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Types & classes120 in github.com/Cadene/pretrained-models.pytorch

⨍ Functions 373◇ Types & classes 120
↓ 48 callersClassBasicConv2d
pretrainedmodels/models/inceptionv4.py:35
↓ 41 callersClassBasicConv2d
pretrainedmodels/models/polynet.py:23
↓ 38 callersClassBasicConv2d
pretrainedmodels/models/inceptionresnetv2.py:34
↓ 33 callersClassLambdaMap
pretrainedmodels/models/resnext_features/resnext101_64x4d_features.py:25
↓ 33 callersClassLambdaMap
pretrainedmodels/models/resnext_features/resnext101_32x4d_features.py:25
↓ 33 callersClassLambdaReduce
pretrainedmodels/models/resnext_features/resnext101_64x4d_features.py:33
↓ 33 callersClassLambdaReduce
pretrainedmodels/models/resnext_features/resnext101_32x4d_features.py:33
↓ 29 callersClassLambda
pretrainedmodels/models/resnext_features/resnext101_64x4d_features.py:17
↓ 29 callersClassLambda
pretrainedmodels/models/resnext_features/resnext101_32x4d_features.py:17
↓ 22 callersClassBranchSeparables
pretrainedmodels/models/nasnet.py:77
↓ 22 callersClassBranchSeparables
pretrainedmodels/models/nasnet_mobile.py:93
↓ 20 callersClassBlock17
pretrainedmodels/models/inceptionresnetv2.py:143
↓ 15 callersClassNormalCell
pretrainedmodels/models/nasnet.py:329
↓ 13 callersClassCell
pretrainedmodels/models/pnasnet.py:226
↓ 12 callersClassBlock
pretrainedmodels/models/xception.py:63
↓ 12 callersClassBranchSeparables
pretrainedmodels/models/pnasnet.py:67
↓ 10 callersClassBlock35
pretrainedmodels/models/inceptionresnetv2.py:86
↓ 10 callersClassBlock8
pretrainedmodels/models/inceptionresnetv2.py:203
↓ 10 callersClassInceptionResNetA2Way
pretrainedmodels/models/polynet.py:349
↓ 10 callersClassInceptionResNetB2Way
pretrainedmodels/models/polynet.py:356
↓ 10 callersClassInceptionResNetBPoly3
pretrainedmodels/models/polynet.py:370
↓ 9 callersClassAverageMeter
Computes and stores the average and current value
examples/imagenet_eval.py:261
↓ 9 callersClassNormalCell
pretrainedmodels/models/nasnet_mobile.py:354
↓ 8 callersClassDualPathBlock
pretrainedmodels/models/dpn.py:248
↓ 8 callersClassPolyConv2d
A block that is used inside poly-N (poly-2, poly-3, and so on) modules. The Convolution layer is shared between all Inception blocks inside a
pretrainedmodels/models/polynet.py:41
↓ 7 callersClassInception_B
pretrainedmodels/models/inceptionv4.py:160
↓ 6 callersClassDPN
pretrainedmodels/models/dpn.py:312
↓ 6 callersClassMaxPool
pretrainedmodels/models/pnasnet.py:33
↓ 6 callersClassMaxPoolPad
pretrainedmodels/models/nasnet_mobile.py:48
↓ 6 callersClassSENet
pretrainedmodels/models/senet.py:207
↓ 5 callersClassBnActConv2d
pretrainedmodels/models/dpn.py:218
↓ 5 callersClassBranchSeparablesReduction
pretrainedmodels/models/nasnet.py:119
↓ 5 callersClassBranchSeparablesReduction
pretrainedmodels/models/nasnet_mobile.py:141
↓ 5 callersClassFBResNet
pretrainedmodels/models/fbresnet.py:103
↓ 5 callersClassInceptionResNetC2Way
pretrainedmodels/models/polynet.py:363
↓ 5 callersClassInceptionResNetCPoly3
pretrainedmodels/models/polynet.py:376
↓ 5 callersClassReluConvBn
pretrainedmodels/models/pnasnet.py:100
↓ 5 callersClassResNet
pretrainedmodels/models/fbresnet/resnet152_load.py:98
↓ 5 callersClassSeparableConv2d
pretrainedmodels/models/xception.py:50
↓ 4 callersClassInception_A
pretrainedmodels/models/inceptionv4.py:107
↓ 4 callersClassSeparableConv2d
pretrainedmodels/models/nasnet.py:60
↓ 4 callersClassSeparableConv2d
pretrainedmodels/models/nasnet_mobile.py:76
↓ 3 callersClassAvgPoolPad
pretrainedmodels/models/nasnet_mobile.py:62
↓ 3 callersClassBranchSeparablesStem
pretrainedmodels/models/nasnet.py:98
↓ 3 callersClassBranchSeparablesStem
pretrainedmodels/models/nasnet_mobile.py:120
↓ 3 callersClassFirstCell
pretrainedmodels/models/nasnet.py:260
↓ 3 callersClassFirstCell
pretrainedmodels/models/nasnet_mobile.py:285
↓ 3 callersClassInception_C
pretrainedmodels/models/inceptionv4.py:221
↓ 3 callersClassNASNetALarge
NASNetALarge (6 @ 4032)
pretrainedmodels/models/nasnet.py:492
↓ 3 callersClassNASNetAMobile
NASNetAMobile (4 @ 1056)
pretrainedmodels/models/nasnet_mobile.py:520
↓ 3 callersClassSEModule
pretrainedmodels/models/senet.py:85
↓ 2 callersClassCatBnAct
pretrainedmodels/models/dpn.py:207
↓ 2 callersClassInceptionResNetV2
pretrainedmodels/models/inceptionresnetv2.py:234
↓ 2 callersClassInceptionV4
pretrainedmodels/models/inceptionv4.py:264
↓ 2 callersClassInputBlock
pretrainedmodels/models/dpn.py:230
↓ 2 callersClassMaxPoolPad
pretrainedmodels/models/nasnet.py:32
↓ 2 callersClassPNASNet5Large
pretrainedmodels/models/pnasnet.py:291
↓ 2 callersClassPolyNet
pretrainedmodels/models/polynet.py:382
↓ 2 callersClassSeparableConv2d
pretrainedmodels/models/pnasnet.py:49
↓ 2 callersClassSpatialCrossMapLRN
pretrainedmodels/models/vggm.py:24
↓ 2 callersClassVGGM
pretrainedmodels/models/vggm.py:67
↓ 2 callersClassXception
Xception optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1610.02357.pdf
pretrainedmodels/models/xception.py:114
↓ 1 callersClassAvgPoolPad
pretrainedmodels/models/nasnet.py:46
↓ 1 callersClassBNInception
pretrainedmodels/models/bninception.py:27
↓ 1 callersClassCellStem0
pretrainedmodels/models/pnasnet.py:181
↓ 1 callersClassCellStem0
pretrainedmodels/models/nasnet.py:137
↓ 1 callersClassCellStem0
pretrainedmodels/models/nasnet_mobile.py:159
↓ 1 callersClassCellStem1
pretrainedmodels/models/nasnet.py:187
↓ 1 callersClassCellStem1
pretrainedmodels/models/nasnet_mobile.py:209
↓ 1 callersClassFactorizedReduction
pretrainedmodels/models/pnasnet.py:117
↓ 1 callersClassLoadImage
pretrainedmodels/utils.py:84
↓ 1 callersClassMixed_3a
pretrainedmodels/models/inceptionv4.py:55
↓ 1 callersClassMixed_4a
pretrainedmodels/models/inceptionv4.py:69
↓ 1 callersClassMixed_5a
pretrainedmodels/models/inceptionv4.py:93
↓ 1 callersClassMixed_5b
pretrainedmodels/models/inceptionresnetv2.py:54
↓ 1 callersClassMixed_6a
pretrainedmodels/models/inceptionresnetv2.py:120
↓ 1 callersClassMixed_7a
pretrainedmodels/models/inceptionresnetv2.py:171
↓ 1 callersClassReductionA
A dimensionality reduction block that is placed after stage-a Inception-ResNet blocks.
pretrainedmodels/models/polynet.py:176
↓ 1 callersClassReductionB
A dimensionality reduction block that is placed after stage-b Inception-ResNet blocks.
pretrainedmodels/models/polynet.py:199
↓ 1 callersClassReductionCell0
pretrainedmodels/models/nasnet.py:382
↓ 1 callersClassReductionCell0
pretrainedmodels/models/nasnet_mobile.py:407
↓ 1 callersClassReductionCell1
pretrainedmodels/models/nasnet.py:437
↓ 1 callersClassReductionCell1
pretrainedmodels/models/nasnet_mobile.py:462
↓ 1 callersClassReduction_A
pretrainedmodels/models/inceptionv4.py:138
↓ 1 callersClassReduction_B
pretrainedmodels/models/inceptionv4.py:194
↓ 1 callersClassResNeXt101_32x4d
pretrainedmodels/models/resnext.py:37
↓ 1 callersClassResNeXt101_64x4d
pretrainedmodels/models/resnext.py:58
↓ 1 callersClassResNet
pretrainedmodels/models/cafferesnet.py:100
↓ 1 callersClassStem
pretrainedmodels/models/polynet.py:67
↓ 1 callersClassToRange255
pretrainedmodels/utils.py:23
↓ 1 callersClassToSpaceBGR
pretrainedmodels/utils.py:9
↓ 1 callersClassTransformImage
pretrainedmodels/utils.py:34
↓ 1 callersClassVoc2007Classification
pretrainedmodels/datasets/voc.py:215
↓ 1 callersClassWideResNet
pretrainedmodels/models/wideresnet.py:58
ClassAdaptiveAvgMaxPool2d
Selectable global pooling layer with dynamic input kernel size
pretrainedmodels/models/dpn.py:431
ClassAveragePrecisionMeter
The APMeter measures the average precision per class. The APMeter is designed to operate on `NxK` Tensors `output` and `target`, and opti
pretrainedmodels/datasets/utils.py:86
ClassBasicBlock
pretrainedmodels/models/fbresnet.py:33
ClassBasicBlock
pretrainedmodels/models/cafferesnet.py:29
ClassBasicBlock
pretrainedmodels/models/fbresnet/resnet152_load.py:26
ClassBlockA
Inception-ResNet-A block.
pretrainedmodels/models/polynet.py:108
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