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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STTS | STTS-main/VideoSwin/mmaction/core/evaluation/eval_detection.py | import json
import numpy as np
from mmcv.utils import print_log
from ...utils import get_root_logger
from .accuracy import interpolated_precision_recall, pairwise_temporal_iou
class ActivityNetLocalization:
"""Class to evaluate detection results on ActivityNet.
Args:
ground_truth_filename (str | No... | 9,363 | 39.017094 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_utils.py | import csv
import logging
import time
from collections import defaultdict
import numpy as np
from .ava_evaluation import object_detection_evaluation as det_eval
from .ava_evaluation import standard_fields
def det2csv(dataset, results, custom_classes):
csv_results = []
for idx in range(len(dataset)):
... | 8,215 | 33.666667 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/eval_hooks.py | import os
import os.path as osp
import warnings
from math import inf
import torch.distributed as dist
from torch.nn.modules.batchnorm import _BatchNorm
from torch.utils.data import DataLoader
try:
from mmcv.runner import EvalHook as BasicEvalHook
from mmcv.runner import DistEvalHook as BasicDistEvalHook
... | 16,695 | 41.700767 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/accuracy.py | import numpy as np
def confusion_matrix(y_pred, y_real, normalize=None):
"""Compute confusion matrix.
Args:
y_pred (list[int] | np.ndarray[int]): Prediction labels.
y_real (list[int] | np.ndarray[int]): Ground truth labels.
normalize (str | None): Normalizes confusion matrix over the ... | 20,710 | 38.449524 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/__init__.py | from .accuracy import (average_precision_at_temporal_iou,
average_recall_at_avg_proposals, confusion_matrix,
get_weighted_score, interpolated_precision_recall,
mean_average_precision, mean_class_accuracy,
mmit_mean_average_preci... | 866 | 50 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/per_image_evaluation.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 16,948 | 46.211699 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/standard_fields.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 5,313 | 44.810345 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/np_box_list.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 4,923 | 34.171429 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/metrics.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 5,689 | 38.79021 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/__init__.py | 0 | 0 | 0 | py | |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/object_detection_evaluation.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 24,757 | 42.057391 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/evaluation/ava_evaluation/np_box_ops.py | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 3,462 | 33.979798 | 80 | py |
STTS | STTS-main/VideoSwin/mmaction/core/bbox/bbox_target.py | import torch
import torch.nn.functional as F
def bbox_target(pos_bboxes_list, neg_bboxes_list, gt_labels, cfg):
"""Generate classification targets for bboxes.
Args:
pos_bboxes_list (list[Tensor]): Positive bboxes list.
neg_bboxes_list (list[Tensor]): Negative bboxes list.
gt_labels (l... | 1,334 | 30.785714 | 73 | py |
STTS | STTS-main/VideoSwin/mmaction/core/bbox/__init__.py | from .assigners import MaxIoUAssignerAVA
from .bbox_target import bbox_target
from .transforms import bbox2result
__all__ = ['MaxIoUAssignerAVA', 'bbox_target', 'bbox2result']
| 177 | 28.666667 | 61 | py |
STTS | STTS-main/VideoSwin/mmaction/core/bbox/transforms.py | import numpy as np
def bbox2result(bboxes, labels, num_classes, thr=0.01):
"""Convert detection results to a list of numpy arrays.
Args:
bboxes (Tensor): shape (n, 4)
labels (Tensor): shape (n, #num_classes)
num_classes (int): class number, including background class
thr (floa... | 1,167 | 30.567568 | 76 | py |
STTS | STTS-main/VideoSwin/mmaction/core/bbox/assigners/__init__.py | from .max_iou_assigner_ava import MaxIoUAssignerAVA
__all__ = ['MaxIoUAssignerAVA']
| 85 | 20.5 | 51 | py |
STTS | STTS-main/VideoSwin/mmaction/core/bbox/assigners/max_iou_assigner_ava.py | import torch
from mmaction.utils import import_module_error_class
try:
from mmdet.core.bbox import AssignResult, MaxIoUAssigner
from mmdet.core.bbox.builder import BBOX_ASSIGNERS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
if mmdet_imported:
@BBOX_ASSI... | 6,032 | 42.402878 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/runner/omnisource_runner.py | # Copyright (c) Open-MMLab. All rights reserved.
import time
import warnings
import mmcv
from mmcv.runner import EpochBasedRunner, Hook
from mmcv.runner.utils import get_host_info
def cycle(iterable):
iterator = iter(iterable)
while True:
try:
yield next(iterator)
except StopItera... | 6,589 | 39.429448 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/runner/__init__.py | from .omnisource_runner import OmniSourceDistSamplerSeedHook, OmniSourceRunner
__all__ = ['OmniSourceRunner', 'OmniSourceDistSamplerSeedHook']
| 144 | 35.25 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/core/hooks/__init__.py | from .output import OutputHook
__all__ = ['OutputHook']
| 57 | 13.5 | 30 | py |
STTS | STTS-main/VideoSwin/mmaction/core/hooks/output.py | import functools
import warnings
import torch
class OutputHook:
"""Output feature map of some layers.
Args:
module (nn.Module): The whole module to get layers.
outputs (tuple[str] | list[str]): Layer name to output. Default: None.
as_tensor (bool): Determine to return a tensor or a n... | 2,040 | 29.014706 | 84 | py |
STTS | STTS-main/VideoSwin/mmaction/core/optimizer/topk_optimizer_constructor.py | import torch
from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
from mmcv.utils import SyncBatchNorm, _BatchNorm, _ConvNd
@OPTIMIZER_BUILDERS.register_module()
class TopkOptimizerConstructor(DefaultOptimizerConstructor):
"""Optimizer constructor in TSM model.
This constructor builds opti... | 2,226 | 32.238806 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/core/optimizer/tsm_optimizer_constructor.py | import torch
from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
from mmcv.utils import SyncBatchNorm, _BatchNorm, _ConvNd
@OPTIMIZER_BUILDERS.register_module()
class TSMOptimizerConstructor(DefaultOptimizerConstructor):
"""Optimizer constructor in TSM model.
This constructor builds optim... | 4,074 | 36.045455 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/core/optimizer/copy_of_sgd.py | from mmcv.runner import OPTIMIZERS
from torch.optim import SGD
@OPTIMIZERS.register_module()
class CopyOfSGD(SGD):
"""A clone of torch.optim.SGD.
A customized optimizer could be defined like CopyOfSGD. You may derive from
built-in optimizers in torch.optim, or directly implement a new optimizer.
"""
| 320 | 25.75 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/core/optimizer/__init__.py | from .copy_of_sgd import CopyOfSGD
from .tsm_optimizer_constructor import TSMOptimizerConstructor
from .topk_optimizer_constructor import TopkOptimizerConstructor
__all__ = ['CopyOfSGD', 'TSMOptimizerConstructor', 'TopkOptimizerConstructor']
| 243 | 39.666667 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/__init__.py | from .backbones import (C3D, X3D, MobileNetV2, MobileNetV2TSM, ResNet,
ResNet2Plus1d, ResNet3d, ResNet3dCSN, ResNet3dLayer,
ResNet3dSlowFast, ResNet3dSlowOnly, ResNetAudio,
ResNetTIN, ResNetTSM, TANet)
from .builder import (BACKBONES, DETECTORS, HE... | 2,178 | 57.891892 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/builder.py | import warnings
from mmcv.cnn import MODELS as MMCV_MODELS
from mmcv.utils import Registry
from mmaction.utils import import_module_error_func
MODELS = Registry('models', parent=MMCV_MODELS)
BACKBONES = MODELS
NECKS = MODELS
HEADS = MODELS
RECOGNIZERS = MODELS
LOSSES = MODELS
LOCALIZERS = MODELS
try:
from mmdet... | 2,741 | 28.804348 | 107 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/base.py | from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import torch
import torch.distributed as dist
import torch.nn as nn
from .. import builder
class BaseLocalizer(nn.Module, metaclass=ABCMeta):
"""Base class for localizers.
All localizers should subclass it. All subclass should over... | 5,143 | 34.722222 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/bsn.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ...localization import temporal_iop
from ..builder import LOCALIZERS, build_loss
from .base import BaseLocalizer
from .utils import post_processing
@LOCALIZERS.register_module()
class TEM(BaseLocalizer):
"""Temporal Evalua... | 15,855 | 39.141772 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/ssn.py | import torch
import torch.nn as nn
from .. import builder
from ..builder import LOCALIZERS
from .base import BaseLocalizer
@LOCALIZERS.register_module()
class SSN(BaseLocalizer):
"""Temporal Action Detection with Structured Segment Networks.
Args:
backbone (dict): Config for building backbone.
... | 5,048 | 36.4 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/__init__.py | from .base import BaseLocalizer
from .bmn import BMN
from .bsn import PEM, TEM
from .ssn import SSN
__all__ = ['PEM', 'TEM', 'BMN', 'SSN', 'BaseLocalizer']
| 157 | 21.571429 | 55 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/bmn.py | import math
import numpy as np
import torch
import torch.nn as nn
from ...localization import temporal_iop, temporal_iou
from ..builder import LOCALIZERS, build_loss
from .base import BaseLocalizer
from .utils import post_processing
@LOCALIZERS.register_module()
class BMN(BaseLocalizer):
"""Boundary Matching Ne... | 17,788 | 41.659472 | 93 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/utils/post_processing.py | from mmaction.localization import soft_nms
def post_processing(result, video_info, soft_nms_alpha, soft_nms_low_threshold,
soft_nms_high_threshold, post_process_top_k,
feature_extraction_interval):
"""Post process for temporal proposals generation.
Args:
result... | 1,807 | 39.177778 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/localizers/utils/__init__.py | from .post_processing import post_processing
__all__ = ['post_processing']
| 76 | 18.25 | 44 | py |
STTS | STTS-main/VideoSwin/mmaction/models/recognizers/base.py | import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import auto_fp16
from .. import builder
class BaseRecognizer(nn.Module, metaclass=ABCMeta):
"""Base cla... | 12,392 | 36.554545 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/recognizers/recognizer3d.py | import torch
from torch import nn
from ..builder import RECOGNIZERS
from .base import BaseRecognizer
def copyParams(module_src, module_dest):
params_src = module_src.named_parameters()
params_dest = module_dest.named_parameters()
dict_dest = dict(params_dest)
for name, param in params_src:
... | 4,166 | 30.330827 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/recognizers/recognizer2d.py | import torch
from torch import nn
from ..builder import RECOGNIZERS
from .base import BaseRecognizer
@RECOGNIZERS.register_module()
class Recognizer2D(BaseRecognizer):
"""2D recognizer model framework."""
def forward_train(self, imgs, labels, **kwargs):
"""Defines the computation performed at every ... | 6,572 | 34.33871 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/recognizers/__init__.py | from .audio_recognizer import AudioRecognizer
from .base import BaseRecognizer
from .recognizer2d import Recognizer2D
from .recognizer3d import Recognizer3D
__all__ = ['BaseRecognizer', 'Recognizer2D', 'Recognizer3D', 'AudioRecognizer']
| 238 | 33.142857 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/recognizers/audio_recognizer.py | from ..builder import RECOGNIZERS
from .base import BaseRecognizer
@RECOGNIZERS.register_module()
class AudioRecognizer(BaseRecognizer):
"""Audio recognizer model framework."""
def forward(self, audios, label=None, return_loss=True):
"""Define the computation performed at every call."""
if re... | 3,632 | 34.617647 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/common/tam.py | import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init, kaiming_init, normal_init
class TAM(nn.Module):
"""Temporal Adaptive Module(TAM) for TANet.
This module is proposed in `TAM: TEMPORAL ADAPTIVE MODULE FOR VIDEO
RECOGNITION <https://arxiv.org/pdf/2005.06803>`_
A... | 5,051 | 36.422222 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/common/conv_audio.py | import torch
import torch.nn as nn
from mmcv.cnn import CONV_LAYERS, ConvModule, constant_init, kaiming_init
from torch.nn.modules.utils import _pair
@CONV_LAYERS.register_module()
class ConvAudio(nn.Module):
"""Conv2d module for AudioResNet backbone.
<https://arxiv.org/abs/2001.08740>`_.
Args:
... | 3,225 | 29.72381 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/models/common/lfb.py | import io
import os.path as osp
import warnings
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info
try:
import lmdb
lmdb_imported = True
except (ImportError, ModuleNotFoundError):
lmdb_imported = False
class LFB:
"""Long-Term Feature Bank (LFB).
... | 7,493 | 38.650794 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/common/conv2plus1d.py | import torch.nn as nn
from mmcv.cnn import CONV_LAYERS, build_norm_layer, constant_init, kaiming_init
from torch.nn.modules.utils import _triple
@CONV_LAYERS.register_module()
class Conv2plus1d(nn.Module):
"""(2+1)d Conv module for R(2+1)d backbone.
https://arxiv.org/pdf/1711.11248.pdf.
Args:
in... | 3,453 | 31.895238 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/common/__init__.py | from .conv2plus1d import Conv2plus1d
from .conv_audio import ConvAudio
from .lfb import LFB
from .tam import TAM
__all__ = ['Conv2plus1d', 'ConvAudio', 'LFB', 'TAM']
| 167 | 23 | 52 | py |
STTS | STTS-main/VideoSwin/mmaction/models/necks/tpn.py | import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, normal_init, xavier_init
from ..builder import NECKS, build_loss
class Identity(nn.Module):
"""Identity mapping."""
def forward(self, x):
return x
class DownSample(nn.Module):
"""DownSample mo... | 16,411 | 35.552339 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/necks/__init__.py | from .tpn import TPN
__all__ = ['TPN']
| 40 | 9.25 | 20 | py |
STTS | STTS-main/VideoSwin/mmaction/models/roi_extractors/__init__.py | from .single_straight3d import SingleRoIExtractor3D
__all__ = ['SingleRoIExtractor3D']
| 88 | 21.25 | 51 | py |
STTS | STTS-main/VideoSwin/mmaction/models/roi_extractors/single_straight3d.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmaction.utils import import_module_error_class
try:
from mmcv.ops import RoIAlign, RoIPool
except (ImportError, ModuleNotFoundError):
@import_module_error_class('mmcv-full')
class RoIAlign(nn.Module):
pass
@import_modul... | 4,474 | 33.689922 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/base.py | from abc import ABCMeta, abstractmethod
import torch.nn as nn
class BaseWeightedLoss(nn.Module, metaclass=ABCMeta):
"""Base class for loss.
All subclass should overwrite the ``_forward()`` method which returns the
normal loss without loss weights.
Args:
loss_weight (float): Factor scalar mu... | 1,181 | 25.266667 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/margin_loss.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from ..builder import LOSSES
def batched_index_select(input, dim, index):
for i in range(1, len(input.shape)):
if i != dim:
index = index.unsqueeze(i)
expanse = list(input.shape)
expanse[0] = -1
expanse[dim] = -1
... | 2,004 | 34.175439 | 91 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/distill_loss.py | import torch.nn.functional as F
import torch.nn as nn
from ..builder import LOSSES
@LOSSES.register_module()
class DistillationLoss(nn.Module):
"""
This module wraps a standard criterion and adds an extra knowledge distillation loss by
taking a teacher model prediction and using it as additional supervis... | 2,144 | 34.75 | 91 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/ohem_hinge_loss.py | import torch
class OHEMHingeLoss(torch.autograd.Function):
"""This class is the core implementation for the completeness loss in
paper.
It compute class-wise hinge loss and performs online hard example mining
(OHEM).
"""
@staticmethod
def forward(ctx, pred, labels, is_positive, ohem_rati... | 2,497 | 37.430769 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/bmn_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .binary_logistic_regression_loss import binary_logistic_regression_loss
@LOSSES.register_module()
class BMNLoss(nn.Module):
"""BMN Loss.
From paper https://arxiv.org/abs/1907.09702,
code https://github.c... | 7,173 | 38.635359 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/nll_loss.py | import torch.nn.functional as F
from ..builder import LOSSES
from .base import BaseWeightedLoss
@LOSSES.register_module()
class NLLLoss(BaseWeightedLoss):
"""NLL Loss.
It will calculate NLL loss given cls_score and label.
"""
def _forward(self, cls_score, label, **kwargs):
"""Forward functi... | 688 | 24.518519 | 74 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/hvu_loss.py | import torch
import torch.nn.functional as F
from ..builder import LOSSES
from .base import BaseWeightedLoss
@LOSSES.register_module()
class HVULoss(BaseWeightedLoss):
"""Calculate the BCELoss for HVU.
Args:
categories (tuple[str]): Names of tag categories, tags are organized in
this ord... | 6,676 | 46.021127 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/binary_logistic_regression_loss.py | import torch
import torch.nn as nn
from ..builder import LOSSES
def binary_logistic_regression_loss(reg_score,
label,
threshold=0.5,
ratio_range=(1.05, 21),
eps=1e-5):
"... | 2,061 | 32.258065 | 75 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/ssn_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .ohem_hinge_loss import OHEMHingeLoss
@LOSSES.register_module()
class SSNLoss(nn.Module):
@staticmethod
def activity_loss(activity_score, labels, activity_indexer):
"""Activity Loss.
It will... | 7,274 | 39.416667 | 102 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/cross_entropy_loss.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from ..builder import LOSSES
from .base import BaseWeightedLoss
@LOSSES.register_module()
class CrossEntropyLoss(BaseWeightedLoss):
"""Cross Entropy Loss.
Support two kinds of labels and their corresponding loss type. It's worth
mentionin... | 4,774 | 39.12605 | 132 | py |
STTS | STTS-main/VideoSwin/mmaction/models/losses/__init__.py | from .base import BaseWeightedLoss
from .binary_logistic_regression_loss import BinaryLogisticRegressionLoss
from .bmn_loss import BMNLoss
from .cross_entropy_loss import BCELossWithLogits, CrossEntropyLoss
from .hvu_loss import HVULoss
from .nll_loss import NLLLoss
from .ohem_hinge_loss import OHEMHingeLoss
from .ssn_... | 520 | 33.733333 | 75 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/mobilenet_v2.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ...utils import get_root_logger
from ..builder import BACKBONES
def make_divisible(value, divisor, min_... | 10,933 | 35.691275 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/c3d.py | import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init, normal_init
from mmcv.runner import load_checkpoint
from mmcv.utils import _BatchNorm
from ...utils import get_root_logger
from ..builder import BACKBONES
@BACKBONES.register_module()
class C3D(nn.Module):
"""C3D backbone.
A... | 4,771 | 33.085714 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/checkpoint.py | # Copyright (c) OpenMMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimize... | 24,129 | 34.021771 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet3d_slowonly.py | from ..builder import BACKBONES
from .resnet3d_slowfast import ResNet3dPathway
try:
from mmdet.models.builder import BACKBONES as MMDET_BACKBONES
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
@BACKBONES.register_module()
class ResNet3dSlowOnly(ResNet3dPathway):
... | 1,643 | 30.018868 | 74 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet3d_csn.py | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.utils import _BatchNorm
from ..builder import BACKBONES
from .resnet3d import Bottleneck3d, ResNet3d
class CSNBottleneck3d(Bottleneck3d):
"""Channel-Separated Bottleneck Block.
This module is proposed in
"Video Classification with Channel-S... | 6,234 | 40.845638 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/swin_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, trunc_normal_
# from mmcv.runner import load_checkpoint
from .checkpoint import load_checkpoint
from mmaction.utils import get_root_logger
from ..bu... | 30,486 | 40.032301 | 156 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet.py | import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import _BatchNorm
from torch.utils import checkpoint as cp
from ...utils import get_root_logger
from ..builder import BACKBONES
class BasicBlock(nn.Module):
... | 21,448 | 35.292724 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet_audio.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.utils import _ntuple
from ...utils import get_root_logger
from ..builder import BACKBONE... | 13,252 | 34.435829 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/topk.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import einops
from einops import rearrange
from math import sqrt
class PredictorLG(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, embed_dim=384):
super().__init__()
self.in_conv = nn.Seque... | 8,524 | 33.375 | 124 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet3d.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, NonLocal3d, build_activation_layer,
constant_init, kaiming_init)
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import _BatchNorm
from torch.nn.modules.utils import _ntuple, _trip... | 40,219 | 38.277344 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet3d_slowfast.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, kaiming_init
from mmcv.runner import _load_checkpoint, load_checkpoint
from mmcv.utils import print_log
from ...utils import get_root_logger
from ..builder import BACKBONES
from .resnet3d import ResNet3d
try:
from mmdet.models import BACKBONES as... | 21,060 | 39.424184 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet2plus1d.py | from ..builder import BACKBONES
from .resnet3d import ResNet3d
@BACKBONES.register_module()
class ResNet2Plus1d(ResNet3d):
"""ResNet (2+1)d backbone.
This model is proposed in `A Closer Look at Spatiotemporal Convolutions for
Action Recognition <https://arxiv.org/abs/1711.11248>`_
"""
def __init... | 1,482 | 28.66 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/mobilenet_v2_tsm.py | from ..builder import BACKBONES
from .mobilenet_v2 import InvertedResidual, MobileNetV2
from .resnet_tsm import TemporalShift
@BACKBONES.register_module()
class MobileNetV2TSM(MobileNetV2):
"""MobileNetV2 backbone for TSM.
Args:
num_segments (int): Number of frame segments. Default: 8.
is_shi... | 1,416 | 33.560976 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet_tsm.py | import torch
import torch.nn as nn
from mmcv.cnn import NonLocal3d
from torch.nn.modules.utils import _ntuple
from ..builder import BACKBONES
from .resnet import ResNet
class NL3DWrapper(nn.Module):
"""3D Non-local wrapper for ResNet50.
Wrap ResNet layers with 3D NonLocal modules.
Args:
block (... | 10,742 | 35.416949 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/resnet_tin.py | import torch
import torch.nn as nn
from mmaction.utils import import_module_error_func
from ..builder import BACKBONES
from .resnet_tsm import ResNetTSM
try:
from mmcv.ops import tin_shift
except (ImportError, ModuleNotFoundError):
@import_module_error_func('mmcv-full')
def tin_shift(*args, **kwargs):
... | 13,132 | 33.651715 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/__init__.py | from .c3d import C3D
from .mobilenet_v2 import MobileNetV2
from .mobilenet_v2_tsm import MobileNetV2TSM
from .resnet import ResNet
from .resnet2plus1d import ResNet2Plus1d
from .resnet3d import ResNet3d, ResNet3dLayer
from .resnet3d_csn import ResNet3dCSN
from .resnet3d_slowfast import ResNet3dSlowFast
from .resnet3d_s... | 807 | 35.727273 | 97 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/tanet.py | from copy import deepcopy
import torch.nn as nn
from torch.utils import checkpoint as cp
from ..builder import BACKBONES
from ..common import TAM
from .resnet import Bottleneck, ResNet
class TABlock(nn.Module):
"""Temporal Adaptive Block (TA-Block) for TANet.
This block is proposed in `TAM: TEMPORAL ADAPTI... | 3,690 | 31.095652 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/backbones/x3d.py | import math
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, Swish, build_activation_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils import _BatchNorm
from ...utils import get_root_logger
from ..builder import... | 19,116 | 35.482824 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/base.py | from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from ...core import top_k_accuracy
from ..builder import build_loss
class AvgConsensus(nn.Module):
"""Average consensus module.
Args:
dim (int): Decide which dim consensus function to apply.
Default: 1.
"""
... | 3,854 | 34.045455 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/fbo_head.py | import copy
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from mmcv.utils import _BatchNorm
from mmaction.models.common import LFB
from mmaction.utils import get_root_logger
try:
from mmdet.models.builder import SHARED_HEAD... | 14,120 | 34.390977 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/ssn_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
def parse_stage_config(stage_cfg):
"""Parse config of STPP for three stages.
Args:
stage_cfg (int | tuple[int]):
Config of structured temporal pyramid pooling.
Returns:
tuple[tupl... | 16,778 | 39.627119 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/audio_tsn_head.py | import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import BaseHead
@HEADS.register_module()
class AudioTSNHead(BaseHead):
"""Classification head for TSN on audio.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number... | 2,421 | 31.72973 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/trn_head.py | import itertools
import numpy as np
import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import BaseHead
class RelationModule(nn.Module):
"""Relation Module of TRN.
Args:
hidden_dim (int): The dimension of hidden layer of MLP in relation
... | 7,868 | 36.293839 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/tsm_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import AvgConsensus, BaseHead
@HEADS.register_module()
class TSMHead(BaseHead):
"""Class head for TSM.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Nu... | 4,170 | 36.241071 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/bbox_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmaction.core.bbox import bbox_target
try:
from mmdet.models.builder import HEADS as MMDET_HEADS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
class BBoxHeadAVA(nn.Module):
"""Simplest R... | 8,768 | 34.358871 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/misc_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.utils import _BatchNorm
try:
from mmdet.models.builder import SHARED_HEADS as MMDET_SHARED_HEADS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
# Note: All the... | 4,040 | 29.613636 | 76 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/tpn_head.py | import torch.nn as nn
from ..builder import HEADS
from .tsn_head import TSNHead
@HEADS.register_module()
class TPNHead(TSNHead):
"""Class head for TPN.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of channels in input feature.
loss_cls (dict)... | 3,306 | 35.340659 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/x3d_head.py | import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import BaseHead
@HEADS.register_module()
class X3DHead(BaseHead):
"""Classification head for I3D.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of channels i... | 2,837 | 30.533333 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/slowfast_head.py | import torch
import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import BaseHead
@HEADS.register_module()
class SlowFastHead(BaseHead):
"""The classification head for SlowFast.
Args:
num_classes (int): Number of classes to be classified.
in_channels ... | 2,542 | 30.7875 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/tsn_head.py | import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import AvgConsensus, BaseHead
@HEADS.register_module()
class TSNHead(BaseHead):
"""Class head for TSN.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of chann... | 3,148 | 33.228261 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/__init__.py | from .audio_tsn_head import AudioTSNHead
from .base import BaseHead
from .bbox_head import BBoxHeadAVA
from .fbo_head import FBOHead
from .i3d_head import I3DHead
from .lfb_infer_head import LFBInferHead
from .misc_head import ACRNHead
from .roi_head import AVARoIHead
from .slowfast_head import SlowFastHead
from .ssn_h... | 704 | 31.045455 | 75 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/lfb_infer_head.py | import os.path as osp
import mmcv
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import get_dist_info
try:
from mmdet.models.builder import SHARED_HEADS as MMDET_SHARED_HEADS
mmdet_imported = True
except (ImportError, ModuleNotFoundError):
mmdet_imported = False
cla... | 5,150 | 34.280822 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/i3d_head.py | import torch.nn as nn
from mmcv.cnn import normal_init
from ..builder import HEADS
from .base import BaseHead
@HEADS.register_module()
class I3DHead(BaseHead):
"""Classification head for I3D.
Args:
num_classes (int): Number of classes to be classified.
in_channels (int): Number of channels i... | 2,446 | 32.067568 | 78 | py |
STTS | STTS-main/VideoSwin/mmaction/models/heads/roi_head.py | import numpy as np
from mmaction.core.bbox import bbox2result
from mmaction.utils import import_module_error_class
try:
from mmdet.core.bbox import bbox2roi
from mmdet.models import HEADS as MMDET_HEADS
from mmdet.models.roi_heads import StandardRoIHead
mmdet_imported = True
except (ImportError, Modul... | 4,487 | 35.487805 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/localization/bsn_utils.py | import os.path as osp
import numpy as np
from .proposal_utils import temporal_iop, temporal_iou
def generate_candidate_proposals(video_list,
video_infos,
tem_results_dir,
temporal_scale,
... | 11,496 | 41.899254 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/localization/proposal_utils.py | import numpy as np
def temporal_iou(proposal_min, proposal_max, gt_min, gt_max):
"""Compute IoU score between a groundtruth bbox and the proposals.
Args:
proposal_min (list[float]): List of temporal anchor min.
proposal_max (list[float]): List of temporal anchor max.
gt_min (float): G... | 3,450 | 35.326316 | 77 | py |
STTS | STTS-main/VideoSwin/mmaction/localization/__init__.py | from .bsn_utils import generate_bsp_feature, generate_candidate_proposals
from .proposal_utils import soft_nms, temporal_iop, temporal_iou
from .ssn_utils import (eval_ap, load_localize_proposal_file,
perform_regression, temporal_nms)
__all__ = [
'generate_candidate_proposals', 'generate_bs... | 465 | 41.363636 | 75 | py |
STTS | STTS-main/VideoSwin/mmaction/localization/ssn_utils.py | from itertools import groupby
import numpy as np
from ..core import average_precision_at_temporal_iou
from . import temporal_iou
def load_localize_proposal_file(filename):
"""Load the proposal file and split it into many parts which contain one
video's information separately.
Args:
filename(str... | 4,902 | 28.011834 | 76 | py |
STTS | STTS-main/VideoSwin/mmaction/datasets/base.py | import copy
import os.path as osp
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict, defaultdict
import mmcv
import numpy as np
import torch
from mmcv.utils import print_log
from torch.utils.data import Dataset
from ..core import (mean_average_precision, mean_class_accuracy,
... | 11,612 | 39.322917 | 105 | py |
STTS | STTS-main/VideoSwin/mmaction/datasets/rawvideo_dataset.py | import copy
import os.path as osp
import random
import mmcv
from .base import BaseDataset
from .builder import DATASETS
@DATASETS.register_module()
class RawVideoDataset(BaseDataset):
"""RawVideo dataset for action recognition, used in the Project OmniSource.
The dataset loads clips of raw videos and apply... | 5,635 | 37.340136 | 79 | py |
STTS | STTS-main/VideoSwin/mmaction/datasets/audio_visual_dataset.py | import os.path as osp
from .builder import DATASETS
from .rawframe_dataset import RawframeDataset
@DATASETS.register_module()
class AudioVisualDataset(RawframeDataset):
"""Dataset that reads both audio and visual data, supporting both rawframes
and videos. The annotation file is same as that of the rawframe ... | 3,073 | 38.922078 | 79 | py |
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