repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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SASA | SASA-main/pcdet/models/roi_heads/pvrcnn_head.py | import torch.nn as nn
from ...ops.pointnet2.pointnet2_stack import pointnet2_modules as pointnet2_stack_modules
from ...utils import common_utils
from .roi_head_template import RoIHeadTemplate
class PVRCNNHead(RoIHeadTemplate):
def __init__(self, input_channels, model_cfg, num_class=1):
super().__init__(... | 7,628 | 40.688525 | 116 | py |
SASA | SASA-main/pcdet/models/roi_heads/pointrcnn_head.py | import torch
import torch.nn as nn
from ...ops.pointnet2.pointnet2_batch import pointnet2_modules
from ...ops.roipoint_pool3d import roipoint_pool3d_utils
from ...utils import common_utils
from .roi_head_template import RoIHeadTemplate
class PointRCNNHead(RoIHeadTemplate):
def __init__(self, input_channels, mode... | 7,866 | 42.705556 | 116 | py |
SASA | SASA-main/pcdet/models/roi_heads/target_assigner/proposal_target_layer.py | import numpy as np
import torch
import torch.nn as nn
from ....ops.iou3d_nms import iou3d_nms_utils
class ProposalTargetLayer(nn.Module):
def __init__(self, roi_sampler_cfg):
super().__init__()
self.roi_sampler_cfg = roi_sampler_cfg
def forward(self, batch_dict):
"""
Args:
... | 9,946 | 42.436681 | 117 | py |
SASA | SASA-main/pcdet/models/model_utils/model_nms_utils.py | import torch
from ...ops.iou3d_nms import iou3d_nms_utils
def class_agnostic_nms(box_scores, box_preds, nms_config, score_thresh=None):
src_box_scores = box_scores
if score_thresh is not None:
scores_mask = (box_scores >= score_thresh)
box_scores = box_scores[scores_mask]
box_preds = ... | 2,419 | 35.666667 | 116 | py |
SASA | SASA-main/pcdet/models/backbones_2d/base_bev_backbone.py | import numpy as np
import torch
import torch.nn as nn
class BaseBEVBackbone(nn.Module):
def __init__(self, model_cfg, input_channels):
super().__init__()
self.model_cfg = model_cfg
if self.model_cfg.get('LAYER_NUMS', None) is not None:
assert len(self.model_cfg.LAYER_NUMS) == ... | 4,318 | 37.221239 | 121 | py |
SASA | SASA-main/pcdet/models/backbones_2d/__init__.py | from .base_bev_backbone import BaseBEVBackbone
__all__ = {
'BaseBEVBackbone': BaseBEVBackbone
}
| 101 | 16 | 46 | py |
SASA | SASA-main/pcdet/models/backbones_2d/map_to_bev/pointpillar_scatter.py | import torch
import torch.nn as nn
class PointPillarScatter(nn.Module):
def __init__(self, model_cfg, grid_size, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
self.nx, self.ny, self.nz = grid_size
assert se... | 1,545 | 39.684211 | 123 | py |
SASA | SASA-main/pcdet/models/backbones_2d/map_to_bev/__init__.py | from .height_compression import HeightCompression
from .pointpillar_scatter import PointPillarScatter
__all__ = {
'HeightCompression': HeightCompression,
'PointPillarScatter': PointPillarScatter
}
| 206 | 24.875 | 51 | py |
SASA | SASA-main/pcdet/models/backbones_2d/map_to_bev/height_compression.py | import torch.nn as nn
class HeightCompression(nn.Module):
def __init__(self, model_cfg, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
def forward(self, batch_dict):
"""
Args:
batch_dict:
... | 870 | 31.259259 | 90 | py |
SASA | SASA-main/pcdet/datasets/dataset.py | from collections import defaultdict
from pathlib import Path
import numpy as np
import torch.utils.data as torch_data
from ..utils import common_utils
from .augmentor.data_augmentor import DataAugmentor
from .processor.data_processor import DataProcessor
from .processor.point_feature_encoder import PointFeatureEncode... | 6,966 | 37.071038 | 118 | py |
SASA | SASA-main/pcdet/datasets/__init__.py | import torch
from torch.utils.data import DataLoader
from torch.utils.data import DistributedSampler as _DistributedSampler
from pcdet.utils import common_utils
from .dataset import DatasetTemplate
from .kitti.kitti_dataset import KittiDataset
from .nuscenes.nuscenes_dataset import NuScenesDataset
from .waymo.waymo_d... | 2,440 | 32.438356 | 101 | py |
SASA | SASA-main/pcdet/datasets/waymo/waymo_utils.py | # OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import numpy as np
from ...utils import common_utils
import tensorflow as tf
from waymo_open_da... | 9,957 | 42.484716 | 114 | py |
SASA | SASA-main/pcdet/datasets/waymo/waymo_dataset.py | # OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import os
import pickle
import copy
import numpy as np
import torch
import multiprocessing
from tqdm import tqdm
from pa... | 15,837 | 41.461126 | 127 | py |
SASA | SASA-main/pcdet/datasets/waymo/waymo_eval.py | # OpenPCDet PyTorch Dataloader and Evaluation Tools for Waymo Open Dataset
# Reference https://github.com/open-mmlab/OpenPCDet
# Written by Shaoshuai Shi, Chaoxu Guo
# All Rights Reserved 2019-2020.
import numpy as np
import pickle
import tensorflow as tf
from google.protobuf import text_format
from waymo_open_datase... | 10,488 | 41.465587 | 116 | py |
SASA | SASA-main/pcdet/datasets/processor/point_feature_encoder.py | import numpy as np
class PointFeatureEncoder(object):
def __init__(self, config, point_cloud_range=None):
super().__init__()
self.point_encoding_config = config
assert list(self.point_encoding_config.src_feature_list[0:3]) == ['x', 'y', 'z']
self.used_feature_list = self.point_enco... | 1,703 | 34.5 | 100 | py |
SASA | SASA-main/pcdet/datasets/processor/data_processor.py | from functools import partial
import numpy as np
from ...utils import box_utils, common_utils
class DataProcessor(object):
def __init__(self, processor_configs, point_cloud_range, training):
self.point_cloud_range = point_cloud_range
self.training = training
self.mode = 'train' if traini... | 8,976 | 43.440594 | 139 | py |
SASA | SASA-main/pcdet/datasets/augmentor/augmentor_utils.py | import numpy as np
import numba
import warnings
from numba.core.errors import NumbaPerformanceWarning
from ...utils import common_utils
from ...utils import box_utils
def random_flip_along_x(enable_prob, gt_boxes, points):
"""
Args:
enable_prob:
gt_boxes: (N, 7 + C), [x, y, z, dx, dy, dz, hea... | 16,240 | 38.229469 | 122 | py |
SASA | SASA-main/pcdet/datasets/augmentor/data_augmentor.py | from functools import partial
import numpy as np
from ...utils import common_utils
from . import augmentor_utils, database_sampler
class DataAugmentor(object):
def __init__(self, root_path, augmentor_configs, class_names, logger=None):
self.root_path = root_path
self.class_names = class_names
... | 5,098 | 35.949275 | 96 | py |
SASA | SASA-main/pcdet/datasets/augmentor/database_sampler.py | import pickle
import numpy as np
from ...ops.iou3d_nms import iou3d_nms_utils
from ...utils import box_utils
class DataBaseSampler(object):
def __init__(self, root_path, sampler_cfg, class_names, logger=None):
self.root_path = root_path
self.class_names = class_names
self.sampler_cfg = s... | 8,518 | 40.354369 | 120 | py |
SASA | SASA-main/pcdet/datasets/nuscenes/nuscenes_utils.py | """
The NuScenes data pre-processing and evaluation is modified from
https://github.com/traveller59/second.pytorch and https://github.com/poodarchu/Det3D
"""
import operator
from functools import reduce
from pathlib import Path
import numpy as np
import tqdm
from nuscenes.utils.data_classes import Box
from nuscenes.u... | 18,474 | 35.876248 | 111 | py |
SASA | SASA-main/pcdet/datasets/nuscenes/nuscenes_dataset.py | import copy
import pickle
from pathlib import Path
import numpy as np
from tqdm import tqdm
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import common_utils
from ..dataset import DatasetTemplate
class NuScenesDataset(DatasetTemplate):
def __init__(self, dataset_cfg, class_names, traini... | 15,322 | 39.861333 | 120 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_dataset.py | import copy
import pickle
import numpy as np
from skimage import io
from ...ops.roiaware_pool3d import roiaware_pool3d_utils
from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti
from ..dataset import DatasetTemplate
class KittiDataset(DatasetTemplate):
def __init__(self, dataset_cfg, ... | 19,046 | 41.995485 | 140 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_utils.py | import numpy as np
from ...utils import box_utils
def transform_annotations_to_kitti_format(annos, map_name_to_kitti=None, info_with_fakelidar=False):
"""
Args:
annos:
map_name_to_kitti: dict, map name to KITTI names (Car, Pedestrian, Cyclist)
info_with_fakelidar:
Returns:
"""... | 1,809 | 39.222222 | 105 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_object_eval_python/rotate_iou.py | #####################
# Based on https://github.com/hongzhenwang/RRPN-revise
# Licensed under The MIT License
# Author: yanyan, scrin@foxmail.com
#####################
import math
import numba
import numpy as np
from numba import cuda
@numba.jit(nopython=True)
def div_up(m, n):
return m // n + (m % n > 0)
@cuda... | 11,552 | 33.903323 | 95 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_object_eval_python/evaluate.py | import time
import fire
import .kitti_common as kitti
from .eval import get_coco_eval_result, get_official_eval_result
def _read_imageset_file(path):
with open(path, 'r') as f:
lines = f.readlines()
return [int(line) for line in lines]
def evaluate(label_path,
result_path,
... | 909 | 25.764706 | 74 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_object_eval_python/kitti_common.py | import concurrent.futures as futures
import os
import pathlib
import re
from collections import OrderedDict
import numpy as np
from skimage import io
def get_image_index_str(img_idx):
return "{:06d}".format(img_idx)
def get_kitti_info_path(idx,
prefix,
info_type=... | 15,309 | 36.070218 | 79 | py |
SASA | SASA-main/pcdet/datasets/kitti/kitti_object_eval_python/eval.py | import io as sysio
import numba
import numpy as np
from .rotate_iou import rotate_iou_gpu_eval
@numba.jit
def get_thresholds(scores: np.ndarray, num_gt, num_sample_pts=41):
scores.sort()
scores = scores[::-1]
current_recall = 0
thresholds = []
for i, score in enumerate(scores):
l_recall ... | 35,743 | 41.051765 | 105 | py |
SASA | SASA-main/pcdet/utils/box_utils.py | import numpy as np
import scipy
import torch
import copy
from scipy.spatial import Delaunay
from ..ops.roiaware_pool3d import roiaware_pool3d_utils
from . import common_utils
def in_hull(p, hull):
"""
:param p: (N, K) test points
:param hull: (M, K) M corners of a box
:return (N) bool
"""
try... | 10,568 | 34.466443 | 118 | py |
SASA | SASA-main/pcdet/utils/loss_utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import box_utils
from ..ops.roiaware_pool3d import roiaware_pool3d_utils
class SigmoidFocalClassificationLoss(nn.Module):
"""
Sigmoid focal cross entropy loss.
"""
def __init__(self, gamma: float = 2.0, alph... | 14,516 | 35.938931 | 105 | py |
SASA | SASA-main/pcdet/utils/box_coder_utils.py | import numpy as np
import torch
class ResidualCoder(object):
def __init__(self, code_size=7, encode_angle_by_sincos=False, **kwargs):
super().__init__()
self.code_size = code_size
self.encode_angle_by_sincos = encode_angle_by_sincos
if self.encode_angle_by_sincos:
self.... | 13,258 | 35.226776 | 107 | py |
SASA | SASA-main/pcdet/utils/object3d_kitti.py | import numpy as np
def get_objects_from_label(label_file):
with open(label_file, 'r') as f:
lines = f.readlines()
objects = [Object3d(line) for line in lines]
return objects
def cls_type_to_id(cls_type):
type_to_id = {'Car': 1, 'Pedestrian': 2, 'Cyclist': 3, 'Van': 4}
if cls_type not in ... | 3,449 | 40.071429 | 119 | py |
SASA | SASA-main/pcdet/utils/common_utils.py | import logging
import os
import pickle
import random
import shutil
import subprocess
import numpy as np
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
def check_numpy_to_torch(x):
if isinstance(x, np.ndarray):
return torch.from_numpy(x).float(), True
return x, False
... | 5,750 | 28.341837 | 97 | py |
SASA | SASA-main/pcdet/utils/calibration_kitti.py | import numpy as np
def get_calib_from_file(calib_file):
with open(calib_file) as f:
lines = f.readlines()
obj = lines[2].strip().split(' ')[1:]
P2 = np.array(obj, dtype=np.float32)
obj = lines[3].strip().split(' ')[1:]
P3 = np.array(obj, dtype=np.float32)
obj = lines[4].strip().split(... | 4,464 | 34.436508 | 116 | py |
SASA | SASA-main/pcdet/utils/visual_utils/visualize_result.py | import argparse
import os
import numpy as np
import visualize_utils
from pcdet.utils import object3d_kitti
from pcdet.datasets import KittiDataset
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--sample_id', type=int, required=True, help='sample index')... | 2,783 | 30.636364 | 106 | py |
SASA | SASA-main/pcdet/utils/visual_utils/visualize_utils.py | import mayavi.mlab as mlab
import numpy as np
import torch
box_colormap = [
[1, 1, 1],
[0, 1, 0],
[0, 1, 1],
[1, 1, 0],
]
def check_numpy_to_torch(x):
if isinstance(x, np.ndarray):
return torch.from_numpy(x).float(), True
return x, False
def rotate_points_along_z(points, angle):
... | 8,540 | 38.541667 | 121 | py |
SASA | SASA-main/pcdet/ops/roipoint_pool3d/roipoint_pool3d_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function
from ...utils import box_utils
from . import roipoint_pool3d_cuda
class RoIPointPool3d(nn.Module):
def __init__(self, num_sampled_points=512, pool_extra_width=1.0):
super().__init__()
self.num_sampled_points = num_sampled_poin... | 2,226 | 31.75 | 112 | py |
SASA | SASA-main/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function, Variable
from . import pointnet2_stack_cuda as pointnet2
class BallQuery(Function):
@staticmethod
def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
new_xyz: torch.Tensor, new_x... | 9,462 | 34.441948 | 123 | py |
SASA | SASA-main/pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py | from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pointnet2_utils
class StackSAModuleMSG(nn.Module):
def __init__(self, *, radii: List[float], nsamples: List[int], mlps: List[List[int]],
use_xyz: bool = True, pool_method='max_pool'):
... | 5,425 | 38.318841 | 113 | py |
SASA | SASA-main/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py | from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function, Variable
from . import pointnet2_batch_cuda as pointnet2
@torch.no_grad()
def calc_dist_matrix_for_sampling(xyz: torch.Tensor, features: torch.Tensor = None,
... | 14,049 | 35.588542 | 116 | py |
SASA | SASA-main/pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py | from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pointnet2_utils
class _PointnetSAModuleBase(nn.Module):
def __init__(self):
super().__init__()
self.npoint = None
self.groupers = None
self.mlps = None
self.pool_meth... | 18,910 | 41.688488 | 119 | py |
SASA | SASA-main/pcdet/ops/iou3d_nms/iou3d_nms_utils.py | """
3D IoU Calculation and Rotated NMS
Written by Shaoshuai Shi
All Rights Reserved 2019-2020.
"""
import torch
from ...utils import common_utils
from . import iou3d_nms_cuda
def boxes_bev_iou_cpu(boxes_a, boxes_b):
"""
Args:
boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading]
boxes_b: (N, 7) [x, ... | 3,650 | 30.205128 | 109 | py |
SASA | SASA-main/pcdet/ops/roiaware_pool3d/roiaware_pool3d_utils.py | import torch
import torch.nn as nn
from torch.autograd import Function
from ...utils import common_utils
from . import roiaware_pool3d_cuda
def points_in_boxes_cpu(points, boxes):
"""
Args:
points: (num_points, 3)
boxes: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO... | 4,688 | 34.522727 | 120 | py |
chainer | chainer-master/setup.py | #!/usr/bin/env python
import os
import pkg_resources
import sys
from setuptools import setup
import chainerx_build_helper
if sys.version_info[:3] == (3, 5, 0):
if not int(os.getenv('CHAINER_PYTHON_350_FORCE', '0')):
msg = """
Chainer does not work with Python 3.5.0.
We strongly recommend to use anothe... | 6,477 | 28.990741 | 78 | py |
chainer | chainer-master/chainerx_build_helper.py | # This script is based on pybind11's example script. See the original via the
# following URL: https://github.com/pybind/cmake_example/blob/master/setup.py
import distutils
import os
import platform
import re
import subprocess
import sys
import setuptools
from setuptools.command import build_ext
def emit_build_info... | 4,576 | 31.928058 | 78 | py |
chainer | chainer-master/chainerx/_cuda.py | import chainerx
try:
# _pybind_cuda is unavailable if ChainerX is built without CUDA.
from chainerx import _pybind_cuda
_available = True
except Exception:
_available = False
try:
import cupy
_cupy_available = True
except Exception:
_cupy_available = False
def cupy_share_allocator():
... | 1,005 | 27.742857 | 76 | py |
chainer | chainer-master/chainerx/_ndarray.py | # This file implements chainerx.ndarray methods that can be defined only in
# Python.
import chainerx
def populate():
def clip(self, a_min, a_max):
"""Returns an array with values limited to [``a_min``, ``a_max``].
.. seealso:: :func:`chainerx.clip` for full documentation,
:meth:`nu... | 434 | 21.894737 | 75 | py |
chainer | chainer-master/chainerx/_fallback_workarounds.py | # This file defines workaround implementation for
# NumPy-compatibility functions that fall back to NumPy/CuPy functions
# for native/cuda devices respecitvely.
# The workaround does not support backprop, and also requires external
# libraries mentioned above.
# Functions defined in this file should be considered to ha... | 6,347 | 30.117647 | 79 | py |
chainer | chainer-master/chainerx/__init__.py | import os
import warnings
try:
from chainerx import _build_info
except ImportError:
raise ImportError(
'''\
Cannot import chainerx because _build_info.py cannot be found.
The chainer and chainerx module being imported was not correctly \
installed by `pip install`.
It may be caused by either of the f... | 2,837 | 28.5625 | 83 | py |
chainer | chainer-master/chainerx/_device.py | import chainerx
def _recover_device(backend_name, device_index):
# Recovers the device instance.
# This function is used together with chainerx.Device.__reduce__.
# TODO(niboshi): Save the context name and lookup the context with it.
context = chainerx.get_default_context()
backend = context.get_b... | 405 | 32.833333 | 74 | py |
chainer | chainer-master/chainerx/random/distributions.py | import numpy
import chainerx
# TODO(sonots): Implement in C++, especially in CUDA
def normal(*args, **kwargs):
"""normal(*args, **kwargs, device=None)
Draws random samples from a normal (Gaussian) distribution.
This is currently equivalent to :func:`numpy.random.normal`
wrapped by :func:`chainerx.a... | 1,057 | 26.842105 | 65 | py |
chainer | chainer-master/chainerx/random/__init__.py | from chainerx.random.distributions import normal # NOQA
from chainerx.random.distributions import uniform # NOQA
| 115 | 37.666667 | 57 | py |
chainer | chainer-master/chainerx/testing/array.py | import numpy.testing
import chainerx
# NumPy-like assertion functions that accept both NumPy and ChainerX arrays
def _as_numpy(x):
if isinstance(x, chainerx.ndarray):
return chainerx.to_numpy(x)
assert isinstance(x, numpy.ndarray) or numpy.isscalar(x)
return x
def _check_dtype_and_strides(x, y... | 6,059 | 39.4 | 79 | py |
chainer | chainer-master/chainerx/testing/helper.py | import functools
import traceback
import warnings
import numpy
import pytest
import chainerx
from chainerx.testing import array
# A test returning this object will have its return value ignored.
#
# This is e.g. useful when a combination of parametrizations and operations
# unintentionally cover non-supported funct... | 12,987 | 39.461059 | 79 | py |
chainer | chainer-master/chainerx/testing/__init__.py | import pytest
pytest.register_assert_rewrite('chainerx.testing.array')
pytest.register_assert_rewrite('chainerx.testing.helper')
from chainerx._testing import _DeviceBuffer # NOQA
from chainerx._testing import _fromnumpy # NOQA
from chainerx.testing import array # NOQA
from chainerx.testing import helper # NOQA
... | 1,307 | 45.714286 | 71 | py |
chainer | chainer-master/chainerx/testing/dtypes.py | import numpy
import pytest
import chainerx
float_dtypes = (
'float16',
'float32',
'float64',
)
signed_dtypes = (
'int8',
'int16',
'int32',
'int64',
'float16',
'float32',
'float64',
)
unsigned_dtypes = (
'uint8',
)
integral_dtypes = (
'uint8',
'int8',
'int1... | 1,613 | 16.933333 | 77 | py |
chainer | chainer-master/chainerx/manipulation/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/chainerx/_docs/routines.py | import chainerx
from chainerx import _docs
def set_docs():
_docs_creation()
_docs_evaluation()
_docs_indexing()
_docs_linalg()
_docs_logic()
_docs_loss()
_docs_manipulation()
_docs_math()
_docs_sorting()
_docs_statistics()
_docs_connection()
_docs_normalization()
_d... | 127,329 | 31.26812 | 86 | py |
chainer | chainer-master/chainerx/_docs/context.py | import chainerx
from chainerx import _docs
def set_docs():
Context = chainerx.Context
_docs.set_doc(
Context,
"""Context()
An isolated execution environment of ChainerX.
In Python binding, a single context is automatically created and set as the
global default context on import. Only advance... | 367 | 20.647059 | 77 | py |
chainer | chainer-master/chainerx/_docs/array.py | import chainerx
from chainerx import _docs
def set_docs():
ndarray = chainerx.ndarray
_docs.set_doc(
ndarray,
"""ndarray(shape, dtype, device=None)
Multi-dimensional array, the central data structure of ChainerX.
This class, along with other APIs in the :mod:`chainerx` module, provides a
sub... | 12,815 | 29.297872 | 79 | py |
chainer | chainer-master/chainerx/_docs/utils.py | import chainerx
from chainerx import _docs
def set_docs():
_docs.set_doc(
chainerx.to_numpy,
"""to_numpy(array, copy=True)
Converts a ChainerX array to NumPy
Args:
array (~chainerx.ndarray): ChainerX array.
copy (bool): If ``True``, a copy is always made. Otherwise, the resulting
... | 410 | 20.631579 | 77 | py |
chainer | chainer-master/chainerx/_docs/backend.py | import chainerx
from chainerx import _docs
def _set_docs_backend():
Backend = chainerx.Backend
_docs.set_doc(
Backend,
"""Pluggable entity that abstracts various computing platforms.
A backend holds one or more :class:`~chainerx.Device`\\ s, each of which
represents a physical computing unit... | 1,249 | 17.115942 | 72 | py |
chainer | chainer-master/chainerx/_docs/backprop.py | import chainerx
from chainerx import _docs
def set_docs():
_docs.set_doc(
chainerx.backward,
"""backward(outputs, *, enable_double_backprop=False)
Runs backpropagation.
On backpropagation (a.k.a. backprop),
the computational graph is traversed backward starting from the output arrays,
up until th... | 4,923 | 32.27027 | 79 | py |
chainer | chainer-master/chainerx/_docs/__init__.py | import inspect
from chainerx import _core
from chainerx._docs import array
from chainerx._docs import backend
from chainerx._docs import backprop
from chainerx._docs import context
from chainerx._docs import device
from chainerx._docs import routines
from chainerx._docs import utils
def set_doc(obj, docstring):
... | 683 | 25.307692 | 74 | py |
chainer | chainer-master/chainerx/_docs/device.py | import chainerx
from chainerx import _docs
def _set_docs_device():
Device = chainerx.Device
_docs.set_doc(
Device,
"""Represents a physical computing unit.
""")
_docs.set_doc(
Device.synchronize,
"""Synchronizes the device.
""")
_docs.set_doc(
Device.name,
... | 2,706 | 20.830645 | 79 | py |
chainer | chainer-master/chainerx/math/misc.py | import chainerx
# TODO(sonots): Implement in C++
def clip(a, a_min, a_max):
"""Clips the values of an array to a given interval.
Given an interval, values outside the interval are clipped to the
interval edges. For example, if an interval of ``[0, 1]`` is specified,
values smaller than 0 become 0, an... | 1,236 | 27.767442 | 75 | py |
chainer | chainer-master/chainerx/math/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/chainerx/creation/from_data.py | import numpy
import chainerx
# TODO(sonots): Support subclassing
def asanyarray(a, dtype=None, device=None):
"""Converts an object to an array.
This is currently equivalent to :func:`~chainerx.asarray`, since there are
no subclasses of ndarray in ChainerX. Note that the original
:func:`numpy.asanyar... | 3,445 | 30.907407 | 87 | py |
chainer | chainer-master/chainerx/creation/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/test_utils.py | import contextlib
import os
import re
import shutil
import subprocess
import sys
import tempfile
class OutputEvaluator(object):
def check(self, outdata):
raise NotImplementedError()
class TemplateOutputEvaluator(OutputEvaluator):
def __init__(self, template, **checks):
self.template = templ... | 11,391 | 33.416918 | 78 | py |
chainer | chainer-master/examples/dcgan/updater.py | #!/usr/bin/env python
import chainer
import chainer.functions as F
from chainer import Variable
class DCGANUpdater(chainer.training.updaters.StandardUpdater):
def __init__(self, *args, **kwargs):
self.gen, self.dis = kwargs.pop('models')
super(DCGANUpdater, self).__init__(*args, **kwargs)
de... | 1,385 | 29.130435 | 67 | py |
chainer | chainer-master/examples/dcgan/net.py | #!/usr/bin/env python
import numpy
import chainer
import chainer.functions as F
import chainer.links as L
import chainerx
def add_noise(device, h, sigma=0.2):
if chainer.config.train:
xp = device.xp
# TODO(niboshi): Support random.randn in ChainerX
if device.xp is chainerx:
fa... | 4,005 | 42.075269 | 79 | py |
chainer | chainer-master/examples/dcgan/train_dcgan.py | #!/usr/bin/env python
import argparse
import os
import warnings
import numpy
import chainer
from chainer import training
from chainer.training import extensions
from net import Discriminator
from net import Generator
from updater import DCGANUpdater
from visualize import out_generated_image
def main():
parser ... | 5,172 | 37.318519 | 79 | py |
chainer | chainer-master/examples/dcgan/visualize.py | #!/usr/bin/env python
import os
import numpy as np
from PIL import Image
import chainer
import chainer.backends.cuda
from chainer import Variable
def out_generated_image(gen, dis, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_imag... | 1,096 | 27.868421 | 68 | py |
chainer | chainer-master/examples/reinforcement_learning/dqn_cartpole.py | #!/usr/bin/env python
"""Example code of DQN and DoubleDQN on OpenAI Gym environments.
For DQN, see: https://www.nature.com/articles/nature14236
For DoubleDQN, see: https://arxiv.org/abs/1509.06461
"""
from __future__ import division
import argparse
import collections
import copy
import random
import warnings
import ... | 8,420 | 38.167442 | 79 | py |
chainer | chainer-master/examples/reinforcement_learning/ddpg_pendulum.py | #!/usr/bin/env python
"""Example code of DDPG on OpenAI Gym environments.
For DDPG, see: https://arxiv.org/abs/1509.02971
"""
from __future__ import division
import argparse
import collections
import copy
import random
import warnings
import gym
import numpy as np
import chainer
from chainer import functions as F
fr... | 9,863 | 37.084942 | 78 | py |
chainer | chainer-master/examples/ptb/train_ptb_custom_loop.py | #!/usr/bin/env python
"""Sample script of recurrent neural network language model.
This code is ported from the following implementation written in Torch.
https://github.com/tomsercu/lstm
This code is a custom loop version of train_ptb.py. That is, we train
models without using the Trainer class in chainer and instea... | 6,844 | 37.672316 | 78 | py |
chainer | chainer-master/examples/ptb/train_ptb.py | #!/usr/bin/env python
"""Sample script of recurrent neural network language model.
This code is ported from the following implementation written in Torch.
https://github.com/tomsercu/lstm
Note for contributors:
This example code is referred to from the "RNN Language Models" tutorial.
If this file is to be modified, p... | 11,423 | 38.666667 | 79 | py |
chainer | chainer-master/examples/ptb/gentxt.py | #!/usr/bin/env python
"""Example to generate text from a recurrent neural network language model.
This code is ported from following implementation.
https://github.com/longjie/chainer-char-rnn/blob/master/sample.py
"""
import argparse
import sys
import numpy as np
import six
import chainer
from chainer.backends imp... | 3,635 | 32.054545 | 78 | py |
chainer | chainer-master/examples/caffe_export/export.py | import argparse
import os
import numpy as np
import chainer
from chainer.exporters import caffe
from chainer.links.model.vision import googlenet
from chainer.links.model.vision import resnet
from chainer.links.model.vision import vgg
archs = {
'googlenet': googlenet.GoogLeNet,
'resnet50': resnet.ResNet50Laye... | 1,953 | 27.318841 | 71 | py |
chainer | chainer-master/examples/word2vec/train_word2vec.py | #!/usr/bin/env python
"""Sample script of word embedding model.
This code implements skip-gram model and continuous-bow model.
"""
import argparse
import collections
import os
import six
import warnings
import numpy as np
import chainer
from chainer.backends import cuda
import chainer.functions as F
import chainer.i... | 10,528 | 34.935154 | 79 | py |
chainer | chainer-master/examples/word2vec/search.py | #!/usr/bin/env python
import argparse
import os
import numpy
import six
n_result = 5 # number of search result to show
parser = argparse.ArgumentParser()
parser.add_argument('--result', default='result',
help='Directory of a training result')
args = parser.parse_args()
with open(os.path.join(... | 1,487 | 25.105263 | 75 | py |
chainer | chainer-master/examples/chainermn/dcgan/updater.py | #!/usr/bin/env python
from __future__ import print_function
import chainer
import chainer.functions as F
from chainer import Variable
class DCGANUpdater(chainer.training.StandardUpdater):
def __init__(self, *args, **kwargs):
self.gen, self.dis = kwargs.pop('models')
super(DCGANUpdater, self).__... | 1,445 | 28.510204 | 68 | py |
chainer | chainer-master/examples/chainermn/dcgan/net.py | #!/usr/bin/env python
from __future__ import print_function
import numpy
import chainer
from chainer import backend
import chainer.functions as F
import chainer.links as L
def add_noise(h, sigma=0.2):
xp = backend.get_array_module(h.array)
if chainer.config.train:
return h + sigma * xp.random.randn... | 3,630 | 40.735632 | 79 | py |
chainer | chainer-master/examples/chainermn/dcgan/train_dcgan.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import os
import chainer
from chainer import training
from chainer.training import extensions
from net import Discriminator
from net import Generator
from updater import DCGANUpdater
from visualize import out_generated_image
import chainerm... | 6,762 | 39.740964 | 79 | py |
chainer | chainer-master/examples/chainermn/dcgan/visualize.py | #!/usr/bin/env python
import os
import numpy as np
from PIL import Image
import chainer
import chainer.cuda
from chainer import Variable
def out_generated_image(gen, dis, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_images = rows... | 1,078 | 27.394737 | 68 | py |
chainer | chainer-master/examples/chainermn/imagenet/train_imagenet.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import multiprocessing
import random
import sys
import numpy as np
import chainer
import chainer.cuda
from chainer import training
from chainer.training import extensions
import chainermn
import models.alex as alex
import models.googlenet... | 8,957 | 38.117904 | 99 | py |
chainer | chainer-master/examples/chainermn/imagenet/compute_mean.py | #!/usr/bin/env python
import argparse
import sys
import numpy as np
import chainer
def compute_mean(dataset):
print('compute mean image')
sum_image = 0
N = len(dataset)
for i, (image, _) in enumerate(dataset):
sum_image += image
sys.stderr.write('{} / {}\r'.format(i, N))
sys.... | 1,037 | 25.615385 | 77 | py |
chainer | chainer-master/examples/chainermn/imagenet/models/nin.py | import chainer
import chainer.functions as F
import chainer.initializers as I
import chainer.links as L
class NIN(chainer.Chain):
"""Network-in-Network example model."""
insize = 227
def __init__(self):
super(NIN, self).__init__()
conv_init = I.HeNormal() # MSRA scaling
with s... | 1,296 | 34.054054 | 74 | py |
chainer | chainer-master/examples/chainermn/imagenet/models/googlenet.py | import chainer
import chainer.functions as F
import chainer.links as L
class GoogLeNet(chainer.Chain):
insize = 224
def __init__(self):
super(GoogLeNet, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(None, 64, 7, stride=2, pad=3)
self.conv2_redu... | 2,922 | 33.388235 | 71 | py |
chainer | chainer-master/examples/chainermn/imagenet/models/googlenetbn.py | import chainer
import chainer.functions as F
import chainer.links as L
class GoogLeNetBN(chainer.Chain):
"""New GoogLeNet of BatchNormalization version."""
insize = 224
def __init__(self):
super(GoogLeNetBN, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution... | 3,429 | 34 | 74 | py |
chainer | chainer-master/examples/chainermn/imagenet/models/resnet50.py | # Original author: yasunorikudo
# (https://github.com/yasunorikudo/chainer-ResNet)
import chainer
import chainer.functions as F
from chainer import initializers
import chainer.links as L
class BottleNeckA(chainer.Chain):
def __init__(self, in_size, ch, out_size, stride=2):
super(BottleNeckA, self).__ini... | 3,533 | 31.422018 | 74 | py |
chainer | chainer-master/examples/chainermn/imagenet/models/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/chainermn/imagenet/models/alex.py | import chainer
import chainer.functions as F
import chainer.links as L
class Alex(chainer.Chain):
"""Single-GPU AlexNet without partition toward the channel axis."""
insize = 227
def __init__(self):
super(Alex, self).__init__()
with self.init_scope():
self.conv1 = L.Convolut... | 1,365 | 34.025641 | 74 | py |
chainer | chainer-master/examples/chainermn/mnist/train_mnist_model_parallel.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import chainer
import chainer.cuda
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainermn
import chainermn.datasets
import chainermn.functions
chainer.disable_experimental_fe... | 4,438 | 30.48227 | 76 | py |
chainer | chainer-master/examples/chainermn/mnist/train_mnist_dual_parallel.py | #!/usr/bin/env python
# coding: utf-8
import argparse
import chainer
import chainer.cuda
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainermn
import chainermn.datasets
import chainermn.functions
chainer.disable_experimental_fe... | 5,279 | 32.630573 | 76 | py |
chainer | chainer-master/examples/chainermn/mnist/train_mnist.py | #!/usr/bin/env python
from __future__ import print_function
import argparse
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx
import chainermn
class MLP(chainer.Chain):
def __init__(self, n_units, n_out):... | 5,320 | 37.839416 | 78 | py |
chainer | chainer-master/examples/chainermn/cifar/train_cifar.py | import argparse
import chainer
import chainer.links as L
from chainer import training
from chainer.training import extensions
from chainer.datasets import get_cifar10
from chainer.datasets import get_cifar100
import chainermn
import models.VGG
def main():
parser = argparse.ArgumentParser(description='ChainerM... | 4,765 | 37.128 | 77 | py |
chainer | chainer-master/examples/chainermn/cifar/models/VGG.py | from chainer.utils import argument
import chainer
import chainer.functions as F
import chainer.links as L
import warnings
class Block(chainer.Chain):
"""A convolution, batch norm, ReLU block.
A block in a feedforward network that performs a
convolution followed by batch normalization followed
by a ... | 4,303 | 30.647059 | 76 | py |
chainer | chainer-master/examples/chainermn/cifar/models/__init__.py | 0 | 0 | 0 | py | |
chainer | chainer-master/examples/chainermn/seq2seq/europal.py | from __future__ import unicode_literals
import collections
import gzip
import io
import os
import re
import numpy
import progressbar
split_pattern = re.compile(r'([.,!?"\':;)(])')
digit_pattern = re.compile(r'\d')
def split_sentence(s):
s = s.lower()
s = s.replace('\u2019', '\'')
s = digit_pattern.sub... | 2,164 | 23.325843 | 67 | py |
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