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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MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/tdtr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
import os
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import (
CharPolygons,
... | 11,925 | 39.020134 | 120 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import numpy as np
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 th... | 1,498 | 26.254545 | 72 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/utils.py | #!/usr/bin/env python3
import os
import shlex
import shutil
import subprocess
def extract_archive(dataset_archive, tmp_data_path):
if not os.path.isfile(dataset_archive):
return False
dataset_ext = os.path.splitext(dataset_archive)[1]
if dataset_ext != ".gz" and dataset_ext != ".tar":
re... | 926 | 22.769231 | 85 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/synthtext.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
import os
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import (
SegmentationCh... | 9,096 | 38.042918 | 116 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/scut.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
import os
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import (
CharPolygons,
... | 13,327 | 39.510638 | 116 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/icdar.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
import os
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import (
SegmentationCh... | 11,125 | 39.458182 | 120 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/total_text.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
import os
import numpy as np
import torch
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import (
CharPolygons,
... | 12,866 | 39.589905 | 120 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .coco import COCODataset
from .concat_dataset import ConcatDataset, MixDataset
from .icdar import IcdarDataset
from .scut import ScutDataset
from .synthtext import SynthtextDataset
from .total_text import TotaltextDataset
__all__ = [
"COC... | 459 | 24.555556 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
class COCODataset(torchvision.datasets.coco.CocoDetection):
def __ini... | 2,363 | 34.818182 | 89 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/datasets/list_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Simple dataset class that wraps a list of path names
"""
from PIL import Image
from maskrcnn_benchmark.structures.bounding_box import BoxList
class ListDataset(object):
def __init__(self, image_lists, transforms=None):
self.imag... | 943 | 24.513514 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/samplers/grouped_batch_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import torch
from torch.utils.data.sampler import BatchSampler
from torch.utils.data.sampler import Sampler
class GroupedBatchSampler(BatchSampler):
"""
Wraps another sampler to yield a mini-batch of indices.
It enfo... | 4,844 | 41.130435 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/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 |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/samplers/distributed.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Code is copy-pasted exactly as in torch.utils.data.distributed,
# with a modification in the import to use the deprecated backend
# FIXME remove this once c10d fixes the bug it has
import math
import torch
import torch.distributed as dist
from to... | 2,777 | 38.126761 | 86 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/samplers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .distributed import DistributedSampler
from .grouped_batch_sampler import GroupedBatchSampler
from .iteration_based_batch_sampler import IterationBasedBatchSampler
__all__ = ["DistributedSampler", "GroupedBatchSampler", "IterationBasedBatchSa... | 328 | 46 | 85 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/transforms/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .transforms import Compose
from .transforms import Resize
from .transforms import RandomHorizontalFlip
from .transforms import ToTensor
from .transforms import Normalize
from .build import build_transforms
| 285 | 27.6 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/transforms/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from . import transforms as T
def build_transforms(cfg, is_train=True):
to_bgr255 = cfg.INPUT.TO_BGR255
normalize_transform = T.Normalize(
mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD, to_bgr255=to_bgr255
)
if is_tra... | 2,636 | 36.140845 | 125 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/transforms/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import random
import cv2
import numpy as np
from PIL import Image
from shapely import affinity
from shapely.geometry import Polygon
from torchvision.transforms import functional as F
class Compose(object):
def __init__(self, transforms):
... | 12,621 | 32.480106 | 83 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/registry.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.utils.registry import Registry
BACKBONES = Registry()
RPN_HEADS = Registry()
ROI_BOX_FEATURE_EXTRACTORS = Registry()
ROI_BOX_PREDICTOR = Registry()
ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry()
ROI_KEYPOINT_PREDICTOR = Regi... | 400 | 29.846154 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/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... | 4,845 | 44.28972 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/make_layers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Miscellaneous utility functions
"""
import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.config import cfg
from maskrcnn_benchmark.layers import Conv2d
from maskrcnn_benchmark.modeling.poolers import P... | 3,557 | 27.926829 | 78 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/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 ... | 404 | 22.823529 | 97 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/poolers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.layers import ROIAlign
from .utils import cat
class LevelMapper(object):
"""Determine which FPN level each RoI in a set of RoIs should map... | 4,171 | 32.645161 | 90 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/balanced_positive_negative_sampler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO
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):
... | 2,678 | 37.271429 | 83 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/box_coder.py | #!/usr/bin/env python3
# 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, bbo... | 3,462 | 33.979798 | 86 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/backbone/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",
# "ResNe... | 24,619 | 30.808786 | 90 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/backbone/backbone.py | # # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# from collections import OrderedDict
# from torch import nn
# from . import fpn as fpn_module
# from . import resnet
# def build_resnet_backbone(cfg):
# body = resnet.ResNet(cfg)
# model = nn.Sequential(OrderedDict([("body", body)]))... | 4,395 | 32.30303 | 89 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/backbone/resnet34.py | import torch
import torch.nn.functional as F
from torch import nn
import math
from maskrcnn_benchmark.layers import FrozenBatchNorm2d
from maskrcnn_benchmark.layers import Conv2d
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return Conv2d(in_planes, out_planes, kernel_size=3, stri... | 2,729 | 26.857143 | 67 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/backbone/fpn.py | # #!/usr/bin/env python3
# # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# import torch
# import torch.nn.functional as F
# from torch import nn
# class FPN(nn.Module):
# """
# Module that adds FPN on top of a list of feature maps.
# The feature maps are currently supposed to be ... | 7,331 | 40.659091 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/backbone/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .backbone import build_backbone
| 109 | 35.666667 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/detector/detectors.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .generalized_rcnn import GeneralizedRCNN
_DETECTION_META_ARCHITECTURES = {"GeneralizedRCNN": GeneralizedRCNN}
def build_detection_model(cfg):
meta_arch = _DETECTION_META_ARCHITECTURES[cfg.MODEL.META_ARCHITECTURE]... | 347 | 28 | 74 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/detector/generalized_rcnn.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
Implements the Generalized R-CNN framework
"""
import torch
from torch import nn
from maskrcnn_benchmark.structures.image_list import to_image_list
from ..backbone import build_backbone
from ..rpn.rpn import build_rpn
... | 3,837 | 35.903846 | 101 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/detector/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .detectors import build_detection_model
| 117 | 38.333333 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/segmentation/inference.py | #!/usr/bin/env python3
import numpy as np
import torch
import cv2
import pyclipper
from shapely.geometry import Polygon
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist, cat_boxlist_gt
from maskrcnn_benchmark.structures.boxlist_ops import ... | 15,089 | 38.815303 | 148 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/segmentation/loss.py | #!/usr/bin/env python3
"""
This file contains specific functions for computing losses on the SEG
file
"""
import torch
class SEGLossComputation(object):
"""
This class computes the SEG loss.
"""
def __init__(self, cfg):
self.eps = 1e-6
self.cfg = cfg
def __call__(self, preds, ta... | 2,122 | 31.661538 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/segmentation/segmentation.py | #!/usr/bin/env python3
import torch
from torch import nn
from .inference import make_seg_postprocessor
from .loss import make_seg_loss_evaluator
import time
def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes, out_planes, kernel_siz... | 6,573 | 34.923497 | 110 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/rpn/inference.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark... | 7,490 | 35.720588 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/rpn/anchor_generator.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self... | 8,907 | 32.742424 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/rpn/loss.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
This file contains specific functions for computing losses on the RPN
file
"""
import torch
from torch.nn import functional as F
from ..balanced_positive_negative_sampler import BalancedPositiveNegativeSampler
from ..ut... | 6,123 | 39.026144 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/rpn/rpn.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from .loss import make_rpn_loss_evaluator
from .anchor_generator import make_anchor_generator
from ... | 5,453 | 37.680851 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/rpn/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# from .rpn import build_rpn
| 101 | 33 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/roi_heads.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .box_head.box_head import build_roi_box_head
from .mask_head.mask_head import build_roi_mask_head
class CombinedROIHeads(torch.nn.ModuleDict):
"""
Combines a set of individual heads (for box predi... | 2,237 | 36.932203 | 86 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
from PIL import Image
from torch import nn
import cv2
from torch.nn import functional as F
from maskrcnn_benchmark.structures.bounding_box import BoxList
# TODO check if want to return a single BoxList or a composi... | 8,971 | 34.184314 | 209 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_feature_extractors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
from torch.nn import functional as F
from ..box_head.roi_box_feature_extractors import ResNet50Conv5ROIFeatureExtractor
from maskrcnn_benchmark.modeling.poolers import Pooler
from maskrcnn_benchmark.layers import Conv2d
clas... | 2,762 | 36.849315 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.utils import cat
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_iou
from to... | 8,431 | 34.428571 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_seq_predictors.py | # Written by Minghui Liao
import math
import random
import numpy as np
import torch
from maskrcnn_benchmark.utils.chars import char2num, num2char
from torch import nn
from torch.nn import functional as F
gpu_device = torch.device("cuda")
cpu_device = torch.device("cpu")
def reduce_mul(l):
out = 1.0
for x i... | 16,629 | 42.534031 | 134 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/roi_mask_predictors.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.layers import Conv2d, ConvTranspose2d
from torch import nn
from torch.nn import functional as F
from .roi_seq_predictors import make_roi_seq_predictor
class MaskRCNNC4Predictor(nn.Module):
def __init__(self, cfg):
... | 11,124 | 40.356877 | 122 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/mask_head/mask_head.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from maskrcnn_benchmark.modeling.matcher import Matcher
from maskrcnn_benchmark.modeling.utils import cat
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmar... | 27,041 | 44.679054 | 160 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/inference.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torch.nn.functional as F
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_nms
from maskrcnn_benchmark.struc... | 7,693 | 42.468927 | 148 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.nn import functional as F
from maskrcnn_benchmark.modeling.backbone import resnet
from maskrcnn_benchmark.modeling.poolers import Pooler
from maskrcnn_benchmark.modeling.utils imp... | 6,713 | 38.263158 | 145 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/box_head.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .inference import make_roi_box_post_processor
from .loss import make_roi_box_loss_evaluator
from .roi_box_feature_extractors import make_roi_box_feature_extractor
from .roi_box_predictors import make_roi_bo... | 3,104 | 35.529412 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.layers import smooth_l1_loss
from maskrcnn_benchmark.modeling.balanced_positive_negative_sampler import (
BalancedPositiveNegativeSampler,
)
from maskrcnn_benchmark.modeling.box_coder ... | 7,135 | 37.572973 | 91 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_predictors.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from torch import nn
class FastRCNNPredictor(nn.Module):
def __init__(self, config, pretrained=None):
super(FastRCNNPredictor, self).__init__()
stage_index = 4
stage2_relative_factor = 2 ** (sta... | 2,501 | 33.273973 | 76 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/modeling/roi_heads/box_head/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/structures/image_list.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
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 a field the original sizes of ea... | 4,459 | 35.859504 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/structures/segmentation_mask.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import cv2
import numpy as np
import pycocotools.mask as mask_utils
import torch
from maskrcnn_benchmark.utils.chars import char2num
import pyclipper
# from PIL import Image
from shapely import affinity
from shapely.geometry ... | 27,175 | 34.431551 | 161 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/structures/bounding_box.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
# from shapely import affinity
# from shapely.geometry import box
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BoxList(object):
"""
This class represents a set of bounding boxes.
The boun... | 11,570 | 35.617089 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/structures/boxlist_ops.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from maskrcnn_benchmark.layers import nms as _box_nms
from .bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
import numpy as np
import shapely
from shapely... | 7,393 | 30.46383 | 90 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/structures/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/weighted_editdistance.py | def weighted_edit_distance(word1, word2, scores):
m = len(word1)
n = len(word2)
dp = [[0 for __ in range(m + 1)] for __ in range(n + 1)]
for j in range(m + 1):
dp[0][j] = j
for i in range(n + 1):
dp[i][0] = i
for i in range(1, n + 1): ## word2
for j in range(1, m + 1): #... | 1,813 | 31.981818 | 107 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/rotated_icdar2013/e2e/rrc_evaluation_funcs.py | #!/usr/bin/env python2
#encoding: UTF-8
import json
import sys;sys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
def print_help():
sys.stdout.write('Usage: python %s.py -g=<gtFile... | 15,410 | 40.764228 | 359 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/rotated_icdar2013/e2e/script.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# encoding=utf8
from collections import namedtuple
import rrc_evaluation_funcs
import importlib
from prepare_results import prepare_results_for_evaluation
def evaluation_imports():
"""
evaluation_imports: Dictionary ( key = module name , value = alias ) with pyth... | 20,045 | 42.578261 | 322 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/rotated_icdar2013/e2e/prepare_results.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import os
sys.path.append('./')
import shapely
from shapely.geometry import Polygon,MultiPoint
import numpy as np
import editdistance
sys.path.append('../../')
from weighted_editdistance import weighted_edit_distance
from tqdm import tqdm
try:
import pickle
... | 10,790 | 39.41573 | 239 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/icdar2015/e2e/rrc_evaluation_funcs.py | #!/usr/bin/env python2
#encoding: UTF-8
import json
import sys;sys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
def print_help():
sys.stdout.write('Usage: python %s.py -g=<gtFile... | 15,410 | 40.764228 | 359 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/icdar2015/e2e/script.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# encoding=utf8
from collections import namedtuple
import rrc_evaluation_funcs
import importlib
from prepare_results import prepare_results_for_evaluation
def evaluation_imports():
"""
evaluation_imports: Dictionary ( key = module name , value = alias ) with pyth... | 20,101 | 42.605206 | 322 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/icdar2015/e2e/prepare_results.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import os
sys.path.append('./')
import shapely
from shapely.geometry import Polygon,MultiPoint
import numpy as np
import editdistance
sys.path.append('../../')
from weighted_editdistance import weighted_edit_distance
from tqdm import tqdm
try:
import pickle
... | 10,659 | 39.532319 | 243 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/totaltext/e2e/rrc_evaluation_funcs.py | #!/usr/bin/env python2
#encoding: UTF-8
import json
import sys;sys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
def print_help():
sys.stdout.write('Usage: python %s.py -g=<gtFile... | 15,410 | 40.764228 | 359 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/totaltext/e2e/script.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# encoding=utf8
from collections import namedtuple
import rrc_evaluation_funcs_total_text as rrc_evaluation_funcs
import importlib
from prepare_results import prepare_results_for_evaluation
def evaluation_imports():
"""
evaluation_imports: Dictionary ( key = module... | 19,780 | 42.763274 | 322 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/totaltext/e2e/prepare_results.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import os
import glob
sys.path.append('./')
import shapely
from shapely.geometry import Polygon,MultiPoint
import numpy as np
import editdistance
sys.path.append('../../')
from weighted_editdistance import weighted_edit_distance
from tqdm import tqdm
try:
im... | 9,314 | 38.807692 | 243 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/evaluation/totaltext/e2e/rrc_evaluation_funcs_total_text.py | #!/usr/bin/env python2
#encoding: UTF-8
import json
import sys;sys.path.append('./')
import zipfile
import re
import sys
import os
import codecs
import importlib
from io import StringIO
def print_help():
sys.stdout.write('Usage: python %s.py -g=<gtFile> -s=<submFile> -o=<outputFolder> [-i=<gtImagesFile> -p=<jsonPa... | 15,482 | 41.652893 | 359 | py |
HASOC-2021---Hate-Speech-Detection | HASOC-2021---Hate-Speech-Detection-main/main.py | import getopt
import sys
import tensorflow as tf
import os
import json
import numpy as np
import file_utils
from datetime import datetime
import matplotlib.pyplot as plt
import h5py
from bert.tokenization.bert_tokenization import FullTokenizer
from bert import BertModelLayer
from bert.loader import StockBertConfig, map... | 7,085 | 37.934066 | 179 | py |
HASOC-2021---Hate-Speech-Detection | HASOC-2021---Hate-Speech-Detection-main/data_loader.py | import pandas as pd
import numpy as np
from bert.tokenization.bert_tokenization import FullTokenizer
from ekphrasis.classes.preprocessor import TextPreProcessor
from ekphrasis.classes.tokenizer import SocialTokenizer
from ekphrasis.dicts.emoticons import emoticons
from tensorflow.keras.preprocessing.text import Tokeniz... | 10,369 | 31.507837 | 144 | py |
HASOC-2021---Hate-Speech-Detection | HASOC-2021---Hate-Speech-Detection-main/file_utils.py | import os
import json
def save_list_to_file(input_list: list, file_path):
file = open(file_path, 'w')
file.write("\n".join(str(item) for item in input_list))
file.close()
def save_string_to_file(text, file_path):
file = open(file_path, 'w')
file.write(text)
file.close()
def read_file_to_set(f... | 1,070 | 23.906977 | 59 | py |
HASOC-2021---Hate-Speech-Detection | HASOC-2021---Hate-Speech-Detection-main/models.py | import tensorflow as tf
import numpy as np
from bert import BertModelLayer
from bert.loader import StockBertConfig, map_stock_config_to_params, load_stock_weights
from tensorflow import keras
from tensorflow.keras import layers
class MultiHeadSelfAttention(layers.Layer):
def __init__(self, embed_dim, num_heads=8):... | 17,504 | 41.799511 | 166 | py |
steer | steer-master/ffjord/train_vae_flow.py | # !/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import time
import torch
import torch.utils.data
import torch.optim as optim
import numpy as np
import math
import random
import os
import datetime
import lib.utils as utils
import lib.layers.odefunc as odefunc
imp... | 14,613 | 39.707521 | 124 | py |
steer | steer-master/ffjord/train_toy.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import argparse
import os
import time
import torch
import torch.optim as optim
import lib.toy_data as toy_data
import lib.utils as utils
from lib.visualize_flow import visualize_transform
import lib.layers.odefunc as odefunc
from train_misc imp... | 9,288 | 39.038793 | 119 | py |
steer | steer-master/ffjord/train_img2d.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import argparse
import os
import time
import torch
import torch.optim as optim
import lib.utils as utils
from lib.visualize_flow import visualize_transform
import lib.layers.odefunc as odefunc
from train_misc import standard_normal_logprob
from... | 9,789 | 37.543307 | 119 | py |
steer | steer-master/ffjord/train_cnf.py | import argparse
import os
import time
import numpy as np
import torch
import torch.optim as optim
import torchvision.datasets as dset
import torchvision.transforms as tforms
from torchvision.utils import save_image
import lib.layers as layers
import lib.utils as utils
import lib.odenvp as odenvp
import lib.multiscale... | 18,212 | 39.654018 | 119 | py |
steer | steer-master/ffjord/train_discrete_tabular.py | import argparse
import os
import time
import torch
import lib.utils as utils
from lib.custom_optimizers import Adam
import lib.layers as layers
import datasets
from train_misc import standard_normal_logprob, count_parameters
parser = argparse.ArgumentParser()
parser.add_argument(
'--data', choices=['power', 'g... | 8,301 | 34.177966 | 120 | py |
steer | steer-master/ffjord/train_tabular.py | import argparse
import os
import time
import torch
import lib.utils as utils
import lib.layers.odefunc as odefunc
from lib.custom_optimizers import Adam
import datasets
from train_misc import standard_normal_logprob
from train_misc import set_cnf_options, count_nfe, count_parameters, count_total_time
from train_mis... | 12,249 | 38.90228 | 120 | py |
steer | steer-master/ffjord/train_discrete_toy.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import argparse
import os
import time
import torch
import torch.optim as optim
import lib.layers as layers
import lib.toy_data as toy_data
import lib.utils as utils
from lib.visualize_flow import visualize_transform
from train_misc import stand... | 6,334 | 32.877005 | 119 | py |
steer | steer-master/ffjord/train_misc.py | import six
import math
import lib.layers.wrappers.cnf_regularization as reg_lib
import lib.spectral_norm as spectral_norm
import lib.layers as layers
from lib.layers.odefunc import divergence_bf, divergence_approx
def standard_normal_logprob(z):
logZ = -0.5 * math.log(2 * math.pi)
return logZ - z.pow(2) / 2
... | 6,311 | 30.402985 | 117 | py |
steer | steer-master/ffjord/vae_lib/models/VAE.py | from __future__ import print_function
import torch
import torch.nn as nn
import vae_lib.models.flows as flows
from vae_lib.models.layers import GatedConv2d, GatedConvTranspose2d
class VAE(nn.Module):
"""
The base VAE class containing gated convolutional encoder and decoder architecture.
Can be used as a ... | 25,211 | 33.255435 | 120 | py |
steer | steer-master/ffjord/vae_lib/models/CNFVAE.py | import torch
import torch.nn as nn
from train_misc import build_model_tabular
import lib.layers as layers
from .VAE import VAE
import lib.layers.diffeq_layers as diffeq_layers
from lib.layers.odefunc import NONLINEARITIES
from torchdiffeq import odeint_adjoint as odeint
def get_hidden_dims(args):
return tuple(ma... | 14,405 | 33.881356 | 116 | py |
steer | steer-master/ffjord/vae_lib/models/layers.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import numpy as np
import torch.nn.functional as F
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class GatedConv2d(nn.Module):
def __init__(self... | 7,128 | 32.947619 | 115 | py |
steer | steer-master/ffjord/vae_lib/models/__init__.py | 0 | 0 | 0 | py | |
steer | steer-master/ffjord/vae_lib/models/flows.py | """
Collection of flow strategies
"""
from __future__ import print_function
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from vae_lib.models.layers import MaskedConv2d, MaskedLinear
class Planar(nn.Module):
"""
PyTorch implementation of planar flows... | 9,939 | 32.133333 | 118 | py |
steer | steer-master/ffjord/vae_lib/optimization/loss.py | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from vae_lib.utils.distributions import log_normal_diag, log_normal_standard, log_bernoulli
import torch.nn.functional as F
def binary_loss_function(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes the b... | 10,566 | 37.849265 | 116 | py |
steer | steer-master/ffjord/vae_lib/optimization/training.py | from __future__ import print_function
import time
import torch
from vae_lib.optimization.loss import calculate_loss
from vae_lib.utils.visual_evaluation import plot_reconstructions
from vae_lib.utils.log_likelihood import calculate_likelihood
import numpy as np
from train_misc import count_nfe, override_divergence_fn... | 5,518 | 31.087209 | 120 | py |
steer | steer-master/ffjord/vae_lib/optimization/__init__.py | 0 | 0 | 0 | py | |
steer | steer-master/ffjord/vae_lib/utils/distributions.py | from __future__ import print_function
import torch
import torch.utils.data
import math
MIN_EPSILON = 1e-5
MAX_EPSILON = 1. - 1e-5
PI = torch.FloatTensor([math.pi])
if torch.cuda.is_available():
PI = PI.cuda()
# N(x | mu, var) = 1/sqrt{2pi var} exp[-1/(2 var) (x-mean)(x-mean)]
# log N(x| mu, var) = -log sqrt(2pi... | 1,768 | 25.80303 | 86 | py |
steer | steer-master/ffjord/vae_lib/utils/plotting.py | from __future__ import division
from __future__ import print_function
import numpy as np
import matplotlib
# noninteractive background
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def plot_training_curve(train_loss, validation_loss, fname='training_curve.pdf', labels=None):
"""
Plots train_loss and ... | 4,021 | 37.304762 | 106 | py |
steer | steer-master/ffjord/vae_lib/utils/log_likelihood.py | from __future__ import print_function
import time
import numpy as np
from scipy.misc import logsumexp
from vae_lib.optimization.loss import calculate_loss_array
def calculate_likelihood(X, model, args, logger, S=5000, MB=500):
# set auxiliary variables for number of training and test sets
N_test = X.size(0)
... | 1,592 | 25.114754 | 110 | py |
steer | steer-master/ffjord/vae_lib/utils/load_data.py | from __future__ import print_function
import torch
import torch.utils.data as data_utils
import pickle
from scipy.io import loadmat
import numpy as np
import os
def load_static_mnist(args, **kwargs):
"""
Dataloading function for static mnist. Outputs image data in vectorized form: each image is a vector of... | 7,592 | 35.859223 | 116 | py |
steer | steer-master/ffjord/vae_lib/utils/__init__.py | 0 | 0 | 0 | py | |
steer | steer-master/ffjord/vae_lib/utils/visual_evaluation.py | from __future__ import print_function
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
def plot_reconstructions(data, recon_mean, loss, loss_type, epoch, args):
if args.input_type == 'multinomial':
# data is already between 0 and 1
... | 2,063 | 37.222222 | 119 | py |
steer | steer-master/ffjord/datasets/power.py | import numpy as np
import datasets
class POWER:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
trn, val, tst = load_data_normalised()
self.trn = self.Data(trn)
self.val = self.Da... | 1,940 | 24.88 | 108 | py |
steer | steer-master/ffjord/datasets/hepmass.py | import pandas as pd
import numpy as np
from collections import Counter
from os.path import join
import datasets
class HEPMASS:
"""
The HEPMASS data set.
http://archive.ics.uci.edu/ml/datasets/HEPMASS
"""
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)... | 2,730 | 28.365591 | 112 | py |
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