# -*- coding: utf-8 -*-
# @Author : Lintao Peng
# @File : Ushape_Trans.py
# coding=utf-8
# Design based on the pix2pix
import torch.nn as nn
import torch.nn.functional as F
import torch
import datetime
import os
import time
import timeit
import copy
import numpy as np
from torch.nn import ModuleList
from torch.nn import Conv2d
from torch.nn import LeakyReLU
from net.block import *
from net.block import _equalized_conv2d
from net.SGFMT import TransformerModel
from net.PositionalEncoding import FixedPositionalEncoding,LearnedPositionalEncoding
from net.CMSFFT import ChannelTransformer
##权重初始化
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
class Generator(nn.Module):
"""
MSG-Unet-GAN的生成器部分
"""
def __init__(self,
img_dim=256,
patch_dim=16,
embedding_dim=512,
num_channels=3,
num_heads=8,
num_layers=4,
hidden_dim=256,
dropout_rate=0.0,
attn_dropout_rate=0.0,
in_ch=3,
out_ch=3,
conv_patch_representation=True,
positional_encoding_type="learned",
use_eql=True):
super(Generator, self).__init__()
assert embedding_dim % num_heads == 0
assert img_dim % patch_dim == 0
self.out_ch=out_ch #输出通道数
self.in_ch=in_ch #输入通道数
self.img_dim = img_dim #输入图片尺寸
self.embedding_dim = embedding_dim #512
self.num_heads = num_heads #多头注意力中头的数量
self.patch_dim = patch_dim #每个patch的尺寸
self.num_channels = num_channels #图片通道数?
self.dropout_rate = dropout_rate #drop-out比率
self.attn_dropout_rate = attn_dropout_rate #注意力模块的dropout比率
self.conv_patch_representation = conv_patch_representation #True
self.num_patches = int((img_dim // patch_dim) ** 2) #将三通道图片分成多少块
self.seq_length = self.num_patches #每个sequence的长度为patches的大小
self.flatten_dim = 128 * num_channels #128*3=384
#线性编码
self.linear_encoding = nn.Linear(self.flatten_dim, self.embedding_dim)
#位置编码
if positional_encoding_type == "learned":
self.position_encoding = LearnedPositionalEncoding(
self.seq_length, self.embedding_dim, self.seq_length
)
elif positional_encoding_type == "fixed":
self.position_encoding = FixedPositionalEncoding(
self.embedding_dim,
)
self.pe_dropout = nn.Dropout(p=self.dropout_rate)
self.transformer = TransformerModel(
embedding_dim, #512
num_layers, #4
num_heads, #8
hidden_dim, #4096
self.dropout_rate,
self.attn_dropout_rate,
)
#layer Norm
self.pre_head_ln = nn.LayerNorm(embedding_dim)
if self.conv_patch_representation:
self.Conv_x = nn.Conv2d(
256,
self.embedding_dim, #512
kernel_size=3,
stride=1,
padding=1
)
self.bn = nn.BatchNorm2d(256)
self.relu = nn.ReLU(inplace=True)
#modulelist
self.rgb_to_feature=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
self.feature_to_rgb=ModuleList([to_rgb(32),to_rgb(64),to_rgb(128),to_rgb(256)])
self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.Maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.Conv1=conv_block(self.in_ch, 16)
self.Conv1_1 = conv_block(16, 32)
self.Conv2 = conv_block(32, 32)
self.Conv2_1 = conv_block(32, 64)
self.Conv3 = conv_block(64,64)
self.Conv3_1 = conv_block(64,128)
self.Conv4 = conv_block(128,128)
self.Conv4_1 = conv_block(128,256)
self.Conv5 = conv_block(512,256)
#self.Conv_x = conv_block(256,512)
self.mtc = ChannelTransformer(channel_num=[32,64,128,256],
patchSize=[32, 16, 8, 4])
self.Up5 = up_conv(256, 256)
self.coatt5 = CCA(F_g=256, F_x=256)
self.Up_conv5 = conv_block(512, 256)
self.Up_conv5_1 = conv_block(256, 256)
self.Up4 = up_conv(256, 128)
self.coatt4 = CCA(F_g=128, F_x=128)
self.Up_conv4 = conv_block(256, 128)
self.Up_conv4_1 = conv_block(128, 128)
self.Up3 = up_conv(128, 64)
self.coatt3 = CCA(F_g=64, F_x=64)
self.Up_conv3 = conv_block(128, 64)
self.Up_conv3_1 = conv_block(64, 64)
self.Up2 = up_conv(64, 32)
self.coatt2 = CCA(F_g=32, F_x=32)
self.Up_conv2 = conv_block(64, 32)
self.Up_conv2_1 = conv_block(32, 32)
self.Conv = nn.Conv2d(32, self.out_ch, kernel_size=1, stride=1, padding=0)
# self.active = torch.nn.Sigmoid()
#
def reshape_output(self,x): #将transformer的输出resize为原来的特征图尺寸
x = x.view(
x.size(0),
int(self.img_dim / self.patch_dim),
int(self.img_dim / self.patch_dim),
self.embedding_dim,
)#B,16,16,512
x = x.permute(0, 3, 1, 2).contiguous()
return x
def forward(self, x):
#print(x.shape)
output=[]
x_1=self.Maxpool(x)
x_2=self.Maxpool(x_1)
x_3=self.Maxpool(x_2)
e1 = self.Conv1(x)
#print(e1.shape)
e1 = self.Conv1_1(e1)
e2 = self.Maxpool1(e1)
#32*128*128
x_1=self.rgb_to_feature[0](x_1)
#e2=torch.cat((x_1,e2), dim=1)
e2=x_1+e2
e2 = self.Conv2(e2)
e2 = self.Conv2_1(e2)
e3 = self.Maxpool2(e2)
#64*64*64
x_2=self.rgb_to_feature[1](x_2)
#e3=torch.cat((x_2,e3), dim=1)
e3=x_2+e3
e3 = self.Conv3(e3)
e3 = self.Conv3_1(e3)
e4 = self.Maxpool3(e3)
#128*32*32
x_3=self.rgb_to_feature[2](x_3)
#e4=torch.cat((x_3,e4), dim=1)
e4=x_3+e4
e4 = self.Conv4(e4)
e4 = self.Conv4_1(e4)
e5 = self.Maxpool4(e4)
#256*16*16
#channel-wise transformer-based attention
e1,e2,e3,e4,att_weights = self.mtc(e1,e2,e3,e4)
#spatial-wise transformer-based attention
residual=e5
#中间的隐变量
#conv_x应该接受256通道，输出512通道的中间隐变量
e5= self.bn(e5)
e5=self.relu(e5)
e5= self.Conv_x(e5) #out->512*16*16 shape->B,512,16,16
e5= e5.permute(0, 2, 3, 1).contiguous() # B,512,16,16->B,16,16,512
e5= e5.view(e5.size(0), -1, self.embedding_dim) #B,16,16,512->B,16*16,512 线性映射层
e5= self.position_encoding(e5) #位置编码
e5= self.pe_dropout(e5) #预dropout层
# apply transformer
e5= self.transformer(e5)
e5= self.pre_head_ln(e5)
e5= self.reshape_output(e5)#out->512*16*16 shape->B,512,16,16
e5=self.Conv5(e5) #out->256,16,16 shape->B,256,16,16
#residual是否要加bn和relu？
e5=e5+residual
d5 = self.Up5(e5)
e4_att = self.coatt5(g=d5, x=e4)
d5 = torch.cat((e4_att, d5), dim=1)
d5 = self.Up_conv5(d5)
d5 = self.Up_conv5_1(d5)
#256
out3=self.feature_to_rgb[3](d5)
output.append(out3)#32*32orH/8,W/8
d4 = self.Up4(d5)
e3_att = self.coatt4(g=d4, x=e3)
d4 = torch.cat((e3_att, d4), dim=1)
d4 = self.Up_conv4(d4)
d4 = self.Up_conv4_1(d4)
#128
out2=self.feature_to_rgb[2](d4)
output.append(out2)#64*64orH/4,W/4
d3 = self.Up3(d4)
e2_att = self.coatt3(g=d3, x=e2)
d3 = torch.cat((e2_att, d3), dim=1)
d3 = self.Up_conv3(d3)
d3 = self.Up_conv3_1(d3)
#64
out1=self.feature_to_rgb[1](d3)
output.append(out1)#128#128orH/2,W/2
d2 = self.Up2(d3)
e1_att = self.coatt2(g=d2, x=e1)
d2 = torch.cat((e1_att, d2), dim=1)
d2 = self.Up_conv2(d2)
d2 = self.Up_conv2_1(d2)
#32
out0=self.feature_to_rgb[0](d2)
output.append(out0)#256*256
#out = self.Conv(d2)
#d1 = self.active(out)
#output=np.array(output)
return output[3]
class Discriminator(nn.Module):
def __init__(self, in_channels=3,use_eql=True):
super(Discriminator, self).__init__()
self.use_eql=use_eql
self.in_channels=in_channels
#modulelist
self.rgb_to_feature1=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
self.rgb_to_feature2=ModuleList([from_rgb(32),from_rgb(64),from_rgb(128)])
self.layer=_equalized_conv2d(self.in_channels*2, 64, (1, 1), bias=True)
# pixel_wise feature normalizer:
self.pixNorm = PixelwiseNorm()
# leaky_relu:
self.lrelu = LeakyReLU(0.2)
self.layer0=DisGeneralConvBlock(64,64,use_eql=self.use_eql)
#128*128*32
self.layer1=DisGeneralConvBlock(128,128,use_eql=self.use_eql)
#64*64*64
self.layer2=DisGeneralConvBlock(256,256,use_eql=self.use_eql)
#32*32*128
self.layer3=DisGeneralConvBlock(512,512,use_eql=self.use_eql)
#16*16*256
self.layer4=DisFinalBlock(512,use_eql=self.use_eql)
#8*8*512
def forward(self, img_A, inputs):
#inputs图片尺寸从小到大
# Concatenate image and condition image by channels to produce input
#img_input = torch.cat((img_A, img_B), 1)
#img_A_128= F.interpolate(img_A, size=[128, 128])
#img_A_64= F.interpolate(img_A, size=[64, 64])
#img_A_32= F.interpolate(img_A, size=[32, 32])
x=torch.cat((img_A[3], inputs[3]), 1)
y = self.pixNorm(self.lrelu(self.layer(x)))
y=self.layer0(y)
#128*128*64
x1=self.rgb_to_feature1[0](img_A[2])
x2=self.rgb_to_feature2[0](inputs[2])
x=torch.cat((x1,x2),1)
y=torch.cat((x,y),1)
y=self.layer1(y)
#64*64*128
x1=self.rgb_to_feature1[1](img_A[1])
x2=self.rgb_to_feature2[1](inputs[1])
x=torch.cat((x1,x2),1)
y=torch.cat((x,y),1)
y=self.layer2(y)
#32*32*256
x1=self.rgb_to_feature1[2](img_A[0])
x2=self.rgb_to_feature2[2](inputs[0])
x=torch.cat((x1,x2),1)
y=torch.cat((x,y),1)
y=self.layer3(y)
#16*16*512
y=self.layer4(y)
#8*8*512
return y