Conv2dRelu

class Conv2dRelu(*args, **kwargs)[source]

Bases: sphinx.ext.autodoc.importer._MockObject

Block holding one Conv2d and one ReLU layer

forward(input_batch)[source]

Forward batch though layers

Parameters:input_batch (torch.Tensor) – input batch
Returns:result
Return type:torch.Tensor

Img224x224Kernel7x7SeparatedDims

class Img224x224Kernel7x7SeparatedDims(in_channels, out_params, norm_class, p_dropout=0)[source]

Bases: shapenet.networks.abstract_network.AbstractFeatureExtractor

static _build_model(in_channels, out_params, norm_class, p_dropout)[source]

Build the actual model structure

Parameters:
  • in_channels (int) – number of input channels
  • out_params (int) – number of outputs
  • norm_class (Any) – class implementing a normalization
  • p_dropout (float) – dropout probability
Returns:

ensembled model
Return type:

torch.nn.Module
forward(input_batch)

Feed batch through network

Parameters:input_batch (torch.Tensor) – batch to feed through network
Returns:exracted features
Return type:torch.Tensor