repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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BiteNet | BiteNet-master/model_utils/attentionLayers.py | import tensorflow as tf
from tensorflow import keras
import numpy as np
from tensorflow.python.ops import math_ops
from functools import reduce
from operator import mul
from model_utils.normalization_layer import LayerNormalization
VERY_BIG_NUMBER = 1e30
VERY_SMALL_NUMBER = 1e-30
VERY_POSITIVE_NUMBER = VERY_BIG_NUMBER... | 19,161 | 40.297414 | 126 | py |
BiteNet | BiteNet-master/model_utils/position_encoding_layer.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from tensorflow import keras
class PositionEncoding(keras.layers.Layer):
def __init__(self, hidden_size, is_encoding, **kwargs):
# Pass dtype=float32, as we hav... | 1,488 | 42.794118 | 88 | py |
BiteNet | BiteNet-master/model_utils/mh_layer.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow import keras
class MultiHeadAttention(keras.layers.Layer):
def __init__(self, direction, train, dropout, num_units, num_heads=10, **kwargs):
super(MultiHea... | 3,717 | 39.413043 | 98 | py |
BiCM | BiCM-master/docs/source/conf.py | # 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 ex... | 2,195 | 30.826087 | 79 | py |
DynamicRCNN | DynamicRCNN-master/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# !/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ex... | 2,190 | 29.013699 | 77 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_2x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_2x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_2x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_2x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_dcnv2_fpn_mstrain_3x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_dcnv2_fpn_mstrain_3x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_dcnv2_fpn_mstrain_3x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_dcnv2_fpn_mstrain_3x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_1x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_1x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_1x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_1x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_2x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_2x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_2x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_2x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_mstrain_3x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_mstrain_3x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_mstrain_3x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r101_fpn_mstrain_3x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x/test.py | import argparse
import os
from tqdm import tqdm
import logging
import time
import datetime
import cv2
import numpy as np
import torch
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.engine.checkpoint... | 8,999 | 34.714286 | 81 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from config import config as cfg
from dynamic_rcnn.basemodels import resnet
from dynamic_rcnn.det_opr.box_coder import BoxCoder
from dynamic_rcnn.det_opr.fpn.fpn import build_resnet_fpn_backbone
from dynamic_rcnn.det_opr.rpn.anchor_generator import mak... | 10,341 | 44.964444 | 78 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x/dataset.py | from config import config as cfg
import torch.utils.data
from dynamic_rcnn.datasets.coco import COCODataset
from dynamic_rcnn.datasets.concat_dataset import ConcatDataset
from dynamic_rcnn.datasets.transforms import build_transforms
from dynamic_rcnn.datasets import samplers
from dynamic_rcnn.datasets.collate_batch im... | 3,340 | 40.246914 | 105 | py |
DynamicRCNN | DynamicRCNN-master/models/zhanghongkai/dynamic_rcnn/coco/dynamic_rcnn_r50_fpn_1x/train.py | import os
import argparse
import time
import datetime
import torch
import torch.distributed as dist
from config import config as cfg
from network import Network
from dataset import make_data_loader
from dynamic_rcnn.engine.comm import synchronize, get_rank, get_world_size
from dynamic_rcnn.utils.logger import setup_... | 7,991 | 34.838565 | 135 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class BalancedPositiveNegativeSampler(object):
"""
This class samples batches, ensuring that they contain a fixed proportion of positives
"""
def __init__(self, batch_size_per_image, positive_fraction):
"""
... | 2,718 | 38.405797 | 90 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
class Matcher(object):
"""
This class assigns to each predicted "element" (e.g., a box) a ground-truth
element. Each predicted element will have exactly zero or one matches; each
ground-truth element may be assigned t... | 5,129 | 44.39823 | 88 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from dynamic_rcnn import _C
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar... | 4,159 | 30.515152 | 118 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/poolers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from dynamic_rcnn.kernels.ops.roi_align import ROIAlign
from dynamic_rcnn.utils.torch_utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a se... | 4,593 | 33.541353 | 90 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/box_coder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
class BoxCoder(object):
"""
This class encodes and decodes a set of bounding boxes into
the representation used for training the regressors.
"""
def __init__(self, weights, bbox_xform_clip=math.log(1... | 3,367 | 34.083333 | 86 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rcnn/post_processing.py | import torch
import torch.nn.functional as F
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import boxlist_nms, cat_boxlist
def filter_results(
boxlist, num_classes, score_thresh, nms_thresh, detections_per_img):
# unwrap the boxlist to... | 3,764 | 35.201923 | 81 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rcnn/proposal_target_opr.py | import torch
from dynamic_rcnn.det_opr.matcher import Matcher
from dynamic_rcnn.det_opr.sampler import BalancedPositiveNegativeSampler
from dynamic_rcnn.datasets.structures.boxlist_ops import boxlist_iou
def proposal_target_opr(
proposals, targets, box_coder, high_threshold, low_threshold,
batch_size_... | 4,352 | 38.93578 | 86 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rcnn/mask_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
# TODO check if want to return a single BoxList or a composite
# object
class MaskPostProcessor(nn.Module):
"""
From th... | 6,450 | 31.255 | 87 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rcnn/mask_head/mask_target_opr.py | import torch
from dynamic_rcnn.det_opr.matcher import Matcher
from dynamic_rcnn.datasets.structures.boxlist_ops import boxlist_iou
def project_masks_on_boxes(segmentation_masks, proposals, discretization_size):
"""
Given segmentation masks and the bounding boxes corresponding
to the location of the masks ... | 3,757 | 37.346939 | 80 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rcnn/cascade_rcnn/proposal_opr.py | import torch
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import cat_boxlist
# TODO: this should be implemented in RPN, but now a little different
def add_gt_proposals(proposals, targets):
"""
Arguments:
proposals: list[BoxList]
... | 1,717 | 32.686275 | 71 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from torch import nn
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self, buffers=... | 10,106 | 33.261017 | 82 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/proposal_opr.py | import torch
from dynamic_rcnn.utils.torch_utils import permute_and_flatten
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import cat_boxlist, boxlist_nms, \
remove_small_boxes
def proposal_opr(
rpn_anchors, rpn_cls_logits, rpn_bbox_pred... | 4,839 | 36.8125 | 84 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/anchor_target_opr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from dynamic_rcnn.det_opr.matcher import Matcher
from dynamic_rcnn.det_opr.sampler import BalancedPositiveNegativeSampler
from dynamic_rcnn.datasets.structures.boxlist_ops import cat_boxlist, boxlist_iou
def anchor_target_opr(
... | 2,959 | 37.441558 | 81 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/fcos/post_processing.py | import torch
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import cat_boxlist, boxlist_nms, \
remove_small_boxes
def post_processing_opr(
fcos_locations, cls_logits, bbox_preds, centernesses, image_sizes,
pre_nms_top_n, pre_nms_... | 5,313 | 38.073529 | 84 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/fcos/scale.py | import torch
from torch import nn
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
| 270 | 21.583333 | 66 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/fcos/fcos_target_opr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
INF = 100000000
def get_sample_region(
gt, strides, num_points_per_level, gt_xs, gt_ys, radius=1):
gt = gt[None].expand(gt_xs.shape[0], gt.shape[0], 4)
center_x = (gt[..., 0] + gt[..., 2]) / 2
center_y = (gt[...,... | 6,238 | 37.751553 | 80 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/retinanet/post_processing.py | import torch
from dynamic_rcnn.utils.torch_utils import permute_and_flatten
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import cat_boxlist, boxlist_nms, \
remove_small_boxes
def post_processing_opr(
retina_anchors, cls_logits, bbox_pr... | 5,010 | 35.845588 | 84 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/rpn/retinanet/anchor_target_opr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from dynamic_rcnn.det_opr.matcher import Matcher
from dynamic_rcnn.datasets.structures.boxlist_ops import boxlist_iou
def anchor_target_opr(
anchors, targets, box_coder, high_threshold, low_threshold,
allow_low_quali... | 2,079 | 35.491228 | 80 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/det_opr/fpn/fpn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch.nn.functional as F
from torch import nn
from collections import OrderedDict
from dynamic_rcnn.utils.misc import conv_with_kaiming_uniform
class FPN(nn.Module):
"""
Module that adds FPN on top of a list of feature maps.
T... | 5,496 | 36.910345 | 86 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/voc.py | import os
import torch
import torch.utils.data
from PIL import Image
import sys
if sys.version_info[0] == 2:
import xml.etree.cElementTree as ET
else:
import xml.etree.ElementTree as ET
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
class PascalVOCDataset(torch.utils.data.Dataset):
... | 4,171 | 29.676471 | 118 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
class ConcatDataset(_ConcatDataset):
"""
Same as torch.utils.data.dataset.ConcatDataset, but exposes an extra
method for querying the sizes of the ima... | 766 | 30.958333 | 72 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.segmentation_mask import SegmentationMask
from dynamic_rcnn.datasets.structures.keypoint import PersonKeypoints... | 5,053 | 38.484375 | 85 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/evaluation/coco/coco_eval.py | import tempfile
import os
import torch
from collections import OrderedDict
from tqdm import tqdm
from dynamic_rcnn.det_opr.rcnn.mask_head.inference import Masker
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.datasets.structures.boxlist_ops import boxlist_iou
def do_coco_evaluati... | 13,734 | 34.127877 | 88 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/samplers/grouped_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import bisect
import copy
import torch
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
def _quantize(x, bins):
bins = copy.copy(bins)
bins = sorted(bins)
quantized = list... | 5,462 | 39.169118 | 88 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/samplers/iteration_based_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch.utils.data.sampler import BatchSampler
class IterationBasedBatchSampler(BatchSampler):
"""
Wraps a BatchSampler, resampling from it until
a specified number of iterations have been sampled
"""
def __init__(self, ba... | 1,164 | 35.40625 | 71 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/samplers/distributed.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed.
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
class DistributedSampler(S... | 2,569 | 37.358209 | 86 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import torch
import torchvision
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, image, target):
for ... | 3,477 | 27.508197 | 83 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/structures/image_list.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
import torch
class ImageList(object):
"""
Structure that holds a list of images (of possibly
varying sizes) as a single tensor.
This works by padding the images to the same size,
and storing in... | 2,485 | 33.054795 | 87 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/structures/segmentation_mask.py | import cv2
import copy
import torch
import numpy as np
from dynamic_rcnn.utils.misc import interpolate
from dynamic_rcnn.utils.pyt_utils import findContours
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons... | 18,772 | 31.479239 | 94 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The bounding boxes are represented as a Nx4 Tensor.
In order to uniquely determine the bou... | 9,645 | 35.127341 | 92 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/structures/boxlist_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from dynamic_rcnn.kernels.ops.nms import nms as _box_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores"):
"""
Performs non-maximum suppression on a boxlist, wit... | 3,719 | 27.615385 | 97 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/datasets/structures/keypoint.py | import torch
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device ... | 6,555 | 33.687831 | 97 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/engine/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_s... | 1,817 | 33.301887 | 80 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/engine/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import torch
import logging
from collections import OrderedDict
from dynamic_rcnn.utils.pyt_utils import link_file
from dynamic_rcnn.basemodels.c2_model_loading import load_resnet_c2_format
def align_and_update_state_dicts(model_state_d... | 6,589 | 36.657143 | 91 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/engine/comm.py | """
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import pickle
import time
import torch
import torch.distributed as dist
def get_world_size():
if not dist.is_available():
return 1
if not dist.is_initialized():
return 1
ret... | 3,372 | 27.584746 | 84 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/engine/bbox_aug.py | import torch
import torchvision.transforms as TT
from dynamic_rcnn.datasets import transforms as T
from dynamic_rcnn.datasets.structures.image_list import to_image_list
from dynamic_rcnn.datasets.structures.bounding_box import BoxList
from dynamic_rcnn.det_opr.rcnn.post_processing import filter_results
def im_detect... | 4,559 | 36.073171 | 93 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/basemodels/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
def _rename_basic_resnet_weights(layer_keys):
layer_keys = [k.replace("_", ".") for k in layer_keys]
layer_keys = [k.replace(".w", ".weight") for k in layer_ke... | 7,704 | 39.984043 | 129 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/basemodels/resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Variant of the resnet module that takes cfg as an argument.
Example usage. Strings may be specified in the config file.
model = ResNet(
"StemWithFixedBatchNorm",
"BottleneckWithFixedBatchNorm",
"ResNet50StagesTo4",
... | 14,048 | 30.359375 | 85 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __... | 1,862 | 26.80597 | 82 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/utils/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 11,025 | 30.593123 | 88 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/utils/torch_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
def cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if ... | 1,908 | 33.709091 | 97 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from dynamic_rcnn import _C
class _ROIPool(Function):
@staticmethod
de... | 1,849 | 27.90625 | 74 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from dynamic_rcnn import _C
class _ROIAlign(Function):
@staticmethod
d... | 2,103 | 29.941176 | 85 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/dcn/deform_conv_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from dynamic_rcnn import _C
class DeformConvFunction(Function):
@staticmethod
def forward(
ctx,
input,
offset,
weight,
str... | 8,303 | 30.574144 | 83 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/dcn/deform_pool_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from dynamic_rcnn import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
... | 2,589 | 25.979167 | 99 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/dcn/deform_pool_module.py | from torch import nn
from .deform_pool_func import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 6,307 | 40.774834 | 79 | py |
DynamicRCNN | DynamicRCNN-master/dynamic_rcnn/kernels/ops/dcn/deform_conv_module.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .deform_conv_func import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
... | 5,802 | 31.601124 | 78 | py |
stargan | stargan-master/main.py | import os
import argparse
from solver import Solver
from data_loader import get_loader
from torch.backends import cudnn
def str2bool(v):
return v.lower() in ('true')
def main(config):
# For fast training.
cudnn.benchmark = True
# Create directories if not exist.
if not os.path.exists(config.log_... | 5,677 | 50.618182 | 116 | py |
stargan | stargan-master/data_loader.py | from torch.utils import data
from torchvision import transforms as T
from torchvision.datasets import ImageFolder
from PIL import Image
import torch
import os
import random
class CelebA(data.Dataset):
"""Dataset class for the CelebA dataset."""
def __init__(self, image_dir, attr_path, selected_attrs, transfo... | 3,244 | 34.271739 | 84 | py |
stargan | stargan-master/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class ResidualBlock(nn.Module):
"""Residual Block with instance normalization."""
def __init__(self, dim_in, dim_out):
super(ResidualBlock, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(di... | 3,719 | 40.797753 | 116 | py |
stargan | stargan-master/solver.py | from model import Generator
from model import Discriminator
from torch.autograd import Variable
from torchvision.utils import save_image
import torch
import torch.nn.functional as F
import numpy as np
import os
import time
import datetime
class Solver(object):
"""Solver for training and testing StarGAN."""
d... | 27,540 | 46.321306 | 122 | py |
astrocook | astrocook-master/py2md.py | from astrocook.cookbook_absorbers import CookbookAbsorbers as Absorbers
from astrocook.cookbook_continuum import CookbookContinuum as Continuum
from astrocook.cookbook_edit import CookbookEdit as Edit
from astrocook.cookbook_flux import CookbookFlux as Flux
from astrocook.cookbook_general import CookbookGeneral as Gene... | 4,659 | 30.066667 | 87 | py |
CCA-SSG | CCA-SSG-main/main.py | import argparse
from model import CCA_SSG, LogReg
from aug import random_aug
from dataset import load
import numpy as np
import torch as th
import torch.nn as nn
import warnings
warnings.filterwarnings('ignore')
parser = argparse.ArgumentParser(description='CCA-SSG')
parser.add_argument('--dataname', type=str, de... | 4,978 | 31.122581 | 122 | py |
CCA-SSG | CCA-SSG-main/aug.py | import torch as th
import numpy as np
import dgl
def random_aug(graph, x, feat_drop_rate, edge_mask_rate):
n_node = graph.number_of_nodes()
edge_mask = mask_edge(graph, edge_mask_rate)
feat = drop_feature(x, feat_drop_rate)
ng = dgl.graph([])
ng.add_nodes(n_node)
src = graph.edges()[0]
ds... | 885 | 21.717949 | 57 | py |
CCA-SSG | CCA-SSG-main/model.py | import torch.nn as nn
import torch.nn.functional as F
from dgl.nn import GraphConv
class LogReg(nn.Module):
def __init__(self, hid_dim, out_dim):
super(LogReg, self).__init__()
self.fc = nn.Linear(hid_dim, out_dim)
def forward(self, x):
ret = self.fc(x)
return ret
class MLP(... | 2,125 | 25.246914 | 76 | py |
CCA-SSG | CCA-SSG-main/dataset.py | import numpy as np
import torch as th
from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
from dgl.data import AmazonCoBuyPhotoDataset, AmazonCoBuyComputerDataset
from dgl.data import CoauthorCSDataset, CoauthorPhysicsDataset
def load(name):
if name == 'cora':
dataset = CoraGra... | 1,940 | 30.306452 | 79 | py |
IAI | IAI-main/setup.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import platform
import shutil
import sys
import warnings
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
... | 7,936 | 35.408257 | 125 | py |
IAI | IAI-main/tools/test.py | import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
fro... | 8,655 | 38.167421 | 79 | py |
IAI | IAI-main/tools/train.py | import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector... | 6,889 | 35.648936 | 79 | py |
IAI | IAI-main/tools/deployment/mmdet2torchserve.py | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except ImportError:
package_model = None
def mmdet2t... | 3,605 | 32.388889 | 78 | py |
IAI | IAI-main/tools/deployment/onnx2tensorrt.py | import argparse
import os
import os.path as osp
import numpy as np
import onnx
import onnxruntime as ort
import torch
from mmcv.ops import get_onnxruntime_op_path
from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt,
save_trt_engine)
from mmcv.visualization.image import ... | 5,713 | 30.744444 | 79 | py |
IAI | IAI-main/tools/deployment/mmdet_handler.py | import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties = context.system_properties
self.map_loc... | 2,462 | 34.185714 | 79 | py |
IAI | IAI-main/tools/deployment/pytorch2onnx.py | import argparse
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmcv import DictAction
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
... | 8,830 | 35.044898 | 78 | py |
IAI | IAI-main/tools/model_converters/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,125 | 27.15 | 77 | py |
IAI | IAI-main/tools/model_converters/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
IAI | IAI-main/tools/model_converters/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,800 | 31.385714 | 79 | py |
IAI | IAI-main/tools/model_converters/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,530 | 41.542169 | 78 | py |
IAI | IAI-main/tools/analysis_tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTenso... | 3,802 | 32.359649 | 79 | py |
IAI | IAI-main/tools/analysis_tools/get_flops.py | import argparse
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a det... | 2,566 | 30.304878 | 79 | py |
IAI | IAI-main/tools/analysis_tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from pycocotools.coco import... | 15,365 | 38.299233 | 79 | py |
IAI | IAI-main/configs/iai/ytvis2019_iai_condinst_r50_ms.py | # model settings
batch_size = 4
max_obj_num = 20
model = dict(
type='IAICondInst',
pretrained='torchvision://resnet50',
id_cfg=dict(num_frames=5, batch_size=batch_size, max_obj_num=max_obj_num),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3... | 5,086 | 29.279762 | 96 | py |
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