Stack n bottleneck modules where n is inferred from the depth of the network. Args: block: block type used to construct ResNext

Computes the precision@k for the specified values of k

Stack n bottleneck modules where n is inferred from the depth of the network. Args: name: string name of the current block.

3x3 convolution with padding

3x3 convolution with padding

make dir if not exist

Constructor Args: baseWidth: baseWidth for ResNeXt. cardinality: number of convolution groups. layers: co

Constructor Args: cardinality: number of convolution groups. depth: number of layers. num_classes: number

paths is a distionary with {name:filepath} pair

Constructor Args: inplanes: input channel dimensionality planes: output channel dimensionality baseWidth:

Constructor Args: in_channels: input channel dimensionality out_channels: output channel dimensionality s

r"""AlexNet model architecture from the `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.

Converts a one-channel grayscale image to a color heatmap image

Constructs a ResNet model.

Compute the mean and std value of dataset.

Init layer parameters.

Constructs a ResNet model.

Constructs a ResNet model.

Constructs a ResNeXt.

Construct ResNeXt-101.

Construct ResNeXt-152.

Construct ResNeXt-50.

VGG 11-layer model (configuration "A") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 11-layer model (configuration "A") with batch normalization

VGG 13-layer model (configuration "B") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 13-layer model (configuration "B") with batch normalization

VGG 16-layer model (configuration "D") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 16-layer model (configuration "D") with batch normalization

VGG 19-layer model (configuration "E") Args: pretrained (bool): If True, returns a model pre-trained on ImageNet

VGG 19-layer model (configuration 'E') with batch normalization

Constructs a Wide Residual Networks.