Network.py

import torch.nn as nn

vgg19_cfg = [3, 64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"]
decoder_cfg = [512, 256, "U", 256, 256, 256, 128, "U", 128, 64, 'U', 64, 3]

def vgg19(weights=None):
 """
 Build vgg19 network. Load weights if weights are given.

 Args:
 weights (dict): vgg19 pretrained weights

 Return:
 layers (nn.Sequential): vgg19 layers
 """

 modules = make_block(vgg19_cfg)
 modules = [nn.Conv2d(3, 3, kernel_size=1)] + list(modules.children())
 layers = nn.Sequential(*modules)

 if weights:
 layers.load_state_dict(weights)
 
 return layers


def decoder(weights=None):
 """
 Build decoder network. Load weights if weights are given.

 Args:
 weights (dict): decoder pretrained weights

 Return:
 layers (nn.Sequential): decoder layers
 """

 modules = make_block(decoder_cfg)
 layers = nn.Sequential(*list(modules.children())[:-1]) # no relu at the last layer

 if weights:
 layers.load_state_dict(weights)

 return layers


def make_block(config):
 """
 Helper function for building blocks of convolutional layers.

 Args:
 config (list): List of layer configs. "M"
 "M" - Max pooling layer. 
 "U" - Upsampling layer. 
 i (int) - Convolutional layer (i filters) plus ReLU activation. 
 Return:
 layers (nn.Sequential): block layers
 """
 layers = []
 in_channels = config[0]
 
 for c in config[1:]:
 if c == "M":
 layers.append(nn.MaxPool2d(kernel_size=2, stride=2, padding=0))
 elif c == "U":
 layers.append(nn.Upsample(scale_factor=2, mode='nearest'))
 else:
 assert(isinstance(c, int))
 layers.append(nn.Conv2d(in_channels, c, kernel_size=3, padding=1))
 layers.append(nn.ReLU(inplace=True))
 in_channels = c

 return nn.Sequential(*layers)