network/architecture.py

import torch
import torch.nn as nn
import torchvision

def get_batchnorm_layer(opts):
    if opts.norm_layer == "batch":
        norm_layer = nn.BatchNorm2d
    elif opts.layer == "spectral_instance":
        norm_layer = nn.InstanceNorm2d
    else:
        print("not implemented")
        exit()
    return norm_layer

def get_conv2d_layer(in_c, out_c, k, s, p=0, dilation=1, groups=1):
    return nn.Conv2d(in_channels=in_c,
                    out_channels=out_c,
                    kernel_size=k,
                    stride=s,
                    padding=p,dilation=dilation, groups=groups)

def get_deconv2d_layer(in_c, out_c, k=1, s=1, p=1):
    return nn.Sequential(
        nn.Upsample(scale_factor=2, mode="bilinear"),
        nn.Conv2d(
            in_channels=in_c,
            out_channels=out_c,
            kernel_size=k,
            stride=s,
            padding=p
        )
    )

class Identity(nn.Module):

    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, x):
        return x