layers.py

from torch import nn
## LAYER UTILITIES ####################################################################
def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, norm=True, norm_mode='batch'):
    """Creates a transposed-convolutional layer, with optional batch/instance normalization.
    """
    layers = []
    layers.append(
        nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
    )
    if norm:
        if norm_mode == 'instance':
            layers.append(
                nn.InstanceNorm2d(out_channels)
            )
        elif norm_mode == 'batch':
            layers.append(
                nn.BatchNorm2d(out_channels)
            )
    return nn.Sequential(*layers)

def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, norm=True, norm_mode='batch'):
    """Creates a convolutional layer, with optional batch/instance normalization.
    """
    layers = []
    layers.append(
        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False)
    )
    if norm:
        if norm_mode == 'instance':
            layers.append(
                nn.InstanceNorm2d(out_channels)
            )
        elif norm_mode == 'batch':
            layers.append(
                nn.BatchNorm2d(out_channels)
            )
    return nn.Sequential(*layers)

class ResidualBlock(nn.Module):
    """Instatiates a residual block with kernel_size = 3
    """
    def __init__(self, conv_dim):
        super(ResidualBlock, self).__init__()
        self._conv = conv(
            conv_dim, conv_dim, kernel_size=3, stride=1, padding=1
        )
    
    def forward(self, x):
        return x + self._conv(x)