id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
31,337 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _enhance_level_to_arg(level, _hparams):
# range [0.1, 1.9]
return (level / _MAX_LEVEL) * 1.8 + 0.1, | null |
31,338 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _enhance_increasing_level_to_arg(level, _hparams):
# the ... | null |
31,339 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _shear_level_to_arg(level, _hparams):
# range [-0.3, 0.3]... | null |
31,340 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _translate_abs_level_to_arg(level, hparams):
translate_co... | null |
31,341 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _randomly_negate(v):
"""With 50% prob, negate the value"""
return -v if random.random() > 0.5 else v
def _translate_rel_level_to_arg(level, hparams):
# default ra... | null |
31,342 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
def _posterize_level_to_arg(level, _hparams):
# As per Tensorflow TPU EfficientNet impl
# range [0, 4], 'keep 0 up to 4 MSB of original image'
# intensity/severity of augmentation decr... | null |
31,343 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _posterize_original_level_to_arg(level, _hparams):
# As per original AutoAugment paper description
# range [4, 8], 'keep 4 up to 8 MSB of image'
# intensity/sever... | null |
31,344 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
def _solarize_level_to_arg(level, _hparams):
# range [0, 256]
# intensity/severity of augmentation decreases with level
return int((level / _MAX_LEVEL) * 256),
def _solarize_increasin... | null |
31,345 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
def _solarize_add_level_to_arg(level, _hparams):
# range [0, 110]
return int((level / _MAX_LEVEL) * 110), | null |
31,346 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
def auto_augment_policy(name='v0', hparams=None):
hparams = hparams or _HPARAMS_DEFAULT
if name == 'original':
return auto_augment_policy_original(hparams)
elif name == 'origin... | Create a AutoAugment transform :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr'). The remaining sections, not order sepecific determine 'mstd' - fl... |
31,347 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
_MAX_LEVEL = 10.
_RAND_TRANSFORMS = [
'AutoContrast',
'Equalize',
'Invert',
'Rotate',
'Posterize',
'Solarize',
'SolarizeAdd',
'Color',
'Contrast',
'Brightne... | Create a RandAugment transform :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order sepecific determine 'm' - integer mag... |
31,348 | import random
import math
import re
from PIL import Image, ImageOps, ImageEnhance, ImageChops
import PIL
import numpy as np
def augmix_ops(magnitude=10, hparams=None, transforms=None):
hparams = hparams or _HPARAMS_DEFAULT
transforms = transforms or _AUGMIX_TRANSFORMS
return [AugmentOp(
name, prob=1... | Create AugMix PyTorch transform :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining sections, not order sepecific determine 'm' - integer ma... |
31,349 | import os
import contextlib
import copy
import numpy as np
import paddle
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def convert_to_xywh(boxes):
xmin, ymin, xmax, ymax = boxes.unbind(1)
return paddle.stack((xmin, ... | null |
31,350 | import os
import contextlib
import copy
import numpy as np
import paddle
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def merge(img_ids, eval_imgs):
def create_common_coco_eval(coco_eval, img_ids, eval_imgs):
img_ids, ... | null |
31,351 | import os
import contextlib
import copy
import numpy as np
import paddle
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
The provided code snippet includes necessary dependencies for implementing the `evaluate` function. Writ... | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None |
31,352 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
The provided code snippet includes necessary d... | crop image and target with region Args: image: np.array target: label dict contains labels, boxes, or masks fields, see coco.py for details regtion: list, crop region [top, left, height, width] Returns: cropped_image: cropped image target: corresponding targets |
31,353 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
The provided code snippet includes necessary d... | horizontal flip image and corresponding labels |
31,354 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def resize(image, target, size, max_size=None)... | null |
31,355 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def pad(image, target, padding):
padded_im... | null |
31,356 | import copy
import pickle
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.optimizer.lr import LRScheduler
The provided code snippet includes necessary dependencies for implementing the `reduce_dict` function. Write a Python function `def reduce_dict(input_dict, average=True)` to solve th... | Impl all_reduce for dict of tensors in DDP |
31,357 | import copy
import pickle
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.optimizer.lr import LRScheduler
def accuracy(output, target, topk=(1,)):
if target.numel() == 0:
return [paddle.zeros([])]
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk... | null |
31,358 | import copy
import pickle
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.optimizer.lr import LRScheduler
The provided code snippet includes necessary dependencies for implementing the `all_gather` function. Write a Python function `def all_gather(data)` to solve the following problem:
r... | run all_gather on any picklable data (do not requires tensors) Args: data: picklable object Returns: data_list: list of data gathered from each rank |
31,359 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
def print_model_named_params(model):
for name, param in model.named_par... | null |
31,360 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
def print_model_named_buffers(model):
for name, buff in model.named_buf... | null |
31,361 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
def torch_to_paddle_mapping():
map1 = torch_to_paddle_mapping_backbone()... | null |
31,362 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
class NestedTensor():
"""Each NestedTensor has .tensor and .mask attrib... | null |
31,363 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
class NestedTensor():
"""Each NestedTensor has .tensor and .mask attrib... | null |
31,366 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
def torch_to_paddle_mapping():
def convert(torch_model, paddle_model):
... | null |
31,367 | import sys
from misc import NestedTensor as ThNestedTensor
import os
import argparse
import numpy as np
import paddle
import torch
from detr import build_detr
from utils import NestedTensor
from config import get_config
import misc as th_utils
class NestedTensor():
def __init__(self, tensors, mask):
def deco... | null |
31,369 | from scipy.optimize import linear_sum_assignment
from scipy.spatial import distance
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from box_ops import box_cxcywh_to_xyxy
from box_ops import generalized_box_iou
def cdist_p1(x, y):
# x: [batch * num_queries, 4]
# y: [batch * num_boxes, 4]
... | null |
31,370 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.distributed as dist
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from utils import Avera... | return arguments, this will overwrite the config after loading yaml file |
31,371 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.distributed as dist
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from utils import Avera... | null |
31,372 | import os
import numpy as np
from PIL import Image
import paddle
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
import transforms as T
from utils import collate_fn
The provided code snippet includes necessary dependencies for implementing the `convert_coco_poly_to_mask` function. Write a P... | Convert coco anno from polygons to image masks |
31,373 | import os
import numpy as np
from PIL import Image
import paddle
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
import transforms as T
from utils import collate_fn
class CocoDetection(paddle.io.Dataset):
""" COCO Detection dataset
This class gets images and annotations for paddle tr... | Return CocoDetection dataset according to image_set: ['train', 'val'] |
31,374 | import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `box_xyxy_to_cxcywh_numpy` function. Write a Python function `def box_xyxy_to_cxcywh_numpy(box)` to solve the following problem:
convert box from top-left/bottom-right format: [x0, y0, x1, y1] to center-size... | convert box from top-left/bottom-right format: [x0, y0, x1, y1] to center-size format: [center_x, center_y, width, height] Args: box: numpy array, last_dim=4, stop-left/bottom-right format boxes Return: numpy array, last_dim=4, center-size format boxes |
31,375 | import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `box_cxcywh_to_xyxy` function. Write a Python function `def box_cxcywh_to_xyxy(box)` to solve the following problem:
convert box from center-size format: [center_x, center_y, width, height] to top-left/botto... | convert box from center-size format: [center_x, center_y, width, height] to top-left/bottom-right format: [x0, y0, x1, y1] Args: box: paddle.Tensor, last_dim=4, stores center-size format boxes Return: paddle.Tensor, last_dim=4, top-left/bottom-right format boxes |
31,376 | import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `box_xyxy_to_cxcywh` function. Write a Python function `def box_xyxy_to_cxcywh(box)` to solve the following problem:
convert box from top-left/bottom-right format: [x0, y0, x1, y1] to center-size format: [ce... | convert box from top-left/bottom-right format: [x0, y0, x1, y1] to center-size format: [center_x, center_y, width, height] Args: box: paddle.Tensor, last_dim=4, stop-left/bottom-right format boxes Return: paddle.Tensor, last_dim=4, center-size format boxes |
31,377 | import numpy as np
import paddle
def box_iou(boxes1, boxes2):
"""compute iou of 2 sets of boxes in (x1, y1, x2, y2) format
This method returns the iou between every pair of boxes
in two sets of boxes.
Args:
boxes1: paddle.Tensor, shape=N x 4, boxes are stored in (x1, y1, x2, y2) format
... | Compute GIoU of each pais in boxes1 and boxes2 GIoU = IoU - |A_c - U| / |A_c| where A_c is the smallest convex hull that encloses both boxes, U is the union of boxes Details illustrations can be found in https://giou.stanford.edu/ Args: boxes1: paddle.Tensor, shape=N x 4, boxes are stored in (x1, y1, x2, y2) format box... |
31,378 | import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `masks_to_boxes` function. Write a Python function `def masks_to_boxes(masks)` to solve the following problem:
convert masks to bboxes Args: masks: paddle.Tensor, NxHxW Return: boxes: paddle.Tensor, Nx4
Her... | convert masks to bboxes Args: masks: paddle.Tensor, NxHxW Return: boxes: paddle.Tensor, Nx4 |
31,379 | import os
import paddle
from paddle.io import Dataset, DataLoader
from paddle.vision import transforms, datasets, image_load, set_image_backend
import numpy as np
import argparse
from PIL import Image
import cv2
from config import *
class ImageNet1MDataset(Dataset):
def __init__(self, file_folder, mode="train", tra... | null |
31,380 | import os
import paddle
from paddle.io import Dataset, DataLoader
from paddle.vision import transforms, datasets, image_load, set_image_backend
import numpy as np
import argparse
from PIL import Image
import cv2
from config import *
def get_loader(config, dataset_train, dataset_test=None, multi=False):
# multigpu ... | null |
31,381 | import os
from yacs.config import CfgNode as CN
import yaml
def _update_config_from_file(config, cfg_file):
config.defrost()
with open(cfg_file, 'r') as infile:
yaml_cfg = yaml.load(infile, Loader=yaml.FullLoader)
for cfg in yaml_cfg.setdefault('BASE', ['']):
if cfg:
_update_conf... | Update config by ArgumentParser Args: args: ArgumentParser contains options Return: config: updated config |
31,382 | import os
from yacs.config import CfgNode as CN
import yaml
_C = CN()
_C.BASE = ['']
_C.DATA = CN()
_C.DATA.BATCH_SIZE = 256
_C.DATA.BATCH_SIZE_EVAL = 8
_C.DATA.DATA_PATH = '/dataset/coco/'
_C.DATA.DATASET = 'coco'
_C.DATA.NUM_WORKERS = 2
_C.DATA.IMAGENET_MEAN = [0.485, 0.456, 0.406]
_C.DATA.IMAGENET_STD = [0.229, 0.22... | Return a clone of config or load from yaml file |
31,383 | import paddle
import paddle.nn as nn
import paddle.nn.functional as F
def dice_loss(inputs, targets, num_boxes):
inputs = F.sigmoid(inputs)
inputs = inputs.flatten(1)
numerator = 2 * (inputs * targets).sum(1)
denominaror = inputs.sum(-1) + target.sum(-1)
loss = 1 - (numerator + 1) / (denominator +1... | null |
31,384 | import paddle
import paddle.nn as nn
import paddle.nn.functional as F
def sigmoid_focal_loss(inputs, targets, num_boxes, alpha=.25, gamma=2.):
prob = F.sigmoid(inputs)
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
p_t = prob * targets + (1 - prob) * (1 - targets)
loss ... | null |
31,385 | import numpy as np
import paddle
import paddle.nn.Functional as F
def one_hot(x, num_classes, on_value=1., off_value=0.):
one_hot = F.one_hot(x, num_classes)
return paddle.scatter_(paddle.full((x.shape[0], num_classes), off_value), x, on_value)
def mixup_target(target, num_classes, lam=1., smoothing=0.0, devic... | null |
31,386 | import numpy as np
import paddle
import paddle.nn.Functional as F
def rand_bbox(img_shape, lam, margin=0., count=None):
""" Standard CutMix bounding-box
Generates a random square bbox based on lambda value. This impl includes
support for enforcing a border margin as percent of bbox dimensions.
Args:
... | Generate bbox and apply lambda correction. |
31,387 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from config import get_config
from config import update_c... | return arguments, this will overwrite the config after loading yaml file |
31,388 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from config import get_config
from config import update_c... | set logging file and format Args: filename: str, full path of the logger file to write logger_name: str, the logger name, e.g., 'master_logger', 'local_logger' Return: logger: python logger |
31,389 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from config import get_config
from config import update_c... | Training for one epoch Args: dataloader: paddle.io.DataLoader, dataloader instance model: nn.Layer, DETR model criterion: criterion defined in DETR postprocessors: PostProcess, converts output to the coco format base_ds: coco api instance for generate CocoEvaluator, pycocotools.coco.COCO(anno_file) optimizer: nn.optimi... |
31,390 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from config import get_config
from config import update_c... | Validate for whole dataset Args: dataloader: paddle.io.DataLoader, dataloader instance model: nn.Layer, DETR model criterion: criterion defined in DETR postprocessors: PostProcess, converts output to the coco format base_ds: coco api instance for generate CocoEvaluator, pycocotools.coco.COCO(anno_file) total_batch: int... |
31,391 | import random
import math
import paddle
def _get_pixels(per_pixel, rand_color, patch_size, dtype="float32"):
if per_pixel:
return paddle.normal(shape=patch_size).astype(dtype)
elif rand_color:
return paddle.normal(shape=(patch_size[0], 1, 1)).astype(dtype)
else:
return paddle.zeros(... | null |
31,392 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
def init_weights(lr):
weight_attr = paddle.ParamAttr(learning_rate=lr)
bias_attr = paddle.ParamAttr(learning_rate=lr)
return weight_attr, bias_attr | null |
31,393 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
... | null |
31,394 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
... | null |
31,395 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
class BottleneckBlock(nn.Layer):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
... | null |
31,396 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
class BottleneckBlock(nn.Layer):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
... | null |
31,397 | from functools import partial
import paddle
import paddle.nn as nn
from paddle.utils.download import get_weights_path_from_url
class BottleneckBlock(nn.Layer):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
... | null |
31,398 | import os
import contextlib
import copy
import numpy as np
import paddle
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def convert_to_xywh(boxes):
#xmin, ymin, xmax, ymax = boxes.unbind(1)
#return paddle.stack((xmin... | null |
31,399 | import os
import contextlib
import copy
import numpy as np
import paddle
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def merge(img_ids, eval_imgs):
#all_img_ids = [img_ids]
#all_eval_imgs = [eval_imgs]
all_img_... | null |
31,401 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def crop(image, target, region):
cropped_i... | null |
31,402 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def hflip(image, target):
flipped_image = ... | null |
31,403 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def resize(image, target, size, max_size=None)... | null |
31,404 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def pad(image, target, padding=None, size_divi... | null |
31,405 | import copy
import pickle
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.optimizer.lr import LRScheduler
def _max_by_axis(the_list):
maxes = the_list[0]
for sublist in the_list[1:]:
for idx, item in enumerate(sublist):
maxes[idx] = max(maxes[idx], item)
re... | make the batch handle different image sizes This method take a list of tensors with different sizes, then max size is selected as the final batch size, smaller samples are padded with zeros(bottom-right), and corresponding masks are generated. |
31,409 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.distributed as dist
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from pvtv2_det import b... | null |
31,410 | import os
import copy
import numpy as np
from PIL import Image
import paddle
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
import transforms as T
from utils import nested_tensor_from_tensor_list
The provided code snippet includes necessary dependencies for implementing the `convert_coco_p... | Convert coco anno from polygons to image masks |
31,411 | import os
import copy
import numpy as np
from PIL import Image
import paddle
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
import transforms as T
from utils import nested_tensor_from_tensor_list
class CocoDetection(paddle.io.Dataset):
""" COCO Detection dataset
This class gets imag... | Return CocoDetection dataset according to image_set: ['train', 'val'] |
31,412 | import os
import copy
import numpy as np
from PIL import Image
import paddle
from pycocotools.coco import COCO
from pycocotools import mask as coco_mask
import transforms as T
from utils import nested_tensor_from_tensor_list
def collate_fn(batch):
"""Collate function for batching samples
Samples varies in sizes... | return dataloader on train/val set for single/multi gpu Arguments: dataset: paddle.io.Dataset, coco dataset batch_size: int, num of samples in one batch mode: str, ['train', 'val'], dataset to use multi_gpu: bool, if True, DistributedBatchSampler is used for DDP |
31,417 | import numpy as np
import paddle
The provided code snippet includes necessary dependencies for implementing the `masks_to_boxes` function. Write a Python function `def masks_to_boxes(masks)` to solve the following problem:
convert masks to bboxes Args: masks: paddle.Tensor, NxHxW Return: boxes: paddle.Tensor, Nx4
Her... | convert masks to bboxes Args: masks: paddle.Tensor, NxHxW Return: boxes: paddle.Tensor, Nx4 |
31,418 | import os
from yacs.config import CfgNode as CN
import yaml
def _update_config_from_file(config, cfg_file):
config.defrost()
with open(cfg_file, 'r') as infile:
yaml_cfg = yaml.load(infile, Loader=yaml.FullLoader)
for cfg in yaml_cfg.setdefault('BASE', ['']):
if cfg:
_update_conf... | Update config by ArgumentParser Args: args: ArgumentParser contains options Return: config: updated config |
31,419 | import os
from yacs.config import CfgNode as CN
import yaml
_C = CN()
_C.BASE = ['']
_C.DATA = CN()
_C.DATA.BATCH_SIZE = 8
_C.DATA.BATCH_SIZE_EVAL = 1
_C.DATA.WEIGHT_PATH = './weights/pvtv2_b0_maskrcnn.pdparams'
_C.DATA.VAL_DATA_PATH = "/dataset/coco/"
_C.DATA.DATASET = 'coco'
_C.DATA.IMAGE_SIZE = 640
_C.DATA.CROP_PCT ... | Return a clone config or load from yaml file |
31,420 | import math
import paddle
import paddle.nn as nn
from paddle.nn.initializer import Normal, Constant
from retinanet_loss import RetinaNetLoss
from post_process import RetinaNetPostProcess
from det_utils.generator_utils import AnchorGenerator
def transpose_to_bs_hwa_k(tensor, k):
assert tensor.dim() == 4
bs, _, ... | null |
31,421 | import paddle
from .box_utils import boxes_iou, bbox2delta
def anchor_target_matcher(match_quality_matrix,
positive_thresh,
negative_thresh,
allow_low_quality_matches,
low_thresh = -float("inf")):
'''
This c... | Args: anchors (tensor): shape [-1, 4] the sum of muti-level anchors. gt_boxes (list): gt_boxes[i] is the i-th img's gt_boxes. positive_thresh (float): the positive class threshold of iou between anchors and gt. negative_thresh (float): the negative class threshold of iou between anchors and gt. batch_size_per_image (in... |
31,422 | import paddle
from .box_utils import boxes_iou, bbox2delta
def anchor_target_matcher(match_quality_matrix,
positive_thresh,
negative_thresh,
allow_low_quality_matches,
low_thresh = -float("inf")):
'''
This c... | It performs box matching between "roi" and "target",and assigns training labels to the proposals. Args: proposals (list[tensor]): the batch RoIs from rpn_head. gt_boxes (list[tensor]): gt_boxes[i] is the i'th img's gt_boxes. gt_classes (list[tensor]): gt_classes[i] is the i'th img's gt_classes. num_classes (int): the n... |
31,423 | import math
import paddle
import paddle.nn as nn
from paddle.fluid.framework import Variable, in_dygraph_mode
from paddle.fluid import core
The provided code snippet includes necessary dependencies for implementing the `generate_proposals` function. Write a Python function `def generate_proposals(scores, ... | **Generate proposal Faster-RCNN** This operation proposes RoIs according to each box with their probability to be a foreground object and the box can be calculated by anchors. Bbox_deltais and scores to be an object are the output of RPN. Final proposals could be used to train detection net. For generating proposals, t... |
31,424 | import math
import paddle
import paddle.nn as nn
from paddle.fluid.framework import Variable, in_dygraph_mode
from paddle.fluid import core
The provided code snippet includes necessary dependencies for implementing the `roi_align` function. Write a Python function `def roi_align(input, rois, ... | Region of interest align (also known as RoI align) is to perform bilinear interpolation on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7). Args: input (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The... |
31,425 | import math
import paddle
import paddle.nn as nn
from paddle.fluid.framework import Variable, in_dygraph_mode
from paddle.fluid import core
The provided code snippet includes necessary dependencies for implementing the `distribute_fpn_proposals` function. Write a Python function `def distribute_fpn_proposals(fpn_rois,... | **This op only takes LoDTensor as input.** In Feature Pyramid Networks (FPN) models, it is needed to distribute all proposals into different FPN level, with respect to scale of the proposals, the referring scale and the referring level. Besides, to restore the order of proposals, we return an array which indicates the ... |
31,426 | import math
import paddle
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
The provided code snippet includes necessary dependencies for implementing the `delta2bbox` function. Write a Python function `def delta2bbox(deltas, boxes, weigh... | The inverse process of bbox2delta. |
31,427 | import math
import paddle
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
def boxes_area(boxes):
'''
Compute boxes area.
Args:
boxes (tensor): shape [M, 4] | [N, M, 4].
Returns:
areas (tensor): shape [M] ... | Compute the ious of two boxes tensor and the coordinate format of boxes is xyxy. Args: boxes1 (tensor): when mode == 'a': shape [N, M, 4]; when mode == 'b': shape [N, M, 4] boxes2 (tensor): when mode == 'a': shape [N, R, 4]; when mode == 'b': shape [N, M, 4] mode (string | 'a' or 'b'): when mode == 'a': compute one to ... |
31,428 | import math
import paddle
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
def nonempty_bbox(boxes, min_size=0, return_mask=False):
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
mask = paddle.logical_and(h > min... | null |
31,429 | import math
import paddle
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
The provided code snippet includes necessary dependencies for implementing the `multiclass_nms` function. Write a Python function `def multiclass_nms(bboxes, ... | This operator is to do multi-class non maximum suppression (NMS) on boxes and scores. In the NMS step, this operator greedily selects a subset of detection bounding boxes that have high scores larger than score_threshold, if providing this threshold, then selects the largest nms_top_k confidences scores if nms_top_k is... |
31,430 | from itertools import repeat
import collections.abc
import numpy as np
import paddle
import paddle.nn as nn
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable):
return x
return tuple(repeat(x, n))
return parse | null |
31,431 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.distributed as dist
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from pvtv2_det import b... | Training for one epoch Args: dataloader: paddle.io.DataLoader, dataloader instance model: nn.Layer, DETR model criterion: nn.Layer postprocessors: nn.Layer base_ds: coco api instance train_loss_rpn_cls_meter.avg epoch: int, current epoch total_epoch: int, total num of epoch, for logging debug_steps: int, num of iters t... |
31,432 | import sys
import os
import time
import logging
import argparse
import random
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.distributed as dist
from coco import build_coco
from coco import get_dataloader
from coco_eval import CocoEvaluator
from pvtv2_det import b... | Validation for whole dataset Args: dataloader: paddle.io.DataLoader, dataloader instance model: nn.Layer, a ViT model criterion: criterion postprocessors: postprocessor for generating bboxes base_ds: COCO instance total_epoch: int, total num of epoch, for logging debug_steps: int, num of iters to log info Returns: val_... |
31,434 | import copy
import paddle
import paddle.nn as nn
from model_utils import DropPath
class PyramidVisionTransformerV2(nn.Layer):
"""PyramidVisionTransformerV2 class
Attributes:
patch_size: int, size of patch
image_size: int, size of image
num_classes: int, num of image classes
in_ch... | null |
31,435 | import paddle
import paddle.nn as nn
from config import get_config
from pvtv2_backbone import build_pvtv2
from det_necks.fpn import FPN, LastLevelMaxPool
from det_heads.maskrcnn_head.rpn_head import RPNHead
from det_heads.maskrcnn_head.roi_head import RoIHead
class PVTv2Det(nn.Layer):
def __init__(self, config):
... | null |
31,436 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
def print_model_named_params(mod... | null |
31,437 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
def print_model_named_buffers(mo... | null |
31,438 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
def torch_to_paddle_mapping():
... | null |
31,439 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
class NestedTensor():
"""Eac... | null |
31,440 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
class NestedTensor():
"""Eac... | null |
31,465 | import sys
import os
import argparse
import numpy as np
import paddle
import torch
from config import get_config
from pvtv2_det import build_pvtv2_det
from model_utils import DropPath
from utils import NestedTensor
from misc import NestedTensor as ThNestedTensor
import misc as th_utils
class NestedTensor():
def _... | null |
31,472 | import paddle
from .box_utils import boxes_iou, bbox2delta
def anchor_target_matcher(match_quality_matrix,
positive_thresh,
negative_thresh,
allow_low_quality_matches,
low_thresh = -float("inf")):
'''
This c... | It performs box matching between "roi" and "target",and assigns training labels to the proposals. Args: proposals (list[tensor]): the batch RoIs from rpn_head. gt_boxes (list[tensor]): gt_boxes[i] is the i'th img's gt_boxes. gt_classes (list[tensor]): gt_classes[i] is the i'th img's gt_classes. num_classes (int): the n... |
31,481 | import os
import contextlib
import copy
import numpy as np
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def convert_to_xywh(boxes):
#xmin, ymin, xmax, ymax = boxes.unbind(1)
#return paddle.stack((xmin, ymin, xmax -... | null |
31,482 | import os
import contextlib
import copy
import numpy as np
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
def merge(img_ids, eval_imgs):
#all_img_ids = [img_ids]
#all_eval_imgs = [eval_imgs]
all_img_ids = all_gath... | null |
31,483 | import os
import contextlib
import copy
import numpy as np
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from utils import all_gather
The provided code snippet includes necessary dependencies for implementing the `evaluate` function. Write a Python fun... | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None |
31,486 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def resize(image, target, size, max_size=None)... | null |
31,487 | import random
import numpy as np
import PIL
import paddle
import paddle.vision.transforms as T
from paddle.vision.transforms import functional as F
from random_erasing import RandomErasing
from box_ops import box_xyxy_to_cxcywh
from box_ops import box_xyxy_to_cxcywh_numpy
def pad(image, target, padding):
padded_im... | null |
31,492 | import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from model_utils import DropPath, _ntuple
The provided code snippet includes necessary dependencies for implementing the `windows_partition` function. Write a Python function `def windows_partition(x, window_size)` to solve the fo... | partite windows into window_size x window_size Args: x: Tensor, shape=[b, h, w, c] window_size: int, window size Returns: x: Tensor, shape=[num_windows*b, window_size, window_size, c] |
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