' context manager support for Python 2.6 import tests.backport_assert_raises # noqa from nose.tools import assert_raises from moto import mock_cloudformation_deprecated, mock_s3_deprecated, mock_route53_deprecated from moto.cloudformation import cloudformation_backends dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 1", "Resources": {}, } dummy_template2 = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 2", "Resources": {}, } # template with resource which has no delete attribute defined dummy_template3 = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 3", "Resources": { "VPC": { "Properties": { "CidrBlock": "192.168.0.0/16", }, "Type": "AWS::EC2::VPC" } }, } dummy_template_json = json.dumps(dummy_template) dummy_template_json2 = json.dumps(dummy_template2) dummy_template_json3 = json.dumps(dummy_template3) @mock_cloudformation_deprecated def test_create_stack(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) stack = conn.describe_stacks()[0] stack.stack_name.should.equal('test_stack') stack.get_template().should.equal({ 'GetTemplateResponse': { 'GetTemplateResult': { 'TemplateBody': dummy_template_json, 'ResponseMetadata': { 'RequestId': '2d06e36c-ac1d-11e0-a958-f9382b6eb86bEXAMPLE' } } } }) @mock_cloudformation_deprecated @mock_route53_deprecated def test_create_stack_hosted_zone_by_id(): conn = boto.connect_cloudformation() dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 1", "Parameters": { }, "Resources": { "Bar": { "Type" : "AWS::Route53::HostedZone", "Properties" : { "Name" : "foo.bar.baz", } }, }, } dummy_template2 = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 2", "Parameters": { "ZoneId": { "Type": "String" } }, "Resources": { "Foo": { "Properties": { "HostedZoneId": {"Ref": "ZoneId"}, "RecordSets": [] }, "Type": "AWS::Route53::RecordSetGroup" } }, } conn.create_stack( "test_stack", template_body=json.dumps(dummy_template), parameters={}.items() ) r53_conn = boto.connect_route53() zone_id = r53_conn.get_zones()[0].id conn.create_stack( "test_stack", template_body=json.dumps(dummy_template2), parameters={"ZoneId": zone_id}.items() ) stack = conn.describe_stacks()[0] assert stack.list_resources() @mock_cloudformation_deprecated def test_creating_stacks_across_regions(): west1_conn = boto.cloudformation.connect_to_region("us-west-1") west1_conn.create_stack("test_stack", template_body=dummy_template_json) west2_conn = boto.cloudformation.connect_to_region("us-west-2") west2_conn.create_stack("test_stack", template_body=dummy_template_json) list(west1_conn.describe_stacks()).should.have.length_of(1) list(west2_conn.describe_stacks()).should.have.length_of(1) @mock_cloudformation_deprecated def test_create_stack_with_notification_arn(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack_with_notifications", template_body=dummy_template_json, notification_arns='arn:aws:sns:us-east-1:123456789012:fake-queue' ) stack = conn.describe_stacks()[0] [n.value for n in stack.notification_arns].should.contain( 'arn:aws:sns:us-east-1:123456789012:fake-queue') @mock_cloudformation_deprecated @mock_s3_deprecated def test_create_stack_from_s3_url(): s3_conn = boto.s3.connect_to_region('us-west-1') bucket = s3_conn.create_bucket("foobar") key = boto.s3.key.Key(bucket) key.key = "template-key" key.set_contents_from_string(dummy_template_json) key_url = key.generate_url(expires_in=0, query_auth=False) conn = boto.cloudformation.connect_to_region('us-west-1') conn.create_stack('new-stack', template_url=key_url) stack = conn.describe_stacks()[0] stack.stack_name.should.equal('new-stack') stack.get_template().should.equal( { 'GetTemplateResponse': { 'GetTemplateResult': { 'TemplateBody': dummy_template_json, 'ResponseMetadata': { 'RequestId': '2d06e36c-ac1d-11e0-a958-f9382b6eb86bEXAMPLE' } } } }) @mock_cloudformation_deprecated def test_describe_stack_by_name(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) stack = conn.describe_stacks("test_stack")[0] stack.stack_name.should.equal('test_stack') @mock_cloudformation_deprecated def test_describe_stack_by_stack_id(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) stack = conn.describe_stacks("test_stack")[0] stack_by_id = conn.describe_stacks(stack.stack_id)[0] stack_by_id.stack_id.should.equal(stack.stack_id) stack_by_id.stack_name.should.equal("test_stack") @mock_cloudformation_deprecated def test_describe_deleted_stack(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) stack = conn.describe_stacks("test_stack")[0] stack_id = stack.stack_id conn.delete_stack(stack.stack_id) stack_by_id = conn.describe_stacks(stack_id)[0] stack_by_id.stack_id.should.equal(stack.stack_id) stack_by_id.stack_name.should.equal("test_stack") stack_by_id.stack_status.should.equal("DELETE_COMPLETE") @mock_cloudformation_deprecated def test_get_template_by_name(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) template = conn.get_template("test_stack") template.should.equal({ 'GetTemplateResponse': { 'GetTemplateResult': { 'TemplateBody': dummy_template_json, 'ResponseMetadata': { 'RequestId': '2d06e36c-ac1d-11e0-a958-f9382b6eb86bEXAMPLE' } } } }) @mock_cloudformation_deprecated def test_list_stacks(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) conn.create_stack( "test_stack2", template_body=dummy_template_json, ) stacks = conn.list_stacks() stacks.should.have.length_of(2) stacks[0].template_description.should.equal("Stack 1") @mock_cloudformation_deprecated def test_delete_stack_by_name(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) conn.describe_stacks().should.have.length_of(1) conn.delete_stack("test_stack") conn.describe_stacks().should.have.length_of(0) @mock_cloudformation_deprecated def test_delete_stack_by_id(): conn = boto.connect_cloudformation() stack_id = conn.create_stack( "test_stack", template_body=dummy_template_json, ) conn.describe_stacks().should.have.length_of(1) conn.delete_stack(stack_id) conn.describe_stacks().should.have.length_of(0) with assert_raises(BotoServerError): conn.describe_stacks("test_stack") conn.describe_stacks(stack_id).should.have.length_of(1) @mock_cloudformation_deprecated def test_delete_stack_with_resource_missing_delete_attr(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json3, ) conn.describe_stacks().should.have.length_of(1) conn.delete_stack("test_stack") conn.describe_stacks().should.have.length_of(0) @mock_cloudformation_deprecated def test_bad_describe_stack(): conn = boto.connect_cloudformation() with assert_raises(BotoServerError): conn.describe_stacks("bad_stack") @mock_cloudformation_deprecated() def test_cloudformation_params(): dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 1", "Resources": {}, "Parameters": { "APPNAME": { "Default": "app-name", "Description": "The name of the app", "Type": "String" } } } dummy_template_json = json.dumps(dummy_template) cfn = boto.connect_cloudformation() cfn.create_stack('test_stack1', template_body=dummy_template_json, parameters=[ ('APPNAME', 'testing123')]) stack = cfn.describe_stacks('test_stack1')[0] stack.parameters.should.have.length_of(1) param = stack.parameters[0] param.key.should.equal('APPNAME') param.value.should.equal('testing123') @mock_cloudformation_deprecated def test_cloudformation_params_conditions_and_resources_are_distinct(): dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack 1", "Conditions": { "FooEnabled": { "Fn::Equals": [ { "Ref": "FooEnabled" }, "true" ] }, "FooDisabled": { "Fn::Not": [ { "Fn::Equals": [ { "Ref": "FooEnabled" }, "true" ] } ] } }, "Parameters": { "FooEnabled": { "Type": "String", "AllowedValues": [ "true", "false" ] } }, "Resources": { "Bar": { "Properties": { "CidrBlock": "192.168.0.0/16", }, "Condition": "FooDisabled", "Type": "AWS::EC2::VPC" } } } dummy_template_json = json.dumps(dummy_template) cfn = boto.connect_cloudformation() cfn.create_stack('test_stack1', template_body=dummy_template_json, parameters=[('FooEnabled', 'true')]) stack = cfn.describe_stacks('test_stack1')[0] resources = stack.list_resources() assert not [resource for resource in resources if resource.logical_resource_id == 'Bar'] @mock_cloudformation_deprecated def test_stack_tags(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, tags={"foo": "bar", "baz": "bleh"}, ) stack = conn.describe_stacks()[0] dict(stack.tags).should.equal({"foo": "bar", "baz": "bleh"}) @mock_cloudformation_deprecated def test_update_stack(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) conn.update_stack("test_stack", dummy_template_json2) stack = conn.describe_stacks()[0] stack.stack_status.should.equal("UPDATE_COMPLETE") stack.get_template().should.equal({ 'GetTemplateResponse': { 'GetTemplateResult': { 'TemplateBody': dummy_template_json2, 'ResponseMetadata': { 'RequestId': '2d06e36c-ac1d-11e0-a958-f9382b6eb86bEXAMPLE' } } } }) @mock_cloudformation_deprecated def test_update_stack_with_previous_template(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, ) conn.update_stack("test_stack", use_previous_template=True) stack = conn.describe_stacks()[0] stack.stack_status.should.equal("UPDATE_COMPLETE") stack.get_template().should.equal({ 'GetTemplateResponse': { 'GetTemplateResult': { 'TemplateBody': dummy_template_json, 'ResponseMetadata': { 'RequestId': '2d06e36c-ac1d-11e0-a958-f9382b6eb86bEXAMPLE' } } } }) @mock_cloudformation_deprecated def test_update_stack_with_parameters(): dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack", "Resources": { "VPC": { "Properties": { "CidrBlock": {"Ref": "Bar"} }, "Type": "AWS::EC2::VPC" } }, "Parameters": { "Bar": { "Type": "String" } } } dummy_template_json = json.dumps(dummy_template) conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, parameters=[("Bar", "192.168.0.0/16")] ) conn.update_stack( "test_stack", template_body=dummy_template_json, parameters=[("Bar", "192.168.0.1/16")] ) stack = conn.describe_stacks()[0] assert stack.parameters[0].value == "192.168.0.1/16" @mock_cloudformation_deprecated def test_update_stack_replace_tags(): conn = boto.connect_cloudformation() conn.create_stack( "test_stack", template_body=dummy_template_json, tags={"foo": "bar"}, ) conn.update_stack( "test_stack", template_body=dummy_template_json, tags={"foo": "baz"}, ) stack = conn.describe_stacks()[0] stack.stack_status.should.equal("UPDATE_COMPLETE") # since there is one tag it doesn't come out as a list dict(stack.tags).should.equal({"foo": "baz"}) @mock_cloudformation_deprecated def test_update_stack_when_rolled_back(): conn = boto.connect_cloudformation() stack_id = conn.create_stack( "test_stack", template_body=dummy_template_json) cloudformation_backends[conn.region.name].stacks[ stack_id].status = 'ROLLBACK_COMPLETE' with assert_raises(BotoServerError) as err: conn.update_stack("test_stack", dummy_template_json) ex = err.exception ex.body.should.match( r'is in ROLLBACK_COMPLETE state and can not be updated') ex.error_code.should.equal('ValidationError') ex.reason.should.equal('Bad Request') ex.status.should.equal(400) @mock_cloudformation_deprecated def test_describe_stack_events_shows_create_update_and_delete(): conn = boto.connect_cloudformation() stack_id = conn.create_stack( "test_stack", template_body=dummy_template_json) conn.update_stack(stack_id, template_body=dummy_template_json2) conn.delete_stack(stack_id) # assert begins and ends with stack events events = conn.describe_stack_events(stack_id) events[0].resource_type.should.equal("AWS::CloudFormation::Stack") events[-1].resource_type.should.equal("AWS::CloudFormation::Stack") # testing ordering of stack events without assuming resource events will not exist # the AWS API returns events in reverse chronological order stack_events_to_look_for = iter([ ("DELETE_COMPLETE", None), ("DELETE_IN_PROGRESS", "User Initiated"), ("UPDATE_COMPLETE", None), ("UPDATE_IN_PROGRESS", "User Initiated"), ("CREATE_COMPLETE", None), ("CREATE_IN_PROGRESS", "User Initiated"), ]) try: for event in events: event.stack_id.should.equal(stack_id) event.stack_name.should.equal("test_stack") event.event_id.should.match(r"[0-9a-f]{8}-([0-9a-f]{4}-){3}[0-9a-f]{12}") if event.resource_type == "AWS::CloudFormation::Stack": event.logical_resource_id.should.equal("test_stack") event.physical_resource_id.should.equal(stack_id) status_to_look_for, reason_to_look_for = next( stack_events_to_look_for) event.resource_status.should.equal(status_to_look_for) if reason_to_look_for is not None: event.resource_status_reason.should.equal( reason_to_look_for) except StopIteration: assert False, "Too many stack events" list(stack_events_to_look_for).should.be.empty @mock_cloudformation_deprecated def test_create_stack_lambda_and_dynamodb(): conn = boto.connect_cloudformation() dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack Lambda Test 1", "Parameters": { }, "Resources": { "func1": { "Type" : "AWS::Lambda::Function", "Properties" : { "Code": { "S3Bucket": "bucket_123", "S3Key": "key_123" }, "FunctionName": "func1", "Handler": "handler.handler", "Role": "role1", "Runtime": "python2.7", "Description": "descr", "MemorySize": 12345, } }, "func1version": { "Type": "AWS::Lambda::Version", "Properties": { "FunctionName": { "Ref": "func1" } } }, "tab1": { "Type" : "AWS::DynamoDB::Table", "Properties" : { "TableName": "tab1", "KeySchema": [{ "AttributeName": "attr1", "KeyType": "HASH" }], "AttributeDefinitions": [{ "AttributeName": "attr1", "AttributeType": "string" }], "ProvisionedThroughput": { "ReadCapacityUnits": 10, "WriteCapacityUnits": 10 } } }, "func1mapping": { "Type": "AWS::Lambda::EventSourceMapping", "Properties": { "FunctionName": { "Ref": "func1" }, "EventSourceArn": "arn:aws:dynamodb:region:XXXXXX:table/tab1/stream/2000T00:00:00.000", "StartingPosition": "0", "BatchSize": 100, "Enabled": True } } }, } validate_s3_before = os.environ.get('VALIDATE_LAMBDA_S3', '') try: os.environ['VALIDATE_LAMBDA_S3'] = 'false' conn.create_stack( "test_stack_lambda_1", template_body=json.dumps(dummy_template), parameters={}.items() ) finally: os.environ['VALIDATE_LAMBDA_S3'] = validate_s3_before stack = conn.describe_stacks()[0] resources = stack.list_resources() assert len(resources) == 4 @mock_cloudformation_deprecated def test_create_stack_kinesis(): conn = boto.connect_cloudformation() dummy_template = { "AWSTemplateFormatVersion": "2010-09-09", "Description": "Stack Kinesis Test 1", "Parameters": {}, "Resources": { "stream1": { "Type" : "AWS::Kinesis::Stream", "Properties" : { "Name": "stream1", "ShardCount": 2 } } } } conn.create_stack( "test_stack_kinesis_1", template_body=json.dumps(dummy_template), parameters={}.items() ) stack = conn.describe_stacks()[0] resources = stack.list_resources() assert len(resources) == 1

30.434269

107

0.592118

12a3ed4de4ebd1b11a9391998dea31bfa12159ec

3,412

py

Python

app/app/settings.py

bigmaho/recipe-app-api

835fcc457474d49544455407d0866bf0d29517cd

[ "MIT" ]

null

app/app/settings.py

bigmaho/recipe-app-api

835fcc457474d49544455407d0866bf0d29517cd

[ "MIT" ]

null

app/app/settings.py

bigmaho/recipe-app-api

835fcc457474d49544455407d0866bf0d29517cd

[ "MIT" ]

null

""" Django settings for app project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'j#g+f@s6+t3h%t8#*%v1j^x-gh=q)rqq55w#%ubw=07*adss7d' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'core', 'user', 'recipe', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'HOST': os.environ.get('DB_HOST'), 'NAME': os.environ.get('DB_NAME'), 'USER': os.environ.get('DB_USER'), 'PASSWORD': os.environ.get('DB_PASS'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/media/' MEDIA_ROOT = '/vol/web/media' STATIC_ROOT = '/vol/web/static' AUTH_USER_MODEL = 'core.User'

25.274074

91

0.684642

c4d4ce780be2fa5a2617874ddb608e41edf70c36

10,679

py

Python

tensorflow/python/kernel_tests/dynamic_stitch_op_test.py

tianyapiaozi/tensorflow

fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a

[ "Apache-2.0" ]

71

2017-05-25T16:02:15.000Z

2021-06-09T16:08:08.000Z

tensorflow/python/kernel_tests/dynamic_stitch_op_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

[ "Apache-2.0" ]

133

2017-04-26T16:49:49.000Z

2019-10-15T11:39:26.000Z

tensorflow/python/kernel_tests/dynamic_stitch_op_test.py

shrikunjsarda/tensorflow

7e8927e7af0c51ac20a63bd4eab6ff83df1a39ae

[ "Apache-2.0" ]

26

2017-04-12T16:25:44.000Z

2018-10-30T10:10:15.000Z

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for tensorflow.ops.data_flow_ops.{,parallel_}dynamic_stitch.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.ops import array_ops from tensorflow.python.ops import data_flow_ops from tensorflow.python.ops import gradients_impl import tensorflow.python.ops.data_flow_grad # pylint: disable=unused-import from tensorflow.python.platform import test class DynamicStitchTestBase(object): def __init__(self, stitch_op): self.stitch_op = stitch_op def testScalar(self): with self.test_session(use_gpu=True): indices = [constant_op.constant(0), constant_op.constant(1)] data = [constant_op.constant(40), constant_op.constant(60)] for step in -1, 1: stitched_t = self.stitch_op(indices[::step], data) stitched_val = stitched_t.eval() self.assertAllEqual([40, 60][::step], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([2], stitched_t.get_shape().as_list()) def testShapeInferenceForScalarWithNonConstantIndices(self): with self.test_session(use_gpu=True): indices = [ array_ops.placeholder(dtype=dtypes.int32), constant_op.constant(1) ] data = [constant_op.constant(40), constant_op.constant(60)] for step in -1, 1: stitched_t = self.stitch_op(indices[::step], data) # Dimension 0 is max(flatten(indices))+1, but the first indices input is # not a constant tensor, so we can only infer it as a vector of unknown # length. self.assertEqual([None], stitched_t.get_shape().as_list()) def testSimpleOneDimensional(self): with self.test_session(use_gpu=True): indices = [ constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), constant_op.constant([10, 60, 20, 30, 50]) ] stitched_t = self.stitch_op(indices, data) stitched_val = stitched_t.eval() self.assertAllEqual([0, 10, 20, 30, 40, 50, 60, 70], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([8], stitched_t.get_shape().as_list()) def testOneListOneDimensional(self): with self.test_session(use_gpu=True): indices = [constant_op.constant([1, 6, 2, 3, 5, 0, 4, 7])] data = [constant_op.constant([10, 60, 20, 30, 50, 0, 40, 70])] stitched_t = self.stitch_op(indices, data) stitched_val = stitched_t.eval() self.assertAllEqual([0, 10, 20, 30, 40, 50, 60, 70], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([8], stitched_t.get_shape().as_list()) def testSimpleTwoDimensional(self): with self.test_session(use_gpu=True): indices = [ constant_op.constant([0, 4, 7]), constant_op.constant([1, 6]), constant_op.constant([2, 3, 5]) ] data = [ constant_op.constant([[0, 1], [40, 41], [70, 71]]), constant_op.constant([[10, 11], [60, 61]]), constant_op.constant([[20, 21], [30, 31], [50, 51]]) ] stitched_t = self.stitch_op(indices, data) stitched_val = stitched_t.eval() self.assertAllEqual([[0, 1], [10, 11], [20, 21], [30, 31], [40, 41], [50, 51], [60, 61], [70, 71]], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([8, 2], stitched_t.get_shape().as_list()) def testHigherRank(self): with self.test_session(use_gpu=True) as sess: indices = [ constant_op.constant(6), constant_op.constant([4, 1]), constant_op.constant([[5, 2], [0, 3]]) ] data = [ constant_op.constant([61., 62.]), constant_op.constant([[41., 42.], [11., 12.]]), constant_op.constant([[[51., 52.], [21., 22.]], [[1., 2.], [31., 32.]]]) ] stitched_t = self.stitch_op(indices, data) stitched_val = stitched_t.eval() correct = 10. * np.arange(7)[:, None] + [1., 2.] self.assertAllEqual(correct, stitched_val) self.assertEqual([7, 2], stitched_t.get_shape().as_list()) # Test gradients stitched_grad = 7. * stitched_val grads = gradients_impl.gradients(stitched_t, indices + data, stitched_grad) self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients for datum, grad in zip(data, sess.run(grads[3:])): self.assertAllEqual(7. * datum.eval(), grad) def testErrorIndicesMultiDimensional(self): indices = [ constant_op.constant([0, 4, 7]), constant_op.constant([[1, 6, 2, 3, 5]]) ] data = [ constant_op.constant([[0, 40, 70]]), constant_op.constant([10, 60, 20, 30, 50]) ] with self.assertRaises(ValueError): self.stitch_op(indices, data) def testErrorDataNumDimsMismatch(self): indices = [ constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), constant_op.constant([[10, 60, 20, 30, 50]]) ] with self.assertRaises(ValueError): self.stitch_op(indices, data) def testErrorDataDimSizeMismatch(self): indices = [ constant_op.constant([0, 4, 5]), constant_op.constant([1, 6, 2, 3]) ] data = [ constant_op.constant([[0], [40], [70]]), constant_op.constant([[10, 11], [60, 61], [20, 21], [30, 31]]) ] with self.assertRaises(ValueError): self.stitch_op(indices, data) def testErrorDataAndIndicesSizeMismatch(self): indices = [ constant_op.constant([0, 4, 7]), constant_op.constant([1, 6, 2, 3, 5]) ] data = [ constant_op.constant([0, 40, 70]), constant_op.constant([10, 60, 20, 30]) ] with self.assertRaises(ValueError): self.stitch_op(indices, data) class DynamicStitchTest(DynamicStitchTestBase, test.TestCase): def __init__(self, *test_case_args): test.TestCase.__init__(self, *test_case_args) DynamicStitchTestBase.__init__(self, data_flow_ops.dynamic_stitch) class ParallelDynamicStitchTest(DynamicStitchTestBase, test.TestCase): def __init__(self, *test_case_args): test.TestCase.__init__(self, *test_case_args) DynamicStitchTestBase.__init__(self, data_flow_ops.parallel_dynamic_stitch) def testScalar(self): with self.test_session(use_gpu=True): indices = [constant_op.constant(0), constant_op.constant(1)] data = [constant_op.constant(40.0), constant_op.constant(60.0)] for step in -1, 1: stitched_t = data_flow_ops.dynamic_stitch(indices[::step], data) stitched_val = stitched_t.eval() self.assertAllEqual([40.0, 60.0][::step], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([2], stitched_t.get_shape().as_list()) def testHigherRank(self): with self.test_session(use_gpu=True) as sess: indices = [ constant_op.constant(6), constant_op.constant([4, 1]), constant_op.constant([[5, 2], [0, 3]]) ] data = [ constant_op.constant([61, 62], dtype=dtypes.float32), constant_op.constant([[41, 42], [11, 12]], dtype=dtypes.float32), constant_op.constant( [[[51, 52], [21, 22]], [[1, 2], [31, 32]]], dtype=dtypes.float32) ] stitched_t = data_flow_ops.dynamic_stitch(indices, data) stitched_val = stitched_t.eval() correct = 10 * np.arange(7)[:, None] + [1.0, 2.0] self.assertAllEqual(correct, stitched_val) self.assertEqual([7, 2], stitched_t.get_shape().as_list()) # Test gradients stitched_grad = 7 * stitched_val grads = gradients_impl.gradients(stitched_t, indices + data, stitched_grad) self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients for datum, grad in zip(data, sess.run(grads[3:])): self.assertAllEqual(7.0 * datum.eval(), grad) # GPU version unit tests def testScalarGPU(self): with self.test_session(): indices = [constant_op.constant(0), constant_op.constant(1)] data = [constant_op.constant(40.0), constant_op.constant(60.0)] for step in -1, 1: stitched_t = data_flow_ops.dynamic_stitch(indices[::step], data) stitched_val = stitched_t.eval() self.assertAllEqual([40.0, 60.0][::step], stitched_val) # Dimension 0 is max(flatten(indices))+1. self.assertEqual([2], stitched_t.get_shape().as_list()) def testHigherRankGPU(self): with self.test_session() as sess: indices = [ constant_op.constant(6), constant_op.constant([4, 1]), constant_op.constant([[5, 2], [0, 3]]) ] data = [ constant_op.constant([61, 62], dtype=dtypes.float32), constant_op.constant([[41, 42], [11, 12]], dtype=dtypes.float32), constant_op.constant( [[[51, 52], [21, 22]], [[1, 2], [31, 32]]], dtype=dtypes.float32) ] stitched_t = data_flow_ops.dynamic_stitch(indices, data) stitched_val = stitched_t.eval() correct = 10 * np.arange(7)[:, None] + [1.0, 2.0] self.assertAllEqual(correct, stitched_val) self.assertEqual([7, 2], stitched_t.get_shape().as_list()) # Test gradients stitched_grad = 7 * stitched_val grads = gradients_impl.gradients(stitched_t, indices + data, stitched_grad) self.assertEqual(grads[:3], [None] * 3) # Indices have no gradients for datum, grad in zip(data, sess.run(grads[3:])): self.assertAllEqual(7.0 * datum.eval(), grad) if __name__ == "__main__": test.main()

39.117216

80

0.62459

cb8617c6015c3c5b01f17e7a53d5e728843f42f0

617

py

Python

exercicio103.py

juniooor/Exercicios-python

aed87da4f93d0e6083b1a8c3af4081a028f145de

[ "MIT" ]

null

exercicio103.py

juniooor/Exercicios-python

aed87da4f93d0e6083b1a8c3af4081a028f145de

[ "MIT" ]

null

exercicio103.py

juniooor/Exercicios-python

aed87da4f93d0e6083b1a8c3af4081a028f145de

[ "MIT" ]

null

#Faça um programa com uma função chamada somaImposto. A função possui dois parâmetros formais: taxaImposto, que é a quantia de imposto sobre vendas expressa em porcentagem e custo, que é o custo de um item antes do imposto. A função “altera” o valor de custo para incluir o imposto sobre vendas. print('-----DESAFIO 103-----') def somaimposto(taxa,prod): taxa=taxa/100 juros=prod*taxa soma=juros+prod return soma produto=float(input('Digite o valor do produto: ')) taxa=float(input('Digite o valor do imposto [%]: ')) result=somaimposto(taxa,produto) print(f'O produto com imposto fica R${result}')

44.071429

295

0.73906

0ed8bd55456c710ea2d7e025552a71e1556b3d37

1,225

py

Python

server/view/auth/signUp.py

DSMFOREST/Forest.NewBackend

7bfb8e18660af00ef45a6453d120b72446d2eff0

[ "MIT" ]

null

server/view/auth/signUp.py

DSMFOREST/Forest.NewBackend

7bfb8e18660af00ef45a6453d120b72446d2eff0

[ "MIT" ]

null

server/view/auth/signUp.py

DSMFOREST/Forest.NewBackend

7bfb8e18660af00ef45a6453d120b72446d2eff0

[ "MIT" ]

null

import random from flask import request, Response, url_for, render_template from flasgger import swag_from from flask_restful import Resource from view import send_email from view.auth.confirm import Confirm from docs.auth import SIGN_UP_POST from model.user import UserModel class SignUp(Resource): @swag_from(SIGN_UP_POST) def post(self): payload = request.json ID = payload['userId'] # 아이디 EM = payload['email'] PW = payload['password'] if UserModel.objects(userId=ID).first(): return {"status": "The ID already exists."}, 409 else: UserModel( userId=ID, password=PW, name=payload['name'], student_number=payload['student_number'], email=EM, token=int(random.randint(100000, 999999)), confirmed=False, admin=False ).save() code = UserModel.objects(userId=ID).first().token send_email( to=EM, link="http://aws.jaehoon.kim:5002/confirm/" + str(ID) + "." + str(code) ) return {"status": "successfully processed!"}, 201

27.840909

87

0.570612

9403b60c56f4651c882f09e50818ba3b281951e8

83

py

Python

cicl/apps.py

chitzuchang/CICL

561e647e8651a55b64f861036f1c031e24b58f1c

[ "MIT" ]

1

2021-07-07T20:15:37.000Z

cicl/apps.py

chitzuchang/CICL

561e647e8651a55b64f861036f1c031e24b58f1c

[ "MIT" ]

null

cicl/apps.py

chitzuchang/CICL

561e647e8651a55b64f861036f1c031e24b58f1c

[ "MIT" ]

null

from django.apps import AppConfig class CICLConfig(AppConfig): name = "cicl"

13.833333

33

0.73494

64e683d132bf109720e6c710637d443d111fe2c9

3,232

bzl

Python

python/pip.bzl

Cecilwang/rules_python

9e027001f7821aafa082a8c06e7c672342c429b9

[ "Apache-2.0" ]

null

python/pip.bzl

Cecilwang/rules_python

9e027001f7821aafa082a8c06e7c672342c429b9

[ "Apache-2.0" ]

null

python/pip.bzl

Cecilwang/rules_python

9e027001f7821aafa082a8c06e7c672342c429b9

[ "Apache-2.0" ]

null

# Copyright 2017 The Bazel Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Import pip requirements into Bazel.""" def _pip_import_impl(repository_ctx): """Core implementation of pip_import.""" # Add an empty top-level BUILD file. # This is because Bazel requires BUILD files along all paths accessed # via //this/sort/of:path and we wouldn't be able to load our generated # requirements.bzl without it. repository_ctx.file("BUILD", "") # To see the output, pass: quiet=False result = repository_ctx.execute([ repository_ctx.attr.python_interpreter, repository_ctx.path(repository_ctx.attr._script), "--python_interpreter", repository_ctx.attr.python_interpreter, "--name", repository_ctx.attr.name, "--input", repository_ctx.path(repository_ctx.attr.requirements), "--output", repository_ctx.path("requirements.bzl"), "--directory", repository_ctx.path(""), ]) if result.return_code: fail("pip_import failed: %s (%s)" % (result.stdout, result.stderr)) pip_import = repository_rule( attrs = { "python_interpreter": attr.string(default="python"), "requirements": attr.label( mandatory = True, allow_single_file = True, ), "_script": attr.label( executable = True, default = Label("//tools:piptool.par"), cfg = "host", ), }, implementation = _pip_import_impl, ) """A rule for importing <code>requirements.txt</code> dependencies into Bazel. This rule imports a <code>requirements.txt</code> file and generates a new <code>requirements.bzl</code> file. This is used via the <code>WORKSPACE</code> pattern: <pre><code>pip_import( name = "foo", requirements = ":requirements.txt", ) load("@foo//:requirements.bzl", "pip_install") pip_install() </code></pre> You can then reference imported dependencies from your <code>BUILD</code> file with: <pre><code>load("@foo//:requirements.bzl", "requirement") py_library( name = "bar", ... deps = [ "//my/other:dep", requirement("futures"), requirement("mock"), ], ) </code></pre> Or alternatively: <pre><code>load("@foo//:requirements.bzl", "all_requirements") py_binary( name = "baz", ... deps = [ ":foo", ] + all_requirements, ) </code></pre> Args: requirements: The label of a requirements.txt file. """ def pip_repositories(): """Pull in dependencies needed for pulling in pip dependencies. A placeholder method that will eventually pull in any dependencies needed to install pip dependencies. """ pass

29.651376

80

0.6612

31c103ee14a5d9be16e6ad38842a4987cb8868d5

602

py

Python

notification/notifier/slack.py

gueux/openduty

442a9a358f06408f27149c5cd311e51859ed7bc0

[ "MIT" ]

595

2015-01-04T15:34:04.000Z

2022-01-21T14:13:32.000Z

notification/notifier/slack.py

gueux/openduty

442a9a358f06408f27149c5cd311e51859ed7bc0

[ "MIT" ]

65

2015-01-02T01:57:37.000Z

2019-01-28T12:18:12.000Z

notification/notifier/slack.py

gueux/openduty

442a9a358f06408f27149c5cd311e51859ed7bc0

[ "MIT" ]

214

2015-01-03T20:48:55.000Z

2022-03-22T09:18:12.000Z

from slacker import Slacker class SlackNotifier: def __init__(self, config): self.__config = config def notify(self, notification): slack = Slacker(self.__config['apikey']) response = slack.chat.post_message(notification.user_to_notify.profile.slack_room_name, notification.message, username="Openduty", icon_url="https://slack.global.ssl.fastly.net/1937/img/services/pagerduty_48.png") if not response.error: print "Slack message sent" else: print "Failed to send Slack message"

37.625

146

0.641196

91b7fc25bf5b20af3458adc23d73def8fc869db5

7,124

py

Python

philbinss/flipflops.py

martinohanlon/PhilbinSS

994284fdca7c5ee0eba795ce045415e5dc6d7a00

[ "MIT" ]

4

2017-05-27T21:39:07.000Z

2020-12-20T13:21:34.000Z

philbinss/flipflops.py

martinohanlon/PhilbinSS

994284fdca7c5ee0eba795ce045415e5dc6d7a00

[ "MIT" ]

5

2017-05-24T10:46:45.000Z

2017-05-24T18:12:08.000Z

philbinss/flipflops.py

martinohanlon/PhilbinSS

994284fdca7c5ee0eba795ce045415e5dc6d7a00

[ "MIT" ]

null

from interfaces import Interface from logicgates import Nor, And, Not, Or, Nand from components import Split from primitives import Cathode from mixins import InputSetMixin, InputResetMixin, InputDMixin, InputJMixin, InputKMixin, InputClockMixin, OutputQ_Mixin, OutputQMixin from mixins import InputAMixin, InputBMixin, InputCMixin, OutputMixin class SRFlipFlop(Interface, InputSetMixin, InputResetMixin, OutputQ_Mixin, OutputQMixin): """ The implementation of a SR (set / reset) flip flop """ def __init__(self): inputs = {} outputs = {} n1 = Nor() n2 = Nor() inputs["set"] = n1.input_a inputs["reset"] = n2.input_b output_q_ = Cathode() output_q = Cathode() n1_split = Split() n2_split = Split() n1.output.connect(n1_split.input) n1_split.connect(n2.input_a) n1_split.connect(output_q_) n2.output.connect(n2_split.input) n2_split.connect(n1.input_b) n2_split.connect(output_q) outputs["output_q_"] = output_q_ outputs["output_q"] = output_q super(SRFlipFlop, self).__init__(inputs, outputs) def __str__(self): return "SRFlipFlop: " + super(SRFlipFlop, self).__str__() class JKFlipFlop(Interface, InputJMixin, InputKMixin, InputClockMixin, OutputQ_Mixin, OutputQMixin): """ The implementation of a JK flip flop """ def __init__(self): inputs = {} outputs = {} aj1 = And() aj2 = And() ak1 = And() ak2 = And() sr = SRFlipFlop() clk_split = Split() q_split = Split() qsplit = Split() #connect up the inputs inputs["input_j"] = aj1.input_a inputs["clock"] = clk_split.input clk_split.connect(aj1.input_b) clk_split.connect(ak1.input_a) inputs["input_k"] = ak1.input_b #connect the 2nd AND gates to the SR flip flop aj1.output.connect(aj2.input_b) ak1.output.connect(ak2.input_a) aj2.output.connect(sr.set) ak2.output.connect(sr.reset) #connect up the sr outputs output_q_ = Cathode() output_q = Cathode() sr.output_q_.connect(q_split.input) q_split.connect(aj2.input_a) q_split.connect(output_q_) sr.output_q.connect(qsplit.input) qsplit.connect(ak2.input_b) qsplit.connect(output_q) outputs["output_q_"] = output_q_ outputs["output_q"] = output_q super(JKFlipFlop, self).__init__(inputs, outputs) def __str__(self): return "JKFlipFlop: " + super(JKFlipFlop, self).__str__() class ThreeInputNand(Interface, InputAMixin, InputBMixin, InputCMixin, OutputMixin): """ The implementation of a Nand gate, it accepts a three inputs and has a single output """ def __init__(self): a1 = And() a2 = And() n = Not() inputs = {} inputs["input_a"] = a1.input_a inputs["input_b"] = a1.input_b inputs["input_c"] = a2.input_a a1.output.connect(a2.input_b) a2.output.connect(n.input) outputs = {} outputs["output"] = n.output super(ThreeInputNand, self).__init__(inputs, outputs) def __str__(self): return "ThreeInputNand: " + super(ThreeInputNand, self).__str__() class MasterSlaveJKFlipFlop(Interface, InputJMixin, InputKMixin, InputClockMixin, OutputQ_Mixin, OutputQMixin): """ The implementation of a JK flip flop """ def __init__(self): inputs = {} outputs = {} n1 = ThreeInputNand() n2 = ThreeInputNand() n3 = Nand() n4 = Nand() n5 = Nand() n6 = Nand() n7 = Nand() n8 = Nand() n = Not() clk_split = Split() n3_split = Split() n4_split = Split() n_split = Split() n7_split = Split() n8_split = Split() output_q_ = Cathode() output_q = Cathode() self.components = {} self.components["clk_split"] = clk_split self.components["n3_split"] = n3_split self.components["n4_split"] = n4_split self.components["n_split"] = n_split # inputs inputs["input_j"] = n1.input_b inputs["clock"] = clk_split.input inputs["input_k"] = n2.input_b # clock split clk_split.connect(n1.input_c) clk_split.connect(n2.input_a) clk_split.connect(n.input) # nand 1 n1.output.connect(n3.input_a) # nand 2 n2.output.connect(n4.input_b) # nand 3 n3.output.connect(n3_split.input) # nand 4 n4.output.connect(n4_split.input) # not n.output.connect(n_split.input) # nand 3 split n3_split.connect(n4.input_a) n3_split.connect(n5.input_a) # nand 4 split n4_split.connect(n3.input_b) n4_split.connect(n6.input_b) # not split n_split.connect(n5.input_b) n_split.connect(n6.input_a) # nand 5 n5.output.connect(n7.input_a) # nand 6 n6.output.connect(n8.input_b) # nand 7 n7.output.connect(n7_split.input) # nand 8 n8.output.connect(n8_split.input) # nand 7 split n7_split.connect(n8.input_a) n7_split.connect(output_q) n7_split.connect(n2.input_c) # nand 8 split n8_split.connect(n7.input_b) n8_split.connect(output_q_) n8_split.connect(n1.input_a) outputs["output_q_"] = output_q_ outputs["output_q"] = output_q super(MasterSlaveJKFlipFlop, self).__init__(inputs, outputs) def __str__(self): return "MasterSlaveJKFlipFlop: " + super(MasterSlaveJKFlipFlop, self).__str__() class DFlipFlop(Interface, InputDMixin, InputClockMixin, OutputQ_Mixin, OutputQMixin): """ The implementation of a D flip flop """ def __init__(self): inputs = {} outputs = {} n = Not() a1 = And() a2 = And() sr = SRFlipFlop() clk_split = Split() d_split = Split() #connect up the inputs inputs["input_d"] = d_split.input d_split.connect(n.input) d_split.connect(a2.input_b) n.output.connect(a1.input_a) inputs["clock"] = clk_split.input clk_split.connect(a1.input_b) clk_split.connect(a2.input_a) a1.output.connect(sr.set) a2.output.connect(sr.reset) outputs["output_q_"] = sr.output_q_ outputs["output_q"] = sr.output_q super(DFlipFlop, self).__init__(inputs, outputs) def __str__(self): return "DFlipFlop: " + super(DFlipFlop, self).__str__()

27.505792

135

0.569905

d27ed7c0c049247ff4b361a42b854e6d8097f890

3,481

py

Python

WebUtils/Tests/TestHTMLTag.py

PeaceWorksTechnologySolutions/w4py

74f5a03a63f1a93563502b908474aefaae2abda2

[ "MIT" ]

18

2016-08-01T20:15:59.000Z

2019-12-24T16:00:03.000Z

WebUtils/Tests/TestHTMLTag.py

WebwareForPython/w4py

bba08f5974d49f5da7e88abe3eeda1037d0824a3

[ "MIT" ]

6

2016-09-13T05:48:45.000Z

2020-01-09T18:29:12.000Z

WebUtils/Tests/TestHTMLTag.py

WebwareForPython/w4py

bba08f5974d49f5da7e88abe3eeda1037d0824a3

[ "MIT" ]

6

2016-09-16T14:32:29.000Z

2020-01-03T18:52:16.000Z

import os import sys import unittest sys.path.insert(1, os.path.abspath('../..')) from MiscUtils import StringIO from WebUtils.HTMLTag import HTMLReader class HTMLTagTest(unittest.TestCase): def setUp(self): self._html = """\ <html> <head> <title>Example</title> </head> <body lang="en"> <p>What's up, <i>doc</i>?</p> <hr> <table id="dataTable"> <tr> <th> x </th> <th> y </th> </tr> <tr><td class="datum">0</td><td class="datum">0</td></tr> </table> </body> </html>""" def checkBasics(self): reader = HTMLReader() tag = reader.readString('<html> </html>') self.assertEqual(tag.name(), 'html') self.assertEqual(reader.rootTag(), tag) self.assertTrue(reader.filename() is None) out = StringIO() tag.pprint(out) self.assertEqual(out.getvalue(), '<html>\n</html>\n') def checkReuseReader(self): reader = HTMLReader() reader.readString('<html> </html>') tag = reader.readString('<html> <body> </body> </html>') self.assertFalse(reader.rootTag() is None) self.assertEqual(reader.rootTag(), tag) tag = reader.readString('<html> </html>', retainRootTag=0) self.assertFalse(tag is None) self.assertTrue(reader.rootTag() is None) def checkAccess(self): html = HTMLReader().readString(self._html) # Name self.assertEqual(html.name(), 'html') # Attrs self.assertEqual(html.numAttrs(), 0) self.assertFalse(html.hasAttr('foo')) self.assertRaises(KeyError, html.attr, 'foo') self.assertTrue(html.attr('foo', None) is None) # Children and subtags, when both are the same. for numFoos, fooAt, foos in [ [html.numChildren, html.childAt, html.children], [html.numSubtags, html.subtagAt, html.subtags]]: self.assertEqual(numFoos(), 2) self.assertEqual(len(foos()), 2) self.assertEqual(fooAt(0).name(), 'head') self.assertEqual(fooAt(1).name(), 'body') # Children and subtags when they're different body = html.subtagAt(1) p = body.subtagAt(0) self.assertEqual(p.name(), 'p') self.assertEqual(p.numChildren(), 3) self.assertEqual(p.numSubtags(), 1) def checkMatchingAttr(self): html = HTMLReader().readString(self._html) self.assertEqual( html.tagWithMatchingAttr('lang', 'en').name(), 'body') self.assertEqual( html.tagWithMatchingAttr('id', 'dataTable').name(), 'table') self.assertEqual(html.tagWithId('dataTable').name(), 'table') def checkInvalidHTML(self): from WebUtils.HTMLTag import ( HTMLTagUnbalancedError, HTMLTagIncompleteError) reader = HTMLReader() html = '<html> <body> <table> </body> </html>' self.assertRaises(HTMLTagUnbalancedError, reader.readString, html) html = '<html> <body>' self.assertRaises(HTMLTagIncompleteError, reader.readString, html) def tearDown(self): del self._html def makeTestSuite(): cases = ['Basics', 'ReuseReader', 'Access', 'MatchingAttr', 'InvalidHTML'] tests = [HTMLTagTest('check'+case) for case in cases] return unittest.TestSuite(tests) if __name__ == '__main__': runner = unittest.TextTestRunner(stream=sys.stdout) unittest.main(defaultTest='makeTestSuite', testRunner=runner)

31.36036

78

0.608733

a4adbf1e52f40e1d2dcc0139e8a92868967a1ad3

4,774

py

Python

skka/settings.py

McCarthyCode/Susan-Kill-Kegan-and-Associates

4b3416aa9d94d409232ecb970d75d36cfd8928f9

[ "MIT" ]

null

skka/settings.py

McCarthyCode/Susan-Kill-Kegan-and-Associates

4b3416aa9d94d409232ecb970d75d36cfd8928f9

[ "MIT" ]

1

2020-06-09T02:06:39.000Z

skka/settings.py

McCarthyCode/Susan-Kill-Kegan-and-Associates

4b3416aa9d94d409232ecb970d75d36cfd8928f9

[ "MIT" ]

null

""" Django settings for skka project. Generated by 'django-admin startproject' using Django 3.0.4. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os import pytz # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Retrieve production stage environment variable class MissingEnvironmentVariable(Exception): pass class InvalidEnvironmentVariable(Exception): pass try: STAGE = os.environ['STAGE'] except KeyError: raise MissingEnvironmentVariable( 'Environment variable STAGE is not defined.') # SECURITY WARNING: don't run with debug turned on in production! if STAGE == 'development' or STAGE == 'staging': DEBUG = True elif STAGE == 'production': DEBUG = False else: raise InvalidEnvironmentVariable( 'The value of environment variable STAGE is not valid.') # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY_FILE = '%s/auth/secret.txt' % BASE_DIR with open(SECRET_KEY_FILE, 'r', encoding='utf8') as f: content = f.readline() SECRET_KEY = content[:-1] if STAGE == 'development': ALLOWED_HOSTS = [ 'localhost', ] elif STAGE == 'staging': ALLOWED_HOSTS = [ 'staging.skkeganlandscapes.com', ] elif STAGE == 'production': ALLOWED_HOSTS = [ 'skkeganlandscapes.com', 'www.skkeganlandscapes.com', ] # Application definition INSTALLED_APPS = [ 'home', 'gallery', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'skka.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'skka.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases if STAGE == 'development': DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } else: PGPASSWORD_FILE = '%s/auth/.pgpass' % BASE_DIR with open(PGPASSWORD_FILE, 'r', encoding='utf8') as f: content = f.readline() PGPASSWORD = content[14:-1] DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'skka', 'USER': 'skka', 'PASSWORD': PGPASSWORD, 'HOST': 'localhost', 'PORT': '', } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'America/Chicago' TZ = pytz.timezone(TIME_ZONE) USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles/') # Media files MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media') # Additional variables NAME = 'Susan Kill Kegan & Associates'

25.529412

83

0.664851

c6fc1b7dc38c271496ee13679cd5c310a441fc61

16,745

py

Python

test/functional/rpc_decodescript.py

gingfinger/divi99

3b0602b41bf35fb1e30c12b1bf06ef1da58935eb

[ "MIT" ]

null

test/functional/rpc_decodescript.py

gingfinger/divi99

3b0602b41bf35fb1e30c12b1bf06ef1da58935eb

[ "MIT" ]

null

test/functional/rpc_decodescript.py

gingfinger/divi99

3b0602b41bf35fb1e30c12b1bf06ef1da58935eb

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2015-2018 The Divi Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test decoding scripts via decodescript RPC command.""" from test_framework.messages import CTransaction, sha256 from test_framework.test_framework import DiviTestFramework from test_framework.util import assert_equal, bytes_to_hex_str, hex_str_to_bytes from io import BytesIO class DecodeScriptTest(DiviTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 def decodescript_script_sig(self): signature = '304502207fa7a6d1e0ee81132a269ad84e68d695483745cde8b541e3bf630749894e342a022100c1f7ab20e13e22fb95281a870f3dcf38d782e53023ee313d741ad0cfbc0c509001' push_signature = '48' + signature public_key = '03b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb2' push_public_key = '21' + public_key # below are test cases for all of the standard transaction types # 1) P2PK scriptSig # the scriptSig of a public key scriptPubKey simply pushes a signature onto the stack rpc_result = self.nodes[0].decodescript(push_signature) assert_equal(signature, rpc_result['asm']) # 2) P2PKH scriptSig rpc_result = self.nodes[0].decodescript(push_signature + push_public_key) assert_equal(signature + ' ' + public_key, rpc_result['asm']) # 3) multisig scriptSig # this also tests the leading portion of a P2SH multisig scriptSig # OP_0 <A sig> <B sig> rpc_result = self.nodes[0].decodescript('00' + push_signature + push_signature) assert_equal('0 ' + signature + ' ' + signature, rpc_result['asm']) # 4) P2SH scriptSig # an empty P2SH redeemScript is valid and makes for a very simple test case. # thus, such a spending scriptSig would just need to pass the outer redeemScript # hash test and leave true on the top of the stack. rpc_result = self.nodes[0].decodescript('5100') assert_equal('1 0', rpc_result['asm']) # 5) null data scriptSig - no such thing because null data scripts can not be spent. # thus, no test case for that standard transaction type is here. def decodescript_script_pub_key(self): public_key = '03b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb2' push_public_key = '21' + public_key public_key_hash = '5dd1d3a048119c27b28293056724d9522f26d945' push_public_key_hash = '14' + public_key_hash uncompressed_public_key = '04b0da749730dc9b4b1f4a14d6902877a92541f5368778853d9c4a0cb7802dcfb25e01fc8fde47c96c98a4f3a8123e33a38a50cf9025cc8c4494a518f991792bb7' push_uncompressed_public_key = '41' + uncompressed_public_key p2wsh_p2pk_script_hash = 'd8590cf8ea0674cf3d49fd7ca249b85ef7485dea62c138468bddeb20cd6519f7' # below are test cases for all of the standard transaction types # 1) P2PK scriptPubKey # <pubkey> OP_CHECKSIG rpc_result = self.nodes[0].decodescript(push_public_key + 'ac') assert_equal(public_key + ' OP_CHECKSIG', rpc_result['asm']) # P2PK is translated to P2WPKH assert_equal('0 ' + public_key_hash, rpc_result['segwit']['asm']) # 2) P2PKH scriptPubKey # OP_DUP OP_HASH160 <PubKeyHash> OP_EQUALVERIFY OP_CHECKSIG rpc_result = self.nodes[0].decodescript('76a9' + push_public_key_hash + '88ac') assert_equal('OP_DUP OP_HASH160 ' + public_key_hash + ' OP_EQUALVERIFY OP_CHECKSIG', rpc_result['asm']) # P2PKH is translated to P2WPKH assert_equal('0 ' + public_key_hash, rpc_result['segwit']['asm']) # 3) multisig scriptPubKey # <m> <A pubkey> <B pubkey> <C pubkey> <n> OP_CHECKMULTISIG # just imagine that the pub keys used below are different. # for our purposes here it does not matter that they are the same even though it is unrealistic. multisig_script = '52' + push_public_key + push_public_key + push_public_key + '53ae' rpc_result = self.nodes[0].decodescript(multisig_script) assert_equal('2 ' + public_key + ' ' + public_key + ' ' + public_key + ' 3 OP_CHECKMULTISIG', rpc_result['asm']) # multisig in P2WSH multisig_script_hash = bytes_to_hex_str(sha256(hex_str_to_bytes(multisig_script))) assert_equal('0 ' + multisig_script_hash, rpc_result['segwit']['asm']) # 4) P2SH scriptPubKey # OP_HASH160 <Hash160(redeemScript)> OP_EQUAL. # push_public_key_hash here should actually be the hash of a redeem script. # but this works the same for purposes of this test. rpc_result = self.nodes[0].decodescript('a9' + push_public_key_hash + '87') assert_equal('OP_HASH160 ' + public_key_hash + ' OP_EQUAL', rpc_result['asm']) # P2SH does not work in segwit secripts. decodescript should not return a result for it. assert 'segwit' not in rpc_result # 5) null data scriptPubKey # use a signature look-alike here to make sure that we do not decode random data as a signature. # this matters if/when signature sighash decoding comes along. # would want to make sure that no such decoding takes place in this case. signature_imposter = '48304502207fa7a6d1e0ee81132a269ad84e68d695483745cde8b541e3bf630749894e342a022100c1f7ab20e13e22fb95281a870f3dcf38d782e53023ee313d741ad0cfbc0c509001' # OP_RETURN <data> rpc_result = self.nodes[0].decodescript('6a' + signature_imposter) assert_equal('OP_RETURN ' + signature_imposter[2:], rpc_result['asm']) # 6) a CLTV redeem script. redeem scripts are in-effect scriptPubKey scripts, so adding a test here. # OP_NOP2 is also known as OP_CHECKLOCKTIMEVERIFY. # just imagine that the pub keys used below are different. # for our purposes here it does not matter that they are the same even though it is unrealistic. # # OP_IF # <receiver-pubkey> OP_CHECKSIGVERIFY # OP_ELSE # <lock-until> OP_CHECKLOCKTIMEVERIFY OP_DROP # OP_ENDIF # <sender-pubkey> OP_CHECKSIG # # lock until block 500,000 cltv_script = '63' + push_public_key + 'ad670320a107b17568' + push_public_key + 'ac' rpc_result = self.nodes[0].decodescript(cltv_script) assert_equal('OP_IF ' + public_key + ' OP_CHECKSIGVERIFY OP_ELSE 500000 OP_CHECKLOCKTIMEVERIFY OP_DROP OP_ENDIF ' + public_key + ' OP_CHECKSIG', rpc_result['asm']) # CLTV script in P2WSH cltv_script_hash = bytes_to_hex_str(sha256(hex_str_to_bytes(cltv_script))) assert_equal('0 ' + cltv_script_hash, rpc_result['segwit']['asm']) # 7) P2PK scriptPubKey # <pubkey> OP_CHECKSIG rpc_result = self.nodes[0].decodescript(push_uncompressed_public_key + 'ac') assert_equal(uncompressed_public_key + ' OP_CHECKSIG', rpc_result['asm']) # uncompressed pubkeys are invalid for checksigs in segwit scripts. # decodescript should not return a P2WPKH equivalent. assert 'segwit' not in rpc_result # 8) multisig scriptPubKey with an uncompressed pubkey # <m> <A pubkey> <B pubkey> <n> OP_CHECKMULTISIG # just imagine that the pub keys used below are different. # the purpose of this test is to check that a segwit script is not returned for bare multisig scripts # with an uncompressed pubkey in them. rpc_result = self.nodes[0].decodescript('52' + push_public_key + push_uncompressed_public_key +'52ae') assert_equal('2 ' + public_key + ' ' + uncompressed_public_key + ' 2 OP_CHECKMULTISIG', rpc_result['asm']) # uncompressed pubkeys are invalid for checksigs in segwit scripts. # decodescript should not return a P2WPKH equivalent. assert 'segwit' not in rpc_result # 9) P2WPKH scriptpubkey # 0 <PubKeyHash> rpc_result = self.nodes[0].decodescript('00' + push_public_key_hash) assert_equal('0 ' + public_key_hash, rpc_result['asm']) # segwit scripts do not work nested into each other. # a nested segwit script should not be returned in the results. assert 'segwit' not in rpc_result # 10) P2WSH scriptpubkey # 0 <ScriptHash> # even though this hash is of a P2PK script which is better used as bare P2WPKH, it should not matter # for the purpose of this test. rpc_result = self.nodes[0].decodescript('0020' + p2wsh_p2pk_script_hash) assert_equal('0 ' + p2wsh_p2pk_script_hash, rpc_result['asm']) # segwit scripts do not work nested into each other. # a nested segwit script should not be returned in the results. assert 'segwit' not in rpc_result def decoderawtransaction_asm_sighashtype(self): """Test decoding scripts via RPC command "decoderawtransaction". This test is in with the "decodescript" tests because they are testing the same "asm" script decodes. """ # this test case uses a random plain vanilla mainnet transaction with a single P2PKH input and output tx = '0100000001696a20784a2c70143f634e95227dbdfdf0ecd51647052e70854512235f5986ca010000008a47304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb014104d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536ffffffff0100e1f505000000001976a914eb6c6e0cdb2d256a32d97b8df1fc75d1920d9bca88ac00000000' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('304402207174775824bec6c2700023309a168231ec80b82c6069282f5133e6f11cbb04460220570edc55c7c5da2ca687ebd0372d3546ebc3f810516a002350cac72dfe192dfb[ALL] 04d3f898e6487787910a690410b7a917ef198905c27fb9d3b0a42da12aceae0544fc7088d239d9a48f2828a15a09e84043001f27cc80d162cb95404e1210161536', rpc_result['vin'][0]['scriptSig']['asm']) # this test case uses a mainnet transaction that has a P2SH input and both P2PKH and P2SH outputs. # it's from James D'Angelo's awesome introductory videos about multisig: https://www.youtube.com/watch?v=zIbUSaZBJgU and https://www.youtube.com/watch?v=OSA1pwlaypc # verify that we have not altered scriptPubKey decoding. tx = '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' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('8e3730608c3b0bb5df54f09076e196bc292a8e39a78e73b44b6ba08c78f5cbb0', rpc_result['txid']) assert_equal('0 3045022100ae3b4e589dfc9d48cb82d41008dc5fa6a86f94d5c54f9935531924602730ab8002202f88cf464414c4ed9fa11b773c5ee944f66e9b05cc1e51d97abc22ce098937ea[ALL] 3045022100b44883be035600e9328a01b66c7d8439b74db64187e76b99a68f7893b701d5380220225bf286493e4c4adcf928c40f785422572eb232f84a0b83b0dea823c3a19c75[ALL] 5221020743d44be989540d27b1b4bbbcfd17721c337cb6bc9af20eb8a32520b393532f2102c0120a1dda9e51a938d39ddd9fe0ebc45ea97e1d27a7cbd671d5431416d3dd87210213820eb3d5f509d7438c9eeecb4157b2f595105e7cd564b3cdbb9ead3da41eed53ae', rpc_result['vin'][0]['scriptSig']['asm']) assert_equal('OP_DUP OP_HASH160 dc863734a218bfe83ef770ee9d41a27f824a6e56 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm']) assert_equal('OP_HASH160 2a5edea39971049a540474c6a99edf0aa4074c58 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm']) txSave = CTransaction() txSave.deserialize(BytesIO(hex_str_to_bytes(tx))) # make sure that a specifically crafted op_return value will not pass all the IsDERSignature checks and then get decoded as a sighash type tx = '01000000015ded05872fdbda629c7d3d02b194763ce3b9b1535ea884e3c8e765d42e316724020000006b48304502204c10d4064885c42638cbff3585915b322de33762598321145ba033fc796971e2022100bb153ad3baa8b757e30a2175bd32852d2e1cb9080f84d7e32fcdfd667934ef1b012103163c0ff73511ea1743fb5b98384a2ff09dd06949488028fd819f4d83f56264efffffffff0200000000000000000b6a0930060201000201000180380100000000001976a9141cabd296e753837c086da7a45a6c2fe0d49d7b7b88ac00000000' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('OP_RETURN 300602010002010001', rpc_result['vout'][0]['scriptPubKey']['asm']) # verify that we have not altered scriptPubKey processing even of a specially crafted P2PKH pubkeyhash and P2SH redeem script hash that is made to pass the der signature checks tx = '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' rpc_result = self.nodes[0].decoderawtransaction(tx) assert_equal('OP_DUP OP_HASH160 3011020701010101010101020601010101010101 OP_EQUALVERIFY OP_CHECKSIG', rpc_result['vout'][0]['scriptPubKey']['asm']) assert_equal('OP_HASH160 3011020701010101010101020601010101010101 OP_EQUAL', rpc_result['vout'][1]['scriptPubKey']['asm']) # some more full transaction tests of varying specific scriptSigs. used instead of # tests in decodescript_script_sig because the decodescript RPC is specifically # for working on scriptPubKeys (argh!). push_signature = bytes_to_hex_str(txSave.vin[0].scriptSig)[2:(0x48*2+4)] signature = push_signature[2:] der_signature = signature[:-2] signature_sighash_decoded = der_signature + '[ALL]' signature_2 = der_signature + '82' push_signature_2 = '48' + signature_2 signature_2_sighash_decoded = der_signature + '[NONE|ANYONECANPAY]' # 1) P2PK scriptSig txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal(signature_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # make sure that the sighash decodes come out correctly for a more complex / lesser used case. txSave.vin[0].scriptSig = hex_str_to_bytes(push_signature_2) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal(signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # 2) multisig scriptSig txSave.vin[0].scriptSig = hex_str_to_bytes('00' + push_signature + push_signature_2) rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal('0 ' + signature_sighash_decoded + ' ' + signature_2_sighash_decoded, rpc_result['vin'][0]['scriptSig']['asm']) # 3) test a scriptSig that contains more than push operations. # in fact, it contains an OP_RETURN with data specially crafted to cause improper decode if the code does not catch it. txSave.vin[0].scriptSig = hex_str_to_bytes('6a143011020701010101010101020601010101010101') rpc_result = self.nodes[0].decoderawtransaction(bytes_to_hex_str(txSave.serialize())) assert_equal('OP_RETURN 3011020701010101010101020601010101010101', rpc_result['vin'][0]['scriptSig']['asm']) def run_test(self): self.decodescript_script_sig() self.decodescript_script_pub_key() self.decoderawtransaction_asm_sighashtype() if __name__ == '__main__': DecodeScriptTest().main()

71.255319

761

0.760287

0dceb2da1992b75d3ed0c0ea2cd7c49fae19753f

2,599

py

Python

src/graia/amnesia/builtins/uvicorn.py

GraiaProject/Amnesia

d48d3084f776f788767939d73774146086358887

[ "MIT" ]

2

2022-03-13T09:12:44.000Z

2022-03-28T10:53:06.000Z

src/graia/amnesia/builtins/uvicorn.py

GraiaProject/Amnesia

d48d3084f776f788767939d73774146086358887

[ "MIT" ]

null

src/graia/amnesia/builtins/uvicorn.py

GraiaProject/Amnesia

d48d3084f776f788767939d73774146086358887

[ "MIT" ]

null

import asyncio import logging from launart.manager import Launart from launart.service import Service from launart.utilles import wait_fut from loguru import logger from uvicorn import Config, Server from graia.amnesia.builtins.common import ASGIHandlerProvider class LoguruHandler(logging.Handler): def emit(self, record: logging.LogRecord) -> None: try: level = logger.level(record.levelname).name except ValueError: level = str(record.levelno) frame, depth = logging.currentframe(), 2 while frame and frame.f_code.co_filename == logging.__file__: frame = frame.f_back depth += 1 logger.opt(depth=depth, exception=record.exc_info).log( level, record.getMessage(), ) class WithoutSigHandlerServer(Server): def install_signal_handlers(self) -> None: return class UvicornService(Service): supported_interface_types = set() supported_description_types = set() id = "http.asgi_runner" server: Server host: str port: int def __init__(self, host: str = "127.0.0.1", port: int = 8000): self.host = host self.port = port super().__init__() def get_interface(self, interface_type): pass @property def required(self): return {"http.universal_server"} @property def stages(self): return {"preparing", "cleanup"} async def launch(self, mgr: Launart): async with self.stage("preparing"): asgi_handler = mgr.get_interface(ASGIHandlerProvider).get_asgi_handler() self.server = WithoutSigHandlerServer(Config(asgi_handler, host=self.host, port=self.port)) # TODO: 使用户拥有更多的对 Config 的配置能力. PATCHES = "uvicorn.error", "uvicorn.asgi", "uvicorn.access", "" level = logging.getLevelName(20) # default level for uvicorn logging.basicConfig(handlers=[LoguruHandler()], level=level) for name in PATCHES: target = logging.getLogger(name) target.handlers = [LoguruHandler(level=level)] target.propagate = False serve_task = asyncio.create_task(self.server.serve()) async with self.stage("cleanup"): logger.warning("try to shutdown uvicorn server...") self.server.should_exit = True await wait_fut([serve_task, asyncio.sleep(10)], return_when=asyncio.FIRST_COMPLETED) if not serve_task.done(): logger.warning("timeout, force exit uvicorn server...")

32.898734

103

0.640246

92c597bc083c0998370be284591335cb2f8261cf

1,973

py

Python

SEM-internal-cell-texture/analysis_scripts/3_feature_collection.py

ocarmo/EMP1-trafficking_PTP7-analysis

54138d63ed6bc60033d3e2412d373530f888ed86

[ "MIT" ]

null

SEM-internal-cell-texture/analysis_scripts/3_feature_collection.py

ocarmo/EMP1-trafficking_PTP7-analysis

54138d63ed6bc60033d3e2412d373530f888ed86

[ "MIT" ]

null

SEM-internal-cell-texture/analysis_scripts/3_feature_collection.py

ocarmo/EMP1-trafficking_PTP7-analysis

54138d63ed6bc60033d3e2412d373530f888ed86

[ "MIT" ]

null

import os import numpy as np import pandas as pd import matplotlib.pyplot as plt # import seaborn as sns import skimage.io from skimage import measure import functools from loguru import logger logger.info('Import OK') image_folder = f'SEM-internal-cell-texture/python_results/SEM-internal-cell-texture_Analyzed/initial_cleanup_zoom/' mask_folder = f'SEM-internal-cell-texture/python_results/SEM-internal-cell-texture_Analyzed/napari_masking/' output_folder = f'SEM-internal-cell-texture/python_results/SEM-internal-cell-texture_Analyzed/feature_collection/' if not os.path.exists(output_folder): os.makedirs(output_folder) def feature_extractor(mask, properties=False): if not properties: properties = ['area', 'coords', 'centroid', 'convex_area', 'eccentricity', 'euler_number', 'label', 'local_centroid', 'major_axis_length', 'minor_axis_length', 'orientation', 'perimeter', 'solidity'] return pd.DataFrame(skimage.measure.regionprops_table(mask, properties=properties)) # ----------------Initialise file lists---------------- # read in masks filtered_masks = {masks.replace('_mask.npy', ''): np.load( f'{mask_folder}{masks}') for masks in os.listdir(f'{mask_folder}') if '.npy' in masks} # ----------------collect feature information---------------- feature_information = [] for image_name, stack in filtered_masks.items(): image_name logger.info(f'Processing {image_name}') mask = (stack[1, :, :]).astype(int) feature_properties = feature_extractor(mask) feature_properties['ROI_type'] = 'knob' # properties = pd.concat([feature_properties]) feature_properties['image_name'] = image_name feature_information.append(feature_properties) feature_information = pd.concat(feature_information) logger.info('Completed feature collection') feature_information.drop('coords', axis=1, inplace=True) # ----------------save to csv---------------- feature_information.to_csv(f'{output_folder}feature_summary.csv')

38.686275

207

0.732387

67334cb6867d53406d0246c0bdfa59bacd9fac78

5,409

py

Python

src/server/fitbit.py

gyevnarb/IntelligentSleepAssistant

a167066df71d0f352e474fcd806857525a32e4f0

[ "MIT" ]

1

2018-03-11T12:22:19.000Z

src/server/fitbit.py

gyevnarb/IntelligentSleepAssistant

a167066df71d0f352e474fcd806857525a32e4f0

[ "MIT" ]

null

src/server/fitbit.py

gyevnarb/IntelligentSleepAssistant

a167066df71d0f352e474fcd806857525a32e4f0

[ "MIT" ]

null

""" A Python library for accessing the FitBit API. This library provides a wrapper to the FitBit API and does not provide storage of tokens or caching if that is required. Most of the code has been adapted from: https://groups.google.com/group/fitbit-api/browse_thread/thread/0a45d0ebed3ebccb 5/22/2012 - JCF - Updated to work with python-oauth2 https://github.com/dgouldin/python-oauth2 10/22/2015 - JG - Removed use of oauth2 library (singing is not necessary anymore), updated to use /oauth2/ authentication infrastructure to get access to more stats. """ import os, base64, requests, urllib class Fitbit(): # All information must be as on the https://dev.fitbit.com/apps page. CLIENT_ID = '22CNND' CLIENT_SECRET = 'd50713c7425870e331710a35954fb293' REDIRECT_URI = 'http://127.0.0.1:5000' # Decide which information the FitBit.py should have access to. # Options: 'activity', 'heartrate', 'location', 'nutrition', # 'profile', 'settings', 'sleep', 'social', 'weight' API_SCOPES = ('activity', 'heartrate', 'location', 'nutrition', 'profile', 'settings', 'sleep', 'social', 'weight') # These settings should probably not be changed. API_SERVER = 'api.fitbit.com' WWW_SERVER = 'www.fitbit.com' AUTHORIZE_URL = 'https://%s/oauth2/authorize' % WWW_SERVER TOKEN_URL = 'https://%s/oauth2/token' % API_SERVER def GetAuthorizationUri(self): # Parameters for authorization, make sure to select params = { 'client_id': self.CLIENT_ID, 'response_type': 'code', 'scope': ' '.join(self.API_SCOPES), 'redirect_uri': self.REDIRECT_URI } # Encode parameters and construct authorization url to be returned to user. urlparams = urllib.urlencode(params) return "%s?%s" % (self.AUTHORIZE_URL, urlparams) # Tokes are requested based on access code. Access code must be fresh (10 minutes) def GetAccessToken(self, access_code): # Construct the authentication header auth_header = base64.b64encode(self.CLIENT_ID + ':' + self.CLIENT_SECRET) headers = { 'Authorization': 'Basic %s' % auth_header, 'Content-Type' : 'application/x-www-form-urlencoded' } # Paramaters for requesting tokens (auth + refresh) params = { 'code': access_code, 'grant_type': 'authorization_code', 'client_id': self.CLIENT_ID, 'redirect_uri': self.REDIRECT_URI } # Place request resp = requests.post(self.TOKEN_URL, data=params, headers=headers) status_code = resp.status_code resp = resp.json() if status_code != 200: raise Exception("Something went wrong exchanging code for token (%s): %s" % (resp['errors'][0]['errorType'], resp['errors'][0]['message'])) # Strip the goodies token = dict() token['access_token'] = resp['access_token'] token['refresh_token'] = resp['refresh_token'] return token # Get new tokens based if authentication token is expired def RefAccessToken(self, token): # Construct the authentication header auth_header = base64.b64encode(self.CLIENT_ID + ':' + self.CLIENT_SECRET) headers = { 'Authorization': 'Basic %s' % auth_header, 'Content-Type' : 'application/x-www-form-urlencoded' } # Set up parameters for refresh request params = { 'grant_type': 'refresh_token', 'refresh_token': token['refresh_token'] } # Place request resp = requests.post(self.TOKEN_URL, data=params, headers=headers) status_code = resp.status_code resp = resp.json() if status_code != 200: raise Exception("Something went wrong refreshing (%s): %s" % (resp['errors'][0]['errorType'], resp['errors'][0]['message'])) # Distil token['access_token'] = resp['access_token'] token['refresh_token'] = resp['refresh_token'] return token # Place api call to retrieve data def ApiCall(self, token, apiCall='/1/user/-/activities/log/steps/date/today/1d.json'): # Other API Calls possible, or read the FitBit documentation for the full list # (https://dev.fitbit.com/docs/), e.g.: # apiCall = '/1/user/-/devices.json' # apiCall = '/1/user/-/profile.json' # apiCall = '/1/user/-/activities/date/2015-10-22.json' headers = { 'Authorization': 'Bearer %s' % token['access_token'] } final_url = 'https://' + self.API_SERVER + apiCall resp = requests.get(final_url, headers=headers) status_code = resp.status_code resp = resp.json() resp['token'] = token if status_code == 200: return resp elif status_code == 401: print "The access token you provided has been expired let me refresh that for you." # Refresh the access token with the refresh token if expired. Access tokens should be good for 1 hour. token = self.RefAccessToken(token) return self.ApiCall(token, apiCall) else: raise Exception("Something went wrong requesting (%s): %s" % (resp['errors'][0]['errorType'], resp['errors'][0]['message']))

38.635714

151

0.622111

329399076b083b5ff57cf7b7ebe4fb2d666411ed

698

py

Python

ros/src/pybullet_ros/plugins/joint_state_pub.py

domire8/pybullet_ros

3edc1e2bf8a8a9a9f3557e283ade2602d3905e2c

[ "MIT" ]

null

ros/src/pybullet_ros/plugins/joint_state_pub.py

domire8/pybullet_ros

3edc1e2bf8a8a9a9f3557e283ade2602d3905e2c

[ "MIT" ]

1

2021-05-20T12:32:00.000Z

ros/src/pybullet_ros/plugins/joint_state_pub.py

domire8/pybullet_ros

3edc1e2bf8a8a9a9f3557e283ade2602d3905e2c

[ "MIT" ]

null

#!/usr/bin/env python3 """ Query robot state and publish position, velocity and effort values to /robot_name/joint_states. """ import rospy from sensor_msgs.msg import JointState class JointStatePub: def __init__(self, pybullet, robot): self._pb = pybullet self._robot = robot self._publisher = rospy.Publisher(robot.namespace + "joint_states", JointState, queue_size=1) def execute(self): """ Execute the plugin. This function is called from main update loop in the pybullet ros node. """ joint_msg = self._robot.get_joint_state_msg() joint_msg.header.stamp = rospy.Time.now() self._publisher.publish(joint_msg)

29.083333

101

0.687679

d8757ff1a9769875527b8b6429f74bb123ecda56

9,374

py

Python

bode.py

vanish125/DS1054_BodePlotter

569d3b97d8a0a657dd27d7b30b152fa78203995b

[ "MIT" ]

null

bode.py

vanish125/DS1054_BodePlotter

569d3b97d8a0a657dd27d7b30b152fa78203995b

[ "MIT" ]

null

bode.py

vanish125/DS1054_BodePlotter

569d3b97d8a0a657dd27d7b30b152fa78203995b

[ "MIT" ]

null

# bode.py # Program to plot bode diagrams using a DS1054Z and a jds6600 # Jan Böhmer (c) 2019 # published under MIT license. See file "LICENSE" for full license text # from jds6600 import * 1st try import fygen import numpy as np import time from ds1054z import DS1054Z import argparse import dft import matplotlib.pyplot as plt import scipy.signal parser = argparse.ArgumentParser(description="This program plots Bode Diagrams of a DUT using an JDS6600 and Rigol DS1054Z") parser.add_argument('MIN_FREQ', metavar='min', type=float, help="The minimum frequency for which should be tested") parser.add_argument('MAX_FREQ', metavar='max', type=float, help="The maximum frequency for which should be tested") parser.add_argument('COUNT', metavar='N', nargs="?", default=50, type=int, help='The number of frequencies for which should be probed') parser.add_argument("--awg_port", dest="AWG_PORT", default="/dev/ttyUSB0", help="The serial port where the AWG is connected to") parser.add_argument("--ds_ip", default="auto", dest="OSC_IP", help="The IP address of the DS1054Z. Set to auto, to auto discover the oscilloscope via Zeroconf") parser.add_argument("--linear", dest="LINEAR", action="store_true", help="Set this flag to use a linear scale") parser.add_argument("--awg_voltage", dest="VOLTAGE", default=5, type=float, help="The amplitude of the signal used for the generator") parser.add_argument("--awg_offset", dest="OFFSET", default=0, type=float, help="The offset of the signal used for the generator") parser.add_argument("--step_time", dest="TIMEOUT", default=0.00, type=float, help="The pause between to measurements in ms.") parser.add_argument("--phase", dest="PHASE", action="store_true", help="Set this flag if you want to plot the Phase diagram too") parser.add_argument("--no_smoothing", dest="SMOOTH", action="store_false", help="Set this to disable the smoothing of the data with a Savitzky–Golay filter") parser.add_argument("--use_manual_settings", dest="MANUAL_SETTINGS", action="store_true", help="When this option is set, the options on the oscilloscope for voltage and time base are not changed by this program.") parser.add_argument("--output", dest="file", type=argparse.FileType("w"), help="Write the measured data to the given CSV file.") parser.add_argument("--no_plots", dest="PLOTS", action="store_false", help="When this option is set no plots are shown. Useful in combination with --output") parser.add_argument("--normalize", dest="NORMALIZE", action="store_true", help="Set this option if you dont want to get the absolute voltage levels on the output, but the value normalized on the input level.") parser.add_argument("--use_dft", dest="DFT", action="store_true", help="Use Discrete Fourier Transform on raw data; more accurate but slower.") args = parser.parse_args() if args.OSC_IP == "auto": import ds1054z.discovery results = ds1054z.discovery.discover_devices() if not results: print("No Devices found! Try specifying the IP Address manually.") exit() OSC_IP = results[0].ip print("Found Oscilloscope! Using IP Address " + OSC_IP) else: OSC_IP = args.OSC_IP DEFAULT_PORT = args.AWG_PORT MIN_FREQ = args.MIN_FREQ MAX_FREQ = args.MAX_FREQ STEP_COUNT = args.COUNT # Do some validity checs if MIN_FREQ < 0 or MAX_FREQ < 0: exit("Frequencies has to be greater 0!") if MIN_FREQ >= MAX_FREQ: exit("MAX_FREQ has to be greater then min frequency") if STEP_COUNT <= 0: exit("The step count has to be positive") TIMEOUT = args.TIMEOUT AWG_CHANNEL = 0 # //channel 1 AWG_VOLT = args.VOLTAGE AWG_OFFSET = args.OFFSET print("Init AWG") # awg = jds6600(DEFAULT_PORT) awg = fygen.FYGen(DEFAULT_PORT) # AWG_MAX_FREQ = awg.getinfo_devicetype() AWG_MODEL = awg.get_model() AWG_MAX_FREQ = float(AWG_MODEL[7:9]) print("Maximum Generator Frequency: %d MHz"% AWG_MAX_FREQ) if MAX_FREQ > AWG_MAX_FREQ * 1e6: exit("Your MAX_FREQ is higher than your AWG can achieve!") # We use sine for sweep # awg.setwaveform(AWG_CHANNEL, "sine") awg.set(AWG_CHANNEL, enable=True, wave='sin') # Init scope scope = DS1054Z(OSC_IP) # Set some options for the oscilloscope if not args.MANUAL_SETTINGS: # Center vertically scope.set_channel_offset(1, 0) scope.set_channel_offset(2, 0) # Display one period in 2 divs period = (1/MIN_FREQ) / 2 scope.timebase_scale = period scope.run() # Set the sensitivity according to the selected voltage scope.set_channel_scale(1, args.VOLTAGE / 3, use_closest_match=True) # Be a bit more pessimistic for the default voltage, because we run into problems if it is too confident scope.set_channel_scale(2, args.VOLTAGE / 2, use_closest_match=True) freqs = np.linspace(MIN_FREQ, MAX_FREQ, num=STEP_COUNT) if not args.LINEAR: freqs = np.logspace(np.log10(MIN_FREQ), np.log10(MAX_FREQ), num=STEP_COUNT) else: freqs = np.linspace(MIN_FREQ, MAX_FREQ, num=STEP_COUNT) # Set amplitude awg.set(AWG_CHANNEL, volts=AWG_VOLT, offset_volts=AWG_OFFSET, enable=True) volts = list() phases = list() # We have to wait a bit before we measure the first value awg.set(AWG_CHANNEL, freq_hz=float(freqs[0]), enable=True) time.sleep(0.3) if not args.MANUAL_SETTINGS:# initialize voltage reading to see if scope is set in correct vertical scale, in case vout is bigger than vin scope.display_channel(1, enable=True) scope.display_channel(2, enable=True) volt = scope.get_channel_measurement(2, 'vpp') vscalelist = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10] scopevscale = scope.get_channel_scale(2) index = vscalelist.index(scopevscale) while volt is None: # increase voltage scale until vpp is read #print("vscale ", vscalelist[index]) scope.set_channel_scale(2, vscalelist[index] , use_closest_match=True) time.sleep(1) volt = scope.get_channel_measurement(2, 'vpp') print("vpp: ", volt) if index < 9: index = index + 3 else: index = 12 for freq in freqs: awg.set(AWG_CHANNEL, freq_hz=float(freq), enable=True) time.sleep(TIMEOUT) if args.DFT: volt0, volt, phase = dft.measure_with_dft(scope, freq) phases.append(phase) else: volt0 = scope.get_channel_measurement(1, 'vpp') volt = scope.get_channel_measurement(2, 'vpp') phase = scope.get_channel_measurement('CHAN1, CHAN2', 'rphase') if phase: phase = -1*phase phases.append(phase) if not args.NORMALIZE: volts.append(volt) else: if volt0 < 0.01: print("Input voltage is very low, check your connections and retry") exit() else: volts.append(volt/volt0) # Use a better timebase if not args.MANUAL_SETTINGS: # Display one period in 2 divs period = (1/freq) / 2 scope.timebase_scale = period # Use better voltage scale for next time if volt: scope.set_channel_scale(2, volt / 2, use_closest_match=True) else: scope.set_channel_scale(2, AWG_VOLT / 2, use_closest_match=True) print(freq, " volt0: ", volt0, " volt: ", volt, " phase: ", phase) # Write data to file if needed if args.file: if args.PHASE: args.file.write("Frequency in Hz; Amplitude in V; Phase in Degree\n") else: args.file.write("Frequency in Hz; Amplitude in V\n") for n in range(0, len(freqs)): if volts[n]: volt = volts[n] else: volt = float("nan") if args.PHASE: if phases[n]: phase = phases[n] else: phase = phases[n] args.file.write("%f;%f;%f \n"%(freqs[n], volt, phase)) else: args.file.write("%f;%f \n"%(freqs[n], volt)) args.file.close() # Plot graphics if not args.PLOTS: exit() plt.plot(freqs, volts, label="Measured data") if args.SMOOTH: try: yhat = scipy.signal.savgol_filter(volts, 9, 3) # window size 51, polynomial order 3 plt.plot(freqs, yhat, "--", color="red", label="Smoothed data") except: print("Error during smoothing amplitude data") plt.title("Amplitude diagram (N=%d)"%STEP_COUNT) plt.xlabel("Frequency [Hz]") plt.ylabel("Voltage Peak-Peak [V]") plt.legend() # Set log x axis if not args.LINEAR: plt.xscale("log") plt.show() if args.PHASE: try: plt.plot(freqs, phases) plt.title("Phase diagram (N=%d)"%STEP_COUNT) plt.ylabel("Phase [°]") plt.xlabel("Frequency [Hz]") except: print("Phase was not correctly measured, check your connections") if args.SMOOTH: try: yhat = scipy.signal.savgol_filter(phases, 9, 3) # window size 51, polynomial order 3 plt.plot(freqs, yhat, "--", color="red", label="Smoothed data") except: print("Error during smoothing phase data") # Set log x axis if not args.LINEAR: plt.xscale("log") plt.show()

36.053846

214

0.65607

9a9849da8f7906bc7a6a0f801a3574242e9b1eb0

2,521

py

Python

docs/conf.py

mozkzki/moz-youtube

e74cb17a618d5e73af5cf83d50ec349ac75e5113

[ "MIT" ]

null

docs/conf.py

mozkzki/moz-youtube

e74cb17a618d5e73af5cf83d50ec349ac75e5113

[ "MIT" ]

1

2021-09-18T20:08:07.000Z

docs/conf.py

mozkzki/moz-youtube

e74cb17a618d5e73af5cf83d50ec349ac75e5113

[ "MIT" ]

null

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os import sys sys.path.insert(0, "../src/moz_youtube") # -- Project information ----------------------------------------------------- project = "moz_youtube" copyright = "2021, mozkzki" author = "mozkzki" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.viewcode", "sphinx.ext.todo", "sphinx.ext.napoleon", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = "alabaster" html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # -- Extension configuration ------------------------------------------------- # -- Options for todo extension ---------------------------------------------- # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = True

33.613333

79

0.652519

4fc1ac5d29967dda5fac5876c6d7667916683f8f

106,763

py

Python

pysnmp_mibs/DOCS-CABLE-DEVICE-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

6

2017-04-21T13:48:08.000Z

2022-01-06T19:42:52.000Z

pysnmp_mibs/DOCS-CABLE-DEVICE-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

1

2020-05-05T16:42:25.000Z

pysnmp_mibs/DOCS-CABLE-DEVICE-MIB.py

jackjack821/pysnmp-mibs

9835ea0bb2420715caf4ee9aaa07d59bb263acd6

[ "BSD-2-Clause" ]

6

2020-02-08T20:28:49.000Z

2021-09-14T13:36:46.000Z

# # PySNMP MIB module DOCS-CABLE-DEVICE-MIB (http://pysnmp.sf.net) # ASN.1 source http://mibs.snmplabs.com:80/asn1/DOCS-CABLE-DEVICE-MIB # Produced by pysmi-0.0.7 at Sun Feb 14 00:09:24 2016 # On host bldfarm platform Linux version 4.1.13-100.fc21.x86_64 by user goose # Using Python version 3.5.0 (default, Jan 5 2016, 17:11:52) # ( ObjectIdentifier, Integer, OctetString, ) = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString") ( NamedValues, ) = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") ( ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion, ValueSizeConstraint, ValueRangeConstraint, ) = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "SingleValueConstraint", "ConstraintsUnion", "ValueSizeConstraint", "ValueRangeConstraint") ( diffServMIBCounterGroup, diffServClfrStatus, diffServMIBMultiFieldClfrGroup, diffServAlgDropStatus, diffServMIBDscpMarkActGroup, diffServClfrElementStorage, diffServDataPathStatus, diffServMIBActionGroup, diffServMultiFieldClfrDstAddr, diffServMIBDataPathGroup, diffServActionStorage, diffServClfrStorage, diffServClfrElementStatus, diffServMultiFieldClfrStorage, diffServMIBAlgDropGroup, diffServMultiFieldClfrSrcAddr, diffServMIBClfrGroup, diffServDataPathStorage, diffServAlgDropType, diffServAlgDropStorage, diffServMultiFieldClfrAddrType, diffServCountActStorage, diffServMIBClfrElementGroup, ) = mibBuilder.importSymbols("DIFFSERV-MIB", "diffServMIBCounterGroup", "diffServClfrStatus", "diffServMIBMultiFieldClfrGroup", "diffServAlgDropStatus", "diffServMIBDscpMarkActGroup", "diffServClfrElementStorage", "diffServDataPathStatus", "diffServMIBActionGroup", "diffServMultiFieldClfrDstAddr", "diffServMIBDataPathGroup", "diffServActionStorage", "diffServClfrStorage", "diffServClfrElementStatus", "diffServMultiFieldClfrStorage", "diffServMIBAlgDropGroup", "diffServMultiFieldClfrSrcAddr", "diffServMIBClfrGroup", "diffServDataPathStorage", "diffServAlgDropType", "diffServAlgDropStorage", "diffServMultiFieldClfrAddrType", "diffServCountActStorage", "diffServMIBClfrElementGroup") ( InterfaceIndexOrZero, ) = mibBuilder.importSymbols("IF-MIB", "InterfaceIndexOrZero") ( InetAddress, InetAddressType, ) = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddress", "InetAddressType") ( ZeroBasedCounter32, ) = mibBuilder.importSymbols("RMON2-MIB", "ZeroBasedCounter32") ( SnmpAdminString, ) = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString") ( ObjectGroup, NotificationGroup, ModuleCompliance, ) = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance") ( mib_2, Counter64, MibIdentifier, ModuleIdentity, Unsigned32, TimeTicks, Counter32, ObjectIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, zeroDotZero, iso, Integer32, NotificationType, Bits, IpAddress, Gauge32, ) = mibBuilder.importSymbols("SNMPv2-SMI", "mib-2", "Counter64", "MibIdentifier", "ModuleIdentity", "Unsigned32", "TimeTicks", "Counter32", "ObjectIdentity", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "zeroDotZero", "iso", "Integer32", "NotificationType", "Bits", "IpAddress", "Gauge32") ( StorageType, TextualConvention, DateAndTime, RowStatus, DisplayString, RowPointer, TruthValue, ) = mibBuilder.importSymbols("SNMPv2-TC", "StorageType", "TextualConvention", "DateAndTime", "RowStatus", "DisplayString", "RowPointer", "TruthValue") docsDev = ModuleIdentity((1, 3, 6, 1, 2, 1, 69)).setRevisions(("2006-12-20 00:00", "1999-08-19 00:00",)) if mibBuilder.loadTexts: docsDev.setLastUpdated('200612200000Z') if mibBuilder.loadTexts: docsDev.setOrganization('IETF IP over Cable Data Network\n Working Group') if mibBuilder.loadTexts: docsDev.setContactInfo(' Rich Woundy\n Postal: Comcast Cable\n 27 Industrial Avenue\n Chelmsford, MA 01824 U.S.A.\n Phone: +1 978 244 4010\n E-mail: richard_woundy@cable.comcast.com\n\n Kevin Marez\n Postal: Motorola Corporation\n 6450 Sequence Drive\n San Diego, CA 92121 U.S.A.\n Phone: +1 858 404 3785\n E-mail: kevin.marez@motorola.com\n\n IETF IPCDN Working Group\n General Discussion: ipcdn@ietf.org\n Subscribe: http://www.ietf.org/mailman/listinfo/ipcdn\n Archive: ftp://ftp.ietf.org/ietf-mail-archive/ipcdn\n Co-chairs: Richard Woundy,\n richard_woundy@cable.comcast.com\n Jean-Francois Mule,\n jf.mule@cablelabs.com') if mibBuilder.loadTexts: docsDev.setDescription('This is the MIB Module for DOCSIS-compliant cable modems\n\n and cable-modem termination systems.\n\n Copyright (C) The IETF Trust (2006). This version\n of this MIB module was published in RFC 4639; for full\n legal notices see the RFC itself.') docsDevMIBObjects = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1)) docsDevBase = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 1)) docsDevRole = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("cm", 1), ("cmtsActive", 2), ("cmtsBackup", 3),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevRole.setDescription("Defines the current role of this device. cm(1) is a\n Cable Modem, cmtsActive(2) is a Cable Modem Termination\n System that is controlling the system of cable modems,\n and cmtsBackup(3) is a CMTS that is currently connected\n but is not controlling the system (not currently used).\n\n In general, if this device is a 'cm', its role will not\n change during operation or between reboots. If the\n device is a 'cmts' it may change between cmtsActive and\n cmtsBackup and back again during normal operation. NB:\n At this time, the DOCSIS standards do not support the\n concept of a backup CMTS, but cmtsBackup is included for\n completeness.") docsDevDateTime = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 2), DateAndTime()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevDateTime.setDescription("The current date and time, with time zone information\n (if known).\n\n If the real data and time cannot be determined, this\n shall represent elapsed time from boot relative to\n the standard epoch '1970-1-1,0:0:0.0'. In other\n words, if this agent has been up for 3 minutes and\n not been able to determine what the actual date and\n time are, this object will return the value\n '1970-1-1,0:03:0.0'.") docsDevResetNow = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 3), TruthValue()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevResetNow.setDescription('Setting this object to true(1) causes the device to\n reset. Reading this object always returns false(2).') docsDevSerialNumber = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 4), SnmpAdminString()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevSerialNumber.setDescription("The manufacturer's serial number for this device.") docsDevSTPControl = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("stEnabled", 1), ("noStFilterBpdu", 2), ("noStPassBpdu", 3),)).clone('noStFilterBpdu')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSTPControl.setDescription('This object controls operation of the spanning tree\n protocol (as distinguished from transparent bridging).\n\n If set to stEnabled(1), then the spanning tree protocol\n is enabled, subject to bridging constraints.\n\n If noStFilterBpdu(2), then spanning tree is not active,\n and Bridge PDUs received are discarded.\n\n If noStPassBpdu(3), then spanning tree is not active,\n and Bridge PDUs are transparently forwarded.\n\n Note that a device need not implement all of these\n options, but that noStFilterBpdu(2) is required.') docsDevIgmpModeControl = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 6), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("passive", 1), ("active", 2),)).clone('passive')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevIgmpModeControl.setDescription('This object controls the IGMP mode of operation for\n the CM or CMTS. In passive mode, the device forwards\n IGMP between interfaces as based on knowledge of\n Multicast Session activity on the subscriber side\n interface and the rules defined in the DOCSIS RFI\n specification. In active mode, the device terminates\n at and initiates IGMP through its interfaces as based\n on the knowledge of Multicast Session activity on the\n subscriber side interface.') docsDevMaxCpe = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 1, 7), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0,255))).setUnits('CPEs').setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevMaxCpe.setDescription('The maximum number of CPEs that can be granted access\n through a CM during a CM epoch. This value can be\n obtained from the CM configuration file; however,\n it may be adjusted by the CM according to hardware or\n software limitations that have been imposed on the\n implementation.') docsDevNmAccessTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 2), ) if mibBuilder.loadTexts: docsDevNmAccessTable.setDescription('This table controls access to SNMP objects by network\n management stations. If the table is empty, access to\n SNMP objects is unrestricted. The objects in this table\n MUST NOT persist across reboots. The objects in this\n table are only accessible from cable devices that are\n not capable of operating in SNMP Coexistence mode\n (RFC 3584) or in SNMPv3 mode (RFC 3410).\n See the conformance section for\n details. Note that some devices are required by other\n specifications (e.g., the DOCSIS OSSIv1.1 specification)\n to support the legacy SNMPv1/v2c docsDevNmAccess mode\n for backward compatibility.\n\n This table is deprecated. Instead, use the SNMP\n coexistence MIBs from RFC 3584, the TARGET and\n NOTIFICATION MIBs from RFC 3413, and\n the View-Based Access Control Model (VACM) MIBs for\n all SNMP protocol versions from RFC 3415.') docsDevNmAccessEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 2, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessIndex")) if mibBuilder.loadTexts: docsDevNmAccessEntry.setDescription('An entry describing access to SNMP objects by a\n particular network management station. An entry in\n this table is not readable unless the management station\n has read-write permission (either implicit if the table\n is empty, or explicit through an entry in this table).\n Entries are ordered by docsDevNmAccessIndex. The first\n\n matching entry (e.g., matching IP address and community\n string) is used to derive access.') docsDevNmAccessIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevNmAccessIndex.setDescription('Index used to order the application of access\n entries.') docsDevNmAccessIp = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 2), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessIp.setDescription('The IP address (or subnet) of the network management\n station. The address 0.0.0.0 is defined to mean\n any Network Management Station (NMS). If traps are\n enabled for this entry, then the value must be the\n address of a specific device. Implementations MAY\n recognize 255.255.255.255 as equivalent to 0.0.0.0.') docsDevNmAccessIpMask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 3), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessIpMask.setDescription('The IP subnet mask of the network management stations.\n If traps are enabled for this entry, then the value must\n be 0.0.0.0. Implementations MAY recognize\n 255.255.255.255 as equivalent to 0.0.0.0.') docsDevNmAccessCommunity = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 4), OctetString().clone('public')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessCommunity.setDescription('The community string to be matched for access by this\n entry. If set to a zero-length string, then any\n community string will match. When read, this object\n SHOULD return a zero-length string.') docsDevNmAccessControl = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6,))).clone(namedValues=NamedValues(("none", 1), ("read", 2), ("readWrite", 3), ("roWithTraps", 4), ("rwWithTraps", 5), ("trapsOnly", 6),)).clone('read')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessControl.setDescription("Specifies the type of access allowed to this NMS.\n Setting this object to none(1) causes the table entry\n to be destroyed. Read(2) allows access by 'get' and\n 'get-next' PDUs. ReadWrite(3) allows access by 'set' as\n well. RoWithtraps(4), rwWithTraps(5), and trapsOnly(6)\n control distribution of Trap PDUs transmitted by this\n device.") docsDevNmAccessInterfaces = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 6), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1,32))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessInterfaces.setDescription("Specifies the set of interfaces from which requests from\n this NMS will be accepted. Each octet within\n the value of this object specifies a set of eight\n\n interfaces, the first octet specifying ports 1\n through 8, the second octet specifying interfaces 9\n through 16, etc. Within each octet, the most\n significant bit represents the lowest numbered\n interface, and the least significant bit represents the\n highest numbered interface. Thus, each interface is\n represented by a single bit within the value of this\n object. If that bit has a value of '1' then that\n interface is included in the set.\n\n Note that entries in this table apply only to link-layer\n interfaces (e.g., Ethernet and CATV MAC). Bits\n representing upstream and downstream channel interfaces\n MUST NOT be set to '1'.\n\n Note that if bits corresponding to non-existing\n interfaces are set, the result is implementation\n specific.\n\n Note that according to the DOCSIS OSSIv1.1\n specification, when ifIndex '1' is included in the\n set, then this row applies to all CPE\n (customer-facing) interfaces.\n\n The size of this object is the minimum required to\n represent all configured interfaces for this device.") docsDevNmAccessStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 7), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessStatus.setDescription('Controls and reflects the status of rows in this\n table. Rows in this table may be created by either the\n create-and-go or create-and-wait paradigm. There is no\n restriction on changing values in a row of this table\n while the row is active.\n\n The following objects MUST have valid values before this\n object can be set to active: docsDevNmAccessIp,\n docsDevNmAccessStatus, docsDevNmAccessIpMask,\n docsDevNmAccessCommunity, docsDevNmAccessControl, and\n docsDevNmAccessInterfaces.') docsDevNmAccessTrapVersion = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 2, 1, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("disableSNMPv2trap", 1), ("enableSNMPv2trap", 2),)).clone('disableSNMPv2trap')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevNmAccessTrapVersion.setDescription('Specifies the TRAP version that is sent to this NMS.\n Setting this object to disableSNMPv2trap (1) causes the\n trap in SNMPv1 format to be sent to a particular NMS.\n Setting this object to enableSNMPv2trap (2) causes the\n trap in SNMPv2 format be sent to a particular NMS.') docsDevSoftware = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 3)) docsDevSwServer = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 1), IpAddress()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwServer.setDescription('The address of the TFTP server used for software\n upgrades. If the TFTP server is unknown or is a\n non-IPv4 address, return 0.0.0.0.\n\n This object is deprecated. See docsDevSwServerAddress\n for its replacement. This object will have its value\n modified, given a valid SET to docsDevSwServerAddress.') docsDevSwFilename = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 2), SnmpAdminString().subtype(subtypeSpec=ValueSizeConstraint(0,64))).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwFilename.setDescription('The filename of the software image to be downloaded via\n TFTP, or the abs_path (as defined in RFC 2616) of the\n software image to be downloaded via HTTP.\n\n Unless set via SNMP, this is the filename or abs_path\n specified by the provisioning server during the boot\n process that corresponds to the software version that\n\n is desired for this device.\n\n If unknown, the value of this object is the zero-length\n string.') docsDevSwAdminStatus = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("upgradeFromMgt", 1), ("allowProvisioningUpgrade", 2), ("ignoreProvisioningUpgrade", 3),)).clone('allowProvisioningUpgrade')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwAdminStatus.setDescription('If set to upgradeFromMgt(1), the device will initiate a\n TFTP or HTTP software image download. After\n successfully receiving an image, the device will set\n its state to ignoreProvisioningUpgrade(3) and reboot.\n If the download process is interrupted (e.g., by a reset\n or power failure), the device will load the previous\n image and, after re-initialization, continue to attempt\n loading the image specified in docsDevSwFilename.\n\n If set to allowProvisioningUpgrade(2), the device will\n use the software version information supplied by the\n provisioning server when next rebooting (this does not\n cause a reboot).\n\n When set to ignoreProvisioningUpgrade(3), the device\n will disregard software image upgrade information\n from the provisioning server.\n\n Note that reading this object can return\n upgradeFromMgt(1). This indicates that a software\n download is currently in progress, and that the device\n will reboot after successfully receiving an image.') docsDevSwOperStatus = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5,))).clone(namedValues=NamedValues(("inProgress", 1), ("completeFromProvisioning", 2), ("completeFromMgt", 3), ("failed", 4), ("other", 5),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevSwOperStatus.setDescription('InProgress(1) indicates that a TFTP or HTTP download is\n underway, either as a result of a version mismatch at\n provisioning or as a result of a upgradeFromMgt request.\n No other docsDevSw* objects can be modified in\n this state.\n\n CompleteFromProvisioning(2) indicates that the last\n software upgrade was a result of version mismatch at\n provisioning.\n\n CompleteFromMgt(3) indicates that the last software\n upgrade was a result of setting docsDevSwAdminStatus to\n upgradeFromMgt.\n\n Failed(4) indicates that the last attempted download\n failed, ordinarily due to TFTP or HTTP timeout.') docsDevSwCurrentVers = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 5), SnmpAdminString()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevSwCurrentVers.setDescription("The software version currently operating in this device.\n This string's syntax is that used by the\n individual vendor to identify software versions.\n For a CM, this string will describe the current\n software load. For a CMTS, this object SHOULD contain\n a human-readable representation either of the vendor\n specific designation of the software for the chassis,\n or of the software for the control processor. If\n neither of these is applicable, the value MUST be a\n zero-length string.") docsDevSwServerAddressType = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 6), InetAddressType()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwServerAddressType.setDescription('The type of address of the TFTP or HTTP server used for\n\n software upgrades.\n\n If docsDevSwServerTransportProtocol is currently set to\n tftp(1), attempting to set this object to dns(16) MUST\n result in an error.') docsDevSwServerAddress = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 7), InetAddress()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwServerAddress.setDescription('The address of the TFTP or HTTP server used for software\n upgrades.\n\n If the TFTP/HTTP server is unknown, return the zero-\n length address string (see the TextualConvention).\n\n If docsDevSwServer is also implemented in this agent,\n this object is tied to it. A set of this object to an\n IPv4 address will result in also setting the value of\n docsDevSwServer to that address. If this object is set\n to an IPv6 address, docsDevSwServer is set to 0.0.0.0.\n If docsDevSwServer is set, this object is also set to\n that value. Note that if both are set in the same\n action, the order of which one sets the other is\n undefined.') docsDevSwServerTransportProtocol = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 3, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("tftp", 1), ("http", 2),)).clone('tftp')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevSwServerTransportProtocol.setDescription('This object specifies the transport protocol (TFTP or\n HTTP) to be used for software upgrades.\n\n If the value of this object is tftp(1), then the cable\n device uses TFTP (RFC 1350) read request packets to\n download the docsDevSwFilename from the\n docsDevSwServerAddress in octet mode.\n\n If the value of this object is http(2), then the cable\n device uses HTTP 1.0 (RFC 1945) or HTTP 1.1 (RFC 2616)\n GET requests sent to host docsDevSwServerAddress to\n\n download the software image from path docsDevSwFilename.\n\n If docsDevSwServerAddressType is currently set to\n dns(16), attempting to set this object to tftp(1) MUST\n result in an error.') docsDevServer = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 4)) docsDevServerBootState = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,))).clone(namedValues=NamedValues(("operational", 1), ("disabled", 2), ("waitingForDhcpOffer", 3), ("waitingForDhcpResponse", 4), ("waitingForTimeServer", 5), ("waitingForTftp", 6), ("refusedByCmts", 7), ("forwardingDenied", 8), ("other", 9), ("unknown", 10),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerBootState.setDescription('If operational(1), the device has completed loading and\n processing of configuration parameters, and the CMTS has\n completed the Registration exchange.\n\n If disabled(2), then the device was administratively\n disabled, possibly by being refused network access in\n the configuration file.\n\n If waitingForDhcpOffer(3), then a Dynamic Host\n Configuration Protocol (DHCP) Discover has been\n transmitted, and no offer has yet been received.\n\n If waitingForDhcpResponse(4), then a DHCP Request has\n been transmitted, and no response has yet been received.\n\n If waitingForTimeServer(5), then a Time Request has been\n transmitted, and no response has yet been received.\n\n If waitingForTftp(6), then a request to the TFTP\n parameter server has been made, and no response\n received.\n\n If refusedByCmts(7), then the Registration\n Request/Response exchange with the CMTS failed.\n\n If forwardingDenied(8), then the registration process\n was completed, but the network access option in the\n received configuration file prohibits forwarding.\n\n If other(9), then the registration process reached a\n point that does not fall into one of the above\n categories.\n\n If unknown(10), then the device has not yet begun the\n registration process or is in some other indeterminate\n state.') docsDevServerDhcp = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 2), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerDhcp.setDescription('The IP address of the DHCP server that assigned an IP\n address to this device. Returns 0.0.0.0 if DHCP is not\n used for IP address assignment, or if this agent is\n not assigned an IPv4 address.\n\n This object is deprecated and is replaced by\n docsDevServerDhcpAddress.') docsDevServerTime = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 3), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerTime.setDescription('The IP address of the Time server (RFC 0868). Returns\n 0.0.0.0 if the time server IP address is unknown, or if\n the time server is not an IPv4 server.\n\n This object is deprecated and is replaced by\n\n docsDevServerTimeAddress.') docsDevServerTftp = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 4), IpAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerTftp.setDescription('The IP address of the TFTP server responsible for\n downloading provisioning and configuration parameters\n to this device. Returns 0.0.0.0 if the TFTP server\n address is unknown or is not an IPv4 address.\n\n This object is deprecated and is replaced by\n docsDevServerConfigTftpAddress.') docsDevServerConfigFile = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 5), SnmpAdminString()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerConfigFile.setDescription('The name of the device configuration file read from\n the TFTP server. Returns a zero-length string if\n the configuration file name is unknown.') docsDevServerDhcpAddressType = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 6), InetAddressType()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerDhcpAddressType.setDescription('The type of address of docsDevServerDhcpAddress. If\n DHCP was not used, this value should return\n unknown(0).') docsDevServerDhcpAddress = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 7), InetAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerDhcpAddress.setDescription('The internet address of the DHCP server that assigned\n an IP address to this device. Returns the zero length\n octet string if DHCP was not used for IP address\n assignment.') docsDevServerTimeAddressType = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 8), InetAddressType()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerTimeAddressType.setDescription('The type of address of docsDevServerTimeAddress. If\n no time server exists, this value should return\n unknown(0).') docsDevServerTimeAddress = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 9), InetAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerTimeAddress.setDescription('The Internet address of the RFC 868 Time server,\n as provided by DHCP option 4.\n\n Note that if multiple values are provided to the\n CM in DHCP option 4, the value of this MIB object\n MUST be the Time server address from which the Time\n of Day reference was acquired as based on the DOCSIS\n RFI specification. During the period of time where\n the Time of Day have not been acquired, the Time\n server address reported by the CM may report the\n first address value in the DHCP option value or the\n last server address the CM attempted to get the Time\n of day value.\n\n Returns the zero-length octet string if the time server\n IP address is not provisioned.') docsDevServerConfigTftpAddressType = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 10), InetAddressType()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerConfigTftpAddressType.setDescription('The type of address of docsDevServerConfigTftpAddress.\n If no TFTP server exists, this value should return\n unknown(0).') docsDevServerConfigTftpAddress = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 4, 11), InetAddress()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevServerConfigTftpAddress.setDescription('The internet address of the TFTP server responsible for\n downloading provisioning and configuration parameters\n to this device. Returns the zero-length octet string if\n the config server address is unknown. There are certain\n security risks that are involved with using TFTP.') docsDevEvent = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 5)) docsDevEvControl = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("resetLog", 1), ("useDefaultReporting", 2),))).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvControl.setDescription('Setting this object to resetLog(1) empties the event\n log. All data is deleted. Setting it to\n useDefaultReporting(2) returns all event priorities to\n their factory-default reporting. Reading this object\n always returns useDefaultReporting(2).') docsDevEvSyslog = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 2), IpAddress()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvSyslog.setDescription('The IP address of the Syslog server. If 0.0.0.0, either\n syslog transmission is inhibited, or the Syslog server\n address is not an IPv4 address.\n\n This object is deprecated and is replaced by\n docsDevEvSyslogAddress.') docsDevEvThrottleAdminStatus = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4,))).clone(namedValues=NamedValues(("unconstrained", 1), ("maintainBelowThreshold", 2), ("stopAtThreshold", 3), ("inhibited", 4),)).clone('unconstrained')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvThrottleAdminStatus.setDescription('Controls the transmission of traps and syslog messages\n with respect to the trap pacing threshold.\n\n unconstrained(1) causes traps and syslog messages to be\n transmitted without regard to the threshold settings.\n\n maintainBelowThreshold(2) causes trap transmission and\n syslog messages to be suppressed if the number of traps\n would otherwise exceed the threshold.\n\n stopAtThreshold(3) causes trap transmission to cease at\n the threshold and not to resume until directed to do so.\n\n inhibited(4) causes all trap transmission and syslog\n messages to be suppressed.\n\n A single event is always treated as a single event for\n threshold counting. That is, an event causing both a\n trap and a syslog message is still treated as a single\n event.\n\n Writing to this object resets the thresholding state.') docsDevEvThrottleInhibited = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 4), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvThrottleInhibited.setDescription('If true(1), trap and syslog transmission is currently\n inhibited due to thresholds and/or the current setting\n of docsDevEvThrottleAdminStatus. In addition, this is\n true(1) when transmission is inhibited because no\n syslog (docsDevEvSyslog) or trap (docsDevNmAccessEntry)\n destinations have been set.\n\n This object is deprecated and is replaced by\n docsDevEvThrottleThresholdExceeded.') docsDevEvThrottleThreshold = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 5), Unsigned32()).setUnits('events').setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvThrottleThreshold.setDescription('Number of events per docsDevEvThrottleInterval permitted\n before throttling is to occur.\n\n A single event, whether the notification could result in\n messages transmitted using syslog, SNMP, or both\n protocols, and regardless of the number of destinations,\n (including zero) is always treated as a single event for\n threshold counting. For example, an event causing both\n a trap and a syslog message is still treated as a single\n event.\n\n All system notifications that occur within the device\n should be taken into consideration when calculating\n and monitoring the threshold.') docsDevEvThrottleInterval = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647)).clone(1)).setUnits('seconds').setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvThrottleInterval.setDescription('The interval over which docsDevEvThrottleThreshold\n applies.') docsDevEvControlTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 5, 7), ) if mibBuilder.loadTexts: docsDevEvControlTable.setDescription('This table allows control of the reporting of event\n classes. For each event priority, a combination of\n\n logging and reporting mechanisms may be chosen. The\n mapping of event types to priorities is\n vendor dependent. Vendors may also choose to allow\n the user to control that mapping through proprietary\n means. Table entries MUST persist across reboots for\n CMTS devices and MUST NOT persist across reboots for CM\n devices.') docsDevEvControlEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 5, 7, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevEvPriority")) if mibBuilder.loadTexts: docsDevEvControlEntry.setDescription('Allows configuration of the reporting mechanisms for a\n particular event priority.') docsDevEvPriority = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 7, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8,))).clone(namedValues=NamedValues(("emergency", 1), ("alert", 2), ("critical", 3), ("error", 4), ("warning", 5), ("notice", 6), ("information", 7), ("debug", 8),))) if mibBuilder.loadTexts: docsDevEvPriority.setDescription('The priority level that is controlled by this\n entry. These are ordered from most (emergency) to least\n (debug) critical. Each event with a CM or CMTS has a\n particular priority level associated with it (as defined\n by the vendor).\n\n emergency(1) events indicate vendor-specific fatal\n hardware or software errors that prevent normal system\n operation.\n\n alert(2) events indicate a serious failure that causes\n the reporting system to reboot but is not caused by\n hardware or software malfunctioning.\n\n critical(3) events indicate a serious failure that\n requires attention and prevents the device from\n transmitting data but that could be recovered without\n rebooting the system.\n\n error(4) and warning(5) events indicate that a failure\n occurred that could interrupt the normal data flow but\n that does not cause the device to re-register.\n\n notice(6) and information(7) events indicate a\n milestone or checkpoint in normal operation that could\n be of particular importance for troubleshooting.\n\n debug(8) events are reserved for vendor-specific\n events.\n\n During normal operation, no event more\n critical than notice(6) should be generated. Events\n between warning and emergency should be generated at\n appropriate levels of problems (e.g., emergency when the\n box is about to crash).') docsDevEvReporting = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 7, 1, 2), Bits().clone(namedValues=NamedValues(("local", 0), ("traps", 1), ("syslog", 2), ("localVolatile", 8), ("stdInterface", 9),))).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvReporting.setDescription('Defines the action to be taken on occurrence of this\n event class. Implementations may not necessarily\n support all options for all event classes but at\n minimum must allow traps and syslogging to be\n disabled.\n\n If the local(0) bit is set, then log to the internal\n log and update non-volatile store, for backward\n compatibility with the original RFC 2669 definition.\n If the traps(1) bit is set, then generate\n an SNMP trap; if the syslog(2) bit is set, then\n send a syslog message (assuming that the syslog address\n is set). If the localVolatile(8) bit is set, then\n log to the internal log without updating non-volatile\n store. If the stdInterface(9) bit is set, then the\n agent ignores all other bits except the local(0),\n syslog(2), and localVolatile(8) bits. Setting the\n stdInterface(9) bit indicates that RFC3413 and\n RFC3014 are being used to control event reporting\n mechanisms.') docsDevEventTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 5, 8), ) if mibBuilder.loadTexts: docsDevEventTable.setDescription('Contains a log of network and device events that may be\n of interest in fault isolation and troubleshooting.\n If the local(0) bit is set in docsDevEvReporting,\n entries in this table MUST persist across reboots.') docsDevEventEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevEvIndex")) if mibBuilder.loadTexts: docsDevEventEntry.setDescription('Describes a network or device event that may be of\n interest in fault isolation and troubleshooting.\n Multiple sequential identical events are represented by\n incrementing docsDevEvCounts and setting\n docsDevEvLastTime to the current time rather than\n creating multiple rows.\n\n Entries are created with the first occurrence of an\n event. docsDevEvControl can be used to clear the\n table. Individual events cannot be deleted.') docsDevEvIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevEvIndex.setDescription('Provides relative ordering of the objects in the event\n log. This object will always increase except when\n (a) the log is reset via docsDevEvControl,\n (b) the device reboots and does not implement\n non-volatile storage for this log, or (c) it reaches\n the value 2^31. The next entry for all the above\n cases is 1.') docsDevEvFirstTime = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 2), DateAndTime()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvFirstTime.setDescription('The value of docsDevDateTime at the time this entry was\n created.') docsDevEvLastTime = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 3), DateAndTime()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvLastTime.setDescription('When an entry reports only one event, this object will\n have the same value as the corresponding instance of\n docsDevEvFirstTime. When an entry reports multiple\n events, this object will record the value that\n docsDevDateTime had when the most recent event for this\n entry occurred.') docsDevEvCounts = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 4), Counter32()).setUnits('events').setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvCounts.setDescription('The number of consecutive event instances reported by\n this entry. This starts at 1 with the creation of this\n row and increments by 1 for each subsequent duplicate\n event.') docsDevEvLevel = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8,))).clone(namedValues=NamedValues(("emergency", 1), ("alert", 2), ("critical", 3), ("error", 4), ("warning", 5), ("notice", 6), ("information", 7), ("debug", 8),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvLevel.setDescription('The priority level of this event, as defined by the\n vendor. These are ordered from most serious (emergency)\n to least serious (debug).\n\n emergency(1) events indicate vendor-specific fatal\n hardware or software errors that prevent normal system\n operation.\n\n alert(2) events indicate a serious failure that causes\n the reporting system to reboot but that is not caused by\n hardware or software malfunctioning.\n\n critical(3) events indicate a serious failure that\n requires attention and prevents the device from\n transmitting data but that could be recovered without\n rebooting the system.\n\n error(4) and warning(5) events indicate that a failure\n occurred that could interrupt the normal data flow but\n that does not cause the device to re-register.\n\n notice(6) and information(7) events indicate a\n milestone or checkpoint in normal operation that could\n be of particular importance for troubleshooting.\n\n debug(8) events are reserved for vendor-specific\n\n events.\n\n During normal operation, no event more\n critical than notice(6) should be generated. Events\n between warning and emergency should be generated at\n appropriate levels of problems (e.g., emergency when the\n box is about to crash).') docsDevEvId = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 6), Unsigned32()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvId.setDescription('For this product, uniquely identifies the type of event\n that is reported by this entry.') docsDevEvText = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 5, 8, 1, 7), SnmpAdminString()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvText.setDescription('Provides a human-readable description of the event,\n including all relevant context (interface numbers,\n etc.).') docsDevEvSyslogAddressType = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 9), InetAddressType().clone('unknown')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvSyslogAddressType.setDescription('The type of address of docsDevEvSyslogAddress. If\n no syslog server exists, this value should return\n unknown(0).') docsDevEvSyslogAddress = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 10), InetAddress()).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevEvSyslogAddress.setDescription('The Internet address of the Syslog server, as provided\n by DHCP option 7 or set via SNMP management. If the\n address of the server is set to the zero-length\n string, the 0.0.0.0 IPv4 address, or the 0: IPv6\n address, Syslog transmission is inhibited.\n\n Note that if multiple values are provided to the CM in\n DHCP option 7, the value of this MIB object MUST be the\n first Syslog server address received.\n\n By default at agent boot, this object returns the zero\n length string.') docsDevEvThrottleThresholdExceeded = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 5, 11), TruthValue()).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevEvThrottleThresholdExceeded.setDescription('If true(1), trap and syslog transmission is currently\n inhibited due to exceeding the trap/syslog event\n threshold in the current interval.') docsDevFilter = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 6)) docsDevFilterLLCUnmatchedAction = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 6, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("discard", 1), ("accept", 2),)).clone('accept')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevFilterLLCUnmatchedAction.setDescription('LLC (Link Level Control) filters can be defined on an\n inclusive or exclusive basis: CMs can be configured to\n forward only packets matching a set of layer three\n protocols, or to drop packets matching a set of layer\n three protocols. Typical use of these filters is to\n\n filter out possibly harmful (given the context of a\n large metropolitan LAN) protocols.\n\n If set to discard(1), any L2 packet that does not match\n at least one filter in the docsDevFilterLLCTable will be\n discarded. If set to accept(2), any L2 packet that\n does not match at least one filter in the\n docsDevFilterLLCTable will be accepted for further\n processing (e.g., bridging). In other words, if the\n packet does not match an entry in the table, it takes\n this action; if it does match an entry in the table, it\n takes the opposite of this action.') docsDevFilterLLCTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 6, 2), ) if mibBuilder.loadTexts: docsDevFilterLLCTable.setDescription('A list of filters to apply to (bridged) LLC\n traffic. The filters in this table are applied to\n incoming traffic on the appropriate interface(s) prior\n to any further processing (e.g., before the packet\n is handed off for level 3 processing, or for bridging).\n The specific action taken when no filter is matched is\n controlled by docsDevFilterLLCUnmatchedAction. Table\n entries MUST NOT persist across reboots for any device.') docsDevFilterLLCEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCIndex")) if mibBuilder.loadTexts: docsDevFilterLLCEntry.setDescription('Describes a single filter to apply to (bridged) LLC\n traffic received on a specified interface. ') docsDevFilterLLCIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevFilterLLCIndex.setDescription('Index used for the identification of filters (note that\n LLC filter order is irrelevant).') docsDevFilterLLCStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 2), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterLLCStatus.setDescription('Controls and reflects the status of rows in this\n table. There is no restriction on changing any of the\n associated columns for this row while this object is set\n to active.\n\n Specifying only this object (with the\n appropriate index) on a CM is sufficient to create a\n filter row that matches all inbound packets on the\n ethernet interface and results in the packets being\n discarded. docsDevFilterLLCIfIndex (at least) must be\n specified on a CMTS to create a row.') docsDevFilterLLCIfIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 3), InterfaceIndexOrZero()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterLLCIfIndex.setDescription("The entry interface to which this filter applies. The\n value corresponds to ifIndex for either a CATV MAC or\n another network interface. If the value is zero, the\n filter applies to all interfaces. In Cable Modems, the\n default value is the customer side interface(s). In\n CMTSs, this object has to be specified to\n create a row in this table.\n\n Note that according to the DOCSIS OSSIv1.1\n specification, ifIndex '1' in the CM means that this\n row applies to all Cable Modem-to-CPE Interfaces\n (CMCI).") docsDevFilterLLCProtocolType = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("ethertype", 1), ("dsap", 2),)).clone('ethertype')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterLLCProtocolType.setDescription('The format of the value in docsDevFilterLLCProtocol:\n either a two-byte Ethernet Ethertype, or a one-byte\n 802.2 Service Access Point (SAP) value. ethertype(1)\n also applies to Standard Network Access Protocol\n (SNAP) encapsulated frames.') docsDevFilterLLCProtocol = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterLLCProtocol.setDescription('The layer-three protocol for which this filter applies.\n The protocol value format depends on\n docsDevFilterLLCProtocolType. Note that for SNAP\n frames, ethertype filtering is performed rather than\n Destination Service Access Point (DSAP) =0xAA.') docsDevFilterLLCMatches = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 2, 1, 6), Counter32()).setUnits('matches').setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevFilterLLCMatches.setDescription('Counts the number of times this filter was matched.') docsDevFilterIpDefault = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 6, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("discard", 1), ("accept", 2),)).clone('accept')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevFilterIpDefault.setDescription('The default behavior for (bridged) packets that do not\n match IP filters (or Internet filters, if implemented)\n is defined by docsDevFilterIpDefault.\n\n If set to discard(1), all packets not matching an IP\n filter in docsDevFilterIpTable will be discarded. If\n set to accept(2), all packets not matching an IP filter\n or an Internet filter will be accepted for further\n processing (e.g., bridging).') docsDevFilterIpTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 6, 4), ) if mibBuilder.loadTexts: docsDevFilterIpTable.setDescription('An ordered list of filters or classifiers to apply to\n IP traffic. Filter application is ordered by the filter\n index, rather than by a best match algorithm (note that\n this implies that the filter table may have gaps in the\n index values). Packets that match no filters will have\n policy 0 in the docsDevFilterPolicyTable applied to\n them, if it exists. Otherwise, Packets that match no\n filters are discarded or forwarded according to the\n setting of docsDevFilterIpDefault.\n\n Any IP packet can theoretically match multiple rows of\n this table. When considering a packet, the table is\n scanned in row index order (e.g., filter 10 is checked\n before filter 20). If the packet matches that filter\n (which means that it matches ALL criteria for that row),\n actions appropriate to docsDevFilterIpControl and\n docsDevFilterPolicyId are taken. If the packet was\n discarded processing is complete. If\n docsDevFilterIpContinue is set to true, the filter\n comparison continues with the next row in the table,\n looking for additional matches.\n\n If the packet matches no filter in the table, the packet\n is accepted or dropped for further processing\n according to the setting of docsDevFilterIpDefault.\n If the packet is accepted, the actions specified by\n policy group 0 (e.g., the rows in\n docsDevFilterPolicyTable that have a value of 0 for\n docsDevFilterPolicyId) are taken, if that policy\n\n group exists.\n\n Logically, this table is consulted twice during the\n processing of any IP packet: once upon its acceptance\n from the L2 entity, and once upon its transmission to\n the L2 entity. In actuality, for cable modems, IP\n filtering is generally the only IP processing done for\n transit traffic. This means that inbound and outbound\n filtering can generally be done at the same time with\n one pass through the filter table.\n\n The objects in this table are only accessible from cable\n devices that are not operating in DiffServ MIB mode\n (RFC 3289). See the conformance section for details.\n\n Note that some devices are required by other\n specifications (e.g., the DOCSIS OSSIv1.1 specification)\n to support the legacy SNMPv1/v2c docsDevFilter mode\n for backward compatibility.\n\n Table entries MUST NOT persist across reboots for any\n device.\n\n This table is deprecated. Instead, use the DiffServ MIB\n from RFC 3289.') docsDevFilterIpEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpIndex")) if mibBuilder.loadTexts: docsDevFilterIpEntry.setDescription('Describes a filter to apply to IP traffic received on a\n specified interface. All identity objects in this table\n (e.g., source and destination address/mask, protocol,\n source/dest port, TOS/mask, interface and direction)\n must match their respective fields in the packet for\n any given filter to match.\n\n To create an entry in this table, docsDevFilterIpIfIndex\n must be specified.') docsDevFilterIpIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevFilterIpIndex.setDescription('Index used to order the application of filters.\n The filter with the lowest index is always applied\n first.') docsDevFilterIpStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 2), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpStatus.setDescription('Controls and reflects the status of rows in this\n table. Specifying only this object (with the\n appropriate index) on a CM is sufficient to create a\n filter row that matches all inbound packets on the\n ethernet interface and results in the packets being\n discarded. docsDevFilterIpIfIndex (at least) must be\n specified on a CMTS to create a row. Creation of the\n rows may be done via either create-and-wait or\n create-and-go, but the filter is not applied until this\n object is set to (or changes to) active. There is no\n restriction in changing any object in a row while this\n object is set to active.') docsDevFilterIpControl = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("discard", 1), ("accept", 2), ("policy", 3),)).clone('discard')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpControl.setDescription('If set to discard(1), all packets matching this filter\n will be discarded, and scanning of the remainder of the\n filter list will be aborted. If set to accept(2), all\n packets matching this filter will be accepted for\n further processing (e.g., bridging). If\n docsDevFilterIpContinue is set to true, see if there\n are other matches; otherwise, done. If set to\n policy (3), execute the policy entries\n matched by docsDevFilterIpPolicyId in\n docsDevFilterPolicyTable.\n\n If docsDevFilterIpContinue is set to true, continue\n scanning the table for other matches; otherwise, done.') docsDevFilterIpIfIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 4), InterfaceIndexOrZero()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpIfIndex.setDescription("The entry interface to which this filter applies. The\n value corresponds to ifIndex for either a CATV MAC or\n another interface. If the value is zero, the\n filter applies to all interfaces. Default value in CMs\n is the index of the customer-side (e.g., ethernet)\n interface(s). In CMTSes, this object MUST be\n specified to create a row in this table.\n\n Note that according to the DOCSIS OSSIv1.1\n specification, ifIndex '1' in the Cable Modem means\n that this row applies to all CMCI (customer-facing)\n interfaces.") docsDevFilterIpDirection = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("inbound", 1), ("outbound", 2), ("both", 3),)).clone('inbound')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpDirection.setDescription('Determines whether the filter is applied to inbound(1)\n traffic, outbound(2) traffic, or traffic in both(3)\n directions.') docsDevFilterIpBroadcast = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 6), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpBroadcast.setDescription('If set to true(1), the filter only applies to multicast\n and broadcast traffic. If set to false(2), the filter\n applies to all traffic.') docsDevFilterIpSaddr = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 7), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpSaddr.setDescription('The source IP address, or portion thereof, that is to be\n matched for this filter. The source address is first\n masked (ANDed) against docsDevFilterIpSmask before\n being compared to this value. A value of 0 for this\n object and 0 for the mask matches all IP addresses.') docsDevFilterIpSmask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 8), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpSmask.setDescription('A bit mask that is to be applied to the source address\n prior to matching. This mask is not necessarily the\n same as a subnet mask, but 1s bits must be leftmost and\n contiguous.') docsDevFilterIpDaddr = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 9), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpDaddr.setDescription('The destination IP address, or portion thereof, that is\n to be matched for this filter. The destination address\n is first masked (ANDed) against docsDevFilterIpDmask\n before being compared to this value. A value of\n 00000000 for this object and 00000000 for the mask\n matches all IP addresses.') docsDevFilterIpDmask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 10), IpAddress().clone(hexValue="00000000")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpDmask.setDescription('A bit mask that is to be applied to the destination\n address prior to matching. This mask is not necessarily\n the same as a subnet mask, but 1s bits MUST be leftmost\n and contiguous.') docsDevFilterIpProtocol = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 11), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,256)).clone(256)).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpProtocol.setDescription('The IP protocol value that is to be matched. For\n example, icmp is 1, tcp is 6, and udp is 17. A value of\n 256 matches ANY protocol.') docsDevFilterIpSourcePortLow = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 12), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpSourcePortLow.setDescription('This is the inclusive lower bound of the transport-layer\n source port range that is to be matched. If the IP\n protocol of the packet is neither UDP nor TCP, this\n\n object is ignored during matching.') docsDevFilterIpSourcePortHigh = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 13), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,65535)).clone(65535)).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpSourcePortHigh.setDescription('This is the inclusive upper bound of the transport-layer\n source port range that is to be matched. If the IP\n protocol of the packet is neither UDP nor TCP, this\n object is ignored during matching.') docsDevFilterIpDestPortLow = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 14), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,65535))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpDestPortLow.setDescription('This is the inclusive lower bound of the transport-layer\n destination port range that is to be matched. If the IP\n protocol of the packet is neither UDP nor TCP, this\n object is ignored during matching.') docsDevFilterIpDestPortHigh = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 15), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,65535)).clone(65535)).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpDestPortHigh.setDescription('This is the inclusive upper bound of the transport-layer\n destination port range that is to be matched. If the IP\n protocol of the packet is neither UDP nor TCP, this\n object is ignored during matching.') docsDevFilterIpMatches = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 16), ZeroBasedCounter32()).setUnits('matches').setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevFilterIpMatches.setDescription('Counts the number of times this filter was matched.\n This object is initialized to 0 at boot, or at row\n creation, and is reset only upon reboot.') docsDevFilterIpTos = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 17), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1,1)).setFixedLength(1).clone(hexValue="00")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpTos.setDescription("This is the value to be matched to the packet's\n TOS (Type of Service) value (after the TOS value\n is ANDed with docsDevFilterIpTosMask). A value for this\n object of 0 and a mask of 0 matches all TOS values.") docsDevFilterIpTosMask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 18), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1,1)).setFixedLength(1).clone(hexValue="00")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpTosMask.setDescription("The mask to be applied to the packet's TOS value before\n matching.") docsDevFilterIpContinue = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 19), TruthValue().clone('false')).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpContinue.setDescription('If this value is set to true and docsDevFilterIpControl\n is anything but discard (1), continue scanning and\n applying policies. See Section 3.3.3 for more\n details.') docsDevFilterIpPolicyId = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 4, 1, 20), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,2147483647))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterIpPolicyId.setDescription('This object points to an entry in\n docsDevFilterPolicyTable. If docsDevFilterIpControl\n\n is set to policy (3), execute all matching policies\n in docsDevFilterPolicyTable. If no matching policy\n exists, treat as if docsDevFilterIpControl were set\n to accept (1). If this object is set to the value of\n 0, there is no matching policy, and\n docsDevFilterPolicyTable MUST NOT be consulted.') docsDevFilterPolicyTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 6, 5), ) if mibBuilder.loadTexts: docsDevFilterPolicyTable.setDescription('A Table that maps between a policy group ID and a set\n of pointers to policies to be applied. All rows with\n the same docsDevFilterPolicyId are part of the same\n group of policy pointers and are applied in the order\n in this table. docsDevFilterPolicyTable exists to\n allow multiple policy actions (referenced by policy\n pointers) to be applied to any given classified packet.\n The policy actions are applied in index order.\n For example:\n\n Index ID Type Action\n 1 1 TOS 1\n 9 5 TOS 1\n 12 1 IPSEC 3\n\n This says that a packet that matches a filter with\n policy id 1 first has TOS policy 1 applied (which might\n set the TOS bits to enable a higher priority) and next\n has the IPSEC policy 3 applied (which may result in the\n packets being dumped into a secure VPN to a remote\n encryptor).\n\n Policy ID 0 is reserved for default actions and is\n applied only to packets that match no filters in\n docsDevFilterIpTable.\n\n Table entries MUST NOT persist across reboots for any\n device.\n\n This table is deprecated. Instead, use the DiffServ MIB\n\n from RFC 3289.') docsDevFilterPolicyEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 6, 5, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevFilterPolicyIndex")) if mibBuilder.loadTexts: docsDevFilterPolicyEntry.setDescription('An entry in the docsDevFilterPolicyTable. Entries are\n created by Network Management. To create an entry,\n docsDevFilterPolicyId MUST be specified.') docsDevFilterPolicyIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 5, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevFilterPolicyIndex.setDescription('Index value for the table.') docsDevFilterPolicyId = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 5, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0,2147483647))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterPolicyId.setDescription('Policy ID for this entry. If a policy ID can apply to\n multiple rows of this table, all relevant policies are\n executed. Policy 0 (if populated) is applied to all\n packets that do not match any of the filters. N.B. If\n docsDevFilterIpPolicyId is set to 0, it DOES NOT match\n policy 0 of this table.') docsDevFilterPolicyStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 5, 1, 5), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterPolicyStatus.setDescription('Object used to create an entry in this table. There is\n no restriction in changing any object in a row while\n this object is set to active.\n The following object MUST have a valid value before this\n object can be set to active: docsDevFilterPolicyPtr.') docsDevFilterPolicyPtr = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 5, 1, 6), RowPointer().clone((0, 0))).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterPolicyPtr.setDescription('This object points to a row in an applicable filter\n policy table. Currently, the only standard policy\n table is docsDevFilterTosTable.\n\n Per the textual convention, this object points to the\n first accessible object in the row; e.g., to point to a\n row in docsDevFilterTosTable with an index of 21, the\n value of this object would be the object identifier\n docsDevTosStatus.21.\n\n Vendors are recommended to adhere to the same convention\n when adding vendor-specific policy table extensions.\n\n If this pointer references an empty or non-existent\n row, then no policy action is taken.\n\n The default upon row creation is a null pointer that\n results in no policy action being taken.') docsDevFilterTosTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 6, 6), ) if mibBuilder.loadTexts: docsDevFilterTosTable.setDescription('Table used to describe Type of Service (TOS) bits\n\n processing.\n\n This table is an adjunct to the docsDevFilterIpTable\n and the docsDevFilterPolicy table. Entries in the\n latter table can point to specific rows in this (and\n other) tables and cause specific actions to be taken.\n This table permits the manipulation of the value of the\n Type of Service bits in the IP header of the matched\n packet as follows:\n\n Set the tosBits of the packet to\n (tosBits & docsDevFilterTosAndMask) |\n docsDevFilterTosOrMask\n\n This construct allows you to do a clear and set of all\n the TOS bits in a flexible manner.\n\n Table entries MUST NOT persist across reboots for any\n device.\n\n This table is deprecated. Instead, use the DiffServ MIB\n from RFC 3289.') docsDevFilterTosEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 6, 6, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevFilterTosIndex")) if mibBuilder.loadTexts: docsDevFilterTosEntry.setDescription('A TOS policy entry.') docsDevFilterTosIndex = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 6, 1, 1), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1,2147483647))) if mibBuilder.loadTexts: docsDevFilterTosIndex.setDescription('The unique index for this row. There are no ordering\n requirements for this table, and any valid index may be\n specified.') docsDevFilterTosStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 6, 1, 2), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterTosStatus.setDescription('The object used to create and delete entries in this\n table. A row created by specifying just this object\n results in a row that specifies no change to the TOS\n bits. A row may be created using either the\n create-and-go or create-and-wait paradigms. There is\n no restriction on the ability to change values in this\n row while the row is active.') docsDevFilterTosAndMask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 6, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1,1)).setFixedLength(1).clone(hexValue="ff")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterTosAndMask.setDescription("This value is bitwise ANDed with the matched packet's\n TOS bits.") docsDevFilterTosOrMask = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 6, 6, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1,1)).setFixedLength(1).clone(hexValue="00")).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevFilterTosOrMask.setDescription("This value is bitwise ORed with the result from the\n AND procedure (tosBits & docsDevFilterTosAndMask).\n The result then replaces the packet's TOS bits.") docsDevCpe = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 1, 7)) docsDevCpeEnroll = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 7, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2,))).clone(namedValues=NamedValues(("none", 1), ("any", 2),)).clone('any')).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevCpeEnroll.setDescription('This object controls the population of\n docsDevFilterCpeTable.\n If set to none, the filters must be set manually\n by a network management action (either configuration\n or SNMP set).\n If set to any, the CM wiretaps the packets originating\n from the ethernet and enrolls up to docsDevCpeIpMax\n addresses as based on the source IPv4 or v6 addresses of\n those packets.') docsDevCpeIpMax = MibScalar((1, 3, 6, 1, 2, 1, 69, 1, 7, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(-1,2147483647)).clone(-1)).setMaxAccess("readwrite") if mibBuilder.loadTexts: docsDevCpeIpMax.setDescription('This object controls the maximum number of CPEs allowed\n to be learned behind this device. If set to zero, any\n number of CPEs may connect up to the maximum permitted\n for the device.\n If set to -1, no filtering is done on CPE source\n addresses, and no entries are made in the\n docsDevFilterCpeTable via learning. If an attempt is\n made to set this to a number greater than that\n permitted for the device, it is set to that maximum.') docsDevCpeTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 7, 3), ) if mibBuilder.loadTexts: docsDevCpeTable.setDescription('This table lists the IPv4 addresses seen (or permitted)\n as source addresses in packets originating from the\n customer interface on this device. In addition, this\n table can be provisioned with the specific addresses\n permitted for the CPEs via the normal row creation\n mechanisms. Table entries MUST NOT persist across\n reboots for any device.\n\n N.B. Management action can add entries in this table\n and in docsDevCpeIpTable past the value of\n\n docsDevCpeIpMax. docsDevCpeIpMax ONLY restricts the\n ability of the CM to add learned addresses\n automatically.\n\n This table is deprecated and is replaced by\n docsDevCpeInetTable.') docsDevCpeEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 7, 3, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevCpeIp")) if mibBuilder.loadTexts: docsDevCpeEntry.setDescription('An entry in the docsDevFilterCpeTable. There is one\n entry for each IPv4 CPE seen or provisioned. If\n docsDevCpeIpMax is set to -1, this table is ignored;\n otherwise, upon receipt of an IP packet from the\n customer interface of the CM, the source IP address is\n checked against this table. If the address is in the\n table, packet processing continues. If the address is\n not in the table but docsDevCpeEnroll is set to any\n and the sum of the table sizes of docsDevCpeTable and\n docsDevCpeInetTable is less than docsDevCpeIpMax, the\n address is added to the table, and packet processing\n continues. Otherwise, the packet is dropped.\n\n The filtering actions specified by this table occur\n after any LLC filtering (docsDevFilterLLCTable), but\n prior to any IP filtering (docsDevFilterIpTable,\n docsDevNmAccessTable).') docsDevCpeIp = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 3, 1, 1), IpAddress()) if mibBuilder.loadTexts: docsDevCpeIp.setDescription('The IPv4 address to which this entry applies.\n\n N.B. Attempts to set all zeros or all ones address\n values MUST be rejected.') docsDevCpeSource = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 3, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3,))).clone(namedValues=NamedValues(("other", 1), ("manual", 2), ("learned", 3),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevCpeSource.setDescription('This object describes how this entry was created. If\n the value is manual(2), this row was created by a\n network management action (either configuration or\n SNMP set). If set to learned(3), then it was found via\n looking at the source IPv4 address of a received packet.\n The value other(1) is used for any entries that do not\n meet manual(2) or learned(3) criteria.') docsDevCpeStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 3, 1, 3), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevCpeStatus.setDescription('Standard object to manipulate rows. To create a row in\n this table, one only needs to specify this object.\n Management stations SHOULD use the create-and-go\n mechanism for creating rows in this table.') docsDevCpeInetTable = MibTable((1, 3, 6, 1, 2, 1, 69, 1, 7, 4), ) if mibBuilder.loadTexts: docsDevCpeInetTable.setDescription('This table lists the IP addresses seen (or permitted) as\n source addresses in packets originating from the\n customer interface on this device. In addition, this\n table can be provisioned with the specific addresses\n permitted for the CPEs via the normal row creation\n mechanisms.\n\n N.B. Management action can add entries in this table\n and in docsDevCpeIpTable past the value of\n docsDevCpeIpMax. docsDevCpeIpMax ONLY restricts the\n ability of the CM to add learned addresses\n automatically.\n\n Table entries MUST NOT persist across reboots for any\n device.\n\n This table exactly mirrors docsDevCpeTable and applies\n to IPv4 and IPv6 addresses.') docsDevCpeInetEntry = MibTableRow((1, 3, 6, 1, 2, 1, 69, 1, 7, 4, 1), ).setIndexNames((0, "DOCS-CABLE-DEVICE-MIB", "docsDevCpeInetType"), (0, "DOCS-CABLE-DEVICE-MIB", "docsDevCpeInetAddr")) if mibBuilder.loadTexts: docsDevCpeInetEntry.setDescription('An entry in the docsDevFilterCpeInetTable. There is one\n entry for each IP CPE seen or provisioned. If\n docsDevCpeIpMax is set to -1, this table is ignored;\n otherwise, upon receipt of an IP packet from the\n customer interface of the CM, the source IP address is\n checked against this table. If the address is in the\n table, packet processing continues. If the address is\n not in the table but docsDevCpeEnroll is set to any and\n the sum of the table sizes for docsDevCpeTable and\n docsDevCpeInetTable is less than docsDevCpeIpMax, the\n address is added to the table, and packet processing\n continues. Otherwise, the packet is dropped.\n\n The filtering actions specified by this table occur\n after any LLC filtering (docsDevFilterLLCTable), but\n prior to any IP filtering (docsDevFilterIpTable,\n docsDevNmAccessTable).\n\n When an agent (cable modem) restarts, then all\n dynamically created rows are lost.') docsDevCpeInetType = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 4, 1, 1), InetAddressType()) if mibBuilder.loadTexts: docsDevCpeInetType.setDescription('The type of internet address of docsDevCpeInetAddr.') docsDevCpeInetAddr = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 4, 1, 2), InetAddress()) if mibBuilder.loadTexts: docsDevCpeInetAddr.setDescription('The Internet address to which this entry applies.\n\n Implementors need to be aware that if the size of\n docsDevCpeInetAddr exceeds 114 octets OIDs of\n instances of columns in this row will have more\n than 128 sub-identifiers and cannot be accessed\n using SNMPv1, SNMPv2c, or SNMPv3. Only unicast\n address are allowed for this object.') docsDevCpeInetSource = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 4, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(2, 3,))).clone(namedValues=NamedValues(("manual", 2), ("learned", 3),))).setMaxAccess("readonly") if mibBuilder.loadTexts: docsDevCpeInetSource.setDescription('This object describes how this entry was created. If\n the value is manual(2), this row was created by a\n network management action (either configuration or\n SNMP set). If set to learned(3), then it was found\n via looking at the source IP address of a received\n packet.') docsDevCpeInetRowStatus = MibTableColumn((1, 3, 6, 1, 2, 1, 69, 1, 7, 4, 1, 4), RowStatus()).setMaxAccess("readcreate") if mibBuilder.loadTexts: docsDevCpeInetRowStatus.setDescription('Standard object to manipulate rows. To create a row in\n this table, one only needs to specify this object.\n Management stations SHOULD use the create-and-go\n mechanism for creating rows in this table.') docsDevNotification = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 2)) docsDevNotifications = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 0)) docsDevConformance = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 3)) docsDevGroups = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 3, 1)) docsDevCompliances = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 3, 2)) docsDevBasicCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 69, 3, 2, 1)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevBaseGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEventGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSoftwareGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeGroup"),)) if mibBuilder.loadTexts: docsDevBasicCompliance.setDescription('The RFC 2669 compliance statement for MCNS/DOCSIS\n Cable Modems and Cable Modem Termination Systems.') docsDevBaseGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 1)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevRole"), ("DOCS-CABLE-DEVICE-MIB", "docsDevDateTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevResetNow"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSerialNumber"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSTPControl"),)) if mibBuilder.loadTexts: docsDevBaseGroup.setDescription('A collection of objects providing device status and\n control.') docsDevNmAccessGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 2)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessIp"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessIpMask"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessCommunity"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessControl"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessInterfaces"), ("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessStatus"),)) if mibBuilder.loadTexts: docsDevNmAccessGroup.setDescription('A collection of objects for controlling access to SNMP\n objects on cable devices.\n\n This group has been deprecated because all the\n objects have been deprecated in favor of SNMPv3 and\n Coexistence MIBs.') docsDevSoftwareGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 3)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevSwServer"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwFilename"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwAdminStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwOperStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwCurrentVers"),)) if mibBuilder.loadTexts: docsDevSoftwareGroup.setDescription('A collection of objects for controlling software\n downloads.\n\n This group has been deprecated and replaced by\n docsDevSoftwareGroupV2. Object docsDevSwServer\n has been replaced by docsDevSwServerAddressType\n and docsDevSwServerAddress, and\n docsDevSwServerTransportProtocol has been added to\n support TFTP and HTTP firmware downloads.') docsDevServerGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 4)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevServerBootState"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerDhcp"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerTftp"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerConfigFile"),)) if mibBuilder.loadTexts: docsDevServerGroup.setDescription('A collection of objects providing status about server\n provisioning.\n\n This group has been deprecated and replaced by\n docsDevServerGroupV2. The objects docsDevServerDhcp,\n docsDevServerTime, and docsDevServerTftp have\n been replaced by docsDevServerDhcpAddressType,\n docsDevServerDhcpAddress, docsDevServerTimeAddressType,\n docsDevServerTimeAddress,\n docsDevServerConfigTftpAddressType, and\n docsDevServerConfigTftpAddress.') docsDevEventGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 5)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevEvControl"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvSyslog"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleAdminStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleInhibited"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleThreshold"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleInterval"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvReporting"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvFirstTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvLastTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvCounts"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvLevel"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvId"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvText"),)) if mibBuilder.loadTexts: docsDevEventGroup.setDescription('A collection of objects used to control and monitor\n events.\n\n This group has been deprecated and replaced by\n docsDevEventGroupV2. The object docsDevEvSyslog has\n\n been replaced by docsDevEvSyslogAddressType and\n docsDevEvSyslogAddress, and docsDevEvThrottleInhibited\n has been replaced by\n docsDevEvThrottleThresholdExceeded.') docsDevFilterGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 6)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCUnmatchedAction"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDefault"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCIfIndex"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCProtocolType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCProtocol"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCMatches"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpControl"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpIfIndex"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDirection"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpBroadcast"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpSaddr"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpSmask"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDaddr"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDmask"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpProtocol"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpSourcePortLow"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpSourcePortHigh"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDestPortLow"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpDestPortHigh"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpMatches"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpTos"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpTosMask"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpContinue"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterIpPolicyId"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterPolicyId"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterPolicyStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterPolicyPtr"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterTosStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterTosAndMask"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterTosOrMask"),)) if mibBuilder.loadTexts: docsDevFilterGroup.setDescription('A collection of objects to specify filters at the link\n layer and IPv4 layer.\n\n This group has been deprecated and replaced by various\n groups from the DiffServ MIB.') docsDevCpeGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 1, 7)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevCpeEnroll"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeIpMax"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeSource"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeStatus"),)) if mibBuilder.loadTexts: docsDevCpeGroup.setDescription('A collection of objects used to control the number\n and specific values of IPv4 addresses allowed for\n associated Customer Premises Equipment (CPE).\n\n This group has been deprecated and replaced by\n docsDevInetCpeGroup. The object docsDevCpeSource has\n been replaced by docsDevCpeInetSource, and\n docsDevCpeStatus has been replaced by\n docsDevCpeInetRowStatus.') docsDevGroupsV2 = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 3, 3)) docsDevCompliancesV2 = MibIdentifier((1, 3, 6, 1, 2, 1, 69, 3, 4)) docsDevCmCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 69, 3, 4, 1)).setObjects(*(("DIFFSERV-MIB", "diffServMIBDataPathGroup"), ("DIFFSERV-MIB", "diffServMIBClfrGroup"), ("DIFFSERV-MIB", "diffServMIBClfrElementGroup"), ("DIFFSERV-MIB", "diffServMIBMultiFieldClfrGroup"), ("DIFFSERV-MIB", "diffServMIBActionGroup"), ("DIFFSERV-MIB", "diffServMIBDscpMarkActGroup"), ("DIFFSERV-MIB", "diffServMIBCounterGroup"), ("DIFFSERV-MIB", "diffServMIBAlgDropGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevBaseGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevBaseIgmpGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevBaseMaxCpeGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSoftwareGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEventGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevInetCpeGroup"),)) if mibBuilder.loadTexts: docsDevCmCompliance.setDescription('The compliance statement for DOCSIS Cable Modems.\n\n This compliance statement applies to implementations\n of DOCSIS versions that are not IPv6 capable.') docsDevCmtsCompliance = ModuleCompliance((1, 3, 6, 1, 2, 1, 69, 3, 4, 2)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevBaseGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevBaseIgmpGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevBaseMaxCpeGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSoftwareGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEventGroupV2"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCGroup"), ("DOCS-CABLE-DEVICE-MIB", "docsDevInetCpeGroup"),)) if mibBuilder.loadTexts: docsDevCmtsCompliance.setDescription('The compliance statement for DOCSIS Cable Modem\n Termination Systems.\n\n This compliance statement applies to implementations\n of DOCSIS versions that are not IPv6 capable.') docsDevBaseIgmpGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 1)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevIgmpModeControl"),)) if mibBuilder.loadTexts: docsDevBaseIgmpGroup.setDescription('An object providing cable device IGMP status and\n control.') docsDevBaseMaxCpeGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 2)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevMaxCpe"),)) if mibBuilder.loadTexts: docsDevBaseMaxCpeGroup.setDescription('An object providing management of the maximum number of\n CPEs permitted access through a cable modem.') docsDevNmAccessExtGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 3)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevNmAccessTrapVersion"),)) if mibBuilder.loadTexts: docsDevNmAccessExtGroup.setDescription('An object, in addition to the objects in\n docsDevNmAccessGroup, for controlling access to\n SNMP objects on cable devices.\n\n This group is included in this MIB due to existing\n implementations of docsDevNmAccessTrapVersion in\n DOCSIS cable modems.\n\n This group has been deprecated because the object has\n been deprecated in favor of SNMPv3 and Coexistence\n MIBs.') docsDevSoftwareGroupV2 = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 4)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevSwFilename"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwAdminStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwOperStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwCurrentVers"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwServerAddressType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwServerAddress"), ("DOCS-CABLE-DEVICE-MIB", "docsDevSwServerTransportProtocol"),)) if mibBuilder.loadTexts: docsDevSoftwareGroupV2.setDescription('A collection of objects for controlling software\n downloads. This group replaces docsDevSoftwareGroup.') docsDevServerGroupV2 = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 5)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevServerBootState"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerDhcpAddressType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerDhcpAddress"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerTimeAddressType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerTimeAddress"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerConfigTftpAddressType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerConfigTftpAddress"), ("DOCS-CABLE-DEVICE-MIB", "docsDevServerConfigFile"),)) if mibBuilder.loadTexts: docsDevServerGroupV2.setDescription('A collection of objects providing status about server\n provisioning. This group replaces docsDevServerGroup.') docsDevEventGroupV2 = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 6)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevEvControl"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleAdminStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleThreshold"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleInterval"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvReporting"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvFirstTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvLastTime"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvCounts"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvLevel"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvId"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvText"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvSyslogAddressType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvSyslogAddress"), ("DOCS-CABLE-DEVICE-MIB", "docsDevEvThrottleThresholdExceeded"),)) if mibBuilder.loadTexts: docsDevEventGroupV2.setDescription('A collection of objects used to control and monitor\n events. This group replaces docsDevEventGroup.\n The event reporting mechanism, and more specifically\n docsDevEvReporting, can be used to take advantage of\n the event reporting features of RFC3413 and RFC3014.') docsDevFilterLLCGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 7)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCUnmatchedAction"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCStatus"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCIfIndex"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCProtocolType"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCProtocol"), ("DOCS-CABLE-DEVICE-MIB", "docsDevFilterLLCMatches"),)) if mibBuilder.loadTexts: docsDevFilterLLCGroup.setDescription('A collection of objects to specify link layer filters.') docsDevInetCpeGroup = ObjectGroup((1, 3, 6, 1, 2, 1, 69, 3, 3, 8)).setObjects(*(("DOCS-CABLE-DEVICE-MIB", "docsDevCpeEnroll"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeIpMax"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeInetSource"), ("DOCS-CABLE-DEVICE-MIB", "docsDevCpeInetRowStatus"),)) if mibBuilder.loadTexts: docsDevInetCpeGroup.setDescription('A collection of objects used to control the number\n and specific values of Internet (e.g., IPv4 and IPv6)\n addresses allowed for associated Customer Premises\n Equipment (CPE).') mibBuilder.exportSymbols("DOCS-CABLE-DEVICE-MIB", docsDevBase=docsDevBase, docsDevEventEntry=docsDevEventEntry, docsDevNmAccessGroup=docsDevNmAccessGroup, docsDevFilterLLCMatches=docsDevFilterLLCMatches, docsDevCpeTable=docsDevCpeTable, docsDevNmAccessControl=docsDevNmAccessControl, docsDevCpeInetSource=docsDevCpeInetSource, docsDevFilterIpTable=docsDevFilterIpTable, docsDevCmtsCompliance=docsDevCmtsCompliance, docsDevBasicCompliance=docsDevBasicCompliance, docsDevServerDhcpAddress=docsDevServerDhcpAddress, docsDevNotification=docsDevNotification, docsDevFilterLLCEntry=docsDevFilterLLCEntry, docsDevGroups=docsDevGroups, docsDevFilterIpSmask=docsDevFilterIpSmask, docsDevServerBootState=docsDevServerBootState, docsDevFilterLLCTable=docsDevFilterLLCTable, docsDevEvSyslog=docsDevEvSyslog, docsDevEvThrottleInhibited=docsDevEvThrottleInhibited, docsDevServerGroupV2=docsDevServerGroupV2, docsDevEventGroupV2=docsDevEventGroupV2, docsDevCpeGroup=docsDevCpeGroup, docsDevNmAccessInterfaces=docsDevNmAccessInterfaces, docsDevEvId=docsDevEvId, docsDevFilterIpDefault=docsDevFilterIpDefault, docsDevCompliances=docsDevCompliances, docsDevFilterPolicyTable=docsDevFilterPolicyTable, docsDevEventTable=docsDevEventTable, docsDevFilterLLCIfIndex=docsDevFilterLLCIfIndex, docsDevNotifications=docsDevNotifications, docsDevEvReporting=docsDevEvReporting, docsDevServerConfigFile=docsDevServerConfigFile, docsDevFilterLLCProtocol=docsDevFilterLLCProtocol, docsDevCpeInetType=docsDevCpeInetType, docsDevNmAccessIp=docsDevNmAccessIp, docsDevConformance=docsDevConformance, docsDevCpeInetEntry=docsDevCpeInetEntry, docsDevServerTimeAddressType=docsDevServerTimeAddressType, docsDevEvControl=docsDevEvControl, docsDevCmCompliance=docsDevCmCompliance, docsDevEvLastTime=docsDevEvLastTime, docsDevSwServerAddressType=docsDevSwServerAddressType, docsDevNmAccessIndex=docsDevNmAccessIndex, docsDevFilterTosTable=docsDevFilterTosTable, docsDevFilterIpSourcePortLow=docsDevFilterIpSourcePortLow, docsDevFilterTosIndex=docsDevFilterTosIndex, docsDevNmAccessTrapVersion=docsDevNmAccessTrapVersion, docsDevFilterIpControl=docsDevFilterIpControl, docsDevSwFilename=docsDevSwFilename, docsDevFilterTosAndMask=docsDevFilterTosAndMask, docsDevMIBObjects=docsDevMIBObjects, docsDevNmAccessExtGroup=docsDevNmAccessExtGroup, docsDevSwCurrentVers=docsDevSwCurrentVers, docsDevCpeInetAddr=docsDevCpeInetAddr, docsDevFilterTosStatus=docsDevFilterTosStatus, docsDevEvThrottleInterval=docsDevEvThrottleInterval, docsDevGroupsV2=docsDevGroupsV2, docsDevEvControlTable=docsDevEvControlTable, docsDevFilterGroup=docsDevFilterGroup, docsDevFilterLLCStatus=docsDevFilterLLCStatus, docsDevNmAccessTable=docsDevNmAccessTable, docsDevSwServer=docsDevSwServer, docsDevFilterIpDestPortHigh=docsDevFilterIpDestPortHigh, docsDevServerGroup=docsDevServerGroup, docsDevNmAccessIpMask=docsDevNmAccessIpMask, docsDevSTPControl=docsDevSTPControl, docsDevEvControlEntry=docsDevEvControlEntry, docsDevMaxCpe=docsDevMaxCpe, PYSNMP_MODULE_ID=docsDev, docsDevEvent=docsDevEvent, docsDevEvFirstTime=docsDevEvFirstTime, docsDevNmAccessStatus=docsDevNmAccessStatus, docsDevSerialNumber=docsDevSerialNumber, docsDevEvThrottleThreshold=docsDevEvThrottleThreshold, docsDevCpeIp=docsDevCpeIp, docsDev=docsDev, docsDevCpeEnroll=docsDevCpeEnroll, docsDevBaseMaxCpeGroup=docsDevBaseMaxCpeGroup, docsDevEventGroup=docsDevEventGroup, docsDevBaseIgmpGroup=docsDevBaseIgmpGroup, docsDevFilterIpEntry=docsDevFilterIpEntry, docsDevSoftwareGroup=docsDevSoftwareGroup, docsDevEvPriority=docsDevEvPriority, docsDevEvThrottleThresholdExceeded=docsDevEvThrottleThresholdExceeded, docsDevEvSyslogAddress=docsDevEvSyslogAddress, docsDevNmAccessCommunity=docsDevNmAccessCommunity, docsDevEvThrottleAdminStatus=docsDevEvThrottleAdminStatus, docsDevEvCounts=docsDevEvCounts, docsDevFilterIpIndex=docsDevFilterIpIndex, docsDevResetNow=docsDevResetNow, docsDevSwServerAddress=docsDevSwServerAddress, docsDevNmAccessEntry=docsDevNmAccessEntry, docsDevDateTime=docsDevDateTime, docsDevSwServerTransportProtocol=docsDevSwServerTransportProtocol, docsDevServerTime=docsDevServerTime, docsDevServerDhcpAddressType=docsDevServerDhcpAddressType, docsDevInetCpeGroup=docsDevInetCpeGroup, docsDevServerConfigTftpAddress=docsDevServerConfigTftpAddress, docsDevFilterIpBroadcast=docsDevFilterIpBroadcast, docsDevFilterTosEntry=docsDevFilterTosEntry, docsDevRole=docsDevRole, docsDevFilterIpDaddr=docsDevFilterIpDaddr, docsDevFilterIpMatches=docsDevFilterIpMatches, docsDevCompliancesV2=docsDevCompliancesV2, docsDevEvLevel=docsDevEvLevel, docsDevFilterIpContinue=docsDevFilterIpContinue, docsDevCpe=docsDevCpe, docsDevEvSyslogAddressType=docsDevEvSyslogAddressType, docsDevServerDhcp=docsDevServerDhcp, docsDevFilterIpDestPortLow=docsDevFilterIpDestPortLow, docsDevFilterPolicyIndex=docsDevFilterPolicyIndex, docsDevServerTftp=docsDevServerTftp, docsDevFilterPolicyEntry=docsDevFilterPolicyEntry, docsDevFilterLLCProtocolType=docsDevFilterLLCProtocolType, docsDevEvIndex=docsDevEvIndex, docsDevFilterPolicyStatus=docsDevFilterPolicyStatus, docsDevServerConfigTftpAddressType=docsDevServerConfigTftpAddressType, docsDevFilterPolicyPtr=docsDevFilterPolicyPtr, docsDevSoftwareGroupV2=docsDevSoftwareGroupV2, docsDevServer=docsDevServer, docsDevFilterLLCIndex=docsDevFilterLLCIndex, docsDevEvText=docsDevEvText, docsDevCpeIpMax=docsDevCpeIpMax, docsDevFilterIpSourcePortHigh=docsDevFilterIpSourcePortHigh, docsDevBaseGroup=docsDevBaseGroup, docsDevFilterPolicyId=docsDevFilterPolicyId, docsDevFilterLLCUnmatchedAction=docsDevFilterLLCUnmatchedAction, docsDevFilterIpSaddr=docsDevFilterIpSaddr, docsDevFilterTosOrMask=docsDevFilterTosOrMask, docsDevCpeEntry=docsDevCpeEntry, docsDevFilterIpIfIndex=docsDevFilterIpIfIndex, docsDevFilterIpPolicyId=docsDevFilterIpPolicyId, docsDevCpeInetRowStatus=docsDevCpeInetRowStatus, docsDevServerTimeAddress=docsDevServerTimeAddress, docsDevSoftware=docsDevSoftware, docsDevFilterIpTos=docsDevFilterIpTos, docsDevCpeSource=docsDevCpeSource, docsDevFilterIpProtocol=docsDevFilterIpProtocol, docsDevIgmpModeControl=docsDevIgmpModeControl, docsDevCpeStatus=docsDevCpeStatus, docsDevFilterIpTosMask=docsDevFilterIpTosMask, docsDevFilterIpDmask=docsDevFilterIpDmask, docsDevSwOperStatus=docsDevSwOperStatus, docsDevFilterLLCGroup=docsDevFilterLLCGroup, docsDevFilter=docsDevFilter, docsDevSwAdminStatus=docsDevSwAdminStatus, docsDevFilterIpStatus=docsDevFilterIpStatus, docsDevFilterIpDirection=docsDevFilterIpDirection, docsDevCpeInetTable=docsDevCpeInetTable)

350.042623

6,497

0.704139

c6e04bc0342dca732e1080ec65ab77a24c9e06e4

11,513

py

Python

samples/openapi3/client/petstore/python/petstore_api/model/some_object_with_self_attr.py

rotty3000/openapi-generator

40d3331e789412d8f42df0148cc089a9d330b759

[ "Apache-2.0" ]

1

2022-01-11T15:49:34.000Z

samples/openapi3/client/petstore/python/petstore_api/model/some_object_with_self_attr.py

rotty3000/openapi-generator

40d3331e789412d8f42df0148cc089a9d330b759

[ "Apache-2.0" ]

9

2021-11-01T08:59:31.000Z

2022-03-31T08:31:57.000Z

samples/openapi3/client/petstore/python/petstore_api/model/some_object_with_self_attr.py

rotty3000/openapi-generator

40d3331e789412d8f42df0148cc089a9d330b759

[ "Apache-2.0" ]

1

2022-02-19T21:56:04.000Z

""" OpenAPI Petstore This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from petstore_api.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, OpenApiModel ) from petstore_api.exceptions import ApiAttributeError class SomeObjectWithSelfAttr(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } @cached_property def additional_properties_type(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded """ return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501 _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { '_self': (str,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { '_self': 'self', # noqa: E501 } read_only_vars = { } _composed_schemas = {} @classmethod @convert_js_args_to_python_args def _from_openapi_data(cls, *args, **kwargs): # noqa: E501 """SomeObjectWithSelfAttr - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) _self (str): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', True) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) self = super(OpenApiModel, cls).__new__(cls) if args: for arg in args: if isinstance(arg, dict): kwargs.update(arg) else: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) return self required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """SomeObjectWithSelfAttr - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) _self (str): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: for arg in args: if isinstance(arg, dict): kwargs.update(arg) else: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value) if var_name in self.read_only_vars: raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate " f"class with read only attributes.")

43.609848

174

0.560149

f328284d63a7544b1d3bb113e149cf446d9cd2ce

2,492

py

Python

src/the_tale/the_tale/blogs/migrations/0001_initial.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

1

2020-04-02T11:51:20.000Z

src/the_tale/the_tale/blogs/migrations/0001_initial.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

null

src/the_tale/the_tale/blogs/migrations/0001_initial.py

devapromix/the-tale

2a10efd3270734f8cf482b4cfbc5353ef8f0494c

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from django.db import models, migrations import rels.django import django.db.models.deletion from django.conf import settings class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('forum', '0001_initial'), ] operations = [ migrations.CreateModel( name='Post', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ('caption', models.CharField(max_length=256)), ('text', models.TextField(default=b'', blank=True)), ('state', rels.django.RelationIntegerField(db_index=True)), ('votes', models.IntegerField(default=0)), ('author', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, null=True)), ('forum_thread', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, blank=True, to='forum.Thread', null=True)), ('moderator', models.ForeignKey(related_name='+', on_delete=django.db.models.deletion.SET_NULL, blank=True, to=settings.AUTH_USER_MODEL, null=True)), ], options={ 'permissions': (('moderate_post', '\u041c\u043e\u0436\u0435\u0442 \u0440\u0435\u0434\u0430\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439'),), }, bases=(models.Model,), ), migrations.CreateModel( name='Vote', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('post', models.ForeignKey(related_name='+', to='blogs.Post')), ('voter', models.ForeignKey(related_name='+', to=settings.AUTH_USER_MODEL)), ], options={ }, bases=(models.Model,), ), migrations.AlterUniqueTogether( name='vote', unique_together=set([('voter', 'post')]), ), ]

46.148148

299

0.60634

5a598cef6f499f4f2bcb63e8cf4de64ebac72c7a

4,224

py

Python

build_windows/gui_interface.py

grigorievich/Viy

c78cfb55e548ce40b9fe3756e4ce2a923b84a552

[ "Apache-2.0" ]

14

2017-09-08T09:20:37.000Z

2020-09-20T10:56:22.000Z

build_windows/gui_interface.py

grigorievich/Viy

c78cfb55e548ce40b9fe3756e4ce2a923b84a552

[ "Apache-2.0" ]

null

build_windows/gui_interface.py

grigorievich/Viy

c78cfb55e548ce40b9fe3756e4ce2a923b84a552

[ "Apache-2.0" ]

2

2018-02-14T16:52:33.000Z

2018-06-20T22:16:02.000Z

from __future__ import print_function, division import math import ui import messages_from_ui class GtkUI(object): """Gtk+ UI class.""" def __init__(self, font): """Initialize the UI instance.""" pass def start(self, bridge): """Start the UI event loop.""" bridge.attach(80, 24, True) self._bridge = bridge def register_poller(method, period): import time from threading import Thread def poller(): while True: time.sleep(period) method() t = Thread(target = poller, daemon = True) t.start() def input_poll(): inp = messages_from_ui.check_for_user_input() if inp != '': self._bridge.input(inp) def resize_poll(): new_size = messages_from_ui.check_for_resize() '''expects (cols, rows) format''' if new_size[0] != -1: self._bridge.resize(new_size[0], new_size[1]) def quit_poll(): need_to_quit = messages_from_ui.check_for_quit() if need_to_quit == 1: print("calling self._bridge.exit()") self._bridge.exit() register_poller(input_poll, 0.001) register_poller(resize_poll, 0.1) register_poller(quit_poll, 0.1) def quit(self): ui.quit() def switch_to_navigator(self): ui.switch_to_navigator() def _nvim_resize(self, columns, rows): '''expects (rows, columns) format''' ui.resize(rows, columns) def _nvim_clear(self): ui.clear() def _nvim_lock_update_mutex(self): ui.lock_update_mutex() def _nvim_unlock_update_mutex(self): ui.unlock_update_mutex() def _nvim_redraw(self): ui.schedule_redraw() def _nvim_eol_clear(self): ui.eol_clear() def _nvim_cursor_goto(self, row, col): ui.cursor_goto(row, col) def _nvim_busy_start(self): pass def _nvim_busy_stop(self): pass def _nvim_mouse_on(self): pass def _nvim_mouse_off(self): pass def _nvim_mode_change(self, mode): if mode == 'normal': ui.change_mode(0) if mode == 'insert': ui.change_mode(1) def _nvim_set_scroll_region(self, top, bot, left, right): ui.set_scroll_region(top, bot, left, right) def _nvim_scroll(self, count): ui.scroll(count) def _nvim_highlight_set(self, attrs): if attrs == {}: '''reset highlight set''' ui.highlight_set(-1, 0) return attributeIDs = {'bold' : 0, 'underline' : 1, 'undercurl' : 2, 'italic' : 3, 'reverse' : 4, 'foreground' : 5, 'background' : 6, 'special' : 7} for attr, value in attrs.items(): if (type(value) == bool): if (value == True): cvalue = 1 else: cvalue = 0 ui.highlight_set(attributeIDs[attr], cvalue) #RPC(5, intToBytes(attributeIDs[attr]) + intToBytes(cvalue)) else: ui.highlight_set(attributeIDs[attr], value) #RPC(5, intToBytes(attributeIDs[attr]) + intToBytes(value)) def _nvim_put(self, text): ui.put(text) def _nvim_bell(self): pass def _nvim_visual_bell(self): pass def _nvim_update_fg(self, fg): ''' fg could be negative, but update_fg receives an unsigned int. The resulting conversion is what is apparently expected by nvim, so simply passing should work. ''' if fg < 0: fg = 0 ui.update_fg(fg) def _nvim_update_bg(self, bg): #bg could be negative if bg < 0: bg = 0xFFFFFF ui.update_bg(bg) def _nvim_suspend(self): pass def _nvim_set_title(self, title): ui.set_title(title) def _nvim_set_icon(self, icon): pass

26.236025

76

0.533617

fad35c486de48f087787b3e7b228c44cc14f5e49

632

py

Python

nima/train/emd_loss.py

jjmao-cs/nima.pytorch

62f1dda7b146d47084bb414933380c324487fb4e

[ "MIT" ]

73

2020-10-15T06:38:56.000Z

2022-03-24T07:32:10.000Z

nima/train/emd_loss.py

jjmao-cs/nima.pytorch

62f1dda7b146d47084bb414933380c324487fb4e

[ "MIT" ]

11

2020-12-17T06:21:27.000Z

2022-02-04T04:56:57.000Z

nima/train/emd_loss.py

jjmao-cs/nima.pytorch

62f1dda7b146d47084bb414933380c324487fb4e

[ "MIT" ]

10

2020-10-16T13:11:34.000Z

2022-03-24T12:19:43.000Z

import torch import torch.nn as nn from torch.autograd import Variable class EDMLoss(nn.Module): def __init__(self): super(EDMLoss, self).__init__() def forward(self, p_target: Variable, p_estimate: Variable): assert p_target.shape == p_estimate.shape # cdf for values [1, 2, ..., 10] cdf_target = torch.cumsum(p_target, dim=1) # cdf for values [1, 2, ..., 10] cdf_estimate = torch.cumsum(p_estimate, dim=1) cdf_diff = cdf_estimate - cdf_target samplewise_emd = torch.sqrt(torch.mean(torch.pow(torch.abs(cdf_diff), 2))) return samplewise_emd.mean()

33.263158

82

0.653481

2c399793654938b9bfa06a8ac3bd0bbc7b8657de

1,474

py

Python

python_schools/iterators.py

nussbrot/code-exchange

3427798c4391e5f9c17cd19399ac56997c091ee2

[ "MIT" ]

null

python_schools/iterators.py

nussbrot/code-exchange

3427798c4391e5f9c17cd19399ac56997c091ee2

[ "MIT" ]

null

python_schools/iterators.py

nussbrot/code-exchange

3427798c4391e5f9c17cd19399ac56997c091ee2

[ "MIT" ]

null

# the iterator protocol needs functions __iter__() and __next__() class iterator_protocol(object): def __init__(self, *args): self._data = args self._idx = 0 def __iter__(self): return self def __next__(self): if self._idx < len(self._data): tmp = self._data[self._idx] self._idx += 1 return tmp else: raise StopIteration iterator = iterator_protocol(1, "Suppe", None, 3.12) for item in iterator: print(item) # the same iterator implemented as generator function def generator(*args): for item in args: yield item for item in generator(1, "Suppe", None, 3.12): print(item) # Generators can also be created by putting list comprehensions in round brackets print("\nSquares") squares = (x*x for x in range(10)) for item in squares: print(item) # iterators also work recursively. # let's take a look at Guido's binary tree inorder traversal: def inorder(t): if t: for x in inorder(t.left): yield x yield t.dat for x in inorder(t.right): yield x class tree(object): def __init__(self, dat, left=None, right=None): self.dat = dat self.left = left self.right = right # A small test tree: # 10 # /\ # 7 13 # /\ # 5 9 my_tree = tree(10, tree(7, tree(5), tree(9)), tree(13)) print("\nBinary tree traversal") for node in inorder(my_tree): print(node)

21.676471

81

0.612619

42fe8f6b717c18e981e9e6dd510736e9d42f38a2

8,310

py

Python

unsupervised_learning/clustering/OPTICS/sample_scratch.py

niektuytel/Machine_Learning

0cd5656ca8076c383fd81c5e32a49969a20ad042

[ "MIT" ]

11

2021-07-05T15:51:35.000Z

2022-03-19T15:17:37.000Z

unsupervised_learning/clustering/OPTICS/sample_scratch.py

niektuytel/Machine_Learning

0cd5656ca8076c383fd81c5e32a49969a20ad042

[ "MIT" ]

null

unsupervised_learning/clustering/OPTICS/sample_scratch.py

niektuytel/Machine_Learning

0cd5656ca8076c383fd81c5e32a49969a20ad042

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt import operator, math from scipy.spatial.distance import pdist from scipy.spatial.distance import squareform import warnings warnings.filterwarnings("ignore", category=FutureWarning) # data X = np.array([[15, 39], [15, 81], [16, 6], [16, 77], [17, 40], [17, 76], [18, 6], [18, 94], [19, 3], [19, 72], [19, 14], [19, 99], [20, 15], [20, 77], [20, 13], [20, 79], [21, 35], [21, 66], [23, 29], [23, 98], [24, 35], [24, 73], [25, 5], [25, 73], [28, 14], [28, 82], [28, 32], [28, 61], [29, 31], [29, 87], [30, 4], [30, 73], [33, 4], [33, 92], [33, 14], [33, 81], [34, 17], [34, 73], [37, 26], [37, 75], [38, 35], [38, 92], [39, 36], [39, 61], [39, 28], [39, 65], [40, 55], [40, 47], [40, 42], [40, 42], [42, 52], [42, 60], [43, 54], [43, 60], [43, 45], [43, 41], [44, 50], [44, 46], [46, 51], [46, 46], [46, 56], [46, 55], [47, 52], [47, 59], [48, 51], [48, 59], [48, 50], [48, 48], [48, 59], [48, 47], [49, 55], [49, 42], [50, 49], [50, 56], [54, 47], [54, 54], [54, 53], [54, 48], [54, 52], [54, 42], [54, 51], [54, 55], [54, 41], [54, 44], [54, 57], [54, 46], [57, 58], [57, 55], [58, 60], [58, 46], [59, 55], [59, 41], [60, 49], [60, 40], [60, 42], [60, 52], [60, 47], [60, 50], [61, 42], [61, 49], [62, 41], [62, 48], [62, 59], [62, 55], [62, 56], [62, 42], [63, 50], [63, 46], [63, 43], [63, 48], [63, 52], [63, 54], [64, 42], [64, 46], [65, 48], [65, 50], [65, 43], [65, 59], [67, 43], [67, 57], [67, 56], [67, 40], [69, 58], [69, 91], [70, 29], [70, 77], [71, 35], [71, 95], [71, 11], [71, 75], [71, 9], [71, 75], [72, 34], [72, 71], [73, 5], [73, 88], [73, 7], [73, 73], [74, 10], [74, 72], [75, 5], [75, 93], [76, 40], [76, 87], [77, 12], [77, 97], [77, 36], [77, 74], [78, 22], [78, 90], [78, 17], [78, 88], [78, 20], [78, 76], [78, 16], [78, 89], [78, 1], [78, 78], [78, 1], [78, 73], [79, 35], [79, 83], [81, 5], [81, 93], [85, 26], [85, 75], [86, 20], [86, 95], [87, 27], [87, 63], [87, 13], [87, 75], [87, 10], [87, 92], [88, 13], [88, 86], [88, 15], [88, 69], [93, 14], [93, 90], [97, 32], [97, 86], [98, 15], [98, 88], [99, 39], [99, 97], [101, 24], [101, 68], [103, 17], [103, 85], [103, 23], [103, 69], [113, 8], [113, 91], [120, 16], [120, 79], [126, 28], [126, 74], [137, 18], [137, 83]]) # Algorithms def euclidean_distance(x1, x2): """ Calculates the l2 distance between two vectors """ distance = 0 # Squared distance between each coordinate for i in range(len(x1)): distance += pow((x1[i] - x2[i]), 2) return math.sqrt(distance) class OPTICS(): def __init__(self, epsilon=10, min_samples=10, metric="euclidean"): self.epsilon=epsilon self.min_samples=min_samples self.metric=metric def _get_core_distances(self): # min_index that will been checked min_index = self.min_samples - 1 # list to array # get matrix value of the min_samples temp = self.adjacency_matrix[ np.arange(self.adjacency_matrix.shape[0]), np.argsort(self.adjacency_matrix)[:, min_index] ] # return matrix with all values that are less then self.epsilon else it will be -1 return np.where(temp <= self.epsilon, temp, -1) def _get_neighbors(self, sample_i): """ Return a list of indexes of neighboring samples A sample_2 is considered a neighbor of sample_1 if the distance between them is smaller than this.epsilon """ data = self.X neighbors = [] all_indexes = np.arange(len(data)) for i, _sample in enumerate(data[all_indexes != sample_i]): distance = euclidean_distance(data[sample_i], _sample) if distance <= self.epsilon: neighbors.append(i) return np.array(neighbors) def _update_reachable_distances(self, sample_i, neighbors, seeds=dict()): # Iterate through neighbors and expand higest reachable distance from them for neighbor in neighbors: if self.visited_samples[neighbor]: continue # First calculate the reachable distance of the changed point for sample_i new_reach_dist = max( self.core_distances[sample_i], self.adjacency_matrix[sample_i][neighbor] ) seeds[neighbor] = self.reachable_distances[neighbor] = min( self.reachable_distances[neighbor], new_reach_dist ) return seeds def _get_cluster_labels(self, orders): # find the index of the point in the ordered list that is smaller than epsilon, # that is the index corresponding to the ordered list clusters = np.where(self.reachable_distances[orders] <= self.epsilon)[0] # Normally: the value of current should be one more index than the value of pre. # If it is larger than one index, it means that it is not a category. pre = clusters[0] - 1 clusterId = 0 # Will make sure all outliers have same cluster label labels = np.full(shape=self.X.shape[0], fill_value=0) for cluster_i, cluster in enumerate(clusters): # Normally: the value of cluster should be one more index than the value of pre. # If it is larger than one index, it means that it is not a category. if(cluster - pre != 1): clusterId = clusterId + 1 labels[orders[cluster]]=clusterId pre=cluster return labels def fit_predict(self, X, is_adjacency_matrix=False): self.X = X # Compute the adjacency matrix if not is_adjacency_matrix: dist = pdist(X, metric=self.metric) self.adjacency_matrix = squareform(dist) else: self.adjacency_matrix = X self.visited_samples = np.zeros(self.X.shape[0]) self.reachable_distances = np.full(self.X.shape[0], np.inf) self.core_distances = self._get_core_distances() # all matched values as a summed Matrix summed_matches = np.sum(np.where(self.adjacency_matrix <= self.epsilon, 1, 0), axis=1) core_samples = np.where(summed_matches >= self.min_samples)[0] used_samples = [] # Iterate through core samples and itterate to the next core samples # so we get all grouped clusters at the end for core_sample in core_samples: if self.visited_samples[core_sample]: continue # unique noted data self.visited_samples[core_sample] = int(True) used_samples.append(core_sample) # Find all points (samples) in epsilon range neighbors = self._get_neighbors(core_sample) nearest_samples = self._update_reachable_distances(core_sample, neighbors) # check closest sample from core point while len(nearest_samples) > 0: closest_sample = sorted(nearest_samples.items(), key=operator.itemgetter(1))[0][0] del nearest_samples[closest_sample] # unique noted data self.visited_samples[closest_sample] = int(True) used_samples.append(closest_sample) # Find all points (samples) in epsilon range sample_neighbors = self._get_neighbors(closest_sample) if len(sample_neighbors) >= self.min_samples: nearest_samples = self._update_reachable_distances(closest_sample, sample_neighbors, nearest_samples) cluster_labels = self._get_cluster_labels(used_samples) return cluster_labels if __name__ == "__main__": # define model model = OPTICS(epsilon=23, min_samples=15) # assign cluster to each sample yhat = model.fit_predict(X) # retrieve unique clusters clusters = np.unique(yhat) # create scatter plot for samples from each cluster for cluster in clusters: # get row indexes for samples with this cluster row_ix = np.where(yhat == cluster) # create scatter of these samples plt.scatter(X[row_ix, 0], X[row_ix, 1]) # show the plot plt.show()

47.485714

2,014

0.570517

d28a5b3b01dc07a7aeaaa89942a0c20a00da447f

68,272

py

Python

src/twisted/web/test/test_static.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

2

2021-05-30T16:35:00.000Z

2021-06-03T12:23:33.000Z

src/twisted/web/test/test_static.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

src/twisted/web/test/test_static.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

2

2021-05-29T21:12:22.000Z

2021-05-30T04:56:50.000Z

# Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Tests for L{twisted.web.static}. """ import errno import inspect import mimetypes import os import re import sys import warnings from io import BytesIO as StringIO from unittest import skipIf from zope.interface.verify import verifyObject from twisted.internet import abstract, interfaces from twisted.python.runtime import platform from twisted.python.filepath import FilePath from twisted.python import compat, log from twisted.python.compat import networkString from twisted.trial.unittest import TestCase from twisted.web import static, http, script, resource from twisted.web.server import UnsupportedMethod from twisted.web.test.requesthelper import DummyRequest from twisted.web.test._util import _render from twisted.web._responses import FOUND class StaticDataTests(TestCase): """ Tests for L{Data}. """ def test_headRequest(self): """ L{Data.render} returns an empty response body for a I{HEAD} request. """ data = static.Data(b"foo", "bar") request = DummyRequest([""]) request.method = b"HEAD" d = _render(data, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"") d.addCallback(cbRendered) return d def test_invalidMethod(self): """ L{Data.render} raises L{UnsupportedMethod} in response to a non-I{GET}, non-I{HEAD} request. """ data = static.Data(b"foo", b"bar") request = DummyRequest([b""]) request.method = b"POST" self.assertRaises(UnsupportedMethod, data.render, request) class StaticFileTests(TestCase): """ Tests for the basic behavior of L{File}. """ def _render(self, resource, request): return _render(resource, request) def test_ignoredExtTrue(self): """ Passing C{1} as the value to L{File}'s C{ignoredExts} argument issues a warning and sets the ignored extensions to the wildcard C{"*"}. """ with warnings.catch_warnings(record=True) as caughtWarnings: file = static.File(self.mktemp(), ignoredExts=1) self.assertEqual(file.ignoredExts, ["*"]) self.assertEqual(len(caughtWarnings), 1) def test_ignoredExtFalse(self): """ Passing C{1} as the value to L{File}'s C{ignoredExts} argument issues a warning and sets the ignored extensions to the empty list. """ with warnings.catch_warnings(record=True) as caughtWarnings: file = static.File(self.mktemp(), ignoredExts=0) self.assertEqual(file.ignoredExts, []) self.assertEqual(len(caughtWarnings), 1) def test_allowExt(self): """ Passing C{1} as the value to L{File}'s C{allowExt} argument issues a warning and sets the ignored extensions to the wildcard C{*}. """ with warnings.catch_warnings(record=True) as caughtWarnings: file = static.File(self.mktemp(), ignoredExts=True) self.assertEqual(file.ignoredExts, ["*"]) self.assertEqual(len(caughtWarnings), 1) def test_invalidMethod(self): """ L{File.render} raises L{UnsupportedMethod} in response to a non-I{GET}, non-I{HEAD} request. """ request = DummyRequest([b""]) request.method = b"POST" path = FilePath(self.mktemp()) path.setContent(b"foo") file = static.File(path.path) self.assertRaises(UnsupportedMethod, file.render, request) def test_notFound(self): """ If a request is made which encounters a L{File} before a final segment which does not correspond to any file in the path the L{File} was created with, a not found response is sent. """ base = FilePath(self.mktemp()) base.makedirs() file = static.File(base.path) request = DummyRequest([b"foobar"]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(request.responseCode, 404) d.addCallback(cbRendered) return d def test_emptyChild(self): """ The C{''} child of a L{File} which corresponds to a directory in the filesystem is a L{DirectoryLister}. """ base = FilePath(self.mktemp()) base.makedirs() file = static.File(base.path) request = DummyRequest([b""]) child = resource.getChildForRequest(file, request) self.assertIsInstance(child, static.DirectoryLister) self.assertEqual(child.path, base.path) def test_emptyChildUnicodeParent(self): """ The C{u''} child of a L{File} which corresponds to a directory whose path is text is a L{DirectoryLister} that renders to a binary listing. @see: U{https://twistedmatrix.com/trac/ticket/9438} """ textBase = FilePath(self.mktemp()).asTextMode() textBase.makedirs() textBase.child("text-file").open("w").close() textFile = static.File(textBase.path) request = DummyRequest([b""]) child = resource.getChildForRequest(textFile, request) self.assertIsInstance(child, static.DirectoryLister) nativePath = compat.nativeString(textBase.path) self.assertEqual(child.path, nativePath) response = child.render(request) self.assertIsInstance(response, bytes) def test_securityViolationNotFound(self): """ If a request is made which encounters a L{File} before a final segment which cannot be looked up in the filesystem due to security considerations, a not found response is sent. """ base = FilePath(self.mktemp()) base.makedirs() file = static.File(base.path) request = DummyRequest([b".."]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(request.responseCode, 404) d.addCallback(cbRendered) return d @skipIf(platform.isWindows(), "Cannot remove read permission on Windows") def test_forbiddenResource(self): """ If the file in the filesystem which would satisfy a request cannot be read, L{File.render} sets the HTTP response code to I{FORBIDDEN}. """ base = FilePath(self.mktemp()) base.setContent(b"") # Make sure we can delete the file later. self.addCleanup(base.chmod, 0o700) # Get rid of our own read permission. base.chmod(0) file = static.File(base.path) request = DummyRequest([b""]) d = self._render(file, request) def cbRendered(ignored): self.assertEqual(request.responseCode, 403) d.addCallback(cbRendered) return d def test_undecodablePath(self): """ A request whose path cannot be decoded as UTF-8 receives a not found response, and the failure is logged. """ path = self.mktemp() if isinstance(path, bytes): path = path.decode("ascii") base = FilePath(path) base.makedirs() file = static.File(base.path) request = DummyRequest([b"\xff"]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(request.responseCode, 404) self.assertEqual(len(self.flushLoggedErrors(UnicodeDecodeError)), 1) d.addCallback(cbRendered) return d def test_forbiddenResource_default(self): """ L{File.forbidden} defaults to L{resource.ForbiddenResource}. """ self.assertIsInstance(static.File(b".").forbidden, resource.ForbiddenResource) def test_forbiddenResource_customize(self): """ The resource rendered for forbidden requests is stored as a class member so that users can customize it. """ base = FilePath(self.mktemp()) base.setContent(b"") markerResponse = b"custom-forbidden-response" def failingOpenForReading(): raise OSError(errno.EACCES, "") class CustomForbiddenResource(resource.Resource): def render(self, request): return markerResponse class CustomStaticFile(static.File): forbidden = CustomForbiddenResource() fileResource = CustomStaticFile(base.path) fileResource.openForReading = failingOpenForReading request = DummyRequest([b""]) result = fileResource.render(request) self.assertEqual(markerResponse, result) def test_indexNames(self): """ If a request is made which encounters a L{File} before a final empty segment, a file in the L{File} instance's C{indexNames} list which exists in the path the L{File} was created with is served as the response to the request. """ base = FilePath(self.mktemp()) base.makedirs() base.child("foo.bar").setContent(b"baz") file = static.File(base.path) file.indexNames = ["foo.bar"] request = DummyRequest([b""]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"baz") self.assertEqual( request.responseHeaders.getRawHeaders(b"content-length")[0], b"3" ) d.addCallback(cbRendered) return d def test_staticFile(self): """ If a request is made which encounters a L{File} before a final segment which names a file in the path the L{File} was created with, that file is served as the response to the request. """ base = FilePath(self.mktemp()) base.makedirs() base.child("foo.bar").setContent(b"baz") file = static.File(base.path) request = DummyRequest([b"foo.bar"]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"baz") self.assertEqual( request.responseHeaders.getRawHeaders(b"content-length")[0], b"3" ) d.addCallback(cbRendered) return d @skipIf( sys.getfilesystemencoding().lower() not in ("utf-8", "mcbs"), "Cannot write unicode filenames with file system encoding of" " {}".format(sys.getfilesystemencoding()), ) def test_staticFileUnicodeFileName(self): """ A request for a existing unicode file path encoded as UTF-8 returns the contents of that file. """ name = "\N{GREEK SMALL LETTER ETA WITH PERISPOMENI}" content = b"content" base = FilePath(self.mktemp()) base.makedirs() base.child(name).setContent(content) file = static.File(base.path) request = DummyRequest([name.encode("utf-8")]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), content) self.assertEqual( request.responseHeaders.getRawHeaders(b"content-length")[0], networkString(str(len(content))), ) d.addCallback(cbRendered) return d def test_staticFileDeletedGetChild(self): """ A L{static.File} created for a directory which does not exist should return childNotFound from L{static.File.getChild}. """ staticFile = static.File(self.mktemp()) request = DummyRequest([b"foo.bar"]) child = staticFile.getChild(b"foo.bar", request) self.assertEqual(child, staticFile.childNotFound) def test_staticFileDeletedRender(self): """ A L{static.File} created for a file which does not exist should render its C{childNotFound} page. """ staticFile = static.File(self.mktemp()) request = DummyRequest([b"foo.bar"]) request2 = DummyRequest([b"foo.bar"]) d = self._render(staticFile, request) d2 = self._render(staticFile.childNotFound, request2) def cbRendered2(ignored): def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"".join(request2.written)) d.addCallback(cbRendered) return d d2.addCallback(cbRendered2) return d2 def test_getChildChildNotFound_customize(self): """ The resource rendered for child not found requests can be customize using a class member. """ base = FilePath(self.mktemp()) base.setContent(b"") markerResponse = b"custom-child-not-found-response" class CustomChildNotFoundResource(resource.Resource): def render(self, request): return markerResponse class CustomStaticFile(static.File): childNotFound = CustomChildNotFoundResource() fileResource = CustomStaticFile(base.path) request = DummyRequest([b"no-child.txt"]) child = fileResource.getChild(b"no-child.txt", request) result = child.render(request) self.assertEqual(markerResponse, result) def test_headRequest(self): """ L{static.File.render} returns an empty response body for I{HEAD} requests. """ path = FilePath(self.mktemp()) path.setContent(b"foo") file = static.File(path.path) request = DummyRequest([b""]) request.method = b"HEAD" d = _render(file, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"") d.addCallback(cbRendered) return d def test_processors(self): """ If a request is made which encounters a L{File} before a final segment which names a file with an extension which is in the L{File}'s C{processors} mapping, the processor associated with that extension is used to serve the response to the request. """ base = FilePath(self.mktemp()) base.makedirs() base.child("foo.bar").setContent( b"from twisted.web.static import Data\n" b"resource = Data(b'dynamic world', 'text/plain')\n" ) file = static.File(base.path) file.processors = {".bar": script.ResourceScript} request = DummyRequest([b"foo.bar"]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"dynamic world") self.assertEqual( request.responseHeaders.getRawHeaders(b"content-length")[0], b"13" ) d.addCallback(cbRendered) return d def test_ignoreExt(self): """ The list of ignored extensions can be set by passing a value to L{File.__init__} or by calling L{File.ignoreExt} later. """ file = static.File(b".") self.assertEqual(file.ignoredExts, []) file.ignoreExt(".foo") file.ignoreExt(".bar") self.assertEqual(file.ignoredExts, [".foo", ".bar"]) file = static.File(b".", ignoredExts=(".bar", ".baz")) self.assertEqual(file.ignoredExts, [".bar", ".baz"]) def test_ignoredExtensionsIgnored(self): """ A request for the I{base} child of a L{File} succeeds with a resource for the I{base<extension>} file in the path the L{File} was created with if such a file exists and the L{File} has been configured to ignore the I{<extension>} extension. """ base = FilePath(self.mktemp()) base.makedirs() base.child("foo.bar").setContent(b"baz") base.child("foo.quux").setContent(b"foobar") file = static.File(base.path, ignoredExts=(".bar",)) request = DummyRequest([b"foo"]) child = resource.getChildForRequest(file, request) d = self._render(child, request) def cbRendered(ignored): self.assertEqual(b"".join(request.written), b"baz") d.addCallback(cbRendered) return d def test_directoryWithoutTrailingSlashRedirects(self): """ A request for a path which is a directory but does not have a trailing slash will be redirected to a URL which does have a slash by L{File}. """ base = FilePath(self.mktemp()) base.makedirs() base.child("folder").makedirs() file = static.File(base.path) request = DummyRequest([b"folder"]) request.uri = b"http://dummy/folder#baz?foo=bar" child = resource.getChildForRequest(file, request) self.successResultOf(self._render(child, request)) self.assertEqual(request.responseCode, FOUND) self.assertEqual( request.responseHeaders.getRawHeaders(b"location"), [b"http://dummy/folder/#baz?foo=bar"], ) def _makeFilePathWithStringIO(self): """ Create a L{File} that when opened for reading, returns a L{StringIO}. @return: 2-tuple of the opened "file" and the L{File}. @rtype: L{tuple} """ fakeFile = StringIO() path = FilePath(self.mktemp()) path.touch() file = static.File(path.path) # Open our file instead of a real one file.open = lambda: fakeFile return fakeFile, file def test_HEADClosesFile(self): """ A HEAD request opens the file, gets the size, and then closes it after the request. """ fakeFile, file = self._makeFilePathWithStringIO() request = DummyRequest([""]) request.method = b"HEAD" self.successResultOf(_render(file, request)) self.assertEqual(b"".join(request.written), b"") self.assertTrue(fakeFile.closed) def test_cachedRequestClosesFile(self): """ A GET request that is cached closes the file after the request. """ fakeFile, file = self._makeFilePathWithStringIO() request = DummyRequest([""]) request.method = b"GET" # This request will always return saying that it is cached request.setLastModified = lambda _: http.CACHED self.successResultOf(_render(file, request)) self.assertEqual(b"".join(request.written), b"") self.assertTrue(fakeFile.closed) class StaticMakeProducerTests(TestCase): """ Tests for L{File.makeProducer}. """ def makeResourceWithContent(self, content, type=None, encoding=None): """ Make a L{static.File} resource that has C{content} for its content. @param content: The L{bytes} to use as the contents of the resource. @param type: Optional value for the content type of the resource. """ fileName = FilePath(self.mktemp()) fileName.setContent(content) resource = static.File(fileName._asBytesPath()) resource.encoding = encoding resource.type = type return resource def contentHeaders(self, request): """ Extract the content-* headers from the L{DummyRequest} C{request}. This returns the subset of C{request.outgoingHeaders} of headers that start with 'content-'. """ contentHeaders = {} for k, v in request.responseHeaders.getAllRawHeaders(): if k.lower().startswith(b"content-"): contentHeaders[k.lower()] = v[0] return contentHeaders def test_noRangeHeaderGivesNoRangeStaticProducer(self): """ makeProducer when no Range header is set returns an instance of NoRangeStaticProducer. """ resource = self.makeResourceWithContent(b"") request = DummyRequest([]) with resource.openForReading() as file: producer = resource.makeProducer(request, file) self.assertIsInstance(producer, static.NoRangeStaticProducer) def test_noRangeHeaderSets200OK(self): """ makeProducer when no Range header is set sets the responseCode on the request to 'OK'. """ resource = self.makeResourceWithContent(b"") request = DummyRequest([]) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.OK, request.responseCode) def test_noRangeHeaderSetsContentHeaders(self): """ makeProducer when no Range header is set sets the Content-* headers for the response. """ length = 123 contentType = "text/plain" contentEncoding = "gzip" resource = self.makeResourceWithContent( b"a" * length, type=contentType, encoding=contentEncoding ) request = DummyRequest([]) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual( { b"content-type": networkString(contentType), b"content-length": b"%d" % (length,), b"content-encoding": networkString(contentEncoding), }, self.contentHeaders(request), ) def test_singleRangeGivesSingleRangeStaticProducer(self): """ makeProducer when the Range header requests a single byte range returns an instance of SingleRangeStaticProducer. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3") resource = self.makeResourceWithContent(b"abcdef") with resource.openForReading() as file: producer = resource.makeProducer(request, file) self.assertIsInstance(producer, static.SingleRangeStaticProducer) def test_singleRangeSets206PartialContent(self): """ makeProducer when the Range header requests a single, satisfiable byte range sets the response code on the request to 'Partial Content'. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3") resource = self.makeResourceWithContent(b"abcdef") with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.PARTIAL_CONTENT, request.responseCode) def test_singleRangeSetsContentHeaders(self): """ makeProducer when the Range header requests a single, satisfiable byte range sets the Content-* headers appropriately. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3") contentType = "text/plain" contentEncoding = "gzip" resource = self.makeResourceWithContent( b"abcdef", type=contentType, encoding=contentEncoding ) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual( { b"content-type": networkString(contentType), b"content-encoding": networkString(contentEncoding), b"content-range": b"bytes 1-3/6", b"content-length": b"3", }, self.contentHeaders(request), ) def test_singleUnsatisfiableRangeReturnsSingleRangeStaticProducer(self): """ makeProducer still returns an instance of L{SingleRangeStaticProducer} when the Range header requests a single unsatisfiable byte range. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=4-10") resource = self.makeResourceWithContent(b"abc") with resource.openForReading() as file: producer = resource.makeProducer(request, file) self.assertIsInstance(producer, static.SingleRangeStaticProducer) def test_singleUnsatisfiableRangeSets416ReqestedRangeNotSatisfiable(self): """ makeProducer sets the response code of the request to of 'Requested Range Not Satisfiable' when the Range header requests a single unsatisfiable byte range. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=4-10") resource = self.makeResourceWithContent(b"abc") with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.REQUESTED_RANGE_NOT_SATISFIABLE, request.responseCode) def test_singleUnsatisfiableRangeSetsContentHeaders(self): """ makeProducer when the Range header requests a single, unsatisfiable byte range sets the Content-* headers appropriately. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=4-10") contentType = "text/plain" resource = self.makeResourceWithContent(b"abc", type=contentType) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual( { b"content-type": b"text/plain", b"content-length": b"0", b"content-range": b"bytes */3", }, self.contentHeaders(request), ) def test_singlePartiallyOverlappingRangeSetsContentHeaders(self): """ makeProducer when the Range header requests a single byte range that partly overlaps the resource sets the Content-* headers appropriately. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=2-10") contentType = "text/plain" resource = self.makeResourceWithContent(b"abc", type=contentType) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual( { b"content-type": b"text/plain", b"content-length": b"1", b"content-range": b"bytes 2-2/3", }, self.contentHeaders(request), ) def test_multipleRangeGivesMultipleRangeStaticProducer(self): """ makeProducer when the Range header requests a single byte range returns an instance of MultipleRangeStaticProducer. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3,5-6") resource = self.makeResourceWithContent(b"abcdef") with resource.openForReading() as file: producer = resource.makeProducer(request, file) self.assertIsInstance(producer, static.MultipleRangeStaticProducer) def test_multipleRangeSets206PartialContent(self): """ makeProducer when the Range header requests a multiple satisfiable byte ranges sets the response code on the request to 'Partial Content'. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3,5-6") resource = self.makeResourceWithContent(b"abcdef") with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.PARTIAL_CONTENT, request.responseCode) def test_mutipleRangeSetsContentHeaders(self): """ makeProducer when the Range header requests a single, satisfiable byte range sets the Content-* headers appropriately. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3,5-6") resource = self.makeResourceWithContent(b"abcdefghijkl", encoding="gzip") with resource.openForReading() as file: producer = resource.makeProducer(request, file) contentHeaders = self.contentHeaders(request) # The only content-* headers set are content-type and content-length. self.assertEqual( {b"content-length", b"content-type"}, set(contentHeaders.keys()) ) # The content-length depends on the boundary used in the response. expectedLength = 5 for boundary, offset, size in producer.rangeInfo: expectedLength += len(boundary) self.assertEqual( b"%d" % (expectedLength,), contentHeaders[b"content-length"] ) # Content-type should be set to a value indicating a multipart # response and the boundary used to separate the parts. self.assertIn(b"content-type", contentHeaders) contentType = contentHeaders[b"content-type"] self.assertNotIdentical( None, re.match(br'multipart/byteranges; boundary="[^"]*"\Z', contentType), ) # Content-encoding is not set in the response to a multiple range # response, which is a bit wussy but works well enough with the way # static.File does content-encodings... self.assertNotIn(b"content-encoding", contentHeaders) def test_multipleUnsatisfiableRangesReturnsMultipleRangeStaticProducer(self): """ makeProducer still returns an instance of L{SingleRangeStaticProducer} when the Range header requests multiple ranges, none of which are satisfiable. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=10-12,15-20") resource = self.makeResourceWithContent(b"abc") with resource.openForReading() as file: producer = resource.makeProducer(request, file) self.assertIsInstance(producer, static.MultipleRangeStaticProducer) def test_multipleUnsatisfiableRangesSets416ReqestedRangeNotSatisfiable(self): """ makeProducer sets the response code of the request to of 'Requested Range Not Satisfiable' when the Range header requests multiple ranges, none of which are satisfiable. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=10-12,15-20") resource = self.makeResourceWithContent(b"abc") with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.REQUESTED_RANGE_NOT_SATISFIABLE, request.responseCode) def test_multipleUnsatisfiableRangeSetsContentHeaders(self): """ makeProducer when the Range header requests multiple ranges, none of which are satisfiable, sets the Content-* headers appropriately. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=4-10") contentType = "text/plain" request.requestHeaders.addRawHeader(b"range", b"bytes=10-12,15-20") resource = self.makeResourceWithContent(b"abc", type=contentType) with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual( { b"content-length": b"0", b"content-range": b"bytes */3", b"content-type": b"text/plain", }, self.contentHeaders(request), ) def test_oneSatisfiableRangeIsEnough(self): """ makeProducer when the Range header requests multiple ranges, at least one of which matches, sets the response code to 'Partial Content'. """ request = DummyRequest([]) request.requestHeaders.addRawHeader(b"range", b"bytes=1-3,100-200") resource = self.makeResourceWithContent(b"abcdef") with resource.openForReading() as file: resource.makeProducer(request, file) self.assertEqual(http.PARTIAL_CONTENT, request.responseCode) class StaticProducerTests(TestCase): """ Tests for the abstract L{StaticProducer}. """ def test_stopProducingClosesFile(self): """ L{StaticProducer.stopProducing} closes the file object the producer is producing data from. """ fileObject = StringIO() producer = static.StaticProducer(None, fileObject) producer.stopProducing() self.assertTrue(fileObject.closed) def test_stopProducingSetsRequestToNone(self): """ L{StaticProducer.stopProducing} sets the request instance variable to None, which indicates to subclasses' resumeProducing methods that no more data should be produced. """ fileObject = StringIO() producer = static.StaticProducer(DummyRequest([]), fileObject) producer.stopProducing() self.assertIdentical(None, producer.request) class NoRangeStaticProducerTests(TestCase): """ Tests for L{NoRangeStaticProducer}. """ def test_implementsIPullProducer(self): """ L{NoRangeStaticProducer} implements L{IPullProducer}. """ verifyObject(interfaces.IPullProducer, static.NoRangeStaticProducer(None, None)) def test_resumeProducingProducesContent(self): """ L{NoRangeStaticProducer.resumeProducing} writes content from the resource to the request. """ request = DummyRequest([]) content = b"abcdef" producer = static.NoRangeStaticProducer(request, StringIO(content)) # start calls registerProducer on the DummyRequest, which pulls all # output from the producer and so we just need this one call. producer.start() self.assertEqual(content, b"".join(request.written)) def test_resumeProducingBuffersOutput(self): """ L{NoRangeStaticProducer.start} writes at most C{abstract.FileDescriptor.bufferSize} bytes of content from the resource to the request at once. """ request = DummyRequest([]) bufferSize = abstract.FileDescriptor.bufferSize content = b"a" * (2 * bufferSize + 1) producer = static.NoRangeStaticProducer(request, StringIO(content)) # start calls registerProducer on the DummyRequest, which pulls all # output from the producer and so we just need this one call. producer.start() expected = [ content[0:bufferSize], content[bufferSize : 2 * bufferSize], content[2 * bufferSize :], ] self.assertEqual(expected, request.written) def test_finishCalledWhenDone(self): """ L{NoRangeStaticProducer.resumeProducing} calls finish() on the request after it is done producing content. """ request = DummyRequest([]) finishDeferred = request.notifyFinish() callbackList = [] finishDeferred.addCallback(callbackList.append) producer = static.NoRangeStaticProducer(request, StringIO(b"abcdef")) # start calls registerProducer on the DummyRequest, which pulls all # output from the producer and so we just need this one call. producer.start() self.assertEqual([None], callbackList) class SingleRangeStaticProducerTests(TestCase): """ Tests for L{SingleRangeStaticProducer}. """ def test_implementsIPullProducer(self): """ L{SingleRangeStaticProducer} implements L{IPullProducer}. """ verifyObject( interfaces.IPullProducer, static.SingleRangeStaticProducer(None, None, None, None), ) def test_resumeProducingProducesContent(self): """ L{SingleRangeStaticProducer.resumeProducing} writes the given amount of content, starting at the given offset, from the resource to the request. """ request = DummyRequest([]) content = b"abcdef" producer = static.SingleRangeStaticProducer(request, StringIO(content), 1, 3) # DummyRequest.registerProducer pulls all output from the producer, so # we just need to call start. producer.start() self.assertEqual(content[1:4], b"".join(request.written)) def test_resumeProducingBuffersOutput(self): """ L{SingleRangeStaticProducer.start} writes at most C{abstract.FileDescriptor.bufferSize} bytes of content from the resource to the request at once. """ request = DummyRequest([]) bufferSize = abstract.FileDescriptor.bufferSize content = b"abc" * bufferSize producer = static.SingleRangeStaticProducer( request, StringIO(content), 1, bufferSize + 10 ) # DummyRequest.registerProducer pulls all output from the producer, so # we just need to call start. producer.start() expected = [ content[1 : bufferSize + 1], content[bufferSize + 1 : bufferSize + 11], ] self.assertEqual(expected, request.written) def test_finishCalledWhenDone(self): """ L{SingleRangeStaticProducer.resumeProducing} calls finish() on the request after it is done producing content. """ request = DummyRequest([]) finishDeferred = request.notifyFinish() callbackList = [] finishDeferred.addCallback(callbackList.append) producer = static.SingleRangeStaticProducer(request, StringIO(b"abcdef"), 1, 1) # start calls registerProducer on the DummyRequest, which pulls all # output from the producer and so we just need this one call. producer.start() self.assertEqual([None], callbackList) class MultipleRangeStaticProducerTests(TestCase): """ Tests for L{MultipleRangeStaticProducer}. """ def test_implementsIPullProducer(self): """ L{MultipleRangeStaticProducer} implements L{IPullProducer}. """ verifyObject( interfaces.IPullProducer, static.MultipleRangeStaticProducer(None, None, None), ) def test_resumeProducingProducesContent(self): """ L{MultipleRangeStaticProducer.resumeProducing} writes the requested chunks of content from the resource to the request, with the supplied boundaries in between each chunk. """ request = DummyRequest([]) content = b"abcdef" producer = static.MultipleRangeStaticProducer( request, StringIO(content), [(b"1", 1, 3), (b"2", 5, 1)] ) # DummyRequest.registerProducer pulls all output from the producer, so # we just need to call start. producer.start() self.assertEqual(b"1bcd2f", b"".join(request.written)) def test_resumeProducingBuffersOutput(self): """ L{MultipleRangeStaticProducer.start} writes about C{abstract.FileDescriptor.bufferSize} bytes of content from the resource to the request at once. To be specific about the 'about' above: it can write slightly more, for example in the case where the first boundary plus the first chunk is less than C{bufferSize} but first boundary plus the first chunk plus the second boundary is more, but this is unimportant as in practice the boundaries are fairly small. On the other side, it is important for performance to bundle up several small chunks into one call to request.write. """ request = DummyRequest([]) content = b"0123456789" * 2 producer = static.MultipleRangeStaticProducer( request, StringIO(content), [(b"a", 0, 2), (b"b", 5, 10), (b"c", 0, 0)] ) producer.bufferSize = 10 # DummyRequest.registerProducer pulls all output from the producer, so # we just need to call start. producer.start() expected = [ b"a" + content[0:2] + b"b" + content[5:11], content[11:15] + b"c", ] self.assertEqual(expected, request.written) def test_finishCalledWhenDone(self): """ L{MultipleRangeStaticProducer.resumeProducing} calls finish() on the request after it is done producing content. """ request = DummyRequest([]) finishDeferred = request.notifyFinish() callbackList = [] finishDeferred.addCallback(callbackList.append) producer = static.MultipleRangeStaticProducer( request, StringIO(b"abcdef"), [(b"", 1, 2)] ) # start calls registerProducer on the DummyRequest, which pulls all # output from the producer and so we just need this one call. producer.start() self.assertEqual([None], callbackList) class RangeTests(TestCase): """ Tests for I{Range-Header} support in L{twisted.web.static.File}. @type file: L{file} @ivar file: Temporary (binary) file containing the content to be served. @type resource: L{static.File} @ivar resource: A leaf web resource using C{file} as content. @type request: L{DummyRequest} @ivar request: A fake request, requesting C{resource}. @type catcher: L{list} @ivar catcher: List which gathers all log information. """ def setUp(self): """ Create a temporary file with a fixed payload of 64 bytes. Create a resource for that file and create a request which will be for that resource. Each test can set a different range header to test different aspects of the implementation. """ path = FilePath(self.mktemp()) # This is just a jumble of random stuff. It's supposed to be a good # set of data for this test, particularly in order to avoid # accidentally seeing the right result by having a byte sequence # repeated at different locations or by having byte values which are # somehow correlated with their position in the string. self.payload = ( b"\xf8u\xf3E\x8c7\xce\x00\x9e\xb6a0y0S\xf0\xef\xac\xb7" b"\xbe\xb5\x17M\x1e\x136k{\x1e\xbe\x0c\x07\x07\t\xd0" b"\xbckY\xf5I\x0b\xb8\x88oZ\x1d\x85b\x1a\xcdk\xf2\x1d" b"&\xfd%\xdd\x82q/A\x10Y\x8b" ) path.setContent(self.payload) self.file = path.open() self.resource = static.File(self.file.name) self.resource.isLeaf = 1 self.request = DummyRequest([b""]) self.request.uri = self.file.name self.catcher = [] log.addObserver(self.catcher.append) def tearDown(self): """ Clean up the resource file and the log observer. """ self.file.close() log.removeObserver(self.catcher.append) def _assertLogged(self, expected): """ Asserts that a given log message occurred with an expected message. """ logItem = self.catcher.pop() self.assertEqual(logItem["message"][0], expected) self.assertEqual( self.catcher, [], "An additional log occurred: {!r}".format(logItem) ) def test_invalidRanges(self): """ L{File._parseRangeHeader} raises L{ValueError} when passed syntactically invalid byte ranges. """ f = self.resource._parseRangeHeader # there's no = self.assertRaises(ValueError, f, b"bytes") # unknown isn't a valid Bytes-Unit self.assertRaises(ValueError, f, b"unknown=1-2") # there's no - in =stuff self.assertRaises(ValueError, f, b"bytes=3") # both start and end are empty self.assertRaises(ValueError, f, b"bytes=-") # start isn't an integer self.assertRaises(ValueError, f, b"bytes=foo-") # end isn't an integer self.assertRaises(ValueError, f, b"bytes=-foo") # end isn't equal to or greater than start self.assertRaises(ValueError, f, b"bytes=5-4") def test_rangeMissingStop(self): """ A single bytes range without an explicit stop position is parsed into a two-tuple giving the start position and L{None}. """ self.assertEqual(self.resource._parseRangeHeader(b"bytes=0-"), [(0, None)]) def test_rangeMissingStart(self): """ A single bytes range without an explicit start position is parsed into a two-tuple of L{None} and the end position. """ self.assertEqual(self.resource._parseRangeHeader(b"bytes=-3"), [(None, 3)]) def test_range(self): """ A single bytes range with explicit start and stop positions is parsed into a two-tuple of those positions. """ self.assertEqual(self.resource._parseRangeHeader(b"bytes=2-5"), [(2, 5)]) def test_rangeWithSpace(self): """ A single bytes range with whitespace in allowed places is parsed in the same way as it would be without the whitespace. """ self.assertEqual(self.resource._parseRangeHeader(b" bytes=1-2 "), [(1, 2)]) self.assertEqual(self.resource._parseRangeHeader(b"bytes =1-2 "), [(1, 2)]) self.assertEqual(self.resource._parseRangeHeader(b"bytes= 1-2"), [(1, 2)]) self.assertEqual(self.resource._parseRangeHeader(b"bytes=1 -2"), [(1, 2)]) self.assertEqual(self.resource._parseRangeHeader(b"bytes=1- 2"), [(1, 2)]) self.assertEqual(self.resource._parseRangeHeader(b"bytes=1-2 "), [(1, 2)]) def test_nullRangeElements(self): """ If there are multiple byte ranges but only one is non-null, the non-null range is parsed and its start and stop returned. """ self.assertEqual( self.resource._parseRangeHeader(b"bytes=1-2,\r\n, ,\t"), [(1, 2)] ) def test_multipleRanges(self): """ If multiple byte ranges are specified their starts and stops are returned. """ self.assertEqual( self.resource._parseRangeHeader(b"bytes=1-2,3-4"), [(1, 2), (3, 4)] ) def test_bodyLength(self): """ A correct response to a range request is as long as the length of the requested range. """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=0-43") self.resource.render(self.request) self.assertEqual(len(b"".join(self.request.written)), 44) def test_invalidRangeRequest(self): """ An incorrect range request (RFC 2616 defines a correct range request as a Bytes-Unit followed by a '=' character followed by a specific range. Only 'bytes' is defined) results in the range header value being logged and a normal 200 response being sent. """ range = b"foobar=0-43" self.request.requestHeaders.addRawHeader(b"range", range) self.resource.render(self.request) expected = "Ignoring malformed Range header {!r}".format(range.decode()) self._assertLogged(expected) self.assertEqual(b"".join(self.request.written), self.payload) self.assertEqual(self.request.responseCode, http.OK) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-length")[0], b"%d" % (len(self.payload),), ) def parseMultipartBody(self, body, boundary): """ Parse C{body} as a multipart MIME response separated by C{boundary}. Note that this with fail the calling test on certain syntactic problems. """ sep = b"\r\n--" + boundary parts = body.split(sep) self.assertEqual(b"", parts[0]) self.assertEqual(b"--\r\n", parts[-1]) parsed_parts = [] for part in parts[1:-1]: before, header1, header2, blank, partBody = part.split(b"\r\n", 4) headers = header1 + b"\n" + header2 self.assertEqual(b"", before) self.assertEqual(b"", blank) partContentTypeValue = re.search( b"^content-type: (.*)$", headers, re.I | re.M ).group(1) start, end, size = re.search( b"^content-range: bytes ([0-9]+)-([0-9]+)/([0-9]+)$", headers, re.I | re.M, ).groups() parsed_parts.append( { b"contentType": partContentTypeValue, b"contentRange": (start, end, size), b"body": partBody, } ) return parsed_parts def test_multipleRangeRequest(self): """ The response to a request for multiple bytes ranges is a MIME-ish multipart response. """ startEnds = [(0, 2), (20, 30), (40, 50)] rangeHeaderValue = b",".join( [networkString("{}-{}".format(s, e)) for (s, e) in startEnds] ) self.request.requestHeaders.addRawHeader(b"range", b"bytes=" + rangeHeaderValue) self.resource.render(self.request) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) boundary = re.match( b'^multipart/byteranges; boundary="(.*)"$', self.request.responseHeaders.getRawHeaders(b"content-type")[0], ).group(1) parts = self.parseMultipartBody(b"".join(self.request.written), boundary) self.assertEqual(len(startEnds), len(parts)) for part, (s, e) in zip(parts, startEnds): self.assertEqual(networkString(self.resource.type), part[b"contentType"]) start, end, size = part[b"contentRange"] self.assertEqual(int(start), s) self.assertEqual(int(end), e) self.assertEqual(int(size), self.resource.getFileSize()) self.assertEqual(self.payload[s : e + 1], part[b"body"]) def test_multipleRangeRequestWithRangeOverlappingEnd(self): """ The response to a request for multiple bytes ranges is a MIME-ish multipart response, even when one of the ranged falls off the end of the resource. """ startEnds = [(0, 2), (40, len(self.payload) + 10)] rangeHeaderValue = b",".join( [networkString("{}-{}".format(s, e)) for (s, e) in startEnds] ) self.request.requestHeaders.addRawHeader(b"range", b"bytes=" + rangeHeaderValue) self.resource.render(self.request) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) boundary = re.match( b'^multipart/byteranges; boundary="(.*)"$', self.request.responseHeaders.getRawHeaders(b"content-type")[0], ).group(1) parts = self.parseMultipartBody(b"".join(self.request.written), boundary) self.assertEqual(len(startEnds), len(parts)) for part, (s, e) in zip(parts, startEnds): self.assertEqual(networkString(self.resource.type), part[b"contentType"]) start, end, size = part[b"contentRange"] self.assertEqual(int(start), s) self.assertEqual(int(end), min(e, self.resource.getFileSize() - 1)) self.assertEqual(int(size), self.resource.getFileSize()) self.assertEqual(self.payload[s : e + 1], part[b"body"]) def test_implicitEnd(self): """ If the end byte position is omitted, then it is treated as if the length of the resource was specified by the end byte position. """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=23-") self.resource.render(self.request) self.assertEqual(b"".join(self.request.written), self.payload[23:]) self.assertEqual(len(b"".join(self.request.written)), 41) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-range")[0], b"bytes 23-63/64", ) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-length")[0], b"41" ) def test_implicitStart(self): """ If the start byte position is omitted but the end byte position is supplied, then the range is treated as requesting the last -N bytes of the resource, where N is the end byte position. """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=-17") self.resource.render(self.request) self.assertEqual(b"".join(self.request.written), self.payload[-17:]) self.assertEqual(len(b"".join(self.request.written)), 17) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-range")[0], b"bytes 47-63/64", ) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-length")[0], b"17" ) def test_explicitRange(self): """ A correct response to a bytes range header request from A to B starts with the A'th byte and ends with (including) the B'th byte. The first byte of a page is numbered with 0. """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=3-43") self.resource.render(self.request) written = b"".join(self.request.written) self.assertEqual(written, self.payload[3:44]) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-range")[0], b"bytes 3-43/64", ) self.assertEqual( b"%d" % (len(written),), self.request.responseHeaders.getRawHeaders(b"content-length")[0], ) def test_explicitRangeOverlappingEnd(self): """ A correct response to a bytes range header request from A to B when B is past the end of the resource starts with the A'th byte and ends with the last byte of the resource. The first byte of a page is numbered with 0. """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=40-100") self.resource.render(self.request) written = b"".join(self.request.written) self.assertEqual(written, self.payload[40:]) self.assertEqual(self.request.responseCode, http.PARTIAL_CONTENT) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-range")[0], b"bytes 40-63/64", ) self.assertEqual( b"%d" % (len(written),), self.request.responseHeaders.getRawHeaders(b"content-length")[0], ) def test_statusCodeRequestedRangeNotSatisfiable(self): """ If a range is syntactically invalid due to the start being greater than the end, the range header is ignored (the request is responded to as if it were not present). """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=20-13") self.resource.render(self.request) self.assertEqual(self.request.responseCode, http.OK) self.assertEqual(b"".join(self.request.written), self.payload) self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-length")[0], b"%d" % (len(self.payload),), ) def test_invalidStartBytePos(self): """ If a range is unsatisfiable due to the start not being less than the length of the resource, the response is 416 (Requested range not satisfiable) and no data is written to the response body (RFC 2616, section 14.35.1). """ self.request.requestHeaders.addRawHeader(b"range", b"bytes=67-108") self.resource.render(self.request) self.assertEqual( self.request.responseCode, http.REQUESTED_RANGE_NOT_SATISFIABLE ) self.assertEqual(b"".join(self.request.written), b"") self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-length")[0], b"0" ) # Sections 10.4.17 and 14.16 self.assertEqual( self.request.responseHeaders.getRawHeaders(b"content-range")[0], networkString("bytes */%d" % (len(self.payload),)), ) class DirectoryListerTests(TestCase): """ Tests for L{static.DirectoryLister}. """ def _request(self, uri): request = DummyRequest([b""]) request.uri = uri return request def test_renderHeader(self): """ L{static.DirectoryLister} prints the request uri as header of the rendered content. """ path = FilePath(self.mktemp()) path.makedirs() lister = static.DirectoryLister(path.path) data = lister.render(self._request(b"foo")) self.assertIn(b"<h1>Directory listing for foo</h1>", data) self.assertIn(b"<title>Directory listing for foo</title>", data) def test_renderUnquoteHeader(self): """ L{static.DirectoryLister} unquote the request uri before printing it. """ path = FilePath(self.mktemp()) path.makedirs() lister = static.DirectoryLister(path.path) data = lister.render(self._request(b"foo%20bar")) self.assertIn(b"<h1>Directory listing for foo bar</h1>", data) self.assertIn(b"<title>Directory listing for foo bar</title>", data) def test_escapeHeader(self): """ L{static.DirectoryLister} escape "&", "<" and ">" after unquoting the request uri. """ path = FilePath(self.mktemp()) path.makedirs() lister = static.DirectoryLister(path.path) data = lister.render(self._request(b"foo%26bar")) self.assertIn(b"<h1>Directory listing for foo&bar</h1>", data) self.assertIn(b"<title>Directory listing for foo&bar</title>", data) def test_renderFiles(self): """ L{static.DirectoryLister} is able to list all the files inside a directory. """ path = FilePath(self.mktemp()) path.makedirs() path.child("file1").setContent(b"content1") path.child("file2").setContent(b"content2" * 1000) lister = static.DirectoryLister(path.path) data = lister.render(self._request(b"foo")) body = b"""<tr class="odd"> <td><a href="file1">file1</a></td> <td>8B</td> <td>[text/html]</td> <td></td> </tr> <tr class="even"> <td><a href="file2">file2</a></td> <td>7K</td> <td>[text/html]</td> <td></td> </tr>""" self.assertIn(body, data) def test_renderDirectories(self): """ L{static.DirectoryLister} is able to list all the directories inside a directory. """ path = FilePath(self.mktemp()) path.makedirs() path.child("dir1").makedirs() path.child("dir2 & 3").makedirs() lister = static.DirectoryLister(path.path) data = lister.render(self._request(b"foo")) body = b"""<tr class="odd"> <td><a href="dir1/">dir1/</a></td> <td></td> <td>[Directory]</td> <td></td> </tr> <tr class="even"> <td><a href="dir2%20%26%203/">dir2 & 3/</a></td> <td></td> <td>[Directory]</td> <td></td> </tr>""" self.assertIn(body, data) def test_renderFiltered(self): """ L{static.DirectoryLister} takes an optional C{dirs} argument that filter out the list of directories and files printed. """ path = FilePath(self.mktemp()) path.makedirs() path.child("dir1").makedirs() path.child("dir2").makedirs() path.child("dir3").makedirs() lister = static.DirectoryLister(path.path, dirs=["dir1", "dir3"]) data = lister.render(self._request(b"foo")) body = b"""<tr class="odd"> <td><a href="dir1/">dir1/</a></td> <td></td> <td>[Directory]</td> <td></td> </tr> <tr class="even"> <td><a href="dir3/">dir3/</a></td> <td></td> <td>[Directory]</td> <td></td> </tr>""" self.assertIn(body, data) def test_oddAndEven(self): """ L{static.DirectoryLister} gives an alternate class for each odd and even rows in the table. """ lister = static.DirectoryLister(None) elements = [ {"href": "", "text": "", "size": "", "type": "", "encoding": ""} for i in range(5) ] content = lister._buildTableContent(elements) self.assertEqual(len(content), 5) self.assertTrue(content[0].startswith('<tr class="odd">')) self.assertTrue(content[1].startswith('<tr class="even">')) self.assertTrue(content[2].startswith('<tr class="odd">')) self.assertTrue(content[3].startswith('<tr class="even">')) self.assertTrue(content[4].startswith('<tr class="odd">')) def test_contentType(self): """ L{static.DirectoryLister} produces a MIME-type that indicates that it is HTML, and includes its charset (UTF-8). """ path = FilePath(self.mktemp()) path.makedirs() lister = static.DirectoryLister(path.path) req = self._request(b"") lister.render(req) self.assertEqual( req.responseHeaders.getRawHeaders(b"content-type")[0], b"text/html; charset=utf-8", ) def test_mimeTypeAndEncodings(self): """ L{static.DirectoryLister} is able to detect mimetype and encoding of listed files. """ path = FilePath(self.mktemp()) path.makedirs() path.child("file1.txt").setContent(b"file1") path.child("file2.py").setContent(b"python") path.child("file3.conf.gz").setContent(b"conf compressed") path.child("file4.diff.bz2").setContent(b"diff compressed") directory = os.listdir(path.path) directory.sort() contentTypes = { ".txt": "text/plain", ".py": "text/python", ".conf": "text/configuration", ".diff": "text/diff", } lister = static.DirectoryLister(path.path, contentTypes=contentTypes) dirs, files = lister._getFilesAndDirectories(directory) self.assertEqual(dirs, []) self.assertEqual( files, [ { "encoding": "", "href": "file1.txt", "size": "5B", "text": "file1.txt", "type": "[text/plain]", }, { "encoding": "", "href": "file2.py", "size": "6B", "text": "file2.py", "type": "[text/python]", }, { "encoding": "[gzip]", "href": "file3.conf.gz", "size": "15B", "text": "file3.conf.gz", "type": "[text/configuration]", }, { "encoding": "[bzip2]", "href": "file4.diff.bz2", "size": "15B", "text": "file4.diff.bz2", "type": "[text/diff]", }, ], ) @skipIf(not platform._supportsSymlinks(), "No symlink support") def test_brokenSymlink(self): """ If on the file in the listing points to a broken symlink, it should not be returned by L{static.DirectoryLister._getFilesAndDirectories}. """ path = FilePath(self.mktemp()) path.makedirs() file1 = path.child("file1") file1.setContent(b"file1") file1.linkTo(path.child("file2")) file1.remove() lister = static.DirectoryLister(path.path) directory = os.listdir(path.path) directory.sort() dirs, files = lister._getFilesAndDirectories(directory) self.assertEqual(dirs, []) self.assertEqual(files, []) def test_childrenNotFound(self): """ Any child resource of L{static.DirectoryLister} renders an HTTP I{NOT FOUND} response code. """ path = FilePath(self.mktemp()) path.makedirs() lister = static.DirectoryLister(path.path) request = self._request(b"") child = resource.getChildForRequest(lister, request) result = _render(child, request) def cbRendered(ignored): self.assertEqual(request.responseCode, http.NOT_FOUND) result.addCallback(cbRendered) return result def test_repr(self): """ L{static.DirectoryLister.__repr__} gives the path of the lister. """ path = FilePath(self.mktemp()) lister = static.DirectoryLister(path.path) self.assertEqual(repr(lister), "<DirectoryLister of {!r}>".format(path.path)) self.assertEqual(str(lister), "<DirectoryLister of {!r}>".format(path.path)) def test_formatFileSize(self): """ L{static.formatFileSize} format an amount of bytes into a more readable format. """ self.assertEqual(static.formatFileSize(0), "0B") self.assertEqual(static.formatFileSize(123), "123B") self.assertEqual(static.formatFileSize(4567), "4K") self.assertEqual(static.formatFileSize(8900000), "8M") self.assertEqual(static.formatFileSize(1234000000), "1G") self.assertEqual(static.formatFileSize(1234567890000), "1149G") class LoadMimeTypesTests(TestCase): """ Tests for the MIME type loading routine. @cvar UNSET: A sentinel to signify that C{self.paths} has not been set by the mock init. """ UNSET = object() def setUp(self): self.paths = self.UNSET def _fakeInit(self, paths): """ A mock L{mimetypes.init} that records the value of the passed C{paths} argument. @param paths: The paths that will be recorded. """ self.paths = paths def test_defaultArgumentIsNone(self): """ By default, L{None} is passed to C{mimetypes.init}. """ static.loadMimeTypes(init=self._fakeInit) self.assertIdentical(self.paths, None) def test_extraLocationsWork(self): """ Passed MIME type files are passed to C{mimetypes.init}. """ paths = ["x", "y", "z"] static.loadMimeTypes(paths, init=self._fakeInit) self.assertIdentical(self.paths, paths) def test_usesGlobalInitFunction(self): """ By default, C{mimetypes.init} is called. """ # Checking mimetypes.inited doesn't always work, because # something, somewhere, calls mimetypes.init. Yay global # mutable state :) if getattr(inspect, "signature", None): signature = inspect.signature(static.loadMimeTypes) self.assertIs(signature.parameters["init"].default, mimetypes.init) else: args, _, _, defaults = inspect.getargspec(static.loadMimeTypes) defaultInit = defaults[args.index("init")] self.assertIs(defaultInit, mimetypes.init) class StaticDeprecationTests(TestCase): def test_addSlashDeprecated(self): """ L{twisted.web.static.addSlash} is deprecated. """ from twisted.web.static import addSlash addSlash(DummyRequest([b""])) warnings = self.flushWarnings([self.test_addSlashDeprecated]) self.assertEqual(len(warnings), 1) self.assertEqual( warnings[0]["message"], "twisted.web.static.addSlash was deprecated in Twisted 16.0.0", )

37.409315

88

0.613634

2340742f71f589e3c3bace832d0090d0e4289bea

9,008

py

Python

pennylane/tape/tapes/reversible.py

theRoughCode/pennylane

317f82ef00c752beeef7d2412b88119a753467b4

[ "Apache-2.0" ]

null

pennylane/tape/tapes/reversible.py

theRoughCode/pennylane

317f82ef00c752beeef7d2412b88119a753467b4

[ "Apache-2.0" ]

1

2020-10-04T22:45:45.000Z

pennylane/tape/tapes/reversible.py

theRoughCode/pennylane

317f82ef00c752beeef7d2412b88119a753467b4

[ "Apache-2.0" ]

null

# Copyright 2018-2020 Xanadu Quantum Technologies Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Quantum tape that implements reversible backpropagation. """ # pylint: disable=attribute-defined-outside-init,protected-access from copy import copy from functools import reduce from string import ascii_letters as ABC import numpy as np import pennylane as qml from .tape import QuantumTape ABC_ARRAY = np.array(list(ABC)) class ReversibleTape(QuantumTape): r"""Quantum tape for computing gradients via reversible analytic differentiation. .. note:: The reversible analytic differentation method has the following restrictions: * As it requires knowledge of the statevector, only statevector simulator devices can be used. * Differentiation is only supported for the parametrized quantum operations :class:`~.RX`, :class:`~.RY`, :class:`~.RZ`, and :class:`~.Rot`. This class extends the :class:`~.jacobian` method of the quantum tape to support analytic gradients of qubit operations using reversible analytic differentiation. This gradient method returns *exact* gradients, however requires use of a statevector simulator. Simply create the tape, and then call the Jacobian method: >>> tape.jacobian(dev) For more details on the quantum tape, please see :class:`~.QuantumTape`. **Reversible analytic differentiation** Assume a circuit has a gate :math:`G(\theta)` that we want to differentiate. Without loss of generality, we can write the circuit in the form three unitaries: :math:`UGV`. Starting from the initial state :math:`\vert 0\rangle`, the quantum state is evolved up to the "pre-measurement" state :math:`\vert\psi\rangle=UGV\vert 0\rangle`, which is saved (this can be reused for each variable being differentiated). We then apply the unitary :math:`V^{-1}` to evolve this state backwards in time until just after the gate :math:`G` (hence the name "reversible"). The generator of :math:`G` is then applied as a gate, and we evolve forward using :math:`V` again. At this stage, the state of the simulator is proportional to :math:`\frac{\partial}{\partial\theta}\vert\psi\rangle`. Some further post-processing of this gives the derivative :math:`\frac{\partial}{\partial\theta} \langle \hat{O} \rangle` for any observable O. The reversible approach is similar to backpropagation, but trades off extra computation for enhanced memory efficiency. Where backpropagation caches the state tensors at each step during a forward pass, the reversible method only caches the final pre-measurement state. Compared to the parameter-shift rule, the reversible method can be faster or slower, depending on the density and location of parametrized gates in a circuit (circuits with higher density of parametrized gates near the end of the circuit will see a benefit). """ def _grad_method(self, idx, use_graph=True, default_method="A"): return super()._grad_method(idx, use_graph=use_graph, default_method=default_method) @staticmethod def _matrix_elem(vec1, obs, vec2, device): r"""Computes the matrix element of an observable. That is, given two basis states :math:`\mathbf{i}`, :math:`\mathbf{j}`, this method returns :math:`\langle \mathbf{i} \vert \hat{O} \vert \mathbf{j} \rangle`. Unmeasured wires are contracted, and a scalar is returned. Args: vec1 (array[complex]): a length :math:`2^N` statevector obs (.Observable): a PennyLane observable vec2 (array[complex]): a length :math:`2^N` statevector device (.QubitDevice): the device used to compute the matrix elements """ # pylint: disable=protected-access mat = device._reshape(obs.matrix, [2] * len(obs.wires) * 2) wires = obs.wires vec1_indices = ABC[: device.num_wires] obs_in_indices = "".join(ABC_ARRAY[wires.tolist()].tolist()) obs_out_indices = ABC[device.num_wires : device.num_wires + len(wires)] obs_indices = "".join([obs_in_indices, obs_out_indices]) vec2_indices = reduce( lambda old_string, idx_pair: old_string.replace(idx_pair[0], idx_pair[1]), zip(obs_in_indices, obs_out_indices), vec1_indices, ) einsum_str = "{vec1_indices},{obs_indices},{vec2_indices}->".format( vec1_indices=vec1_indices, obs_indices=obs_indices, vec2_indices=vec2_indices, ) return device._einsum(einsum_str, device._conj(vec1), mat, vec2) def jacobian(self, device, params=None, **options): # The parameter_shift_var method needs to evaluate the circuit # at the unshifted parameter values; the pre-rotated statevector is then stored # self._state attribute. Here, we set the value of the attribute to None # before each Jacobian call, so that the statevector is calculated only once. self._state = None return super().jacobian(device, params, **options) def analytic_pd(self, idx, device, params=None, **options): t_idx = list(self.trainable_params)[idx] op = self._par_info[t_idx]["op"] p_idx = self._par_info[t_idx]["p_idx"] # The reversible tape only support differentiating # expectation values of observables for now. for m in self.measurements: if ( m.return_type is qml.operation.Variance or m.return_type is qml.operation.Probability ): raise ValueError( f"{m.return_type} is not supported with the reversible gradient method" ) # The reversible tape only supports the RX, RY, RZ, and Rot operations for now: # # * CRX, CRY, CRZ ops have a non-unitary matrix as generator. # # * PauliRot, MultiRZ, U2, and U3 do not have generators specified. # # TODO: the controlled rotations can be supported by multiplying ``state`` # directly by these generators within this function # (or by allowing non-unitary matrix multiplies in the simulator backends) if op.name not in ["RX", "RY", "RZ", "Rot"]: raise ValueError( "The {} gate is not currently supported with the " "reversible gradient method.".format(op.name) ) if self._state is None: self.execute_device(params, device) self._state = device._pre_rotated_state self.set_parameters(params) # create a new circuit which rewinds the pre-measurement state to just after `op`, # applies the generator of `op`, and then plays forward back to # pre-measurement step wires = op.wires op_idx = self.operations.index(op) # TODO: likely better to use circuitgraph to determine minimally necessary ops between_ops = self.operations[op_idx + 1 :] if op.name == "Rot": decomp = op.decomposition(*op.parameters, wires=wires) generator, multiplier = decomp[p_idx].generator between_ops = decomp[p_idx + 1 :] + between_ops else: generator, multiplier = op.generator generator = generator(wires) diff_circuit = QuantumTape() diff_circuit._ops = [copy(op).inv() for op in between_ops[::-1]] + [generator] + between_ops # set the simulator state to be the pre-measurement state device._state = self._state # evolve the pre-measurement state under this new circuit device.execute(diff_circuit) dstate = device._pre_rotated_state # TODO: this will only work for QubitDevices # compute matrix element <d(state)|O|state> for each observable O matrix_elems = device._asarray( [self._matrix_elem(dstate, ob, self._state, device) for ob in self.observables] # TODO: if all observables act on same number of wires, could # do all at once with einsum ) # reset state back to pre-measurement value device._pre_rotated_state = self._state return 2 * multiplier * device._imag(matrix_elems)

43.941463

103

0.658637

fe4e8b1a8acfdf7aaa8e2d98cac419a3504f8ce3

45,630

py

Python

third_party/python/Parser/asdl_c.py

appotry/cosmopolitan

af4687cc3f2331a23dc336183ab58fe001cda082

[ "ISC" ]

null

third_party/python/Parser/asdl_c.py

appotry/cosmopolitan

af4687cc3f2331a23dc336183ab58fe001cda082

[ "ISC" ]

null

third_party/python/Parser/asdl_c.py

appotry/cosmopolitan

af4687cc3f2331a23dc336183ab58fe001cda082

[ "ISC" ]

null

#! /usr/bin/env python """Generate C code from an ASDL description.""" import os, sys import asdl TABSIZE = 4 MAX_COL = 80 def get_c_type(name): """Return a string for the C name of the type. This function special cases the default types provided by asdl. """ if name in asdl.builtin_types: return name else: return "%s_ty" % name def reflow_lines(s, depth): """Reflow the line s indented depth tabs. Return a sequence of lines where no line extends beyond MAX_COL when properly indented. The first line is properly indented based exclusively on depth * TABSIZE. All following lines -- these are the reflowed lines generated by this function -- start at the same column as the first character beyond the opening { in the first line. """ size = MAX_COL - depth * TABSIZE if len(s) < size: return [s] lines = [] cur = s padding = "" while len(cur) > size: i = cur.rfind(' ', 0, size) # XXX this should be fixed for real if i == -1 and 'GeneratorExp' in cur: i = size + 3 assert i != -1, "Impossible line %d to reflow: %r" % (size, s) lines.append(padding + cur[:i]) if len(lines) == 1: # find new size based on brace j = cur.find('{', 0, i) if j >= 0: j += 2 # account for the brace and the space after it size -= j padding = " " * j else: j = cur.find('(', 0, i) if j >= 0: j += 1 # account for the paren (no space after it) size -= j padding = " " * j cur = cur[i+1:] else: lines.append(padding + cur) return lines def is_simple(sum): """Return True if a sum is a simple. A sum is simple if its types have no fields, e.g. unaryop = Invert | Not | UAdd | USub """ for t in sum.types: if t.fields: return False return True class EmitVisitor(asdl.VisitorBase): """Visit that emits lines""" def __init__(self, file): self.file = file self.identifiers = set() super(EmitVisitor, self).__init__() def emit_identifier(self, name): name = str(name) if name in self.identifiers: return self.emit("_Py_IDENTIFIER(%s);" % name, 0) self.identifiers.add(name) def emit(self, s, depth, reflow=True): # XXX reflow long lines? if reflow: lines = reflow_lines(s, depth) else: lines = [s] for line in lines: line = (" " * TABSIZE * depth) + line + "\n" self.file.write(line) class TypeDefVisitor(EmitVisitor): def visitModule(self, mod): for dfn in mod.dfns: self.visit(dfn) def visitType(self, type, depth=0): self.visit(type.value, type.name, depth) def visitSum(self, sum, name, depth): if is_simple(sum): self.simple_sum(sum, name, depth) else: self.sum_with_constructors(sum, name, depth) def simple_sum(self, sum, name, depth): enum = [] for i in range(len(sum.types)): type = sum.types[i] enum.append("%s=%d" % (type.name, i + 1)) enums = ", ".join(enum) ctype = get_c_type(name) s = "typedef enum _%s { %s } %s;" % (name, enums, ctype) self.emit(s, depth) self.emit("", depth) def sum_with_constructors(self, sum, name, depth): ctype = get_c_type(name) s = "typedef struct _%(name)s *%(ctype)s;" % locals() self.emit(s, depth) self.emit("", depth) def visitProduct(self, product, name, depth): ctype = get_c_type(name) s = "typedef struct _%(name)s *%(ctype)s;" % locals() self.emit(s, depth) self.emit("", depth) class StructVisitor(EmitVisitor): """Visitor to generate typedefs for AST.""" def visitModule(self, mod): for dfn in mod.dfns: self.visit(dfn) def visitType(self, type, depth=0): self.visit(type.value, type.name, depth) def visitSum(self, sum, name, depth): if not is_simple(sum): self.sum_with_constructors(sum, name, depth) def sum_with_constructors(self, sum, name, depth): def emit(s, depth=depth): self.emit(s % sys._getframe(1).f_locals, depth) enum = [] for i in range(len(sum.types)): type = sum.types[i] enum.append("%s_kind=%d" % (type.name, i + 1)) emit("enum _%(name)s_kind {" + ", ".join(enum) + "};") emit("struct _%(name)s {") emit("enum _%(name)s_kind kind;", depth + 1) emit("union {", depth + 1) for t in sum.types: self.visit(t, depth + 2) emit("} v;", depth + 1) for field in sum.attributes: # rudimentary attribute handling type = str(field.type) assert type in asdl.builtin_types, type emit("%s %s;" % (type, field.name), depth + 1); emit("};") emit("") def visitConstructor(self, cons, depth): if cons.fields: self.emit("struct {", depth) for f in cons.fields: self.visit(f, depth + 1) self.emit("} %s;" % cons.name, depth) self.emit("", depth) def visitField(self, field, depth): # XXX need to lookup field.type, because it might be something # like a builtin... ctype = get_c_type(field.type) name = field.name if field.seq: if field.type == 'cmpop': self.emit("asdl_int_seq *%(name)s;" % locals(), depth) else: self.emit("asdl_seq *%(name)s;" % locals(), depth) else: self.emit("%(ctype)s %(name)s;" % locals(), depth) def visitProduct(self, product, name, depth): self.emit("struct _%(name)s {" % locals(), depth) for f in product.fields: self.visit(f, depth + 1) for field in product.attributes: # rudimentary attribute handling type = str(field.type) assert type in asdl.builtin_types, type self.emit("%s %s;" % (type, field.name), depth + 1); self.emit("};", depth) self.emit("", depth) class PrototypeVisitor(EmitVisitor): """Generate function prototypes for the .h file""" def visitModule(self, mod): for dfn in mod.dfns: self.visit(dfn) def visitType(self, type): self.visit(type.value, type.name) def visitSum(self, sum, name): if is_simple(sum): pass # XXX else: for t in sum.types: self.visit(t, name, sum.attributes) def get_args(self, fields): """Return list of C argument into, one for each field. Argument info is 3-tuple of a C type, variable name, and flag that is true if type can be NULL. """ args = [] unnamed = {} for f in fields: if f.name is None: name = f.type c = unnamed[name] = unnamed.get(name, 0) + 1 if c > 1: name = "name%d" % (c - 1) else: name = f.name # XXX should extend get_c_type() to handle this if f.seq: if f.type == 'cmpop': ctype = "asdl_int_seq *" else: ctype = "asdl_seq *" else: ctype = get_c_type(f.type) args.append((ctype, name, f.opt or f.seq)) return args def visitConstructor(self, cons, type, attrs): args = self.get_args(cons.fields) attrs = self.get_args(attrs) ctype = get_c_type(type) self.emit_function(cons.name, ctype, args, attrs) def emit_function(self, name, ctype, args, attrs, union=True): args = args + attrs if args: argstr = ", ".join(["%s %s" % (atype, aname) for atype, aname, opt in args]) argstr += ", PyArena *arena" else: argstr = "PyArena *arena" margs = "a0" for i in range(1, len(args)+1): margs += ", a%d" % i self.emit("#define %s(%s) _Py_%s(%s)" % (name, margs, name, margs), 0, reflow=False) self.emit("%s _Py_%s(%s);" % (ctype, name, argstr), False) def visitProduct(self, prod, name): self.emit_function(name, get_c_type(name), self.get_args(prod.fields), self.get_args(prod.attributes), union=False) class FunctionVisitor(PrototypeVisitor): """Visitor to generate constructor functions for AST.""" def emit_function(self, name, ctype, args, attrs, union=True): def emit(s, depth=0, reflow=True): self.emit(s, depth, reflow) argstr = ", ".join(["%s %s" % (atype, aname) for atype, aname, opt in args + attrs]) if argstr: argstr += ", PyArena *arena" else: argstr = "PyArena *arena" self.emit("%s" % ctype, 0) emit("%s(%s)" % (name, argstr)) emit("{") emit("%s p;" % ctype, 1) for argtype, argname, opt in args: if not opt and argtype != "int": emit("if (!%s) {" % argname, 1) emit("PyErr_SetString(PyExc_ValueError,", 2) msg = "field %s is required for %s" % (argname, name) emit(' "%s");' % msg, 2, reflow=False) emit('return NULL;', 2) emit('}', 1) emit("p = (%s)PyArena_Malloc(arena, sizeof(*p));" % ctype, 1); emit("if (!p)", 1) emit("return NULL;", 2) if union: self.emit_body_union(name, args, attrs) else: self.emit_body_struct(name, args, attrs) emit("return p;", 1) emit("}") emit("") def emit_body_union(self, name, args, attrs): def emit(s, depth=0, reflow=True): self.emit(s, depth, reflow) emit("p->kind = %s_kind;" % name, 1) for argtype, argname, opt in args: emit("p->v.%s.%s = %s;" % (name, argname, argname), 1) for argtype, argname, opt in attrs: emit("p->%s = %s;" % (argname, argname), 1) def emit_body_struct(self, name, args, attrs): def emit(s, depth=0, reflow=True): self.emit(s, depth, reflow) for argtype, argname, opt in args: emit("p->%s = %s;" % (argname, argname), 1) for argtype, argname, opt in attrs: emit("p->%s = %s;" % (argname, argname), 1) class PickleVisitor(EmitVisitor): def visitModule(self, mod): for dfn in mod.dfns: self.visit(dfn) def visitType(self, type): self.visit(type.value, type.name) def visitSum(self, sum, name): pass def visitProduct(self, sum, name): pass def visitConstructor(self, cons, name): pass def visitField(self, sum): pass class Obj2ModPrototypeVisitor(PickleVisitor): def visitProduct(self, prod, name): code = "static int obj2ast_%s(PyObject* obj, %s* out, PyArena* arena);" self.emit(code % (name, get_c_type(name)), 0) visitSum = visitProduct class Obj2ModVisitor(PickleVisitor): def funcHeader(self, name): ctype = get_c_type(name) self.emit("int", 0) self.emit("obj2ast_%s(PyObject* obj, %s* out, PyArena* arena)" % (name, ctype), 0) self.emit("{", 0) self.emit("int isinstance;", 1) self.emit("", 0) def sumTrailer(self, name, add_label=False): self.emit("", 0) # there's really nothing more we can do if this fails ... error = "expected some sort of %s, but got %%R" % name format = "PyErr_Format(PyExc_TypeError, \"%s\", obj);" self.emit(format % error, 1, reflow=False) if add_label: self.emit("failed:", 1) self.emit("Py_XDECREF(tmp);", 1) self.emit("return 1;", 1) self.emit("}", 0) self.emit("", 0) def simpleSum(self, sum, name): self.funcHeader(name) for t in sum.types: line = ("isinstance = PyObject_IsInstance(obj, " "(PyObject *)%s_type);") self.emit(line % (t.name,), 1) self.emit("if (isinstance == -1) {", 1) self.emit("return 1;", 2) self.emit("}", 1) self.emit("if (isinstance) {", 1) self.emit("*out = %s;" % t.name, 2) self.emit("return 0;", 2) self.emit("}", 1) self.sumTrailer(name) def buildArgs(self, fields): return ", ".join(fields + ["arena"]) def complexSum(self, sum, name): self.funcHeader(name) self.emit("PyObject *tmp = NULL;", 1) for a in sum.attributes: self.visitAttributeDeclaration(a, name, sum=sum) self.emit("", 0) # XXX: should we only do this for 'expr'? self.emit("if (obj == Py_None) {", 1) self.emit("*out = NULL;", 2) self.emit("return 0;", 2) self.emit("}", 1) for a in sum.attributes: self.visitField(a, name, sum=sum, depth=1) for t in sum.types: line = "isinstance = PyObject_IsInstance(obj, (PyObject*)%s_type);" self.emit(line % (t.name,), 1) self.emit("if (isinstance == -1) {", 1) self.emit("return 1;", 2) self.emit("}", 1) self.emit("if (isinstance) {", 1) for f in t.fields: self.visitFieldDeclaration(f, t.name, sum=sum, depth=2) self.emit("", 0) for f in t.fields: self.visitField(f, t.name, sum=sum, depth=2) args = [f.name for f in t.fields] + [a.name for a in sum.attributes] self.emit("*out = %s(%s);" % (t.name, self.buildArgs(args)), 2) self.emit("if (*out == NULL) goto failed;", 2) self.emit("return 0;", 2) self.emit("}", 1) self.sumTrailer(name, True) def visitAttributeDeclaration(self, a, name, sum=sum): ctype = get_c_type(a.type) self.emit("%s %s;" % (ctype, a.name), 1) def visitSum(self, sum, name): if is_simple(sum): self.simpleSum(sum, name) else: self.complexSum(sum, name) def visitProduct(self, prod, name): ctype = get_c_type(name) self.emit("int", 0) self.emit("obj2ast_%s(PyObject* obj, %s* out, PyArena* arena)" % (name, ctype), 0) self.emit("{", 0) self.emit("PyObject* tmp = NULL;", 1) for f in prod.fields: self.visitFieldDeclaration(f, name, prod=prod, depth=1) for a in prod.attributes: self.visitFieldDeclaration(a, name, prod=prod, depth=1) self.emit("", 0) for f in prod.fields: self.visitField(f, name, prod=prod, depth=1) for a in prod.attributes: self.visitField(a, name, prod=prod, depth=1) args = [f.name for f in prod.fields] args.extend([a.name for a in prod.attributes]) self.emit("*out = %s(%s);" % (name, self.buildArgs(args)), 1) self.emit("return 0;", 1) self.emit("failed:", 0) self.emit("Py_XDECREF(tmp);", 1) self.emit("return 1;", 1) self.emit("}", 0) self.emit("", 0) def visitFieldDeclaration(self, field, name, sum=None, prod=None, depth=0): ctype = get_c_type(field.type) if field.seq: if self.isSimpleType(field): self.emit("asdl_int_seq* %s;" % field.name, depth) else: self.emit("asdl_seq* %s;" % field.name, depth) else: ctype = get_c_type(field.type) self.emit("%s %s;" % (ctype, field.name), depth) def isSimpleSum(self, field): # XXX can the members of this list be determined automatically? return field.type in ('expr_context', 'boolop', 'operator', 'unaryop', 'cmpop') def isNumeric(self, field): return get_c_type(field.type) in ("int", "bool") def isSimpleType(self, field): return self.isSimpleSum(field) or self.isNumeric(field) def visitField(self, field, name, sum=None, prod=None, depth=0): ctype = get_c_type(field.type) if field.opt: check = "exists_not_none(obj, &PyId_%s)" % (field.name,) else: check = "_PyObject_HasAttrId(obj, &PyId_%s)" % (field.name,) self.emit("if (%s) {" % (check,), depth, reflow=False) self.emit("int res;", depth+1) if field.seq: self.emit("Py_ssize_t len;", depth+1) self.emit("Py_ssize_t i;", depth+1) self.emit("tmp = _PyObject_GetAttrId(obj, &PyId_%s);" % field.name, depth+1) self.emit("if (tmp == NULL) goto failed;", depth+1) if field.seq: self.emit("if (!PyList_Check(tmp)) {", depth+1) self.emit("PyErr_Format(PyExc_TypeError, \"%s field \\\"%s\\\" must " "be a list, not a %%.200s\", tmp->ob_type->tp_name);" % (name, field.name), depth+2, reflow=False) self.emit("goto failed;", depth+2) self.emit("}", depth+1) self.emit("len = PyList_GET_SIZE(tmp);", depth+1) if self.isSimpleType(field): self.emit("%s = _Py_asdl_int_seq_new(len, arena);" % field.name, depth+1) else: self.emit("%s = _Py_asdl_seq_new(len, arena);" % field.name, depth+1) self.emit("if (%s == NULL) goto failed;" % field.name, depth+1) self.emit("for (i = 0; i < len; i++) {", depth+1) self.emit("%s val;" % ctype, depth+2) self.emit("res = obj2ast_%s(PyList_GET_ITEM(tmp, i), &val, arena);" % field.type, depth+2, reflow=False) self.emit("if (res != 0) goto failed;", depth+2) self.emit("if (len != PyList_GET_SIZE(tmp)) {", depth+2) self.emit("PyErr_SetString(PyExc_RuntimeError, \"%s field \\\"%s\\\" " "changed size during iteration\");" % (name, field.name), depth+3, reflow=False) self.emit("goto failed;", depth+3) self.emit("}", depth+2) self.emit("asdl_seq_SET(%s, i, val);" % field.name, depth+2) self.emit("}", depth+1) else: self.emit("res = obj2ast_%s(tmp, &%s, arena);" % (field.type, field.name), depth+1) self.emit("if (res != 0) goto failed;", depth+1) self.emit("Py_CLEAR(tmp);", depth+1) self.emit("} else {", depth) if not field.opt: message = "required field \\\"%s\\\" missing from %s" % (field.name, name) format = "PyErr_SetString(PyExc_TypeError, \"%s\");" self.emit(format % message, depth+1, reflow=False) self.emit("return 1;", depth+1) else: if self.isNumeric(field): self.emit("%s = 0;" % field.name, depth+1) elif not self.isSimpleType(field): self.emit("%s = NULL;" % field.name, depth+1) else: raise TypeError("could not determine the default value for %s" % field.name) self.emit("}", depth) class MarshalPrototypeVisitor(PickleVisitor): def prototype(self, sum, name): ctype = get_c_type(name) self.emit("static int marshal_write_%s(PyObject **, int *, %s);" % (name, ctype), 0) visitProduct = visitSum = prototype class PyTypesDeclareVisitor(PickleVisitor): def visitProduct(self, prod, name): self.emit("static PyTypeObject *%s_type;" % name, 0) self.emit("static PyObject* ast2obj_%s(void*);" % name, 0) if prod.attributes: for a in prod.attributes: self.emit_identifier(a.name) self.emit("static char *%s_attributes[] = {" % name, 0) for a in prod.attributes: self.emit('"%s",' % a.name, 1) self.emit("};", 0) if prod.fields: for f in prod.fields: self.emit_identifier(f.name) self.emit("static char *%s_fields[]={" % name,0) for f in prod.fields: self.emit('"%s",' % f.name, 1) self.emit("};", 0) def visitSum(self, sum, name): self.emit("static PyTypeObject *%s_type;" % name, 0) if sum.attributes: for a in sum.attributes: self.emit_identifier(a.name) self.emit("static char *%s_attributes[] = {" % name, 0) for a in sum.attributes: self.emit('"%s",' % a.name, 1) self.emit("};", 0) ptype = "void*" if is_simple(sum): ptype = get_c_type(name) tnames = [] for t in sum.types: tnames.append(str(t.name)+"_singleton") tnames = ", *".join(tnames) self.emit("static PyObject *%s;" % tnames, 0) self.emit("static PyObject* ast2obj_%s(%s);" % (name, ptype), 0) for t in sum.types: self.visitConstructor(t, name) def visitConstructor(self, cons, name): self.emit("static PyTypeObject *%s_type;" % cons.name, 0) if cons.fields: for t in cons.fields: self.emit_identifier(t.name) self.emit("static char *%s_fields[]={" % cons.name, 0) for t in cons.fields: self.emit('"%s",' % t.name, 1) self.emit("};",0) class PyTypesVisitor(PickleVisitor): def visitModule(self, mod): self.emit(""" typedef struct { PyObject_HEAD PyObject *dict; } AST_object; static void ast_dealloc(AST_object *self) { /* bpo-31095: UnTrack is needed before calling any callbacks */ PyObject_GC_UnTrack(self); Py_CLEAR(self->dict); Py_TYPE(self)->tp_free(self); } static int ast_traverse(AST_object *self, visitproc visit, void *arg) { Py_VISIT(self->dict); return 0; } static int ast_clear(AST_object *self) { Py_CLEAR(self->dict); return 0; } static int ast_type_init(PyObject *self, PyObject *args, PyObject *kw) { _Py_IDENTIFIER(_fields); Py_ssize_t i, numfields = 0; int res = -1; PyObject *key, *value, *fields; fields = _PyObject_GetAttrId((PyObject*)Py_TYPE(self), &PyId__fields); if (!fields) PyErr_Clear(); if (fields) { numfields = PySequence_Size(fields); if (numfields == -1) goto cleanup; } res = 0; /* if no error occurs, this stays 0 to the end */ if (PyTuple_GET_SIZE(args) > 0) { if (numfields != PyTuple_GET_SIZE(args)) { PyErr_Format(PyExc_TypeError, "%.400s constructor takes %s" "%zd positional argument%s", Py_TYPE(self)->tp_name, numfields == 0 ? "" : "either 0 or ", numfields, numfields == 1 ? "" : "s"); res = -1; goto cleanup; } for (i = 0; i < PyTuple_GET_SIZE(args); i++) { /* cannot be reached when fields is NULL */ PyObject *name = PySequence_GetItem(fields, i); if (!name) { res = -1; goto cleanup; } res = PyObject_SetAttr(self, name, PyTuple_GET_ITEM(args, i)); Py_DECREF(name); if (res < 0) goto cleanup; } } if (kw) { i = 0; /* needed by PyDict_Next */ while (PyDict_Next(kw, &i, &key, &value)) { res = PyObject_SetAttr(self, key, value); if (res < 0) goto cleanup; } } cleanup: Py_XDECREF(fields); return res; } /* Pickling support */ static PyObject * ast_type_reduce(PyObject *self, PyObject *unused) { PyObject *res; _Py_IDENTIFIER(__dict__); PyObject *dict = _PyObject_GetAttrId(self, &PyId___dict__); if (dict == NULL) { if (PyErr_ExceptionMatches(PyExc_AttributeError)) PyErr_Clear(); else return NULL; } if (dict) { res = Py_BuildValue("O()O", Py_TYPE(self), dict); Py_DECREF(dict); return res; } return Py_BuildValue("O()", Py_TYPE(self)); } static PyMethodDef ast_type_methods[] = { {"__reduce__", ast_type_reduce, METH_NOARGS, NULL}, {NULL} }; static PyGetSetDef ast_type_getsets[] = { {"__dict__", PyObject_GenericGetDict, PyObject_GenericSetDict}, {NULL} }; static PyTypeObject AST_type = { PyVarObject_HEAD_INIT(&PyType_Type, 0) "_ast.AST", sizeof(AST_object), 0, (destructor)ast_dealloc, /* tp_dealloc */ 0, /* tp_print */ 0, /* tp_getattr */ 0, /* tp_setattr */ 0, /* tp_reserved */ 0, /* tp_repr */ 0, /* tp_as_number */ 0, /* tp_as_sequence */ 0, /* tp_as_mapping */ 0, /* tp_hash */ 0, /* tp_call */ 0, /* tp_str */ PyObject_GenericGetAttr, /* tp_getattro */ PyObject_GenericSetAttr, /* tp_setattro */ 0, /* tp_as_buffer */ Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC, /* tp_flags */ 0, /* tp_doc */ (traverseproc)ast_traverse, /* tp_traverse */ (inquiry)ast_clear, /* tp_clear */ 0, /* tp_richcompare */ 0, /* tp_weaklistoffset */ 0, /* tp_iter */ 0, /* tp_iternext */ ast_type_methods, /* tp_methods */ 0, /* tp_members */ ast_type_getsets, /* tp_getset */ 0, /* tp_base */ 0, /* tp_dict */ 0, /* tp_descr_get */ 0, /* tp_descr_set */ offsetof(AST_object, dict),/* tp_dictoffset */ (initproc)ast_type_init, /* tp_init */ PyType_GenericAlloc, /* tp_alloc */ PyType_GenericNew, /* tp_new */ PyObject_GC_Del, /* tp_free */ }; static PyTypeObject* make_type(char *type, PyTypeObject* base, char**fields, int num_fields) { PyObject *fnames, *result; int i; fnames = PyTuple_New(num_fields); if (!fnames) return NULL; for (i = 0; i < num_fields; i++) { PyObject *field = PyUnicode_FromString(fields[i]); if (!field) { Py_DECREF(fnames); return NULL; } PyTuple_SET_ITEM(fnames, i, field); } result = PyObject_CallFunction((PyObject*)&PyType_Type, "s(O){sOss}", type, base, "_fields", fnames, "__module__", "_ast"); Py_DECREF(fnames); return (PyTypeObject*)result; } static int add_attributes(PyTypeObject* type, char**attrs, int num_fields) { int i, result; _Py_IDENTIFIER(_attributes); PyObject *s, *l = PyTuple_New(num_fields); if (!l) return 0; for (i = 0; i < num_fields; i++) { s = PyUnicode_FromString(attrs[i]); if (!s) { Py_DECREF(l); return 0; } PyTuple_SET_ITEM(l, i, s); } result = _PyObject_SetAttrId((PyObject*)type, &PyId__attributes, l) >= 0; Py_DECREF(l); return result; } /* Conversion AST -> Python */ static PyObject* ast2obj_list(asdl_seq *seq, PyObject* (*func)(void*)) { Py_ssize_t i, n = asdl_seq_LEN(seq); PyObject *result = PyList_New(n); PyObject *value; if (!result) return NULL; for (i = 0; i < n; i++) { value = func(asdl_seq_GET(seq, i)); if (!value) { Py_DECREF(result); return NULL; } PyList_SET_ITEM(result, i, value); } return result; } static PyObject* ast2obj_object(void *o) { if (!o) o = Py_None; Py_INCREF((PyObject*)o); return (PyObject*)o; } #define ast2obj_singleton ast2obj_object #define ast2obj_constant ast2obj_object #define ast2obj_identifier ast2obj_object #define ast2obj_string ast2obj_object #define ast2obj_bytes ast2obj_object static PyObject* ast2obj_int(long b) { return PyLong_FromLong(b); } /* Conversion Python -> AST */ static int obj2ast_singleton(PyObject *obj, PyObject** out, PyArena* arena) { if (obj != Py_None && obj != Py_True && obj != Py_False) { PyErr_SetString(PyExc_ValueError, "AST singleton must be True, False, or None"); return 1; } *out = obj; return 0; } static int obj2ast_object(PyObject* obj, PyObject** out, PyArena* arena) { if (obj == Py_None) obj = NULL; if (obj) { if (PyArena_AddPyObject(arena, obj) < 0) { *out = NULL; return -1; } Py_INCREF(obj); } *out = obj; return 0; } static int obj2ast_constant(PyObject* obj, PyObject** out, PyArena* arena) { if (obj) { if (PyArena_AddPyObject(arena, obj) < 0) { *out = NULL; return -1; } Py_INCREF(obj); } *out = obj; return 0; } static int obj2ast_identifier(PyObject* obj, PyObject** out, PyArena* arena) { if (!PyUnicode_CheckExact(obj) && obj != Py_None) { PyErr_SetString(PyExc_TypeError, "AST identifier must be of type str"); return 1; } return obj2ast_object(obj, out, arena); } static int obj2ast_string(PyObject* obj, PyObject** out, PyArena* arena) { if (!PyUnicode_CheckExact(obj) && !PyBytes_CheckExact(obj)) { PyErr_SetString(PyExc_TypeError, "AST string must be of type str"); return 1; } return obj2ast_object(obj, out, arena); } static int obj2ast_bytes(PyObject* obj, PyObject** out, PyArena* arena) { if (!PyBytes_CheckExact(obj)) { PyErr_SetString(PyExc_TypeError, "AST bytes must be of type bytes"); return 1; } return obj2ast_object(obj, out, arena); } static int obj2ast_int(PyObject* obj, int* out, PyArena* arena) { int i; if (!PyLong_Check(obj)) { PyErr_Format(PyExc_ValueError, "invalid integer value: %R", obj); return 1; } i = _PyLong_AsInt(obj); if (i == -1 && PyErr_Occurred()) return 1; *out = i; return 0; } static int add_ast_fields(void) { PyObject *empty_tuple, *d; if (PyType_Ready(&AST_type) < 0) return -1; d = AST_type.tp_dict; empty_tuple = PyTuple_New(0); if (!empty_tuple || PyDict_SetItemString(d, "_fields", empty_tuple) < 0 || PyDict_SetItemString(d, "_attributes", empty_tuple) < 0) { Py_XDECREF(empty_tuple); return -1; } Py_DECREF(empty_tuple); return 0; } static int exists_not_none(PyObject *obj, _Py_Identifier *id) { int isnone; PyObject *attr = _PyObject_GetAttrId(obj, id); if (!attr) { PyErr_Clear(); return 0; } isnone = attr == Py_None; Py_DECREF(attr); return !isnone; } """, 0, reflow=False) self.emit("static int init_types(void)",0) self.emit("{", 0) self.emit("static int initialized;", 1) self.emit("if (initialized) return 1;", 1) self.emit("if (add_ast_fields() < 0) return 0;", 1) for dfn in mod.dfns: self.visit(dfn) self.emit("initialized = 1;", 1) self.emit("return 1;", 1); self.emit("}", 0) def visitProduct(self, prod, name): if prod.fields: fields = name+"_fields" else: fields = "NULL" self.emit('%s_type = make_type("%s", &AST_type, %s, %d);' % (name, name, fields, len(prod.fields)), 1) self.emit("if (!%s_type) return 0;" % name, 1) if prod.attributes: self.emit("if (!add_attributes(%s_type, %s_attributes, %d)) return 0;" % (name, name, len(prod.attributes)), 1) else: self.emit("if (!add_attributes(%s_type, NULL, 0)) return 0;" % name, 1) def visitSum(self, sum, name): self.emit('%s_type = make_type("%s", &AST_type, NULL, 0);' % (name, name), 1) self.emit("if (!%s_type) return 0;" % name, 1) if sum.attributes: self.emit("if (!add_attributes(%s_type, %s_attributes, %d)) return 0;" % (name, name, len(sum.attributes)), 1) else: self.emit("if (!add_attributes(%s_type, NULL, 0)) return 0;" % name, 1) simple = is_simple(sum) for t in sum.types: self.visitConstructor(t, name, simple) def visitConstructor(self, cons, name, simple): if cons.fields: fields = cons.name+"_fields" else: fields = "NULL" self.emit('%s_type = make_type("%s", %s_type, %s, %d);' % (cons.name, cons.name, name, fields, len(cons.fields)), 1) self.emit("if (!%s_type) return 0;" % cons.name, 1) if simple: self.emit("%s_singleton = PyType_GenericNew(%s_type, NULL, NULL);" % (cons.name, cons.name), 1) self.emit("if (!%s_singleton) return 0;" % cons.name, 1) class ASTModuleVisitor(PickleVisitor): def visitModule(self, mod): self.emit("static struct PyModuleDef _astmodule = {", 0) self.emit(' PyModuleDef_HEAD_INIT, "_ast"', 0) self.emit("};", 0) self.emit("PyMODINIT_FUNC", 0) self.emit("PyInit__ast(void)", 0) self.emit("{", 0) self.emit("PyObject *m, *d;", 1) self.emit("if (!init_types()) return NULL;", 1) self.emit('m = PyModule_Create(&_astmodule);', 1) self.emit("if (!m) return NULL;", 1) self.emit("d = PyModule_GetDict(m);", 1) self.emit('if (PyDict_SetItemString(d, "AST", (PyObject*)&AST_type) < 0) return NULL;', 1) self.emit('if (PyModule_AddIntMacro(m, PyCF_ONLY_AST) < 0)', 1) self.emit("return NULL;", 2) for dfn in mod.dfns: self.visit(dfn) self.emit("return m;", 1) self.emit("}", 0) def visitProduct(self, prod, name): self.addObj(name) def visitSum(self, sum, name): self.addObj(name) for t in sum.types: self.visitConstructor(t, name) def visitConstructor(self, cons, name): self.addObj(cons.name) def addObj(self, name): self.emit('if (PyDict_SetItemString(d, "%s", (PyObject*)%s_type) < 0) return NULL;' % (name, name), 1) _SPECIALIZED_SEQUENCES = ('stmt', 'expr') def find_sequence(fields, doing_specialization): """Return True if any field uses a sequence.""" for f in fields: if f.seq: if not doing_specialization: return True if str(f.type) not in _SPECIALIZED_SEQUENCES: return True return False def has_sequence(types, doing_specialization): for t in types: if find_sequence(t.fields, doing_specialization): return True return False class StaticVisitor(PickleVisitor): CODE = '''Very simple, always emit this static code. Override CODE''' def visit(self, object): self.emit(self.CODE, 0, reflow=False) class ObjVisitor(PickleVisitor): def func_begin(self, name): ctype = get_c_type(name) self.emit("PyObject*", 0) self.emit("ast2obj_%s(void* _o)" % (name), 0) self.emit("{", 0) self.emit("%s o = (%s)_o;" % (ctype, ctype), 1) self.emit("PyObject *result = NULL, *value = NULL;", 1) self.emit('if (!o) {', 1) self.emit("Py_INCREF(Py_None);", 2) self.emit('return Py_None;', 2) self.emit("}", 1) self.emit('', 0) def func_end(self): self.emit("return result;", 1) self.emit("failed:", 0) self.emit("Py_XDECREF(value);", 1) self.emit("Py_XDECREF(result);", 1) self.emit("return NULL;", 1) self.emit("}", 0) self.emit("", 0) def visitSum(self, sum, name): if is_simple(sum): self.simpleSum(sum, name) return self.func_begin(name) self.emit("switch (o->kind) {", 1) for i in range(len(sum.types)): t = sum.types[i] self.visitConstructor(t, i + 1, name) self.emit("}", 1) for a in sum.attributes: self.emit("value = ast2obj_%s(o->%s);" % (a.type, a.name), 1) self.emit("if (!value) goto failed;", 1) self.emit('if (_PyObject_SetAttrId(result, &PyId_%s, value) < 0)' % a.name, 1) self.emit('goto failed;', 2) self.emit('Py_DECREF(value);', 1) self.func_end() def simpleSum(self, sum, name): self.emit("PyObject* ast2obj_%s(%s_ty o)" % (name, name), 0) self.emit("{", 0) self.emit("switch(o) {", 1) for t in sum.types: self.emit("case %s:" % t.name, 2) self.emit("Py_INCREF(%s_singleton);" % t.name, 3) self.emit("return %s_singleton;" % t.name, 3) self.emit("default:", 2) self.emit('/* should never happen, but just in case ... */', 3) code = "PyErr_Format(PyExc_SystemError, \"unknown %s found\");" % name self.emit(code, 3, reflow=False) self.emit("return NULL;", 3) self.emit("}", 1) self.emit("}", 0) def visitProduct(self, prod, name): self.func_begin(name) self.emit("result = PyType_GenericNew(%s_type, NULL, NULL);" % name, 1); self.emit("if (!result) return NULL;", 1) for field in prod.fields: self.visitField(field, name, 1, True) for a in prod.attributes: self.emit("value = ast2obj_%s(o->%s);" % (a.type, a.name), 1) self.emit("if (!value) goto failed;", 1) self.emit('if (_PyObject_SetAttrId(result, &PyId_%s, value) < 0)' % a.name, 1) self.emit('goto failed;', 2) self.emit('Py_DECREF(value);', 1) self.func_end() def visitConstructor(self, cons, enum, name): self.emit("case %s_kind:" % cons.name, 1) self.emit("result = PyType_GenericNew(%s_type, NULL, NULL);" % cons.name, 2); self.emit("if (!result) goto failed;", 2) for f in cons.fields: self.visitField(f, cons.name, 2, False) self.emit("break;", 2) def visitField(self, field, name, depth, product): def emit(s, d): self.emit(s, depth + d) if product: value = "o->%s" % field.name else: value = "o->v.%s.%s" % (name, field.name) self.set(field, value, depth) emit("if (!value) goto failed;", 0) emit('if (_PyObject_SetAttrId(result, &PyId_%s, value) == -1)' % field.name, 0) emit("goto failed;", 1) emit("Py_DECREF(value);", 0) def emitSeq(self, field, value, depth, emit): emit("seq = %s;" % value, 0) emit("n = asdl_seq_LEN(seq);", 0) emit("value = PyList_New(n);", 0) emit("if (!value) goto failed;", 0) emit("for (i = 0; i < n; i++) {", 0) self.set("value", field, "asdl_seq_GET(seq, i)", depth + 1) emit("if (!value1) goto failed;", 1) emit("PyList_SET_ITEM(value, i, value1);", 1) emit("value1 = NULL;", 1) emit("}", 0) def set(self, field, value, depth): if field.seq: # XXX should really check for is_simple, but that requires a symbol table if field.type == "cmpop": # While the sequence elements are stored as void*, # ast2obj_cmpop expects an enum self.emit("{", depth) self.emit("Py_ssize_t i, n = asdl_seq_LEN(%s);" % value, depth+1) self.emit("value = PyList_New(n);", depth+1) self.emit("if (!value) goto failed;", depth+1) self.emit("for(i = 0; i < n; i++)", depth+1) # This cannot fail, so no need for error handling self.emit("PyList_SET_ITEM(value, i, ast2obj_cmpop((cmpop_ty)asdl_seq_GET(%s, i)));" % value, depth+2, reflow=False) self.emit("}", depth) else: self.emit("value = ast2obj_list(%s, ast2obj_%s);" % (value, field.type), depth) else: ctype = get_c_type(field.type) self.emit("value = ast2obj_%s(%s);" % (field.type, value), depth, reflow=False) class PartingShots(StaticVisitor): CODE = """ PyObject* PyAST_mod2obj(mod_ty t) { if (!init_types()) return NULL; return ast2obj_mod(t); } /* mode is 0 for "exec", 1 for "eval" and 2 for "single" input */ mod_ty PyAST_obj2mod(PyObject* ast, PyArena* arena, int mode) { mod_ty res; PyObject *req_type[3]; char *req_name[] = {"Module", "Expression", "Interactive"}; int isinstance; req_type[0] = (PyObject*)Module_type; req_type[1] = (PyObject*)Expression_type; req_type[2] = (PyObject*)Interactive_type; assert(0 <= mode && mode <= 2); if (!init_types()) return NULL; isinstance = PyObject_IsInstance(ast, req_type[mode]); if (isinstance == -1) return NULL; if (!isinstance) { PyErr_Format(PyExc_TypeError, "expected %s node, got %.400s", req_name[mode], Py_TYPE(ast)->tp_name); return NULL; } if (obj2ast_mod(ast, &res, arena) != 0) return NULL; else return res; } int PyAST_Check(PyObject* obj) { if (!init_types()) return -1; return PyObject_IsInstance(obj, (PyObject*)&AST_type); } """ class ChainOfVisitors: def __init__(self, *visitors): self.visitors = visitors def visit(self, object): for v in self.visitors: v.visit(object) v.emit("", 0) def main(srcfile, dump_module=False): argv0 = sys.argv[0] components = argv0.split(os.sep) argv0 = os.sep.join(components[-2:]) mod = asdl.parse(srcfile) if dump_module: print('Parsed Module:') print(mod) if not asdl.check(mod): sys.exit(1) if H_FILE: with open(H_FILE, "w") as f: f.write("\ #ifndef COSMOPOLITAN_THIRD_PARTY_PYTHON_INCLUDE_PYTHON_AST_H_\n\ #define COSMOPOLITAN_THIRD_PARTY_PYTHON_INCLUDE_PYTHON_AST_H_\n\ #include \"third_party/python/Include/asdl.h\"\n\ #if !(__ASSEMBLER__ + __LINKER__ + 0)\n\ COSMOPOLITAN_C_START_\n\ /* clang-format off */\n\ /* File automatically generated by %s. */\n\ \n\ " % argv0) c = ChainOfVisitors(TypeDefVisitor(f), StructVisitor(f), PrototypeVisitor(f), ) c.visit(mod) f.write("\ PyObject* PyAST_mod2obj(mod_ty t);\n\ mod_ty PyAST_obj2mod(PyObject* ast, PyArena* arena, int mode);\n\ int PyAST_Check(PyObject* obj);\n\ \n\ COSMOPOLITAN_C_END_\n\ #endif /* !(__ASSEMBLER__ + __LINKER__ + 0) */\n\ #endif /* COSMOPOLITAN_THIRD_PARTY_PYTHON_INCLUDE_PYTHON_AST_H_ */\n") if C_FILE: with open(C_FILE, "w") as f: f.write('\ #include "third_party/python/Include/%s-ast.h"\n\ #include "third_party/python/Include/abstract.h"\n\ #include "third_party/python/Include/boolobject.h"\n\ #include "third_party/python/Include/descrobject.h"\n\ #include "third_party/python/Include/dictobject.h"\n\ #include "third_party/python/Include/listobject.h"\n\ #include "third_party/python/Include/longobject.h"\n\ #include "third_party/python/Include/modsupport.h"\n\ #include "third_party/python/Include/object.h"\n\ #include "third_party/python/Include/objimpl.h"\n\ #include "third_party/python/Include/pyerrors.h"\n\ #include "third_party/python/Include/pythonrun.h"\n\ #include "third_party/python/Include/tupleobject.h"\n\ #include "third_party/python/Include/yoink.h"\n\ /* clang-format off */\n\ \n\ PYTHON_PROVIDE("_ast");\n\ \n\ /* File automatically generated by %s. */\n\ \n\ static PyTypeObject AST_type;\n\ ' % (mod.name, argv0)) v = ChainOfVisitors( PyTypesDeclareVisitor(f), PyTypesVisitor(f), Obj2ModPrototypeVisitor(f), FunctionVisitor(f), ObjVisitor(f), Obj2ModVisitor(f), ASTModuleVisitor(f), PartingShots(f), ) v.visit(mod) if __name__ == "__main__": import getopt H_FILE = '' C_FILE = '' dump_module = False opts, args = getopt.getopt(sys.argv[1:], "dh:c:") for o, v in opts: if o == '-h': H_FILE = v if o == '-c': C_FILE = v if o == '-d': dump_module = True if H_FILE and C_FILE: print('Must specify exactly one output file') sys.exit(1) elif len(args) != 1: print('Must specify single input file') sys.exit(1) main(args[0], dump_module)

33.379663

110

0.539667

59d05a4fabb908c5d24ef1c8efff8300a1df26c3

362

py

Python

mptt_graph/admin.py

synw/django-mptt-graph

e4ff6b1d77f43b70fa2d58b3ba9a3155e279a1e2

[ "MIT" ]

4

2019-04-14T11:00:44.000Z

2021-06-06T09:56:44.000Z

mptt_graph/admin.py

synw/django-mptt-graph

e4ff6b1d77f43b70fa2d58b3ba9a3155e279a1e2

[ "MIT" ]

3

2017-08-21T10:53:12.000Z

2020-01-02T12:36:15.000Z

mptt_graph/admin.py

synw/django-mptt-graph

e4ff6b1d77f43b70fa2d58b3ba9a3155e279a1e2

[ "MIT" ]

null

# -*- coding: utf-8 -*- from django.contrib import admin from mptt.admin import MPTTModelAdmin from mptt_graph.models import GraphModel, TreeNode @admin.register(GraphModel) class UrlTreeAdmin(admin.ModelAdmin): list_display = ["title", "model_path", "model_pk"] @admin.register(TreeNode) class TreeNodeAdmin(MPTTModelAdmin): mptt_level_indent = 30

22.625

54

0.765193

f2c0be66df871726673ea1149e9d3a6832043b3e

539

py

Python

Python3/0029-Divide-Two-Integers/soln.py

wyaadarsh/LeetCode-Solutions

3719f5cb059eefd66b83eb8ae990652f4b7fd124

[ "MIT" ]

5

2020-07-24T17:48:59.000Z

2020-12-21T05:56:00.000Z

Python3/0029-Divide-Two-Integers/soln.py

zhangyaqi1989/LeetCode-Solutions

2655a1ffc8678ad1de6c24295071308a18c5dc6e

[ "MIT" ]

null

Python3/0029-Divide-Two-Integers/soln.py

zhangyaqi1989/LeetCode-Solutions

2655a1ffc8678ad1de6c24295071308a18c5dc6e

[ "MIT" ]

2

2020-07-24T17:49:01.000Z

2020-08-31T19:57:35.000Z

class Solution: def divide(self, dividend, divisor): """ :type dividend: int :type divisor: int :rtype: int """ sign = -1 if dividend * divisor < 0 else +1 a, b = abs(dividend), abs(divisor) res = 0 while a >= b: temp, unit = b, 1 while a >= temp: a -= temp res += unit temp *= 10 unit *= 10 res *= sign return res if -2**31 <= res <= 2**31 - 1 else 2**31 - 1

28.368421

63

0.408163

356b22c632cf5199319e3d46ba764f04833632c3

50,030

py

Python

python/paddle/nn/layer/loss.py

wangna11BD/Paddle

bc379ca3d5895eadbc1748bc5b71606011563ee1

[ "Apache-2.0" ]

1

2021-04-28T13:47:27.000Z

python/paddle/nn/layer/loss.py

wangna11BD/Paddle

bc379ca3d5895eadbc1748bc5b71606011563ee1

[ "Apache-2.0" ]

null

python/paddle/nn/layer/loss.py

wangna11BD/Paddle

bc379ca3d5895eadbc1748bc5b71606011563ee1

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -* # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: define loss functions of neural network import numpy as np import paddle.fluid as fluid import paddle.fluid.core as core import paddle from .. import functional as F from paddle.fluid.framework import core, in_dygraph_mode, _varbase_creator class BCEWithLogitsLoss(fluid.dygraph.Layer): r""" This operator combines the sigmoid layer and the :ref:`api_nn_loss_BCELoss` layer. Also, we can see it as the combine of ``sigmoid_cross_entropy_with_logits`` layer and some reduce operations. This measures the element-wise probability error in classification tasks in which each class is independent. This can be thought of as predicting labels for a data-point, where labels are not mutually exclusive. For example, a news article can be about politics, technology or sports at the same time or none of these. First this operator calculate loss function as follows: .. math:: Out = -Labels * \\log(\\sigma(Logit)) - (1 - Labels) * \\log(1 - \\sigma(Logit)) We know that :math:`\\sigma(Logit) = \\frac{1}{1 + \\e^{-Logit}}`. By substituting this we get: .. math:: Out = Logit - Logit * Labels + \\log(1 + \\e^{-Logit}) For stability and to prevent overflow of :math:`\\e^{-Logit}` when Logit < 0, we reformulate the loss as follows: .. math:: Out = \\max(Logit, 0) - Logit * Labels + \\log(1 + \\e^{-\|Logit\|}) Then, if ``weight`` or ``pos_weight`` is not None, this operator multiply the weight tensor on the loss `Out`. The ``weight`` tensor will attach different weight on every items in the batch. The ``pos_weight`` will attach different weight on the positive label of each class. Finally, this operator applies reduce operation on the loss. If :attr:`reduction` set to ``'none'``, the operator will return the original loss `Out`. If :attr:`reduction` set to ``'mean'``, the reduced mean loss is :math:`Out = MEAN(Out)`. If :attr:`reduction` set to ``'sum'``, the reduced sum loss is :math:`Out = SUM(Out)`. Note that the target labels ``label`` should be numbers between 0 and 1. Args: weight (Tensor, optional): A manual rescaling weight given to the loss of each batch element. If given, it has to be a 1D Tensor whose size is `[N, ]`, The data type is float32, float64. Default is ``'None'``. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'mean'``. pos_weight (Tensor, optional): A weight of positive examples. Must be a vector with length equal to the number of classes. The data type is float32, float64. Default is ``'None'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shapes: logit (Tensor): The input predications tensor. 2-D tensor with shape: [N, *], N is batch_size, `*` means number of additional dimensions. The ``logit`` is usually the output of Linear layer. Available dtype is float32, float64. label (Tensor): The target labels tensor. 2-D tensor with the same shape as ``logit``. The target labels which values should be numbers between 0 and 1. Available dtype is float32, float64. output (Tensor): If ``reduction`` is ``'none'``, the shape of output is same as ``logit`` , else the shape of output is scalar. Returns: A callable object of BCEWithLogitsLoss. Examples: .. code-block:: python import paddle logit = paddle.to_tensor([5.0, 1.0, 3.0], dtype="float32") label = paddle.to_tensor([1.0, 0.0, 1.0], dtype="float32") bce_logit_loss = paddle.nn.BCEWithLogitsLoss() output = bce_logit_loss(logit, label) print(output.numpy()) # [0.45618808] """ def __init__(self, weight=None, reduction='mean', pos_weight=None, name=None): if reduction not in ['sum', 'mean', 'none']: raise ValueError( "The value of 'reduction' in BCEWithLogitsLoss should be 'sum', 'mean' or 'none', but " "received %s, which is not allowed." % reduction) super(BCEWithLogitsLoss, self).__init__() self.weight = weight self.reduction = reduction self.pos_weight = pos_weight self.name = name def forward(self, logit, label): out = paddle.nn.functional.binary_cross_entropy_with_logits( logit, label, self.weight, self.reduction, self.pos_weight, self.name) return out class CrossEntropyLoss(fluid.dygraph.Layer): r""" By default, this operator implements the cross entropy loss function with softmax. This function combines the calculation of the softmax operation and the cross entropy loss function to provide a more numerically stable computing. This operator will calculate the cross entropy loss function without softmax when use_softmax=False. By default, this operator will calculate the mean of the result, and you can also affect the default behavior by using the reduction parameter. Please refer to the part of parameters for details. This operator can be used to calculate the softmax cross entropy loss with soft and hard labels. Where, the hard labels mean the actual label value, 0, 1, 2, etc. And the soft labels mean the probability of the actual label, 0.6, 0.8, 0.2, etc. The calculation of this operator includes the following two steps. - **I.softmax cross entropy** 1. Hard label (each sample can only be assigned into one category) 1.1. when use_softmax=True .. math:: \\loss_j=-\text{logits}_{label_j}+\log\left(\sum_{i=0}^{C}\exp(\text{logits}_i)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories. 1.2. when use_softmax=False .. math:: \\loss_j=-\log\left({P}_{label_j}\right) , j = 1,...,N where, N is the number of samples and C is the number of categories, P is input(the output of softmax). 2. Soft label (each sample is assigned to multiple categories with a certain probability, and the probability sum is 1). 2.1. when use_softmax=True .. math:: \\loss_j=-\sum_{i=0}^{C}\text{label}_i\left(\text{logits}_i-\log\left(\sum_{i=0}^{C}\exp(\text{logits}_i)\right)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories. 2.2. when use_softmax=False .. math:: \\loss_j=-\sum_{j=0}^{C}\left({label}_j*\log\left({P}_{label_j}\right)\right) , j = 1,...,N where, N is the number of samples and C is the number of categories, P is input(the output of softmax). - **II.Weight and reduction processing** 1. Weight If the ``weight`` parameter is ``None`` , go to the next step directly. If the ``weight`` parameter is not ``None`` , the cross entropy of each sample is weighted by weight according to soft_label = False or True as follows. 1.1. Hard labels (soft_label = False) .. math:: \\loss_j=loss_j*weight[label_j] 1.2. Soft labels (soft_label = True) .. math:: \\loss_j=loss_j*\sum_{i}\left(weight[label_i]*logits_i\right) 2. reduction 2.1 if the ``reduction`` parameter is ``none`` Return the previous result directly 2.2 if the ``reduction`` parameter is ``sum`` Return the sum of the previous results .. math:: \\loss=\sum_{j}loss_j 2.3 if the ``reduction`` parameter is ``mean`` , it will be processed according to the ``weight`` parameter as follows. 2.3.1. If the ``weight`` parameter is ``None`` Return the average value of the previous results .. math:: \\loss=\sum_{j}loss_j/N where, N is the number of samples and C is the number of categories. 2.3.2. If the 'weight' parameter is not 'None', the weighted average value of the previous result will be returned 1. Hard labels (soft_label = False) .. math:: \\loss=\sum_{j}loss_j/\sum_{j}weight[label_j] 2. Soft labels (soft_label = True) .. math:: \\loss=\sum_{j}loss_j/\sum_{j}\left(\sum_{i}weight[label_i]\right) Parameters: - **weight** (Tensor, optional) a manual rescaling weight given to each class. If given, has to be a Tensor of size C and the data type is float32, float64. Default is ``'None'`` . - **ignore_index** (int64, optional) Specifies a target value that is ignored and does not contribute to the loss. A negative value means that no label value needs to be ignored. Only valid when soft_label = False. Default is ``-100`` . - **reduction** (str, optional) Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. - **soft_label** (bool, optional) Indicate whether label is soft. If soft_label=False, the label is hard. If soft_label=True, the label is soft. Default is ``False``. - **axis** (int, optional) The index of dimension to perform softmax calculations. It should be in range :math:`[-1, rank - 1]`, where :math:`rank` is the number of dimensions of input :attr:`input`. Default is ``-1`` . - **use_softmax** (bool, optional) Indicate whether compute softmax before cross_entropy. Default is ``True``. - **name** (str, optional) The name of the operator. Default is ``None`` . For more information, please refer to :ref:`api_guide_Name` . Shape: - **input** (Tensor) Input tensor, the data type is float32, float64. Shape is :math:`[N_1, N_2, ..., N_k, C]`, where C is number of classes , ``k >= 1`` . Note: 1. when use_softmax=True, it expects unscaled logits. This operator should not be used with the output of softmax operator, which will produce incorrect results. 2. when use_softmax=False, it expects the output of softmax operator. - **label** (Tensor) 1. If soft_label=False, the shape is :math:`[N_1, N_2, ..., N_k]` or :math:`[N_1, N_2, ..., N_k, 1]`, k >= 1. the data type is int32, int64, float32, float64, where each value is [0, C-1]. 2. If soft_label=True, the shape and data type should be same with ``input`` , and the sum of the labels for each sample should be 1. - **output** (Tensor) Return the softmax cross_entropy loss of ``input`` and ``label``. The data type is the same as input. If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the dimension of return value is ``1``. If :attr:`reduction` is ``'none'``: 1. If soft_label = False, the dimension of return value is the same with ``label`` . 2. if soft_label = True, the dimension of return value is :math:`[N_1, N_2, ..., N_k, 1]` . Example1(hard labels): .. code-block:: python import paddle paddle.seed(99999) N=100 C=200 reduction='mean' input = paddle.rand([N, C], dtype='float64') label = paddle.randint(0, C, shape=[N], dtype='int64') weight = paddle.rand([C], dtype='float64') cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss( weight=weight, reduction=reduction) dy_ret = cross_entropy_loss( input, label) print(dy_ret.numpy()) #[5.41993642] Example2(soft labels): .. code-block:: python import paddle paddle.seed(99999) axis = -1 ignore_index = -100 N = 4 C = 3 shape = [N, C] reduction='mean' weight = None logits = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0) labels = paddle.uniform(shape, dtype='float64', min=0.1, max=1.0) labels /= paddle.sum(labels, axis=axis, keepdim=True) paddle_loss_mean = paddle.nn.functional.cross_entropy( logits, labels, soft_label=True, axis=axis, weight=weight, reduction=reduction) print(paddle_loss_mean.numpy()) #[1.12908343] """ def __init__(self, weight=None, ignore_index=-100, reduction='mean', soft_label=False, axis=-1, use_softmax=True, name=None): super(CrossEntropyLoss, self).__init__() self.weight = weight self.reduction = reduction self.ignore_index = ignore_index self.soft_label = soft_label self.axis = axis self.use_softmax = use_softmax self.name = name def forward(self, input, label): ret = paddle.nn.functional.cross_entropy( input, label, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction, soft_label=self.soft_label, axis=self.axis, use_softmax=self.use_softmax, name=self.name) return ret class HSigmoidLoss(fluid.dygraph.Layer): """ Hierarchical Sigmoid Layer. The hierarchical sigmoid organizes the classes into a complete binary tree to reduce the computational complexity and speed up the model training, especially the training of language model. Each leaf node of the complete binary tree represents a class(word) and each non-leaf node acts as a binary classifier. For each class(word), there's a unique path from root to itself, hsigmoid calculate the cost for each non-leaf node on the path, and sum them to get a total cost. Comparing to softmax, the OP can reduce the computational complexity from :math:`O(N)` to :math:`O(logN)`, where :math:`N` represents the number of classes or the size of word dict. The OP supports default tree and custom tree. For the default tree, you can refer to `Hierarchical Probabilistic Neural Network Language Model <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>_`. For the custom tree, you need to set :attr:`is_custom` to True, and do the following steps (take the language model as an example): 1. Using a custom word dict to build a binary tree, each leaf node should be an word in the word dict. 2. Creating a dict map word_id -> path that from the word to the root node, we call it path_table. 3. Creating a dict map word_id -> code of path that from the word to the root node, we call it path_code. Code means the label of each binary classifier, 1 indicate true, 0 indicate false. 4. Now, each word should has its path and code along the path, you can pass a batch of path and code related to the same batch of inputs. Parameters: feature_size (int): The number of features. num_classes (int): The number of classes or the size of word dict, must be greater than 2. If the default tree is used (:attr:`is_custom` is set to False), :attr:`num_classes` should not be None. If the custom tree is used (:attr:`is_custom` is set to True), :attr:`num_classes` should be the number of non-leaf nodes, which indicates the num of classes using by the binary classifier. weight_attr (ParamAttr, optional): The parameter attribute for the learnable weights of hsigmoid. If it is set to None or one attribute of ParamAttr, hsigmoid will create a ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with Xavier. Default is None. bias_attr (ParamAttr|bool, optional): The parameter attribute for the bias of hsigmoid. If it is set to False, no bias will be added. If it is set to None or one attribute of ParamAttr, hsigmoid will create a ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default is None. is_custom (bool, optional): Whether use custom binary tree. If it's True, `path_table` and `path_code` should be passed to its forward method, otherwise `path_table` and `path_code` should not be passed to its forward method. Default is False. is_sparse (bool, optional): Whether use sparse updating instead of dense updating, if it's True, the gradient of weight and input will be sparse. Default is False. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input (Tensor): The input tensor. The shapes is [N, D], where N is batch size and D is feature size. It's data type should be float32, float64. label (Tensor): It's shapes is [N, 1]. It's data type should be int64. output (Tensor): The HSigmoid Loss of ``input`` and ``label``. Shape is [N, 1] Examples: .. code-block:: python import paddle paddle.set_device('cpu') input = paddle.uniform([2, 3]) # [[-0.2820413 0.9528898 -0.81638825] # random # [-0.6733154 -0.33866507 0.25770962]] # random label = paddle.to_tensor([0, 1, 4, 5]) m = paddle.nn.HSigmoidLoss(3, 5) out = m(input, label) # [[2.4543471] # [1.9359267]] """ def __init__(self, feature_size, num_classes, weight_attr=None, bias_attr=None, is_custom=False, is_sparse=False, name=None): super(HSigmoidLoss, self).__init__() if (num_classes < 2) and (not is_custom): raise ValueError( "num_classes must not be less than 2 with default tree") if (not is_custom) and (is_sparse): print("Sparse mode should not be used without custom tree") is_sparse = False self._feature_size = feature_size self._num_classes = num_classes self._is_custom = is_custom self._is_sparse = is_sparse self._weight_attr = weight_attr self._bias_attr = bias_attr self._name = name self._dtype = paddle.get_default_dtype() remote_prefetch = is_sparse print("With sparse mode, if your models has only" " small parameter prefetch may cause speed down") C = self._num_classes if is_custom else self._num_classes - 1 self.weight = self.create_parameter( [C, self._feature_size], attr=self._weight_attr, is_bias=False, dtype=self._dtype) self.bias = self.create_parameter( [C, 1], attr=self._bias_attr, is_bias=True, dtype=self._dtype) def forward(self, input, label, path_table=None, path_code=None): out = F.hsigmoid_loss( input, label, self._num_classes, self.weight, self.bias, path_table=path_table, path_code=path_code, is_sparse=self._is_sparse, name=self._name) return out class MSELoss(fluid.dygraph.layers.Layer): r""" **Mean Square Error Loss** Computes the mean square error (squared L2 norm) of given input and label. If :attr:`reduction` is set to ``'none'``, loss is calculated as: .. math:: Out = (input - label)^2 If :attr:`reduction` is set to ``'mean'``, loss is calculated as: .. math:: Out = \operatorname{mean}((input - label)^2) If :attr:`reduction` is set to ``'sum'``, loss is calculated as: .. math:: Out = \operatorname{sum}((input - label)^2) where `input` and `label` are `float32` tensors of same shape. Parameters: reduction (string, optional): The reduction method for the output, could be 'none' | 'mean' | 'sum'. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. Shape: input (Tensor): Input tensor, the data type is float32 or float64 label (Tensor): Label tensor, the data type is float32 or float64 output (Tensor): output tensor storing the MSE loss of input and label, the data type is same as input. Examples: .. code-block:: python import numpy as np import paddle input_data = np.array([1.5]).astype("float32") label_data = np.array([1.7]).astype("float32") mse_loss = paddle.nn.loss.MSELoss() input = paddle.to_tensor(input_data) label = paddle.to_tensor(label_data) output = mse_loss(input, label) print(output) # [0.04000002] """ def __init__(self, reduction='mean'): super(MSELoss, self).__init__() if reduction not in ['sum', 'mean', 'none']: raise ValueError( "'reduction' in 'MSELoss' should be 'sum', 'mean' or 'none', " "but received {}.".format(reduction)) self.reduction = reduction def forward(self, input, label): if not fluid.framework.in_dygraph_mode(): fluid.data_feeder.check_variable_and_dtype( input, 'input', ['float32', 'float64'], 'MSELoss') fluid.data_feeder.check_variable_and_dtype( label, 'label', ['float32', 'float64'], 'MSELoss') square_out = fluid.layers.square( fluid.layers.elementwise_sub(input, label)) if self.reduction == 'none': return square_out reduce_op = 'reduce_mean' if self.reduction == 'sum': reduce_op = 'reduce_sum' return getattr(fluid.layers, reduce_op)(square_out) class L1Loss(fluid.dygraph.Layer): r""" This interface is used to construct a callable object of the ``L1Loss`` class. The L1Loss layer calculates the L1 Loss of ``input`` and ``label`` as follows. If `reduction` set to ``'none'``, the loss is: .. math:: Out = \lvert input - label\rvert If `reduction` set to ``'mean'``, the loss is: .. math:: Out = MEAN(\lvert input - label\rvert) If `reduction` set to ``'sum'``, the loss is: .. math:: Out = SUM(\lvert input - label\rvert) Parameters: reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'none'``, the unreduced loss is returned; If `reduction` is ``'mean'``, the reduced mean loss is returned. If `reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input (Tensor): The input tensor. The shapes is [N, *], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64, int32, int64. label (Tensor): label. The shapes is [N, *], same shape as ``input`` . It's data type should be float32, float64, int32, int64. output (Tensor): The L1 Loss of ``input`` and ``label``. If `reduction` is ``'none'``, the shape of output loss is [N, *], the same as ``input`` . If `reduction` is ``'mean'`` or ``'sum'``, the shape of output loss is [1]. Examples: .. code-block:: python import paddle import numpy as np input_data = np.array([[1.5, 0.8], [0.2, 1.3]]).astype("float32") label_data = np.array([[1.7, 1], [0.4, 0.5]]).astype("float32") input = paddle.to_tensor(input_data) label = paddle.to_tensor(label_data) l1_loss = paddle.nn.L1Loss() output = l1_loss(input, label) print(output.numpy()) # [0.35] l1_loss = paddle.nn.L1Loss(reduction='sum') output = l1_loss(input, label) print(output.numpy()) # [1.4] l1_loss = paddle.nn.L1Loss(reduction='none') output = l1_loss(input, label) print(output) # [[0.20000005 0.19999999] # [0.2 0.79999995]] """ def __init__(self, reduction='mean', name=None): if reduction not in ['sum', 'mean', 'none']: raise ValueError( "The value of 'reduction' in L1Loss should be 'sum', 'mean' or 'none', but " "received %s, which is not allowed." % reduction) super(L1Loss, self).__init__() self.reduction = reduction self.name = name def forward(self, input, label): return paddle.nn.functional.l1_loss( input, label, self.reduction, name=self.name) class BCELoss(fluid.dygraph.Layer): """ This interface is used to construct a callable object of the ``BCELoss`` class. The BCELoss layer measures the binary_cross_entropy loss between input predictions ``input`` and target labels ``label`` . The binary_cross_entropy loss can be described as: If :attr:`weight` is set, the loss is: .. math:: Out = -1 * weight * (label * log(input) + (1 - label) * log(1 - input)) If :attr:`weight` is None, the loss is: .. math:: Out = -1 * (label * log(input) + (1 - label) * log(1 - input)) If :attr:`reduction` set to ``'none'``, the interface will return the original loss `Out`. If :attr:`reduction` set to ``'mean'``, the reduced mean loss is: .. math:: Out = MEAN(Out) If :attr:`reduction` set to ``'sum'``, the reduced sum loss is: .. math:: Out = SUM(Out) Note that the input predictions ``input`` always be the output of sigmoid, and the target labels ``label`` should be numbers between 0 and 1. Parameters: weight (Tensor, optional): A manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch and the data type is float32, float64. Default is ``'None'``. reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the summed loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input (Tensor): 2-D tensor with shape: [N, *], N is batch_size, `*` means number of additional dimensions. The input ``input`` should always be the output of sigmod. Available dtype is float32, float64. label (Tensor): 2-D tensor with the same shape as ``input``. The target labels which values should be numbers between 0 and 1. Available dtype is float32, float64. output (Tensor): If ``reduction`` is ``'none'``, the shape of output is same as ``input`` , else the shape of output is scalar. Returns: A callable object of BCELoss. Examples: .. code-block:: python import numpy as np import paddle input_data = np.array([0.5, 0.6, 0.7]).astype("float32") label_data = np.array([1.0, 0.0, 1.0]).astype("float32") input = paddle.to_tensor(input_data) label = paddle.to_tensor(label_data) bce_loss = paddle.nn.BCELoss() output = bce_loss(input, label) print(output) # [0.65537095] """ def __init__(self, weight=None, reduction='mean', name=None): if reduction not in ['sum', 'mean', 'none']: raise ValueError( "The value of 'reduction' in bce_loss should be 'sum', 'mean' or 'none', but " "received %s, which is not allowed." % reduction) super(BCELoss, self).__init__() self.weight = weight self.reduction = reduction self.name = name def forward(self, input, label): out = paddle.nn.functional.binary_cross_entropy( input, label, self.weight, self.reduction, self.name) return out class NLLLoss(fluid.dygraph.Layer): r""" :alias_main: paddle.nn.NLLLoss :alias: paddle.nn.NLLLoss,paddle.nn.layer.NLLLoss,paddle.nn.layer.loss.NLLLoss This class accepts input and target label and returns negative log likelihood cross error. It is useful to train a classification problem with C classes. The input for the loss is epected to contain log-probabilities of each classes. It has to be a Tensor of size either (batch_size, C) or (batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case. The label for the loss should be a class index in the range [0, C-1] where C is the number of classes. If ignore_index is specified, the specified target value does not contribute to the input gradient. If the optional argument `weight` is provided, it should be a 1D Tensor assigning weight to each of the classed. This is particularly useful when you have an unbalanced training set. The loss is calculated as follows. The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as: .. math:: \ell(x, y) = L = \{l_1,\dots,l_N\}^\\top, \quad l_n = - w_{y_n} x_{n,y_n}, \quad w_{c} = \\text{weight}[c] \cdot \mathbb{1}\{c \\not= \\text{ignore\\_index}\}, where :math:`N` is the batch size. If :attr:`reduction` is not ``'none'`` (default ``'mean'``), then .. math:: \ell(x, y) = \\begin{cases} \\sum_{n=1}^N \\frac{1}{\\sum_{n=1}^N w_{y_n}} l_n, & \\text{if reduction} = \\text{'mean';}\\\\ \\sum_{n=1}^N l_n, & \\text{if reduction} = \\text{'sum'.} \\end{cases} Parameters: weight (Tensor, optional): Weight tensor, a manual rescaling weight given to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise, it treated as if having all ones. the data type is float32, float64, Default is ``'None'``. ignore_index (int64, optional): Specifies a target value that is ignored and does not contribute to the input gradient. reduction (str, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; if `reduction` is ``'sum'``, the reduced sum loss is returned; if `reduction` is ``'none'``, no reduction will be apllied. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes. But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`. The data type is float32, float64. label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`. The data type is int64. output (Tensor): the `negative log likelihood loss` between input `x` and `label`. If `reduction` is `'none'`, the shape is `[N, *]`. If `reduction` is `'sum'` or `'mean'`, the shape is `[1]`. Examples: .. code-block:: python import paddle nll_loss = paddle.nn.loss.NLLLoss() log_softmax = paddle.nn.LogSoftmax(axis=1) input = paddle.to_tensor([[0.88103855, 0.9908683 , 0.6226845 ], [0.53331435, 0.07999352, 0.8549948 ], [0.25879037, 0.39530203, 0.698465 ], [0.73427284, 0.63575995, 0.18827209], [0.05689114, 0.0862954 , 0.6325046 ]], "float32") log_out = log_softmax(input) label = paddle.to_tensor([0, 2, 1, 1, 0], "int64") result = nll_loss(log_out, label) print(result) # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True, [1.07202101]) """ def __init__(self, weight=None, ignore_index=-100, reduction='mean', name=None): if reduction not in ['sum', 'mean', 'none']: raise ValueError( "The value of 'reduction' in nll_loss should be 'sum', 'mean' or " "'none', but received %s, which is not allowed." % reduction) super(NLLLoss, self).__init__() self._weight = weight self._ignore_index = ignore_index self._reduction = reduction self._name = name def forward(self, input, label): return F.nll_loss( input, label, weight=self._weight, ignore_index=self._ignore_index, reduction=self._reduction, name=self._name) class KLDivLoss(fluid.dygraph.Layer): r""" This interface calculates the Kullback-Leibler divergence loss between Input(X) and Input(Target). Notes that Input(X) is the log-probability and Input(Target) is the probability. KL divergence loss is calculated as follows: $$l(x, y) = y * (\log(y) - x)$$ Parameters: reduction (Tensor): Indicate how to average the loss, the candicates are ``'none'`` | ``'batchmean'`` | ``'mean'`` | ``'sum'``. If `reduction` is ``'mean'``, the reduced mean loss is returned; If `reduction` is ``'batchmean'``, the sum loss divided by batch size is returned; if `reduction` is ``'sum'``, the reduced sum loss is returned; if `reduction` is ``'none'``, no reduction will be apllied. Default is ``'mean'``. Shape: - input (Tensor): (N, *), where * means, any number of additional dimensions. - label (Tensor): (N, *), same shape as input. - output (Tensor): tensor with shape: [1] by default. Examples: .. code-block:: python import paddle import numpy as np import paddle.nn as nn shape = (5, 20) x = np.random.uniform(-10, 10, shape).astype('float32') target = np.random.uniform(-10, 10, shape).astype('float32') # 'batchmean' reduction, loss shape will be [1] kldiv_criterion = nn.KLDivLoss(reduction='batchmean') pred_loss = kldiv_criterion(paddle.to_tensor(x), paddle.to_tensor(target)) # shape=[1] # 'mean' reduction, loss shape will be [1] kldiv_criterion = nn.KLDivLoss(reduction='mean') pred_loss = kldiv_criterion(paddle.to_tensor(x), paddle.to_tensor(target)) # shape=[1] # 'sum' reduction, loss shape will be [1] kldiv_criterion = nn.KLDivLoss(reduction='sum') pred_loss = kldiv_criterion(paddle.to_tensor(x), paddle.to_tensor(target)) # shape=[1] # 'none' reduction, loss shape is same with X shape kldiv_criterion = nn.KLDivLoss(reduction='none') pred_loss = kldiv_criterion(paddle.to_tensor(x), paddle.to_tensor(target)) # shape=[5, 20] """ def __init__(self, reduction='mean'): super(KLDivLoss, self).__init__() self.reduction = reduction def forward(self, input, label): out = F.kl_div(input, label, self.reduction) return out class MarginRankingLoss(fluid.dygraph.Layer): r""" This interface is used to construct a callable object of the ``MarginRankingLoss`` class. The MarginRankingLoss layer calculates the margin rank loss between the input, other and label , use the math function as follows. .. math:: margin\_rank\_loss = max(0, -label * (input - other) + margin) If :attr:`reduction` set to ``'mean'``, the reduced mean loss is: .. math:: Out = MEAN(margin\_rank\_loss) If :attr:`reduction` set to ``'sum'``, the reduced sum loss is: .. math:: Out = SUM(margin\_rank\_loss) If :attr:`reduction` set to ``'none'``, just return the origin ``margin_rank_loss``. Parameters: margin (float, optional): The margin value to add, default value is 0; reduction (str, optional): Indicate the reduction to apply to the loss, the candicates are ``'none'``, ``'mean'``, ``'sum'``.If :attr:`reduction` is ``'none'``, the unreduced loss is returned; If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned. If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. Default is ``'mean'``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. other: N-D Tensor, `other` have the same shape and dtype as `input`. label: N-D Tensor, label have the same shape and dtype as `input`. output: If :attr:`reduction` is ``'mean'`` or ``'sum'`` , the out shape is :math:`[1]`, otherwise the shape is the same as `input` .The same dtype as input tensor. Returns: A callable object of MarginRankingLoss. Examples: .. code-block:: python import paddle input = paddle.to_tensor([[1, 2], [3, 4]], dtype="float32") other = paddle.to_tensor([[2, 1], [2, 4]], dtype="float32") label = paddle.to_tensor([[1, -1], [-1, -1]], dtype="float32") margin_rank_loss = paddle.nn.MarginRankingLoss() loss = margin_rank_loss(input, other, label) print(loss) # [0.75] """ def __init__(self, margin=0.0, reduction='mean', name=None): if reduction not in ['sum', 'mean', 'none']: raise ValueError( "The value of 'reduction' in MarginRankingLoss should be 'sum', 'mean' or 'none', but " "received %s, which is not allowed." % reduction) super(MarginRankingLoss, self).__init__() self.margin = margin self.reduction = reduction self.name = name def forward(self, input, other, label): out = paddle.nn.functional.margin_ranking_loss( input, other, label, self.margin, self.reduction, self.name) return out class CTCLoss(fluid.dygraph.Layer): """ An operator integrating the open source Warp-CTC library (https://github.com/baidu-research/warp-ctc) to compute Connectionist Temporal Classification (CTC) loss. It can be aliased as softmax with CTC, since a native softmax activation is interated to the Warp-CTC library to normalize values for each row of the input tensor. Parameters: blank (int, optional): The blank label index of Connectionist Temporal Classification (CTC) loss, which is in the half-opened interval [0, num_classes + 1). The data type must be int32. Default is 0. reduction (string, optional): Indicate how to average the loss, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the output loss will be divided by the label_lengths, and then return the mean of quotient; If :attr:`reduction` is ``'sum'``, return the sum of loss; If :attr:`reduction` is ``'none'``, no reduction will be applied. Default is ``'mean'``. Shape: log_probs (Tensor): The unscaled probability sequence with padding, which is a 3-D Tensor. The tensor shape is [max_logit_length, batch_size, num_classes + 1], where max_logit_length is the longest length of input logit sequence. The data type should be float32 or float64. labels (Tensor): The ground truth sequence with padding, which must be a 3-D Tensor. The tensor shape is [batch_size, max_label_length], where max_label_length is the longest length of label sequence. The data type must be int32. input_lengths (Tensor): The length for each input sequence, it should have shape [batch_size] and dtype int64. label_lengths (Tensor): The length for each label sequence, it should have shape [batch_size] and dtype int64. norm_by_times (bool, default false) – Whether to normalize the gradients by the number of time-step, which is also the sequence’s length. There is no need to normalize the gradients if reduction mode is 'mean'. Returns: Tensor, The Connectionist Temporal Classification (CTC) loss between ``log_probs`` and ``labels``. If attr:`reduction` is ``'none'``, the shape of loss is [batch_size], otherwise, the shape of loss is [1]. Data type is the same as ``log_probs``. Examples: .. code-block:: python # declarative mode import numpy as np import paddle # length of the longest logit sequence max_seq_length = 4 #length of the longest label sequence max_label_length = 3 # number of logit sequences batch_size = 2 # class num class_num = 3 np.random.seed(1) log_probs = np.array([[[4.17021990e-01, 7.20324516e-01, 1.14374816e-04], [3.02332580e-01, 1.46755889e-01, 9.23385918e-02]], [[1.86260208e-01, 3.45560730e-01, 3.96767467e-01], [5.38816750e-01, 4.19194520e-01, 6.85219526e-01]], [[2.04452246e-01, 8.78117442e-01, 2.73875929e-02], [6.70467496e-01, 4.17304814e-01, 5.58689833e-01]], [[1.40386939e-01, 1.98101491e-01, 8.00744593e-01], [9.68261600e-01, 3.13424170e-01, 6.92322612e-01]], [[8.76389146e-01, 8.94606650e-01, 8.50442126e-02], [3.90547849e-02, 1.69830427e-01, 8.78142476e-01]]]).astype("float32") labels = np.array([[1, 2, 2], [1, 2, 2]]).astype("int32") input_lengths = np.array([5, 5]).astype("int64") label_lengths = np.array([3, 3]).astype("int64") log_probs = paddle.to_tensor(log_probs) labels = paddle.to_tensor(labels) input_lengths = paddle.to_tensor(input_lengths) label_lengths = paddle.to_tensor(label_lengths) loss = paddle.nn.CTCLoss(blank=0, reduction='none')(log_probs, labels, input_lengths, label_lengths) print(loss) #[3.9179852 2.9076521] loss = paddle.nn.CTCLoss(blank=0, reduction='mean')(log_probs, labels, input_lengths, label_lengths) print(loss) #[1.1376063] """ def __init__(self, blank=0, reduction='mean'): super(CTCLoss, self).__init__() self.blank = blank self.reduction = reduction def forward(self, log_probs, labels, input_lengths, label_lengths, norm_by_times=False): return paddle.nn.functional.ctc_loss( log_probs, labels, input_lengths, label_lengths, self.blank, self.reduction, norm_by_times=norm_by_times) class SmoothL1Loss(fluid.dygraph.Layer): r""" This operator calculates smooth_l1_loss. Creates a criterion that uses a squared term if the absolute element-wise error falls below 1 and an L1 term otherwise. In some cases it can prevent exploding gradients and it is more robust and less sensitivity to outliers. Also known as the Huber loss: .. math:: loss(x,y) = \\frac{1}{n}\\sum_{i}z_i where z_i is given by: .. math:: \\mathop{z_i} = \\left\\{\\begin{array}{rcl} 0.5(x_i - y_i)^2 & & {if |x_i - y_i| < delta} \\\\ delta * |x_i - y_i| - 0.5 * delta^2 & & {otherwise} \\end{array} \\right. Parameters: reduction (str, optional): Indicate how to average the loss by batch_size, the candicates are ``'none'`` | ``'mean'`` | ``'sum'``. If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned; If :attr:`reduction` is ``'sum'``, the reduced sum loss is returned. If :attr:`reduction` is ``'none'``, the unreduced loss is returned. Default is ``'mean'``. delta (float, optional): Specifies the hyperparameter delta to be used. The value determines how large the errors need to be to use L1. Errors smaller than delta are minimized with L2. Parameter is ignored for negative/zero values. Default = 1.0 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Call Parameters: input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1. label (Tensor): Label tensor, the data type is float32 or float64. The shape of label is the same as the shape of input. Returns: The tensor storing the smooth_l1_loss of input and label. Return type: Tensor. Examples: .. code-block:: python import paddle import numpy as np input_data = np.random.rand(3,3).astype("float32") label_data = np.random.rand(3,3).astype("float32") input = paddle.to_tensor(input_data) label = paddle.to_tensor(label_data) loss = paddle.nn.SmoothL1Loss() output = loss(input, label) print(output) """ def __init__(self, reduction='mean', delta=1.0, name=None): super(SmoothL1Loss, self).__init__() self.reduction = reduction self.delta = delta self.name = name def forward(self, input, label): return F.smooth_l1_loss( input, label, reduction=self.reduction, delta=self.delta, name=self.name)

41.656953

403

0.589246

9a53e42f5c7bf6242872577e9fdbe58ab9869727

29,916

py

Python

core/platform/search/gae_search_services_test.py

steve7158/oppia

e2cae72fa5d3503c64d195f09d3460507697730c

[ "Apache-2.0" ]

2

2018-12-14T05:46:31.000Z

2019-01-04T21:52:44.000Z

core/platform/search/gae_search_services_test.py

steve7158/oppia

e2cae72fa5d3503c64d195f09d3460507697730c

[ "Apache-2.0" ]

5

2018-06-09T02:05:45.000Z

2018-09-20T13:53:42.000Z

core/platform/search/gae_search_services_test.py

steve7158/oppia

e2cae72fa5d3503c64d195f09d3460507697730c

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2014 The Oppia Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an 'AS-IS' BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for the appengine search api wrapper.""" import datetime import time from core.platform.search import gae_search_services from core.tests import test_utils from google.appengine.api import search class SearchAddToIndexTests(test_utils.GenericTestBase): """Test inserting documents into search indexes.""" def test_insert_document_with_id(self): date = datetime.date(year=2015, month=3, day=14) datetime_value = datetime.datetime( year=1991, month=6, day=20, hour=16, minute=30, second=14) doc_id = 'abcdefghijklmnop' doc = { 'id': doc_id, 'numberfield': 5, 'stringfield': 'abc', 'datefield': date, 'datetimefield': datetime_value } result = gae_search_services.add_documents_to_index([doc], 'my_index') self.assertEqual(result, [doc_id]) result_doc = search.Index('my_index').get(doc_id) self.assertEqual(result_doc.doc_id, doc_id) self.assertEqual(result_doc.field('numberfield').value, 5) self.assertEqual(result_doc.field('stringfield').value, 'abc') # The search api returns date fields as datetime fields with # time at midnight. self.assertEqual( result_doc.field('datefield').value, datetime.datetime.combine( date=date, time=datetime.datetime.min.time())) self.assertEqual( result_doc.field('datetimefield').value, datetime_value) def test_insert_document_without_id(self): doc = {'abc': 'def'} result = gae_search_services.add_documents_to_index([doc], 'my_index') retrieved_doc = search.Index('my_index').get(result[0]) self.assertEqual(retrieved_doc.field('abc').value, 'def') def test_insert_multiple_with_id(self): docs = [{'id': 'id%d' % n, 'name': 'doc%d' % n} for n in range(5)] result = gae_search_services.add_documents_to_index(docs, 'my_index') index = search.Index('my_index') for ind in range(5): retrieved_doc = index.get('id%d' % ind) self.assertEqual(retrieved_doc.field('name').value, 'doc%d' % ind) self.assertEqual(result[ind], 'id%d' % ind) def test_insert_document_with_multi_valued_property(self): doc = {'id': 'doc', 'prop': ['val1', 'val2', 'val3']} gae_search_services.add_documents_to_index([doc], 'index') resultdoc = search.Index('index').get('doc') values = set([field.value for field in resultdoc['prop']]) self.assertEqual(values, set(['val1', 'val2', 'val3'])) def test_disallow_unsuported_value_types(self): with self.assertRaises(ValueError): doc = {'abc': set('xyz')} gae_search_services.add_documents_to_index(doc, 'my_index') def test_add_document_with_rank(self): doc = {'id': 'my_doc', 'field': 'value', 'rank': 42} gae_search_services.add_documents_to_index([doc], 'my_index') index = search.Index('my_index') self.assertEqual(index.get('my_doc').rank, 42) def test_add_document_with_existing_id_updates_it(self): doc1 = {'id': 'doc', 'version': 1, 'rank': 10} doc2 = {'id': 'doc', 'version': 2, 'rank': 20} gae_search_services.add_documents_to_index([doc1], 'my_index') index = search.Index('my_index') self.assertEqual(index.get('doc').field('version').value, 1) self.assertEqual(index.get('doc').rank, 10) gae_search_services.add_documents_to_index([doc2], 'my_index') self.assertEqual(index.get('doc').field('version').value, 2) self.assertEqual(index.get('doc').rank, 20) def test_validate_list_values(self): doc1 = {'f': ['a', 'b', ['c', 'd']]} doc2 = {'f': ['a', 'b', 3, set([4, 5, 6])]} # The str() of list and set are passed in to ensure that we mention the # type the user passed in, in our error message.. with self.assertRaisesRegexp(ValueError, str(list)): gae_search_services.add_documents_to_index([doc1], 'my_index') with self.assertRaisesRegexp(ValueError, str(set)): gae_search_services.add_documents_to_index([doc2], 'my_index') def test_index_must_be_string(self): index = search.Index('test') # Check that the error message mentions the type the user passed in. with self.assertRaisesRegexp(ValueError, str(type(index))): gae_search_services.add_documents_to_index( {'id': 'one', 'key': 'value'}, index) def _get_put_error(self, num_res, transient=None): """returns a PutError. with num_res results. If transient is given, it should be an index in the results array. The result at that index will have a transient error code. """ non_trans_code = search.OperationResult.INVALID_REQUEST trans_code = search.OperationResult.TRANSIENT_ERROR results = [ search.PutResult(code=non_trans_code) for _ in range(num_res)] if transient is not None: results[transient] = search.PutResult(code=trans_code) return search.PutError('lol', results) def test_use_default_num_retries(self): doc = {'id': 'doc', 'prop': 'val'} exception = self._get_put_error(1, transient=0) failing_put = test_utils.FailingFunction( search.Index.put, exception, gae_search_services.DEFAULT_NUM_RETRIES, ) add_docs_counter = test_utils.CallCounter( gae_search_services.add_documents_to_index) put_ctx = self.swap(search.Index, 'put', failing_put) add_docs_ctx = self.swap( gae_search_services, 'add_documents_to_index', add_docs_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with put_ctx, add_docs_ctx, assert_raises_ctx as context_mgr: gae_search_services.add_documents_to_index([doc], 'my_index') self.assertEqual(context_mgr.exception.original_exception, exception) self.assertEqual( add_docs_counter.times_called, gae_search_services.DEFAULT_NUM_RETRIES) def test_use_custom_number_of_retries(self): doc = {'id': 'doc', 'prop': 'val'} exception = self._get_put_error(1, transient=0) failing_put = test_utils.FailingFunction( search.Index.put, exception, 42) add_docs_counter = test_utils.CallCounter( gae_search_services.add_documents_to_index) put_ctx = self.swap(search.Index, 'put', failing_put) add_docs_ctx = self.swap( gae_search_services, 'add_documents_to_index', add_docs_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with put_ctx, add_docs_ctx, assert_raises_ctx: gae_search_services.add_documents_to_index( [doc], 'my_index', retries=42) self.assertEqual(add_docs_counter.times_called, 42) def test_arguments_are_preserved_in_retries(self): doc = {'id': 'doc', 'prop': 'val'} exception = self._get_put_error(1, transient=0) failing_put = test_utils.FailingFunction( search.Index.put, exception, 3 ) add_docs_counter = test_utils.CallCounter( gae_search_services.add_documents_to_index) put_ctx = self.swap(search.Index, 'put', failing_put) add_docs_ctx = self.swap( gae_search_services, 'add_documents_to_index', add_docs_counter) with put_ctx, add_docs_ctx: gae_search_services.add_documents_to_index( [doc], 'my_index', retries=4) self.assertEqual(add_docs_counter.times_called, 4) result = search.Index('my_index').get('doc') self.assertEqual(result.field('prop').value, 'val') def test_put_error_with_transient_result(self): docs = [{'id': 'doc1', 'prop': 'val1'}, {'id': 'doc2', 'prop': 'val2'}, {'id': 'doc3', 'prop': 'val3'}] error = self._get_put_error(3, transient=1) failing_put = test_utils.FailingFunction( search.Index.put, error, 4) add_docs_counter = test_utils.CallCounter( gae_search_services.add_documents_to_index) put_ctx = self.swap(search.Index, 'put', failing_put) add_docs_ctx = self.swap( gae_search_services, 'add_documents_to_index', add_docs_counter) with put_ctx, add_docs_ctx: gae_search_services.add_documents_to_index( docs, 'my_index', retries=5) self.assertEqual(add_docs_counter.times_called, 5) for i in xrange(1, 4): result = search.Index('my_index').get('doc' + str(i)) self.assertEqual(result.field('prop').value, 'val' + str(i)) def test_put_error_without_transient_result(self): docs = [{'id': 'doc1', 'prop': 'val1'}, {'id': 'doc2', 'prop': 'val2'}, {'id': 'doc3', 'prop': 'val3'}] error = self._get_put_error(3) failing_put = test_utils.FailingFunction(search.Index.put, error, 1) add_docs_counter = test_utils.CallCounter( gae_search_services.add_documents_to_index) add_docs_ctx = self.swap( gae_search_services, 'add_documents_to_index', add_docs_counter) put_ctx = self.swap(search.Index, 'put', failing_put) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with add_docs_ctx, put_ctx, assert_raises_ctx as e: gae_search_services.add_documents_to_index(docs, 'my_index') # Assert that the method only gets called once, since the error is not # transient. self.assertEqual(add_docs_counter.times_called, 1) self.assertEqual(e.exception.original_exception, error) class SearchRemoveFromIndexTests(test_utils.GenericTestBase): """Test deleting documents from search indexes.""" def test_delete_single_document(self): doc = search.Document(doc_id='doc_id', fields=[ search.TextField(name='k', value='v')]) index = search.Index('my_index') index.put([doc]) gae_search_services.delete_documents_from_index(['doc_id'], 'my_index') self.assertIsNone(index.get('doc_id')) def test_delete_multiple_documents(self): index = search.Index('my_index') for i in xrange(10): field = search.TextField(name='k', value='v%d' % i) doc = search.Document(doc_id='doc%d' % i, fields=[field]) index.put([doc]) gae_search_services.delete_documents_from_index( ['doc' + str(i) for i in xrange(10)], 'my_index') for i in xrange(10): self.assertIsNone(index.get('doc%d' % i)) def test_doc_ids_must_be_strings(self): with self.assertRaisesRegexp(ValueError, str(dict)): gae_search_services.delete_documents_from_index( ['d1', {'id': 'd2'}], 'index') def test_index_must_be_string(self): with self.assertRaises(ValueError): gae_search_services.delete_documents_from_index( ['doc_id'], search.Index('ind')) def _get_delete_error(self, num_res, transient=None): """returns a DeleteError. with num_res results. If transient is given, it should be an index in the results array. The result at that index will have a transient error code. """ non_trans_code = search.OperationResult.INVALID_REQUEST trans_code = search.OperationResult.TRANSIENT_ERROR results = [ search.DeleteResult(code=non_trans_code) for _ in range(num_res)] if transient is not None: results[transient] = search.PutResult(code=trans_code) return search.DeleteError('lol', results=results) def test_use_default_num_retries(self): exception = self._get_delete_error(1, transient=0) failing_delete = test_utils.FailingFunction( search.Index.delete, exception, gae_search_services.DEFAULT_NUM_RETRIES ) delete_docs_counter = test_utils.CallCounter( gae_search_services.delete_documents_from_index) delete_ctx = self.swap(search.Index, 'delete', failing_delete) delete_docs_ctx = self.swap( gae_search_services, 'delete_documents_from_index', delete_docs_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with delete_ctx, delete_docs_ctx, assert_raises_ctx as context_mgr: gae_search_services.delete_documents_from_index( ['doc'], 'my_index') self.assertEqual(context_mgr.exception.original_exception, exception) self.assertEqual( delete_docs_counter.times_called, gae_search_services.DEFAULT_NUM_RETRIES) def test_use_custom_number_of_retries(self): exception = self._get_delete_error(1, transient=0) failing_delete = test_utils.FailingFunction( search.Index.delete, exception, 42) delete_docs_counter = test_utils.CallCounter( gae_search_services.delete_documents_from_index) delete_ctx = self.swap(search.Index, 'delete', failing_delete) delete_docs_ctx = self.swap( gae_search_services, 'delete_documents_from_index', delete_docs_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with delete_ctx, delete_docs_ctx, assert_raises_ctx: gae_search_services.delete_documents_from_index( ['id'], 'index', retries=42) self.assertEqual(delete_docs_counter.times_called, 42) def test_arguments_are_preserved_in_retries(self): index = search.Index('index') index.put([search.Document(doc_id='doc', fields=[ search.TextField(name='prop', value='val') ])]) exception = self._get_delete_error(1, transient=0) failing_delete = test_utils.FailingFunction( search.Index.delete, exception, 3) delete_docs_counter = test_utils.CallCounter( gae_search_services.delete_documents_from_index) index_ctx = self.swap(search.Index, 'delete', failing_delete) delete_docs_ctx = self.swap( gae_search_services, 'delete_documents_from_index', delete_docs_counter) with index_ctx, delete_docs_ctx: gae_search_services.delete_documents_from_index( ['doc'], 'index', retries=4) self.assertEqual(delete_docs_counter.times_called, 4) result = search.Index('my_index').get('doc') self.assertIsNone(result) def test_delete_error_with_transient_result(self): error = self._get_delete_error(3, transient=1) failing_delete = test_utils.FailingFunction( search.Index.delete, error, 4) delete_docs_counter = test_utils.CallCounter( gae_search_services.delete_documents_from_index) index = search.Index('my_index') for i in xrange(3): index.put(search.Document(doc_id='d' + str(i), fields=[ search.TextField(name='prop', value='value') ])) delete_ctx = self.swap(search.Index, 'delete', failing_delete) delete_docs_ctx = self.swap( gae_search_services, 'delete_documents_from_index', delete_docs_counter) with delete_ctx, delete_docs_ctx: gae_search_services.delete_documents_from_index( ['d0', 'd1', 'd2'], 'my_index', retries=5) self.assertEqual(delete_docs_counter.times_called, 5) for i in xrange(3): result = search.Index('my_index').get('doc' + str(i)) self.assertIsNone(result) def test_put_error_without_transient_result(self): error = self._get_delete_error(3) delete_spy = test_utils.FailingFunction(search.Index.delete, error, 1) delete_docs_counter = test_utils.CallCounter( gae_search_services.delete_documents_from_index) delete_docs_ctx = self.swap( gae_search_services, 'delete_documents_from_index', delete_docs_counter) delete_ctx = self.swap(search.Index, 'delete', delete_spy) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with delete_docs_ctx, delete_ctx, assert_raises_ctx as e: gae_search_services.delete_documents_from_index( ['a', 'b', 'c'], 'my_index') # Assert that the method only gets called once, since the error is not # transient. self.assertEqual(delete_docs_counter.times_called, 1) self.assertEqual(e.exception.original_exception, error) class SearchQueryTests(test_utils.GenericTestBase): """Test searching for documents in an index.""" def test_search_all_documents(self): doc1 = search.Document(doc_id='doc1', language='en', rank=1, fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', language='en', rank=2, fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', language='en', rank=3, fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) result = gae_search_services.search('k:abc', 'my_index')[0] self.assertIn({ 'id': 'doc1', 'k': 'abc def ghi', 'rank': 1, 'language_code': 'en' }, result) self.assertIn({ 'id': 'doc2', 'k': 'abc jkl mno', 'rank': 2, 'language_code': 'en' }, result) self.assertIn({ 'id': 'doc3', 'k': 'abc jkl ghi', 'rank': 3, 'language_code': 'en' }, result) def test_respect_search_query(self): doc1 = search.Document(doc_id='doc1', rank=1, language='en', fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', rank=1, language='en', fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', rank=1, language='en', fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) result = gae_search_services.search('k:jkl', 'my_index')[0] self.assertNotIn({ 'id': 'doc1', 'k': 'abc def ghi', 'language_code': 'en', 'rank': 1 }, result) self.assertIn({ 'id': 'doc2', 'k': 'abc jkl mno', 'language_code': 'en', 'rank': 1 }, result) self.assertIn({ 'id': 'doc3', 'k': 'abc jkl ghi', 'language_code': 'en', 'rank': 1 }, result) def test_respect_limit(self): doc1 = search.Document(doc_id='doc1', fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) result = gae_search_services.search('k:abc', 'my_index', limit=2)[0] self.assertEqual(len(result), 2) def test_use_cursor(self): doc1 = search.Document(doc_id='doc1', language='en', rank=1, fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', language='en', rank=1, fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', language='en', rank=1, fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) result1, cursor = gae_search_services.search( 'k:abc', 'my_index', limit=2) result2, cursor = gae_search_services.search( 'k:abc', 'my_index', cursor=cursor) self.assertEqual(len(result1), 2) self.assertEqual(len(result2), 1) dict1 = {'id': 'doc1', 'k': 'abc def ghi', 'language_code': 'en', 'rank': 1} self.assertIn(dict1, result1 + result2) dict2 = {'id': 'doc2', 'k': 'abc jkl mno', 'language_code': 'en', 'rank': 1} self.assertIn(dict2, result1 + result2) dict3 = {'id': 'doc3', 'k': 'abc jkl ghi', 'language_code': 'en', 'rank': 1} self.assertIn(dict3, result1 + result2) def test_ids_only(self): doc1 = search.Document(doc_id='doc1', fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) result = gae_search_services.search( 'k:abc', 'my_index', ids_only=True)[0] self.assertIn('doc1', result) self.assertIn('doc2', result) self.assertIn('doc3', result) def test_cursor_is_none_if_no_more_results(self): doc1 = search.Document(doc_id='doc1', fields=[ search.TextField(name='k', value='abc def ghi')]) doc2 = search.Document(doc_id='doc2', fields=[ search.TextField(name='k', value='abc jkl mno')]) doc3 = search.Document(doc_id='doc3', fields=[ search.TextField(name='k', value='abc jkl ghi')]) index = search.Index('my_index') index.put([doc1, doc2, doc3]) cursor = gae_search_services.search('k:abc', 'my_index')[1] self.assertIsNone(cursor) def test_default_rank_is_descending_date(self): # Time is only saved with 1 second accuracy, # so I'm putting a 1 second delay between puts. dict1 = {'id': 'doc1', 'k': 'abc def'} dict2 = {'id': 'doc2', 'k': 'abc ghi'} dict3 = {'id': 'doc3', 'k': 'abc jkl'} gae_search_services.add_documents_to_index([dict1], 'my_index') time.sleep(1) gae_search_services.add_documents_to_index([dict2], 'my_index') time.sleep(1) gae_search_services.add_documents_to_index([dict3], 'my_index') result = gae_search_services.search( 'k:abc', index='my_index', ids_only=True)[0] self.assertEqual(result, ['doc3', 'doc2', 'doc1']) def test_search_with_custom_rank_and_language(self): doc1 = {'id': 'doc1', 'k': 'abc def', 'rank': 3, 'language_code': 'en'} doc2 = {'id': 'doc2', 'k': 'abc ghi', 'rank': 1, 'language_code': 'fr'} doc3 = {'id': 'doc3', 'k': 'abc jkl', 'rank': 2, 'language_code': 'nl'} gae_search_services.add_documents_to_index([doc1, doc2, doc3], 'index') result = gae_search_services.search('k:abc', index='index')[0] self.assertEqual(result, [doc1, doc3, doc2]) def test_search_using_single_sort_expression(self): doc1 = {'id': 'doc1', 'k': 'abc ghi'} doc2 = {'id': 'doc2', 'k': 'abc def'} doc3 = {'id': 'doc3', 'k': 'abc jkl'} gae_search_services.add_documents_to_index([doc1, doc2, doc3], 'index') result = gae_search_services.search('k:abc', 'index', sort='+k')[0] self.assertEqual(result[0].get('id'), 'doc2') self.assertEqual(result[1].get('id'), 'doc1') self.assertEqual(result[2].get('id'), 'doc3') result = gae_search_services.search('k:abc', 'index', sort='-k')[0] self.assertEqual(result[0].get('id'), 'doc3') self.assertEqual(result[1].get('id'), 'doc1') self.assertEqual(result[2].get('id'), 'doc2') def test_search_using_multiple_sort_expressions(self): doc1 = {'id': 'doc1', 'k1': 2, 'k2': 'abc ghi'} doc2 = {'id': 'doc2', 'k1': 1, 'k2': 'abc def'} doc3 = {'id': 'doc3', 'k1': 1, 'k2': 'abc jkl'} gae_search_services.add_documents_to_index([doc1, doc2, doc3], 'index') result = gae_search_services.search( 'k2:abc', 'index', sort='+k1 -k2')[0] self.assertEqual(result[0].get('id'), 'doc3') self.assertEqual(result[1].get('id'), 'doc2') self.assertEqual(result[2].get('id'), 'doc1') def test_use_default_num_retries(self): exception = search.TransientError('oops') failing_index_search = test_utils.FailingFunction( search.Index.search, exception, gae_search_services.DEFAULT_NUM_RETRIES) search_counter = test_utils.CallCounter(gae_search_services.search) search_ctx = self.swap(search.Index, 'search', failing_index_search) search_counter_ctx = self.swap( gae_search_services, 'search', search_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with search_ctx, search_counter_ctx, assert_raises_ctx as context_mgr: gae_search_services.search('query', 'my_index') self.assertEqual(context_mgr.exception.original_exception, exception) self.assertEqual( search_counter.times_called, gae_search_services.DEFAULT_NUM_RETRIES) def test_use_custom_number_of_retries(self): exception = search.TransientError('oops') failing_index_search = test_utils.FailingFunction( search.Index.search, exception, 3) search_counter = test_utils.CallCounter(gae_search_services.search) index_ctx = self.swap(search.Index, 'search', failing_index_search) search_counter_ctx = self.swap( gae_search_services, 'search', search_counter) assert_raises_ctx = self.assertRaises( gae_search_services.SearchFailureError) with index_ctx, search_counter_ctx, assert_raises_ctx: gae_search_services.search('query', 'my_index', retries=3) self.assertEqual(search_counter.times_called, 3) def test_arguments_are_preserved_in_retries(self): for i in xrange(3): doc = search.Document(doc_id='doc%d' % i, fields=[ search.TextField('prop', 'val'), search.NumberField('index', i) ]) search.Index('my_index').put(doc) exception = search.TransientError('oops') failing_index_search = test_utils.FailingFunction( search.Index.search, exception, 3) search_counter = test_utils.CallCounter(gae_search_services.search) gae_search_ctx = self.swap( search.Index, 'search', failing_index_search) search_counter_ctx = self.swap( gae_search_services, 'search', search_counter) with gae_search_ctx, search_counter_ctx: result, cursor = gae_search_services.search( 'prop:val', 'my_index', sort='-index', limit=2, ids_only=True, retries=4) failing_index_search2 = test_utils.FailingFunction( search.Index.search, exception, 3) search_counter2 = test_utils.CallCounter(gae_search_services.search) gae_search_ctx2 = self.swap( search.Index, 'search', failing_index_search2) search_counter_ctx2 = self.swap( gae_search_services, 'search', search_counter2) with gae_search_ctx2, search_counter_ctx2: result2, cursor = gae_search_services.search( 'prop:val', 'my_index', sort='-index', limit=2, cursor=cursor, ids_only=True, retries=4) self.assertEqual(search_counter.times_called, 4) self.assertEqual(result, ['doc2', 'doc1']) # Also check that the cursor is preserved. self.assertEqual(search_counter2.times_called, 4) self.assertEqual(result2, ['doc0']) class SearchGetFromIndexTests(test_utils.GenericTestBase): def test_get_document_from_index(self): document = search.Document(doc_id='my_doc', fields=[ search.TextField(name='my_field', value='value') ]) search.Index('my_index').put(document) result = gae_search_services.get_document_from_index( 'my_doc', 'my_index') self.assertEqual(result.get('id'), 'my_doc') self.assertEqual(result.get('my_field'), 'value')

42.314003

79

0.623813

f4a9328cbae72f8ad4383d107f73cfd06c30a1c1

3,928

py

Python

MaxLatestVersion/gettoken.py

getlove555/getbotline

639e157495849e12ac7dd4bae6012841cf511892

[ "MIT" ]

6

2020-05-23T21:47:52.000Z

2021-03-30T00:19:08.000Z

MaxLatestVersion/gettoken.py

getlove555/getbotline

639e157495849e12ac7dd4bae6012841cf511892

[ "MIT" ]

4

2020-08-01T10:10:14.000Z

2021-01-03T00:55:05.000Z

MaxLatestVersion/gettoken.py

LOUREN03/lourenelle

5448a8634d438f35df98e43ad135f232cf74d2b1

[ "MIT" ]

20

2020-05-11T08:53:30.000Z

2021-07-16T09:50:20.000Z

from thrift.protocol import TCompactProtocol from thrift.transport import THttpClient from TEAM_BOT_MAX.ttypes import IdentityProvider, LoginResultType, LoginRequest, LoginType import json, requests, livejson, LineService def loggedIn(func): def checkLogin(*args, **kwargs): if args[0].isLogin: return func(*args, **kwargs) else: args[0].callback.other('You want to call the function, you must login to TEAMBOTMAXv2') return checkLogin class getToken: isLogin = False def __init__(self): self.isLogin = True @loggedIn def DESKTOPMAC(self): Headers = { 'User-Agent': "Line/8.3.2", 'X-Line-Application': "DESKTOPMAC\t5.10.0\tRynTokens\tTools\t10.13.2", "x-lal": "ja-US_US", } return Headers @loggedIn def DESKTOPWIN(self): Headers = { 'User-Agent': "Line/8.3.2", 'X-Line-Application': "DESKTOPWIN\t5.10.0\tRynTokens\tTools\t10.13.2", "x-lal": "ja-US_US", } return Headers @loggedIn def IOSIPAD(self): Headers = { 'User-Agent': "Line/8.3.2", 'X-Line-Application': "IOSIPAD\t8.14.2\tRynTokens\tTools\t11.2.5", "x-lal": "ja-US_US", } return Headers @loggedIn def CHROMEOS(self): Headers = { 'User-Agent': "Line/8.3.2", 'X-Line-Application': "CHROMEOS\t2.1.5\tRynTokens\tTools\t11.2.5", "x-lal": "ja-US_US", } return Headers @loggedIn def WIN10(self): Headers = { 'User-Agent': "Line/8.3.2", 'X-Line-Application': "WIN10\t5.5.5\tRynTokens\tTools\t11.2.5", "x-lal": "ja-US_US", } return Headers @loggedIn def token(self,to,token,msg_id,sender,nametoken): try: a = token a.update({'x-lpqs' : '/api/v4/TalkService.do'}) transport = THttpClient.THttpClient('https://gd2.line.naver.jp/api/v4/TalkService.do') transport.setCustomHeaders(a) protocol = TCompactProtocol.TCompactProtocol(transport) clienttoken = LineService.Client(protocol) qr = clienttoken.getAuthQrcode(keepLoggedIn=1, systemName='RynTokens') link = "line://au/q/" + qr.verifier #self.sendReplyMessage(msg_id, to, "Click This Link Only For 2 Minute :)\n\n{}".format(link)) data = { "type": "template", "altText": "Token", "template": { "type": "buttons", "title": "Token %s" % nametoken, "text": "Click This Button\nOnly For 2 Minutes", "actions": [ { "type": "uri", "label": "Click Me", "uri": link }, { "type": "uri", "label": "Link ?", "uri": 'line://app/1603968955-ORWb9RdY/?type=text&text=%s' % link } ] } } self.postTemplate(to, data) a.update({"x-lpqs" : '/api/v4/TalkService.do', 'X-Line-Access': qr.verifier}) json.loads(requests.session().get('https://gd2.line.naver.jp/Q', headers=a).text) a.update({'x-lpqs' : '/api/v4p/rs'}) transport = THttpClient.THttpClient('https://gd2.line.naver.jp/api/v4p/rs') transport.setCustomHeaders(a) protocol = TCompactProtocol.TCompactProtocol(transport) clienttoken = LineService.Client(protocol) req = LoginRequest() req.type = 1 req.verifier = qr.verifier req.e2eeVersion = 1 res = clienttoken.loginZ(req) try: settings = livejson.File('setting.json', True, True, 4) settings['token']['token'] = res.authToken settings['token']['status'] = True self.sendMessage(to, 'Success get your token,\nCek Your Private Chat') except Exception as e: self.sendMessage(to, str(e)) except Exception as error: self.sendMessage(to, "Login Kampank") self.sendMessage(to, str(error))

32.46281

102

0.581721

397db2baaf5a72e97303d97bab28509dd6d9d086

6,018

py

Python

tests/test_run.py

fingul/psdash

67638b8df867cf32868b282d574e54945f677ee3

[ "CC0-1.0" ]

null

tests/test_run.py

fingul/psdash

67638b8df867cf32868b282d574e54945f677ee3

[ "CC0-1.0" ]

null

tests/test_run.py

fingul/psdash

67638b8df867cf32868b282d574e54945f677ee3

[ "CC0-1.0" ]

null

import os from psdash.run import PsDashRunner from psdash.node import LocalNode import gevent import socket import unittest import tempfile import time class TestRunner(unittest.TestCase): def test_args_log(self): _, filename = tempfile.mkstemp() r = PsDashRunner(args=['-l', filename]) self.assertEqual(r.app.config['PSDASH_LOGS'][0], filename) def test_args_bind(self): r = PsDashRunner(args=['-b', '10.0.0.1']) self.assertEqual(r.app.config['PSDASH_BIND_HOST'], '10.0.0.1') def test_args_port(self): r = PsDashRunner(args=['-p', '5555']) self.assertEqual(r.app.config['PSDASH_PORT'], 5555) def test_args_debug(self): r = PsDashRunner(args=['-d']) self.assertTrue(r.app.debug) def test_default_args_dont_override_config(self): _, filename = tempfile.mkstemp() with open(filename, "w") as f: f.write("PSDASH_LOGS = ['/var/log/boot.log', '/var/log/dmesg']\n") f.flush() os.environ['PSDASH_CONFIG'] = filename r = PsDashRunner() self.assertEquals(r.app.config['PSDASH_LOGS'], ['/var/log/boot.log', '/var/log/dmesg']) del os.environ['PSDASH_CONFIG'] def test_reload_logs(self): _, filename = tempfile.mkstemp() r = PsDashRunner(args=['-l', filename]) pre_count = len(r.get_local_node().logs.available) r.get_local_node().logs.add_patterns(r.app.config['PSDASH_LOGS']) post_count = len(r.get_local_node().logs.available) self.assertEqual(pre_count, post_count) def test_update_net_io_counters(self): r = PsDashRunner() socket.getaddrinfo('example.org', 80) counters = r.get_local_node().net_io_counters.update() for c in counters.itervalues(): if c['rx_per_sec'] > 0 and c['tx_per_sec'] > 0: break else: self.fail("Didn't find any changed network interface") def test_local_node_is_added(self): r = PsDashRunner() self.assertIsInstance(r.get_local_node(), LocalNode) def test_register_node_creates_proper_node_dict(self): r = PsDashRunner() now = int(time.time()) node = r.register_node('examplehost', 'example.org', 5000) self.assertEqual(node.host, 'example.org') self.assertEqual(node.port, 5000) self.assertEqual(node.last_registered, now) def test_reregister_node(self): r = PsDashRunner() now = int(time.time()) r.register_node('examplehost', 'example.org', 5000) node = r.register_node('examplehost', 'example.org', 5000) self.assertEqual(node.host, 'example.org') self.assertEqual(node.port, 5000) self.assertEqual(node.last_registered, now) def test_get_all_nodes(self): r = PsDashRunner() r.register_node('examplehost', 'example.org', 5000) self.assertEqual(len(r.get_nodes()), 2) # local + registered def test_nodes_from_config(self): config = { 'PSDASH_NODES': [ { 'name': 'test-node', 'host': 'remotehost.org', 'port': 5000 } ] } r = PsDashRunner(config) self.assertEqual(len(r.get_nodes()), 2) self.assertIn('remotehost.org:5000', r.get_nodes()) self.assertEqual(r.get_nodes()['remotehost.org:5000'].name, 'test-node') self.assertEqual(r.get_nodes()['remotehost.org:5000'].host, 'remotehost.org') self.assertEqual(r.get_nodes()['remotehost.org:5000'].port, 5000) def test_register_agent(self): jobs = [] agent_options = { 'PSDASH_AGENT': True, 'PSDASH_PORT': 5001, 'PSDASH_REGISTER_TO': 'http://localhost:5000', 'PSDASH_REGISTER_AS': 'the_agent' } r = PsDashRunner() agent = PsDashRunner(agent_options) jobs.append(gevent.spawn(r.run)) gevent.sleep(0.3) jobs.append(gevent.spawn(agent.run)) gevent.sleep(0.3) self.assertIn('127.0.0.1:5001', r.get_nodes()) self.assertEquals(r.get_node('127.0.0.1:5001').name, 'the_agent') self.assertEquals(r.get_node('127.0.0.1:5001').port, 5001) r.server.close() agent.server.close() gevent.killall(jobs) def test_register_agent_without_name_defaults_to_hostname(self): agent_options = { 'PSDASH_AGENT': True, 'PSDASH_PORT': 5001, 'PSDASH_REGISTER_TO': 'http://localhost:5000' } r = PsDashRunner() agent = PsDashRunner(agent_options) jobs = [] jobs.append(gevent.spawn(r.run)) gevent.sleep(0.3) jobs.append(gevent.spawn(agent.run)) gevent.sleep(0.3) self.assertIn('127.0.0.1:5001', r.get_nodes()) self.assertEquals(r.get_node('127.0.0.1:5001').name, socket.gethostname()) self.assertEquals(r.get_node('127.0.0.1:5001').port, 5001) r.server.close() agent.server.close() gevent.killall(jobs) def test_register_agent_to_auth_protected_host(self): r = PsDashRunner({ 'PSDASH_AUTH_USERNAME': 'user', 'PSDASH_AUTH_PASSWORD': 'pass' }) agent = PsDashRunner({ 'PSDASH_AGENT': True, 'PSDASH_PORT': 5001, 'PSDASH_REGISTER_TO': 'http://localhost:5000', 'PSDASH_AUTH_USERNAME': 'user', 'PSDASH_AUTH_PASSWORD': 'pass' }) jobs = [] jobs.append(gevent.spawn(r.run)) gevent.sleep(0.3) jobs.append(gevent.spawn(agent.run)) gevent.sleep(0.3) self.assertIn('127.0.0.1:5001', r.get_nodes()) self.assertEquals(r.get_node('127.0.0.1:5001').name, socket.gethostname()) self.assertEquals(r.get_node('127.0.0.1:5001').port, 5001) r.server.close() agent.server.close() gevent.killall(jobs)

34.988372

95

0.599202

3fdd50d19c7d5aa1fd7a245a82630fddd521c667

16,984

py

Python

torchvision/datasets/video_utils.py

jdsgomes/vision

c890a7e75ebeaaa75ae9ace4c203b7fc145df068

[ "BSD-3-Clause" ]

1

2022-02-14T09:16:02.000Z

torchvision/datasets/video_utils.py

jdsgomes/vision

c890a7e75ebeaaa75ae9ace4c203b7fc145df068

[ "BSD-3-Clause" ]

null

torchvision/datasets/video_utils.py

jdsgomes/vision

c890a7e75ebeaaa75ae9ace4c203b7fc145df068

[ "BSD-3-Clause" ]

null

import bisect import math import warnings from fractions import Fraction from typing import Any, Dict, List, Optional, Callable, Union, Tuple, TypeVar, cast import torch from torchvision.io import ( _probe_video_from_file, _read_video_from_file, read_video, read_video_timestamps, ) from .utils import tqdm T = TypeVar("T") def pts_convert(pts: int, timebase_from: Fraction, timebase_to: Fraction, round_func: Callable = math.floor) -> int: """convert pts between different time bases Args: pts: presentation timestamp, float timebase_from: original timebase. Fraction timebase_to: new timebase. Fraction round_func: rounding function. """ new_pts = Fraction(pts, 1) * timebase_from / timebase_to return round_func(new_pts) def unfold(tensor: torch.Tensor, size: int, step: int, dilation: int = 1) -> torch.Tensor: """ similar to tensor.unfold, but with the dilation and specialized for 1d tensors Returns all consecutive windows of `size` elements, with `step` between windows. The distance between each element in a window is given by `dilation`. """ if tensor.dim() != 1: raise ValueError(f"tensor should have 1 dimension instead of {tensor.dim()}") o_stride = tensor.stride(0) numel = tensor.numel() new_stride = (step * o_stride, dilation * o_stride) new_size = ((numel - (dilation * (size - 1) + 1)) // step + 1, size) if new_size[0] < 1: new_size = (0, size) return torch.as_strided(tensor, new_size, new_stride) class _VideoTimestampsDataset: """ Dataset used to parallelize the reading of the timestamps of a list of videos, given their paths in the filesystem. Used in VideoClips and defined at top level so it can be pickled when forking. """ def __init__(self, video_paths: List[str]) -> None: self.video_paths = video_paths def __len__(self) -> int: return len(self.video_paths) def __getitem__(self, idx: int) -> Tuple[List[int], Optional[float]]: return read_video_timestamps(self.video_paths[idx]) def _collate_fn(x: T) -> T: """ Dummy collate function to be used with _VideoTimestampsDataset """ return x class VideoClips: """ Given a list of video files, computes all consecutive subvideos of size `clip_length_in_frames`, where the distance between each subvideo in the same video is defined by `frames_between_clips`. If `frame_rate` is specified, it will also resample all the videos to have the same frame rate, and the clips will refer to this frame rate. Creating this instance the first time is time-consuming, as it needs to decode all the videos in `video_paths`. It is recommended that you cache the results after instantiation of the class. Recreating the clips for different clip lengths is fast, and can be done with the `compute_clips` method. Args: video_paths (List[str]): paths to the video files clip_length_in_frames (int): size of a clip in number of frames frames_between_clips (int): step (in frames) between each clip frame_rate (int, optional): if specified, it will resample the video so that it has `frame_rate`, and then the clips will be defined on the resampled video num_workers (int): how many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0) output_format (str): The format of the output video tensors. Can be either "THWC" (default) or "TCHW". """ def __init__( self, video_paths: List[str], clip_length_in_frames: int = 16, frames_between_clips: int = 1, frame_rate: Optional[int] = None, _precomputed_metadata: Optional[Dict[str, Any]] = None, num_workers: int = 0, _video_width: int = 0, _video_height: int = 0, _video_min_dimension: int = 0, _video_max_dimension: int = 0, _audio_samples: int = 0, _audio_channels: int = 0, output_format: str = "THWC", ) -> None: self.video_paths = video_paths self.num_workers = num_workers # these options are not valid for pyav backend self._video_width = _video_width self._video_height = _video_height self._video_min_dimension = _video_min_dimension self._video_max_dimension = _video_max_dimension self._audio_samples = _audio_samples self._audio_channels = _audio_channels self.output_format = output_format.upper() if self.output_format not in ("THWC", "TCHW"): raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.") if _precomputed_metadata is None: self._compute_frame_pts() else: self._init_from_metadata(_precomputed_metadata) self.compute_clips(clip_length_in_frames, frames_between_clips, frame_rate) def _compute_frame_pts(self) -> None: self.video_pts = [] self.video_fps = [] # strategy: use a DataLoader to parallelize read_video_timestamps # so need to create a dummy dataset first import torch.utils.data dl: torch.utils.data.DataLoader = torch.utils.data.DataLoader( _VideoTimestampsDataset(self.video_paths), # type: ignore[arg-type] batch_size=16, num_workers=self.num_workers, collate_fn=_collate_fn, ) with tqdm(total=len(dl)) as pbar: for batch in dl: pbar.update(1) clips, fps = list(zip(*batch)) # we need to specify dtype=torch.long because for empty list, # torch.as_tensor will use torch.float as default dtype. This # happens when decoding fails and no pts is returned in the list. clips = [torch.as_tensor(c, dtype=torch.long) for c in clips] self.video_pts.extend(clips) self.video_fps.extend(fps) def _init_from_metadata(self, metadata: Dict[str, Any]) -> None: self.video_paths = metadata["video_paths"] assert len(self.video_paths) == len(metadata["video_pts"]) self.video_pts = metadata["video_pts"] assert len(self.video_paths) == len(metadata["video_fps"]) self.video_fps = metadata["video_fps"] @property def metadata(self) -> Dict[str, Any]: _metadata = { "video_paths": self.video_paths, "video_pts": self.video_pts, "video_fps": self.video_fps, } return _metadata def subset(self, indices: List[int]) -> "VideoClips": video_paths = [self.video_paths[i] for i in indices] video_pts = [self.video_pts[i] for i in indices] video_fps = [self.video_fps[i] for i in indices] metadata = { "video_paths": video_paths, "video_pts": video_pts, "video_fps": video_fps, } return type(self)( video_paths, self.num_frames, self.step, self.frame_rate, _precomputed_metadata=metadata, num_workers=self.num_workers, _video_width=self._video_width, _video_height=self._video_height, _video_min_dimension=self._video_min_dimension, _video_max_dimension=self._video_max_dimension, _audio_samples=self._audio_samples, _audio_channels=self._audio_channels, ) @staticmethod def compute_clips_for_video( video_pts: torch.Tensor, num_frames: int, step: int, fps: int, frame_rate: Optional[int] = None ) -> Tuple[torch.Tensor, Union[List[slice], torch.Tensor]]: if fps is None: # if for some reason the video doesn't have fps (because doesn't have a video stream) # set the fps to 1. The value doesn't matter, because video_pts is empty anyway fps = 1 if frame_rate is None: frame_rate = fps total_frames = len(video_pts) * (float(frame_rate) / fps) _idxs = VideoClips._resample_video_idx(int(math.floor(total_frames)), fps, frame_rate) video_pts = video_pts[_idxs] clips = unfold(video_pts, num_frames, step) if not clips.numel(): warnings.warn( "There aren't enough frames in the current video to get a clip for the given clip length and " "frames between clips. The video (and potentially others) will be skipped." ) idxs: Union[List[slice], torch.Tensor] if isinstance(_idxs, slice): idxs = [_idxs] * len(clips) else: idxs = unfold(_idxs, num_frames, step) return clips, idxs def compute_clips(self, num_frames: int, step: int, frame_rate: Optional[int] = None) -> None: """ Compute all consecutive sequences of clips from video_pts. Always returns clips of size `num_frames`, meaning that the last few frames in a video can potentially be dropped. Args: num_frames (int): number of frames for the clip step (int): distance between two clips frame_rate (int, optional): The frame rate """ self.num_frames = num_frames self.step = step self.frame_rate = frame_rate self.clips = [] self.resampling_idxs = [] for video_pts, fps in zip(self.video_pts, self.video_fps): clips, idxs = self.compute_clips_for_video(video_pts, num_frames, step, fps, frame_rate) self.clips.append(clips) self.resampling_idxs.append(idxs) clip_lengths = torch.as_tensor([len(v) for v in self.clips]) self.cumulative_sizes = clip_lengths.cumsum(0).tolist() def __len__(self) -> int: return self.num_clips() def num_videos(self) -> int: return len(self.video_paths) def num_clips(self) -> int: """ Number of subclips that are available in the video list. """ return self.cumulative_sizes[-1] def get_clip_location(self, idx: int) -> Tuple[int, int]: """ Converts a flattened representation of the indices into a video_idx, clip_idx representation. """ video_idx = bisect.bisect_right(self.cumulative_sizes, idx) if video_idx == 0: clip_idx = idx else: clip_idx = idx - self.cumulative_sizes[video_idx - 1] return video_idx, clip_idx @staticmethod def _resample_video_idx(num_frames: int, original_fps: int, new_fps: int) -> Union[slice, torch.Tensor]: step = float(original_fps) / new_fps if step.is_integer(): # optimization: if step is integer, don't need to perform # advanced indexing step = int(step) return slice(None, None, step) idxs = torch.arange(num_frames, dtype=torch.float32) * step idxs = idxs.floor().to(torch.int64) return idxs def get_clip(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any], int]: """ Gets a subclip from a list of videos. Args: idx (int): index of the subclip. Must be between 0 and num_clips(). Returns: video (Tensor) audio (Tensor) info (Dict) video_idx (int): index of the video in `video_paths` """ if idx >= self.num_clips(): raise IndexError(f"Index {idx} out of range ({self.num_clips()} number of clips)") video_idx, clip_idx = self.get_clip_location(idx) video_path = self.video_paths[video_idx] clip_pts = self.clips[video_idx][clip_idx] from torchvision import get_video_backend backend = get_video_backend() if backend == "pyav": # check for invalid options if self._video_width != 0: raise ValueError("pyav backend doesn't support _video_width != 0") if self._video_height != 0: raise ValueError("pyav backend doesn't support _video_height != 0") if self._video_min_dimension != 0: raise ValueError("pyav backend doesn't support _video_min_dimension != 0") if self._video_max_dimension != 0: raise ValueError("pyav backend doesn't support _video_max_dimension != 0") if self._audio_samples != 0: raise ValueError("pyav backend doesn't support _audio_samples != 0") if backend == "pyav": start_pts = clip_pts[0].item() end_pts = clip_pts[-1].item() video, audio, info = read_video(video_path, start_pts, end_pts) else: _info = _probe_video_from_file(video_path) video_fps = _info.video_fps audio_fps = None video_start_pts = cast(int, clip_pts[0].item()) video_end_pts = cast(int, clip_pts[-1].item()) audio_start_pts, audio_end_pts = 0, -1 audio_timebase = Fraction(0, 1) video_timebase = Fraction(_info.video_timebase.numerator, _info.video_timebase.denominator) if _info.has_audio: audio_timebase = Fraction(_info.audio_timebase.numerator, _info.audio_timebase.denominator) audio_start_pts = pts_convert(video_start_pts, video_timebase, audio_timebase, math.floor) audio_end_pts = pts_convert(video_end_pts, video_timebase, audio_timebase, math.ceil) audio_fps = _info.audio_sample_rate video, audio, _ = _read_video_from_file( video_path, video_width=self._video_width, video_height=self._video_height, video_min_dimension=self._video_min_dimension, video_max_dimension=self._video_max_dimension, video_pts_range=(video_start_pts, video_end_pts), video_timebase=video_timebase, audio_samples=self._audio_samples, audio_channels=self._audio_channels, audio_pts_range=(audio_start_pts, audio_end_pts), audio_timebase=audio_timebase, ) info = {"video_fps": video_fps} if audio_fps is not None: info["audio_fps"] = audio_fps if self.frame_rate is not None: resampling_idx = self.resampling_idxs[video_idx][clip_idx] if isinstance(resampling_idx, torch.Tensor): resampling_idx = resampling_idx - resampling_idx[0] video = video[resampling_idx] info["video_fps"] = self.frame_rate assert len(video) == self.num_frames, f"{video.shape} x {self.num_frames}" if self.output_format == "TCHW": # [T,H,W,C] --> [T,C,H,W] video = video.permute(0, 3, 1, 2) return video, audio, info, video_idx def __getstate__(self) -> Dict[str, Any]: video_pts_sizes = [len(v) for v in self.video_pts] # To be back-compatible, we convert data to dtype torch.long as needed # because for empty list, in legacy implementation, torch.as_tensor will # use torch.float as default dtype. This happens when decoding fails and # no pts is returned in the list. video_pts = [x.to(torch.int64) for x in self.video_pts] # video_pts can be an empty list if no frames have been decoded if video_pts: video_pts = torch.cat(video_pts) # type: ignore[assignment] # avoid bug in https://github.com/pytorch/pytorch/issues/32351 # TODO: Revert it once the bug is fixed. video_pts = video_pts.numpy() # type: ignore[attr-defined] # make a copy of the fields of self d = self.__dict__.copy() d["video_pts_sizes"] = video_pts_sizes d["video_pts"] = video_pts # delete the following attributes to reduce the size of dictionary. They # will be re-computed in "__setstate__()" del d["clips"] del d["resampling_idxs"] del d["cumulative_sizes"] # for backwards-compatibility d["_version"] = 2 return d def __setstate__(self, d: Dict[str, Any]) -> None: # for backwards-compatibility if "_version" not in d: self.__dict__ = d return video_pts = torch.as_tensor(d["video_pts"], dtype=torch.int64) video_pts = torch.split(video_pts, d["video_pts_sizes"], dim=0) # don't need this info anymore del d["video_pts_sizes"] d["video_pts"] = video_pts self.__dict__ = d # recompute attributes "clips", "resampling_idxs" and other derivative ones self.compute_clips(self.num_frames, self.step, self.frame_rate)

40.056604

116

0.628062

b90882c596aaba10157cb2c8fc31b9401d948ac4

1,265

py

Python

modules/tools/navigation/planning/provider_localization.py

DavidSplit/apollo-3.0

9f82838e857e4c9146952946cbc34b9f35098deb

[ "Apache-2.0" ]

6

2019-10-11T07:57:49.000Z

2022-02-23T15:23:41.000Z

modules/tools/navigation/planning/provider_localization.py

DavidSplit/apollo-3.0

9f82838e857e4c9146952946cbc34b9f35098deb

[ "Apache-2.0" ]

null

modules/tools/navigation/planning/provider_localization.py

DavidSplit/apollo-3.0

9f82838e857e4c9146952946cbc34b9f35098deb

[ "Apache-2.0" ]

12

2019-10-11T07:57:49.000Z

2022-03-16T05:13:00.000Z

#!/usr/bin/env python ############################################################################### # Copyright 2017 The Apollo Authors. All Rights Reserved. # Modifications Copyright (c) 2018 LG Electronics, Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### class LocalizationProvider: def __init__(self): self.localization_pb = None self.x = 0 self.y = 0 self.heading = 0 def update(self, localization_pb): self.localization_pb = localization_pb self.x = self.localization_pb.pose.position.x self.y = self.localization_pb.pose.position.y self.heading = self.localization_pb.pose.heading

38.333333

79

0.633202

409460ada1d5007d3b9de62aa4aa7a04ae4bf7de

1,468

py

Python

core/sentence_embeddings.py

venomousboxer/chabot-backend

1d9c4b0da5f0cfd3572edb0619a43b9d207bbd1e

[ "MIT" ]

1

2019-08-30T05:42:40.000Z

core/sentence_embeddings.py

venomousboxer/chabot-backend

1d9c4b0da5f0cfd3572edb0619a43b9d207bbd1e

[ "MIT" ]

15

2020-01-28T22:39:27.000Z

2022-02-10T00:10:30.000Z

core/sentence_embeddings.py

venomousboxer/chabot-backend

1d9c4b0da5f0cfd3572edb0619a43b9d207bbd1e

[ "MIT" ]

1

2020-04-25T06:22:30.000Z

from InferSent.models import InferSent import torch import numpy as np def cosine(u, v): return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v)) def Start_chatbot(): model_version = 1 MODEL_PATH = "../InferSent/encoder/infersent%s.pkl" % model_version params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048, 'pool_type': 'max', 'dpout_model': 0.0, 'version': model_version} model = InferSent(params_model) model.load_state_dict(torch.load(MODEL_PATH)) use_cuda = False model = model.cuda() if use_cuda else model W2V_PATH = '../data/glove.6B.300d.txt' if model_version == 1 else '../dataset/fastText/crawl-300d-2M.vec' model.set_w2v_path(W2V_PATH) model.build_vocab_k_words(K=570000) dict = {} embeddings = {} questions = [] answers = [] with open('../data/questions.txt') as f: content = f.readlines() questions = [x.strip() for x in content] with open('../data/answers.txt') as f: content = f.readlines() answers = [x.strip() for x in content] for i in range(len(questions)): dict[questions[i]] = answers[i] embeddings[questions[i]] = model.encode([questions[i]])[0] return model, dict, embeddings def give_similarity_score(u, v, model, embeddings): return cosine(embeddings[u], model.encode([v])[0]) #print(give_similarity_score('how are you today?', 'would you tell me how you are today?'))

31.913043

109

0.651226

1bf1c2bf68b98b0feaf20f65334a22472f98e1b1

2,562

py

Python

tb_rest_client/models/models_pe/coap_device_type_configuration.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

30

2020-06-19T06:42:50.000Z

2021-08-23T21:16:36.000Z

tb_rest_client/models/models_pe/coap_device_type_configuration.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

25

2021-08-30T01:17:27.000Z

2022-03-16T14:10:14.000Z

tb_rest_client/models/models_pe/coap_device_type_configuration.py

samson0v/python_tb_rest_client

08ff7898740f7cec2170e85d5c3c89e222e967f7

[ "Apache-2.0" ]

23

2020-07-06T13:41:54.000Z

2021-08-23T21:04:50.000Z

# coding: utf-8 """ ThingsBoard REST API ThingsBoard Professional Edition IoT platform REST API documentation. # noqa: E501 OpenAPI spec version: 3.3.3PAAS-RC1 Contact: info@thingsboard.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class CoapDeviceTypeConfiguration(object): """NOTE: This class is auto generated by the swagger code generator program. from tb_rest_client.api_client import ApiClient Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { } attribute_map = { } def __init__(self): # noqa: E501 """CoapDeviceTypeConfiguration - a model defined in Swagger""" # noqa: E501 self.discriminator = None def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(CoapDeviceTypeConfiguration, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, CoapDeviceTypeConfiguration): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

30.141176

88

0.57299

ce95eff40ac06c7a0b24ab49ce51ae527ca9a8a8

417

py

Python

exericios_faculdade/vetores_matrizes/questao_02.py

Ssousuke/python

869bb1cc385d4f54961a131d6db46a7bf99fd743

[ "MIT" ]

null

exericios_faculdade/vetores_matrizes/questao_02.py

Ssousuke/python

869bb1cc385d4f54961a131d6db46a7bf99fd743

[ "MIT" ]

null

exericios_faculdade/vetores_matrizes/questao_02.py

Ssousuke/python

869bb1cc385d4f54961a131d6db46a7bf99fd743

[ "MIT" ]

null

# Faça um programa que preencha um vetor com 10 números reais, calcule e mostre # a quantidade de números negativos e a soma dos números positivos desse vetor. vetor = [] for i in range(0, 10): valor = int(input()) vetor.append(valor) negativos = 0 positivos = 0 for i in vetor: if i < 0: negativos += 1 else: positivos += 1 print(f'Positivos: {positivos}, Negativos: {negativos}')

23.166667

79

0.661871

37537a9d932fcfb7edbf0b1c1523a1f73919cfd9

2,027

py

Python

test/test_utils.py

fengzhongye/face-alignment

6a7731168dbb1a15f9ecd5fe4c79c992f179a622

[ "BSD-3-Clause" ]

2

2021-02-22T12:16:34.000Z

2021-05-02T15:23:10.000Z

test/test_utils.py

fengzhongye/face-alignment

6a7731168dbb1a15f9ecd5fe4c79c992f179a622

[ "BSD-3-Clause" ]

3

2021-09-08T02:24:48.000Z

2022-03-12T00:44:53.000Z

test/test_utils.py

fengzhongye/face-alignment

6a7731168dbb1a15f9ecd5fe4c79c992f179a622

[ "BSD-3-Clause" ]

1

2020-02-10T05:13:12.000Z

import sys sys.path.append('.') import unittest from face_alignment.utils import * import numpy as np import torch class Tester(unittest.TestCase): def test_flip_is_label(self): # Generate the points heatmaps = torch.from_numpy(np.random.randint(1, high=250, size=(68, 64, 64)).astype('float32')) flipped_heatmaps = flip(flip(heatmaps.clone(), is_label=True), is_label=True) assert np.allclose(heatmaps.numpy(), flipped_heatmaps.numpy()) def test_flip_is_image(self): fake_image = torch.torch.rand(3, 256, 256) fliped_fake_image = flip(flip(fake_image.clone())) assert np.allclose(fake_image.numpy(), fliped_fake_image.numpy()) def test_getpreds(self): pts = torch.from_numpy(np.random.randint(1, high=63, size=(68, 2)).astype('float32')) heatmaps = np.zeros((68, 256, 256)) for i in range(68): if pts[i, 0] > 0: heatmaps[i] = draw_gaussian(heatmaps[i], pts[i], 2) heatmaps = torch.from_numpy(np.expand_dims(heatmaps, axis=0)) preds, _ = get_preds_fromhm(heatmaps) assert np.allclose(pts.numpy(), preds.numpy(), atol=5) def test_create_heatmaps(self): reference_scale = 195 target_landmarks = torch.randint(0, 255, (1, 68, 2)).type(torch.float) # simulated dataset bb = create_bounding_box(target_landmarks) centers = torch.stack([bb[:, 2] - (bb[:, 2] - bb[:, 0]) / 2.0, bb[:, 3] - (bb[:, 3] - bb[:, 1]) / 2.0], dim=1) centers[:, 1] = centers[:, 1] - (bb[:, 3] - bb[:, 1]) * 0.12 # Not sure where 0.12 comes from scales = (bb[:, 2] - bb[:, 0] + bb[:, 3] - bb[:, 1]) / reference_scale heatmaps = create_target_heatmap(target_landmarks, centers, scales) preds = get_preds_fromhm(heatmaps, centers.squeeze(), scales.squeeze())[1] assert np.allclose(preds.numpy(), target_landmarks.numpy(), atol=5) if __name__ == '__main__': unittest.main()

39.745098

119

0.610261

795962c0027739c3411dab6366b3e1c4af16695b

5,457

py

Python

tests/model_connectors/test_time_series_heating_indirect.py

mingzhe37/geojson-modelica-translator

23c969fa5a1b776dfd6dd773b9dd8f6e3a0ce28b

[ "BSD-3-Clause-LBNL" ]

11

2019-08-19T16:58:23.000Z

2022-01-25T14:23:49.000Z

tests/model_connectors/test_time_series_heating_indirect.py

mingzhe37/geojson-modelica-translator

23c969fa5a1b776dfd6dd773b9dd8f6e3a0ce28b

[ "BSD-3-Clause-LBNL" ]

331

2019-07-24T16:15:52.000Z

2022-03-10T04:58:15.000Z

tests/model_connectors/test_time_series_heating_indirect.py

mingzhe37/geojson-modelica-translator

23c969fa5a1b776dfd6dd773b9dd8f6e3a0ce28b

[ "BSD-3-Clause-LBNL" ]

10

2019-07-12T22:21:32.000Z

2022-02-22T06:30:25.000Z

""" **************************************************************************************************** :copyright (c) 2019-2021 URBANopt, Alliance for Sustainable Energy, LLC, and other contributors. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. Redistribution of this software, without modification, must refer to the software by the same designation. Redistribution of a modified version of this software (i) may not refer to the modified version by the same designation, or by any confusingly similar designation, and (ii) must refer to the underlying software originally provided by Alliance as “URBANopt”. Except to comply with the foregoing, the term “URBANopt”, or any confusingly similar designation may not be used to refer to any modified version of this software or any modified version of the underlying software originally provided by Alliance without the prior written consent of Alliance. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. **************************************************************************************************** """ import os import pytest from geojson_modelica_translator.geojson.urbanopt_geojson import ( UrbanOptGeoJson ) from geojson_modelica_translator.model_connectors.couplings.coupling import ( Coupling ) from geojson_modelica_translator.model_connectors.couplings.graph import ( CouplingGraph ) from geojson_modelica_translator.model_connectors.districts.district import ( District ) from geojson_modelica_translator.model_connectors.energy_transfer_systems.ets_cold_water_stub import ( EtsColdWaterStub ) from geojson_modelica_translator.model_connectors.energy_transfer_systems.heating_indirect import ( HeatingIndirect ) from geojson_modelica_translator.model_connectors.load_connectors.time_series import ( TimeSeries ) from geojson_modelica_translator.model_connectors.networks.network_heated_water_stub import ( NetworkHeatedWaterStub ) from geojson_modelica_translator.system_parameters.system_parameters import ( SystemParameters ) from ..base_test_case import TestCaseBase @pytest.mark.simulation class DistrictSystemTest(TestCaseBase): def test_district_system(self): project_name = "time_series_heating_indirect" self.data_dir, self.output_dir = self.set_up(os.path.dirname(__file__), project_name) # load in the example geojson with a single office building filename = os.path.join(self.data_dir, "time_series_ex1.json") self.gj = UrbanOptGeoJson(filename) # load system parameter data filename = os.path.join(self.data_dir, "time_series_system_params_ets.json") sys_params = SystemParameters(filename) # Create the time series load, ets and their coupling time_series_load = TimeSeries(sys_params, self.gj.buildings[0]) geojson_load_id = self.gj.buildings[0].feature.properties["id"] heating_indirect_system = HeatingIndirect(sys_params, geojson_load_id) ts_hi_coupling = Coupling(time_series_load, heating_indirect_system) # create heated water stub for the ets heated_water_stub = NetworkHeatedWaterStub(sys_params) hi_hw_coupling = Coupling(heating_indirect_system, heated_water_stub) # create cold water stub for the load cold_water_stub = EtsColdWaterStub(sys_params) ts_cw_coupling = Coupling(time_series_load, cold_water_stub) graph = CouplingGraph([ ts_hi_coupling, hi_hw_coupling, ts_cw_coupling, ]) district = District( root_dir=self.output_dir, project_name=project_name, system_parameters=sys_params, coupling_graph=graph ) district.to_modelica() root_path = os.path.abspath(os.path.join(district._scaffold.districts_path.files_dir)) self.run_and_assert_in_docker(os.path.join(root_path, 'DistrictEnergySystem.mo'), project_path=district._scaffold.project_path, project_name=district._scaffold.project_name)

45.857143

102

0.741983

5181d54812305017e672fb7764c09948bc888f6c

1,311

py

Python

python/graphscope/nx/readwrite/__init__.py

doudoubobo/GraphScope

ec000ba93033bb3097b5a9407b7037115f1f2a4c

[ "Apache-2.0" ]

null

python/graphscope/nx/readwrite/__init__.py

doudoubobo/GraphScope

ec000ba93033bb3097b5a9407b7037115f1f2a4c

[ "Apache-2.0" ]

null

python/graphscope/nx/readwrite/__init__.py

doudoubobo/GraphScope

ec000ba93033bb3097b5a9407b7037115f1f2a4c

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Copyright 2020 Alibaba Group Holding Limited. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from graphscope.nx.readwrite.adjlist import * from graphscope.nx.readwrite.edgelist import * from graphscope.nx.readwrite.gexf import * from graphscope.nx.readwrite.gml import * from graphscope.nx.readwrite.gpickle import * from graphscope.nx.readwrite.graph6 import * from graphscope.nx.readwrite.graphml import * from graphscope.nx.readwrite.json_graph import * from graphscope.nx.readwrite.leda import * from graphscope.nx.readwrite.multiline_adjlist import * from graphscope.nx.readwrite.nx_shp import * from graphscope.nx.readwrite.nx_yaml import * from graphscope.nx.readwrite.pajek import * from graphscope.nx.readwrite.sparse6 import *

39.727273

74

0.78566

7454c70d55b8a3b478eb708a873d34590d8050c6

194,266

py

Python

python/paddle/fluid/framework.py

laipaang/Paddle

d7f35434b761707a8479b75636546a624399369a

[ "Apache-2.0" ]

1

2020-06-24T14:53:24.000Z

python/paddle/fluid/framework.py

laipaang/Paddle

d7f35434b761707a8479b75636546a624399369a

[ "Apache-2.0" ]

null

python/paddle/fluid/framework.py

laipaang/Paddle

d7f35434b761707a8479b75636546a624399369a

[ "Apache-2.0" ]

null

# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import collections from collections import defaultdict from collections import Iterable import contextlib from .wrapped_decorator import signature_safe_contextmanager, wrap_decorator import os import re import traceback import six import numpy as np import subprocess import multiprocessing import sys import logging from .. import compat as cpt from .proto import framework_pb2 from . import core from . import unique_name import paddle.version as fluid_version import warnings __all__ = [ 'Program', 'default_startup_program', 'default_main_program', 'program_guard', 'name_scope', 'cuda_places', 'cpu_places', 'cuda_pinned_places', 'in_dygraph_mode', 'is_compiled_with_cuda', 'Variable', 'ComplexVariable', 'load_op_library', 'require_version', 'device_guard', 'set_flags', 'get_flags', ] EMPTY_VAR_NAME = core.kEmptyVarName() TEMP_VAR_NAME = core.kTempVarName() GRAD_VAR_SUFFIX = core.kGradVarSuffix() ZERO_VAR_SUFFIX = core.kZeroVarSuffix() CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName() _dygraph_tracer_ = None _dygraph_current_expected_place_ = None _current_device = None global_prog_seed = 0 def require_version(min_version, max_version=None): """ Check if the installed version of PaddlePaddle is in [min_version, max_version], if the installed version is lower than ``min_version`` or higher than ``max_version``, an exception will be thrown, NO returns if the installed version is satisfied. Args: min_version (str): the minimum version required (like '1.4.0'). max_version (str, optional): the max version required (like '1.6.0'), default is None, meaning any version equal or higher than ``min_version`` is acceptable. Returns: None. Raises: TypeError: if the type of ``min_version`` is not str. TypeError: if the type of ``max_version`` is not str or type(None). ValueError: if the value of ``min_version`` is not in version format. ValueError: if the value of ``max_version`` is not in version format or None. Exception: if the installed version is lower than ``min_version`` or higher than ``max_version``. Examples: .. code-block:: python import paddle.fluid as fluid # any version >= 0.1.0 is acceptable. fluid.require_version('0.1.0') # if 0.1.0 <= version <= 10.0.0, it is acceptable. fluid.require_version(min_version='0.1.0', max_version='10.0.0') """ if not isinstance(min_version, str): raise TypeError( "The type of 'min_version' in require_version must be str, but received %s." % (type(min_version))) if not isinstance(max_version, (str, type(None))): raise TypeError( "The type of 'max_version' in require_version must be str or type(None), but received %s." % (type(max_version))) check_format = re.match(r'\d+(\.\d+){0,3}', min_version) if check_format is None or check_format.group() != min_version: raise ValueError( "The value of 'min_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', " "like '1.5.2.0', but received %s" % min_version) if max_version is not None: check_format = re.match(r'\d+(\.\d+){0,3}', max_version) if check_format is None or check_format.group() != max_version: raise ValueError( "The value of 'max_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', " "like '1.5.2.0', but received %s" % max_version) version_installed = [ fluid_version.major, fluid_version.minor, fluid_version.patch, fluid_version.rc ] zero_version = ['0', '0', '0', '0'] def version_cmp(ver_a, ver_b): for i in six.moves.range(len(ver_a)): if int(ver_a[i]) > int(ver_b[i]): return 1 elif int(ver_a[i]) < int(ver_b[i]): return -1 return 0 if version_cmp(version_installed, zero_version) == 0: if max_version is not None: warnings.warn( "PaddlePaddle version in [%s, %s] required, but %s installed. " "Maybe you are using a develop version, " "please make sure the version is good with your code." % (min_version, max_version, fluid_version.full_version)) else: warnings.warn( "PaddlePaddle version %s or higher is required, but %s installed, " "Maybe you are using a develop version, " "please make sure the version is good with your code." % (min_version, fluid_version.full_version)) return min_version_split = min_version.split('.') min_version_to_check = min_version_split + zero_version[len( min_version_split):] if max_version is not None: max_version_split = max_version.split('.') max_version_to_check = max_version_split + zero_version[len( max_version_split):] if version_cmp(version_installed, max_version_to_check) > 0 or version_cmp( version_installed, min_version_to_check) < 0: raise Exception( "VersionError: PaddlePaddle version in [%s, %s] required, but %s installed." % (min_version, max_version, fluid_version.full_version)) else: if version_cmp(version_installed, min_version_to_check) < 0: raise Exception( "VersionError: PaddlePaddle version %s or higher is required, but %s installed, " "please upgrade your PaddlePaddle to %s or other higher version." % (min_version, fluid_version.full_version, min_version)) def in_dygraph_mode(): """ :alias_main: paddle.in_dygraph_mode :alias: paddle.in_dygraph_mode :old_api: paddle.fluid.framework.in_dygraph_mode This function checks whether the program runs in dynamic graph mode or not. You can enter dynamic graph mode with :ref:`api_fluid_dygraph_guard` api, or enable and disable dynamic graph mode with :ref:`api_fluid_dygraph_enable` and :ref:`api_fluid_dygraph_disable` api . Returns: bool: Whether the program is running in dynamic graph mode. Examples: .. code-block:: python import paddle.fluid as fluid fluid.enable_dygraph() # Now we are in dygragh mode print(fluid.in_dygraph_mode()) # True fluid.disable_dygraph() print(fluid.in_dygraph_mode()) # False """ return _dygraph_tracer_ is not None def _dygraph_not_support_(func): def __impl__(*args, **kwargs): assert not in_dygraph_mode( ), "We don't support %s in imperative mode" % func.__name__ return func(*args, **kwargs) return __impl__ def _dygraph_only_(func): def __impl__(*args, **kwargs): assert in_dygraph_mode( ), "We Only support %s in imperative mode, please use fluid.dygraph.guard() as context to run it in imperative Mode" % func.__name__ return func(*args, **kwargs) return __impl__ # NOTE(zhiqiu): This decorator is used for the APIs of Variable which is only # used to make Variable and VarBase has same interfaces, like numpy. Since VarBase is not exposed in our # official docments, logically, we want to keep VarBase and logically consistent. While, actually, # in our implementation, there some APIs not supported, like numpy, because Variable contains the desc. # So, those APIs are listed under class Variable to generate docs only. # TODO(zhiqiu): We should make VarBase consistent with Variable in future, for example, by inheritting # same base class. def _fake_interface_only_(func): def __impl__(*args, **kwargs): raise AssertionError( "'%s' should be called by imperative Varible in imperative mode, please use fluid.dygraph.guard() as context to run it in imperative mode" % func.__name__) return __impl__ dygraph_not_support = wrap_decorator(_dygraph_not_support_) dygraph_only = wrap_decorator(_dygraph_only_) fake_interface_only = wrap_decorator(_fake_interface_only_) def _dygraph_tracer(): return _dygraph_tracer_ def _current_expected_place(): return _dygraph_current_expected_place_ # TODO(zhiqiu): remove this function. def _var_base_to_np(var_base): """ convert VarBase tp numpy Args: var_base(VarBase) : the VarBase to convert Returns (np.ndarray): the np.ndarray contain the value of VarBase """ warnings.warn( "paddle.fluid.framework._var_base_to_np is deprecated, please use var_base.numpy() instead of _var_base_to_np(var_base)." ) return var_base.numpy() def _cpu_num(): if "CPU_NUM" not in os.environ.keys(): if multiprocessing.cpu_count() > 1: sys.stderr.write( '!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list.\n' 'CPU_NUM indicates that how many CPUPlace are used in the current task.\n' 'And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster.\n\n' 'export CPU_NUM={} # for example, set CPU_NUM as number of physical CPU core which is {}.\n\n' '!!! The default number of CPU_NUM=1.\n'.format( multiprocessing.cpu_count(), multiprocessing.cpu_count())) os.environ['CPU_NUM'] = str(1) cpu_num = os.environ.get('CPU_NUM') return int(cpu_num) def _cuda_ids(): gpus_env = os.getenv("FLAGS_selected_gpus") if gpus_env: device_ids = [int(s) for s in gpus_env.split(",")] else: device_ids = six.moves.range(core.get_cuda_device_count()) return device_ids def is_compiled_with_cuda(): """ Whether this whl package can be used to run the model on GPU. Returns (bool): support gpu or not. Examples: .. code-block:: python import paddle.fluid as fluid support_gpu = fluid.is_compiled_with_cuda() """ return core.is_compiled_with_cuda() def cuda_places(device_ids=None): """ **Note**: For multi-card tasks, please use `FLAGS_selected_gpus` environment variable to set the visible GPU device. The next version will fix the problem with `CUDA_VISIBLE_DEVICES` environment variable. This function creates a list of :code:`fluid.CUDAPlace` objects. If :code:`device_ids` is None, environment variable of :code:`FLAGS_selected_gpus` would be checked first. For example, if :code:`FLAGS_selected_gpus=0,1,2`, the returned list would be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)]. If :code:`FLAGS_selected_gpus` is not set, all visible gpu places would be returned according to the :code:`CUDA_VISIBLE_DEVICES` environment variable. If :code:`device_ids` is not None, it should be the device ids of GPUs. For example, if :code:`device_ids=[0,1,2]`, the returned list would be [fluid.CUDAPlace(0), fluid.CUDAPlace(1), fluid.CUDAPlace(2)]. Parameters: device_ids (list or tuple of int, optional): list of GPU device ids. Returns: list of fluid.CUDAPlace: Created GPU place list. Examples: .. code-block:: python import paddle.fluid as fluid cuda_places = fluid.cuda_places() """ assert core.is_compiled_with_cuda(), \ "Not compiled with CUDA" if device_ids is None: device_ids = _cuda_ids() elif not isinstance(device_ids, (list, tuple)): device_ids = [device_ids] return [core.CUDAPlace(dev_id) for dev_id in device_ids] def cpu_places(device_count=None): """ This function creates a list of :code:`fluid.CPUPlace` objects, and returns the created list. If :code:`device_count` is None, the device count would be determined by environment variable :code:`CPU_NUM`. If :code:`CPU_NUM` is not set, the default value is 1, i.e. CPU_NUM=1. :code:`CPU_NUM` indicates the number of devices used in the current task. The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores. Parameters: device_count (int, optional): device number. Default: None. Returns: list of fluid.CPUPlace: Created list of CPU places. Examples: .. code-block:: python import paddle.fluid as fluid cpu_places = fluid.cpu_places() """ if device_count is None: device_count = _cpu_num() return [core.CPUPlace()] * device_count def cuda_pinned_places(device_count=None): """ This function creates a list of :code:`fluid.CUDAPinnedPlace` objects. If :code:`device_count` is None, the device count would be determined by environment variable :code:`CPU_NUM`. If :code:`CPU_NUM` is not set, the default value is 1, i.e. CPU_NUM=1. :code:`CPU_NUM` indicates the number of devices used in the current task. The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores. Parameters: device_count (int, optional): device number. Default: None. Returns: list of fluid.CUDAPinnedPlace: Created list of CUDA pinned places. Examples: .. code-block:: python import paddle.fluid as fluid cuda_pinned_places_cpu_num = fluid.cuda_pinned_places() # or cuda_pinned_places = fluid.cuda_pinned_places(1) """ assert core.is_compiled_with_cuda(), \ "Not compiled with CUDA" if device_count is None: device_count = len(_cuda_ids()) return [core.CUDAPinnedPlace()] * device_count class NameScope(object): def __init__(self, name="", parent=None): self._children = dict() self._name = name self._parent = parent def child(self, prefix): if prefix not in self._children: new_child = NameScope(prefix, self) self._children[prefix] = [new_child] else: new_child = NameScope(prefix + "_%d" % len(self._children[prefix]), self) self._children[prefix].append(new_child) return new_child def parent(self): return self._parent def name(self): return self._name _name_scope = NameScope() @signature_safe_contextmanager def name_scope(prefix=None): """ :api_attr: Static Graph Generate hierarchical name prefix for the operators. Note: This should only used for debugging and visualization purpose. Don't use it for serious analysis such as graph/program transformations. Args: prefix(str, optional): prefix. Default is none. Examples: .. code-block:: python import paddle.fluid as fluid with fluid.name_scope("s1"): a = fluid.data(name='data', shape=[None, 1], dtype='int32') b = a + 1 with fluid.name_scope("s2"): c = b * 1 with fluid.name_scope("s3"): d = c / 1 with fluid.name_scope("s1"): f = fluid.layers.pow(d, 2.0) with fluid.name_scope("s4"): g = f - 1 # Op are created in the default main program. for op in fluid.default_main_program().block(0).ops: # elementwise_add is created in /s1/ if op.type == 'elementwise_add': assert op.desc.attr("op_namescope") == '/s1/' # elementwise_mul is created in '/s1/s2' elif op.type == 'elementwise_mul': assert op.desc.attr("op_namescope") == '/s1/s2/' # elementwise_div is created in '/s1/s3' elif op.type == 'elementwise_div': assert op.desc.attr("op_namescope") == '/s1/s3/' # elementwise_sum is created in '/s4' elif op.type == 'elementwise_sub': assert op.desc.attr("op_namescope") == '/s4/' # pow is created in /s1_1/ elif op.type == 'pow': assert op.desc.attr("op_namescope") == '/s1_1/' """ # TODO(panyx0718): Only [0-9a-z]. # in dygraph we don't need namescope since it will cause mem leak if in_dygraph_mode(): yield else: assert prefix, "namescope prefix can not be empty." global _name_scope _name_scope = _name_scope.child(prefix) try: yield finally: _name_scope = _name_scope.parent() def _full_name_scope(): global _name_scope scope = _name_scope name = "" while scope: name = scope.name() + "/" + name scope = scope.parent() return name def generate_control_dev_var_name(): import random return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random()) def grad_var_name(var_name): """ Returns: str: gradient name for a certain var name """ return var_name + GRAD_VAR_SUFFIX def convert_np_dtype_to_dtype_(np_dtype): """ Convert the data type in numpy to the data type in Paddle Args: np_dtype(np.dtype): the data type in numpy. Returns: core.VarDesc.VarType: the data type in Paddle. """ dtype = np.dtype(np_dtype) if dtype == np.float32: return core.VarDesc.VarType.FP32 elif dtype == np.float64: return core.VarDesc.VarType.FP64 elif dtype == np.float16: return core.VarDesc.VarType.FP16 elif dtype == np.int32: return core.VarDesc.VarType.INT32 elif dtype == np.int16: return core.VarDesc.VarType.INT16 elif dtype == np.int64: return core.VarDesc.VarType.INT64 elif dtype == np.bool: return core.VarDesc.VarType.BOOL elif dtype == np.uint16: return core.VarDesc.VarType.INT16 elif dtype == np.uint8: return core.VarDesc.VarType.UINT8 elif dtype == np.int8: return core.VarDesc.VarType.INT8 else: raise ValueError("Not supported numpy dtype %s" % dtype) def dtype_is_floating(dtype): """ Check the data type is floating or not. Args: dtype(np.dtype|core.VarDesc.VarType): data type. Could be numpy format or Paddle format Returns(bool): True if data type is a float value """ if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) return dtype in [ core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP64 ] def _debug_string_(proto, throw_on_error=True): """ Get the debug string of a protobuf message. The message could be not initialized. Args: proto(google.protobuf.message.Message): The protobuf message throw_on_error(bool): True if raise an error when the protobuf message is not initialized. Returns(str): The debug string of the protobuf message """ error_fields = list() if not proto.IsInitialized(error_fields) and throw_on_error: raise ValueError("{0} are not initialized.\nThe message is {1}:\n". format(error_fields, proto)) return proto.__str__() def _varbase_creator(type=core.VarDesc.VarType.LOD_TENSOR, name=None, shape=None, dtype=None, persistable=None, **kwargs): if dtype is not None: if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) return core.VarBase(dtype if dtype else core.VarDesc.VarType.FP32, list(shape) if shape else [], name, type if type else core.VarDesc.VarType.LOD_TENSOR, True if persistable else False) class VariableMetaClass(type): @classmethod def __instancecheck__(cls, instance): t = type(instance) if in_dygraph_mode(): return issubclass(t, core.VarBase) else: return issubclass(t, Variable) class ParameterMetaClass(VariableMetaClass): @classmethod def __instancecheck__(cls, instance): t = type(instance) if in_dygraph_mode(): return issubclass(t, ParamBase) else: return issubclass(t, Parameter) def _getitem_impl_(var, item): """ Slice the variable. Args: item(int/slice/tuple) : the index. Returns: Sliced variable """ if not isinstance(item, tuple): item = [item] decrease_axis = [] slice_axis = [] slice_start = [] slice_end = [] slice_step = [] use_strided_slice = False reverse_axis = [] target_block = default_main_program().current_block() def fill_constant(shape, value, force_cpu=False, out=None): var.block.append_op( type='fill_constant', inputs={}, outputs={'Out': [out]}, attrs={ 'shape': shape, 'dtype': out.dtype, 'value': float(value), 'force_cpu': force_cpu }) out.stop_gradient = True return out for dim, slice_item in enumerate(item): if isinstance(slice_item, slice): start = slice_item.start end = slice_item.stop step = slice_item.step if start is None and end is None and step is None: continue if step is None: step = 1 if start is None and end is None: assert (step == -1) reverse_axis.append(dim) continue if start is None: start = 0 if end is None: end = 10000000 if step != 1: use_strided_slice = True slice_axis.append(dim) slice_start.append(start) slice_end.append(end) slice_step.append(step) else: decrease_axis.append(dim) slice_axis.append(dim) slice_start.append(slice_item) slice_step.append(1) if isinstance(slice_item, Variable): temp_1 = var.block.create_var(dtype=slice_item.dtype) fill_constant([1], 1, force_cpu=True, out=temp_1) temp_end = target_block.create_var(dtype=slice_item.dtype) target_block.append_op( type='elementwise_add', inputs={'X': slice_item, 'Y': temp_1}, outputs={'Out': temp_end}, attrs={'axis': -1}) slice_end.append(temp_end) else: slice_end.append(slice_item + 1 if slice_item != -1 else 10000000) def contain_var(one_list): for ele in one_list: if isinstance(ele, Variable): return True return False def get_new_list_tensor(old_list): new_list_tensor = [] for dim in old_list: if isinstance(dim, Variable): dim.stop_gradient = True new_list_tensor.append(dim) else: assert (isinstance(dim, int)) temp_out = var.block.create_var(dtype='int32') fill_constant([1], dim, force_cpu=True, out=temp_out) new_list_tensor.append(temp_out) return new_list_tensor inputs = {'Input': [var]} attrs = { 'axes': slice_axis, 'starts': [], 'ends': [], 'decrease_axis': decrease_axis } if (use_strided_slice == True): attrs['strides'] = [] infer_flags = list(1 for i in range(len(slice_axis))) # starts if contain_var(slice_start): inputs['StartsTensorList'] = get_new_list_tensor(slice_start) for i, dim in enumerate(slice_start): if isinstance(dim, Variable): attrs['starts'].append(-1) infer_flags[i] = -1 else: attrs['starts'].append(dim) else: attrs['starts'] = slice_start # ends if contain_var(slice_end): inputs['EndsTensorList'] = get_new_list_tensor(slice_end) for i, dim in enumerate(slice_end): if isinstance(dim, Variable): attrs['ends'].append(-1) infer_flags[i] = -1 else: attrs['ends'].append(dim) else: attrs['ends'] = slice_end # strides if use_strided_slice == True: if contain_var(slice_step): inputs['StridesTensorList'] = get_new_list_tensor(slice_step) for i, dim in enumerate(slice_step): if isinstance(dim, Variable): attrs['strides'].append(-1) infer_flags[i] = -1 else: attrs['strides'].append(dim) else: attrs['strides'] = slice_step # infer_flags attrs['infer_flags'] = infer_flags out = var if use_strided_slice == False and len(slice_axis) > 0: # append slice_op here slice_out_var = target_block.create_var( name=unique_name.generate_with_ignorable_key(var.name + "_slice"), dtype=var.dtype) target_block.append_op( type="slice", inputs=inputs, outputs={'Out': [slice_out_var]}, attrs=attrs) out = slice_out_var elif use_strided_slice == True and len(slice_axis) > 0: strided_slice_out_var = target_block.create_var( name=unique_name.generate_with_ignorable_key(var.name + "_strided_slice"), dtype=var.dtype) target_block.append_op( type="strided_slice", inputs=inputs, outputs={'Out': [strided_slice_out_var]}, attrs=attrs) out = strided_slice_out_var if len(reverse_axis) > 0: reverse_out_var = target_block.create_var( name=unique_name.generate_with_ignorable_key(var.name + "_slice_reverse"), dtype=var.dtype) target_block.append_op( type="reverse", inputs={'X': out}, outputs={'Out': [reverse_out_var]}, attrs={'axis': reverse_axis}) out = reverse_out_var return out @six.add_metaclass(VariableMetaClass) class Variable(object): """ **Notes**: **The constructor of Variable should not be invoked directly.** **In Static Graph Mode: Please use** `Block.create_var` **to create a Static variable which has no data until being feed.** **In Dygraph Mode: Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph variable with real data** In Fluid, every input and output of an OP is a variable. In most cases, variables are used for holding different kinds of data or training labels. A variable belongs to a :ref:`api_guide_Block_en` . All variable has its own name and two variables in different :ref:`api_guide_Block_en` could have the same name. There are many kinds of variables. Each kind of them has its own attributes and usages. Please refer to the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details. Most of a Variable's member variables can be set to be None. It mean it is not available or will be specified later. Examples: In Static Graph Mode: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') In `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ Mode: .. code-block:: python import paddle.fluid as fluid import numpy as np with fluid.dygraph.guard(): new_variable = fluid.dygraph.to_variable(np.arange(10)) """ def __init__(self, block, type=core.VarDesc.VarType.LOD_TENSOR, name=None, shape=None, dtype=None, lod_level=None, capacity=None, persistable=None, error_clip=None, stop_gradient=False, is_data=False, need_check_feed=False, belong_to_optimizer=False, **kwargs): self.block = block if name is None: name = unique_name.generate('_generated_var') if dtype is not None: if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) self.belong_to_optimizer = belong_to_optimizer self.error_clip = error_clip is_new_var = False name = cpt.to_text(name) self.desc = self.block.desc.find_var(cpt.to_bytes(name)) if self.desc is None: self.desc = self.block.desc.var(cpt.to_bytes(name)) is_new_var = True if is_new_var: self.desc.set_type(type) elif self.desc.type() != type: raise ValueError("Variable {0} has been created before. The " "previous type is {1}; the new type is {2}. They" " are not matched".format(self.name, self.desc.type(), type)) if shape is not None: if is_new_var: self.desc.set_shape(shape) else: old_shape = self.shape shape = tuple(shape) if shape != old_shape: raise ValueError( "Variable {0} has been created before. the previous " "shape is {1}; the new shape is {2}. They are not " "matched.".format(self.name, old_shape, shape)) if dtype is not None: if is_new_var: self.desc.set_dtype(dtype) else: old_dtype = self.dtype if dtype != old_dtype: raise ValueError("Variable {0} has been created before. " "The previous data type is {1}; the new " "data type is {2}. They are not " "matched.".format(self.name, old_dtype, dtype)) if lod_level is not None: if is_new_var: self.desc.set_lod_level(lod_level) else: if lod_level != self.lod_level: raise ValueError("Variable {0} has been created before. " "The previous lod_level is {1}; the new " "lod_level is {2}. They are not " "matched".format(self.name, self.lod_level, lod_level)) if persistable is not None: if is_new_var: self.desc.set_persistable(persistable) else: if persistable != self.persistable: raise ValueError( "Variable {0} has been created before." "The previous persistable is {1}; the new " "persistable is {2}. They are not matched".format( self.name, self.persistable, persistable)) if need_check_feed and is_new_var: self.desc.set_need_check_feed(need_check_feed) if capacity is not None: if is_new_var: self.desc.set_capacity(capacity) else: # TODO(abhinavarora) : Compare with set capacity once, # get_capacity is implemented pass self.block.vars[name] = self self.op = None self._stop_gradient = stop_gradient self.is_data = is_data @fake_interface_only def detach(self): """ **Notes**: **This API is ONLY available in Dygraph mode** Returns a new Variable, detached from the current graph. Returns: ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable. Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph import Linear import numpy as np data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32') with fluid.dygraph.guard(): linear = Linear(32, 64) data = to_variable(data) x = linear(data) y = x.detach() """ pass @fake_interface_only def numpy(self): """ **Notes**: **This API is ONLY available in Dygraph mode** Returns a numpy array shows the value of current :ref:`api_guide_Variable_en` Returns: ndarray: The numpy value of current Variable. Returns type: ndarray: dtype is same as current Variable Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph import Linear import numpy as np data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32') with fluid.dygraph.guard(): linear = Linear(32, 64) data = to_variable(data) x = linear(data) print(x.numpy()) """ pass @fake_interface_only def set_value(self, value): """ **Notes**: **This API is ONLY available in Dygraph mode** Set a new value for this Variable. Args: value (Variable|np.ndarray): the new value. Examples: .. code-block:: python import paddle.fluid as fluid from paddle.fluid.dygraph.base import to_variable from paddle.fluid.dygraph import Linear import numpy as np data = np.ones([3, 1024], dtype='float32') with fluid.dygraph.guard(): linear = fluid.dygraph.Linear(1024, 4) t = to_variable(data) linear(t) # call with default weight custom_weight = np.random.randn(1024, 4).astype("float32") linear.weight.set_value(custom_weight) # change existing weight out = linear(t) # call with different weight """ pass @fake_interface_only def backward(self, backward_strategy=None): """ **Notes**: **This API is ONLY available in Dygraph mode** Run backward of current Graph which starts from current Variable Args: backward_strategy( :ref:`api_fluid_dygraph_BackwardStrategy` ): The Backward Strategy to run backward Returns: NoneType: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np x = np.ones([2, 2], np.float32) with fluid.dygraph.guard(): inputs2 = [] for _ in range(10): tmp = fluid.dygraph.base.to_variable(x) # if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since # there is no one need gradient on it. tmp.stop_gradient=False inputs2.append(tmp) ret2 = fluid.layers.sums(inputs2) loss2 = fluid.layers.reduce_sum(ret2) backward_strategy = fluid.dygraph.BackwardStrategy() backward_strategy.sort_sum_gradient = True loss2.backward(backward_strategy) """ pass @fake_interface_only def gradient(self): """ **Notes**: **This API is ONLY available in Dygraph mode** Get the Gradient of Current Variable Returns: ndarray or tuple of ndarray: if Variable's type is LoDTensor, return numpy value of the gradient of current Variable, if Variable's type is SelectedRows, return tuple of ndarray, first element of tuple is numpy value of the gradient of current Variable, second element of tuple is numpy value of the rows of current Variable. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # example1: return ndarray x = np.ones([2, 2], np.float32) with fluid.dygraph.guard(): inputs2 = [] for _ in range(10): tmp = fluid.dygraph.base.to_variable(x) tmp.stop_gradient=False inputs2.append(tmp) ret2 = fluid.layers.sums(inputs2) loss2 = fluid.layers.reduce_sum(ret2) backward_strategy = fluid.dygraph.BackwardStrategy() backward_strategy.sort_sum_gradient = True loss2.backward(backward_strategy) print(loss2.gradient()) # example2: return tuple of ndarray with fluid.dygraph.guard(): embedding = fluid.dygraph.Embedding( size=[20, 32], param_attr='emb.w', is_sparse=True) x_data = np.arange(12).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, 3, 1)) x = fluid.dygraph.base.to_variable(x_data) out = embedding(x) out.backward() print(embedding.weight.gradient()) """ pass @fake_interface_only def clear_gradient(self): """ **Notes**: **1. This API is ONLY available in Dygraph mode** **2. Use it only Variable has gradient, normally we use this for Parameters since other temporal Variable will be deleted by Python's GC** Clear (set to ``0`` ) the Gradient of Current Variable Returns: None Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np x = np.ones([2, 2], np.float32) with fluid.dygraph.guard(): inputs2 = [] for _ in range(10): tmp = fluid.dygraph.base.to_variable(x) tmp.stop_gradient=False inputs2.append(tmp) ret2 = fluid.layers.sums(inputs2) loss2 = fluid.layers.reduce_sum(ret2) backward_strategy = fluid.dygraph.BackwardStrategy() backward_strategy.sort_sum_gradient = True loss2.backward(backward_strategy) print(loss2.gradient()) loss2.clear_gradient() print("After clear {}".format(loss2.gradient())) """ pass def __str__(self): return self._to_readable_code() def _to_readable_code(self): """ Get readable debug string of Variable. .. note:: If you want to get the debug string in protobuf format, please use :code:`to_string` method. Returns: string: The formatted Variable string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print(new_variable._to_readable_code()) """ if self.type == core.VarDesc.VarType.SELECTED_ROWS or self.type == core.VarDesc.VarType.LOD_TENSOR: var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})".\ format(i="{", e="}", name=self.name, type=self.type, shape=self.shape, dtype=self.dtype) else: var_str = "{name} : fluid.{type})".\ format(i="{", e="}", name=self.name, type=self.type) if type(self) == Parameter: if self.trainable: var_str = "trainable param " + var_str else: var_str = "param " + var_str else: var_str = "var " + var_str if self.persistable: var_str = "persist " + var_str return var_str def to_string(self, throw_on_error, with_details=False): """ Get debug string. Args: throw_on_error (bool): True if raise an exception when self is not initialized. with_details (bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default value is False; Returns: str: The debug string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print(new_variable.to_string(True)) print("=============with detail===============") print(new_variable.to_string(True, True)) """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) protostr = self.desc.serialize_to_string() proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr)) res_str = _debug_string_(proto, throw_on_error) if with_details: additional_attr = ("error_clip", "stop_gradient") for attr_name in additional_attr: res_str += "%s: %s\n" % (attr_name, cpt.to_text(getattr(self, attr_name))) return res_str __repr__ = __str__ @property def stop_gradient(self): """ Indicating if we stop gradient from current Variable **Notes: This Property has default value as** ``True`` **in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, while Parameter's default value is False. However, in Static Graph Mode all Variable's default stop_gradient value is** ``False`` Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np with fluid.dygraph.guard(): value0 = np.arange(26).reshape(2, 13).astype("float32") value1 = np.arange(6).reshape(2, 3).astype("float32") value2 = np.arange(10).reshape(2, 5).astype("float32") linear = fluid.Linear(13, 5, dtype="float32") linear2 = fluid.Linear(3, 3, dtype="float32") a = fluid.dygraph.to_variable(value0) b = fluid.dygraph.to_variable(value1) c = fluid.dygraph.to_variable(value2) out1 = linear(a) out2 = linear2(b) out1.stop_gradient = True out = fluid.layers.concat(input=[out1, out2, c], axis=1) out.backward() assert linear.weight.gradient() is None assert (out1.gradient() == 0).all() """ return self._stop_gradient @stop_gradient.setter def stop_gradient(self, s): self._stop_gradient = s @property def persistable(self): """ Indicating if we current Variable should be long-term alive **Notes: This Property will be deprecated and this API is just to help user understand concept** **1. All Variable's persistable is** ``False`` **except Parameters.** **2. In** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, this property should not be changed** Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("persistable of current Var is: {}".format(new_variable.persistable)) """ return self.desc.persistable() @persistable.setter def persistable(self, p): self.desc.set_persistable(p) @property def name(self): """ Indicating name of current Variable **Notes: If it has two or more Varaible share the same name in the same** :ref:`api_guide_Block_en` **, it means these Variable will share content in no-** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode. This is how we achieve Parameter sharing** Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("name of current Var is: {}".format(new_variable.name)) """ return cpt.to_text(self.desc.name()) @property def grad_name(self): """ Indicating name of the gradient Variable of current Variable. **Notes: This is a read-only property. It simply returns name of gradient Variable from a naming convention but doesn't guarantee the gradient exists.** Examples: .. code-block:: python import paddle.fluid as fluid x = fluid.data(name="x", shape=[-1, 23, 48], dtype='float32') print(x.grad_name) # output is "x@GRAD" """ return self.name + "@GRAD" @name.setter def name(self, new_name): self.desc.set_name(new_name) @property def shape(self): """ Indicating shape of current Variable **Notes: This is a read-only property** Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("shape of current Var is: {}".format(new_variable.shape)) """ # convert to tuple, make it as same as numpy API. return tuple(self.desc.shape()) @property def dtype(self): """ Indicating data type of current Variable **Notes: This is a read-only property** Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("Dtype of current Var is: {}".format(new_variable.dtype)) """ return self.desc.dtype() @property def lod_level(self): """ Indicating ``LoD`` info of current Variable, please refer to :ref:`api_fluid_LoDTensor_en` to check the meaning of ``LoD`` **Notes**: **1. This is a read-only property** **2. Don't support this property in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, it's value should be** ``0(int)`` Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("LoD Level of current Var is: {}".format(new_variable.lod_level)) """ if self.type == core.VarDesc.VarType.SELECTED_ROWS: raise Exception("SelectedRows DO NOT supprt lod") return self.desc.lod_level() @property def type(self): """ Indicating Type of current Variable **Notes: This is a read-only property** Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print("Type of current Var is: {}".format(new_variable.type)) """ return self.desc.type() def _set_error_clip(self, error_clip): """ Set the error_clip. Args: error_clip(BaseErrorClipAttr) : The new error_clip. Returns: None """ self.error_clip = error_clip def _set_info(self, key, value): """ Set key-value information for this variable. Args: key(str): Key for this information. value(object): The value associated to the key. Returns: None """ if not hasattr(self, "_info"): self._info = {} self._info[key] = value def _get_info(self, key): """ Get the information of this variable corresponding to key. Args: key(str): Key for this information. Returns: object """ if hasattr(self, "_info") and key in self._info: return self._info[key] return None def _slice_indices(self, slice, length): """ Reference implementation for the slice.indices method. """ # Compute step and length as integers. step = 1 if slice.step is None else slice.step # Raise ValueError for negative length or zero step. if length < 0: raise ValueError("length should not be negative") if step == 0: raise ValueError("slice step can not be zero") # Find lower and upper bounds for start and stop. lower = -1 if step < 0 else 0 upper = length - 1 if step < 0 else length # Compute start. if slice.start is None: start = upper if step < 0 else lower else: start = slice.start start = max(start + length, lower) if start < 0 else min(start, upper) # Compute stop. if slice.stop is None: stop = lower if step < 0 else upper else: stop = slice.stop stop = max(stop + length, lower) if stop < 0 else min(stop, upper) return start, stop, step def _detectEllipsis(self, item): has_ellipsis = False start = 0 end = len(self.shape) for index, o in enumerate(item): if o is Ellipsis: if has_ellipsis: raise ValueError("Index can have one ellipsis only.") has_ellipsis = True start = index else: if has_ellipsis: end = index return has_ellipsis, start, end def _reconstructSliceinfo(self, item): has_ellipsis, start, end = self._detectEllipsis(item) if has_ellipsis: newitem = [] for i in range(start): newitem.append(item[i]) for i in range(start, end): newitem.append(slice(None, None, None)) for i in range(end, len(item)): newitem.append(item[i]) return newitem else: return None def _detectContinuesSlice(self, item): starts = [] ends = [] for index, o in enumerate(item): if isinstance(o, int): start = int(o) if (index > 0 and index >= self.shape[index]) \ or (index < 0 and (index + self.shape[index]) < 0): raise IndexError("invalid index") start = max(start + self.shape[index], 0) if start < 0 else min( start, self.shape[index]) starts.append(start) ends.append(start + 1) elif isinstance(o, slice): start, stop, step = self._slice_indices(o, self.shape[index]) if step == 1 or step == -1: starts.append(start) ends.append(stop) else: return False, None else: raise IndexError("Valid index accept int or slice or ellipsis") return True, [starts, ends] def _cloneVar(self, copy=False): if not copy: return self.block.create_var( name=unique_name.generate_with_ignorable_key(self.name), dtype=self.dtype) else: return self def _sliceVar(self, axes, starts, ends): new_var = self._cloneVar() self.block.append_op( type="slice", inputs={'Input': [self]}, outputs={'Out': [new_var]}, attrs={'axes': axes, 'starts': starts, 'ends': ends}) return new_var def _concatVar(self, inputs, axis): new_var = self._cloneVar() self.block.append_op( type="concat", inputs={'X': inputs}, outputs={'Out': [new_var]}, attrs={'axis': axis, }) return new_var def _sliceAndConcatVar(self, item, axis): if isinstance(item, slice): if self.shape[axis] < 0: return self._cloneVar(True) start, stop, step = self._slice_indices(item, self.shape[axis]) if step == 1: return self._sliceVar([axis], [start], [stop]) else: vars = [] if step > 0: while start < stop: vars.append( self._sliceVar([axis], [start], [start + 1])) start += step else: while start > stop: vars.append( self._sliceVar([axis], [start], [start + 1])) start += step return self._concatVar(vars, axis) elif isinstance(item, int): if self.shape[axis] < 0: return self._cloneVar(True) index = int(item) if (index > 0 and index >= self.shape[axis]) \ or (index < 0 and (index + self.shape[axis]) < 0): raise IndexError("invalid index") return self._sliceVar([axis], [index], [index + 1]) else: raise IndexError("Valid index accept int or slice or tuple") def __getitem__(self, item): return _getitem_impl_(self, item) def get_all_op_protos(): """ Get all registered op proto from PaddlePaddle C++ end. Returns: list: list of OpProto. """ protostrs = core.get_all_op_protos() ret_values = [] for pbstr in protostrs: op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr)) ret_values.append(op_proto) return ret_values class ComplexVariable(object): """ The Variable defined on the complex number domain. It contains two common real number Variables as its members, :attr:`real` and :attr:`imag` holding the real part and imaginary part of complex numbers respectively. **Notes**: **The constructor of ComplexVariable should not be invoked directly.** **Only support dygraph mode at present. Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph ComplexVariable with complex number data.** Args: real (Variable): The Variable holding real-part data. imag (Variable): The Variable holding imaginery-part data. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np a = np.array([1.0+2.0j, 0.2]) with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(a, name="new_var") print(var.name, var.dtype, var.shape) # ({'real': u'new_var.real', 'imag': u'new_var.imag'}, 'complex128', [2L]) print(var.numpy()) # [1. +2.j 0.2+0.j] """ def __init__(self, real, imag): assert real.shape == imag.shape, "The real part and imaginary part " \ "of a ComplexVariable should have the same shape!" assert real.dtype == imag.dtype, "The real part and imaginary part " \ "of a ComplexVariable should have the same data type!" self.real = real self.imag = imag if self.real.dtype in [ core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32 ]: self._dtype = "complex64" else: self._dtype = "complex128" self._shape = self.real.shape @property def dtype(self): return self._dtype @property def shape(self): return self._shape @property def name(self): return {"real": self.real.name, "imag": self.imag.name} @name.setter def name(self, name): # rename if isinstance(name, str): self.real.name = name + ".real" self.imag.name = name + ".imag" elif (isinstance(name, tuple) or isinstance(name, list)) and len(name) == 2: self.real.name, self.imag.name = name[0], name[1] else: raise ValueError( "An invalid name assigned to the ComplexVariable, " "which must be a string, or a tuple or a list with length 2!") def numpy(self): return self.real.numpy() + 1j * self.imag.numpy() def __str__(self): return "REAL: " + self.real.__str__() + "IMAG: " + self.imag.__str__() __repr__ = __str__ class OpProtoHolder(object): """ A global variable to hold all OpProtos from C++ as a map """ @classmethod def instance(cls): if not hasattr(cls, '_instance'): cls._instance = cls() return cls._instance def __init__(self): assert not hasattr( self.__class__, '_instance'), 'Please use `instance()` to get OpProtoHolder object!' op_protos = get_all_op_protos() self.op_proto_map = {} for proto in op_protos: self.op_proto_map[proto.type] = proto def get_op_proto(self, type): """ Get OpProto by a type string. Args: type(str): The type that operator registered in C++ side. Returns(framework_pb2.OpProto): The OpProto """ if type not in self.op_proto_map: raise ValueError("Operator \"%s\" has not been registered." % type) return self.op_proto_map[type] def update_op_proto(self): op_protos = get_all_op_protos() for proto in op_protos: if proto.type not in self.op_proto_map: self.op_proto_map[proto.type] = proto @staticmethod def generated_op_attr_names(): return { core.op_proto_and_checker_maker.kOpRoleAttrName(), core.op_proto_and_checker_maker.kOpRoleVarAttrName(), core.op_proto_and_checker_maker.kOpNameScopeAttrName(), core.op_proto_and_checker_maker.kOpCreationCallstackAttrName(), core.op_proto_and_checker_maker.kOpDeviceAttrName() } class Operator(object): """ In Fluid, all the operation are represented by Operator, and Operator is regarded as a build in an instruction of a Block. Users can use the build in instructions to describe their neural network. Args: block(Block): The block has the current operator. desc(core.OpDesc): The protobuf description of Operator. type(str): The type of operator. Default None. inputs(dict): The input of this Operator. it is a dictionary, for every element, key is the input parameter name, and value is a list of variables. Default None. outputs(dict): The output of this Operator. it is a dictionary, for every element, key is the input parameter name, and value is a list of variables. Default None. attrs(dict): The attributes of this Operator. it is a dictionary, for every element, key is attribute name, and value is the attribute value. The attribute type should be as same as the type registered in C++ side. Default None. Returns: Operator: The initialized Operator. Raises: ValueError: If the passed input, output and attrs doesn't match the initializing Operator's that registered in C++ side. Notes: The constructor of operator should not be invoked directly. Use Block.append_op or Block._prepend_op instead. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() # var1 += var2 + var3 cur_block.append_op(type="sum", inputs={"X": [var1, var2, var3]}, outputs={"Out": [var1]}) """ OP_WITHOUT_KERNEL_SET = { 'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv', 'listen_and_serv', 'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id', 'c_gen_nccl_id', 'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream', 'queue_generator', 'dequeue', 'enqueue' } def __init__(self, block, desc, type=None, inputs=None, outputs=None, attrs=None): if in_dygraph_mode(): if type is None: raise ValueError( "`type` to initialized an Operator can not be None.") self._type = type self.attrs = attrs if attrs else {} else: self.block = block self.desc = desc # note: not add self.attrs here: # https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173 op_attrs = attrs if op_attrs is None: op_attrs = dict() del attrs op_maker = core.op_proto_and_checker_maker if op_maker.kOpRoleAttrName() not in op_attrs: op_attrs[op_maker.kOpRoleAttrName( )] = self.block.program._op_role role_var_name = op_maker.kOpRoleVarAttrName() if len(self.block.program. _op_role_var) != 0 and role_var_name not in op_attrs: op_attrs[role_var_name] = self.block.program._op_role_var if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0: del op_attrs[role_var_name] if len(self.desc.type()) != 0: return if type is None: raise ValueError( "`type` to initialized an Operator can not be None.") else: callstack_var_name = op_maker.kOpCreationCallstackAttrName() op_attrs[callstack_var_name] = list( reversed(traceback.format_stack()))[1:] self.desc.set_type(type) proto = OpProtoHolder.instance().get_op_proto(type) namescope_var_name = op_maker.kOpNameScopeAttrName() op_attrs[namescope_var_name] = _full_name_scope() # set device for op with kernels, give warning for op without kernels # when force_cpu and device_guard are used at the same time, a warning will be given. # TODO(zhangting2020): when force_cpu is removed, clear warning below. if _current_device is not None: if self._has_kernel(type): op_device = op_maker.kOpDeviceAttrName() op_attrs[op_device] = _current_device else: warnings.warn("The Op(%s) is not support to set device." % type) if 'force_cpu' in op_attrs: if (type is 'less_than' and op_attrs['force_cpu'] != None ) or op_attrs['force_cpu'] != False: warnings.warn( "The Attr(force_cpu) of Op(%s) will be deprecated in the future, " "please use 'device_guard' instead. 'device_guard' has higher priority when they are " "used at the same time." % type) def find_name(var_list, name): for var_name in var_list: if var_list[var_name] is not None and var_name == name: return True return False if inputs is not None: for in_proto in proto.inputs: found = find_name(inputs, in_proto.name) assert found or in_proto.dispensable, "Input {} not found".format( in_proto.name) if found: in_args = inputs[in_proto.name] if not isinstance(in_args, list): in_args = [in_args] if not in_proto.duplicable and len(in_args) > 1: raise ValueError( "Input %s expects only one input, but %d are given." % (in_proto.name, len(in_args))) in_arg_names = [] for index, arg in enumerate(in_args): if isinstance(arg, six.string_types): in_arg_names.append(arg) elif isinstance(arg, six.binary_type): in_arg_names.append(arg.decode()) elif isinstance(arg, (Variable, core.VarBase)): in_arg_names.append(cpt.to_text(arg.name)) else: raise TypeError( "The type of '%s' in operator %s should be " "one of [basestring(), str, Varibale] in python2, " "or one of [str, bytes, Variable] in python3." "but received : %s" % (in_proto.name, type, arg)) self.desc.set_input(in_proto.name, in_arg_names) else: self.desc.set_input(in_proto.name, []) if outputs is not None: for m in proto.outputs: if (m.name not in outputs) and m.dispensable: continue if not ((m.name in outputs) or m.dispensable): raise ValueError(("Incorrect setting for output(s) of " "operator \"%s\", should set: [%s].") % (type, m.name)) for out_proto in proto.outputs: if out_proto.name not in outputs: continue out_args = outputs[out_proto.name] if not isinstance(out_args, list): out_args = [out_args] if not out_proto.duplicable and len(out_args) > 1: raise ValueError( "Output %s expects only one output, but %d are given." % (out_proto.name, len(out_args))) out_arg_names = [] for arg in out_args: out_arg_names.append(cpt.to_text(arg.name)) # TODO(minqiyang): could we remove variable's op in static mode? if not in_dygraph_mode(): arg.op = self self.desc.set_output(out_proto.name, out_arg_names) if op_attrs is not None: if not isinstance(op_attrs, dict): raise TypeError("'attrs' should be a dict.") for attr in proto.attrs: attr_name = attr.name if (attr_name not in op_attrs) or ( op_attrs[attr_name] is None): continue attr_val = op_attrs[attr_name] self._update_desc_attr(attr_name, attr_val) self.desc.check_attrs() if self._has_kernel(type): self.desc.infer_var_type(self.block.desc) self.desc.infer_shape(self.block.desc) def _has_kernel(self, op_type): return op_type not in self.OP_WITHOUT_KERNEL_SET def to_string(self, throw_on_error): """ Get debug string. Args: throw_on_error(bool): Whether to raise exception if self is not initialized. Returns: str: The debug string. """ protostr = self.desc.serialize_to_string() proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr)) return _debug_string_(proto, throw_on_error) def _to_readable_code(self, skip_op_callstack=True): """ Get readable debug string of Operator. .. note:: If you want to get the debug string in protobuf format, please use :code:`to_string` method. Args: skip_op_callstack(bool): whether to skip parsing Operator's attribute op_callstack, default value is True Returns: string: The formatted Operator string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() var = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') new_op = cur_block.append_op(type="abs", inputs={"X": [var]}, outputs={"Out": [var]}) print(new_op._to_readable_code()) """ assert isinstance( skip_op_callstack, bool ), "skip_op_callstack parameter's type is error, expect bool, received %s".format( type(skip_op_callstack)) outputs_str = "{" for i in range(0, len(self.output_names)): outputs_str += "{name}=".format(name=self.output_names[i]) o = self.output(self.output_names[i]) outputs_str += "{value}".format(value=o) if i != len(self.output_names) - 1: outputs_str += ", " outputs_str += "}" inputs_str = "{" for i in range(0, len(self.input_names)): inputs_str += "{name}=".format(name=self.input_names[i]) o = self.input(self.input_names[i]) inputs_str += "{value}".format(value=o) if i != len(self.input_names) - 1: inputs_str += ", " inputs_str += "}" attr_names = sorted(self.attr_names) attrs_str = "" for i in range(0, len(attr_names)): name = attr_names[i] if skip_op_callstack and name == "op_callstack": continue attr_type = self.desc.attr_type(name) if attr_type == core.AttrType.BLOCK: a = "{name} = block[{value}]".format( name=name, type=attr_type, value=self._block_attr_id(name)) attrs_str += a if i != len(attr_names) - 1: attrs_str += ", " continue if attr_type == core.AttrType.BLOCKS: a = "{name} = blocks{value}".format( name=name, type=attr_type, value=self._blocks_attr_ids(name)) attrs_str += a if i != len(attr_names) - 1: attrs_str += ", " continue a = "{name} = {value}".format( name=name, type=attr_type, value=self.desc.attr(name)) attrs_str += a if i != len(attr_names) - 1: attrs_str += ", " if outputs_str != "{}": op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\ format(outputs = outputs_str, op_type=self.type, inputs=inputs_str, attrs=attrs_str) else: op_str = "{op_type}(inputs={inputs}, {attrs})".\ format(op_type=self.type, inputs=inputs_str, attrs=attrs_str) return op_str def __str__(self): return self._to_readable_code() __repr__ = __str__ @property def type(self): return self.desc.type() def input(self, name): """ Get the input arguments according to the input parameter name. Args: name(str): The input parameter name. Returns: list: return the list of argument names that associated with \ the specific parameter name. """ return self.desc.input(name) def _rename_input(self, old_name, new_name): """ Rename the `old_name` to `new_name`. Args: old_name(str): The old name of the Operator's input. new_name(str): The new name of the Operator's input. Returns: None """ self.desc._rename_input(old_name, new_name) def _rename_output(self, old_name, new_name): """ Rename the `old_name` to `new_name`. Args: old_name(str): The old name of the Operator's output. new_name(str): The new name of the Operator's output. Returns: None """ self.desc._rename_output(old_name, new_name) @property def input_names(self): return self.desc.input_names() @property def input_arg_names(self): return self.desc.input_arg_names() @property def output_arg_names(self): return self.desc.output_arg_names() def output(self, name): """ Get output arguments by the output parameter name. Args: name(str): The output parameter name. Returns: list: return the list of argument names associated with \ the specific parameter name. """ return self.desc.output(name) @property def output_names(self): return self.desc.output_names() @property def idx(self): for i, op in enumerate(self.block.ops): if op == self: return i raise ValueError( "Can't find op itself in it's block. It could be a bug of Paddle.") def has_attr(self, name): """ Whether this Operator has the attribute with name or not. Args: name(str): the attribute name. Returns: bool: True if has this attribute. """ return self.desc.has_attr(name) def attr_type(self, name): """ Get the type of attribute by attribute's name. Args: name(str): the attribute name. Returns: core.AttrType: the attribute type. """ return self.desc.attr_type(name) def _set_attr(self, name, val): """ Set the value of attribute by attribute's name. Args: name(str): the attribute name. val(bool|int|str|float|list): the value of the attribute. Raises: ValueError: If the type of value doesn't match with desc.attr_type(name). """ self._update_desc_attr(name, val) def _remove_attr(self, name): self.desc.remove_attr(name) def _update_desc_attr(self, name, val): """ Update the value of desc's attribute by attribute's name. Args: name(str): the attribute name. val(bool|int|str|float|list): the value of the attribute. Raises: ValueError: If the type of value doesn't match with desc.attr_type(name). """ if isinstance(val, Block): self.desc.set_block_attr(name, val.desc) elif isinstance(val, list) and val and all( isinstance(v, Block) for v in val): self.desc.set_blocks_attr(name, [v.desc for v in val]) elif isinstance(val, core.BlockDesc) or \ isinstance(val, core.ProgramDesc): self.desc.set_serialized_attr(name, val.serialize_to_string()) else: self.desc._set_attr(name, val) @property def attr_names(self): return self.desc.attr_names() def attr(self, name): """ Get the attribute by name. Args: name(str): the attribute name. Returns: bool|int|str|float|list: The attribute value. The return value can be any valid attribute type. """ return self.desc.attr(name) def _block_attr_id(self, name): """ Get the block attribute's id by name. Args: name(str): the attribute name. Returns: int: the block index. """ return self.desc._block_attr_id(name) def _block_attr(self, name): """ Get the block attribute by name. Args: name(str): the attribute name. Returns: block: the block attribute. """ id = self._block_attr_id(name) assert (id >= 0 and id < len(self.block.program.blocks)) return self.block.program.blocks[id] def _blocks_attr(self, name): """ Get the blocks attribute by name. Args: name(str): the attribute name. Returns: list: list of the blocks attribute. """ attrs = [] for i in self._blocks_attr_ids(name): assert (i >= 0 and i < len(self.block.program.blocks)) attrs.append(self.block.program.blocks[i]) return attrs def _blocks_attr_ids(self, name): """ Get the blocks attribute's ids by name. Args: name(str): the attribute name. Returns: list: list of the blocks ids. """ return self.desc._blocks_attr_ids(name) def all_attrs(self): """ Get the attribute dict. Returns: dict: The Operator's attribute dict, name->attr. """ attr_names = self.attr_names attr_map = {} for n in attr_names: attr_type = self.desc.attr_type(n) if attr_type == core.AttrType.BLOCK: attr_map[n] = self._block_attr(n) continue if attr_type == core.AttrType.BLOCKS: attr_map[n] = self._blocks_attr(n) continue attr_map[n] = self.attr(n) return attr_map def _is_optimize_op(self): op_maker = core.op_proto_and_checker_maker OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize op_role = self.desc.attr(op_maker.kOpRoleAttrName()) if op_role & int(OPTIMIZE): return True else: return False class Block(object): """ In Fluid, a Program is consistence of multi-Block, and Block stores VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name. One block could have some child blocks, and child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that is stored in the parent block. Please reference the framework.proto for details. Args: program(Program): The Program that the Block belongs to. idx(int): The block's id in the Program. Notes: The constructor of Block should not be invoked directly. Please use `Program._create_block()` to create a block. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() var = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') cur_block.append_op(type="abs", inputs={"X": [var]}, outputs={"Out": [var]}) """ def __init__(self, program, idx): self.desc = program.desc.block(idx) self.vars = collections.OrderedDict() # var_name --> var self.ops = list() # operator list self.program = program self.removed_vars = collections.OrderedDict() def __str__(self): return self._to_readable_code() def _to_readable_code(self, skip_op_callstack=True): """ Get readable debug string of Block. .. note:: If you want to get the debug string in protobuf format, please use :code:`to_string` method. Args: skip_op_callstack(bool): whether to skip parsing Operator's attribute op_callstack, default value is True Returns: string: The formatted Block string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_var = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') new_op = cur_block.append_op(type="abs", inputs={"X": [new_var]}, outputs={"Out": [new_var]}) print(cur_block._to_readable_code()) """ assert isinstance( skip_op_callstack, bool ), "skip_op_callstack parameter's type is error, expect bool, received %s".format( type(skip_op_callstack)) block_str = "{ // block " block_str += "{}\n".format(self.idx) for var in list(self.vars.values()): block_str += " {}\n".format(var._to_readable_code()) block_str += "\n" for op in self.ops: block_str += " {}\n".format( op._to_readable_code(skip_op_callstack)) block_str += "}" return block_str def to_string(self, throw_on_error, with_details=False): """ Get debug string. Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True. with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default False. Returns: str: The debug string. """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) if with_details: re_add_indent = re.compile(r"\n(.)") res_str = "blocks {\n idx: %d\n parent_idx: %d" % ( self.idx, self.parent_idx) for var in list(self.vars.values()): res_str += "\n vars {\n %s }" % re_add_indent.sub( r"\n \1", var.to_string(throw_on_error, with_details)) for op in self.ops: res_str += "\n ops {\n %s }" % re_add_indent.sub( r"\n \1", op.to_string(throw_on_error)) res_str += "\n}" else: protostr = self.desc.serialize_to_string() proto = framework_pb2.BlockDesc.FromString( six.binary_type(protostr)) res_str = _debug_string_(proto, throw_on_error) return res_str __repr__ = __str__ @property def parent_idx(self): return self.desc.parent @property def forward_block_idx(self): return self.desc.get_forward_block_idx() def _set_forward_block_idx(self, idx): """ Set the forward block Idx. Args: idx(int): the block index. Returns: None """ self.desc._set_forward_block_idx(idx) @property def backward_block_idx(self): cur_block_idx = self.idx for block in self.program.blocks: if block.forward_block_idx == cur_block_idx: return block.idx return -1 @property def idx(self): return self.desc.id def var(self, name): """ Get a Variable by name from this block. Args: name(str): the Variable's name. Raises: ValueError: The If input's type is not str, or this block doesn't have a Variable with the giving name. Returns: Variable: the Variable with the giving name. """ if not isinstance(name, six.string_types): raise TypeError( "var require string as parameter, but get %s instead." % (type(name))) v = self.vars.get(name, None) if v is None: raise ValueError("var %s not in this block" % name) return v def _find_var_recursive(self, name): """ Get a Variable by name from this block recursively. Args: name(str): the Variable's name. Returns: Variable: the Variable with the giving name. Or None if not found. """ frontier = list() visited = set() frontier.append(self) prog = self.program while len(frontier) != 0: # BFS cur = frontier[0] frontier = frontier[1:] if id(cur) in visited: continue if cur.has_var(name): return cur.var(name) if cur.parent_idx != -1: frontier.append(prog.block(cur.parent_idx)) if cur.forward_block_idx != -1: frontier.append(prog.block(cur.forward_block_idx)) visited.add(id(cur)) return None def _var_recursive(self, name): """ Get a Variable by name from this block recursively. Args: name(str): the Variable's name. Raises: ValueError: this block and this parent block doesn't have a Variable with the giving name. Returns: Variable: the Variable with the giving name. """ var = self._find_var_recursive(name) if var: return var else: raise ValueError("Var {0} is not found recursively".format(name)) def all_parameters(self): return list(self.iter_parameters()) def iter_parameters(self): return (item[1] for item in six.iteritems(self.vars) if isinstance(item[1], Parameter)) def create_var(self, *args, **kwargs): if in_dygraph_mode(): var = _varbase_creator(*args, **kwargs) else: var = Variable(block=self, *args, **kwargs) if 'initializer' in kwargs: kwargs['initializer'](var, self) return var def has_var(self, name): return name in self.vars def _rename_var(self, name, new_name): """ Rename variable in vars and ops' inputs and outputs Args: name(str): the name that need to be renamed. new_name(str): the name that need to rename to. Raises: ValueError: If this block doesn't have this the giving name, or the type of the var with the giving name is not Parameter or Variable. Returns: Variable: the Variable with the giving name. """ name = cpt.to_text(name) new_name = cpt.to_text(new_name) if not self.has_var(name): raise ValueError("var %s is not in current block" % name) v = self.var(name) if type(v) == Parameter: var_type = "Parameter" stop_gradient = v.stop_gradient trainable = v.trainable optimize_attr = v.optimize_attr regularizer = v.regularizer error_clip = v.error_clip elif type(v) == Variable: var_type = "Variable" error_clip = v.error_clip stop_gradient = v.stop_gradient else: raise ValueError("unsupported var type: %s", type(v)) orig_var_type = v.type self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name)) # NOTE: v is destroyed by C++ after calling _rename_var. d = self.desc.find_var(cpt.to_bytes(new_name)) if var_type == "Parameter": if in_dygraph_mode(): var = ParamBase( d.shape(), d.dtype(), type=orig_var_type, name=new_name, stop_gradient=stop_gradient, trainable=trainable, optimize_attr=optimize_attr, regularizer=regularizer, error_clip=error_clip) else: var = Parameter( self, d.shape(), d.dtype(), type=orig_var_type, name=new_name, stop_gradient=stop_gradient, trainable=trainable, optimize_attr=optimize_attr, regularizer=regularizer, error_clip=error_clip) elif var_type == "Variable": var = Variable( self, type=orig_var_type, name=new_name, error_clip=error_clip, stop_gradient=stop_gradient) # rename the python side, _sync_with_cpp will only add # new vars/ops to python side. self.vars[new_name] = var del self.vars[name] self._sync_with_cpp() return var def _remove_var(self, name): self._sync_with_cpp() self.desc._remove_var(cpt.to_bytes(name)) del self.vars[name] def create_parameter(self, *args, **kwargs): global_block = self.program.global_block() param = None if in_dygraph_mode(): param = ParamBase(*args, **kwargs) else: param = Parameter(global_block, *args, **kwargs) if 'initializer' in kwargs: def _is_inited_by(block, var): init_ops = [] for op in block.ops: if var.name in op.output_arg_names: # In startup_program, "c_broadcast" and "c_sync_comm_stream" # are treated as initialization ops that cause error. # Think of "c_broadcast" and "c_sync_comm_stream" as a special case here. if op.type in ["c_broadcast", "c_sync_comm_stream"]: continue init_ops.append(op) return init_ops initializer = kwargs['initializer'] init_ops = _is_inited_by(global_block, param) init_ops_len = len(init_ops) if init_ops_len > 1: raise RuntimeError("param " + param.name + " is inited by multiple init ops " + str( init_ops)) elif init_ops_len == 1: # TODO already inited, do nothing, should log a warning pass else: initializer(param, self) param.stop_gradient = False return param def append_op(self, *args, **kwargs): """ Appends a new Operator according to the giving arguments. Returns: Operator: the append Operator. """ if in_dygraph_mode(): attrs = kwargs.get("attrs", {}) type = kwargs.get("type", None) op = Operator( block=self, desc=None, type=type, inputs=None, outputs=None, attrs=attrs) # record ops in tracer rather than blocks # # TODO(minqiyang): add op stop_gradient support in static mode too. # currently, we only support stop_gradient in dygraph mode. _dygraph_tracer().trace_op(type, kwargs.get("inputs", {}), kwargs.get("outputs", {}), attrs if attrs else {}, kwargs.get("stop_gradient", False)) else: op_desc = self.desc.append_op() op = Operator( block=self, desc=op_desc, type=kwargs.get("type", None), inputs=kwargs.get("inputs", None), outputs=kwargs.get("outputs", None), attrs=kwargs.get("attrs", None)) self.ops.append(op) return op def _insert_op(self, index, *args, **kwargs): """ Insert a Operator according to the giving arguments. Args: index(int): the place that the operator to insert. Returns: Operator: the insert Operator. """ self._sync_with_cpp() op_desc = self.desc._insert_op(index) op = Operator(block=self, desc=op_desc, *args, **kwargs) self.ops.insert(index, op) return op def _remove_op(self, index): """ Remove the specific position operator. Args: index(int): the position that the operator to insert. Returns: None """ self._sync_with_cpp() self.desc._remove_op(index, index + 1) del self.ops[index] def _slice_ops(self, start, end): """ Return the Operator between start and end. Args: start(int): the start position. end(int): the end position. Returns: list: the Operators between start and end. """ return self.ops[start:end] def _prepend_op(self, *args, **kwargs): if in_dygraph_mode(): type = kwargs.get("type", None) attrs = kwargs.get("attrs", {}) op = Operator( self, None, type=type, inputs=None, outputs=None, attrs=attrs) _dygraph_tracer().trace_op(type, kwargs.get("inputs", {}), kwargs.get("outputs", {}), attrs if attrs else {}, kwargs.get("stop_gradient", False)) else: op_desc = self.desc._prepend_op() op = Operator( self, op_desc, type=kwargs.get("type", None), inputs=kwargs.get("inputs", None), outputs=kwargs.get("outputs", None), attrs=kwargs.get("attrs", None)) self.ops.insert(0, op) return op def _sync_with_cpp(self): """ Sync from the desc on the c++ end. This method is used to synchronize the c++ desc instance generated by backward. """ # sync variables from cpp for var in self.desc.all_vars(): if not self.has_var(var.name()): self.create_var(name=var.name(), desc=var, type=var.type()) # sync variables removed from c++ end for var in list(self.vars.keys()): if not self.desc.find_var(cpt.to_bytes(var)): self.vars.pop(var) # sync operators from cpp ops_in_cpp = [] for op_idx in range(0, self.desc.op_size()): ops_in_cpp.append(self.desc.op(op_idx)) if len(self.ops) != 0: first_op_in_python = self.ops[0].desc last_op_in_python = self.ops[len(self.ops) - 1].desc start_index = None end_index = None for index in range(len(ops_in_cpp)): if first_op_in_python == ops_in_cpp[index]: start_index = index if last_op_in_python == ops_in_cpp[index]: end_index = index assert start_index is not None assert end_index is not None assert start_index <= end_index else: start_index = 0 end_index = -1 # sync ops append to the head of cpp_ops for index in range((start_index - 1 - 1), -1, -1): op_desc = ops_in_cpp[index] op = Operator(self, op_desc) self.ops.insert(0, op) # sync ops append to the end of cpp_ops for index in range((end_index + 1), len(ops_in_cpp)): op_desc = ops_in_cpp[index] op = Operator(self, op_desc) self.ops.append(op) # sync ops removed from c++ end if end_index != -1 and end_index < len(self.ops): ops_in_cpp_index = 0 ops_in_python_index = 0 while ops_in_python_index < len( self.ops) and ops_in_cpp_index < len(ops_in_cpp): if self.ops[ops_in_python_index].desc != ops_in_cpp[ ops_in_cpp_index]: del self.ops[ops_in_python_index] else: ops_in_cpp_index += 1 ops_in_python_index += 1 assert len(self.ops) == len(ops_in_cpp) for index in range(len(self.ops)): assert self.ops[index].desc == ops_in_cpp[index] def _copy_param_info_from(self, other): """ Copy the information of parameters from the other block. Args: other(Block): the other block. Raises: ValueError: If type of input is not Block, or the `other` and this block is not in the same topology. Returns: None """ if not isinstance(other, Block): raise TypeError( "_copy_param_info_from should be invoked with Block") for p in other.iter_parameters(): assert isinstance(p, Parameter) v = self.vars.get(p.name, None) if v is None: # if the Parameter is pruned, v may be None continue assert isinstance(v, Variable) new_p = None if in_dygraph_mode(): new_p = ParamBase( shape=v.shape, dtype=v.dtype, type=v.type, lod_level=v.lod_level, stop_gradient=p.stop_gradient, trainable=p.trainable, optimize_attr=p.optimize_attr, regularizer=p.regularizer, error_clip=p.error_clip, name=v.name) else: new_p = Parameter( block=self, shape=v.shape, dtype=v.dtype, type=v.type, lod_level=v.lod_level, stop_gradient=p.stop_gradient, trainable=p.trainable, optimize_attr=p.optimize_attr, regularizer=p.regularizer, error_clip=p.error_clip, name=v.name) self.vars[new_p.name] = new_p def _clone_variable(self, var, force_persistable=True): """ Clone a variable into current block. Args: var: the variable to be cloned. force_persistable(bool): True means setting the result variable to being persistable. False means setting the persistable the same with that of input var. default: True. Returns: Variable: the new variable cloned from 'var' in current block. """ assert isinstance(var, Variable) ret_var = None # make STEP_SCOPES var can be safely cloned. if var.type == core.VarDesc.VarType.STEP_SCOPES: ret_var = self.create_var( name=var.name, persistable=var.persistable, type=var.type) elif var.type == core.VarDesc.VarType.RAW: ret_var = self.create_var( name=var.name, persistable=var.persistable, type=var.type) elif var.type == core.VarDesc.VarType.SELECTED_ROWS: ret_var = self.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, persistable=True if force_persistable else var.persistable, is_data=var.is_data, need_check_feed=var.desc.need_check_feed()) else: ret_var = self.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True if force_persistable else var.persistable, is_data=var.is_data, need_check_feed=var.desc.need_check_feed()) return ret_var class IrNode(object): """ Python IrNode. Beneath it is a core.Node, which is used for Ir Pass. """ def __init__(self, node): """ Construct an IrNode using core.Node. Args: node(core.Node): C++ Node. """ assert isinstance(node, core.Node), 'node must be the instance of core.Node.' self.node = node def name(self): """ Return the node name. Returns: str: node name. """ return self.node.name() def node_type(self): """ Return the node type. Returns: core.Node.Type: node type(core.Node.Type.Operation or core.Node.Type.Variable). """ return self.node.node_type() def var(self): """ Return the node variable description. Returns: core.VarDesc: node variable description. """ return self.node.var() def op(self): """ Return the node operator description. Returns: core.OpDesc: node operator description. """ return self.node.op() def id(self): """ Return the node id. Returns: int: node id. """ return self.node.id() def is_op(self): """ If the node is an operator, then return true. Returns: bool: indicate whether the node is an operator. """ return self.node.is_op() def is_var(self): """ If the node is a variable, then return true. Returns: bool: indicate whether the node is a variable. """ return self.node.is_var() def is_ctrl_var(self): """ If the node is a control dependence variable, then return true. Returns: bool: indicate whether the node is a control dependence variable. """ return self.node.is_ctrl_var() def clear_inputs(self): """ Clear the node inputs. After executing the `clear_inputs` function, the node inputs will be empty. """ self.node.clear_inputs() def remove_input_by_id(self, node_id): """ Remove a node from inputs by the given node id. Args: node_id(int): the given node id. """ self.node.remove_input(node_id) def remove_input(self, node): """ Remove a node from inputs. Args: node(IrNode): the node being removed. """ self.node.remove_input(node.node) def append_input(self, node): """ Append a node in inputs. Args: node(IrNode): the node being appended. """ self.node.append_input(node.node) def clear_outputs(self): """ Clear the node outputs. After executing the `clear_outputs` function, the node outputs will be empty. """ self.node.clear_outputs() def remove_output_by_id(self, node_id): """ Remove a node from outputs by the given node id. Args: node_id(int): the given node id. """ self.node.remove_output(node_id) def remove_output(self, node): """ Remove a node from outputs. Args: node(IrNode): the node being removed. """ self.node.remove_output(node.node) def append_output(self, node): """ Append a node in outputs. Args: node(IrNode): the node being appended. """ self.node.append_output(node.node) @property def inputs(self): """ Return the node inputs. Returns: list(IrNode): node inputs wrapped by IrNode. """ return [IrNode(n) for n in self.node.inputs] @property def outputs(self): """ Return the node outputs. Returns: list(IrNode): node outputs wrapped by IrNode. """ return [IrNode(n) for n in self.node.outputs] class IrVarNode(IrNode): """ Python IrVarNode. Beneath it is a core.Node, it inherits from IrNode. """ def __init__(self, node): """ Construct an IrVarNode using core.Node. Args: node(core.Node): C++ Node. """ assert isinstance(node, core.Node) and node.is_var(), \ 'node must be the instance of core.Node and it must be a variable node.' super(IrVarNode, self).__init__(node) self.node = node def set_shape(self, shape): """ Set the node variable shape. Args: shape(list): shape to be set. """ assert self.node.var() is not None, \ "The node variable description can not be None." self.node.var().set_shape(shape) def persistable(self): """ If the variable node is a persistable variable, then return true. Returns: bool: indicate whether the variable is persistable. """ assert self.node.var() is not None, \ "The node variable description can not be None." return self.node.var().persistable() def type(self): """ Return the variable type. Returns: core.VarDesc.VarType: the variable type. """ assert self.node.var() is not None, \ "The node variable description can not be None." return self.node.var().type() def dtype(self): """ Return the variable data type. Returns: core.VarDesc.VarType: the variable data type. """ assert self.node.var() is not None, \ "The node variable description can not be None." return self.node.var().dtype() def shape(self): """ Return the variable shape. Returns: list: the variable shape. """ assert self.node.var() is not None, \ "The node variable description can not be None." return self.node.var().shape() @property def inputs(self): """ Return the node inputs. Returns: list(IrOpNode): node inputs wrapped by IrOpNode. """ return [IrOpNode(n) for n in self.node.inputs] @property def outputs(self): """ Return the node outputs. Returns: list(IrOpNode): node outputs wrapped by IrOpNode. """ return [IrOpNode(n) for n in self.node.outputs] class IrOpNode(IrNode): """ Python IrOpNode. Beneath it is a core.Node, it inherits from IrNode. """ def __init__(self, node): """ Construct an IrOpNode using core.Node. Args: node(core.Node): C++ Node. """ assert isinstance(node, core.Node) and node.is_op(), \ 'node must be the instance of core.Node and it must be a operator node.' super(IrOpNode, self).__init__(node) self.node = node def rename_input(self, old_input_name, new_input_name): """ Rename the input of this node. Args: old_input_name(str): the old input name. new_input_name(str): the new input name. """ assert self.node.op() is not None, \ "The node operator description can not be None." self.node.op()._rename_input(old_input_name, new_input_name) def rename_output(self, old_output_name, new_output_name): """ Rename the output of this node. Args: old_output_name(str): the old output name. new_output_name(str): the new output name. """ assert self.node.op() is not None, \ "The node operator description can not be None." self.node.op()._rename_output(old_output_name, new_output_name) def input(self, name): """ Get the argument name list by the parameter name for input. Args: name(str): the parameter name. Returns: list(str): the argument name list. """ assert self.node.op() is not None, \ "The node operator description can not be None." return self.node.op().input(name) def output(self, name): """ Get the argument name list by the parameter name for output. Args: name(str): the parameter name. Returns: list(str): the argument name list. """ assert self.node.op() is not None, \ "The node operator description can not be None." return self.node.op().output(name) def set_type(self, new_type): """ Change the operator type into new type. Args: new_type(str): new operator type to be set. """ assert self.node.op() is not None, \ "The node operator description can not be None." return self.node.op().set_type(new_type) def set_attr(self, name, val): """ Set the value of attribute by attribute's name. Args: name(str): the attribute name. val(bool|int|str|float|list): the value of the attribute. """ self._update_desc_attr(name, val) def _update_desc_attr(self, name, val): """ Update the value of the op desc's attribute by attribute's name. """ assert self.node.op() is not None, \ "The node operator description can not be None." desc = self.node.op() if isinstance(val, Block): desc.set_block_attr(name, val.desc) elif isinstance(val, list) and val and \ all(isinstance(v, Block) for v in val): desc.set_blocks_attr(name, [v.desc for v in val]) elif isinstance(val, core.BlockDesc) or \ isinstance(val, core.ProgramDesc): desc.set_serialized_attr(name, val.serialize_to_string()) else: desc._set_attr(name, val) def input_arg_names(self): """ Return input arguments' names of this op node. Returns: list(str): input arguments' names of this op node. """ assert self.node.op() is not None, \ "The node operator description can not be None." return self.node.op().input_arg_names() def output_arg_names(self): """ Return output arguments' names of this op node. Returns: list(str): output arguments' names of this op node. """ assert self.node.op() is not None, \ "The node operator description can not be None." return self.node.op().output_arg_names() @property def inputs(self): """ Return the node inputs. Returns: list(IrVarNode): node inputs wrapped by IrVarNode. """ return [IrVarNode(n) for n in self.node.inputs] @property def outputs(self): """ Return the node outputs. Returns: list(IrVarNode): node outputs wrapped by IrVarNode. """ return [IrVarNode(n) for n in self.node.outputs] class IrGraph(object): """ Python IrGraph. Beneath it is a core.Graph, which is used for creating a c++ Ir Pass Graph. An IrGraph is just a graph view of a Program. In an IrGraph, both Variables and Operators are graph nodes. """ def __init__(self, graph, for_test=False): """ Construct an IrGraph using core.Graph. Args: graph(core.Graph): C++ Graph. for_test(bool): True for the test graph and false for the train graph. """ assert isinstance( graph, core.Graph), 'graph must be the instance of core.Graph.' self.graph = graph self._for_test = for_test def clone(self): """ Create a new and duplicated IrGraph. Warns: The method only clones the graph structure, not its attributes. Returns: IrGraph: A new and duplicated graph. """ g = self.graph.clone() return IrGraph(g, self._for_test) def is_test(self): """ If the graph is used for testing, the function returns true. Otherwise, returns false. """ return self._for_test def all_nodes(self): """ Return all nodes included in the graph as a set. """ return {IrNode(node) for node in self.graph.nodes()} def all_var_nodes(self): """ Return all variable nodes included in the graph as a set. """ return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()} def all_persistable_nodes(self): """ Return all persistable variable nodes included in the graph as a set. """ persistable_nodes = set() for node in self.graph.nodes(): if node.is_var() and node.var() is not None and node.var( ).persistable(): persistable_nodes.add(node) return {IrVarNode(p) for p in persistable_nodes} def all_op_nodes(self): """ Return all operator nodes included in the graph as a set. """ return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()} def create_persistable_node(self, name, var_type, shape, var_dtype): """ Create a persistable variable node in the graph. In IrGraph, it can not distinguish between persistable variables and parameters. Args: name(str): the name of the persistable variable node. vart_type(core.VarDesc.VarType): the type of the persistable variable node. shape(list): the shape of the persistable variable node. var_dtype(core.VarDesc.VarType): the data type of the persistable variable node. Returns: IrVarNode: the created persistable variable node. """ var_desc = core.VarDesc(name) var_desc.set_type(var_type) var_desc.set_shape(shape) var_desc.set_dtype(var_dtype) var_desc.set_persistable(True) return IrVarNode(self.graph.create_var_node(var_desc)) def create_var_node(self, name, var_type, shape, var_dtype): """ Create a variable node in the graph. The created variable node is not persistable. Args: name(str): the name of the variable node. vart_type(core.VarDesc.VarType): the type of the variable node. shape(list): the shape of the variable node. var_dtype(core.VarDesc.VarType): the data type of the variable node. Returns: IrVarNode: the created variable node. """ var_desc = core.VarDesc(name) var_desc.set_type(var_type) var_desc.set_shape(shape) var_desc.set_dtype(var_dtype) return IrVarNode(self.graph.create_var_node(var_desc)) def create_control_dep_var(self): """ create a control var """ return IrVarNode(self.graph.create_control_dep_var()) def create_var_node_from_desc(self, var_desc): """ Create a variable node by using an existing VarDesc in the graph. Depend on the giving VarDesc, the created variable node may be persistable. Args: var_desc(core.VarDesc): the giving variable description. Returns: IrVarNode: the created variable node. """ return IrVarNode(self.graph.create_var_node(var_desc)) def create_op_node(self, op_type, attrs, inputs, outputs): """ Create a operator node in the graph. Args: op_type(str): the type of the operator node. attrs(dict): the attributes of the operator node. inputs(dict): the inputs of the operator node. outputs(dict): the outputs of the operator node. Returns: IrOpNode: the created operator node. """ op_desc = core.OpDesc() op_desc.set_type(op_type) for attr, value in six.iteritems(attrs): self._update_desc_attr(op_desc, attr, value) for input_name, var_nodes in six.iteritems(inputs): if not isinstance(var_nodes, list): var_nodes = [var_nodes] op_desc.set_input(input_name, [var_node.name() for var_node in var_nodes]) for output_name, var_nodes in six.iteritems(outputs): if not isinstance(var_nodes, list): var_nodes = [var_nodes] op_desc.set_output(output_name, [var_node.name() for var_node in var_nodes]) return IrOpNode(self.graph.create_op_node(op_desc)) def create_op_node_from_desc(self, op_desc): """ Create a operator node by using an existing OpDesc in the graph. Args: op_desc(core.VarDesc): the giving operator description. Returns: IrOpNode: the created operator node. """ return IrOpNode(self.graph.create_op_node(op_desc)) def update_input_link(self, old_input_node, new_input_node, op_node): """ Update the input's link of a operator node. Args: old_input_node(IrNode): the old input node of the giving op_node. new_input_node(IrNode): the new input node of the giving op_node. op_node(IrOpNode): the operator node that is needed to update input's link. """ assert old_input_node.node in self.graph.nodes() and new_input_node.node in \ self.graph.nodes() and op_node.node in self.graph.nodes(), \ 'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.' old_input_node.remove_output(op_node) op_node.remove_input(old_input_node) new_input_node.append_output(op_node) op_node.append_input(new_input_node) op_node.rename_input(old_input_node.name(), new_input_node.name()) def update_output_link(self, old_output_node, new_output_node, op_node): """ Update the output's link of an operator node. Args: old_output_node(IrNode): the old output node of the giving op_node. new_output_node(IrNode): the new output node of the giving op_node. op_node(IrOpNode): the operator node that is needed to update input's link. """ assert old_output_node.node in self.graph.nodes() and new_output_node.node in \ self.graph.nodes() and op_node.node in self.graph.nodes(), \ 'The three arguments(old_output_node &new_output_node &op_node) must be in the graph nodes.' old_output_node.remove_input(op_node) op_node.remove_output(old_output_node) new_output_node.append_input(op_node) op_node.append_output(new_output_node) op_node.rename_output(old_output_node.name(), new_output_node.name()) def link_to(self, node_in, node_out): """ Connect two nodes. Args: node_in(IrNode): the input node. node_out(IrNode): the output node. """ assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \ 'The two arguments(node_in&node_out) must be in the graph nodes.' node_in.append_output(node_out) node_out.append_input(node_in) def safe_remove_nodes(self, remove_nodes): """ Remove nodes safely since links connected to these removed nodes are also removed. Args: remove_nodes(set): the nodes prepared to be removed. """ if not isinstance(remove_nodes, set): if isinstance(remove_nodes, Iterable): remove_nodes = set(remove_nodes) else: remove_nodes = {remove_nodes} original_nodes = {n.node for n in remove_nodes} core.graph_safe_remove_nodes(self.graph, original_nodes) def resolve_hazard(self): ordered_nodes = core.topology_sort(self.graph) var_nodes = dict() for node in ordered_nodes: if node.is_op() and node.op() is not None: for each_var_name in node.op().input_arg_names(): if each_var_name not in var_nodes: var_nodes[each_var_name] = [ self._find_node_by_name(node.inputs, each_var_name) ] for each_var_name in node.op().output_arg_names(): if each_var_name not in var_nodes: var_nodes[each_var_name] = [ self._find_node_by_name(node.outputs, each_var_name) ] else: var_nodes[each_var_name].append( self._find_node_by_name(node.outputs, each_var_name)) self.graph.resolve_hazard(var_nodes) def has_circle(self): """ Check if the graph has a circle. Returns: bool: True if the graph has a circle else False. """ return core.has_circle(self.graph) def graph_num(self): """ Count the number of unconnected graphs in this graph. Returns: int: the number of unconnected graphs. """ return core.graph_num(self.graph) def topology_sort(self): """ Perform the topology sort operation on the graph. Notes: the `graph` can not contain a circle. Returns: list(IrNode): nodes in topology order. """ ordered_nodes = core.topology_sort(self.graph) return [IrNode(n) for n in ordered_nodes] def build_adjacency_list(self): """ Build an adjacency list of operations for the `graph`. Returns: dict{IrNode: set(IrNode)}: the adjacency list. """ adj_list = core.build_adjacency_list(self.graph) wrapped_adj_list = dict() for k, v in six.iteritems(adj_list): wrapped_adj_list[IrNode(k)] = {IrNode(n) for n in v} return wrapped_adj_list def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True): """ Draw the graph. If `dot` command is installed, the drawn graph will be saved as pdf file type, otherwise dot file type is used. Args: save_path(str): the save path of drawn graph. name(str): the name of drawn graph. marked_nodes(set(IrNode)): nodes that are needed to be marked. Default value is None. remove_ctr_var(bool): If it is set True, all control variable nodes in the graph will be removed. Default value is True. """ def _convert_to_pdf(dot_file_path): pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf' exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \ + ' -o ' + pdf_save_path, shell=True) if exited_code != 0: print('The dot command is needed for creating pdf files.') print('The {} is saved as the dot filetype.'.format( dot_file_path)) remove_ctr_vars = set() if remove_ctr_var: for node in self.all_var_nodes(): if node.is_ctrl_var(): remove_ctr_vars.add(node) self.safe_remove_nodes(remove_ctr_vars) print('Total ops num = {}.'.format(len(self.all_op_nodes()))) if marked_nodes is not None: if not isinstance(marked_nodes, set): if isinstance(marked_nodes, Iterable): marked_nodes = set(marked_nodes) else: marked_nodes = {marked_nodes} marked_nodes = {n.node for n in marked_nodes} remove_ctr_vars = {n.node for n in remove_ctr_vars} marked_nodes = marked_nodes - remove_ctr_vars if self.graph.has('__graphviz__marked_node__'): self.graph.erase('__graphviz__marked_node__') self.graph.set('__graphviz__marked_node__', marked_nodes) if not os.path.exists(save_path): os.makedirs(save_path) viz_dot_path = os.path.join(save_path, name) + '.dot' viz_pass = core.get_pass('graph_viz_pass') viz_pass.set('graph_viz_path', viz_dot_path) viz_pass.apply(self.graph) _convert_to_pdf(viz_dot_path) def to_program(self): """ Convert the graph into a Program. WARN: When the graph includes backward operator nodes, the conversion process may be failed. Usually, this function is only used to convert a test graph. Returns: Program: a program converted from the graph. """ convert_pass = core.get_pass('graph_to_program_pass') desc = core.ProgramDesc() convert_pass.set_not_owned('program', desc) convert_pass.apply(self.graph) program = Program._construct_from_desc(desc) return program def _find_node_by_name(self, nodes, node_name): """ Find a node in the giving nodes set by the name. """ target_node = None for n in nodes: if n.name() == node_name: target_node = n assert target_node is not None, "Cannot find the target node in the giving set." return target_node def _update_desc_attr(self, desc, name, val): """ Update the value of desc's attribute by attribute's name. """ if isinstance(val, Block): desc.set_block_attr(name, val.desc) elif isinstance(val, list) and val and all( isinstance(v, Block) for v in val): desc.set_blocks_attr(name, [v.desc for v in val]) elif isinstance(val, core.BlockDesc) or \ isinstance(val, core.ProgramDesc): desc.set_serialized_attr(name, val.serialize_to_string()) else: desc._set_attr(name, val) class Program(object): """ Create Python Program. It has at least one :ref:`api_guide_Block_en`, when the control flow op like conditional_block, while :ref:`api_fluid_layers_While` is included, it will contain nested block. Please reference the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details. A set of Program usually contains startup program and main program. A startup program is set to contain some initial work, eg. initialize the ``Parameter``, and the main program will contain the network structure and vars for train. A set of Program can be used for test or train, in train program , Paddle will contain all content to build a train network, in test program Paddle will prune some content which is irrelevant to test, eg. backward ops and vars. **Notes**: **we have** :ref:`api_fluid_default_startup_program` **and** :ref:`api_fluid_default_main_program` **by default, a pair of them will shared the parameters. The** :ref:`api_fluid_default_startup_program` **only run once to initialize parameters,** :ref:`api_fluid_default_main_program` **run in every mini batch and adjust the weights.** Returns: Program: An empty Program. Examples: .. code-block:: python import paddle.fluid as fluid main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program=main_program, startup_program=startup_program): x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') z = fluid.layers.fc(name="fc", input=x, size=10, act="relu") print("main program is: {}".format(main_program)) print("start up program is: {}".format(startup_program)) """ def __init__(self): self.desc = core.ProgramDesc() self.blocks = [Block(self, 0)] self.current_block_idx = 0 global global_prog_seed self._seed = global_prog_seed self._current_role = core.op_proto_and_checker_maker.OpRole.Forward self.__op_role_var = [] # for distribute training # _is_distributed = True if under distributed training self._is_distributed = False # _is_chief = True if the trainer is the first one, usually No.0 self._is_chief = False # _parameters_on_pservers records all the parameters distributed on parameter servers. self._parameters_on_pservers = None # _endpoints is a list about parameter servers ip:port, such as ["ip:port","ip:port"] self._endpoints = [] # if current role is parameter server, the _ps_endpoint is its "ip:port" self._ps_endpoint = None # trainers_endpoints, it is used for distribution. self._trainers_endpoints = [] # the distributed lookup table names self._distributed_lookup_table = None # use Deep gradient comrepssion or not self._enable_dgc = False self._use_lamb = False self._nccl_comm_num = 1 self._use_hierarchical_allreduce = False self._hierarchical_allreduce_inter_nranks = 0 # if this program has been optimized by distributed optimizer # fleet_opt will be given a value self._fleet_opt = None self._program_config = None # assigned if this program has been parsed by a pipeline optimizer self._pipeline_opt = None # appending gradients times self._appending_grad_times = 0 def global_seed(self, seed=0): """ Set global seed for Program Returns: None. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() print(prog.random_seed) ## 0 ## the default random seed is 0 prog.global_seed(102) prog1 = fluid.default_main_program() print(prog1.random_seed) ## 102 ## the random seed is 102 """ global global_prog_seed global_prog_seed = seed self._seed = global_prog_seed @property def _op_role(self): """ The operator role. In a enum {Forward, Backward, Optimize}. Notes: this is a low level API. It is used only for ParallelExecutor to duplicate or schedule operator to devices. For example, the forward operator should be executed on every device. The backward operator should be executed on every device and the parameter gradient of backward (use :code:`_op_role_var` to get this variable) operator should be merged to one device. The optimization operators should be executed on only one device and broadcast the optimization result, i.e., the new parameter, to every other device. """ return self._current_role @_op_role.setter def _op_role(self, role): self._current_role = role @property def _op_role_var(self): """ The auxiliary variables for :code:`_op_role` property. See Also: :code:`Program._op_role`'s documentation for details. Notes: This is a very low-level API. Users should not use it directly. """ return self.__op_role_var @signature_safe_contextmanager def _backward_role_guard(self): tmp_role = self._current_role OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.Backward try: yield finally: self._current_role = tmp_role @signature_safe_contextmanager def _optimized_guard(self, param_and_grads): """ A with guard to set :code:`Optimization` :code:`OpRole` and :code:`OpRoleVar` automatically. Notes: This is a very low level API. Users should not use it directly. Args: param_and_grads(list): The variables (names) to be optimized. Examples: >>> import paddle.fluid as fluid >>> p, g = backward(...) >>> with program._optimized_guard([p,g]): >>> p = p - 0.001 * g """ tmp_role = self._current_role tmp_var = self.__op_role_var OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.Optimize self.__op_role_var = [ var.name if isinstance(var, Variable) else var for var in param_and_grads ] try: yield finally: self.__op_role_var = tmp_var self._current_role = tmp_role @signature_safe_contextmanager def _lr_schedule_guard(self, is_with_opt=False): """ A with guard to set :code:`LRSched` :code:`OpRole` and :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is set to the target learning rate. Notes: This is a very low level API. Users should not use it directly. Args: is_with_opt: Only set to true if these ops a in the middle of a bunch of optimize ops so that it can be treated correctly. For example, sgd->lr_op->sgd->lr_op->sgd. Examples: >>> import paddle.fluid as fluid >>> p, g = backward(...) >>> with program.lr_schedule_guard(): >>> lr = lr * decay """ tmp_role = self._current_role tmp_var = self.__op_role_var OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.LRSched if is_with_opt: self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize) # TODO(typhoonzero): how to set target learning rate var self.__op_role_var = [] try: yield finally: self.__op_role_var = tmp_var self._current_role = tmp_role def __str__(self): """ Get the protobuf debug string of this Program. Returns: (str): The protobuf debug string. Raises: ValueError: If any of required fields is not set. """ return self._to_readable_code() def _to_readable_code(self, skip_op_callstack=True): """ Get readable debug string of Program. .. note:: If you want to get the debug string in protobuf format, please use :code:`to_string` method. Args: skip_op_callstack(bool): whether to skip parsing Operator's attribute op_callstack, default value is True Returns: string: The formatted Program string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_var = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') new_op = cur_block.append_op(type="abs", inputs={"X": [new_var]}, outputs={"Out": [new_var]}) print(cur_program._to_readable_code()) """ assert isinstance( skip_op_callstack, bool ), "skip_op_callstack parameter's type is error, expect bool, received %s".format( type(skip_op_callstack)) program_str = "" for block in self.blocks: program_str += block._to_readable_code(skip_op_callstack) program_str += '\n' return program_str def to_string(self, throw_on_error, with_details=False): """ To debug string. Args: throw_on_error (bool): raise Value error when any of required fields is not set. with_details (bool): True if more details about variables and parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need to print. Returns: str: The debug string describe current Program. Raises: ValueError: If any of required fields is not set and throw_on_error is True. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() x = fluid.layers.data(name="X", shape=[2,3], dtype="float32", append_batch_size=False) pred = fluid.layers.fc(x, size=3) prog_string = prog.to_string(throw_on_error=True, with_details=False) prog_string_with_details = prog.to_string(throw_on_error=False, with_details=True) print("program string without detail: {}".format(prog_string)) print("program string with detail: {}".format(prog_string_with_details)) """ assert isinstance( throw_on_error, bool ), "The type of throw_on_error parameter is wrong, expected bool, but received {}.".format( type(throw_on_error)) assert isinstance( with_details, bool ), "The type of with_details parameter is wrong, expected bool, but received {}.".format( type(with_details)) if with_details: res_str = "" for block in self.blocks: res_str += block.to_string(throw_on_error, with_details) else: protostr = self.desc.serialize_to_string() proto = framework_pb2.ProgramDesc.FromString( six.binary_type(protostr)) res_str = _debug_string_(proto, throw_on_error) return res_str def _get_desc(self): """ Get the C++ side of `ProgramDesc` object pointer. The C++ object is exposed by :code:`pybind`. Notes: This is a very low level API. Users should not use this API directly. """ return self.desc def _version(self): return self.desc._version() def clone(self, for_test=False): """ **Notes**: **1.** :code:`Program.clone()` **method DOES NOT clone** :ref:`api_fluid_io_DataLoader` . **2. Recommend you to use** :code:`clone` **before using** :code:`Opimizer.minimize`. **3. This API has no effect in Dygraph Mode** Create a new Program with forward content of original one when ``for_test=True``. Create a new Program as same as the original one when ``for_test=False``. Some operators, e.g., :ref:`api_fluid_layers_batch_norm` , behave differently between training and testing. They have an attribute, :code:`is_test`, to control this behaviour. This method will change the :code:`is_test` attribute of them to :code:`True` when :code:`for_test=True`. * Set for_test to False when you want to clone the program for training. * Set for_test to True when you want to clone the program for testing. We will prune the backward and optimize part of the program when you use :code:`clone` after :code:`Opimizer.minimize`, but we still recommend you to use :code:`clone` before using :code:`Opimizer.minimize`. For Example: :: import paddle.fluid as fluid img = fluid.layers.data(name='image', shape=[784]) pred = fluid.layers.fc(input=img, size=10, act='relu') loss = fluid.layers.mean(pred) # Here we use clone before Momentum test_program = fluid.default_main_program().clone(for_test=True) optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9) optimizer.minimize(loss) Args: for_test (bool): True if change the :code:`is_test` attribute of operators to :code:`True` and prune the backward and optimize part of the program. The default value is :code:`False` . Returns: Program: A new Program with forward content of original one when ``for_test=True``. A new Program as same as the original one when ``for_test=False`` Examples: **Notes: The Program's order maybe different after** :code:`clone` **and this will not affect your training or testing progress. In the following example we give you an simple method** :code:`print_prog(program)` **to print Program Descs inorder to make sure you have same print result after** :code:`clone`: .. code-block:: python import paddle.fluid as fluid import six def print_prog(prog): for name, value in sorted(six.iteritems(prog.block(0).vars)): print(value) for op in prog.block(0).ops: print("op type is {}".format(op.type)) print("op inputs are {}".format(op.input_arg_names)) print("op outputs are {}".format(op.output_arg_names)) for key, value in sorted(six.iteritems(op.all_attrs())): if key not in ['op_callstack', 'op_role_var']: print(" [ attrs: {}: {} ]".format(key, value)) 1. To clone a test program, the sample code is: .. code-block:: python import paddle.fluid as fluid import six def print_prog(prog): for name, value in sorted(six.iteritems(prog.block(0).vars)): print(value) for op in prog.block(0).ops: print("op type is {}".format(op.type)) print("op inputs are {}".format(op.input_arg_names)) print("op outputs are {}".format(op.output_arg_names)) for key, value in sorted(six.iteritems(op.all_attrs())): if key not in ['op_callstack', 'op_role_var']: print(" [ attrs: {}: {} ]".format(key, value)) train_program = fluid.Program() startup_program = fluid.Program() # startup_program is used to do some parameter init work, # and main program is used to hold the network with fluid.program_guard(train_program, startup_program): with fluid.unique_name.guard(): img = fluid.layers.data(name='image', shape=[784]) hidden = fluid.layers.fc(input=img, size=200, act='relu') hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) loss = fluid.layers.cross_entropy( input=fluid.layers.fc(hidden, size=10, act='softmax'), label=fluid.layers.data(name='label', shape=[1], dtype='int64')) avg_loss = fluid.layers.mean(loss) test_program = train_program.clone(for_test=True) print_prog(test_program) # Due to parameter sharing usage for train and test, so we need to use startup program of train # instead of using test startup program, while nothing is in test's startup program # In Paddle Fluid we will share weights by using the same Variable name. In train and test program # all parameters will have the same name and this can make train and test program sharing parameters, # that's why we need to use startup program of train. And for startup program of test, it has nothing, # since it is a new program. with fluid.program_guard(train_program, startup_program): with fluid.unique_name.guard(): sgd = fluid.optimizer.SGD(learning_rate=1e-3) sgd.minimize(avg_loss) 2. The clone method can be avoid if you create program for training and program for testing individually. .. code-block:: python import paddle.fluid as fluid import six def print_prog(prog): for name, value in sorted(six.iteritems(prog.block(0).vars)): print(value) for op in prog.block(0).ops: print("op type is {}".format(op.type)) print("op inputs are {}".format(op.input_arg_names)) print("op outputs are {}".format(op.output_arg_names)) for key, value in sorted(six.iteritems(op.all_attrs())): if key not in ['op_callstack', 'op_role_var']: print(" [ attrs: {}: {} ]".format(key, value)) def network(): img = fluid.layers.data(name='image', shape=[784]) hidden = fluid.layers.fc(input=img, size=200, act='relu') hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) loss = fluid.layers.cross_entropy( input=fluid.layers.fc(hidden, size=10, act='softmax'), label=fluid.layers.data(name='label', shape=[1], dtype='int64')) avg_loss = fluid.layers.mean(loss) return avg_loss train_program_2 = fluid.Program() startup_program_2 = fluid.Program() test_program_2 = fluid.Program() with fluid.program_guard(train_program_2, startup_program_2): with fluid.unique_name.guard(): avg_loss = network() sgd = fluid.optimizer.SGD(learning_rate=1e-3) sgd.minimize(avg_loss) # the test startup program is not used. with fluid.program_guard(test_program_2, startup_program_2): with fluid.unique_name.guard(): avg_loss = network() print_prog(test_program_2) The two code snippets above will generate and print same programs. """ #NOTE(zhiqiu): we sync the original program first, since its program may diff with # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc. self._sync_with_cpp() pruned_origin_block_id_map = None if for_test: forward_prog = Program() forward_prog.desc, pruned_origin_block_id_map = core.prune_backward( self.desc) forward_prog.blocks = [ Block(forward_prog, i) for i in six.moves.range(forward_prog.desc.num_blocks()) ] forward_prog._sync_with_cpp() p = forward_prog._inference_optimize(prune_read_op=False) else: p = Program() p.current_block_idx = self.current_block_idx p._seed = self._seed p.desc = core.ProgramDesc(self.desc) p.blocks = [ Block(p, i) for i in six.moves.range(self.desc.num_blocks()) ] p._current_role = self._current_role p.__op_role_var = self.__op_role_var p._appending_grad_times = self._appending_grad_times #NOTE(zhiqiu): we sync the cloned program, to update its program by # its desc. p._sync_with_cpp() p._copy_param_info_from(self) p._copy_data_info_from(self, pruned_origin_block_id_map) p._copy_dist_param_info_from(self) return p def _prune(self, targets): """ Prune operators and variables which are not needed to generate :code:`targets`. Notes: This is a very low level API. Users should not use this API directly. This API is in flux and not stable. Args: targets(list|Variable|Operator): A list of variables, operators, or variable names need to be pruned Returns: Program: A new, pruned program. """ return self._prune_with_input([], targets) def _prune_with_input(self, feeded_var_names, targets): """ Prune operators and variables which are not needed to generate :code:`targets`. Prune operators and variables which are needed to generate feeded_var Notes: This is a very low level API. Users should not use this API directly. This API is in flux and not stable. Args: feeded_var_names(list|str): A list of variable names from where pruning start. If it is set as [], this API works just like _prune() targets(list|Variable|Operator): A list of variables, operators, or variable names need to be pruned Returns: Program: A new, pruned program. """ #NOTE(zhiqiu): we sync the original program first, since its program may diff with # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc. self._sync_with_cpp() if not isinstance(feeded_var_names, list): feeded_var_names = [feeded_var_names] if not isinstance(targets, list): targets = [targets] for var in feeded_var_names: if not isinstance(var, six.string_types): raise ValueError( "All feeded_var_names of Program._prune_with_input() can only be " "str, but received %s." % type(var)) targets_idx = [] for t in targets: if not isinstance(t, Operator): if isinstance(t, Variable): name = t.name elif isinstance(t, six.string_types): name = str(t) else: raise ValueError( "All targets of Program._prune_with_input() can only be " "Variable or Operator, but received %s." % type(t)) # NOTEZ(zhiqiu): For variable to be fed in fetch_list, there two cases: # (1) the variable is leaf, it has no op that generates it; # (2) the variable is not leaf, and we need to prune the op that generates it. # In both cases, wo can just skip target_op of that it. if name in feeded_var_names: continue # After transpiler processing, the op that output this # variable maybe has been changed, so t.op is not reliable # and we need to find the current op that generate this # variable here. target_op = None global_block = self.global_block() for idx, op in enumerate(global_block.ops): if name in op.output_arg_names: # NOTE(zhiqiu): Find op that generate target name. # Skip optimize op except for optimize op in targets, # since optimize op generates parameters. if op._is_optimize_op() and op not in targets: continue else: target_op = op break if target_op is None: raise ValueError( "The target variable used for pruning should have an " "associated operator that generates it.") else: targets_idx.append([target_op.block.idx, target_op.idx]) else: targets_idx.append([t.block.idx, t.idx]) res = Program() res.desc, pruned_origin_block_id_map = core.prune(self.desc, set(feeded_var_names), targets_idx) res.blocks = [ Block(res, i) for i in six.moves.range(res.desc.num_blocks()) ] res._sync_with_cpp() res._copy_param_info_from(self) res._copy_data_info_from(self, pruned_origin_block_id_map) res._copy_dist_param_info_from(self) return res def _inference_optimize(self, prune_read_op=True): """ This method will create a new program and do following adjustments on it: 1. Remove all reader variables and their creator ops if exist. 2. Remove the :code:`read_op` if exists. 3. change the :code:`is_test` attribute of operators to :code:`True`. All the :code:`Parameter` information will be lost. Args: prune_read_op(bool): remove the read ops that are added by py_reader for cpp inference library Notes: This API is a very low level API. Use :code:`Program.clone(for_test=True)` instead. Returns: Program: The new program. """ res = Program() res.desc = core.ProgramDesc(self.desc) # remove all readers and the read_op if exist read_op_idx = 0 root_block = res.desc.block(0) if prune_read_op: while True: if read_op_idx >= root_block.op_size() or root_block.op( read_op_idx).type() == 'read': break read_op_idx += 1 if read_op_idx < root_block.op_size(): root_block._remove_op(0, read_op_idx + 1) for var in root_block.all_vars(): if var.type() == core.VarDesc.VarType.READER: root_block._remove_var(cpt.to_bytes(var.name())) # change all `is_test` attributes to True for i in six.moves.range(res.desc.num_blocks()): block = res.desc.block(i) for j in six.moves.range(block.op_size()): op = block.op(j) if op.has_attr('is_test'): op._set_attr('is_test', True) res.blocks = [ Block(res, i) for i in six.moves.range(res.desc.num_blocks()) ] res._sync_with_cpp() return res @staticmethod def parse_from_string(binary_str): """ **Notes**: **1. All information about parameters will be lost after serialization** **2. This API has no effect in Dygraph mode** Deserialize a Program from `protobuf <https://en.wikipedia.org/wiki/Protocol_Buffers>`_ binary string. This method always use to save and load model Args: binary_str_type (str): the binary prootbuf string. Returns: Program: A deserialized Program. Examples: .. code-block:: python import paddle.fluid as fluid startup_prog = fluid.Program() main_prog = fluid.Program() with fluid.program_guard(startup_prog, main_prog): x = fluid.layers.data( name='X', shape=[1000, 784], dtype='float32', append_batch_size=False) y = fluid.layers.data( name='Y', shape=[784, 100], dtype='float32', append_batch_size=False) z = fluid.layers.mul(x=x, y=y) binary_str = fluid.default_main_program().desc.serialize_to_string() prog_restored = fluid.default_main_program().parse_from_string(binary_str) print(fluid.default_main_program()) print(prog_restored) """ p = Program() p.desc = core.ProgramDesc(binary_str) p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())] p._sync_with_cpp() return p @staticmethod def _construct_from_desc(desc): """ Construct a program from program desc. Args: desc(core.ProgramDesc): The program desc for constructing. Returns: Program: A program. """ p = Program() p.desc = desc p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())] p._sync_with_cpp() return p @property def random_seed(self): """ The default random seed for random operators in Program. ``0`` means get the random seed from random device. **Notes: It must be set before the operators have been added.** Returns: int64: Random seed in current Program Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() random_seed = prog.random_seed x_var = fluid.layers.data(name="X", shape=[3,3], dtype="float32", append_batch_size=False) print(random_seed) ## 0 ## the default random seed is 0 # Here we need to set random seed before we use fluid.layers.dropout prog.random_seed = 1 z_var = fluid.layers.dropout(x_var, 0.7) print(prog.random_seed) ## 1 ## the random seed is change to 1 """ return self._seed @property def num_blocks(self): """ The number of :ref:`api_guide_Block_en` in this Program. **Notes: This API has no effect in Dygraph mode** Returns: int(Platform-dependent size): num of :ref:`api_guide_Block_en` in current Program Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() num_blocks = prog.num_blocks print(num_blocks) """ return self.desc.num_blocks() @random_seed.setter def random_seed(self, seed): if not isinstance(seed, int): raise ValueError( "Program.random_seed's input seed must be an integer, but received %s." % type(seed)) self._seed = seed def __repr__(self): return self.__str__() def global_block(self): """ **Notes**: **This API has no effect in Dygraph mode** Get the first :ref:`api_guide_Block_en` of this Program. Returns: :ref:`api_guide_Block_en`: The first :ref:`api_guide_Block_en` of this Program. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() gb_block = prog.global_block() print(gb_block) """ return self.blocks[0] def block(self, index): """ **Notes**: **This API has no effect in Dygraph mode** Get the :code:`index` :ref:`api_guide_Block_en` of this Program Args: index (int) - The index of :ref:`api_guide_Block_en` to get Returns: :ref:`api_guide_Block_en`: The :code:`index` block Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() block_0 = prog.block(0) print(block_0) """ return self.blocks[index] def current_block(self): """ **Notes**: **This API has no effect in Dygraph mode** Get the current :ref:`api_guide_Block_en` . The :code:`current` :ref:`api_guide_Block_en` is the :ref:`api_guide_Block_en` to append operators. Returns: :ref:`api_guide_Block_en`: The :code:`index` :ref:`api_guide_Block_en` Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() current_blk = prog.current_block() print(current_blk) """ return self.blocks[self.current_block_idx] def _create_block(self, parent_idx=None): """ Create a new block with the :code:`parent_idx` and change the current block to new block. Args: parent_idx(int): The parent block index. Returns: Block: The new block. """ new_block_idx = len(self.blocks) parent = self.current_block() if parent_idx is None else self.block( parent_idx) self.desc.append_block(parent.desc) self.current_block_idx = new_block_idx self.blocks.append(Block(self, self.current_block_idx)) return self.current_block() def _rollback(self): """ Exit a code block, i.e., roll back to the parent block. Returns: None """ self.current_block_idx = self.current_block().parent_idx def _sync_with_cpp(self): """ Synchronize Python instance to its binding C++ object instance. If the program is modified in C++ space, this method should be invoked. Notes: This is a very low level API. Users should not invoke it directly. Returns: None """ for block_idx in range(len(self.blocks), self.desc.num_blocks()): self.blocks.append(Block(self, block_idx)) for block in self.blocks: block._sync_with_cpp() def _copy_param_info_from(self, other): """ Copy the information of parameters from other program. Notes: This is a very low level API. Users should not invoke it directly. Args: other(Program): Other program Returns: None """ if not isinstance(other, Program): raise TypeError( "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s" % type(other)) self.global_block()._copy_param_info_from(other.global_block()) def _copy_dist_param_info_from(self, other): """ Copy the information of distributed information from other program. Args: other(Program): Other program Returns: None """ if not isinstance(other, Program): raise TypeError( "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s" % type(other)) self._is_distributed = other._is_distributed self._is_chief = other._is_chief self._parameters_on_pservers = other._parameters_on_pservers self._endpoints = other._endpoints self._ps_endpoint = other._ps_endpoint self._distributed_lookup_table = other._distributed_lookup_table def _copy_data_info_from(self, other, pruned_origin_block_id_map=None): """ Copy the information of data variables from other program. Notes: This is a very low level API. Users should not invoke it directly. Args: other(Program): Other program pruned_origin_block_id_map(dict{int:int}): A dict which maps the block id in program self to the block id in program other. For example, {0:0, 1:1, 2:3} means block 0 in self is cloned from block 0 in other, etc. Default is None, which means default mapped, {0:0, 1:1,..., n:n}. Returns: None """ if not isinstance(other, Program): raise TypeError( "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s" % type(other)) if not pruned_origin_block_id_map: pruned_origin_block_id_map = { i: i for i in six.moves.range(self.desc.num_blocks()) } # NOTE(zhiqiu): All vars in cloned program exist in original program. # The reverse is not true, due to backward pruning. for i, block in enumerate(self.blocks): other_block = other.blocks[pruned_origin_block_id_map[i]] for var in list(block.vars.values()): other_var = other_block.var(var.name) if other_var.is_data: var.is_data = True if other_var.desc.need_check_feed(): var.desc.set_need_check_feed(True) if other_var.stop_gradient: var.stop_gradient = True def list_vars(self): """ Get all :ref:`api_guide_Variable_en` from this Program. A iterable object is returned. Returns: iterable :ref:`api_guide_Variable_en`: The Generator will yield every variable in this program. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() img = fluid.layers.data(name='img', shape=[1,28,28], dtype='float32') label = fluid.layers.data(name='label', shape=[128,1], dtype='int64') for var in prog.list_vars(): print(var) """ for each_block in self.blocks: for each_var in list(each_block.vars.values()): yield each_var def all_parameters(self): """ Get all :ref:`api_guide_parameter_en` from this Program. A list object is returned. Returns: list[ :ref:`api_guide_parameter_en` ]: The list contians all parameters in this program. Examples: .. code-block:: python import paddle.fluid as fluid program = fluid.default_main_program() data = fluid.data(name='x', shape=[None, 13], dtype='float32') hidden = fluid.layers.fc(input=data, size=10) loss = fluid.layers.mean(hidden) fluid.optimizer.SGD(learning_rate=0.01).minimize(loss) for param in program.all_parameters(): print(param) # Here will print all parameters in current program, in this example, # the result is like: # # name: "fc_0.w_0" # type { # type: LOD_TENSOR # lod_tensor { # tensor { # data_type: FP32 # dims: 13 # dims: 10 # } # } # } # persistable: true # # name: "fc_0.b_0" # type { # type: LOD_TENSOR # lod_tensor { # tensor { # data_type: FP32 # dims: 10 # } # } # } # persistable: true # # Here print(param) will print out all the properties of a parameter, # including name, type and persistable, you can access to specific # property of a parameter, such as param.name, param.type """ parameters = [] for each_block in self.blocks: parameters.extend(each_block.all_parameters()) return parameters @six.add_metaclass(ParameterMetaClass) class Parameter(Variable): """ Parameter is derived from Variable. A parameter is a persistable Variable, and will be updated by optimizers after each iteration. The training of a neural network is essentially the updating of its parameters. Relative to a general Variable, a Parameter has several its own member variables: Args: trainable(bool): True if the parameter need to be updated after iterations. optimize_attr(map): Parameter attributes related with optimizing. Currently, it only contains 'learning_rate'. Default: {'learning_rate': 1.0} regularizer(WeightDecayRegularizer): The Regularizer which will be applied on the parameter. Default: None do_model_average(bool): True if the model average strategy will be applied on this parameter. """ def __init__(self, block, shape, dtype, type=core.VarDesc.VarType.LOD_TENSOR, **kwargs): if shape is None: raise ValueError("The shape of Parameter should not be None") if dtype is None: raise ValueError("The dtype of Parameter should not be None") if len(shape) == 0: raise ValueError( "The dimensions of shape for Parameter must be greater than 0") for each in shape: if each < 0: raise ValueError( "Each dimension of shape for Parameter must be greater than 0, but received %s" % list(shape)) Variable.__init__( self, block, persistable=True, shape=shape, dtype=dtype, type=type, **kwargs) self.trainable = kwargs.get('trainable', True) self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0}) self.regularizer = kwargs.get('regularizer', None) self.do_model_average = kwargs.get('do_model_average', None) self.is_distributed = False def __str__(self): return self._to_readable_code() def to_string(self, throw_on_error, with_details=False): """ To debug string. Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True Returns(str): The debug string. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32') debug_str = prog.to_string(throw_on_error=True, with_details=False) print(debug_str) """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) if with_details: res_str = Variable.to_string(self, throw_on_error, True) additional_attr = ("trainable", "optimize_attr", "regularizer", "do_model_average") for attr_name in additional_attr: res_str += "%s: %s\n" % (attr_name, cpt.to_text(getattr(self, attr_name))) else: res_str = Variable.to_string(self, throw_on_error, False) return res_str __repr__ = __str__ class ParamBase(core.VarBase): """ ParamBase is derived from VarBase( Which is the Variable in Dygraph Mode ). A ParamBase is a persistable VarBase, and will be updated by optimizers after each iteration. The training of a neural network is essentially the updating of its ParamBase. Relative to a general Variable, a ParamBase has several its own member variables: Args: trainable(bool): True if the ParamBase need to be updated after iterations. optimize_attr(map): ParamBase attributes related with optimizing. Currently, it only contains 'learning_rate'. Default: {'learning_rate': 1.0} regularizer(WeightDecayRegularizer): The Regularizer which will be applied on the ParamBase. Default: None do_model_average(bool): True if the model average strategy will be applied on this ParamBase. """ @dygraph_only def __init__(self, shape, dtype, **kwargs): if shape is None: raise ValueError("The shape of Parameter should not be None") if dtype is None: raise ValueError("The dtype of Parameter should not be None") if len(shape) == 0: raise ValueError( "The dimensions of shape for Parameter must be greater than 0") for each in shape: if each < 0: raise ValueError( "Each dimension of shape for Parameter must be greater than 0, but received %s" % list(shape)) if dtype is not None: if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) name = kwargs.get('name', unique_name.generate('_param_base')) super(ParamBase, self).__init__(dtype if dtype else core.VarDesc.VarType.FP32, list(shape) if shape else [], name, core.VarDesc.VarType.LOD_TENSOR, True) self.trainable = kwargs.get('trainable', True) self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0}) self.regularizer = kwargs.get('regularizer', None) self.do_model_average = kwargs.get('do_model_average', None) self.is_distributed = False # self.block = default_main_program().global_block() def __str__(self): return self.to_string(True) def to_string(self, throw_on_error, with_details=False): """ To debug string. Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True Returns(str): The debug string. Examples: .. code-block:: python import paddle.fluid as fluid prog = fluid.default_main_program() rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32') debug_str = prog.to_string(throw_on_error=True, with_details=False) print(debug_str) """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) tensor = self.value().get_tensor() if tensor._is_initialized(): return 'Parameter: %s\n%s' % (self.name, str(tensor)) else: return 'Parameter: %s, not initialized' % (self.name) __repr__ = __str__ # program is a global instance. _main_program_ = Program() _startup_program_ = Program() def default_startup_program(): """ Get default/global startup program. The layer function in :ref:`api_fluid_layers` will create parameters, :ref:`api_paddle_data_reader_reader` , `NCCL <https://developer.nvidia.com/nccl>`_ handles as global variables. The :code:`startup_program` will initialize them by the OPs in startup :ref:`api_fluid_Program` . The :ref:`api_fluid_layers` function will append these initialization operators into startup program. This method will return the :code:`default` or the :code:`current` startup program. Users can use :ref:`api_fluid_program_guard` to switch :ref:`api_fluid_Program` . Returns: current default startup :ref:`api_fluid_Program` Returns type: :ref:`api_fluid_Program` Examples: .. code-block:: python import paddle.fluid as fluid main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program=main_program, startup_program=startup_program): x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') z = fluid.layers.fc(name="fc", input=x, size=10, act="relu") print("main program is: {}".format(fluid.default_main_program())) print("start up program is: {}".format(fluid.default_startup_program())) """ return _startup_program_ def default_main_program(): """ This API can be used to get ``default main program`` which store the descriptions of ``op`` and ``variable``. For example ``z = fluid.layers.elementwise_add(x, y)`` will create a new ``elementwise_add`` ``op`` and a new ``z`` ``variable``, and they will be recorded in ``default main program`` The ``default_main_program`` is the default value for ``Program`` parameter in a lot of ``fluid`` APIs. For example, the :code:`Executor.run()` will execute the :code:`default_main_program` when the program is not specified. If you want to replace the ``default main program``, you can use :ref:`api_fluid_program_guard` Returns: :ref:`api_fluid_Program`: a ``Program`` which holding the descriptions of ops and variables in the network. Examples: .. code-block:: python import paddle.fluid as fluid # Sample Network: data = fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') conv1 = fluid.layers.conv2d(data, 4, 5, 1, act=None) bn1 = fluid.layers.batch_norm(conv1, act='relu') pool1 = fluid.layers.pool2d(bn1, 2, 'max', 2) conv2 = fluid.layers.conv2d(pool1, 16, 5, 1, act=None) bn2 = fluid.layers.batch_norm(conv2, act='relu') pool2 = fluid.layers.pool2d(bn2, 2, 'max', 2) fc1 = fluid.layers.fc(pool2, size=50, act='relu') fc2 = fluid.layers.fc(fc1, size=102, act='softmax') loss = fluid.layers.cross_entropy(input=fc2, label=label) loss = fluid.layers.mean(loss) opt = fluid.optimizer.Momentum( learning_rate=0.1, momentum=0.9, regularization=fluid.regularizer.L2Decay(1e-4)) opt.minimize(loss) #print the number of blocks in the program, 1 in this case print(fluid.default_main_program().num_blocks) #print the description of variable 'image' print(fluid.default_main_program().blocks[0].var('image')) """ return _main_program_ def switch_main_program(program): """ Switch the main program to a new program. Args: program(Program): The new main program Returns: Program: The previous main program """ global _main_program_ prev_program = _main_program_ _main_program_ = program return prev_program def switch_startup_program(program): """ Switch the startup program to a new program Args: program(Program): The new startup program Returns: Program: The previous startup program """ global _startup_program_ prev_program = _startup_program_ _startup_program_ = program return prev_program @signature_safe_contextmanager def program_guard(main_program, startup_program=None): """ :api_attr: Static Graph Change the global main program and startup program with `"with"` statement. Layer functions in the Python `"with"` block will append operators and variables to the new main programs. Args: main_program(Program): New main program inside `"with"` statement. startup_program(Program, optional): New startup program inside `"with"` statement. :code:`None` means not changing startup program, default_startup_program is still used. Default: None. Examples: .. code-block:: python import paddle.fluid as fluid main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') hidden = fluid.layers.fc(input=data, size=10, act='relu') Notes: The temporary :code:`Program` can be used if the user does not need to construct either of startup program or main program. Examples: .. code-block:: python import paddle.fluid as fluid main_program = fluid.Program() # does not care about startup program. Just pass a temporary value. with fluid.program_guard(main_program, fluid.Program()): data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32') """ from .data_feeder import check_type check_type(main_program, 'main_program', Program, 'fluid.program_guard') main_program = switch_main_program(main_program) if startup_program is not None: check_type(startup_program, 'startup_program', Program, 'fluid.program_guard') startup_program = switch_startup_program(startup_program) try: yield finally: switch_main_program(main_program) if startup_program is not None: switch_startup_program(startup_program) def _get_var(name, program=None): """ Get a variable by name from the global block of a program. Args: name(str): name of the variable program(Program|None): program object. If None, default_global_program() will be used. Returns: Variable """ if program is None: program = default_main_program() assert isinstance(name, str) assert isinstance(program, Program) return program.global_block().var(name) @signature_safe_contextmanager def _dygraph_guard(tracer): global _dygraph_tracer_ tmp_trace = _dygraph_tracer_ _dygraph_tracer_ = tracer core._switch_tracer(tracer) try: yield finally: core._switch_tracer(tmp_trace) _dygraph_tracer_ = tmp_trace @signature_safe_contextmanager def _dygraph_place_guard(place): global _dygraph_current_expected_place_ tmp_place = _dygraph_current_expected_place_ _dygraph_current_expected_place_ = place try: yield finally: _dygraph_current_expected_place_ = tmp_place def load_op_library(lib_filename): """ :api_attr: Static Graph Load a dynamic library, including custom operators and kernels. When library is loaded, ops and kernels registered in the library will be available in PaddlePaddle main process. Please note, the type of custom operators can't have the same type with the existing operators in the framework. Args: lib_filename (str): name of dynamic library. Examples: .. code-block:: python import paddle.fluid as fluid #fluid.load_op_library('custom_op.so') """ core.load_op_library(lib_filename) OpProtoHolder.instance().update_op_proto() def switch_device(device): global _current_device pre_device = _current_device _current_device = device return pre_device @signature_safe_contextmanager def device_guard(device=None): """ **Notes**: **The API only supports static mode.** A context manager that specifies the device on which the OP will be placed. Args: device(str|None): Specify the device to use in the context. It should be 'cpu' or 'gpu', When it is set to 'cpu' or 'gpu', all OPs created in the context will be placed on CPUPlace or CUDAPlace. When 'gpu' is set and the program runs on single-card, the device index will be the same as the device on which the executor runs. Default: None, OPs in this context will be automatically assigned devices. Examples: .. code-block:: python import paddle.fluid as fluid support_gpu = fluid.is_compiled_with_cuda() place = fluid.CPUPlace() if support_gpu: place = fluid.CUDAPlace(0) # if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0) data1 = fluid.layers.fill_constant(shape=[1, 3, 8, 8], value=0.5, dtype='float32') data2 = fluid.layers.fill_constant(shape=[1, 3, 5, 5], value=0.5, dtype='float32') shape = fluid.layers.shape(data2) with fluid.device_guard("cpu"): # Ops created here will be placed on CPUPlace shape = fluid.layers.slice(shape, axes=[0], starts=[0], ends=[4]) with fluid.device_guard('gpu'): # if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace out = fluid.layers.crop_tensor(data1, shape=shape) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) result = exe.run(fetch_list=[out]) """ index = None if device and ':' in device: device, index = device.split(':') if device == 'cpu': raise ValueError("Should not set device id for cpu.") if device not in ['cpu', 'gpu', '', None]: raise ValueError( "The Attr(device) should be 'cpu' or 'gpu', and it can also be empty string or None " "when there is no need to specify device. But received %s" % device) if index: device = ":".join([device, index]) pre_device = switch_device(device) try: yield finally: switch_device(pre_device) def set_flags(flags): """ This function sets the GFlags value in Paddle. Args: flags (dict): A dict contains flags and its value. Examples: .. code-block:: python import paddle.fluid as fluid fluid.set_flags({'FLAGS_eager_delete_tensor_gb': 1.0}) """ if not isinstance(flags, dict): raise TypeError('flags in set_flags should be a dict') for key, value in flags.items(): if core.globals().is_public(key): core.globals()[key] = value else: raise ValueError( "Flag %s cannot set its value through this function." % (key)) def get_flags(flags): """ This function gets the GFlags value in Paddle. Args: flags(list|tuple|str): A list/tuple of string or a string which is the flag's name. Returns: flag's value in Paddle. Examples: .. code-block:: python import paddle.fluid as fluid flags = ['FLAGS_eager_delete_tensor_gb', 'FLAGS_check_nan_inf'] res = fluid.get_flags(flags) print(res) # {'FLAGS_eager_delete_tensor_gb': 0.0, 'FLAGS_check_nan_inf': False} """ flags_value = {} if isinstance(flags, (list, tuple)): for key in flags: if (core.globals().is_public(key)): value = core.globals()[key] temp = {key: value} flags_value.update(temp) else: raise ValueError( 'Flag %s cannot get its value through this function.' % (key)) elif isinstance(flags, str): if (core.globals().is_public(flags)): value = core.globals()[flags] temp = {flags: value} flags_value.update(temp) else: raise ValueError( 'Flag %s cannot get its value through this function.' % (flags)) else: raise TypeError('Flags in get_flags should be a list, tuple or string.') return flags_value

35.034445

337

0.562847

8df8574ab78d51967aead8675e625cfdfeb9df8a

389

py

Python

models/parameters.py

mourtadg7/goodwin-keen-model

911913fb0b7389b27a1efeb86ce4336551357690

[ "MIT" ]

null

models/parameters.py

mourtadg7/goodwin-keen-model

911913fb0b7389b27a1efeb86ce4336551357690

[ "MIT" ]

1

2021-09-12T18:10:53.000Z

2021-09-12T18:14:17.000Z

models/parameters.py

mourtadg7/goodwin-keen-model

911913fb0b7389b27a1efeb86ce4336551357690

[ "MIT" ]

2

2021-09-12T15:56:01.000Z

2022-01-12T18:58:21.000Z

alpha = 0.025 # Technological growth rate beta = 0.02 # Population growth rate delta = 0.01 # Deprecation rate # Phillips Curve Parameters from Keen (1995) phi0 = 0.04 / (1 - 0.04 ** 2) phi1 = 0.04 ** 3 / (1 - 0.04 ** 2) # Investment Rate Parameters from Grasselli (2012) kappa0 = -0.0065 kappa1 = math.exp(-5) kappa2 = 20 r = 0.03 # Real interest rate v = 3 # Capital to output ratio

32.416667

50

0.663239

8d5342561beb95e5aeb45b6586ad1f440fa28b9e

1,196

py

Python

bitwise/gate/IMPLY.py

jamesjiang52/Bitwise

c71f151d23034b3f9e2a939f637be0eaa16c45c3

[ "MIT" ]

null

bitwise/gate/IMPLY.py

jamesjiang52/Bitwise

c71f151d23034b3f9e2a939f637be0eaa16c45c3

[ "MIT" ]

null

bitwise/gate/IMPLY.py

jamesjiang52/Bitwise

c71f151d23034b3f9e2a939f637be0eaa16c45c3

[ "MIT" ]

null

""" The following classes are defined: IMPLYGate2 """ from .. import wire from . import OR from . import NOT Wire = wire.Wire class IMPLYGate: """Construct a new IMPLY gate. Args: input_1: An object of type Wire. The first input to the IMPLY gate. input_2: An object of type Wire. The second input to the IMPLY gate. output: An object of type Wire. The output of the IMPLY gate. """ def __init__(self, input_1, input_2, output): wire_1 = Wire() NOT.NOTGate(input_1, wire_1) OR.ORGate2(wire_1, input_2, output) self.input_1 = input_1 self.input_2 = input_2 self.output = output def __str__(self): str_ = "" str_ += "input_1: " + str(self.input_1.value) + "\n" str_ += "input_2: " + str(self.input_2.value) + "\n" str_ += "output: " + str(self.output.value) return str_ def __call__(self, *, input_1=None, input_2=None, output=None): if input_1 is not None: self.input_1.value = input_1 if input_2 is not None: self.input_2.value = input_2 if output is not None: self.output.value = output

27.181818

76

0.597826

b7a9d36441ef4f50311924cb9b25ddbfc8d0e477

12,899

py

Python

federatedml/nn/homo_nn/enter_point.py

yzjba/FATE

9a6d252da637b2583a0f8a51f6cb4c615850bab9

[ "Apache-2.0" ]

1

2021-05-31T16:39:30.000Z

federatedml/nn/homo_nn/enter_point.py

ErikSun2020/FATE

bdda535c7d8a974fc2c43102837964b7da199730

[ "Apache-2.0" ]

9

2020-11-13T18:59:35.000Z

2022-02-10T02:13:58.000Z

federatedml/nn/homo_nn/enter_point.py

ErikSun2020/FATE

bdda535c7d8a974fc2c43102837964b7da199730

[ "Apache-2.0" ]

null

# # Copyright 2019 The FATE Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from arch.api import session from arch.api.utils.log_utils import LoggerFactory from fate_flow.entity.metric import MetricType, MetricMeta, Metric from federatedml.framework.homo.blocks import secure_mean_aggregator, loss_scatter, has_converged from federatedml.framework.homo.blocks.base import HomoTransferBase from federatedml.framework.homo.blocks.has_converged import HasConvergedTransVar from federatedml.framework.homo.blocks.loss_scatter import LossScatterTransVar from federatedml.framework.homo.blocks.secure_aggregator import SecureAggregatorTransVar from federatedml.model_base import ModelBase from federatedml.nn.homo_nn import nn_model from federatedml.nn.homo_nn.nn_model import restore_nn_model from federatedml.optim.convergence import converge_func_factory from federatedml.param.homo_nn_param import HomoNNParam from federatedml.util import consts from federatedml.util.io_check import assert_io_num_rows_equal Logger = LoggerFactory.get_logger() MODEL_META_NAME = "HomoNNModelMeta" MODEL_PARAM_NAME = "HomoNNModelParam" def _build_model_dict(meta, param): return {MODEL_META_NAME: meta, MODEL_PARAM_NAME: param} def _extract_param(model_dict: dict): return model_dict.get(MODEL_PARAM_NAME, None) def _extract_meta(model_dict: dict): return model_dict.get(MODEL_META_NAME, None) class HomoNNBase(ModelBase): def __init__(self, trans_var): super().__init__() self.model_param = HomoNNParam() self.aggregate_iteration_num = 0 self.transfer_variable = trans_var def _suffix(self): return self.aggregate_iteration_num, def _init_model(self, param: HomoNNParam): self.param = param self.enable_secure_aggregate = param.secure_aggregate self.max_aggregate_iteration_num = param.max_iter class HomoNNServer(HomoNNBase): def __init__(self, trans_var): super().__init__(trans_var=trans_var) self.model = None self.aggregator = secure_mean_aggregator.Server(self.transfer_variable.secure_aggregator_trans_var) self.loss_scatter = loss_scatter.Server(self.transfer_variable.loss_scatter_trans_var) self.has_converged = has_converged.Server(self.transfer_variable.has_converged_trans_var) def _init_model(self, param: HomoNNParam): super()._init_model(param=param) early_stop = self.model_param.early_stop self.converge_func = converge_func_factory(early_stop.converge_func, early_stop.eps).is_converge self.loss_consumed = early_stop.converge_func != "weight_diff" def callback_loss(self, iter_num, loss): metric_meta = MetricMeta(name='train', metric_type="LOSS", extra_metas={ "unit_name": "iters", }) self.callback_meta(metric_name='loss', metric_namespace='train', metric_meta=metric_meta) self.callback_metric(metric_name='loss', metric_namespace='train', metric_data=[Metric(iter_num, loss)]) def _is_converged(self): loss = self.loss_scatter.weighted_loss_mean(suffix=self._suffix()) Logger.info(f"loss at iter {self.aggregate_iteration_num}: {loss}") self.callback_loss(self.aggregate_iteration_num, loss) if self.loss_consumed: is_converged = self.converge_func(loss) else: is_converged = self.converge_func(self.model) self.has_converged.remote_converge_status(is_converge=is_converged, suffix=self._suffix()) return is_converged def fit(self, data_inst): while self.aggregate_iteration_num < self.max_aggregate_iteration_num: self.model = self.aggregator.weighted_mean_model(suffix=self._suffix()) self.aggregator.send_aggregated_model(model=self.model, suffix=self._suffix()) if self._is_converged(): Logger.info(f"early stop at iter {self.aggregate_iteration_num}") break self.aggregate_iteration_num += 1 else: Logger.warn(f"reach max iter: {self.aggregate_iteration_num}, not converged") def save_model(self): return self.model class HomoNNClient(HomoNNBase): def __init__(self, trans_var): super().__init__(trans_var=trans_var) self.aggregator = secure_mean_aggregator.Client(self.transfer_variable.secure_aggregator_trans_var) self.loss_scatter = loss_scatter.Client(self.transfer_variable.loss_scatter_trans_var) self.has_converged = has_converged.Client(self.transfer_variable.has_converged_trans_var) self.nn_model = None def _init_model(self, param: HomoNNParam): super()._init_model(param=param) self.batch_size = param.batch_size self.aggregate_every_n_epoch = param.aggregate_every_n_epoch self.nn_define = param.nn_define self.config_type = param.config_type self.optimizer = param.optimizer self.loss = param.loss self.metrics = param.metrics self.encode_label = param.encode_label self.data_converter = nn_model.get_data_converter(self.config_type) self.model_builder = nn_model.get_nn_builder(config_type=self.config_type) def _is_converged(self, data, epoch_degree): metrics = self.nn_model.evaluate(data) Logger.info(f"metrics at iter {self.aggregate_iteration_num}: {metrics}") loss = metrics["loss"] self.loss_scatter.send_loss(loss=(loss, epoch_degree), suffix=self._suffix()) is_converged = self.has_converged.get_converge_status(suffix=self._suffix()) return is_converged def __build_nn_model(self, input_shape): self.nn_model = self.model_builder(input_shape=input_shape, nn_define=self.nn_define, optimizer=self.optimizer, loss=self.loss, metrics=self.metrics) def __build_pytorch_model(self, nn_define): self.nn_model = self.model_builder(nn_define=nn_define, optimizer=self.optimizer, loss=self.loss, metrics=self.metrics) def fit(self, data_inst, *args): data = self.data_converter.convert(data_inst, batch_size=self.batch_size, encode_label=self.encode_label) if self.config_type == "pytorch": self.__build_pytorch_model(self.nn_define) else: self.__build_nn_model(data.get_shape()[0]) epoch_degree = float(len(data)) * self.aggregate_every_n_epoch while self.aggregate_iteration_num < self.max_aggregate_iteration_num: Logger.info(f"start {self.aggregate_iteration_num}_th aggregation") # train self.nn_model.train(data, aggregate_every_n_epoch=self.aggregate_every_n_epoch) # send model for aggregate, then set aggregated model to local self.aggregator.send_weighted_model(weighted_model=self.nn_model.get_model_weights(), weight=epoch_degree * self.aggregate_every_n_epoch, suffix=self._suffix()) weights = self.aggregator.get_aggregated_model(suffix=self._suffix()) self.nn_model.set_model_weights(weights=weights) # calc loss and check convergence if self._is_converged(data, epoch_degree): Logger.info(f"early stop at iter {self.aggregate_iteration_num}") break Logger.info(f"role {self.role} finish {self.aggregate_iteration_num}_th aggregation") self.aggregate_iteration_num += 1 else: Logger.warn(f"reach max iter: {self.aggregate_iteration_num}, not converged") def export_model(self): return _build_model_dict(meta=self._get_meta(), param=self._get_param()) def _get_meta(self): from federatedml.protobuf.generated import nn_model_meta_pb2 meta_pb = nn_model_meta_pb2.NNModelMeta() meta_pb.params.CopyFrom(self.model_param.generate_pb()) meta_pb.aggregate_iter = self.aggregate_iteration_num return meta_pb def _get_param(self): from federatedml.protobuf.generated import nn_model_param_pb2 param_pb = nn_model_param_pb2.NNModelParam() param_pb.saved_model_bytes = self.nn_model.export_model() return param_pb @assert_io_num_rows_equal def predict(self, data_inst): data = self.data_converter.convert(data_inst, batch_size=self.batch_size, encode_label=self.encode_label) predict = self.nn_model.predict(data) num_output_units = predict.shape[1] threshold = self.param.predict_param.threshold if num_output_units == 1: kv = [(x[0], (0 if x[1][0] <= threshold else 1, x[1][0].item())) for x in zip(data.get_keys(), predict)] pred_tbl = session.parallelize(kv, include_key=True, partition=data_inst.get_partitions()) return data_inst.join(pred_tbl, lambda d, pred: [d.label, pred[0], pred[1], {"0": 1 - pred[1], "1": pred[1]}]) else: kv = [(x[0], (x[1].argmax(), [float(e) for e in x[1]])) for x in zip(data.get_keys(), predict)] pred_tbl = session.parallelize(kv, include_key=True, partition=data_inst.get_partitions()) return data_inst.join(pred_tbl, lambda d, pred: [d.label, pred[0].item(), pred[1][pred[0]], {str(v): pred[1][v] for v in range(len(pred[1]))}]) def load_model(self, model_dict): model_dict = list(model_dict["model"].values())[0] model_obj = _extract_param(model_dict) meta_obj = _extract_meta(model_dict) self.model_param.restore_from_pb(meta_obj.params) self._init_model(self.model_param) self.aggregate_iteration_num = meta_obj.aggregate_iter self.nn_model = restore_nn_model(self.config_type, model_obj.saved_model_bytes) # server: Arbiter, clients: Guest and Hosts class HomoNNDefaultTransVar(HomoTransferBase): def __init__(self, server=(consts.ARBITER,), clients=(consts.GUEST, consts.HOST), prefix=None): super().__init__(server=server, clients=clients, prefix=prefix) self.secure_aggregator_trans_var = SecureAggregatorTransVar(server=server, clients=clients, prefix=self.prefix) self.loss_scatter_trans_var = LossScatterTransVar(server=server, clients=clients, prefix=self.prefix) self.has_converged_trans_var = HasConvergedTransVar(server=server, clients=clients, prefix=self.prefix) class HomoNNDefaultClient(HomoNNClient): def __init__(self): super().__init__(trans_var=HomoNNDefaultTransVar()) class HomoNNDefaultServer(HomoNNServer): def __init__(self): super().__init__(trans_var=HomoNNDefaultTransVar()) # server: Arbiter, clients: Guest and Hosts class HomoNNGuestServerTransVar(HomoNNDefaultTransVar): def __init__(self, server=(consts.GUEST,), clients=(consts.HOST,), prefix=None): super().__init__(server=server, clients=clients, prefix=prefix) class HomoNNGuestServerClient(HomoNNClient): def __init__(self): super().__init__(trans_var=HomoNNGuestServerTransVar()) class HomoNNGuestServerServer(HomoNNServer): def __init__(self): super().__init__(trans_var=HomoNNGuestServerTransVar()) # server: Arbiter, clients: Hosts class HomoNNArbiterSubmitTransVar(HomoNNDefaultTransVar): def __init__(self, server=(consts.ARBITER,), clients=(consts.HOST,), prefix=None): super().__init__(server=server, clients=clients, prefix=prefix) class HomoNNArbiterSubmitClient(HomoNNClient): def __init__(self): super().__init__(trans_var=HomoNNArbiterSubmitTransVar()) class HomoNNArbiterSubmitServer(HomoNNServer): def __init__(self): super().__init__(trans_var=HomoNNArbiterSubmitTransVar())

43.577703

119

0.685324

ad28fdea55418dbeba1fb767c070be73ace50e1f

1,675

py

Python

tests/tools.py

altvod/basic-notion

38da5cc6912d73fad530d37f1608b49d90c2034e

[ "MIT" ]

null

tests/tools.py

altvod/basic-notion

38da5cc6912d73fad530d37f1608b49d90c2034e

[ "MIT" ]

null

tests/tools.py

altvod/basic-notion

38da5cc6912d73fad530d37f1608b49d90c2034e

[ "MIT" ]

null

import uuid from typing import Type from notion_client import Client from basic_notion.database import NotionDatabase from basic_notion.page import NotionPage, NotionPageList from basic_notion.query import Query def create_database_from_model( sync_client: Client, model: Type[NotionPage], title: str, parent_page_id: str, ) -> NotionDatabase: database = NotionDatabase.make( title=[title], parent={'page_id': parent_page_id}, properties=model.schema, ) data = sync_client.databases.create(**database.data) created_database = NotionDatabase(data=data) return created_database _DB_REGISTRY: dict[Type[NotionPage], NotionDatabase] = {} def get_database_from_model( sync_client: Client, model: Type[NotionPage], parent_page_id: str, ) -> NotionDatabase: if model in _DB_REGISTRY: return _DB_REGISTRY[model] title = f'Test Database {str(uuid.uuid4())}' database = create_database_from_model( sync_client=sync_client, model=model, title=title, parent_page_id=parent_page_id, ) return database def get_id_list(sync_client: Client, list_model: Type[NotionPageList], database_id: str) -> list[str]: # Get the model class of the item model = list_model.item # Get any property (to sort the query, which is required) any_prop = next(iter(model.schema.properties.values())) data = sync_client.databases.query( **Query(database_id=database_id).sorts( any_prop.sort.ascending ).serialize() ) item_list = list_model(data=data) return [item.id for item in item_list.items()]

28.87931

102

0.696716

a0869dc3cd73f48a8f261748811960cd6117f829

4,361

py

Python

sermons/views.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

null

sermons/views.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

44

2020-05-13T20:15:26.000Z

2022-03-04T02:58:58.000Z

sermons/views.py

mennonitengemeinde/church_site

ae9ef5f0f78811cecd734705339511dc0efb8340

[ "MIT" ]

4

2020-06-05T17:59:52.000Z

2021-02-06T19:09:43.000Z

import logging from django.contrib.auth.mixins import PermissionRequiredMixin from django.urls import reverse_lazy from church_site.views import BaseListView, BaseDetailView, BaseCreateView, AdminListView, BaseUpdateView from churches.models import Church from sermons.forms import SermonCreateForm from sermons.models import Sermon from sermons.selectors import get_filtered_sermons, get_member_sermons from speakers.models import Speaker logger = logging.getLogger(__name__) class SermonsListView(BaseListView): page_title = 'Sermons - Mennoniten Gemeinde' current_page = 'sermons' model = Sermon template_name = 'sermons/sermons-list.html' context_object_name = 'sermons' paginate_by = 18 def get_queryset(self): return get_filtered_sermons(self.request.GET.get('church'), self.request.GET.get('speaker')) def get_context_data(self, *, object_list=None, **kwargs): context = super().get_context_data(**kwargs) context['current_church'] = self.request.GET.get('church') if self.request.GET.get('church') else None context['current_speaker'] = int(self.request.GET.get('speaker')) if self.request.GET.get('speaker') else None context['churches'] = Church.objects.all() context['speakers'] = Speaker.objects.all() context['page_filter'] = self.get_page_filter() return context def get_page_filter(self): """keeps the filter in get request when paginating""" if self.request.GET.get('church') and self.request.GET.get('speaker'): return f"church={self.request.GET.get('church')}&speaker={self.request.GET.get('speaker')}" elif self.request.GET.get('church') and not self.request.GET.get('speaker'): return f"church={self.request.GET.get('church')}" elif self.request.GET.get('speaker') and not self.request.GET.get('church'): return f"speaker={self.request.GET.get('speaker')}" class SermonsDetailView(BaseDetailView): current_page = 'sermons' btn_back_href = reverse_lazy('sermons:sermons-list') model = Sermon template_name = 'sermons/sermons-detail.html' context_object_name = 'sermon' def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context['page_title'] = self.get_page_title() # Track views try: context['sermon'].views += 1 context['sermon'].save() except Exception as e: logger.error(e) return context def get_page_title(self) -> str: return f'{self.object.event.start.strftime("%b %d, %Y")} - {self.object.title}' class SermonsAdminListView(PermissionRequiredMixin, AdminListView): permission_required = 'sermons.view_sermon' model = Sermon ordering = ('-event',) context_object_name = 'sermons' template_name = 'sermons/sermons-admin-list.html' page_title = 'Sermons - Admin' current_page = 'manage' btn_add_href = reverse_lazy('sermons:sermons-admin-create') paginate_by = 25 def get_queryset(self): return get_member_sermons(self.request.user, reverse_order=True) class SermonsAdminCreateView(PermissionRequiredMixin, BaseCreateView): permission_required = 'sermons.add_sermon' model = Sermon template_name = 'sermons/sermons-admin-form.html' form_class = SermonCreateForm success_url = reverse_lazy('sermons:sermons-admin-list') page_title = 'New Sermon - Admin' current_page = 'manage' btn_back_href = reverse_lazy('sermons:sermons-admin-list') def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['user'] = self.request.user return kwargs class SermonAdminUpdateView(PermissionRequiredMixin, BaseUpdateView): permission_required = 'sermons.change_sermon' model = Sermon template_name = 'sermons/sermons-admin-form.html' form_class = SermonCreateForm success_url = reverse_lazy('sermons:sermons-admin-list') page_title = 'Update Sermon - Admin' current_page = 'manage' btn_back_href = reverse_lazy('sermons:sermons-admin-list') def get_queryset(self): return get_member_sermons(self.request.user) def get_form_kwargs(self): kwargs = super().get_form_kwargs() kwargs['user'] = self.request.user return kwargs

37.921739

118

0.701903

fd4dfcca351d821e6e69588e41f9e0c5e34fc004

2,144

py

Python

tests/test_invoice.py

izumimatsuo/micro-billing

94d7acdf5515c6f0be26fe013ae9cd1936dc03af

[ "MIT" ]

null

tests/test_invoice.py

izumimatsuo/micro-billing

94d7acdf5515c6f0be26fe013ae9cd1936dc03af

[ "MIT" ]

null

tests/test_invoice.py

izumimatsuo/micro-billing

94d7acdf5515c6f0be26fe013ae9cd1936dc03af

[ "MIT" ]

null

import pytest def test_select_all(client): res = client.get('/invoices') assert res.status_code == 200 data = res.json() assert 1 == len(data['invoices']) def test_select_one(client): res = client.get('/invoices/1') assert res.status_code == 200 data = res.json() assert data['period_start'].startswith('2021-01-01') def test_not_found(client): res = client.get('/invoices/5') assert res.status_code == 404 def test_all_days(client): res = client.get('/invoices/data') assert res.status_code == 200 assert 4 == len(res.content.splitlines()) assert b'"name","amount","start_date"' in res.content assert b'"Taro",980,"2021-01-01 00:00:00"' in res.content assert b'"Jiro",1500,"2021-01-31 00:00:00"' in res.content assert b'"Hanako",980,"2021-02-28 00:00:00"' in res.content def test_first_day(client): res = client.get('/invoices/data/20210401') assert res.status_code == 200 assert 2 == len(res.content.splitlines()) assert b'"name","amount","start_date"' in res.content assert b'"Taro",980,"2021-01-01 00:00:00"' in res.content def test_last_day(client): res = client.get('/invoices/data/20210430') assert res.status_code == 200 assert 2 == len(res.content.splitlines()) assert b'"name","amount","start_date"' in res.content assert b'"Jiro",1500,"2021-01-31 00:00:00"' in res.content def test_leap_year(client): res = client.get('/invoices/data/20240229') assert res.status_code == 200 assert 2 == len(res.content.splitlines()) assert b'"name","amount","start_date"' in res.content assert b'"Jiro",1500,"2021-01-31 00:00:00"' in res.content def test_not_leap_year(client): res = client.get('/invoices/data/20220228') assert res.status_code == 200 assert 3 == len(res.content.splitlines()) assert b'"name","amount","start_date"' in res.content assert b'"Jiro",1500,"2021-01-31 00:00:00"' in res.content assert b'"Hanako",980,"2021-02-28 00:00:00"' in res.content def test_bad_request(client): res = client.get('/invoices/data/20210431') assert res.status_code == 400

31.072464

63

0.66931

de55a24a2f6b125910827059fd99969c3e9cfc48

29,096

py

Python

kornia/geometry/conversions.py

aardvarkkrill/kornia

e36ca3d15883a1dbbb0e7413719c0965a4b63cee

[ "ECL-2.0", "Apache-2.0" ]

null

kornia/geometry/conversions.py

aardvarkkrill/kornia

e36ca3d15883a1dbbb0e7413719c0965a4b63cee

[ "ECL-2.0", "Apache-2.0" ]

null

kornia/geometry/conversions.py

aardvarkkrill/kornia

e36ca3d15883a1dbbb0e7413719c0965a4b63cee

[ "ECL-2.0", "Apache-2.0" ]

1

2021-05-15T03:22:24.000Z

from typing import Tuple import torch import torch.nn as nn import torch.nn.functional as F from kornia.constants import pi __all__ = [ # functional api "rad2deg", "deg2rad", "pol2cart", "cart2pol", "convert_points_from_homogeneous", "convert_points_to_homogeneous", "convert_affinematrix_to_homography", "convert_affinematrix_to_homography3d", "angle_axis_to_rotation_matrix", "angle_axis_to_quaternion", "rotation_matrix_to_angle_axis", "rotation_matrix_to_quaternion", "quaternion_to_angle_axis", "quaternion_to_rotation_matrix", "quaternion_log_to_exp", "quaternion_exp_to_log", "denormalize_pixel_coordinates", "normalize_pixel_coordinates", "normalize_quaternion", "denormalize_pixel_coordinates3d", "normalize_pixel_coordinates3d", ] def rad2deg(tensor: torch.Tensor) -> torch.Tensor: r"""Function that converts angles from radians to degrees. Args: tensor (torch.Tensor): Tensor of arbitrary shape. Returns: torch.Tensor: Tensor with same shape as input. Example: >>> input = torch.tensor(3.1415926535) * torch.rand(1, 3, 3) >>> output = rad2deg(input) """ if not isinstance(tensor, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(tensor))) return 180. * tensor / pi.to(tensor.device).type(tensor.dtype) def deg2rad(tensor: torch.Tensor) -> torch.Tensor: r"""Function that converts angles from degrees to radians. Args: tensor (torch.Tensor): Tensor of arbitrary shape. Returns: torch.Tensor: tensor with same shape as input. Examples:: >>> input = 360. * torch.rand(1, 3, 3) >>> output = deg2rad(input) """ if not isinstance(tensor, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(tensor))) return tensor * pi.to(tensor.device).type(tensor.dtype) / 180. def pol2cart(rho: torch.Tensor, phi: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r"""Function that converts polar coordinates to cartesian coordinates. Args: rho (torch.Tensor): Tensor of arbitrary shape. phi (torch.Tensor): Tensor of same arbitrary shape. Returns: torch.Tensor, torch.Tensor: Tensor with same shape as input. Example: >>> rho = torch.rand(1, 3, 3) >>> phi = torch.rand(1, 3, 3) >>> x, y = pol2cart(rho, phi) """ if not (isinstance(rho, torch.Tensor) & isinstance(phi, torch.Tensor)): raise TypeError("Input type is not a torch.Tensor. Got {}, {}".format( type(rho), type(phi))) x = rho * torch.cos(phi) y = rho * torch.sin(phi) return x, y def cart2pol(x: torch.Tensor, y: torch.Tensor, eps: float = 1e-8) -> Tuple[torch.Tensor, torch.Tensor]: """Function that converts cartesian coordinates to polar coordinates. Args: rho (torch.Tensor): Tensor of arbitrary shape. phi (torch.Tensor): Tensor of same arbitrary shape. eps (float): To avoid division by zero. Default is 1e-8 Returns: torch.Tensor, torch.Tensor: Tensor with same shape as input. Example: >>> x = torch.rand(1, 3, 3) >>> y = torch.rand(1, 3, 3) >>> rho, phi = cart2pol(x, y) """ if not (isinstance(x, torch.Tensor) & isinstance(y, torch.Tensor)): raise TypeError("Input type is not a torch.Tensor. Got {}, {}".format( type(x), type(y))) rho = torch.sqrt(x**2 + y**2 + eps) phi = torch.atan2(y, x) return rho, phi def convert_points_from_homogeneous( points: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: r"""Function that converts points from homogeneous to Euclidean space. Examples:: >>> input = torch.rand(2, 4, 3) # BxNx3 >>> output = convert_points_from_homogeneous(input) # BxNx2 """ if not isinstance(points, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(points))) if len(points.shape) < 2: raise ValueError("Input must be at least a 2D tensor. Got {}".format( points.shape)) # we check for points at infinity z_vec: torch.Tensor = points[..., -1:] # set the results of division by zeror/near-zero to 1.0 # follow the convention of opencv: # https://github.com/opencv/opencv/pull/14411/files mask: torch.Tensor = torch.abs(z_vec) > eps scale = torch.where(mask, 1. / (z_vec + eps), torch.ones_like(z_vec)) return scale * points[..., :-1] def convert_points_to_homogeneous(points: torch.Tensor) -> torch.Tensor: r"""Function that converts points from Euclidean to homogeneous space. Examples:: >>> input = torch.rand(2, 4, 3) # BxNx3 >>> output = convert_points_to_homogeneous(input) # BxNx4 """ if not isinstance(points, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(points))) if len(points.shape) < 2: raise ValueError("Input must be at least a 2D tensor. Got {}".format( points.shape)) return torch.nn.functional.pad(points, [0, 1], "constant", 1.0) def _convert_affinematrix_to_homography_impl(A: torch.Tensor) -> torch.Tensor: H: torch.Tensor = torch.nn.functional.pad(A, [0, 0, 0, 1], "constant", value=0.) H[..., -1, -1] += 1.0 return H def convert_affinematrix_to_homography(A: torch.Tensor) -> torch.Tensor: r"""Function that converts batch of affine matrices from [Bx2x3] to [Bx3x3]. Examples:: >>> input = torch.rand(2, 2, 3) # Bx2x3 >>> output = convert_affinematrix_to_homography(input) # Bx3x3 """ if not isinstance(A, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(A))) if not (len(A.shape) == 3 and A.shape[-2:] == (2, 3)): raise ValueError("Input matrix must be a Bx2x3 tensor. Got {}" .format(A.shape)) return _convert_affinematrix_to_homography_impl(A) def convert_affinematrix_to_homography3d(A: torch.Tensor) -> torch.Tensor: r"""Function that converts batch of affine matrices from [Bx3x4] to [Bx4x4]. Examples:: >>> input = torch.rand(2, 3, 4) # Bx3x4 >>> output = convert_affinematrix_to_homography3d(input) # Bx4x4 """ if not isinstance(A, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(A))) if not (len(A.shape) == 3 and A.shape[-2:] == (3, 4)): raise ValueError("Input matrix must be a Bx3x4 tensor. Got {}" .format(A.shape)) return _convert_affinematrix_to_homography_impl(A) def angle_axis_to_rotation_matrix(angle_axis: torch.Tensor) -> torch.Tensor: r"""Convert 3d vector of axis-angle rotation to 3x3 rotation matrix Args: angle_axis (torch.Tensor): tensor of 3d vector of axis-angle rotations. Returns: torch.Tensor: tensor of 3x3 rotation matrices. Shape: - Input: :math:`(N, 3)` - Output: :math:`(N, 3, 3)` Example: >>> input = torch.rand(1, 3) # Nx3 >>> output = angle_axis_to_rotation_matrix(input) # Nx3x3 """ if not isinstance(angle_axis, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(angle_axis))) if not angle_axis.shape[-1] == 3: raise ValueError( "Input size must be a (*, 3) tensor. Got {}".format( angle_axis.shape)) def _compute_rotation_matrix(angle_axis, theta2, eps=1e-6): # We want to be careful to only evaluate the square root if the # norm of the angle_axis vector is greater than zero. Otherwise # we get a division by zero. k_one = 1.0 theta = torch.sqrt(theta2) wxyz = angle_axis / (theta + eps) wx, wy, wz = torch.chunk(wxyz, 3, dim=1) cos_theta = torch.cos(theta) sin_theta = torch.sin(theta) r00 = cos_theta + wx * wx * (k_one - cos_theta) r10 = wz * sin_theta + wx * wy * (k_one - cos_theta) r20 = -wy * sin_theta + wx * wz * (k_one - cos_theta) r01 = wx * wy * (k_one - cos_theta) - wz * sin_theta r11 = cos_theta + wy * wy * (k_one - cos_theta) r21 = wx * sin_theta + wy * wz * (k_one - cos_theta) r02 = wy * sin_theta + wx * wz * (k_one - cos_theta) r12 = -wx * sin_theta + wy * wz * (k_one - cos_theta) r22 = cos_theta + wz * wz * (k_one - cos_theta) rotation_matrix = torch.cat( [r00, r01, r02, r10, r11, r12, r20, r21, r22], dim=1) return rotation_matrix.view(-1, 3, 3) def _compute_rotation_matrix_taylor(angle_axis): rx, ry, rz = torch.chunk(angle_axis, 3, dim=1) k_one = torch.ones_like(rx) rotation_matrix = torch.cat( [k_one, -rz, ry, rz, k_one, -rx, -ry, rx, k_one], dim=1) return rotation_matrix.view(-1, 3, 3) # stolen from ceres/rotation.h _angle_axis = torch.unsqueeze(angle_axis, dim=1) theta2 = torch.matmul(_angle_axis, _angle_axis.transpose(1, 2)) theta2 = torch.squeeze(theta2, dim=1) # compute rotation matrices rotation_matrix_normal = _compute_rotation_matrix(angle_axis, theta2) rotation_matrix_taylor = _compute_rotation_matrix_taylor(angle_axis) # create mask to handle both cases eps = 1e-6 mask = (theta2 > eps).view(-1, 1, 1).to(theta2.device) mask_pos = (mask).type_as(theta2) mask_neg = (mask == False).type_as(theta2) # noqa # create output pose matrix batch_size = angle_axis.shape[0] rotation_matrix = torch.eye(3).to(angle_axis.device).type_as(angle_axis) rotation_matrix = rotation_matrix.view(1, 3, 3).repeat(batch_size, 1, 1) # fill output matrix with masked values rotation_matrix[..., :3, :3] = \ mask_pos * rotation_matrix_normal + mask_neg * rotation_matrix_taylor return rotation_matrix # Nx3x3 def rotation_matrix_to_angle_axis( rotation_matrix: torch.Tensor) -> torch.Tensor: r"""Convert 3x3 rotation matrix to Rodrigues vector. Args: rotation_matrix (torch.Tensor): rotation matrix. Returns: torch.Tensor: Rodrigues vector transformation. Shape: - Input: :math:`(N, 3, 3)` - Output: :math:`(N, 3)` Example: >>> input = torch.rand(2, 3, 3) # Nx3x3 >>> output = rotation_matrix_to_angle_axis(input) # Nx3 """ if not isinstance(rotation_matrix, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(rotation_matrix))) if not rotation_matrix.shape[-2:] == (3, 3): raise ValueError( "Input size must be a (*, 3, 3) tensor. Got {}".format( rotation_matrix.shape)) quaternion: torch.Tensor = rotation_matrix_to_quaternion(rotation_matrix) return quaternion_to_angle_axis(quaternion) def rotation_matrix_to_quaternion( rotation_matrix: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: r"""Convert 3x3 rotation matrix to 4d quaternion vector. The quaternion vector has components in (x, y, z, w) format. Args: rotation_matrix (torch.Tensor): the rotation matrix to convert. eps (float): small value to avoid zero division. Default: 1e-8. Return: torch.Tensor: the rotation in quaternion. Shape: - Input: :math:`(*, 3, 3)` - Output: :math:`(*, 4)` Example: >>> input = torch.rand(4, 3, 3) # Nx3x3 >>> output = rotation_matrix_to_quaternion(input) # Nx4 """ if not isinstance(rotation_matrix, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(rotation_matrix))) if not rotation_matrix.shape[-2:] == (3, 3): raise ValueError( "Input size must be a (*, 3, 3) tensor. Got {}".format( rotation_matrix.shape)) def safe_zero_division(numerator: torch.Tensor, denominator: torch.Tensor) -> torch.Tensor: eps: float = torch.finfo(numerator.dtype).tiny # type: ignore return numerator / torch.clamp(denominator, min=eps) rotation_matrix_vec: torch.Tensor = rotation_matrix.view( *rotation_matrix.shape[:-2], 9) m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.chunk( rotation_matrix_vec, chunks=9, dim=-1) trace: torch.Tensor = m00 + m11 + m22 def trace_positive_cond(): sq = torch.sqrt(trace + 1.0) * 2. # sq = 4 * qw. qw = 0.25 * sq qx = safe_zero_division(m21 - m12, sq) qy = safe_zero_division(m02 - m20, sq) qz = safe_zero_division(m10 - m01, sq) return torch.cat([qx, qy, qz, qw], dim=-1) def cond_1(): sq = torch.sqrt(1.0 + m00 - m11 - m22 + eps) * 2. # sq = 4 * qx. qw = safe_zero_division(m21 - m12, sq) qx = 0.25 * sq qy = safe_zero_division(m01 + m10, sq) qz = safe_zero_division(m02 + m20, sq) return torch.cat([qx, qy, qz, qw], dim=-1) def cond_2(): sq = torch.sqrt(1.0 + m11 - m00 - m22 + eps) * 2. # sq = 4 * qy. qw = safe_zero_division(m02 - m20, sq) qx = safe_zero_division(m01 + m10, sq) qy = 0.25 * sq qz = safe_zero_division(m12 + m21, sq) return torch.cat([qx, qy, qz, qw], dim=-1) def cond_3(): sq = torch.sqrt(1.0 + m22 - m00 - m11 + eps) * 2. # sq = 4 * qz. qw = safe_zero_division(m10 - m01, sq) qx = safe_zero_division(m02 + m20, sq) qy = safe_zero_division(m12 + m21, sq) qz = 0.25 * sq return torch.cat([qx, qy, qz, qw], dim=-1) where_2 = torch.where(m11 > m22, cond_2(), cond_3()) where_1 = torch.where( (m00 > m11) & (m00 > m22), cond_1(), where_2) quaternion: torch.Tensor = torch.where( trace > 0., trace_positive_cond(), where_1) return quaternion def normalize_quaternion(quaternion: torch.Tensor, eps: float = 1e-12) -> torch.Tensor: r"""Normalizes a quaternion. The quaternion should be in (x, y, z, w) format. Args: quaternion (torch.Tensor): a tensor containing a quaternion to be normalized. The tensor can be of shape :math:`(*, 4)`. eps (Optional[bool]): small value to avoid division by zero. Default: 1e-12. Return: torch.Tensor: the normalized quaternion of shape :math:`(*, 4)`. Example: >>> quaternion = torch.tensor([1., 0., 1., 0.]) >>> normalize_quaternion(quaternion) tensor([0.7071, 0.0000, 0.7071, 0.0000]) """ if not isinstance(quaternion, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(quaternion))) if not quaternion.shape[-1] == 4: raise ValueError( "Input must be a tensor of shape (*, 4). Got {}".format( quaternion.shape)) return F.normalize(quaternion, p=2, dim=-1, eps=eps) # based on: # https://github.com/matthew-brett/transforms3d/blob/8965c48401d9e8e66b6a8c37c65f2fc200a076fa/transforms3d/quaternions.py#L101 # https://github.com/tensorflow/graphics/blob/master/tensorflow_graphics/geometry/transformation/rotation_matrix_3d.py#L247 def quaternion_to_rotation_matrix(quaternion: torch.Tensor) -> torch.Tensor: r"""Converts a quaternion to a rotation matrix. The quaternion should be in (x, y, z, w) format. Args: quaternion (torch.Tensor): a tensor containing a quaternion to be converted. The tensor can be of shape :math:`(*, 4)`. Return: torch.Tensor: the rotation matrix of shape :math:`(*, 3, 3)`. Example: >>> quaternion = torch.tensor([0., 0., 1., 0.]) >>> quaternion_to_rotation_matrix(quaternion) tensor([[-1., 0., 0.], [ 0., -1., 0.], [ 0., 0., 1.]]) """ if not isinstance(quaternion, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(quaternion))) if not quaternion.shape[-1] == 4: raise ValueError( "Input must be a tensor of shape (*, 4). Got {}".format( quaternion.shape)) # normalize the input quaternion quaternion_norm: torch.Tensor = normalize_quaternion(quaternion) # unpack the normalized quaternion components x, y, z, w = torch.chunk(quaternion_norm, chunks=4, dim=-1) # compute the actual conversion tx: torch.Tensor = 2.0 * x ty: torch.Tensor = 2.0 * y tz: torch.Tensor = 2.0 * z twx: torch.Tensor = tx * w twy: torch.Tensor = ty * w twz: torch.Tensor = tz * w txx: torch.Tensor = tx * x txy: torch.Tensor = ty * x txz: torch.Tensor = tz * x tyy: torch.Tensor = ty * y tyz: torch.Tensor = tz * y tzz: torch.Tensor = tz * z one: torch.Tensor = torch.tensor(1.) matrix: torch.Tensor = torch.stack([ one - (tyy + tzz), txy - twz, txz + twy, txy + twz, one - (txx + tzz), tyz - twx, txz - twy, tyz + twx, one - (txx + tyy) ], dim=-1).view(-1, 3, 3) if len(quaternion.shape) == 1: matrix = torch.squeeze(matrix, dim=0) return matrix def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor: """Convert quaternion vector to angle axis of rotation. The quaternion should be in (x, y, z, w) format. Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h Args: quaternion (torch.Tensor): tensor with quaternions. Return: torch.Tensor: tensor with angle axis of rotation. Shape: - Input: :math:`(*, 4)` where `*` means, any number of dimensions - Output: :math:`(*, 3)` Example: >>> quaternion = torch.rand(2, 4) # Nx4 >>> angle_axis = quaternion_to_angle_axis(quaternion) # Nx3 """ if not torch.is_tensor(quaternion): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(quaternion))) if not quaternion.shape[-1] == 4: raise ValueError( "Input must be a tensor of shape Nx4 or 4. Got {}".format( quaternion.shape)) # unpack input and compute conversion q1: torch.Tensor = quaternion[..., 1] q2: torch.Tensor = quaternion[..., 2] q3: torch.Tensor = quaternion[..., 3] sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3 sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta) cos_theta: torch.Tensor = quaternion[..., 0] two_theta: torch.Tensor = 2.0 * torch.where( cos_theta < 0.0, torch.atan2(-sin_theta, -cos_theta), torch.atan2(sin_theta, cos_theta)) k_pos: torch.Tensor = two_theta / sin_theta k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta) k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg) angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3] angle_axis[..., 0] += q1 * k angle_axis[..., 1] += q2 * k angle_axis[..., 2] += q3 * k return angle_axis def quaternion_log_to_exp(quaternion: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: r"""Applies exponential map to log quaternion. The quaternion should be in (x, y, z, w) format. Args: quaternion (torch.Tensor): a tensor containing a quaternion to be converted. The tensor can be of shape :math:`(*, 3)`. Return: torch.Tensor: the quaternion exponential map of shape :math:`(*, 4)`. Example: >>> quaternion = torch.tensor([0., 0., 0.]) >>> quaternion_log_to_exp(quaternion) tensor([0., 0., 0., 1.]) """ if not isinstance(quaternion, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(quaternion))) if not quaternion.shape[-1] == 3: raise ValueError( "Input must be a tensor of shape (*, 3). Got {}".format( quaternion.shape)) # compute quaternion norm norm_q: torch.Tensor = torch.norm( quaternion, p=2, dim=-1, keepdim=True).clamp(min=eps) # compute scalar and vector quaternion_vector: torch.Tensor = quaternion * torch.sin(norm_q) / norm_q quaternion_scalar: torch.Tensor = torch.cos(norm_q) # compose quaternion and return quaternion_exp: torch.Tensor = torch.cat( [quaternion_vector, quaternion_scalar], dim=-1) return quaternion_exp def quaternion_exp_to_log(quaternion: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: r"""Applies the log map to a quaternion. The quaternion should be in (x, y, z, w) format. Args: quaternion (torch.Tensor): a tensor containing a quaternion to be converted. The tensor can be of shape :math:`(*, 4)`. Return: torch.Tensor: the quaternion log map of shape :math:`(*, 3)`. Example: >>> quaternion = torch.tensor([0., 0., 0., 1.]) >>> quaternion_exp_to_log(quaternion) tensor([0., 0., 0.]) """ if not isinstance(quaternion, torch.Tensor): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(quaternion))) if not quaternion.shape[-1] == 4: raise ValueError( "Input must be a tensor of shape (*, 4). Got {}".format( quaternion.shape)) # unpack quaternion vector and scalar quaternion_vector: torch.Tensor = quaternion[..., 0:3] quaternion_scalar: torch.Tensor = quaternion[..., 3:4] # compute quaternion norm norm_q: torch.Tensor = torch.norm( quaternion_vector, p=2, dim=-1, keepdim=True).clamp(min=eps) # apply log map quaternion_log: torch.Tensor = quaternion_vector * torch.acos( torch.clamp(quaternion_scalar, min=-1.0, max=1.0)) / norm_q return quaternion_log # based on: # https://github.com/facebookresearch/QuaterNet/blob/master/common/quaternion.py#L138 def angle_axis_to_quaternion(angle_axis: torch.Tensor) -> torch.Tensor: r"""Convert an angle axis to a quaternion. The quaternion vector has components in (x, y, z, w) format. Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h Args: angle_axis (torch.Tensor): tensor with angle axis. Return: torch.Tensor: tensor with quaternion. Shape: - Input: :math:`(*, 3)` where `*` means, any number of dimensions - Output: :math:`(*, 4)` Example: >>> angle_axis = torch.rand(2, 3) # Nx3 >>> quaternion = angle_axis_to_quaternion(angle_axis) # Nx4 """ if not torch.is_tensor(angle_axis): raise TypeError("Input type is not a torch.Tensor. Got {}".format( type(angle_axis))) if not angle_axis.shape[-1] == 3: raise ValueError( "Input must be a tensor of shape Nx3 or 3. Got {}".format( angle_axis.shape)) # unpack input and compute conversion a0: torch.Tensor = angle_axis[..., 0:1] a1: torch.Tensor = angle_axis[..., 1:2] a2: torch.Tensor = angle_axis[..., 2:3] theta_squared: torch.Tensor = a0 * a0 + a1 * a1 + a2 * a2 theta: torch.Tensor = torch.sqrt(theta_squared) half_theta: torch.Tensor = theta * 0.5 mask: torch.Tensor = theta_squared > 0.0 ones: torch.Tensor = torch.ones_like(half_theta) k_neg: torch.Tensor = 0.5 * ones k_pos: torch.Tensor = torch.sin(half_theta) / theta k: torch.Tensor = torch.where(mask, k_pos, k_neg) w: torch.Tensor = torch.where(mask, torch.cos(half_theta), ones) quaternion: torch.Tensor = torch.zeros_like(angle_axis) quaternion[..., 0:1] += a0 * k quaternion[..., 1:2] += a1 * k quaternion[..., 2:3] += a2 * k return torch.cat([w, quaternion], dim=-1) # based on: # https://github.com/ClementPinard/SfmLearner-Pytorch/blob/master/inverse_warp.py#L65-L71 def normalize_pixel_coordinates( pixel_coordinates: torch.Tensor, height: int, width: int, eps: float = 1e-8) -> torch.Tensor: r"""Normalize pixel coordinates between -1 and 1. Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1). Args: pixel_coordinates (torch.Tensor): the grid with pixel coordinates. Shape can be :math:`(*, 2)`. width (int): the maximum width in the x-axis. height (int): the maximum height in the y-axis. eps (float): safe division by zero. (default 1e-8). Return: torch.Tensor: the normalized pixel coordinates. """ if pixel_coordinates.shape[-1] != 2: raise ValueError("Input pixel_coordinates must be of shape (*, 2). " "Got {}".format(pixel_coordinates.shape)) # compute normalization factor hw: torch.Tensor = torch.stack([ torch.tensor(width, device=pixel_coordinates.device, dtype=pixel_coordinates.dtype), torch.tensor(height, device=pixel_coordinates.device, dtype=pixel_coordinates.dtype) ]) factor: torch.Tensor = torch.tensor( 2., device=pixel_coordinates.device, dtype=pixel_coordinates.dtype) / (hw - 1).clamp(eps) return factor * pixel_coordinates - 1 def denormalize_pixel_coordinates( pixel_coordinates: torch.Tensor, height: int, width: int, eps: float = 1e-8) -> torch.Tensor: r"""Denormalize pixel coordinates. The input is assumed to be -1 if on extreme left, 1 if on extreme right (x = w-1). Args: pixel_coordinates (torch.Tensor): the normalized grid coordinates. Shape can be :math:`(*, 2)`. width (int): the maximum width in the x-axis. height (int): the maximum height in the y-axis. eps (float): safe division by zero. (default 1e-8). Return: torch.Tensor: the denormalized pixel coordinates. """ if pixel_coordinates.shape[-1] != 2: raise ValueError("Input pixel_coordinates must be of shape (*, 2). " "Got {}".format(pixel_coordinates.shape)) # compute normalization factor hw: torch.Tensor = torch.stack([ torch.tensor(width), torch.tensor(height) ]).to(pixel_coordinates.device).to(pixel_coordinates.dtype) factor: torch.Tensor = torch.tensor(2.) / (hw - 1).clamp(eps) return torch.tensor(1.) / factor * (pixel_coordinates + 1) def normalize_pixel_coordinates3d( pixel_coordinates: torch.Tensor, depth: int, height: int, width: int, eps: float = 1e-8) -> torch.Tensor: r"""Normalize pixel coordinates between -1 and 1. Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1). Args: pixel_coordinates (torch.Tensor): the grid with pixel coordinates. Shape can be :math:`(*, 3)`. depth (int): the maximum depth in the z-axis. height (int): the maximum height in the y-axis. width (int): the maximum width in the x-axis. eps (float): safe division by zero. (default 1e-8). Return: torch.Tensor: the normalized pixel coordinates. """ if pixel_coordinates.shape[-1] != 3: raise ValueError("Input pixel_coordinates must be of shape (*, 3). " "Got {}".format(pixel_coordinates.shape)) # compute normalization factor dhw: torch.Tensor = torch.stack([ torch.tensor(depth), torch.tensor(width), torch.tensor(height) ]).to(pixel_coordinates.device).to(pixel_coordinates.dtype) factor: torch.Tensor = torch.tensor(2.) / (dhw - 1).clamp(eps) return factor * pixel_coordinates - 1 def denormalize_pixel_coordinates3d( pixel_coordinates: torch.Tensor, depth: int, height: int, width: int, eps: float = 1e-8) -> torch.Tensor: r"""Denormalize pixel coordinates. The input is assumed to be -1 if on extreme left, 1 if on extreme right (x = w-1). Args: pixel_coordinates (torch.Tensor): the normalized grid coordinates. Shape can be :math:`(*, 3)`. depth (int): the maximum depth in the x-axis. height (int): the maximum height in the y-axis. width (int): the maximum width in the x-axis. eps (float): safe division by zero. (default 1e-8). Return: torch.Tensor: the denormalized pixel coordinates. """ if pixel_coordinates.shape[-1] != 3: raise ValueError("Input pixel_coordinates must be of shape (*, 3). " "Got {}".format(pixel_coordinates.shape)) # compute normalization factor dhw: torch.Tensor = torch.stack([ torch.tensor(depth), torch.tensor(width), torch.tensor(height) ]).to(pixel_coordinates.device).to(pixel_coordinates.dtype) factor: torch.Tensor = torch.tensor(2.) / (dhw - 1).clamp(eps) return torch.tensor(1.) / factor * (pixel_coordinates + 1)

35.353584

126

0.618676

c38be14607dbe25d1c1b7706a5871dc05ea34fff

1,536

py

Python

Qualification Round/cubic-ufo.py

enigma-pattern/GoogleCodeJam-2018

4b4f6b5bda68eccd92f0b31cde693462aba1282f

[ "MIT" ]

42

2018-04-08T02:33:46.000Z

2021-05-29T10:19:33.000Z

Qualification Round/cubic-ufo.py

enigma-pattern/GoogleCodeJam-2018

4b4f6b5bda68eccd92f0b31cde693462aba1282f

[ "MIT" ]

1

2021-05-05T10:32:33.000Z

2021-05-05T12:05:53.000Z

Qualification Round/cubic-ufo.py

enigma-pattern/GoogleCodeJam-2018

4b4f6b5bda68eccd92f0b31cde693462aba1282f

[ "MIT" ]

16

2018-04-15T18:56:25.000Z

2021-07-22T22:38:03.000Z

# Copyright (c) 2018 kamyu. All rights reserved. # # Google Code Jam 2018 Qualification Round - Problem D. Cubic UFO # https://codingcompetitions.withgoogle.com/codejam/round/00000000000000cb/00000000000079cc # # Time: O(1) # Space: O(1) # import math def matrix_multi(A, B): result = [[0.0 for _ in xrange(len(B[0]))] for _ in xrange(len(A))] for i in xrange(len(A)): for k in xrange(len(A[0])): if A[i][k] == 0.0: continue for j in xrange(len(B[0])): result[i][j] += A[i][k] * B[k][j] return result def rotate_y(matrix, cosx): sinx = math.sqrt(1.0-cosx**2) Ry = [[cosx, 0.0, -sinx], [ 0.0, 1.0, 0.0], [sinx, 0.0, cosx]] return matrix_multi(matrix, Ry) def rotate_x(matrix, cosx): sinx = math.sqrt(1.0-cosx**2) Rx = [[1.0, 0.0, 0.0], [0.0, cosx, sinx], [0.0, -sinx, cosx]] return matrix_multi(matrix, Rx) def cubic_ufo(): A = float(input()) matrix = [[0.5, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 0.5]] matrix = rotate_y(matrix, 1.0/math.sqrt(2)) # rotate y by 45 degrees # rotate x to make shadows = rectangle projection part + two triangle projection part on xz plane # => A = sqrt(2)*sinx + cosx cosx = (A + math.sqrt(2*(3-A**2)))/3 matrix = rotate_x(matrix, cosx) return matrix for case in xrange(input()): print 'Case #%d:' % (case+1) for center in cubic_ufo(): print " ".join(map(str, center))

28.981132

101

0.549479

a6ed2451a6bb93221c0d7fd9d85d1193c2f2a2a6

656

py

Python

setup.py

ZephireNZ/PyFlick

3c4fae7380bcfcea9b1e0cb02444a07626261998

[ "MIT" ]

null

setup.py

ZephireNZ/PyFlick

3c4fae7380bcfcea9b1e0cb02444a07626261998

[ "MIT" ]

null

setup.py

ZephireNZ/PyFlick

3c4fae7380bcfcea9b1e0cb02444a07626261998

[ "MIT" ]

2

2021-06-15T14:17:07.000Z

2021-11-18T07:16:04.000Z

import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name="PyFlick", version="0.0.2", author="ZephireNZ", author_email="brynley+pypi@zephire.nz", description="Python API For Flick Electric in New Zealand", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/ZephireNZ/PyFlick", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', )

29.818182

63

0.666159

0441301d54a2779936a0f432eaf25c8a2c0b8201

3,161

py

Python

modoboa/lib/tests/__init__.py

HarshCasper/modoboa

a00baa0593107992f545ee3e89cd4346b9615a96

[ "0BSD" ]

1,602

2016-12-15T14:25:34.000Z

2022-03-31T16:49:25.000Z

modoboa/lib/tests/__init__.py

sebageek/modoboa

57f5d57ea60a57e8dcac970085dfc07082481fc6

[ "0BSD" ]

1,290

2016-12-14T15:39:05.000Z

2022-03-31T13:49:09.000Z

modoboa/lib/tests/__init__.py

sebageek/modoboa

57f5d57ea60a57e8dcac970085dfc07082481fc6

[ "0BSD" ]

272

2016-12-22T11:58:18.000Z

2022-03-17T15:57:24.000Z

"""Testing utilities.""" import socket from django.core import management from django.test import TestCase from rest_framework.authtoken.models import Token from rest_framework.test import APITestCase from modoboa.core import models as core_models from .. import sysutils try: s = socket.create_connection(("127.0.0.1", 25)) s.close() NO_SMTP = False except socket.error: NO_SMTP = True try: import ldap # noqa NO_LDAP = False except ImportError: NO_LDAP = True class ParametersMixin(object): """Add tools to manage parameters.""" @classmethod def setUpTestData(cls): # noqa """Set LocalConfig instance.""" super(ParametersMixin, cls).setUpTestData() cls.localconfig = core_models.LocalConfig.objects.first() def set_global_parameter(self, name, value, app=None): """Set global parameter for the given app.""" if app is None: app = sysutils.guess_extension_name() self.localconfig.parameters.set_value(name, value, app=app) self.localconfig.save() def set_global_parameters(self, parameters, app=None): """Set/update global parameters for the given app.""" if app is None: app = sysutils.guess_extension_name() self.localconfig.parameters.set_values(parameters, app=app) self.localconfig.save() class ModoTestCase(ParametersMixin, TestCase): """All test cases must inherit from this one.""" @classmethod def setUpTestData(cls): # noqa """Create a default user.""" super(ModoTestCase, cls).setUpTestData() management.call_command("load_initial_data") def setUp(self, username="admin", password="password"): """Initiate test context.""" self.assertEqual( self.client.login(username=username, password=password), True) def ajax_request(self, method, url, params=None, status=200): if params is None: params = {} response = getattr(self.client, method)( url, params, HTTP_X_REQUESTED_WITH="XMLHttpRequest") self.assertEqual(response.status_code, status) return response.json() def ajax_post(self, *args, **kwargs): return self.ajax_request("post", *args, **kwargs) def ajax_put(self, *args, **kwargs): return self.ajax_request("put", *args, **kwargs) def ajax_delete(self, *args, **kwargs): return self.ajax_request("delete", *args, **kwargs) def ajax_get(self, *args, **kwargs): return self.ajax_request("get", *args, **kwargs) class ModoAPITestCase(ParametersMixin, APITestCase): """All test cases must inherit from this one.""" @classmethod def setUpTestData(cls): # noqa """Create a default user.""" super(ModoAPITestCase, cls).setUpTestData() management.call_command("load_initial_data") cls.token = Token.objects.create( user=core_models.User.objects.get(username="admin")) def setUp(self): """Setup.""" super(ModoAPITestCase, self).setUp() self.client.credentials(HTTP_AUTHORIZATION="Token " + self.token.key)

30.990196

77

0.660234

af9e60303c9cf04eeba87f7d92b000ceca27b066

4,384

py

Python

Question_semaseg/answers/bin_loss_pytorch.py

skn047/DeepLearningMugenKnock

73d2b903816b380d56020c8336041883bc0d131c

[ "MIT" ]

10

2021-12-17T06:07:25.000Z

2022-03-25T13:50:05.000Z

Question_semaseg/answers/bin_loss_pytorch.py

skn047/DeepLearningMugenKnock

73d2b903816b380d56020c8336041883bc0d131c

[ "MIT" ]

null

Question_semaseg/answers/bin_loss_pytorch.py

skn047/DeepLearningMugenKnock

73d2b903816b380d56020c8336041883bc0d131c

[ "MIT" ]

2

2022-03-15T02:42:09.000Z

2022-03-30T23:19:55.000Z

import torch import torch.nn.functional as F import argparse import cv2 import numpy as np from glob import glob import matplotlib.pyplot as plt num_classes = 2 img_height, img_width = 64, 64#572, 572 out_height, out_width = 64, 64#388, 388 GPU = False torch.manual_seed(0) class Mynet(torch.nn.Module): def __init__(self): super(Mynet, self).__init__() enc1 = [] for i in range(6): f = 3 if i == 0 else 32 enc1.append(torch.nn.Conv2d(f, 32, kernel_size=3, padding=1, stride=1)) enc1.append(torch.nn.BatchNorm2d(32)) enc1.append(torch.nn.ReLU()) self.enc1 = torch.nn.Sequential(*enc1) self.out = torch.nn.Conv2d(32, 1, kernel_size=1, padding=0, stride=1) def forward(self, x): # block conv1 x = self.enc1(x) x = self.out(x) return x CLS = {'akahara': [0,0,128], 'madara': [0,128,0]} # get train data def data_load(path, hf=False, vf=False): xs = [] ts = [] paths = [] for dir_path in glob(path + '/*'): for path in glob(dir_path + '/*'): x = cv2.imread(path) x = cv2.resize(x, (img_width, img_height)).astype(np.float32) x /= 255. x = x[...,::-1] xs.append(x) gt_path = path.replace("images", "seg_images").replace(".jpg", ".png") gt = cv2.imread(gt_path) gt = cv2.resize(gt, (out_width, out_height), interpolation=cv2.INTER_NEAREST) t = np.zeros((out_height, out_width, 1), dtype=np.int) ind = (gt[...,0] > 0) + (gt[..., 1] > 0) + (gt[...,2] > 0) t[ind] = 1 #print(gt_path) #import matplotlib.pyplot as plt #plt.imshow(t, cmap='gray') #plt.show() ts.append(t) paths.append(path) if hf: xs.append(x[:, ::-1]) ts.append(t[:, ::-1]) paths.append(path) if vf: xs.append(x[::-1]) ts.append(t[::-1]) paths.append(path) if hf and vf: xs.append(x[::-1, ::-1]) ts.append(t[::-1, ::-1]) paths.append(path) xs = np.array(xs) ts = np.array(ts) xs = xs.transpose(0,3,1,2) return xs, ts, paths # train def train(): # GPU device = torch.device("cuda" if GPU else "cpu") # model model = Mynet().to(device) opt = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9) model.train() xs, ts, paths = data_load('../Dataset/train/images/', hf=True, vf=True) # training mb = 4 mbi = 0 train_ind = np.arange(len(xs)) np.random.seed(0) np.random.shuffle(train_ind) for i in range(500): if mbi + mb > len(xs): mb_ind = train_ind[mbi:] np.random.shuffle(train_ind) mb_ind = np.hstack((mb_ind, train_ind[:(mb-(len(xs)-mbi))])) mbi = mb - (len(xs) - mbi) else: mb_ind = train_ind[mbi: mbi+mb] mbi += mb x = torch.tensor(xs[mb_ind], dtype=torch.float).to(device) t = torch.tensor(ts[mb_ind], dtype=torch.float).to(device) opt.zero_grad() y = model(x) y = y.permute(0,2,3,1).contiguous() y = torch.sigmoid(y) loss = torch.nn.BCELoss()(y, t) loss.backward() opt.step() #pred = y.argmax(dim=1, keepdim=True) acc = y.eq(t.view_as(y)).sum().item() / mb print("iter >>", i+1, ',loss >>', loss.item(), ',accuracy >>', acc) torch.save(model.state_dict(), 'cnn.pt') def arg_parse(): parser = argparse.ArgumentParser(description='CNN implemented with Keras') parser.add_argument('--train', dest='train', action='store_true') parser.add_argument('--test', dest='test', action='store_true') args = parser.parse_args() return args # main if __name__ == '__main__': args = arg_parse() if args.train: train() #if args.test: # test() if not (args.train or args.test): print("please select train or test flag") print("train: python main.py --train") print("test: python main.py --test") print("both: python main.py --train --test")

26.251497

89

0.519617

28991c67c708b05679a196cc488333430e102b33

26,790

py

Python

observatory/logic/space_api.py

spookey/observatory

be5cc92f53f12e6341e7e3040f26360e54cfdf7d

[ "MIT" ]

null

observatory/logic/space_api.py

spookey/observatory

be5cc92f53f12e6341e7e3040f26360e54cfdf7d

[ "MIT" ]

1

2020-03-28T09:51:56.000Z

observatory/logic/space_api.py

spookey/dz_stats_page

be5cc92f53f12e6341e7e3040f26360e54cfdf7d

[ "MIT" ]

null

from datetime import datetime from logging import getLogger from observatory.models.mapper import EnumConvert, EnumHorizon from observatory.models.value import Value from observatory.start.environment import SP_API_PREFIX, SP_API_REFRESH # pylint: disable=too-many-arguments # pylint: disable=too-many-public-methods class SpaceApi: def __init__(self): self._log = getLogger(self.__class__.__name__) self._content = None self._last = None @staticmethod def _by_key(*, key): return Value.by_key(key=f'{SP_API_PREFIX}.{key}') @staticmethod def _get(*, key, idx=0): return Value.get(key=f'{SP_API_PREFIX}.{key}', idx=idx) def _get_all(self, *, key): return [ { '_idx': elem.idx, 'value': elem.elem, } for elem in self._by_key(key=key) if elem is not None ] def latest_value(self, *, key, idx=0, convert): sensor = self._get(key=key, idx=idx) if sensor is None or sensor.latest is None: return None return sensor.latest.translate( horizon=EnumHorizon.NORMAL, convert=convert, numeric=False ) def _indices_any(self, *keys): result = set() for key in keys: result = result.union( elem.idx for elem in self._by_key(key=key) if elem is not None ) return sorted(result) def _indices_all(self, first, *keys): result = set( elem.idx for elem in self._by_key(key=first) if elem is not None ) for key in keys: result = result.intersection( elem.idx for elem in self._by_key(key=key) if elem is not None ) return sorted(result) @staticmethod def next_index(indices): if not indices: return 0 top = max(indices) diff = set(range(top)).difference(indices) if diff: return min(diff) return 1 + top @property def cam_indices(self): return self._indices_all('cam') @property def contact_keymasters_indices(self): return self._indices_any( 'contact.keymasters.irc_nick', 'contact.keymasters.phone', 'contact.keymasters.email', 'contact.keymasters.twitter', ) @property def sensors_temperature_indices(self): return self._indices_all( 'sensors.temperature.value', 'sensors.temperature.unit', 'sensors.temperature.location', ) @property def sensors_door_locked_indices(self): return self._indices_all( 'sensors.door_locked.value', 'sensors.door_locked.location' ) @property def sensors_barometer_indices(self): return self._indices_all( 'sensors.barometer.value', 'sensors.barometer.unit', 'sensors.barometer.location', ) def sensors_radiation_indices(self, sub): return self._indices_all( f'sensors.radiation.{sub}.value', f'sensors.radiation.{sub}.unit', ) @property def sensors_humidity_indices(self): return self._indices_all( 'sensors.humidity.value', 'sensors.humidity.unit', 'sensors.humidity.location', ) @property def sensors_beverage_supply_indices(self): return self._indices_all( 'sensors.beverage_supply.value', 'sensors.beverage_supply.unit', ) @property def sensors_power_consumption_indices(self): return self._indices_all( 'sensors.power_consumption.value', 'sensors.power_consumption.unit', 'sensors.power_consumption.location', ) @property def sensors_wind_indices(self): return self._indices_all( 'sensors.wind.properties.speed.value', 'sensors.wind.properties.speed.unit', 'sensors.wind.properties.gust.value', 'sensors.wind.properties.gust.unit', 'sensors.wind.properties.direction.value', 'sensors.wind.properties.direction.unit', 'sensors.wind.properties.elevation.value', 'sensors.wind.properties.elevation.unit', 'sensors.wind.location', ) @property def sensors_account_balance_indices(self): return self._indices_all( 'sensors.account_balance.value', 'sensors.account_balance.unit', ) @property def sensors_total_member_count_indices(self): return self._indices_all('sensors.total_member_count.value') @property def sensors_network_traffic_indices(self): return self._indices_any( 'sensors.network_traffic.properties.bits_per_second.value', 'sensors.network_traffic.properties.packets_per_second.value', ) @property def projects_indices(self): return self._indices_all('projects') @property def links_indices(self): return self._indices_all( 'links.name', 'links.url', ) @property def membership_plans_indices(self): return self._indices_all( 'membership_plans.name', 'membership_plans.value', 'membership_plans.currency', 'membership_plans.billing_interval', ) def get_state(self): self._log.info('gathering state') return { 'icon': { 'open': self._get(key='state.icon.open'), 'closed': self._get(key='state.icon.closed'), }, } def get_events(self): self._log.info('gathering events') return [] def build(self): return { 'api_compatibility': ['14'], 'space': self._get(key='space'), 'logo': self._get(key='logo'), 'url': self._get(key='url'), 'location': { 'address': self._get(key='location.address'), 'lat': self._get(key='location.lat'), 'lon': self._get(key='location.lon'), 'timezone': self._get(key='location.timezone'), }, 'spacefed': { 'spacenet': self._get(key='spacefed.spacenet'), 'spacesaml': self._get(key='spacefed.spacesaml'), }, 'cam': self._get_all(key='cam'), 'state': self.get_state(), 'events': self.get_events(), 'contact': { 'phone': self._get(key='contact.phone'), 'sip': self._get(key='contact.sip'), 'keymasters': [ { '_idx': idx, 'name': self._get( key='contact.keymasters.name', idx=idx ), 'irc_nick': self._get( key='contact.keymasters.irc_nick', idx=idx ), 'phone': self._get( key='contact.keymasters.phone', idx=idx ), 'email': self._get( key='contact.keymasters.email', idx=idx ), 'twitter': self._get( key='contact.keymasters.twitter', idx=idx ), 'xmpp': self._get( key='contact.keymasters.xmpp', idx=idx ), 'mastodon': self._get( key='contact.keymasters.mastodon', idx=idx ), 'matrix': self._get( key='contact.keymasters.matrix', idx=idx ), } for idx in self.contact_keymasters_indices ], 'irc': self._get(key='contact.irc'), 'twitter': self._get(key='contact.twitter'), 'mastodon': self._get(key='contact.mastodon'), 'facebook': self._get(key='contact.facebook'), 'identica': self._get(key='contact.identica'), 'foursquare': self._get(key='contact.foursquare'), 'email': self._get(key='contact.email'), 'ml': self._get(key='contact.ml'), 'xmpp': self._get(key='contact.xmpp'), 'issue_mail': self._get(key='contact.issue_mail'), 'gopher': self._get(key='contact.gopher'), 'matrix': self._get(key='contact.matrix'), 'mumble': self._get(key='contact.mumble'), }, 'sensors': { 'temperature': [ { '_idx': idx, 'value': self.latest_value( key='sensors.temperature.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key='sensors.temperature.unit', idx=idx ), 'location': self._get( key='sensors.temperature.location', idx=idx ), 'name': self._get( key='sensors.temperature.name', idx=idx ), 'description': self._get( key='sensors.temperature.description', idx=idx ), } for idx in self.sensors_temperature_indices ], 'door_locked': [ { '_idx': idx, 'value': self.latest_value( key='sensors.door_locked.value', idx=idx, convert=EnumConvert.BOOLEAN, ), 'location': self._get( key='sensors.door_locked.location', idx=idx ), 'name': self._get( key='sensors.door_locked.name', idx=idx ), 'description': self._get( key='sensors.door_locked.description', idx=idx ), } for idx in self.sensors_door_locked_indices ], 'barometer': [ { '_idx': idx, 'value': self.latest_value( key='sensors.barometer.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key='sensors.barometer.unit', idx=idx ), 'location': self._get( key='sensors.barometer.location', idx=idx ), 'name': self._get( key='sensors.barometer.name', idx=idx ), 'description': self._get( key='sensors.barometer.description', idx=idx ), } for idx in self.sensors_barometer_indices ], 'radiation': { sub: [ { '_idx': idx, 'value': self.latest_value( key=f'sensors.radiation.{sub}.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key=f'sensors.radiation.{sub}.unit', idx=idx ), 'dead_time': self._get( key=f'sensors.radiation.{sub}.dead_time', idx=idx, ), 'conversion_factor': self._get( key=( f'sensors.radiation.{sub}.' 'conversion_factor' ), idx=idx, ), 'location': self._get( key=f'sensors.radiation.{sub}.location', idx=idx, ), 'name': self._get( key=f'sensors.radiation.{sub}.name', idx=idx ), 'description': self._get( key=f'sensors.radiation.{sub}.description', idx=idx, ), } for idx in self.sensors_radiation_indices(sub) ] for sub in ['alpha', 'beta', 'gamma', 'beta_gamma'] }, 'humidity': [ { '_idx': idx, 'value': self.latest_value( key='sensors.humidity.value', idx=idx, convert=EnumConvert.INTEGER, ), 'unit': self._get( key='sensors.humidity.unit', idx=idx ), 'location': self._get( key='sensors.humidity.location', idx=idx ), 'name': self._get( key='sensors.humidity.name', idx=idx ), 'description': self._get( key='sensors.humidity.description', idx=idx ), } for idx in self.sensors_humidity_indices ], 'beverage_supply': [ { '_idx': idx, 'value': self.latest_value( key='sensors.beverage_supply.value', idx=idx, convert=EnumConvert.INTEGER, ), 'unit': self._get( key='sensors.beverage_supply.unit', idx=idx ), 'location': self._get( key='sensors.beverage_supply.location', idx=idx ), 'name': self._get( key='sensors.beverage_supply.name', idx=idx ), 'description': self._get( key='sensors.beverage_supply.description', idx=idx ), } for idx in self.sensors_beverage_supply_indices ], 'power_consumption': [ { '_idx': idx, 'value': self.latest_value( key='sensors.power_consumption.value', idx=idx, convert=EnumConvert.INTEGER, ), 'unit': self._get( key='sensors.power_consumption.unit', idx=idx ), 'location': self._get( key='sensors.power_consumption.location', idx=idx ), 'name': self._get( key='sensors.power_consumption.name', idx=idx ), 'description': self._get( key='sensors.power_consumption.description', idx=idx, ), } for idx in self.sensors_power_consumption_indices ], 'wind': [ { '_idx': idx, 'properties': { 'speed': { 'value': self.latest_value( key='sensors.wind.properties.speed.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key='sensors.wind.properties.speed.unit', idx=idx, ), }, 'gust': { 'value': self.latest_value( key='sensors.wind.properties.gust.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key='sensors.wind.properties.gust.unit', idx=idx, ), }, 'direction': { 'value': self.latest_value( key=( 'sensors.wind.properties.' 'direction.value' ), idx=idx, convert=EnumConvert.INTEGER, ), 'unit': self._get( key=( 'sensors.wind.properties.' 'direction.unit' ), idx=idx, ), }, 'elevation': { 'value': self._get( key=( 'sensors.wind.properties.' 'elevation.value' ), idx=idx, ), 'unit': self._get( key=( 'sensors.wind.properties.' 'elevation.unit' ), idx=idx, ), }, }, 'location': self._get( key='sensors.wind.location', idx=idx ), 'name': self._get(key='sensors.wind.name', idx=idx), 'description': self._get( key='sensors.wind.description', idx=idx ), } for idx in self.sensors_wind_indices ], 'network_connections': [], 'account_balance': [ { '_idx': idx, 'value': self.latest_value( key='sensors.account_balance.value', idx=idx, convert=EnumConvert.NATURAL, ), 'unit': self._get( key='sensors.account_balance.unit', idx=idx ), 'location': self._get( key='sensors.account_balance.location', idx=idx ), 'name': self._get( key='sensors.account_balance.name', idx=idx ), 'description': self._get( key='sensors.account_balance.description', idx=idx ), } for idx in self.sensors_account_balance_indices ], 'total_member_count': [ { '_idx': idx, 'value': self.latest_value( key='sensors.total_member_count.value', idx=idx, convert=EnumConvert.INTEGER, ), 'location': self._get( key='sensors.total_member_count.location', idx=idx ), 'name': self._get( key='sensors.total_member_count.name', idx=idx ), 'description': self._get( key='sensors.total_member_count.description', idx=idx, ), } for idx in self.sensors_total_member_count_indices ], 'people_now_present': [], 'network_traffic': [ { '_idx': idx, 'properties': { 'bits_per_second': { 'value': self.latest_value( key=( 'sensors.network_traffic.properties.' 'bits_per_second.value' ), idx=idx, convert=EnumConvert.NATURAL, ), 'maximum': self._get( key=( 'sensors.network_traffic.properties.' 'bits_per_second.maximum' ), idx=idx, ), }, 'packets_per_second': { 'value': self.latest_value( key=( 'sensors.network_traffic.properties.' 'packets_per_second.value' ), idx=idx, convert=EnumConvert.NATURAL, ), }, }, 'location': self._get( key='sensors.network_traffic.location', idx=idx ), 'name': self._get( key='sensors.network_traffic.name', idx=idx ), 'description': self._get( key='sensors.network_traffic.description', idx=idx ), } for idx in self.sensors_network_traffic_indices ], }, 'feeds': { 'blog': { 'type': self._get(key='feeds.blog.type'), 'url': self._get(key='feeds.blog.url'), }, 'wiki': { 'type': self._get(key='feeds.wiki.type'), 'url': self._get(key='feeds.wiki.url'), }, 'calendar': { 'type': self._get(key='feeds.calendar.type'), 'url': self._get(key='feeds.calendar.url'), }, 'flickr': { 'type': self._get(key='feeds.calendar.type'), 'url': self._get(key='feeds.calendar.url'), }, }, 'projects': self._get_all(key='projects'), 'links': [ { '_idx': idx, 'name': self._get(key='links.name', idx=idx), 'description': self._get(key='links.description', idx=idx), 'url': self._get(key='links.url', idx=idx), } for idx in self.links_indices ], 'membership_plans': [ { '_idx': idx, 'name': self._get(key='membership_plans.name', idx=idx), 'value': self._get(key='membership_plans.value', idx=idx), 'currency': self._get( key='membership_plans.currency', idx=idx ), 'billing_interval': self._get( key='membership_plans.billing_interval', idx=idx ), 'description': self._get( key='membership_plans.description', idx=idx ), } for idx in self.membership_plans_indices ], } @property def outdated(self): if self._content is None: return True if self._last is None: return True if (datetime.utcnow() - self._last).total_seconds() > SP_API_REFRESH: return True return False @property def content(self): if self.outdated: self._log.info('rebuilding content') self._content = self.build() self._last = datetime.utcnow() return self._content def clear(self): self._content = None self._last = None return all((self._content is None, self._last is None)) def reset(self): self._log.info('resetting content') self.clear() return self.content

39.281525

79

0.387495

13bf14f8262c7aa7e66c52e5f45a31272f0379e4

10,917

py

Python

sympy/integrals/rationaltools.py

utkarshdeorah/sympy

dcdf59bbc6b13ddbc329431adf72fcee294b6389

[ "BSD-3-Clause" ]

1

2020-09-09T20:40:17.000Z

sympy/integrals/rationaltools.py

utkarshdeorah/sympy

dcdf59bbc6b13ddbc329431adf72fcee294b6389

[ "BSD-3-Clause" ]

14

2018-02-08T10:11:03.000Z

2019-04-16T10:32:46.000Z

sympy/integrals/rationaltools.py

utkarshdeorah/sympy

dcdf59bbc6b13ddbc329431adf72fcee294b6389

[ "BSD-3-Clause" ]

1

2022-02-04T13:50:29.000Z

"""This module implements tools for integrating rational functions. """ from sympy.core.function import Lambda from sympy.core.numbers import I from sympy.core.singleton import S from sympy.core.symbol import (Dummy, Symbol, symbols) from sympy.functions.elementary.exponential import log from sympy.functions.elementary.trigonometric import atan from sympy.polys.polyroots import roots from sympy.polys.polytools import cancel from sympy.polys.rootoftools import RootSum from sympy.polys import Poly, resultant, ZZ from sympy.solvers.solvers import solve def ratint(f, x, **flags): """ Performs indefinite integration of rational functions. Explanation =========== Given a field :math:`K` and a rational function :math:`f = p/q`, where :math:`p` and :math:`q` are polynomials in :math:`K[x]`, returns a function :math:`g` such that :math:`f = g'`. Examples ======== >>> from sympy.integrals.rationaltools import ratint >>> from sympy.abc import x >>> ratint(36/(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2), x) (12*x + 6)/(x**2 - 1) + 4*log(x - 2) - 4*log(x + 1) References ========== .. [1] M. Bronstein, Symbolic Integration I: Transcendental Functions, Second Edition, Springer-Verlag, 2005, pp. 35-70 See Also ======== sympy.integrals.integrals.Integral.doit sympy.integrals.rationaltools.ratint_logpart sympy.integrals.rationaltools.ratint_ratpart """ if isinstance(f, tuple): p, q = f else: p, q = f.as_numer_denom() p, q = Poly(p, x, composite=False, field=True), Poly(q, x, composite=False, field=True) coeff, p, q = p.cancel(q) poly, p = p.div(q) result = poly.integrate(x).as_expr() if p.is_zero: return coeff*result g, h = ratint_ratpart(p, q, x) P, Q = h.as_numer_denom() P = Poly(P, x) Q = Poly(Q, x) q, r = P.div(Q) result += g + q.integrate(x).as_expr() if not r.is_zero: symbol = flags.get('symbol', 't') if not isinstance(symbol, Symbol): t = Dummy(symbol) else: t = symbol.as_dummy() L = ratint_logpart(r, Q, x, t) real = flags.get('real') if real is None: if isinstance(f, tuple): p, q = f atoms = p.atoms() | q.atoms() else: atoms = f.atoms() for elt in atoms - {x}: if not elt.is_extended_real: real = False break else: real = True eps = S.Zero if not real: for h, q in L: _, h = h.primitive() eps += RootSum( q, Lambda(t, t*log(h.as_expr())), quadratic=True) else: for h, q in L: _, h = h.primitive() R = log_to_real(h, q, x, t) if R is not None: eps += R else: eps += RootSum( q, Lambda(t, t*log(h.as_expr())), quadratic=True) result += eps return coeff*result def ratint_ratpart(f, g, x): """ Horowitz-Ostrogradsky algorithm. Explanation =========== Given a field K and polynomials f and g in K[x], such that f and g are coprime and deg(f) < deg(g), returns fractions A and B in K(x), such that f/g = A' + B and B has square-free denominator. Examples ======== >>> from sympy.integrals.rationaltools import ratint_ratpart >>> from sympy.abc import x, y >>> from sympy import Poly >>> ratint_ratpart(Poly(1, x, domain='ZZ'), ... Poly(x + 1, x, domain='ZZ'), x) (0, 1/(x + 1)) >>> ratint_ratpart(Poly(1, x, domain='EX'), ... Poly(x**2 + y**2, x, domain='EX'), x) (0, 1/(x**2 + y**2)) >>> ratint_ratpart(Poly(36, x, domain='ZZ'), ... Poly(x**5 - 2*x**4 - 2*x**3 + 4*x**2 + x - 2, x, domain='ZZ'), x) ((12*x + 6)/(x**2 - 1), 12/(x**2 - x - 2)) See Also ======== ratint, ratint_logpart """ f = Poly(f, x) g = Poly(g, x) u, v, _ = g.cofactors(g.diff()) n = u.degree() m = v.degree() A_coeffs = [ Dummy('a' + str(n - i)) for i in range(0, n) ] B_coeffs = [ Dummy('b' + str(m - i)) for i in range(0, m) ] C_coeffs = A_coeffs + B_coeffs A = Poly(A_coeffs, x, domain=ZZ[C_coeffs]) B = Poly(B_coeffs, x, domain=ZZ[C_coeffs]) H = f - A.diff()*v + A*(u.diff()*v).quo(u) - B*u result = solve(H.coeffs(), C_coeffs) A = A.as_expr().subs(result) B = B.as_expr().subs(result) rat_part = cancel(A/u.as_expr(), x) log_part = cancel(B/v.as_expr(), x) return rat_part, log_part def ratint_logpart(f, g, x, t=None): r""" Lazard-Rioboo-Trager algorithm. Explanation =========== Given a field K and polynomials f and g in K[x], such that f and g are coprime, deg(f) < deg(g) and g is square-free, returns a list of tuples (s_i, q_i) of polynomials, for i = 1..n, such that s_i in K[t, x] and q_i in K[t], and:: ___ ___ d f d \ ` \ ` -- - = -- ) ) a log(s_i(a, x)) dx g dx /__, /__, i=1..n a | q_i(a) = 0 Examples ======== >>> from sympy.integrals.rationaltools import ratint_logpart >>> from sympy.abc import x >>> from sympy import Poly >>> ratint_logpart(Poly(1, x, domain='ZZ'), ... Poly(x**2 + x + 1, x, domain='ZZ'), x) [(Poly(x + 3*_t/2 + 1/2, x, domain='QQ[_t]'), ...Poly(3*_t**2 + 1, _t, domain='ZZ'))] >>> ratint_logpart(Poly(12, x, domain='ZZ'), ... Poly(x**2 - x - 2, x, domain='ZZ'), x) [(Poly(x - 3*_t/8 - 1/2, x, domain='QQ[_t]'), ...Poly(-_t**2 + 16, _t, domain='ZZ'))] See Also ======== ratint, ratint_ratpart """ f, g = Poly(f, x), Poly(g, x) t = t or Dummy('t') a, b = g, f - g.diff()*Poly(t, x) res, R = resultant(a, b, includePRS=True) res = Poly(res, t, composite=False) assert res, "BUG: resultant(%s, %s) cannot be zero" % (a, b) R_map, H = {}, [] for r in R: R_map[r.degree()] = r def _include_sign(c, sqf): if c.is_extended_real and (c < 0) == True: h, k = sqf[0] c_poly = c.as_poly(h.gens) sqf[0] = h*c_poly, k C, res_sqf = res.sqf_list() _include_sign(C, res_sqf) for q, i in res_sqf: _, q = q.primitive() if g.degree() == i: H.append((g, q)) else: h = R_map[i] h_lc = Poly(h.LC(), t, field=True) c, h_lc_sqf = h_lc.sqf_list(all=True) _include_sign(c, h_lc_sqf) for a, j in h_lc_sqf: h = h.quo(Poly(a.gcd(q)**j, x)) inv, coeffs = h_lc.invert(q), [S.One] for coeff in h.coeffs()[1:]: coeff = coeff.as_poly(inv.gens) T = (inv*coeff).rem(q) coeffs.append(T.as_expr()) h = Poly(dict(list(zip(h.monoms(), coeffs))), x) H.append((h, q)) return H def log_to_atan(f, g): """ Convert complex logarithms to real arctangents. Explanation =========== Given a real field K and polynomials f and g in K[x], with g != 0, returns a sum h of arctangents of polynomials in K[x], such that: dh d f + I g -- = -- I log( ------- ) dx dx f - I g Examples ======== >>> from sympy.integrals.rationaltools import log_to_atan >>> from sympy.abc import x >>> from sympy import Poly, sqrt, S >>> log_to_atan(Poly(x, x, domain='ZZ'), Poly(1, x, domain='ZZ')) 2*atan(x) >>> log_to_atan(Poly(x + S(1)/2, x, domain='QQ'), ... Poly(sqrt(3)/2, x, domain='EX')) 2*atan(2*sqrt(3)*x/3 + sqrt(3)/3) See Also ======== log_to_real """ if f.degree() < g.degree(): f, g = -g, f f = f.to_field() g = g.to_field() p, q = f.div(g) if q.is_zero: return 2*atan(p.as_expr()) else: s, t, h = g.gcdex(-f) u = (f*s + g*t).quo(h) A = 2*atan(u.as_expr()) return A + log_to_atan(s, t) def log_to_real(h, q, x, t): r""" Convert complex logarithms to real functions. Explanation =========== Given real field K and polynomials h in K[t,x] and q in K[t], returns real function f such that: ___ df d \ ` -- = -- ) a log(h(a, x)) dx dx /__, a | q(a) = 0 Examples ======== >>> from sympy.integrals.rationaltools import log_to_real >>> from sympy.abc import x, y >>> from sympy import Poly, S >>> log_to_real(Poly(x + 3*y/2 + S(1)/2, x, domain='QQ[y]'), ... Poly(3*y**2 + 1, y, domain='ZZ'), x, y) 2*sqrt(3)*atan(2*sqrt(3)*x/3 + sqrt(3)/3)/3 >>> log_to_real(Poly(x**2 - 1, x, domain='ZZ'), ... Poly(-2*y + 1, y, domain='ZZ'), x, y) log(x**2 - 1)/2 See Also ======== log_to_atan """ from sympy.simplify.radsimp import collect u, v = symbols('u,v', cls=Dummy) H = h.as_expr().subs({t: u + I*v}).expand() Q = q.as_expr().subs({t: u + I*v}).expand() H_map = collect(H, I, evaluate=False) Q_map = collect(Q, I, evaluate=False) a, b = H_map.get(S.One, S.Zero), H_map.get(I, S.Zero) c, d = Q_map.get(S.One, S.Zero), Q_map.get(I, S.Zero) R = Poly(resultant(c, d, v), u) R_u = roots(R, filter='R') if len(R_u) != R.count_roots(): return None result = S.Zero for r_u in R_u.keys(): C = Poly(c.subs({u: r_u}), v) R_v = roots(C, filter='R') if len(R_v) != C.count_roots(): return None R_v_paired = [] # take one from each pair of conjugate roots for r_v in R_v: if r_v not in R_v_paired and -r_v not in R_v_paired: if r_v.is_negative or r_v.could_extract_minus_sign(): R_v_paired.append(-r_v) elif not r_v.is_zero: R_v_paired.append(r_v) for r_v in R_v_paired: D = d.subs({u: r_u, v: r_v}) if D.evalf(chop=True) != 0: continue A = Poly(a.subs({u: r_u, v: r_v}), x) B = Poly(b.subs({u: r_u, v: r_v}), x) AB = (A**2 + B**2).as_expr() result += r_u*log(AB) + r_v*log_to_atan(A, B) R_q = roots(q, filter='R') if len(R_q) != q.count_roots(): return None for r in R_q.keys(): result += r*log(h.as_expr().subs(t, r)) return result

26.117225

91

0.498947

aabdaa8aa3d4f5c27ad49b1d343958200d30c7e1

348

py

Python

donkeycar/parts/history.py

ilkka/donkey

f1454f720d2a6b3fc0db4f13a46a056f7a59fc19

[ "MIT" ]

null

donkeycar/parts/history.py

ilkka/donkey

f1454f720d2a6b3fc0db4f13a46a056f7a59fc19

[ "MIT" ]

null

donkeycar/parts/history.py

ilkka/donkey

f1454f720d2a6b3fc0db4f13a46a056f7a59fc19

[ "MIT" ]

1

2019-01-28T13:33:40.000Z

from collections import deque class History: def __init__(self, buffer_size): self.on = True self.history_buffer = deque(maxlen=buffer_size) for i in range(buffer_size): self.history_buffer.append(0) def run(self, value): if (value != None): self.history_buffer.append(value) return list(self.history_buffer)

23.2

51

0.701149

52440342b96b7db8121d26c1f8f0f741b1f70a7d

2,836

py

Python

srl_core/algo/a2c_acktr.py

chenzeyin9867/srl

309fdaa2703fa9688d993c24c217edb45a6e4332

[ "MIT" ]

3

2021-11-15T06:57:46.000Z

2022-02-17T03:22:32.000Z

srl_core/algo/a2c_acktr.py

chenzeyin9867/srl

309fdaa2703fa9688d993c24c217edb45a6e4332

[ "MIT" ]

null

srl_core/algo/a2c_acktr.py

chenzeyin9867/srl

309fdaa2703fa9688d993c24c217edb45a6e4332

[ "MIT" ]

null

import torch import torch.nn as nn import torch.optim as optim from srl_core.algo.kfac import KFACOptimizer class A2C_ACKTR(): def __init__(self, actor_critic, value_loss_coef, entropy_coef, lr=None, eps=None, alpha=None, max_grad_norm=None, acktr=False): self.actor_critic = actor_critic self.acktr = acktr self.value_loss_coef = value_loss_coef self.entropy_coef = entropy_coef self.max_grad_norm = max_grad_norm if acktr: self.optimizer = KFACOptimizer(actor_critic) else: self.optimizer = optim.RMSprop( actor_critic.parameters(), lr, eps=eps, alpha=alpha) def update(self, rollouts): obs_shape = rollouts.obs.size()[2:] action_shape = rollouts.actions.size()[-1] num_steps, num_processes, _ = rollouts.rewards.size() values, action_log_probs, dist_entropy, _ = self.actor_critic.evaluate_actions( rollouts.obs[:-1].view(-1, *obs_shape), rollouts.recurrent_hidden_states[0].view( -1, self.actor_critic.recurrent_hidden_state_size), rollouts.masks[:-1].view(-1, 1), rollouts.actions.view(-1, action_shape)) values = values.view(num_steps, num_processes, 1) action_log_probs = action_log_probs.view(num_steps, num_processes, 1) advantages = rollouts.returns[:-1] - values value_loss = advantages.pow(2).mean() action_loss = -(advantages.detach() * action_log_probs).mean() if self.acktr and self.optimizer.steps % self.optimizer.Ts == 0: # Compute fisher, see Martens 2014 self.actor_critic.zero_grad() pg_fisher_loss = -action_log_probs.mean() value_noise = torch.randn(values.size()) if values.is_cuda: value_noise = value_noise.cuda() sample_values = values + value_noise vf_fisher_loss = -(values - sample_values.detach()).pow(2).mean() fisher_loss = pg_fisher_loss + vf_fisher_loss self.optimizer.acc_stats = True fisher_loss.backward(retain_graph=True) self.optimizer.acc_stats = False self.optimizer.zero_grad() (value_loss * self.value_loss_coef + action_loss - dist_entropy * self.entropy_coef).backward() if self.acktr == False: nn.utils.clip_grad_norm_(self.actor_critic.parameters(), self.max_grad_norm) self.optimizer.step() return value_loss.item(), action_loss.item(), dist_entropy.item()

35.012346

88

0.586037

649850757a426b361d09cc13cd552c9fa0215268

715

py

Python

stats/services.py

dbgsprw/youtube_crawler

22b044b7c6f7122a0c6512dc4b7b98580eaedcd9

[ "MIT" ]

null

stats/services.py

dbgsprw/youtube_crawler

22b044b7c6f7122a0c6512dc4b7b98580eaedcd9

[ "MIT" ]

7

2019-12-04T23:27:13.000Z

2022-02-10T07:33:06.000Z

stats/services.py

dbgsprw/youtube_statistics_collector

22b044b7c6f7122a0c6512dc4b7b98580eaedcd9

[ "MIT" ]

null

from stats.models import Stats def calculate_view_diff(video, start_day, end_day): older_stat = video.stats_set.filter(created_at__gte=start_day).order_by('created_at')[0] newer_stat = video.stats_set.filter(created_at__lte=end_day).order_by('-created_at')[0] if newer_stat == older_stat: older_stat = Stats(view_count=0, created_at=video.published_at) total_seconds = (end_day - start_day).total_seconds() return _get_average_view_per_seconds(older_stat, newer_stat) * total_seconds def _get_average_view_per_seconds(older_stat, newer_stat): return (newer_stat.view_count - older_stat.view_count) / \ (newer_stat.created_at - older_stat.created_at).total_seconds()

39.722222

92

0.767832

40a8984e8ae71c36fee0b6af040ca6f0ea09beb8

18,300

py

Python

photo_illum.py

deapplegate/wtgpipeline

9693e8562022cc97bf5a96427e22965e1a5e8497

[ "MIT" ]

1

2019-03-15T04:01:19.000Z

photo_illum.py

deapplegate/wtgpipeline

9693e8562022cc97bf5a96427e22965e1a5e8497

[ "MIT" ]

5

2017-12-11T00:11:39.000Z

2021-07-09T17:05:16.000Z

photo_illum.py

deapplegate/wtgpipeline

9693e8562022cc97bf5a96427e22965e1a5e8497

[ "MIT" ]

2

2017-08-15T21:19:11.000Z

2017-10-12T00:36:35.000Z

#!/usr/bin/env python # Python module for photometric calibration. # It needs the Python modules ppgplot and # mpfit to be installed. # 03.03.2005 Fixed a serious bug in the rejection loop. Instead # of using the remaining points we always used all points # and rejected points until the original fit matched the data # 15.02.2005 Fixed the range of the y-axes in the plots to more # sensible values # 14.02.2005 Fixed a bug when more paramters were fitted than # data points were present # We now rescale points to the airmass/color at which # they are plotted (zero) # Check that label is set # 10.12.2004 Now takes a new argument "label" to be # used as axis label in the color plot import copy import getopt import string import sys import mpfit import Numeric from ppgplot import * import BonnLogger def illum_funct(p, fjac=None, X=None, Y=None): [A,B,C,D,E,F] = p model = A*X**2 + B*Y**2 + C*X*Y + D*X + E*Y + F status = 0 return([status, (model-y)/err]) def phot_funct_2(p, fjac=None, y=None, err=None): model = p[0] status = 0 return([status, (model-y)/err]) def phot_funct_1(p, fjac=None, color=None, y=None, err=None): model = p[0] + p[1]*color status = 0 return([status, (model-y)/err]) def phot_funct_0(p, fjac=None, airmass=None, color1=None, color2=None, y=None, err=None): model = p[0] + p[1]*color1 + p[2]*color2 status = 0 return([status, (model-y)/err]) def readInput(file): f = open(file, "r") instMagList = [] stdMagList = [] magErrList = [] colList = [] airmassList = [] for line in f.readlines(): instMag, stdMag, col, airmass, instMagErr, stdMagErr = string.split(line) magErr = (float(instMagErr)**2. + float(stdMagErr)**2.)**0.5 magErrList.append(magErr) instMagList.append(float(instMag)) stdMagList.append(float(stdMag)) colList.append(float(col)) airmassList.append(float(airmass)) f.close() instMag = Numeric.array(instMagList) stdMag = Numeric.array(stdMagList) data = stdMag - instMag airmass = Numeric.array(airmassList) color = Numeric.array(colList) magErr = Numeric.array(magErrList) return data, airmass, color, magErr #def photCalib(data_save, airmass_save, color_save, err_save, p, sigmareject, maxSigIter=50): def photCalib(dictionary, p, sigmareject, maxSigIter=50): save_len = len(data_save) parinfos = [[{"value": p[0], "fixed": 0},{"value": p[1], "fixed": 0, "limited": [0,1], "limits": [-99, 0]},{"value": p[2], "fixed": 0}],[{"value": p[0], "fixed": 0},{"value": p[1], "fixed": 0}],[{"value": p[0], "fixed": 0}]] phot_functs = [phot_funct_0, phot_funct_1, phot_funct_2] solutions = [] for fit_type in [0,1,2]: airmass = copy.copy(airmass_save) color = copy.copy(color_save) data_tmp = copy.copy(data_save) err = copy.copy(err_save) #first apply coefficients we are holding fixed data = copy.copy(data_tmp) if fit_type == 1: for i in range(len(data_tmp)): data[i] = data_tmp[i] - p[1]*airmass[i] if fit_type == 2: for i in range(len(data_tmp)): data[i] = data_tmp[i] - p[1]*airmass[i] - p[2]*color[i] print data_tmp[0], data[0] data_rec = copy.copy(data) parinfo = parinfos[fit_type] #for j in range(len(parinfo)): #if j in fixedList: # print "Element", j, "is fixed at", p[j] # parinfo[j]["fixed"] = 1 #else: # parinfo[j]["fixed"] = 0 for i in range(maxSigIter): old_len = len(data) fas = [{"airmass": airmass,"color": color, "y": data, "err": err},{"color": color,"y": data, "err": err}, {"y": data, "err": err}] fa = fas[fit_type] phot_funct = phot_functs[fit_type] m = mpfit.mpfit(phot_funct, functkw=fa, parinfo=parinfo, maxiter=1000, quiet=1) print m.covar, m.params, m.perror if (m.status <= 0): print 'error message = ', m.errmsg condition = Numeric.zeros(len(data)) break #airmass = copy.copy(airmass_save) #color = copy.copy(color_save) #data = copy.copy(data_save) #err = copy.copy(err_save) # Compute a 3 sigma rejection criterion #condition = preFilter(m.params, data_save, data, # airmass_save, airmass, # color_save, color) params = [0,0,0] perror = [0,0,0] print m.params,m.perror, m.covar if fit_type == 0: params = copy.copy(m.params) perror = copy.copy(m.perror) if fit_type == 1: params[0] = m.params[0] params[2] = m.params[1] params[1] = p[1] perror[0] = m.perror[0] perror[2] = m.perror[1] if fit_type == 2: params[0] = m.params[0] params[1] = p[1] params[2] = p[2] perror[0] = m.perror[0] # Compute a 3 sigma rejection criterion print params, data_rec[0], data[0] condition, redchisq = SigmaCond(params, data_save, data, airmass_save, airmass, color_save, color, err_save, err, sigmareject) print redchisq # Keep everything (from the full data set!) that is within # the 3 sigma criterion #data_sig = Numeric.compress(condition, data_save) data = Numeric.compress(condition, data_rec) airmass = Numeric.compress(condition, airmass_save) color = Numeric.compress(condition, color_save) err = Numeric.compress(condition, err_save) new_len = len(data) if float(new_len)/float(save_len) < 0.5: print "Rejected more than 50% of all measurements." print "Aborting this fit." break # No change if new_len == old_len: print "Converged! (%d iterations)" % (i+1, ) print "Kept %d/%d stars." % (new_len, save_len) break print params, perror, condition meanerr = Numeric.sum(err_save)/len(err_save) solutions.append([params, perror, redchisq, meanerr, condition]) return solutions def SigmaCond(p, data_save, data, airmass_save, airmass, color_save, color, err_save, err, sigmareject): if len(data_save) > 1: #airmass = airmass[int(0.1*len(airmass)):int(0.9*len(airmass))] #color = color[int(0.1*len(color)):int(0.9*len(color))] #data = data[int(0.1*len(data)):int(0.9*len(data))] mo = p[0] + p[1]*airmass + p[2]*color mo_save = p[0] + p[1]*airmass_save + p[2]*color_save print len(data), len(mo), len(err) reddm = (data-mo)/err redchisq = Numeric.sqrt(Numeric.sum(Numeric.power(reddm, 2)) / (len(reddm) - 1)) dm = data-mo dm_save = data_save - mo_save mean = Numeric.sum(dm)/len(dm) sigma = Numeric.sqrt(Numeric.sum(Numeric.power(mean-dm, 2)) / (len(dm) - 1)) #condition = Numeric.less(Numeric.fabs(dm_save), float(sigmareject) * sigma) condition = Numeric.less(Numeric.fabs(dm_save), float(sigmareject) * err_save) else: condition = Numeric.zeros(len(data_save)) return condition, redchisq def makePlots(data, airmass, color, outfile, solutions, label): file = outfile+".ps" pgbeg(file+"/cps", 2, 3) pgiden() for i in range(3): result = solutions[i] # Airmass plot pgpanl(1, i+1) airMin = 1 airMax = Numeric.sort(airmass)[-1]*1.1 print result dataAirMax = result[0][0]+result[0][1]+1 dataAirMin = result[0][0]+result[0][1]-1 dataColMax = result[0][0]+1 dataColMin = result[0][0]-1 colMinVal = Numeric.sort(color)[0] if colMinVal < 0: colMin = colMinVal*1.1 else: colMin = colMinVal*0.95 colMax = Numeric.sort(color)[-1]*1.1 if result[0] and result[1]: eqStr = "%d parameter fit: Mag-Mag(Inst) = %.2f\\(2233)%.2f + (%.2f\\(2233)%.2f) airmass + "\ "(%.2f\\(2233)%.2f) color" % \ (3-i, result[0][0], result[1][0], result[0][1], result[1][1], result[0][2], result[1][2]) else: eqStr = "%d parameter fit not possible" % (3-i, ) fixenv([1, airMax] , [dataAirMin, dataAirMax], eqStr, label=["Airmass", "Mag - Mag(Inst)"]) condition = result[4] goodAirmass = Numeric.compress(condition, airmass) goodData = Numeric.compress(condition, data) goodColor = Numeric.compress(condition, color) badAirmass = Numeric.compress(Numeric.logical_not(condition), airmass) badData = Numeric.compress(Numeric.logical_not(condition), data) badColor = Numeric.compress(Numeric.logical_not(condition), color) if len(goodData): pgsci(3) # Rescale to zero color and filter for data within # our plotting range plotData = goodData-result[0][2]*goodColor plotCond1 = Numeric.less(plotData, dataAirMax) plotCond2 = Numeric.greater(plotData, dataAirMin) plotCond = Numeric.logical_and(plotCond1, plotCond2) plotAirmass = Numeric.compress(plotCond, goodAirmass) plotData = Numeric.compress(plotCond, plotData) pgpt(plotAirmass, plotData, 5) print type(plotAirmass), type(plotData) if len(badData): pgsci(2) plotData = badData-result[0][2]*badColor plotCond1 = Numeric.less(plotData, dataAirMax) plotCond2 = Numeric.greater(plotData, dataAirMin) plotCond = Numeric.logical_and(plotCond1, plotCond2) plotAirmass = Numeric.compress(plotCond, badAirmass) plotData = Numeric.compress(plotCond, plotData) pgpt(plotAirmass, plotData, 5) pgsci(1) a = Numeric.arange(1, airMax, 0.01) m = result[0][0] + result[0][1] * a pgline(a, m) # Color Plot pgpanl(2, i+1) fixenv([colMin, colMax] , [dataColMin, dataColMax], eqStr, label=[label, "Mag - Mag(Inst)"]) if len(goodData): pgsci(3) # Rescale to zero airmass and filter for data within # our plotting range plotData = goodData-result[0][1]*goodAirmass plotCond1 = Numeric.less(plotData, dataColMax) plotCond2 = Numeric.greater(plotData, dataColMin) plotCond = Numeric.logical_and(plotCond1, plotCond2) plotColor = Numeric.compress(plotCond, goodColor) plotData = Numeric.compress(plotCond, plotData) pgpt(plotColor, plotData, 5) if len(badData): pgsci(2) plotData = badData-result[0][1]*badAirmass plotCond1 = Numeric.less(plotData, dataColMax) plotCond2 = Numeric.greater(plotData, dataColMin) plotCond = Numeric.logical_and(plotCond1, plotCond2) plotColor = Numeric.compress(plotCond, badColor) plotData = Numeric.compress(plotCond, plotData) pgpt(plotColor, plotData, 5) pgsci(1) a = Numeric.arange(colMin, colMax, 0.01) m = result[0][0] + result[0][2] * a pgline(a, m) return def fixenv (xrange=[0,1], yrange=[0,1], fname="none", ci = 1, label=["x", "y"]): # set axis ranges. pgswin(xrange[0], xrange[1], yrange[0], yrange[1]) pgsci(ci) # set color index. pgbox() # draw axes. pgsci(1) # back to color index 1 (white) pglab(label[0], label[1], fname) # label the plot return def saveResults(file, solutions, step, sigmareject, cluster, colorused): f = open(file+".asc", "w") which_solution = 0 import MySQLdb, sys, os, re db2 = MySQLdb.connect(db='subaru', user='weaklensing', passwd='darkmatter', host='ki-rh8') c = db2.cursor() #c.execute("DROP TABLE IF EXISTS photometry_db") for result in solutions: which_solution += 1 if Numeric.sometrue(result[2]): import os , time user_name = os.environ['USER'] bonn_target = os.environ['BONN_TARGET'] bonn_filter = os.environ['BONN_FILTER'] time_now = time.asctime() user = user_name #+ str(time.time()) standardstartype = os.environ['STANDARDSTARTYPE'] floatvars = {'ZP':result[0][0],'AIRMASS':result[0][1],'COLOR':result[0][2],'ZPERR':result[1][0],'AIRMASSERR':result[1][1],'COLORERR':result[1][2],'REDCHISQ':result[2],'MEANERR':result[3]} stringvars = {'USER':user_name,'BONN_TARGET':bonn_target,'BONN_FILTER':bonn_filter,'TIME':time_now,'CHOICE':'', 'NUMBERVARS':4-which_solution,'STANDARDSTARTYPE':standardstartype,'USER': user, 'step': step, 'sigmareject':sigmareject, 'cluster':cluster,'colorused':colorused} # make database if it doesn't exist make_db = reduce(lambda x,y: x + ',' + y,[x + ' float(30)' for x in floatvars.keys()]) make_db += ',' + reduce(lambda x,y: x + ',' + y,[x + ' varchar(80)' for x in stringvars.keys()]) command = "CREATE TABLE IF NOT EXISTS photometry_db ( id MEDIUMINT NOT NULL AUTO_INCREMENT, PRIMARY KEY (id), " + make_db + ")" print command #c.execute(command) # insert new observation names = reduce(lambda x,y: x + ',' + y, [x for x in floatvars.keys()]) values = reduce(lambda x,y: str(x) + ',' + str(y), [floatvars[x] for x in floatvars.keys()]) names += ',' + reduce(lambda x,y: x + ',' + y, [x for x in stringvars.keys()]) values += ',' + reduce(lambda x,y: x + ',' + y, ["'" + str(stringvars[x]) + "'" for x in stringvars.keys()]) command = "INSERT INTO photometry_db (" + names + ") VALUES (" + values + ")" print command #c.execute(command) f.write("%s %s %s\n" % (result[0][0], result[0][1], result[0][2])) f.write("%s %s %s\n" % (result[1][0], result[1][1], result[1][2])) f.write("%s#ReducedChiSq\n" % (result[2])) f.write("%s#MeanError\n" % (result[3])) f.write("%s\n" % (id)) else: f.write("-1 -1 -1\n") f.write("-1 -1 -1\n") f.write("-1#ReducedChiSq\n") f.write("-1#MeanError\n") f.write("%s\n" % (id)) f.close return id def usage(): print "Usage:" print "photo_abs.py -i input -f filter -n GABODSID - e ext. coeff. -c color coeff. -o output -l label" print print " -i, --input=STRING Input file, must have 4 columns: Instrumental Mag, Standard Mag, Color, Airmass" print " -o, --output=STRING Output file basename" print " -n, --night=INT GABODSID, unique numerical night identifier" print " -e, --extinction=FLOAT Default value of extinction coefficient for one/two parameter fit" print " -c, --color=FLOAT Default value of color term for one parameter fit" print " -l, --label=STRING Label for color axis (e.g. B-V)" print print "Author:" print " Joerg Dietrich <dietrich@astro.uni-bonn.de>" print return if __name__ == "__main__": __bonn_logger_id__ = BonnLogger.addCommand('maskBadOverscans.py', sys.argv[1:]) try: opts, args = getopt.getopt(sys.argv[1:], "i:n:o:e:c:l:s:", ["input=", "night=", "extinction=", "color=", "output=", "label=","sigmareject=","step=","cluster=","colorused="]) except getopt.GetoptError: usage() BonnLogger.updateStatus(__bonn_logger_id__, 1) sys.exit(2) print sys.argv[1:] infile = night = extcoeff = colcoeff = outfile = label = sigmareject = step = cluster = colorused = None for o, a in opts: if o in ("-i", "--input"): infile = a elif o in ("-o", "--output"): outfile = a elif o in ("-n", "--night"): night = int(a) elif o in ("-e", "--extinction"): extcoeff = float(a) elif o in ("-c", "--color"): colcoeff = float(a) elif o in ("-l", "--label"): label = a elif o in ("-s", "--sigmareject"): sigmareject = float(a) elif o in ("-t", "--step"): step = a elif o in ("-c", "--cluster"): cluster = a elif o in ("-u", "--colorused"): colorused = a else: print "option:", o usage() BonnLogger.updateStatus(__bonn_logger_id__, 1) sys.exit(2) print cluster #raw_input() if not infile or night==None or not outfile or \ extcoeff==None or colcoeff==None or label==None: #print infile, night, outfile, coeff, color usage() BonnLogger.updateStatus(__bonn_logger_id__, 1) sys.exit(2) data, airmass, color, magErr = readInput(infile) #solutions = photCalib(data, airmass, color, magErr, [24, extcoeff, colcoeff], sigmareject) solutions = photCalib({'data':data, 'dataerr':dataerr, vars:{'airmass':airmass, 'color':color}, 'guesses':{'airmasscoeff': airmasscoeff, 'colorcoeff':colorcoeff}, 'sigmareject':sigmareject, fit=[{'function':func_0(airmass,color)},{'function'['color']]}) solutions = photCalib({'data':data, 'dataerr':dataerr, vars:{'X':X,'Y':Y}, 'sigmareject':sigmareject, fit=[['A','B','C','D','E','F'],['color']]}) makePlots(data, airmass, color, outfile, solutions, label) saveResults(outfile, solutions, step, sigmareject, cluster, colorused) BonnLogger.updateStatus(__bonn_logger_id__, 0)

38.689218

279

0.564809

ef3a8cec2243c6a5b660ecc78e6d1ac39c3c8f38

2,946

py

Python

common/vec_env/dummy_vec_env.py

nju-fuzy/baselines

c97a379944632dcc769167b4b0381f6d61729a4f

[ "MIT" ]

1

2021-05-21T11:57:44.000Z

common/vec_env/dummy_vec_env.py

nju-fuzy/baselines

c97a379944632dcc769167b4b0381f6d61729a4f

[ "MIT" ]

null

common/vec_env/dummy_vec_env.py

nju-fuzy/baselines

c97a379944632dcc769167b4b0381f6d61729a4f

[ "MIT" ]

null

import numpy as np from gym import spaces from . import VecEnv from .util import copy_obs_dict, dict_to_obs, obs_space_info class DummyVecEnv(VecEnv): """ VecEnv that does runs multiple environments sequentially, that is, the step and reset commands are send to one environment at a time. Useful when debugging and when num_env == 1 (in the latter case, avoids communication overhead) """ def __init__(self, env_fns): """ Arguments: env_fns: iterable of callables functions that build environments """ self.envs = [fn() for fn in env_fns] env = self.envs[0] VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space) obs_space = env.observation_space self.keys, shapes, dtypes = obs_space_info(obs_space) self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys } self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool) self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32) self.buf_infos = [{} for _ in range(self.num_envs)] self.actions = None self.specs = [e.spec for e in self.envs] def step_async(self, actions): listify = True try: if len(actions) == self.num_envs: listify = False except TypeError: pass if not listify: self.actions = actions else: assert self.num_envs == 1, "actions {} is either not a list or has a wrong size - cannot match to {} environments".format(actions, self.num_envs) self.actions = [actions] def step_wait(self): for e in range(self.num_envs): action = self.actions[e] if isinstance(self.envs[e].action_space, spaces.Discrete): action = int(action) obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action) if self.buf_dones[e]: obs = self.envs[e].reset() self._save_obs(e, obs) return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), self.buf_infos.copy()) def reset(self): for e in range(self.num_envs): obs = self.envs[e].reset() self._save_obs(e, obs) return self._obs_from_buf() def _save_obs(self, e, obs): for k in self.keys: if k is None: self.buf_obs[k][e] = obs else: self.buf_obs[k][e] = obs[k] def _obs_from_buf(self): return dict_to_obs(copy_obs_dict(self.buf_obs)) def get_images(self): return [env.render(mode='rgb_array') for env in self.envs] def render(self, mode='human'): if self.num_envs == 1: return self.envs[0].render(mode=mode) else: return super().render(mode=mode)

35.926829

157

0.595044

113c657234e858926dd0cfbdaada188f63312a33

453

py

Python

cmdline.py

tonib/tokens-rnn-tensorflow

9da815d3f9f4d215dd4d7041e5638eeb3ac51a0a

[ "MIT" ]

null

cmdline.py

tonib/tokens-rnn-tensorflow

9da815d3f9f4d215dd4d7041e5638eeb3ac51a0a

[ "MIT" ]

null

cmdline.py

tonib/tokens-rnn-tensorflow

9da815d3f9f4d215dd4d7041e5638eeb3ac51a0a

[ "MIT" ]

null

import argparse import os def parse_command_line() -> object: # Get commnad line arguments parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--data_dir', type=str, default=os.path.join( 'data' , 'quixote' ), help='Model directory') parser.add_argument('--mode', type=str, default='character', help='Vocabulary mode: character or word') return parser.parse_args()

41.181818

115

0.730684

c66dacdbc637fe57ea6bd0b6c4dc949eb794022f

131

py

Python

secrets.py

haugstve/Coursera_Capstone

89138706b52bad53d53c30c83b1ad324cfa0eae3

[ "MIT" ]

1

2019-03-20T11:27:42.000Z

secrets.py

haugstve/Coursera_Capstone

89138706b52bad53d53c30c83b1ad324cfa0eae3

[ "MIT" ]

null

secrets.py

haugstve/Coursera_Capstone

89138706b52bad53d53c30c83b1ad324cfa0eae3

[ "MIT" ]

null

CLIENT_ID = 'Your CLIENT_ID goes here' # your Foursquare ID CLIENT_SECRET = 'Your CLIENT_SECRET goes here' # your Foursquare Secret

65.5

71

0.793893

b39f6463680e8368495685ba258f041646535434

1,950

py

Python

config/settings/local.py

mkim0818/my-concert

0cc71bb8da29c1d807cd828e219ebc4ad7a25ce9

[ "MIT" ]

null

config/settings/local.py

mkim0818/my-concert

0cc71bb8da29c1d807cd828e219ebc4ad7a25ce9

[ "MIT" ]

null

config/settings/local.py

mkim0818/my-concert

0cc71bb8da29c1d807cd828e219ebc4ad7a25ce9

[ "MIT" ]

null

# -*- coding: utf-8 -*- ''' Local settings - Run in Debug mode - Use console backend for emails - Add Django Debug Toolbar - Add django-extensions as app ''' from .common import * # noqa # DEBUG # ------------------------------------------------------------------------------ DEBUG = env.bool('DJANGO_DEBUG', default=True) TEMPLATES[0]['OPTIONS']['debug'] = DEBUG # SECRET CONFIGURATION # ------------------------------------------------------------------------------ # See: https://docs.djangoproject.com/en/dev/ref/settings/#secret-key # Note: This key only used for development and testing. SECRET_KEY = env("DJANGO_SECRET_KEY", default='r1&=1f3*&1j)&&&1rug@phzjz95=n#=)o34yt7(e^pcr07(+hn') # Mail settings # ------------------------------------------------------------------------------ EMAIL_HOST = 'localhost' EMAIL_PORT = 1025 EMAIL_BACKEND = env('DJANGO_EMAIL_BACKEND', default='django.core.mail.backends.console.EmailBackend') # CACHING # ------------------------------------------------------------------------------ CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', 'LOCATION': '' } } # django-debug-toolbar # ------------------------------------------------------------------------------ MIDDLEWARE_CLASSES += ('debug_toolbar.middleware.DebugToolbarMiddleware',) INSTALLED_APPS += ('debug_toolbar', ) INTERNAL_IPS = ('127.0.0.1', '10.0.2.2',) DEBUG_TOOLBAR_CONFIG = { 'DISABLE_PANELS': [ 'debug_toolbar.panels.redirects.RedirectsPanel', ], 'SHOW_TEMPLATE_CONTEXT': True, } # django-extensions # ------------------------------------------------------------------------------ INSTALLED_APPS += ('django_extensions', ) # TESTING # ------------------------------------------------------------------------------ TEST_RUNNER = 'django.test.runner.DiscoverRunner' # Your local stuff: Below this line define 3rd party library settings

30.952381

99

0.495897

d312eeee2c7810c33d39f0c8f11da9f2b73357e5

5,085

py

Python

akshare/index/index_cni.py

Joeklepko/akshare

b290fad80cd6fed992b2b18496582cd6c7ae0d90

[ "MIT" ]

1

2021-08-21T14:50:39.000Z

akshare/index/index_cni.py

Joeklepko/akshare

b290fad80cd6fed992b2b18496582cd6c7ae0d90

[ "MIT" ]

null

akshare/index/index_cni.py

Joeklepko/akshare

b290fad80cd6fed992b2b18496582cd6c7ae0d90

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2021/6/16 15:18 Desc: 国证指数 http://www.cnindex.com.cn/index.html """ import zipfile import pandas as pd import requests def index_cni_all() -> pd.DataFrame: """ 国证指数-所有指数 http://www.cnindex.com.cn/zh_indices/sese/index.html?act_menu=1&index_type=-1 :return: 国证指数-所有指数 :rtype: pandas.DataFrame """ url = "http://www.cnindex.com.cn/index/indexList" params = { "channelCode": "-1", "rows": "2000", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["rows"]) temp_df.columns = [ "_", "_", "指数代码", "_", "_", "_", "_", "_", "指数简称", "_", "_", "_", "样本数", "收盘点位", "涨跌幅", "_", "PE滚动", "_", "成交量", "成交额", "总市值", "自由流通市值", "_", "_", ] temp_df = temp_df[ [ "指数代码", "指数简称", "样本数", "收盘点位", "涨跌幅", "PE滚动", "成交量", "成交额", "总市值", "自由流通市值", ] ] return temp_df def index_cni_hist(index: str = "399001") -> pd.DataFrame: """ 指数历史行情数据 http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001 :param index: 指数代码 :type index: str :return: 指数历史行情数据 :rtype: pandas.DataFrame """ url = "http://hq.cnindex.com.cn/market/market/getIndexDailyDataWithDataFormat" params = { "indexCode": index, "startDate": "", "endDate": "", "frequency": "day", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["data"]) temp_df.columns = [ "日期", "_", "最高价", "开盘价", "最低价", "收盘价", "_", "涨跌幅", "成交额", "成交量", "_", ] temp_df = temp_df[ [ "日期", "开盘价", "最高价", "最低价", "收盘价", "涨跌幅", "成交量", "成交额", ] ] temp_df["涨跌幅"] = temp_df["涨跌幅"].str.replace("%", "") temp_df["涨跌幅"] = temp_df["涨跌幅"].astype("float") temp_df["涨跌幅"] = temp_df["涨跌幅"] / 100 return temp_df def index_cni_detail(index: str = '399005', date: str = '2020-11') -> pd.DataFrame: """ 国证指数-样本详情-指定日期的样本成份 http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001 :param index: 指数代码 :type index: str :param date: 指定月份 :type date: str :return: 指定日期的样本成份 :rtype: pandas.DataFrame """ url = 'http://www.cnindex.com.cn/sample-detail/download' params = { 'indexcode': index, 'dateStr': date } r = requests.get(url, params=params) temp_df = pd.read_excel(r.content) temp_df['样本代码'] = temp_df['样本代码'].astype(str).str.zfill(6) temp_df.columns = [ '日期', '样本代码', '样本简称', '所属行业', '自由流通市值', '总市值', '权重', ] return temp_df def index_cni_detail_hist(index: str = '399005') -> pd.DataFrame: """ 国证指数-样本详情-历史样本 http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001 :param index: 指数代码 :type index: str :return: 历史样本 :rtype: pandas.DataFrame """ url = 'http://www.cnindex.com.cn/sample-detail/download-history' params = { 'indexcode': index } r = requests.get(url, params=params) temp_df = pd.read_excel(r.content) temp_df['样本代码'] = temp_df['样本代码'].astype(str).str.zfill(6) temp_df.columns = [ '日期', '样本代码', '样本简称', '所属行业', '自由流通市值', '总市值', '权重', ] return temp_df def index_cni_detail_hist_adjust(index: str = '399231') -> pd.DataFrame: """ 国证指数-样本详情-历史调样 http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001 :param index: 指数代码 :type index: str :return: 历史调样 :rtype: pandas.DataFrame """ url = 'http://www.cnindex.com.cn/sample-detail/download-adjustment' params = { 'indexcode': index } r = requests.get(url, params=params) try: temp_df = pd.read_excel(r.content, engine="openpyxl") except zipfile.BadZipFile as e: return temp_df['样本代码'] = temp_df['样本代码'].astype(str).str.zfill(6) return temp_df if __name__ == "__main__": index_cni_all_df = index_cni_all() print(index_cni_all_df) index_cni_hist_df = index_cni_hist(index="399005") print(index_cni_hist_df) index_cni_detail_df = index_cni_detail(index='399005', date='2020-11') print(index_cni_detail_df) index_cni_detail_hist_df = index_cni_detail_hist(index='399005') print(index_cni_detail_hist_df) index_cni_detail_hist_adjust_df = index_cni_detail_hist_adjust(index='399005') print(index_cni_detail_hist_adjust_df)

23.219178

83

0.534513

3b0759af91584507699635fa3dbbca55b6057a1e

4,179

py

Python

mendeley/models/files.py

membranepotential/mendeley-python-sdk

0336f0164f4d409309e813cbd0140011b5b2ff8f

[ "Apache-2.0" ]

103

2015-01-12T00:40:51.000Z

2022-03-29T07:02:06.000Z

mendeley/models/files.py

membranepotential/mendeley-python-sdk

0336f0164f4d409309e813cbd0140011b5b2ff8f

[ "Apache-2.0" ]

26

2015-01-10T04:08:41.000Z

2021-02-05T16:31:37.000Z

mendeley/models/files.py

membranepotential/mendeley-python-sdk

0336f0164f4d409309e813cbd0140011b5b2ff8f

[ "Apache-2.0" ]

43

2015-03-04T18:11:06.000Z

2022-03-13T02:33:34.000Z

import json import os import re from mendeley.models.annotations import Annotation from mendeley.response import SessionResponseObject class File(SessionResponseObject): """ A file attached to a document. .. attribute:: id .. attribute:: size .. attribute:: file_name .. attribute:: mime_type .. attribute:: filehash .. attribute:: download_url """ content_type = 'application/vnd.mendeley-file.1+json' filename_regex = re.compile('filename="(\S+)"') @property def download_url(self): """ the URL at which the file can be downloaded. This is only valid for a short time, so should not be cached. """ file_url = '/files/%s' % self.id rsp = self.session.get(file_url, allow_redirects=False) return rsp.headers['location'] def document(self, view=None): """ :param view: document view to return. :return: a :class:`UserDocument <mendeley.models.documents.UserDocument>` or :class:`CatalogDocument <mendeley.models.catalog.CatalogDocument>`, depending on which the document is attached to. """ if 'document_id' in self.json: return self.session.documents.get_lazy(self.json['document_id'], view=view) elif 'catalog_id' in self.json: return self.session.catalog.get_lazy(self.json['catalog_id'], view=view) else: return None def download(self, directory): """ Downloads the file. :param directory: the directory to download the file to. This must exist. :return: the path to the downloaded file. """ rsp = self.session.get('/files/%s' % self.id, stream=True) filename = self.filename_regex.search(rsp.headers['content-disposition']).group(1) path = os.path.join(directory, filename) with open(path, 'wb') as f: for block in rsp.iter_content(1024): if not block: break f.write(block) return path def delete(self): """ Deletes the file. """ self.session.delete('/files/%s' % self.id) def add_sticky_note(self, text, x_position, y_position, page_number): """ Adds a sticky note to this file. :param text: the text of the sticky_note. :param x_position: the x position on the file of the sticky_note. :param y_position: the y position on the file of the stick_note. :param page_number: the page_number on the file of the sticky_note. :return: a :class:`Annotation <mendeley.models.annotations.Annotation>`. """ position = {'x': x_position, 'y': y_position} bounding_box = {'top_left': position, 'bottom_right': position, 'page': page_number} annotation = { 'document_id': self.document().id, 'text': text, 'filehash': self.filehash, 'positions': [bounding_box] } rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={ 'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type }) return Annotation(self.session, rsp.json()) def add_highlight(self, bounding_boxes, color): """ Adds a highlight to this file. :param bounding_boxes: the area the highlight covers on the file. :param color: the color of the highlight. :return: a :class:`Annotation <mendeley.models.annotations.Annotation>`. """ annotation = { 'document_id': self.document().id, 'filehash': self.filehash, 'positions': [box.json for box in bounding_boxes], 'color': color.json } rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={ 'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type }) return Annotation(self.session, rsp.json()) @classmethod def fields(cls): return ['id', 'size', 'file_name', 'mime_type', 'filehash']

33.97561

119

0.603494

30b556ce98f818711859a552268bb9944b81c9f0

1,884

py

Python

randomizer/slot.py

jrobeson/ColdSteel3Tools

2db0f9b577f237ed1cffcde3ce7362eb76ebc094

[ "MIT" ]

4

2021-05-12T16:48:06.000Z

2021-11-11T23:41:03.000Z

randomizer/slot.py

jrobeson/ColdSteel3Tools

2db0f9b577f237ed1cffcde3ce7362eb76ebc094

[ "MIT" ]

6

2021-05-16T14:44:12.000Z

2021-09-18T00:15:04.000Z

randomizer/slot.py

jrobeson/ColdSteel3Tools

2db0f9b577f237ed1cffcde3ce7362eb76ebc094

[ "MIT" ]

2

2021-05-16T13:17:43.000Z

2021-09-14T01:34:54.000Z

import random, csv, json from randomizer.base import BaseRandomizer class SlotRandomizer(BaseRandomizer): def __init__(self, projectName=None, seed=None, programMode=True) -> None: super().__init__(projectName=projectName, seed=seed, programMode=programMode) random.seed(self.seed) self.inputPath += 'slot' def randomize(self, randomizeBase=True, randomizeGrowth=True, excludeGuest=False): with open('result.txt', 'a', encoding='utf-8') as resultFile, open('ref/char.json') as charFile: chars = json.load(charFile) resultFile.write('\nEP Randomizer Results:\n') inputPath = self.inputPath with open(f'{inputPath}/SlotEp.csv', newline='', encoding='utf-8') as epFile: statusReader = csv.DictReader(epFile) headers = statusReader.fieldnames slots = list(statusReader) skipId = 16 if excludeGuest else 48 for slot in slots: charId = slot['character_id'] if int(charId) >= skipId: continue resultFile.write(f'\n{chars[charId]}:\n') if randomizeBase: baseEp = random.randrange(50, 251, 5) slot['base_ep'] = baseEp resultFile.write(f'Base EP: {baseEp}\n') if randomizeGrowth: growthEp = [random.randrange(0, 161, 5) for _ in range(7)] growthEp.sort() for i in range(7): slot[f'increase_{i + 1}'] = growthEp[i] resultFile.write(f'EP Growth: {growthEp}\n') with open(f'{inputPath}/SlotEp.csv', 'w', newline='', encoding='utf-8') as epFile: writer = csv.DictWriter(epFile, fieldnames=headers) writer.writeheader() writer.writerows(slots)

43.813953

104

0.571125

4a0f770ab9a598cbd754b2fd243fd0f7fb7fba9e

25,330

py

Python

xfel/command_line/cspad_cbf_metrology.py

indu-in/cctbx_project1

e09447ddc2ba3aa9d91b21008b0162ab290b0c30

[ "BSD-3-Clause-LBNL" ]

2

2021-03-18T12:31:57.000Z

2022-03-14T06:27:06.000Z

xfel/command_line/cspad_cbf_metrology.py

indu-in/cctbx_project1

e09447ddc2ba3aa9d91b21008b0162ab290b0c30

[ "BSD-3-Clause-LBNL" ]

null

xfel/command_line/cspad_cbf_metrology.py

indu-in/cctbx_project1

e09447ddc2ba3aa9d91b21008b0162ab290b0c30

[ "BSD-3-Clause-LBNL" ]

null

# -*- mode: python; coding: utf-8; indent-tabs-mode: nil; python-indent: 2 -*- # # LIBTBX_SET_DISPATCHER_NAME cspad.cbf_metrology # from __future__ import absolute_import, division, print_function from six.moves import range import os, sys, random from iotbx.phil import parse from libtbx import easy_run from libtbx.utils import Sorry import six from six.moves import zip phil_scope = parse(""" method = *hierarchical expanding .type = choice reflections = reindexedstrong *indexed integrated .type = choice .help = Which subset of reflections tag = cspad .type = str .help = Name of this refinement run. Output filenames will use this tag. start_at_hierarchy_level = 0 .type = int .help = Start refinement at this hierarchy level refine_to_hierarchy_level = 2 .type = int .help = maximum level to refine cspad to refine_distance = True .type = bool .help = If true, allow root hierarchy level to refine in Z. Otherwise fix this \ axis. Regardless, higher hierarchy levels will refine in Z. refine_energy = False .type = bool .help = If true, when refining level 0, also refine beam energy. Subsequent hierarchy \ levels will fix the energy in place. flat_refinement = False .type = bool .help = If True, do not refine tilt (Tau2 and Tau3) when refining panel positions. Further, \ don't refine distance at levels 1 or higher (respects refine_distance for level 0). flat_refinement_with_distance = False .type = bool .help = If True, and if using flat refinement, then use constraints to allow disance \ to refine at levels 1 and higher. n_subset = None .type = int .help = Refine a random subset of the provided files split_dataset = False .type = bool .help = After refining the full set of images, if split_dataset is True, the \ data will be split in two using odd and even file numbers and each half \ will be refined independently. For each half, _1 or _2 is appended to \ the tag. If used with n_subset, each half will have n_subset/2 images. data_phil = None .type = str .help = Optional phil file with all experiments and reflections for use during \ refinement. If not provided, the program will use whatever directories \ were specified. rmsd_filter { enable = True .type = bool .help = If enabled, between each round of hierarchical refinement, filter \ the images by positional RMSD iqr_multiplier = 1.5 .type = float .help = Interquartile multiplier } n_subset_method = *random n_refl significance_filter .type = choice .help = Algorithm to be used for choosing the n_subset images/experiments for \ refinement. n_refl chooses the set with the largest numbers of reflections \ listed in the reflection table files, thus giving maximal coverage of the detector tiles \ with the fewest refineable parameters. Significance_filter chooses the subset of \ images with maximum reflections above an I/sigI cutoff n_refl_panel_list = None .type = ints .help = If n_subset_method is n_refl, specify which panels to search on. panel_filter = None .type = ints .help = Specify a list of panels to include during refinement. Default (None) is to use \ all panels. output_lcls_geometry = True .type = bool .help = If True, convert final refined geometry to LCLS format """, process_includes=True) refine_defaults_scope = parse(""" output.include_unused_reflections=False refinement { refinery.engine = SparseLevMar parameterisation { beam.fix=all auto_reduction { action=remove min_nref_per_parameter=3 } } reflections { outlier { algorithm=sauter_poon separate_panels=True separate_experiments=False } } } """) def is_even(filename): import re return int(re.findall(r'\d+', filename)[-1][-1]) % 2 == 0 refine_scope = parse(""" include scope dials.command_line.refine.phil_scope """, process_includes=True) def run(args): print("Parsing input...") if "-c" in args or "-h" in args or "--help" in args: phil_scope.show(attributes_level=2) return user_phil = [] paths = [] refine_phil_file = None for arg in args: if os.path.isfile(arg): try: if os.path.splitext(arg)[1] == ".phil": refine_phil_file = arg continue except Exception as e: raise Sorry("Unrecognized file %s"%arg) if os.path.isdir(arg): paths.append(arg) else: try: user_phil.append(parse(arg)) except Exception as e: raise Sorry("Unrecognized argument: %s"%arg) params = phil_scope.fetch(sources=user_phil).extract() merged_scope = refine_scope.fetch(refine_defaults_scope) if refine_phil_file is not None: merged_scope = merged_scope.fetch(parse(file_name = refine_phil_file)) print("Gathering file names...") all_exp = [] all_ref = [] if params.data_phil is None: for path in paths: exp, ref = find_files(path, params.reflections) all_exp.extend(exp) all_ref.extend(ref) if params.split_dataset: even_exp = [] odd_exp = [] even_ref = [] odd_ref = [] for exp, ref in zip(all_exp, all_ref): if is_even(exp): even_exp.append(exp) even_ref.append(ref) else: odd_exp.append(exp) odd_ref.append(ref) base_tag = params.tag base_n_subset = params.n_subset params.n_subset = base_n_subset // 2 params.tag = base_tag + "_1" odd_combine_phil = write_combine_phil(params, odd_exp, odd_ref) params.tag = base_tag + "_2" even_combine_phil = write_combine_phil(params, even_exp, even_ref) params.tag = base_tag params.n_subset = base_n_subset full_combine_phil = write_combine_phil(params, odd_exp+even_exp, odd_ref+even_ref) print("Refining full dataset using tag", params.tag) refine(params, merged_scope, full_combine_phil) params.tag = base_tag + "_1" print("Refining odd numbered data using tag", params.tag) refine(params, merged_scope, odd_combine_phil) params.tag = base_tag + "_2" print("Refining even numbered data using tag", params.tag) refine(params, merged_scope, even_combine_phil) else: combine_phil = write_combine_phil(params, all_exp, all_ref) refine(params, merged_scope, combine_phil) else: assert len(paths) == 0 assert params.n_subset is None print("Refining full dataset using tag", params.tag) refine(params, merged_scope, params.data_phil) if params.split_dataset: input_scope = parse(""" input { experiments = None .type = str .multiple = True .help = "The experiment list file path" reflections = None .type = str .multiple = True .help = "The reflection table file path" } """) input_params = input_scope.fetch(parse(file_name = params.data_phil)).extract() even_exp = [] odd_exp = [] even_ref = [] odd_ref = [] for f in input_params.input.experiments: if is_even(f): even_exp.append(f) else: odd_exp.append(f) for f in input_params.input.reflections: if is_even(f): even_ref.append(f) else: odd_ref.append(f) base_tag = params.tag params.tag = base_tag + "_1" odd_combine_phil = write_combine_phil(params, odd_exp, odd_ref) params.tag = base_tag + "_2" even_combine_phil = write_combine_phil(params, even_exp, even_ref) params.tag = base_tag + "_1" print("Refining odd numbered data using tag", params.tag) refine(params, merged_scope, odd_combine_phil) params.tag = base_tag + "_2" print("Refining even numbered data using tag", params.tag) refine(params, merged_scope, even_combine_phil) def find_files(path, reflections): all_exp = [] all_ref = [] for filename in os.listdir(path): if reflections in filename: extension = os.path.splitext(filename)[1] if extension not in ['.pickle', '.mpack', '.refl']: continue if extension == ".pickle": exp_path = os.path.join(path, filename.rstrip("_%s%s"%(reflections, extension)) + "_refined_experiments.json") else: exp_path = os.path.join(path, filename.rstrip("_%s%s"%(reflections, extension)) + "_refined.expt") if not os.path.exists(exp_path): if extension == ".pickle": exp_path = os.path.join(path, filename.rstrip("_%s%s"%(reflections, extension)) + "_experiments.json") else: exp_path = os.path.join(path, filename.rstrip("_%s%s"%(reflections, extension)) + "_indexed.expt") if not os.path.exists(exp_path): continue all_exp.append(exp_path) all_ref.append(os.path.join(path, filename)) return all_exp, all_ref def write_combine_phil(params, all_exp, all_ref): combine_phil = "%s_combine.phil"%params.tag f = open(combine_phil, 'w') for exp_path, ref_path in zip(all_exp, all_ref): f.write("input {\n") f.write(" experiments = %s\n"%exp_path) f.write(" reflections = %s\n"%ref_path) f.write("}\n") f.close() return combine_phil def refine(params, merged_scope, combine_phil): print("Combining experiments...") command = "dials.combine_experiments reference_from_experiment.average_detector=True reference_from_experiment.average_hierarchy_level=0 output.experiments_filename=%s_combined.expt output.reflections_filename=%s_combined.refl %s"%(params.tag, params.tag, combine_phil) if params.n_subset is not None: command += " n_subset=%d n_subset_method=%s"%(params.n_subset, params.n_subset_method) if params.n_refl_panel_list is not None: command += " n_refl_panel_list=%s"%(",".join(["%d"%p for p in params.n_refl_panel_list])) if params.refine_energy: command += " reference_from_experiment.beam=0" print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() if params.method == 'hierarchical': refine_hierarchical(params, merged_scope, combine_phil) elif params.method == 'expanding': refine_expanding(params, merged_scope, combine_phil) def refine_hierarchical(params, merged_scope, combine_phil): if params.panel_filter is not None: from libtbx import easy_pickle print("Filtering out all reflections except those on panels %s"%(", ".join(["%d"%p for p in params.panel_filter]))) combined_path = "%s_combined.refl"%params.tag data = easy_pickle.load(combined_path) sel = None for panel_id in params.panel_filter: if sel is None: sel = data['panel'] == panel_id else: sel |= data['panel'] == panel_id print("Retaining", len(data.select(sel)), "out of", len(data), "reflections") easy_pickle.dump(combined_path, data.select(sel)) for i in range(params.start_at_hierarchy_level, params.refine_to_hierarchy_level+1): if params.rmsd_filter.enable: input_name = "filtered" else: if i == params.start_at_hierarchy_level: input_name = "combined" else: input_name = "refined" if params.rmsd_filter.enable: command = "cctbx.xfel.filter_experiments_by_rmsd %s %s output.filtered_experiments=%s output.filtered_reflections=%s" if i == params.start_at_hierarchy_level: command = command%("%s_combined.expt"%params.tag, "%s_combined.refl"%params.tag, "%s_filtered.expt"%params.tag, "%s_filtered.refl"%params.tag) else: command = command%("%s_refined_level%d.expt"%(params.tag, i-1), "%s_refined_level%d.refl"%(params.tag, i-1), "%s_filtered_level%d.expt"%(params.tag, i-1), "%s_filtered_level%d.refl"%(params.tag, i-1)) command += " iqr_multiplier=%f"%params.rmsd_filter.iqr_multiplier print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() print("Refining at hierarchy level", i) refine_phil_file = "%s_refine_level%d.phil"%(params.tag, i) if i == 0: fix_list = ['Tau1'] # fix detector rotz if not params.refine_distance: fix_list.append('Dist') if params.flat_refinement: fix_list.extend(['Tau2','Tau3']) diff_phil = "refinement.parameterisation.detector.fix_list=%s\n"%",".join(fix_list) if params.refine_energy: diff_phil += " refinement.parameterisation.beam.fix=in_spindle_plane+out_spindle_plane\n" # allow energy to refine else: # Note, always need to fix something, so pick a panel group and fix its Tau1 (rotation around Z) always if params.flat_refinement and params.flat_refinement_with_distance: diff_phil = "refinement.parameterisation.detector.fix_list=Group1Tau1,Tau2,Tau3\n" # refine distance, rotz and xy translation diff_phil += "refinement.parameterisation.detector.constraints.parameter=Dist\n" # constrain distance to be refined identically for all panels at this hierarchy level elif params.flat_refinement: diff_phil = "refinement.parameterisation.detector.fix_list=Dist,Group1Tau1,Tau2,Tau3\n" # refine only rotz and xy translation else: diff_phil = "refinement.parameterisation.detector.fix_list=Group1Tau1\n" # refine almost everything if i == params.start_at_hierarchy_level: command = "dials.refine %s %s_%s.expt %s_%s.refl"%(refine_phil_file, params.tag, input_name, params.tag, input_name) else: command = "dials.refine %s %s_%slevel%d.expt %s_%s_level%d.refl"%(refine_phil_file, params.tag, input_name, i-1, params.tag, input_name, i-1) diff_phil += "refinement.parameterisation.detector.hierarchy_level=%d\n"%i command += " output.experiments=%s_refined_level%d.expt output.reflections=%s_refined_level%d.refl"%( \ params.tag, i, params.tag, i) scope = merged_scope.fetch(parse(diff_phil)) f = open(refine_phil_file, 'w') f.write(refine_scope.fetch_diff(scope).as_str()) f.close() print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() output_geometry(params) def refine_expanding(params, merged_scope, combine_phil): assert params.start_at_hierarchy_level == 0 if params.rmsd_filter.enable: input_name = "filtered" command = "cctbx.xfel.filter_experiments_by_rmsd %s %s output.filtered_experiments=%s output.filtered_reflections=%s" command = command%("%s_combined.expt"%params.tag, "%s_combined.refl"%params.tag, "%s_filtered.expt"%params.tag, "%s_filtered.refl"%params.tag) command += " iqr_multiplier=%f"%params.rmsd_filter.iqr_multiplier print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() else: input_name = "combined" # -------------------------- if params.panel_filter is not None: from libtbx import easy_pickle print("Filtering out all reflections except those on panels %s"%(", ".join(["%d"%p for p in params.panel_filter]))) combined_path = "%s_combined.refl"%params.tag data = easy_pickle.load(combined_path) sel = None for panel_id in params.panel_filter: if sel is None: sel = data['panel'] == panel_id else: sel |= data['panel'] == panel_id print("Retaining", len(data.select(sel)), "out of", len(data), "reflections") easy_pickle.dump(combined_path, data.select(sel)) # ---------------------------------- # this is the order to refine the CSPAD in steps = {} steps[0] = [2, 3] steps[1] = steps[0] + [0, 1] steps[2] = steps[1] + [14, 15] steps[3] = steps[2] + [6, 7] steps[4] = steps[3] + [4, 5] steps[5] = steps[4] + [12, 13] steps[6] = steps[5] + [8, 9] steps[7] = steps[6] + [10, 11] for s, panels in six.iteritems(steps): rest = [] for p in panels: rest.append(p+16) rest.append(p+32) rest.append(p+48) panels.extend(rest) levels = {0: (0,1)} # levels 0 and 1 for i in range(7): levels[i+1] = (2,) # level 2 previous_step_and_level = None for j in range(8): from libtbx import easy_pickle print("Filtering out all reflections except those on panels %s"%(", ".join(["%d"%p for p in steps[j]]))) combined_path = "%s_%s.refl"%(params.tag, input_name) output_path = "%s_step%d.refl"%(params.tag, j) data = easy_pickle.load(combined_path) sel = None for panel_id in steps[j]: if sel is None: sel = data['panel'] == panel_id else: sel |= data['panel'] == panel_id print("Retaining", len(data.select(sel)), "out of", len(data), "reflections") easy_pickle.dump(output_path, data.select(sel)) for i in levels[j]: print("Step", j , "refining at hierarchy level", i) refine_phil_file = "%s_refine_step%d_level%d.phil"%(params.tag, j, i) if i == 0: if params.refine_distance: diff_phil = "refinement.parameterisation.detector.fix_list=Tau1" # fix detector rotz else: diff_phil = "refinement.parameterisation.detector.fix_list=Dist,Tau1" # fix detector rotz, distance if params.flat_refinement: diff_phil += ",Tau2,Tau3" # Also fix x and y rotations diff_phil += "\n" if params.refine_energy: diff_phil += "refinement.parameterisation.beam.fix=in_spindle_plane+out_spindle_plane\n" # allow energy to refine else: # Note, always need to fix something, so pick a panel group and fix its Tau1 (rotation around Z) always if params.flat_refinement and params.flat_refinement_with_distance: diff_phil = "refinement.parameterisation.detector.fix_list=Group1Tau1,Tau2,Tau3\n" # refine distance, rotz and xy translation diff_phil += "refinement.parameterisation.detector.constraints.parameter=Dist\n" # constrain distance to be refined identically for all panels at this hierarchy level elif params.flat_refinement: diff_phil = "refinement.parameterisation.detector.fix_list=Dist,Group1Tau1,Tau2,Tau3\n" # refine only rotz and xy translation else: diff_phil = "refinement.parameterisation.detector.fix_list=Group1Tau1\n" # refine almost everything if previous_step_and_level is None: command = "dials.refine %s %s_%s.expt %s_step%d.refl"%( \ refine_phil_file, params.tag, input_name, params.tag, j) else: p_step, p_level = previous_step_and_level if p_step == j: command = "dials.refine %s %s_refined_step%d_level%d.expt %s_refined_step%d_level%d.refl"%( \ refine_phil_file, params.tag, p_step, p_level, params.tag, p_step, p_level) else: command = "dials.refine %s %s_refined_step%d_level%d.expt %s_step%d.refl"%( \ refine_phil_file, params.tag, p_step, p_level, params.tag, j) diff_phil += "refinement.parameterisation.detector.hierarchy_level=%d\n"%i output_experiments = "%s_refined_step%d_level%d.expt"%(params.tag, j, i) command += " output.experiments=%s output.reflections=%s_refined_step%d_level%d.refl"%( \ output_experiments, params.tag, j, i) scope = merged_scope.fetch(parse(diff_phil)) f = open(refine_phil_file, 'w') f.write(refine_scope.fetch_diff(scope).as_str()) f.close() print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() # In expanding mode, if using flat refinement with distance, after having refined this step as a block, unrefined # panels will have been left behind. Read back the new metrology, compute the shift applied to the panels refined # in this step,and apply that shift to the unrefined panels in this step if params.flat_refinement and params.flat_refinement_with_distance and i > 0: from dxtbx.model.experiment_list import ExperimentListFactory from xfel.command_line.cspad_detector_congruence import iterate_detector_at_level, iterate_panels from scitbx.array_family import flex from scitbx.matrix import col from libtbx.test_utils import approx_equal experiments = ExperimentListFactory.from_json_file(output_experiments, check_format=False) assert len(experiments.detectors()) == 1 detector = experiments.detectors()[0] # Displacements: deltas along the vector normal to the detector displacements = flex.double() # Iterate through the panel groups at this level for panel_group in iterate_detector_at_level(detector.hierarchy(), 0, i): # Were there panels refined in this step in this panel group? if params.panel_filter: test = [list(detector).index(panel) in steps[j] for panel in iterate_panels(panel_group) if list(detector).index(panel) in params.panel_filter] else: test = [list(detector).index(panel) in steps[j] for panel in iterate_panels(panel_group)] if not any(test): continue # Compute the translation along the normal of this panel group. This is defined as distance in dials.refine displacements.append(col(panel_group.get_local_fast_axis()).cross(col(panel_group.get_local_slow_axis())).dot(col(panel_group.get_local_origin()))) # Even though the panels are constrained to move the same amount, there is a bit a variation. stats = flex.mean_and_variance(displacements) displacement = stats.mean() print("Average displacement along normals: %f +/- %f"%(stats.mean(), stats.unweighted_sample_standard_deviation())) # Verify the variation isn't significant for k in range(1, len(displacements)): assert approx_equal(displacements[0], displacements[k]) # If all of the panel groups in this level moved, no need to do anything. if len(displacements) != len(list(iterate_detector_at_level(detector.hierarchy(), 0, i))): for panel_group in iterate_detector_at_level(detector.hierarchy(), 0, i): if params.panel_filter: test = [list(detector).index(panel) in steps[j] and list(detector).index(panel) in params.panel_filter for panel in iterate_panels(panel_group)] else: test = [list(detector).index(panel) in steps[j] for panel in iterate_panels(panel_group)] # If any of the panels in this panel group moved, no need to do anything if any(test): continue # None of the panels in this panel group moved in this step, so need to apply displacement from other panel # groups at this level fast = col(panel_group.get_local_fast_axis()) slow = col(panel_group.get_local_slow_axis()) ori = col(panel_group.get_local_origin()) normal = fast.cross(slow) panel_group.set_local_frame(fast, slow, (ori.dot(fast)*fast) + (ori.dot(slow)*slow) + (normal*displacement)) # Check the new displacements. Should be the same across all panels. displacements = [] for panel_group in iterate_detector_at_level(detector.hierarchy(), 0, i): displacements.append(col(panel_group.get_local_fast_axis()).cross(col(panel_group.get_local_slow_axis())).dot(col(panel_group.get_local_origin()))) for k in range(1, len(displacements)): assert approx_equal(displacements[0], displacements[k]) experiments.as_file(output_experiments) previous_step_and_level = j,i output_geometry(params) def output_geometry(params): print("Creating files to deploy to psana calibration directory...") if params.refine_to_hierarchy_level > 2: deploy_level = 2 else: deploy_level = params.refine_to_hierarchy_level if params.method == 'hierarchical': command = "cxi.experiment_json_to_cbf_def %s_refined_level%d.expt output_def_file=%s_refined_detector_level%d.def"%(params.tag, deploy_level, params.tag, deploy_level) elif params.method == 'expanding': command = "cxi.experiment_json_to_cbf_def %s_refined_step7_level%d.expt output_def_file=%s_refined_detector_level%d.def"%(params.tag, deploy_level, params.tag, deploy_level) print(command) result = easy_run.fully_buffered(command=command).raise_if_errors() result.show_stdout() if params.output_lcls_geometry: command = "cxi.cbfheader2slaccalib cbf_header=%s_refined_detector_level%d.def out_metrology_file=0-end.data.%s"%(params.tag, deploy_level, params.tag) print(command) result = easy_run.fully_buffered(command=command) errmsg = "\n".join(result.stderr_lines) if "ImportError" in errmsg and "PSCalib.GeometryAccess" in errmsg: print("Not converting to LCLS geometry as PSDM is not available") print("Done.") else: result.raise_if_errors() result.show_stdout() print("Done. Soft link 0-end.data.%s to 0-end.data in the geometry folder of your calibration folder for your experiment to deploy this metrology."%params.tag) if __name__ == "__main__": run(sys.argv[1:])

43.005093

271

0.680261

51bc60a8feccf33a991c03ff55e36d810ebbf8fa

597

py

Python

scripts/todo.py

Jhsmit/awesome-panel

53f7754f7c505a2666f6724df26c851ae942ec40

[ "Apache-2.0" ]

null

scripts/todo.py

Jhsmit/awesome-panel

53f7754f7c505a2666f6724df26c851ae942ec40

[ "Apache-2.0" ]

null

scripts/todo.py

Jhsmit/awesome-panel

53f7754f7c505a2666f6724df26c851ae942ec40

[ "Apache-2.0" ]

null

def _to_plot(data): gridstyle = {"xgrid_line_color": None} curve_opts = opts.Curve( # pylint: disable=no-member line_width=4, responsive=True, color=get_color_cycle(), ) group_by = [] if len(data.ElementName.unique()) > 1: group_by.append("Element") if len(data.InstanceName.unique()) > 1: group_by.append("Instance") return ( data.rename(columns={"ElementName": "Element", "InstanceName": "Instance"}) .hvplot(x="Datetime", y="Value", by=group_by) .opts(curve_opts,) .opts(show_grid=True) )

35.117647

84

0.599665

cba3e4831238649cdd6a95ba07257b484343a69b

2,277

py

Python

cactusbot/sepal.py

CactusBot/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

23

2016-02-16T05:09:11.000Z

2016-09-20T14:22:51.000Z

cactusbot/sepal.py

Alkali-Metal/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

190

2016-09-30T05:31:59.000Z

2018-12-22T08:46:49.000Z

cactusbot/sepal.py

Alkali-Metal/CactusBot

6d035bf74bdc8f7fb3ee1e79f8d443f5b17e7ea5

[ "MIT" ]

16

2016-10-09T16:51:48.000Z

2017-10-25T05:29:10.000Z

"""Interact with Sepal.""" import json import logging from .packets import MessagePacket, Packet from .services.websocket import WebSocket class Sepal(WebSocket): """Interact with Sepal.""" def __init__(self, channel, service=None): super().__init__("wss://cactus.exoz.one/sepal") self.logger = logging.getLogger(__name__) self.channel = channel self.service = service self.parser = SepalParser() async def send(self, packet_type, **kwargs): """Send a packet to Sepal.""" packet = { "type": packet_type, "channel": self.channel } packet.update(kwargs) await super().send(json.dumps(packet)) async def initialize(self): """Send a subscribe packet.""" await self.send("subscribe") async def parse(self, packet): """Parse a Sepal packet.""" try: packet = json.loads(packet) except (TypeError, ValueError): self.logger.exception("Invalid JSON: %s.", packet) return None else: self.logger.debug(packet) return packet async def handle(self, packet): """Convert a JSON packet to a CactusBot packet.""" assert self.service is not None, "Must have a service to handle" if "event" not in packet: return event = packet["event"] if not hasattr(self.parser, "parse_" + event.lower()): return data = await getattr(self.parser, "parse_" + event)(packet) if data is None: return if isinstance(data, (list, tuple)): for packet in data: await self.service.handle(event, packet) else: await self.service.handle(event, data) class SepalParser: """Parse Sepal packets.""" async def parse_repeat(self, packet): """Parse the incoming repeat packets.""" if "response" in packet["data"]: return MessagePacket.from_json(packet["data"]["response"]) async def parse_config(self, packet): """Parse the incoming config packets.""" return [Packet("config", key=key, values=values) for key, values in packet["data"].items()]

25.58427

72

0.582784

d40e72305aafcb13d79fe80a23f9c3f28fa9c502

3,193

py

Python

indico/util/roles.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

1

2018-11-12T21:29:26.000Z

indico/util/roles.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

9

2020-09-08T09:25:57.000Z

2022-01-13T02:59:05.000Z

indico/util/roles.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

3

2020-07-20T09:09:44.000Z

2020-10-19T00:29:49.000Z

# This file is part of Indico. # Copyright (C) 2002 - 2020 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. from __future__ import unicode_literals import csv from flask import flash, session from indico.core.errors import UserValueError from indico.modules.events.roles.forms import ImportMembersCSVForm from indico.modules.users import User from indico.util.i18n import _, ngettext from indico.util.string import to_unicode, validate_email from indico.web.flask.templating import get_template_module from indico.web.util import jsonify_data, jsonify_template class ImportRoleMembersMixin(object): """Import members from a CSV file into a role.""" logger = None def import_members_from_csv(self, f): reader = csv.reader(f.read().splitlines()) emails = set() for num_row, row in enumerate(reader, 1): if len(row) != 1: raise UserValueError(_('Row {}: malformed CSV data').format(num_row)) email = to_unicode(row[0]).strip().lower() if email and not validate_email(email): raise UserValueError(_('Row {row}: invalid email address: {email}').format(row=num_row, email=email)) if email in emails: raise UserValueError(_('Row {}: email address is not unique').format(num_row)) emails.add(email) users = set(User.query.filter(~User.is_deleted, User.all_emails.in_(emails))) users_emails = {user.email for user in users} unknown_emails = emails - users_emails new_members = users - self.role.members return new_members, users, unknown_emails def _process(self): form = ImportMembersCSVForm() if form.validate_on_submit(): new_members, users, unknown_emails = self.import_members_from_csv(form.source_file.data) if form.remove_existing.data: deleted_members = self.role.members - users for member in deleted_members: self.logger.info('User {} removed from role {} by {}'.format(member, self.role, session.user)) self.role.members = users else: self.role.members |= users for user in new_members: self.logger.info('User {} added to role {} by {}'.format(user, self.role, session.user)) flash(ngettext("{} member has been imported.", "{} members have been imported.", len(users)).format(len(users)), 'success') if unknown_emails: flash(ngettext("There is no user with this email address: {}", "There are no users with these email addresses: {}", len(unknown_emails)).format(', '.join(unknown_emails)), 'warning') tpl = get_template_module('events/roles/_roles.html') return jsonify_data(html=tpl.render_role(self.role, collapsed=False, email_button=False)) return jsonify_template('events/roles/import_members.html', form=form, role=self.role)

43.739726

117

0.639524

ab74115d134f7f85741b18c9cdffb66c814c52ba

639

py

Python

tests/unicamp/test_uni_2009p1f2_olimpiadas.py

hiroshisiq/AEDlogis2021

0e153bd52f2b6cff465d4ffab31bfc28d003b24f

[ "MIT" ]

1

2021-06-03T22:46:10.000Z

tests/unicamp/test_uni_2009p1f2_olimpiadas.py

hiroshisiq/AEDlogis2021

0e153bd52f2b6cff465d4ffab31bfc28d003b24f

[ "MIT" ]

8

2021-05-02T00:47:32.000Z

2021-05-15T23:51:44.000Z

tests/unicamp/test_uni_2009p1f2_olimpiadas.py

hiroshisiq/problem_solving

0e153bd52f2b6cff465d4ffab31bfc28d003b24f

[ "MIT" ]

null

import pytest from tests.run_script import run_script, read_text RESOURCES_PATH = './tests/unicamp/resources/2009p1f2' EXECUTABLE_PATH = './problems/unicamp/uni_2009p1f2_olimpiadas.py' @pytest.mark.parametrize( 'input_path, output_path', [(f'{RESOURCES_PATH}/case_1.in', f'{RESOURCES_PATH}/case_1.out'), (f'{RESOURCES_PATH}/case_2.in', f'{RESOURCES_PATH}/case_2.out'), (f'{RESOURCES_PATH}/case_3.in', f'{RESOURCES_PATH}/case_3.out')] ) def test_2009p1f2_olimpiadas(input_path: str, output_path: str): got = run_script(EXECUTABLE_PATH, input_path) expected = read_text(output_path) assert got == expected

31.95

69

0.741784

5313989d1617583462f31456541a3b466a6dcb38

3,285

py

Python

WebDev/2021_05_14_WebClass-7/project1/project1/settings.py

Arc29/2021-22-Classes

03439d1e70a050758e9f698036a92110cf63cf71

[ "MIT" ]

458

2021-04-20T10:24:39.000Z

2022-03-31T11:37:35.000Z

WebDev/2021_05_14_WebClass-7/project1/project1/settings.py

Shineri/2021-22-Classes

9dd13c9f896ec83eb14170500fedeb20d416b1ba

[ "MIT" ]

5

2021-04-29T02:04:15.000Z

2021-11-26T01:40:35.000Z

WebDev/2021_05_14_WebClass-7/project1/project1/settings.py

Shineri/2021-22-Classes

9dd13c9f896ec83eb14170500fedeb20d416b1ba

[ "MIT" ]

59

2021-04-19T19:37:27.000Z

2022-03-18T08:58:44.000Z

""" Django settings for project1 project. Generated by 'django-admin startproject' using Django 3.2.2. For more information on this file, see https://docs.djangoproject.com/en/3.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.2/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-9n)j_emh+^tzs$vngo8*t^iq#2*b_gp-h5a0^5q3+9^_$%#7s6' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'polls.apps.PollsConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'project1.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'project1.wsgi.application' # Database # https://docs.djangoproject.com/en/3.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Asia/Kolkata' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.2/howto/static-files/ STATIC_URL = '/static/' # Default primary key field type # https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

25.664063

91

0.701065

9eef9ccc8b8b776d3cb58f97fad65b975615b67a

1,318

py

Python

tests/cupyx_tests/scipy_tests/linalg_tests/test_matfuncs.py

ameyachawla99/cupy

43d78a7d6fbbe0d9ca35177a14da31a483a827ef

[ "MIT" ]

null

tests/cupyx_tests/scipy_tests/linalg_tests/test_matfuncs.py

ameyachawla99/cupy

43d78a7d6fbbe0d9ca35177a14da31a483a827ef

[ "MIT" ]

null

tests/cupyx_tests/scipy_tests/linalg_tests/test_matfuncs.py

ameyachawla99/cupy

43d78a7d6fbbe0d9ca35177a14da31a483a827ef

[ "MIT" ]

null

from cupyx.scipy.linalg.matfuncs import sinhm import cupy import unittest class TestMatFuncsNan(unittest.TestCase): def test_sinhm_values(self): # Testing whether it is producing right output or not z = cupy.zeros((2, 2)) z[0][0] = 1 z[0][1] = 2 z[1][0] = 3 z[1][1] = 1 r = cupy.zeros((2, 2)) r[0][0] = 1.1752011936438014 r[0][1] = 3.626860407847019 r[1][0] = 10.017874927409903 r[1][1] = 1.1752011936438014 z = sinhm(z) for i in range(z.shape[0]): for j in range(z.shape[1]): assert r[i][j] == z[i][j] def test_values_nan(self): # Testing for nan values in matrix r = cupy.zeros((3, 3)) r.fill(cupy.nan) self.assertRaises(ValueError, sinhm, r) def test_values_inf(self): # Testing for nan values in matrix z = cupy.zeros((2, 2)) z[0][0] = cupy.inf self.assertRaises(ValueError, sinhm, z) def test_shape(self): # Testing for matrix shape k = cupy.zeros((3, 2)) self.assertRaises(ValueError, sinhm, k) def test_dimension_count(self): # Testing whether it is as 2D array or not g = cupy.zeros((3, 3, 3)) self.assertRaises(ValueError, sinhm, g)

25.843137

61

0.556904

ad11af8e674ca148b7c367c7d7b265deb76eccad

590

py

Python

solutions/basics2/cookie.py

mrparkonline/py_basics

821d388e23cebdb0ac6c741a67d2c7d336ec717e

[ "MIT" ]

null

solutions/basics2/cookie.py

mrparkonline/py_basics

821d388e23cebdb0ac6c741a67d2c7d336ec717e

[ "MIT" ]

null

solutions/basics2/cookie.py

mrparkonline/py_basics

821d388e23cebdb0ac6c741a67d2c7d336ec717e

[ "MIT" ]

null

# Cookie Selling Program # input start = float(input('Enter your starting money: ')) num_cookies = input('Enter the number of cookies sold: ') num_big = input('Enter the number of big cookies sold: ') # processing total_cookies = len(num_cookies) + len(num_big) profit_cookies = len(num_cookies) * 1.25 - len(num_cookies) * 0.50 profit_big = len(num_big) * 2 - len(num_big) * 0.75 total_profit = profit_cookies + profit_big total_money = start + total_profit # output print('We sold', total_cookies, 'cookies.') print('Our profit was:', total_profit) print('We now have:', total_money)

29.5

66

0.730508

2cd3e8f05c04f91070afdb27f8f87f1c2c7af6e3

1,261

py

Python

streamlit/GroMoPo_app.py

Gromopo/GroMoPo

dfe40ebb429762403dbd249593bf00b22255efc0

[ "CC0-1.0" ]

null

streamlit/GroMoPo_app.py

Gromopo/GroMoPo

dfe40ebb429762403dbd249593bf00b22255efc0

[ "CC0-1.0" ]

20

2021-11-12T15:23:33.000Z

2022-02-02T08:49:00.000Z

streamlit/GroMoPo_app.py

Gromopo/GroMoPo

dfe40ebb429762403dbd249593bf00b22255efc0

[ "CC0-1.0" ]

1

2021-11-03T16:31:28.000Z

import streamlit as st # Our app libs - possibly move to own folder # from utils import helpers from utils.multipage import MultiPage from pages import home, about, submit_model, model_finder from pathlib import Path import platform #st.set_page_config(layout="wide") st.sidebar.title('Navigation') app = MultiPage() app.add_page("Home", home.app) # Name, Function app.add_page("Model Finder", model_finder.app) app.add_page("Submit Model", submit_model.app) app.add_page("About", about.app) app.run() st.sidebar.title("Contribute") st.sidebar.info("This an open source project and you are very welcome to **contribute** your awesome comments," " questions, resources and groundwater models to the source code") st.sidebar.title("About") st.sidebar.info("This app is maintained and argued on by the GroMoPo mob") # FIXME this should work independant of the system we are on. Make sure this works on all platforms including streamlit.io if platform.system() == 'Windows': main_path = Path(".") else: main_path = Path("streamlit") img_path = main_path.joinpath('pages','img','GroMoPo_logo_V1.png') st.sidebar.image(str(img_path), caption=None, width=None, use_column_width=None, clamp=False, channels='RGB', output_format='auto')

34.081081

131

0.750991

05ec71370706982a61f732cc1b88e363a77924ea

2,754

py

Python

tests/core/full_node/test_sync_store.py

Storch-Network/chialite

587fc53e8ef452e07c6f3f266f58962d065feb5c

[ "Apache-2.0" ]

2

2021-06-29T14:05:41.000Z

2021-07-15T19:28:26.000Z

tests/core/full_node/test_sync_store.py

Storch-Network/chialite

587fc53e8ef452e07c6f3f266f58962d065feb5c

[ "Apache-2.0" ]

31

2021-06-26T23:11:46.000Z

2022-03-29T00:12:30.000Z

tests/core/full_node/test_sync_store.py

Storch-Network/chialite

587fc53e8ef452e07c6f3f266f58962d065feb5c

[ "Apache-2.0" ]

null

import asyncio import pytest from chialite.full_node.sync_store import SyncStore from chialite.util.hash import std_hash @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop class TestStore: @pytest.mark.asyncio async def test_basic_store(self): store = await SyncStore.create() await SyncStore.create() # Save/get sync for sync_mode in (False, True): store.set_sync_mode(sync_mode) assert sync_mode == store.get_sync_mode() # clear sync info await store.clear_sync_info() store.set_peak_target(std_hash(b"1"), 100) assert store.get_sync_target_hash() == std_hash(b"1") assert store.get_sync_target_height() == 100 peer_ids = [std_hash(bytes([a])) for a in range(3)] assert store.get_peers_that_have_peak([]) == set() assert store.get_peers_that_have_peak([std_hash(b"block1")]) == set() assert store.get_heaviest_peak() is None assert len(store.get_peak_of_each_peer()) == 0 store.peer_has_block(std_hash(b"block10"), peer_ids[0], 500, 10, True) store.peer_has_block(std_hash(b"block1"), peer_ids[0], 300, 1, False) store.peer_has_block(std_hash(b"block1"), peer_ids[1], 300, 1, True) store.peer_has_block(std_hash(b"block10"), peer_ids[2], 500, 10, False) store.peer_has_block(std_hash(b"block1"), peer_ids[2], 300, 1, False) assert store.get_heaviest_peak()[0] == std_hash(b"block10") assert store.get_heaviest_peak()[1] == 10 assert store.get_heaviest_peak()[2] == 500 assert len(store.get_peak_of_each_peer()) == 2 store.peer_has_block(std_hash(b"block1"), peer_ids[2], 500, 1, True) assert len(store.get_peak_of_each_peer()) == 3 assert store.get_peak_of_each_peer()[peer_ids[0]][2] == 500 assert store.get_peak_of_each_peer()[peer_ids[1]][2] == 300 assert store.get_peak_of_each_peer()[peer_ids[2]][2] == 500 assert store.get_peers_that_have_peak([std_hash(b"block1")]) == set(peer_ids) assert store.get_peers_that_have_peak([std_hash(b"block10")]) == {peer_ids[0], peer_ids[2]} store.peer_disconnected(peer_ids[0]) assert store.get_heaviest_peak()[2] == 500 assert len(store.get_peak_of_each_peer()) == 2 assert store.get_peers_that_have_peak([std_hash(b"block10")]) == {peer_ids[2]} store.peer_disconnected(peer_ids[2]) assert store.get_heaviest_peak()[2] == 300 store.peer_has_block(std_hash(b"block30"), peer_ids[0], 700, 30, True) assert store.get_peak_of_each_peer()[peer_ids[0]][2] == 700 assert store.get_heaviest_peak()[2] == 700

39.913043

99

0.663399

a25042678bf8ba9cdf34ff3c081589d92238f6af

4,133

py

Python

src/github3/decorators.py

butsyk/github3.py

72fa5125fce75c916733839963554765c907e9e7

[ "BSD-3-Clause" ]

null

src/github3/decorators.py

butsyk/github3.py

72fa5125fce75c916733839963554765c907e9e7

[ "BSD-3-Clause" ]

null

src/github3/decorators.py

butsyk/github3.py

72fa5125fce75c916733839963554765c907e9e7

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """This module provides decorators to the rest of the library.""" from functools import wraps from requests.models import Response import os from io import BytesIO as StringIO class RequestsStringIO(StringIO): """Shim compatibility for string IO.""" def read(self, n=-1, *args, **kwargs): """Ignore extra args and kwargs.""" # StringIO is an old-style class, so can't use super return StringIO.read(self, n) def requires_auth(func): """Decorator to note which object methods require authorization.""" @wraps(func) def auth_wrapper(self, *args, **kwargs): if hasattr(self, "session") and self.session.has_auth(): return func(self, *args, **kwargs) else: from .exceptions import error_for # Mock a 401 response r = generate_fake_error_response( '{"message": "Requires authentication"}' ) raise error_for(r) return auth_wrapper def requires_basic_auth(func): """Specific (basic) authentication decorator. This is used to note which object methods require username/password authorization and won't work with token based authorization. """ @wraps(func) def auth_wrapper(self, *args, **kwargs): if hasattr(self, "session") and self.session.auth: return func(self, *args, **kwargs) else: from .exceptions import error_for # Mock a 401 response r = generate_fake_error_response( '{"message": "Requires username/password authentication"}' ) raise error_for(r) return auth_wrapper def requires_app_credentials(func): """Require client_id and client_secret to be associated. This is used to note and enforce which methods require a client_id and client_secret to be used. """ @wraps(func) def auth_wrapper(self, *args, **kwargs): client_id, client_secret = self.session.retrieve_client_credentials() if client_id and client_secret: return func(self, *args, **kwargs) else: from .exceptions import error_for # Mock a 401 response r = generate_fake_error_response( '{"message": "Requires username/password authentication"}' ) raise error_for(r) return auth_wrapper def requires_app_bearer_auth(func): """Require the use of application authentication. .. versionadded:: 1.2.0 """ @wraps(func) def auth_wrapper(self, *args, **kwargs): from . import session if isinstance(self.session.auth, session.AppBearerTokenAuth): return func(self, *args, **kwargs) else: from . import exceptions raise exceptions.MissingAppBearerAuthentication( "This method requires GitHub App authentication." ) return auth_wrapper def requires_app_installation_auth(func): """Require the use of App's installation authentication. .. versionadded:: 1.2.0 """ @wraps(func) def auth_wrapper(self, *args, **kwargs): from . import session if isinstance(self.session.auth, session.AppInstallationTokenAuth): return func(self, *args, **kwargs) else: from . import exceptions raise exceptions.MissingAppInstallationAuthentication( "This method requires GitHub App authentication." ) return auth_wrapper def generate_fake_error_response(msg, status_code=401, encoding="utf-8"): """Generate a fake Response from requests.""" r = Response() r.status_code = status_code r.encoding = encoding r.raw = RequestsStringIO(msg.encode()) r._content_consumed = True r._content = r.raw.read() return r # Use mock decorators when generating documentation, so all functino signatures # are displayed correctly if os.getenv("GENERATING_DOCUMENTATION", None) == "github3": requires_auth = requires_basic_auth = lambda x: x # noqa # (No coverage)

28.115646

79

0.638519

127b52e4cd8a53557a326b0657026cc90374cb43

102

py

Python

srs/process_data.py

kubekmonika/get-data-from-gios-api

5e27925527307ef72f189ecaa1422a8cc9e02c9e

[ "MIT" ]

null

srs/process_data.py

kubekmonika/get-data-from-gios-api

5e27925527307ef72f189ecaa1422a8cc9e02c9e

[ "MIT" ]

null

srs/process_data.py

kubekmonika/get-data-from-gios-api

5e27925527307ef72f189ecaa1422a8cc9e02c9e

[ "MIT" ]

null

""" This is a file which will contain functions to process the downloaded data to desired format. """

20.4

74

0.754902

2cb4297e3effae797da7f649566bd71922e12914

4,244

py

Python

fedelemflowlist/subset_list.py

hottleta/Federal-LCA-Commons-Elementary-Flow-List

a5ba34acc2af66b802f07c1f59208af135027882

[ "CC0-1.0" ]

1

2020-07-09T14:28:25.000Z

fedelemflowlist/subset_list.py

hottleta/Federal-LCA-Commons-Elementary-Flow-List

a5ba34acc2af66b802f07c1f59208af135027882

[ "CC0-1.0" ]

1

2021-03-18T14:27:28.000Z

fedelemflowlist/subset_list.py

hottleta/Federal-LCA-Commons-Elementary-Flow-List

a5ba34acc2af66b802f07c1f59208af135027882

[ "CC0-1.0" ]

1

2021-09-20T05:02:28.000Z

# subset_list.py (fedelemflowlist) # !/usr/bin/env python3 # coding=utf-8 """ Functions to filter a flow list for subsets Functions correspond with a subset names stored in the subsets dictionary """ import pandas as pd from fedelemflowlist.globals import inputpath subsets = {"freshwater_resources":"get_freshwater_resource_flows", "water_resources":"get_water_resource_flows", "land_use":"get_land_use_flows", #"mineral_resources":"get_mineral_resource_flows", #"energy":"get_energy_flows", #"metal_emissions":"get_metal_emission_flows", "HAP":"get_hazardous_air_pollutant_flows"} inventory_unit = {"freshwater_resources":"kg", "water_resources":"kg", "land_use":"m2*a", "mineral_resources":"kg", "energy":"MJ", "metal_emissions":"kg", "HAP":"kg"} def get_subsets() -> list(): """ Returns a list of all availabile inventory subsets return: list of inventory subsets """ list_of_inventories = list(subsets) return list_of_inventories def get_inventory_unit(subset): """ Returns the inventory unit for the selected subset :param subset: dictionary key return: (str) unit for inventory method. """ unit = inventory_unit[subset] return unit def get_freshwater_resource_flows(fl): """ Subsets the flow list for all freshwater resource flows, excluding resource/air :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Flowable"]=="Water, fresh"] flows = flows[flows["Context"].str.startswith("resource")] flows = flows[~flows["Context"].str.startswith("resource/air")] return flows def get_water_resource_flows(fl): """ Subsets the flow list for all water resource flows, excluding resource/air :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Flowable"].str.startswith("Water")] flows = flows[flows["Context"].str.startswith("resource")] flows = flows[~flows["Context"].str.startswith("resource/air")] return flows def get_land_use_flows(fl): """ Subsets the flow list for all land use resource flows :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Class"]=="Land"] return flows def get_mineral_resource_flows(fl): """ Subsets the flow list for all mineral resource flows :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Class"]=="Geological"] flows = flows[flows["Context"].str.startswith("resource")] flows = flows[~flows["Flowable"].str.contains(" ore")] flows = flows[flows["Unit"]=="kg"] return flows def get_energy_flows(fl): """ Subsets the flow list for all energy flows :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Unit"]=="MJ"] flows = flows[flows["Context"].str.startswith("resource")] return flows def get_metal_emission_flows(fl): """ Subsets the flow list for all emissions of metals :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Context"].str.startswith("emission")] #TODO Update with list of metals metals = ['Aluminum', 'Antimony'] flows = flows[flows['Flowable'].isin(metals)] return flows def get_hazardous_air_pollutant_flows(fl): """ Subsets the flow list for all HAP emissions based on list of flows from EPA. :param fl: df in standard flowlist format :return: df in standard flowlist format """ flows = fl[fl["Context"].str.startswith("emission/air")] haps = pd.read_csv(inputpath+'HAP_flows.csv', usecols=['Flowable']) # HAPs sourced from EPA via script write_HAP_flows.py # https://www.epa.gov/haps/initial-list-hazardous-air-pollutants-modifications flows = flows[flows['Flowable'].isin(haps.Flowable)] return flows

29.678322

82

0.653629

e2d0be367cd2dc8d71a655fb7a1df6038007fc3c

1,018

py

Python

intro/summary-exercises/examples/plot_cumulative_wind_speed_prediction.py

MarcvdSluys/scipy-lecture-notes

849762a741ba556a7765c9f2d90a10a7104dfccf

[ "CC-BY-4.0" ]

null

intro/summary-exercises/examples/plot_cumulative_wind_speed_prediction.py

MarcvdSluys/scipy-lecture-notes

849762a741ba556a7765c9f2d90a10a7104dfccf

[ "CC-BY-4.0" ]

null

intro/summary-exercises/examples/plot_cumulative_wind_speed_prediction.py

MarcvdSluys/scipy-lecture-notes

849762a741ba556a7765c9f2d90a10a7104dfccf

[ "CC-BY-4.0" ]

null

""" Cumulative wind speed prediction ================================ Generate the image cumulative-wind-speed-prediction.png for the interpolate section of scipy.rst. """ import numpy as np from scipy.interpolate import UnivariateSpline import matplotlib.pyplot as plt max_speeds = np.load('max-speeds.npy') years_nb = max_speeds.shape[0] cprob = (np.arange(years_nb, dtype=np.float32) + 1)/(years_nb + 1) sorted_max_speeds = np.sort(max_speeds) speed_spline = UnivariateSpline(cprob, sorted_max_speeds) nprob = np.linspace(0, 1, 100) fitted_max_speeds = speed_spline(nprob) fifty_prob = 1. - 0.02 fifty_wind = speed_spline(fifty_prob) plt.figure() plt.plot(sorted_max_speeds, cprob, 'o') plt.plot(fitted_max_speeds, nprob, 'g--') plt.plot([fifty_wind], [fifty_prob], 'o', ms=8., mfc='y', mec='y') plt.text(30, 0.05, '$V_{50} = %.2f \, m/s$' % fifty_wind) plt.plot([fifty_wind, fifty_wind], [plt.axis()[2], fifty_prob], 'k--') plt.xlabel('Annual wind speed maxima [$m/s$]') plt.ylabel('Cumulative probability')

30.848485

70

0.708251

6d90f549ff22982469410e82293d0fafafd0de6b

1,546

py

Python

djangoecommerce/catalog/models.py

roneysousa/djangoecommerce

9eacd2231ef2c6a54f57e0e2a6f1ffe04f5bfb4c

[ "MIT" ]

null

djangoecommerce/catalog/models.py

roneysousa/djangoecommerce

9eacd2231ef2c6a54f57e0e2a6f1ffe04f5bfb4c

[ "MIT" ]

null

djangoecommerce/catalog/models.py

roneysousa/djangoecommerce

9eacd2231ef2c6a54f57e0e2a6f1ffe04f5bfb4c

[ "MIT" ]

null

# coding=utf-8 from django.db import models from django.core.urlresolvers import reverse # Create your models here. class Category(models.Model): """docstring for """ name = models.CharField('Nome', max_length=100) slug = models.SlugField('Identificador', max_length=100) create = models.DateTimeField('Criado em', auto_now_add=True) modified = models.DateTimeField('Modificado em', auto_now=True) class Meta: verbose_name = 'Categoria' verbose_name_plural = 'Categorias' ordering = ['name'] def __str__(self): return self.name def get_absolute_url(self): return reverse('catalog:category', kwargs={'slug':self.slug}) class Product(models.Model): """docstring for """ name = models.CharField('Nome', max_length=100) slug = models.SlugField('Identificador', max_length=100) category = models.ForeignKey('catalog.Category', verbose_name='Categoria') description = models.TextField('Descrição', blank=True) price = models.DecimalField('Preço', decimal_places=4, max_digits=15) create = models.DateTimeField('Criado em', auto_now_add=True) modified = models.DateTimeField('Modificado em', auto_now=True) class Meta: verbose_name = 'Produto' verbose_name_plural = 'Produtos' ordering = ['name'] def __str__(self): return self.name def get_absolute_url(self): return reverse('catalog:product', kwargs={'slug':self.slug})

32.893617

82

0.65718

4335ca804db98a79312141632b21c119bd8f09b2

10,587

py

Python

intel-sds-proto/vsm_configure_guide/packages/vsmclient/python-vsmclient/build/lib.linux-x86_64-2.7/vsmclient/v1/vsms.py

opensds/proposals

03735f5e19203bdff698454f2633ca483c92129d

[ "Apache-2.0" ]

5

2017-03-21T09:11:55.000Z

2018-11-19T14:44:36.000Z

intel-sds-proto/vsm_configure_guide/packages/vsmclient/python-vsmclient/vsmclient/v1/vsms.py

opensds/proposals

03735f5e19203bdff698454f2633ca483c92129d

[ "Apache-2.0" ]

3

2018-02-06T06:17:10.000Z

2020-07-10T17:29:47.000Z

intel-sds-proto/vsm_configure_guide/packages/vsmclient/python-vsmclient/build/lib.linux-x86_64-2.7/vsmclient/v1/vsms.py

opensds/proposals

03735f5e19203bdff698454f2633ca483c92129d

[ "Apache-2.0" ]

7

2018-02-06T03:54:13.000Z

2021-09-08T10:51:38.000Z

# Copyright 2011 Denali Systems, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Volume interface (1.1 extension). """ import urllib from vsmclient import base class Volume(base.Resource): """A vsm is an extra block level storage to the OpenStack instances.""" def __repr__(self): try: return "<Volume: %s>" % self.id except AttributeError: return "<VSM: summary>" def delete(self): """Delete this vsm.""" self.manager.delete(self) def update(self, **kwargs): """Update the display_name or display_description for this vsm.""" self.manager.update(self, **kwargs) def attach(self, instance_uuid, mountpoint): """Set attachment metadata. :param instance_uuid: uuid of the attaching instance. :param mountpoint: mountpoint on the attaching instance. """ return self.manager.attach(self, instance_uuid, mountpoint) def detach(self): """Clear attachment metadata.""" return self.manager.detach(self) def reserve(self, vsm): """Reserve this vsm.""" return self.manager.reserve(self) def unreserve(self, vsm): """Unreserve this vsm.""" return self.manager.unreserve(self) def begin_detaching(self, vsm): """Begin detaching vsm.""" return self.manager.begin_detaching(self) def roll_detaching(self, vsm): """Roll detaching vsm.""" return self.manager.roll_detaching(self) def initialize_connection(self, vsm, connector): """Initialize a vsm connection. :param connector: connector dict from nova. """ return self.manager.initialize_connection(self, connector) def terminate_connection(self, vsm, connector): """Terminate a vsm connection. :param connector: connector dict from nova. """ return self.manager.terminate_connection(self, connector) def set_metadata(self, vsm, metadata): """Set or Append metadata to a vsm. :param type : The :class: `Volume` to set metadata on :param metadata: A dict of key/value pairs to set """ return self.manager.set_metadata(self, metadata) def upload_to_image(self, force, image_name, container_format, disk_format): """Upload a vsm to image service as an image.""" self.manager.upload_to_image(self, force, image_name, container_format, disk_format) def force_delete(self): """Delete the specified vsm ignoring its current state. :param vsm: The UUID of the vsm to force-delete. """ self.manager.force_delete(self) class VolumeManager(base.ManagerWithFind): """ Manage :class:`Volume` resources. """ resource_class = Volume def create(self, size, snapshot_id=None, source_volid=None, display_name=None, display_description=None, vsm_type=None, user_id=None, project_id=None, availability_zone=None, metadata=None, imageRef=None): """ Create a vsm. :param size: Size of vsm in GB :param snapshot_id: ID of the snapshot :param display_name: Name of the vsm :param display_description: Description of the vsm :param vsm_type: Type of vsm :rtype: :class:`Volume` :param user_id: User id derived from context :param project_id: Project id derived from context :param availability_zone: Availability Zone to use :param metadata: Optional metadata to set on vsm creation :param imageRef: reference to an image stored in glance :param source_volid: ID of source vsm to clone from """ if metadata is None: vsm_metadata = {} else: vsm_metadata = metadata body = {'vsm': {'size': size, 'snapshot_id': snapshot_id, 'display_name': display_name, 'display_description': display_description, 'vsm_type': vsm_type, 'user_id': user_id, 'project_id': project_id, 'availability_zone': availability_zone, 'status': "creating", 'attach_status': "detached", 'metadata': vsm_metadata, 'imageRef': imageRef, 'source_volid': source_volid, }} return self._create('/vsms', body, 'vsm') def get(self, vsm_id): """ Get a vsm. :param vsm_id: The ID of the vsm to delete. :rtype: :class:`Volume` """ return self._get("/vsms/%s" % vsm_id, "vsm") def list(self, detailed=True, search_opts=None): """ Get a list of all vsms. :rtype: list of :class:`Volume` """ print ' comes to list' if search_opts is None: search_opts = {} qparams = {} for opt, val in search_opts.iteritems(): if val: qparams[opt] = val query_string = "?%s" % urllib.urlencode(qparams) if qparams else "" detail = "" if detailed: detail = "/detail" ret = self._list("/conductor%s%s" % (detail, query_string), "conductor") return ret def delete(self, vsm): """ Delete a vsm. :param vsm: The :class:`Volume` to delete. """ self._delete("/vsms/%s" % base.getid(vsm)) def update(self, vsm, **kwargs): """ Update the display_name or display_description for a vsm. :param vsm: The :class:`Volume` to delete. """ if not kwargs: return body = {"vsm": kwargs} self._update("/vsms/%s" % base.getid(vsm), body) def _action(self, action, vsm, info=None, **kwargs): """ Perform a vsm "action." """ body = {action: info} self.run_hooks('modify_body_for_action', body, **kwargs) url = '/vsms/%s/action' % base.getid(vsm) return self.api.client.post(url, body=body) def host_status(self, req=None): """ Perform a vsm "action." """ body = {'request': req} url = '/conductor/host_status' return self.api.client.post(url, body=body) def create_storage_pool(self, body): """ create a storage pool """ url = '/storage_pool/create' return self.api.client.post(url, body=body) def get_storage_group_list(self): url = '/storage_pool/get_storage_group_list' return self.api.client.get(url) def get_pool_size_list(self): url = '/storage_pool/get_pool_size_list' return self.api.client.get(url) def list_storage_pool(self, req=None, search_opts=None): """ Perform a vsm "action." """ if search_opts is None: search_opts = {} qparams = {} for opt, val in search_opts.iteritems(): if val: qparams[opt] = val query_string = "?%s" % urllib.urlencode(qparams) if qparams else "" #body = {'request': req} url = "/storage_pool/list_storage_pool%s" % (query_string) return self.api.client.get(url) #serer_api def get_server_list(self, req=None): """ host list """ url = "/cluster/servers" return self.api.client.get(url) def add_servers(self, req=None, opts=None): """ add servers """ url = "/cluster/servers/add" return self.api.client.post(url, body=opts) def remove_servers(request, opts=None): """ remove servers """ url = "/cluster/servers/del" return self.api.client.post(url, body=opts) #zone_api def get_zone_list(self, req=None): """ get zone list """ url = "/cluster/zones" return self.api.client.get(url) def create_zone(self, req=None, opts=None): """ create a zone """ url = "/cluster/zones/add" return self.api.client.post(url, body=opts) #cluster list def get_cluster_list(self, req=None): """ get cluster list """ url = "/clusters" return self.api.client.get(url) def create_cluster(self, req=None, opts=None): """ create cluster """ url = "/clusters" return self.api.client.post(url, body=opts) def resource_info(self, req=None): """ Perform a vsm "action." """ body = {'request': req} url = '/conductor/resource_info' return self.api.client.post(url, body=body) def asm_settings(self, req=None): """ Perform a vsm "action." """ body = {'request': req} url = '/conductor/asm_settings' return self.api.client.post(url, body=body) def initialize_connection(self, vsm, connector): """ Initialize a vsm connection. :param vsm: The :class:`Volume` (or its ID). :param connector: connector dict from nova. """ return self._action('os-initialize_connection', vsm, {'connector': connector})[1]['connection_info'] def terminate_connection(self, vsm, connector): """ Terminate a vsm connection. :param vsm: The :class:`Volume` (or its ID). :param connector: connector dict from nova. """ self._action('os-terminate_connection', vsm, {'connector': connector}) def summary(self): """ summary """ url = "/vsms/summary" return self._get(url, 'vsm-summary')

29.738764

79

0.565788

dc45636a5042cba997a059f022aaace695498a5f

1,843

py

Python

thefuck/rules/cd_correction.py

HiteshMah-Jan/thefuck

132c62262246824470934c2c6f46919ef6f00203

[ "MIT" ]

75,504

2015-04-08T18:22:19.000Z

2022-03-31T23:59:52.000Z

thefuck/rules/cd_correction.py

HiteshMah-Jan/thefuck

132c62262246824470934c2c6f46919ef6f00203

[ "MIT" ]

1,160

2015-04-17T18:47:12.000Z

2022-03-30T20:42:26.000Z

thefuck/rules/cd_correction.py

HiteshMah-Jan/thefuck

132c62262246824470934c2c6f46919ef6f00203

[ "MIT" ]

4,399

2015-04-17T18:36:04.000Z

2022-03-31T07:01:03.000Z

"""Attempts to spellcheck and correct failed cd commands""" import os import six from thefuck.specific.sudo import sudo_support from thefuck.rules import cd_mkdir from thefuck.utils import for_app, get_close_matches __author__ = "mmussomele" MAX_ALLOWED_DIFF = 0.6 def _get_sub_dirs(parent): """Returns a list of the child directories of the given parent directory""" return [child for child in os.listdir(parent) if os.path.isdir(os.path.join(parent, child))] @sudo_support @for_app('cd') def match(command): """Match function copied from cd_mkdir.py""" return ( command.script.startswith('cd ') and any(( 'no such file or directory' in command.output.lower(), 'cd: can\'t cd to' in command.output.lower(), 'does not exist' in command.output.lower() ))) @sudo_support def get_new_command(command): """ Attempt to rebuild the path string by spellchecking the directories. If it fails (i.e. no directories are a close enough match), then it defaults to the rules of cd_mkdir. Change sensitivity by changing MAX_ALLOWED_DIFF. Default value is 0.6 """ dest = command.script_parts[1].split(os.sep) if dest[-1] == '': dest = dest[:-1] if dest[0] == '': cwd = os.sep dest = dest[1:] elif six.PY2: cwd = os.getcwdu() else: cwd = os.getcwd() for directory in dest: if directory == ".": continue elif directory == "..": cwd = os.path.split(cwd)[0] continue best_matches = get_close_matches(directory, _get_sub_dirs(cwd), cutoff=MAX_ALLOWED_DIFF) if best_matches: cwd = os.path.join(cwd, best_matches[0]) else: return cd_mkdir.get_new_command(command) return u'cd "{0}"'.format(cwd)

29.725806

96

0.637005