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Functions91 in github.com/alinlab/L2T-ww

⨍ Functions 91◇ Types & classes 17
↓ 12 callersFunctionmake_layers
(cfg, batch_norm=False)
models/vgg_cifar.py:159
↓ 10 callersMethodparameters
(self)
train/meta_optimizers.py:34
↓ 6 callersMethodstep
(self, objective, *args, **kwargs)
train/meta_optimizers.py:49
↓ 4 callersFunction_get_num_features
(model)
train_l2t_ww.py:18
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1)
models/resnet_ilsvrc.py:122
↓ 4 callersMethodset_state
(self, state)
train/meta_optimizers.py:45
↓ 3 callersFunctionaccuracy
Computes the precision@k for the specified values of k
utils/utils.py:28
↓ 3 callersMethodget_state
(self)
train/meta_optimizers.py:39
↓ 2 callersMethod__init__
(self, source_model, pairs)
train_l2t_ww.py:75
↓ 2 callersMethod__init__
(self, *size)
models/modules.py:4
↓ 2 callersMethod__init__
(self, block, layers, num_classes=1000, meta=None)
models/resnet_ilsvrc.py:98
↓ 2 callersFunctioncheck_model
(opt)
check_model.py:5
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
models/resnet_ilsvrc.py:19
↓ 2 callersMethodforward_with_features
(self, x)
models/vgg_cifar.py:139
↓ 2 callersMethodmeta_backward
(self)
train/meta_optimizers.py:56
↓ 2 callersFunctionread
(filename)
cub200.py:8
↓ 2 callersFunctionvalidate
(model, loader)
train_l2t_ww.py:240
↓ 1 callersMethod__init__
(self, features, num_classes=10, init_weights=True)
models/vgg_cifar.py:32
↓ 1 callersFunction_copy
(state)
train/meta_optimizers.py:7
↓ 1 callersMethod_initialize_weights
(self)
models/vgg_cifar.py:57
↓ 1 callersMethod_initialize_weights
(self)
models/vgg_cifar.py:145
↓ 1 callersFunctioncheck_dataset
(opt)
check_dataset.py:70
↓ 1 callersFunctioncheck_split
(opt)
check_dataset.py:62
↓ 1 callersMethodforward
(self, x, idx=-1)
models/vgg_cifar.py:116
↓ 1 callersMethodforward
(self, x)
models/resnet_ilsvrc.py:139
↓ 1 callersFunctioninner_objective
(data, matching_only=False)
train_l2t_ww.py:249
↓ 1 callersFunctionmain
()
dog.py:13
↓ 1 callersFunctionmain
()
train_l2t_ww.py:126
↓ 1 callersFunctionmain
()
cub200.py:12
↓ 1 callersFunctionouter_objective
(data)
train_l2t_ww.py:278
↓ 1 callersFunctionouter_objective
()
train/meta_optimizers.py:163
↓ 1 callersMethodreset
(self)
utils/utils.py:15
↓ 1 callersFunctionset_logging_config
(logdir)
utils/utils.py:44
↓ 1 callersMethodupdate
(self, val, n=1)
utils/utils.py:21
↓ 1 callersMethodupdate_classes
(self)
check_dataset.py:16
Method__getitem__
(self, idx)
check_dataset.py:22
Method__getitem__
(self, idx)
check_dataset.py:34
Method__getitem__
(self, idx)
check_dataset.py:55
Method__init__
(self, dataset, classes, indices)
check_dataset.py:9
Method__init__
(self, dataset, classes, indices)
check_dataset.py:29
Method__init__
(self, dataset, classes, indices)
check_dataset.py:41
Method__init__
(self, source_model, target_model, pairs)
train_l2t_ww.py:37
Method__init__
(self, source_model, pairs, weight_type='relu', init=None)
train_l2t_ww.py:94
Method__init__
(self)
utils/utils.py:12
Method__init__
(self, features, num_classes=10, init_weights=True, lwf=False, num_source_cls=200, no_ft=False)
models/vgg_cifar.py:74
Method__init__
(self, inplanes, planes, stride=1, downsample=None, batchnorm_affine=True)
models/modules.py:14
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
models/modules.py:51
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
models/resnet_ilsvrc.py:28
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
models/resnet_ilsvrc.py:60
Method__init__
(self, n, block, num_classes=10, lwf=False, num_source_cls=200, growing=False)
models/resnet_cifar.py:14
Method__init__
(self, params, modules, lr=0.1, momentum=0, weight_decay=0, rollback=False, cpu=False)
train/meta_optimizers.py:27
Method__len__
(self)
check_dataset.py:25
Method__len__
(self)
check_dataset.py:37
Method__len__
(self)
check_dataset.py:58
Method__len__
(self)
train/meta_optimizers.py:98
Functionconv3x3
3x3 convolution with padding
models/modules.py:42
Methodforward
(self, source_features, target_features, weight, beta, loss_weight)
train_l2t_ww.py:46
Methodforward
(self, source_features)
train_l2t_ww.py:84
Methodforward
(self, source_features)
train_l2t_ww.py:108
Methodforward
(self, x)
models/vgg_cifar.py:47
Methodforward
(self, x)
models/modules.py:8
Methodforward
(self, x)
models/modules.py:24
Methodforward
(self, x)
models/modules.py:64
Methodforward
(self, x)
models/resnet_ilsvrc.py:38
Methodforward
(self, x)
models/resnet_ilsvrc.py:73
Methodforward
(self, x, i=None)
models/resnet_cifar.py:74
Methodforward_with_combine_features
(self, x, fs, metanet)
models/vgg_cifar.py:142
Methodforward_with_features
(self, x)
models/resnet_ilsvrc.py:156
Methodforward_with_features
(self, x)
models/resnet_cifar.py:111
Functioninner_objective
()
train/meta_optimizers.py:160
Methodmeta_backward_all
(self, objective, outer_args)
train/meta_optimizers.py:101
Functionread
(filename)
dog.py:9
Functionresnet101
Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
models/resnet_ilsvrc.py:206
Functionresnet152
Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
models/resnet_ilsvrc.py:218
Functionresnet18
Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
models/resnet_ilsvrc.py:160
Functionresnet32
(num_classes=10, growing=False)
models/resnet_cifar.py:140
Functionresnet34
Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
models/resnet_ilsvrc.py:177
Functionresnet50
Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet
models/resnet_ilsvrc.py:194
Functiontest_metaSGD
()
train/meta_optimizers.py:149
Functionvgg11
VGG 11-layer model (configuration "A")
models/vgg_cifar.py:221
Functionvgg11_bn
VGG 11-layer model (configuration "A") with batch normalization
models/vgg_cifar.py:229
Functionvgg13
VGG 13-layer model (configuration "B")
models/vgg_cifar.py:237
Functionvgg13_bn
VGG 13-layer model (configuration "B") with batch normalization
models/vgg_cifar.py:245
Functionvgg16
VGG 16-layer model (configuration "D")
models/vgg_cifar.py:253
Functionvgg16_bn
VGG 16-layer model (configuration "D") with batch normalization
models/vgg_cifar.py:261
Functionvgg19
VGG 19-layer model (configuration "E")
models/vgg_cifar.py:269
Functionvgg19_bn
VGG 19-layer model (configuration 'E') with batch normalization
models/vgg_cifar.py:277
Functionvgg4
VGG 4-layer model (configuration_small "A")
models/vgg_cifar.py:188
Functionvgg4_bn
VGG 4-layer model (configuration_small "A") with batch normalization
models/vgg_cifar.py:196
Functionvgg9
VGG 9-layer model (configuration_small "B")
models/vgg_cifar.py:204
Functionvgg9_bn
VGG 9-layer model (configuration_small "B") with batch normalization
models/vgg_cifar.py:212