claf.modules.conv package

Submodules

class claf.modules.conv.depthwise_separable_conv.DepSepConv(input_size=None, num_filters=None, kernel_size=None)

Bases: torch.nn.modules.module.Module

Depthwise Separable Convolutions

in Xception: Deep Learning with Depthwise Separable Convolutions (https://arxiv.org/abs/1610.02357)

depthwise -> pointwise (1x1 conv)

• Args:

  input_size: the number of input tensor’s dimension num_filters: the number of convolution filter kernel_size: the number of convolution kernel size

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class claf.modules.conv.pointwise_conv.PointwiseConv(input_size, num_filters)

Bases: torch.nn.modules.module.Module

Pointwise Convolution (1x1 Conv)

Convolution 1 Dimension (Faster version) (cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/model_pytorch.py#L45)

• Args:

  input_size: the number of input tensor’s dimension num_filters: the number of convolution filter

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Module contents

class claf.modules.conv.DepSepConv(input_size=None, num_filters=None, kernel_size=None)

Bases: torch.nn.modules.module.Module

Depthwise Separable Convolutions

in Xception: Deep Learning with Depthwise Separable Convolutions (https://arxiv.org/abs/1610.02357)

depthwise -> pointwise (1x1 conv)

• Args:

  input_size: the number of input tensor’s dimension num_filters: the number of convolution filter kernel_size: the number of convolution kernel size

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class claf.modules.conv.PointwiseConv(input_size, num_filters)

Bases: torch.nn.modules.module.Module

Pointwise Convolution (1x1 Conv)

Convolution 1 Dimension (Faster version) (cf. https://github.com/huggingface/pytorch-openai-transformer-lm/blob/ eafc28abdfadfa0732f03a0fc65805c5bfb2ffe7/model_pytorch.py#L45)

• Args:

  input_size: the number of input tensor’s dimension num_filters: the number of convolution filter

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.