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base_net.py
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#!/usr/bin/env python
# coding=utf-8
'''
@Author: wjm
@Date: 2019-10-22 09:46:19
LastEditTime: 2020-11-07 09:47:04
@Description: file content
'''
import torch
import math
import torch.nn as nn
######################################
# common model
######################################
class Upsampler(torch.nn.Module):
def __init__(self, scale, n_feat, bn=False, activation='prelu', bias=True):
super(Upsampler, self).__init__()
modules = []
if scale == 3:
modules.append(ConvBlock(n_feat, 9 * n_feat, 3, 1, 1, bias, activation=None, norm=None))
modules.append(torch.nn.PixelShuffle(3))
if bn:
modules.append(torch.nn.BatchNorm2d(n_feat))
else:
for _ in range(int(math.log(scale, 2))):
modules.append(ConvBlock(n_feat, 4 * n_feat, 3, 1, 1, bias, activation=None, norm=None))
modules.append(torch.nn.PixelShuffle(2))
if bn:
modules.append(torch.nn.BatchNorm2d(n_feat))
self.up = torch.nn.Sequential(*modules)
self.activation = activation
if self.activation == 'relu':
self.act = torch.nn.ReLU(True)
elif self.activation == 'prelu':
self.act = torch.nn.PReLU(init=0.5)
elif self.activation == 'lrelu':
self.act = torch.nn.LeakyReLU(0.2, True)
elif self.activation == 'tanh':
self.act = torch.nn.Tanh()
elif self.activation == 'sigmoid':
self.act = torch.nn.Sigmoid()
def forward(self, x):
out = self.up(x)
if self.activation is not None:
out = self.act(out)
return out
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):
super(MeanShift, self).__init__(3, 3, kernel_size=1)
std = torch.Tensor(rgb_std)
self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
for p in self.parameters():
p.requires_grad = False
class ConvBlock(torch.nn.Module):
def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None, pad_model=None):
super(ConvBlock, self).__init__()
self.pad_model = pad_model
self.norm = norm
self.input_size = input_size
self.output_size = output_size
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.bias = bias
if self.norm =='batch':
self.bn = torch.nn.BatchNorm2d(self.output_size)
elif self.norm == 'instance':
self.bn = torch.nn.InstanceNorm2d(self.output_size)
self.activation = activation
if self.activation == 'relu':
self.act = torch.nn.ReLU(True)
elif self.activation == 'prelu':
self.act = torch.nn.PReLU(init=0.5)
elif self.activation == 'lrelu':
self.act = torch.nn.LeakyReLU(0.2, True)
elif self.activation == 'tanh':
self.act = torch.nn.Tanh()
elif self.activation == 'sigmoid':
self.act = torch.nn.Sigmoid()
if self.pad_model == None:
self.conv = torch.nn.Conv2d(self.input_size, self.output_size, self.kernel_size, self.stride, self.padding, bias=self.bias)
elif self.pad_model == 'reflection':
self.padding = nn.Sequential(nn.ReflectionPad2d(self.padding))
self.conv = torch.nn.Conv2d(self.input_size, self.output_size, self.kernel_size, self.stride, 0, bias=self.bias)
def forward(self, x):
out = x
if self.pad_model is not None:
out = self.padding(out)
if self.norm is not None:
out = self.bn(self.conv(out))
else:
out = self.conv(out)
if self.activation is not None:
return self.act(out)
else:
return out
class TransConvBlock(ConvBlock):
def __init__(self, *args, **kwargs):
super(ConvBlock, self).__init__()
if self.pad_model == None:
self.conv = torch.nn.ConvTranspose2d(self.input_size, self.output_size, self.kernel_size, self.stride, self.padding, bias=self.bias)
elif self.pad_model == 'reflection':
self.padding = nn.Sequential(nn.ReflectionPad2d(self.padding))
self.conv = torch.nn.ConvTranspose2d(self.input_size, self.output_size, self.kernel_size, self.stride, 0, bias=self.bias)
def forward(self, x):
out = x
if self.pad_model is not None:
out = self.padding(out)
if self.norm is not None:
out = self.bn(self.conv(out))
else:
out = self.conv(out)
if self.activation is not None:
return self.act(out)
else:
return out
######################################
# resnet_block
######################################
class ResnetBlock(torch.nn.Module):
def __init__(self, input_size, kernel_size=3, stride=1, padding=1, bias=True, scale=1, activation='prelu', norm='batch', pad_model=None):
super().__init__()
self.norm = norm
self.pad_model = pad_model
self.input_size = input_size
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.bias = bias
self.scale = scale
if self.norm =='batch':
self.normlayer = torch.nn.BatchNorm2d(input_size)
elif self.norm == 'instance':
self.normlayer = torch.nn.InstanceNorm2d(input_size)
else:
self.normlayer = None
self.activation = activation
if self.activation == 'relu':
self.act = torch.nn.ReLU(True)
elif self.activation == 'prelu':
self.act = torch.nn.PReLU(init=0.5)
elif self.activation == 'lrelu':
self.act = torch.nn.LeakyReLU(0.2, True)
elif self.activation == 'tanh':
self.act = torch.nn.Tanh()
elif self.activation == 'sigmoid':
self.act = torch.nn.Sigmoid()
else:
self.act = None
if self.pad_model == None:
self.conv1 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, padding, bias=bias)
self.conv2 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, padding, bias=bias)
self.pad = None
elif self.pad_model == 'reflection':
self.pad = nn.Sequential(nn.ReflectionPad2d(padding))
self.conv1 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, 0, bias=bias)
self.conv2 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, 0, bias=bias)
layers = filter(lambda x: x is not None, [self.pad, self.conv1, self.normlayer, self.act, self.pad, self.conv2, self.normlayer, self.act])
self.layers = nn.Sequential(*layers)
def forward(self, x):
residual = x
out = x
out = self.layers(x)
out = out * self.scale
out = torch.add(out, residual)
return out
class ResnetBlock_triple(ResnetBlock):
def __init__(self, *args, middle_size, output_size, **kwargs):
ResnetBlock.__init__(self, *args, **kwargs)
if self.norm =='batch':
self.normlayer1 = torch.nn.BatchNorm2d(middle_size)
self.normlayer2 = torch.nn.BatchNorm2d(output_size)
elif self.norm == 'instance':
self.normlayer1 = torch.nn.InstanceNorm2d(middle_size)
self.normlayer2 = torch.nn.BatchNorm2d(output_size)
else:
self.normlayer1 = None
self.normlayer2 = None
if self.pad_model == None:
self.conv1 = torch.nn.Conv2d(self.input_size, middle_size, self.kernel_size, self.stride, self.padding, bias=self.bias)
self.conv2 = torch.nn.Conv2d(middle_size, output_size, self.kernel_size, self.stride, self.padding, bias=self.bias)
self.pad = None
elif self.pad_model == 'reflection':
self.pad= nn.Sequential(nn.ReflectionPad2d(self.padding))
self.conv1 = torch.nn.Conv2d(self.input_size, middle_size, self.kernel_size, self.stride, 0, bias=self.bias)
self.conv2 = torch.nn.Conv2d(middle_size, output_size, self.kernel_size, self.stride, 0, bias=self.bias)
layers = filter(lambda x: x is not None, [self.pad, self.conv1, self.normlayer1, self.act, self.pad, self.conv2, self.normlayer2, self.act])
self.layers = nn.Sequential(*layers)
def forward(self, x):
residual = x
out = x
out= self.layers(x)
out = out * self.scale
out = torch.add(out, residual)
return out