trw.layers.encoder_decoder_resnet

Module Contents

Classes

EncoderDecoderResnet

Base class for all neural network modules.

class trw.layers.encoder_decoder_resnet.EncoderDecoderResnet(dimensionality: int, input_channels: int, output_channels: int, encoding_channels: Sequence[int], decoding_channels: Sequence[int], *, nb_residual_blocks: int = 9, convolution_kernel: int = 3, encoding_strides: trw.basic_typing.ConvStrides = 2, decoding_strides: trw.basic_typing.ConvStrides = 2, activation: Optional[trw.basic_typing.Activation] = None, encoding_block: trw.layers.blocks.ConvBlockType = BlockConvNormActivation, decoding_block: trw.layers.blocks.ConvTransposeBlockType = BlockDeconvNormActivation, init_block=partial(BlockConvNormActivation, kernel_size=7), middle_block: Any = BlockRes, out_block=partial(BlockConvNormActivation, kernel_size=7), config: trw.layers.layer_config.LayerConfig = default_layer_config(conv_kwargs={'padding': 'same', 'bias': False, 'padding_mode': 'reflect'}, deconv_kwargs={'padding': 'same', 'bias': False}, norm_type=NormType.BatchNorm, activation=nn.ReLU))

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Variables

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(self, x: trw.basic_typing.TorchTensorNCX) → trw.basic_typing.TorchTensorNCX

forward_with_intermediate(self, x: trw.basic_typing.TorchTensorNCX) → List[trw.basic_typing.TorchTensorNCX]