# -*- 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