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Functions136 in github.com/aaron-xichen/pytorch-playground

⨍ Functions 136◇ Types & classes 25
Methodn_batch
(self)
imagenet/dataset.py:36
Methodn_sample
(self)
imagenet/dataset.py:40
Functionresnet101
(cuda=True, model_root=None)
utee/selector.py:130
Functionresnet101
(pretrained=False, model_root=None, **kwargs)
imagenet/resnet.py:167
Functionresnet152
(cuda=True, model_root=None)
utee/selector.py:138
Functionresnet152
(pretrained=False, model_root=None, **kwargs)
imagenet/resnet.py:174
Functionresnet18
(cuda=True, model_root=None)
utee/selector.py:106
Functionresnet18
(pretrained=False, model_root=None, **kwargs)
imagenet/resnet.py:146
Functionresnet34
(cuda=True, model_root=None)
utee/selector.py:114
Functionresnet34
(pretrained=False, model_root=None, **kwargs)
imagenet/resnet.py:153
Functionresnet50
(cuda=True, model_root=None)
utee/selector.py:122
Functionresnet50
(pretrained=False, model_root=None, **kwargs)
imagenet/resnet.py:160
Functionselect
(model_name, **kwargs)
utee/selector.py:162
Functionsqueezenet1_0
r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" <https://arxi
imagenet/squeezenet.py:116
Functionsqueezenet1_1
r"""SqueezeNet 1.1 model from the `official SqueezeNet repo <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. SqueezeNe
imagenet/squeezenet.py:127
Functionsqueezenet_v0
(cuda=True, model_root=None)
utee/selector.py:146
Functionsqueezenet_v1
(cuda=True, model_root=None)
utee/selector.py:154
Functionstl10
(cuda=True, model_root=None)
utee/selector.py:50
Functionstl10
(n_channel, pretrained=None)
stl10/model.py:47
Functionstr2img
(str_b)
utee/misc.py:141
Functionsvhn
(cuda=True, model_root=None)
utee/selector.py:26
Functionsvhn
(n_channel, pretrained=None)
svhn/model.py:47
Functiontanh_quantize
(input, bits)
utee/quant.py:71
Functiontarget_transform
(target)
svhn/dataset.py:12
Functionvgg11
VGG 11-layer model (configuration "A")
imagenet/vgg.py:82
Functionvgg11_bn
VGG 11-layer model (configuration "A") with batch normalization
imagenet/vgg.py:90
Functionvgg13
VGG 13-layer model (configuration "B")
imagenet/vgg.py:96
Functionvgg13_bn
VGG 13-layer model (configuration "B") with batch normalization
imagenet/vgg.py:104
Functionvgg16
(cuda=True, model_root=None)
utee/selector.py:66
Functionvgg16
VGG 16-layer model (configuration "D")
imagenet/vgg.py:110
Functionvgg16_bn
(cuda=True, model_root=None)
utee/selector.py:74
Functionvgg16_bn
VGG 16-layer model (configuration "D") with batch normalization
imagenet/vgg.py:118
Functionvgg19
(cuda=True, model_root=None)
utee/selector.py:82
Functionvgg19
VGG 19-layer model (configuration "E")
imagenet/vgg.py:124
Functionvgg19_bn
(cuda=True, model_root=None)
utee/selector.py:90
Functionvgg19_bn
VGG 19-layer model (configuration 'E') with batch normalization
imagenet/vgg.py:132
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