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Functions133 in github.com/arunos728/MotionSqueeze

⨍ Functions 133◇ Types & classes 28
↓ 21 callersMethodupdate
(self, val, n=1)
main_kinetics.py:557
↓ 13 callersMethodupdate
(self, val, n=1)
main_something.py:408
↓ 6 callersMethod__init__
(self, block, block2, layers, num_segments, flow_estimation, num_classes=1000, zero_init_residual=False)
resnet_TSM.py:291
↓ 5 callersFunctionconv3x3
3x3 convolution with padding
resnet_TSM.py:27
↓ 5 callersMethodpartialBN
(self, enable)
models.py:190
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, num_segments, stride=1)
resnet_TSM.py:430
↓ 3 callersMethod_prepare_tsn
(self, num_class)
models.py:107
↓ 3 callersFunctionaccuracy
Computes the precision@k for the specified values of k
main_kinetics.py:573
↓ 3 callersFunctionconv1x1
1x1 convolution
resnet_TSM.py:33
↓ 3 callersMethodtrain
Override the default train() to freeze the BN parameters :return:
models.py:169
↓ 2 callersMethodL2normalize
(self, x, d=1)
resnet_TSM.py:152
↓ 2 callersMethodL2normalize
(self, x, d=1)
resnet_TSM.py:174
↓ 2 callersFunctionaccuracy
Computes the precision@k for the specified values of k
main_something.py:425
↓ 2 callersMethodbackward
(self, grad_output)
ops/basic_ops.py:28
↓ 2 callersMethodfill_fix_offset
(more_fix_crop, image_w, image_h, crop_w, crop_h)
transforms.py:241
↓ 2 callersMethodget_augmentation
(self)
models.py:457
↓ 2 callersMethodget_optim_policies
(self, dataset)
models.py:193
↓ 2 callersFunctionsave_validation_score
(score, filename='score.pt')
main_something.py:393
↓ 2 callersFunctionsave_validation_score
(score, filename='score.pt')
main_kinetics.py:542
↓ 2 callersFunctiontsm
(tensor, duration, version='zero')
tsm_util.py:4
↓ 2 callersFunctionvalidate
(val_loader, model, criterion, iter, temperature, logger=None)
main_something.py:317
↓ 2 callersFunctionzero_ops
(m, x, y)
thop/count_hooks.py:13
↓ 1 callersMethod_construct_diff_model
(self, base_model, keep_rgb=False)
models.py:416
↓ 1 callersMethod_construct_flow_model
(self, base_model)
models.py:389
↓ 1 callersMethod_get_diff
(self, input, keep_rgb=False)
models.py:372
↓ 1 callersMethod_get_test_indices
(self, record)
dataset.py:108
↓ 1 callersMethod_get_val_indices
(self, record)
dataset.py:93
↓ 1 callersMethod_load_image
(self, directory, idx)
dataset.py:52
↓ 1 callersMethod_parse_list
(self)
dataset.py:65
↓ 1 callersMethod_prepare_base_model
(self, base_model)
models.py:138
↓ 1 callersMethod_sample_crop_size
(self, im_size)
transforms.py:212
↓ 1 callersMethod_sample_fix_offset
(self, image_w, image_h, crop_w, crop_h)
transforms.py:236
↓ 1 callersMethod_sample_indices
:param record: VideoRecord :return: list
dataset.py:68
↓ 1 callersFunctionadjust_learning_rate
Sets the learning rate to the initial LR decayed by 10 every 30 epochs
main_something.py:415
↓ 1 callersFunctionadjust_learning_rate
Sets the learning rate to the initial LR decayed by 10 every 30 epochs
main_kinetics.py:563
↓ 1 callersMethodapply_gaussian_kernel
(self, corr, h,w,p, sigma=5)
resnet_TSM.py:377
↓ 1 callersMethodcorr_abs_to_rel
(self,corr,h,w)
resnet_TSM.py:180
↓ 1 callersFunctioneval_video
(video_data, num_class, net, this_test_segments, modality, temperature)
main_kinetics.py:432
↓ 1 callersMethodflow_computation
(self, x, pos=2, temperature=100)
resnet_TSM.py:447
↓ 1 callersMethodget
(self, record, indices)
dataset.py:135
↓ 1 callersFunctionmain
()
main_something.py:23
↓ 1 callersFunctionmain
()
main_kinetics.py:26
↓ 1 callersMethodmatch_to_flow_soft
(self, match, k, h,w, temperature=1, mode='softmax')
resnet_TSM.py:395
↓ 1 callersMethodreset
(self)
main_something.py:402
↓ 1 callersMethodreset
(self)
main_kinetics.py:551
↓ 1 callersFunctionresnet18
Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
resnet_TSM.py:500
↓ 1 callersFunctionresnet50
Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
resnet_TSM.py:538
↓ 1 callersFunctionsave_checkpoint
(state, is_best, filename='checkpoint.pth.tar')
main_something.py:386
↓ 1 callersFunctionsave_checkpoint
(state, is_best, filename='checkpoint.pth.tar')
main_kinetics.py:535
↓ 1 callersFunctiontest
(val_loader, model, criterion, iter, temperature, num_class, logger=None)
main_kinetics.py:467
↓ 1 callersFunctiontrain
(train_loader, model, criterion, optimizer, epoch)
main_something.py:233
↓ 1 callersFunctiontrain
(train_loader, model, criterion, optimizer, epoch)
main_kinetics.py:282
↓ 1 callersFunctionvalidate
(val_loader, model, criterion, iter, temperature, logger=None)
main_kinetics.py:365
MethodL2normalize
(self, x, d=1)
resnet_TSM.py:349
Method__call__
(self, img_tuple)
transforms.py:17
Method__call__
(self, img_tuple)
transforms.py:42
Method__call__
(self, img_tuple, is_flow=False)
transforms.py:54
Method__call__
(self, tensor_tuple)
transforms.py:71
Method__call__
(self, img_tuple)
transforms.py:87
Method__call__
(self, img_tuple)
transforms.py:104
Method__call__
(self, img_tuple)
transforms.py:118
Method__call__
(self, img_tuple)
transforms.py:156
Method__call__
(self, img_tuple)
transforms.py:202
Method__call__
(self, img_tuple)
transforms.py:277
Method__call__
(self, img_tuple)
transforms.py:320
Method__call__
(self, pic_tuple)
transforms.py:338
Method__call__
(self, data)
transforms.py:356
Method__getitem__
(self, index)
dataset.py:123
Method__init__
(self, size)
transforms.py:11
Method__init__
(self, size)
transforms.py:39
Method__init__
(self, selective_flip=True, is_flow=False)
transforms.py:50
Method__init__
(self, mean, std)
transforms.py:67
Method__init__
(self, size)
transforms.py:84
Method__init__
(self, size, interpolation=Image.BILINEAR)
transforms.py:101
Method__init__
(self, crop_size, scale_size=None)
transforms.py:110
Method__init__
(self, crop_size, scale_size=None, flip=True)
transforms.py:147
Method__init__
(self, input_size, scales=None, max_distort=1, fix_crop=True, more_fix_crop=True)
transforms.py:194
Method__init__
(self, size, interpolation=Image.BILINEAR)
transforms.py:273
Method__init__
(self, roll=False)
transforms.py:317
Method__init__
(self, div=True)
transforms.py:335
Method__init__
(self, num_class, num_segments, pretrained_parts, modality, base_model='resnet101', dataset='
models.py:11
Method__init__
(self, row)
dataset.py:10
Method__init__
(self, root_path, list_file, num_segments=3, new_length=1, interval=2, modality='RGB', mode=1
dataset.py:27
Method__init__
(self, inplanes, planes, num_segments,stride=1, downsample=None, remainder=0)
resnet_TSM.py:44
Method__init__
(self, inplanes, planes, num_segments, stride=1, downsample=None, remainder=0)
resnet_TSM.py:76
Method__init__
(self, inplanes, planes,num_segments, stride=1, downsample=None, remainder=0)
resnet_TSM.py:109
Method__init__
(self, ks, patch, stride, pad, patch_dilation)
resnet_TSM.py:146
Method__init__
(self, patch)
resnet_TSM.py:170
Method__init__
(self, num_segments, expansion = 1, pos=2)
resnet_TSM.py:226
Method__init__
(self)
main_something.py:399
Method__init__
(self)
main_kinetics.py:548
Method__init__
(self, consensus_type, dim=1)
ops/basic_ops.py:12
Method__init__
(self, consensus_type, dim=1)
ops/basic_ops.py:41
Method__len__
(self)
dataset.py:149
Functionadd_hooks
(m)
thop/profile.py:71
Methodapply_binary_kernel
(self, match, h, w, region)
resnet_TSM.py:357
Functionclass_accuracy
(prediction, label)
ops/utils.py:26
Functionclever_format
(nums, format="%.2f")
thop/utils.py:4
Functionconv1x1x1
1x1x1 convolution
resnet_TSM.py:37
Functioncount_adap_avgpool
(m, x, y)
thop/count_hooks.py:84
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