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net/CMSFFT.py

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+# -*- coding: utf-8 -*-
+# @Author  : Lintao Peng
+# @File    : CMSFFT.py
+# coding=utf-8
+# Design based on the CTrans
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import copy
+import logging
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.nn import Dropout, Softmax, Conv2d, LayerNorm
+from torch.nn.modules.utils import _pair
+
+
+#KV_size = 480
+#transformer.num_heads  = 4
+#transformer.num_layers = 4
+#expand_ratio           = 4
+
+
+
+#线性编码
+class Channel_Embeddings(nn.Module):
+    """Construct the embeddings from patch, position embeddings.
+    """
+    def __init__(self, patchsize, img_size, in_channels):
+        super().__init__()
+        img_size = _pair(img_size)
+        patch_size = _pair(patchsize)
+        n_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1])
+
+        self.patch_embeddings = Conv2d(in_channels=in_channels,
+                                       out_channels=in_channels,
+                                       kernel_size=patch_size,
+                                       stride=patch_size)
+        self.position_embeddings = nn.Parameter(torch.zeros(1, n_patches, in_channels))
+        self.dropout = Dropout(0.1)
+
+    def forward(self, x):
+        if x is None:
+            return None
+        x = self.patch_embeddings(x)  # (B, hidden，n_patches^(1/2), n_patches^(1/2))
+        x = x.flatten(2)
+        x = x.transpose(-1, -2)  # (B, n_patches, hidden)
+        embeddings = x + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+#特征重组
+class Reconstruct(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor):
+        super(Reconstruct, self).__init__()
+        if kernel_size == 3:
+            padding = 1
+        else:
+            padding = 0
+        self.conv = nn.Conv2d(in_channels, out_channels,kernel_size=kernel_size, padding=padding)
+        self.norm = nn.BatchNorm2d(out_channels)
+        self.activation = nn.ReLU(inplace=True)
+        self.scale_factor = scale_factor
+
+    def forward(self, x):
+        if x is None:
+            return None
+
+        # reshape from (B, n_patch, hidden) to (B, h, w, hidden)
+        B, n_patch, hidden = x.size()  
+        h, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch))
+        x = x.permute(0, 2, 1)
+        x = x.contiguous().view(B, hidden, h, w)
+        x = nn.Upsample(scale_factor=self.scale_factor)(x)
+
+        out = self.conv(x)
+        out = self.norm(out)
+        out = self.activation(out)
+        return out
+
+class Attention_org(nn.Module):
+    def __init__(self, vis,channel_num, KV_size=480, num_heads=4):
+        super(Attention_org, self).__init__()
+        self.vis = vis
+        self.KV_size = KV_size
+        self.channel_num = channel_num
+        self.num_attention_heads = num_heads
+
+        self.query1 = nn.ModuleList()
+        self.query2 = nn.ModuleList()
+        self.query3 = nn.ModuleList()
+        self.query4 = nn.ModuleList()
+        self.key = nn.ModuleList()
+        self.value = nn.ModuleList()
+
+        for _ in range(num_heads):
+            query1 = nn.Linear(channel_num[0], channel_num[0], bias=False)
+            query2 = nn.Linear(channel_num[1], channel_num[1], bias=False)
+            query3 = nn.Linear(channel_num[2], channel_num[2], bias=False)
+            query4 = nn.Linear(channel_num[3], channel_num[3], bias=False)
+            key = nn.Linear( self.KV_size,  self.KV_size, bias=False)
+            value = nn.Linear(self.KV_size,  self.KV_size, bias=False)
+            #把所有的值都重新复制一遍，deepcopy为深复制，完全脱离原来的值，即将被复制对象完全再复制一遍作为独立的新个体单独存在
+            self.query1.append(copy.deepcopy(query1))
+            self.query2.append(copy.deepcopy(query2))
+            self.query3.append(copy.deepcopy(query3))
+            self.query4.append(copy.deepcopy(query4))
+            self.key.append(copy.deepcopy(key))
+            self.value.append(copy.deepcopy(value))
+        self.psi = nn.InstanceNorm2d(self.num_attention_heads)
+        self.softmax = Softmax(dim=3)
+        self.out1 = nn.Linear(channel_num[0], channel_num[0], bias=False)
+        self.out2 = nn.Linear(channel_num[1], channel_num[1], bias=False)
+        self.out3 = nn.Linear(channel_num[2], channel_num[2], bias=False)
+        self.out4 = nn.Linear(channel_num[3], channel_num[3], bias=False)
+        self.attn_dropout = Dropout(0.1)
+        self.proj_dropout = Dropout(0.1)
+
+
+
+    def forward(self, emb1,emb2,emb3,emb4, emb_all):
+        multi_head_Q1_list = []
+        multi_head_Q2_list = []
+        multi_head_Q3_list = []
+        multi_head_Q4_list = []
+        multi_head_K_list = []
+        multi_head_V_list = []
+        if emb1 is not None:
+            for query1 in self.query1:
+                Q1 = query1(emb1)
+                multi_head_Q1_list.append(Q1)
+        if emb2 is not None:
+            for query2 in self.query2:
+                Q2 = query2(emb2)
+                multi_head_Q2_list.append(Q2)
+        if emb3 is not None:
+            for query3 in self.query3:
+                Q3 = query3(emb3)
+                multi_head_Q3_list.append(Q3)
+        if emb4 is not None:
+            for query4 in self.query4:
+                Q4 = query4(emb4)
+                multi_head_Q4_list.append(Q4)
+        for key in self.key:
+            K = key(emb_all)
+            multi_head_K_list.append(K)
+        for value in self.value:
+            V = value(emb_all)
+            multi_head_V_list.append(V)
+        # print(len(multi_head_Q4_list))
+
+        multi_head_Q1 = torch.stack(multi_head_Q1_list, dim=1) if emb1 is not None else None
+        multi_head_Q2 = torch.stack(multi_head_Q2_list, dim=1) if emb2 is not None else None
+        multi_head_Q3 = torch.stack(multi_head_Q3_list, dim=1) if emb3 is not None else None
+        multi_head_Q4 = torch.stack(multi_head_Q4_list, dim=1) if emb4 is not None else None
+        multi_head_K = torch.stack(multi_head_K_list, dim=1)
+        multi_head_V = torch.stack(multi_head_V_list, dim=1)
+
+        multi_head_Q1 = multi_head_Q1.transpose(-1, -2) if emb1 is not None else None
+        multi_head_Q2 = multi_head_Q2.transpose(-1, -2) if emb2 is not None else None
+        multi_head_Q3 = multi_head_Q3.transpose(-1, -2) if emb3 is not None else None
+        multi_head_Q4 = multi_head_Q4.transpose(-1, -2) if emb4 is not None else None
+
+        attention_scores1 = torch.matmul(multi_head_Q1, multi_head_K) if emb1 is not None else None
+        attention_scores2 = torch.matmul(multi_head_Q2, multi_head_K) if emb2 is not None else None
+        attention_scores3 = torch.matmul(multi_head_Q3, multi_head_K) if emb3 is not None else None
+        attention_scores4 = torch.matmul(multi_head_Q4, multi_head_K) if emb4 is not None else None
+
+        attention_scores1 = attention_scores1 / math.sqrt(self.KV_size) if emb1 is not None else None
+        attention_scores2 = attention_scores2 / math.sqrt(self.KV_size) if emb2 is not None else None
+        attention_scores3 = attention_scores3 / math.sqrt(self.KV_size) if emb3 is not None else None
+        attention_scores4 = attention_scores4 / math.sqrt(self.KV_size) if emb4 is not None else None
+
+        attention_probs1 = self.softmax(self.psi(attention_scores1)) if emb1 is not None else None
+        attention_probs2 = self.softmax(self.psi(attention_scores2)) if emb2 is not None else None
+        attention_probs3 = self.softmax(self.psi(attention_scores3)) if emb3 is not None else None
+        attention_probs4 = self.softmax(self.psi(attention_scores4)) if emb4 is not None else None
+        # print(attention_probs4.size())
+
+        if self.vis:
+            weights =  []
+            weights.append(attention_probs1.mean(1))
+            weights.append(attention_probs2.mean(1))
+            weights.append(attention_probs3.mean(1))
+            weights.append(attention_probs4.mean(1))
+        else: weights=None
+
+        attention_probs1 = self.attn_dropout(attention_probs1) if emb1 is not None else None
+        attention_probs2 = self.attn_dropout(attention_probs2) if emb2 is not None else None
+        attention_probs3 = self.attn_dropout(attention_probs3) if emb3 is not None else None
+        attention_probs4 = self.attn_dropout(attention_probs4) if emb4 is not None else None
+
+        multi_head_V = multi_head_V.transpose(-1, -2)
+        context_layer1 = torch.matmul(attention_probs1, multi_head_V) if emb1 is not None else None
+        context_layer2 = torch.matmul(attention_probs2, multi_head_V) if emb2 is not None else None
+        context_layer3 = torch.matmul(attention_probs3, multi_head_V) if emb3 is not None else None
+        context_layer4 = torch.matmul(attention_probs4, multi_head_V) if emb4 is not None else None
+
+        context_layer1 = context_layer1.permute(0, 3, 2, 1).contiguous() if emb1 is not None else None
+        context_layer2 = context_layer2.permute(0, 3, 2, 1).contiguous() if emb2 is not None else None
+        context_layer3 = context_layer3.permute(0, 3, 2, 1).contiguous() if emb3 is not None else None
+        context_layer4 = context_layer4.permute(0, 3, 2, 1).contiguous() if emb4 is not None else None
+        context_layer1 = context_layer1.mean(dim=3) if emb1 is not None else None
+        context_layer2 = context_layer2.mean(dim=3) if emb2 is not None else None
+        context_layer3 = context_layer3.mean(dim=3) if emb3 is not None else None
+        context_layer4 = context_layer4.mean(dim=3) if emb4 is not None else None
+
+        O1 = self.out1(context_layer1) if emb1 is not None else None
+        O2 = self.out2(context_layer2) if emb2 is not None else None
+        O3 = self.out3(context_layer3) if emb3 is not None else None
+        O4 = self.out4(context_layer4) if emb4 is not None else None
+        O1 = self.proj_dropout(O1) if emb1 is not None else None
+        O2 = self.proj_dropout(O2) if emb2 is not None else None
+        O3 = self.proj_dropout(O3) if emb3 is not None else None
+        O4 = self.proj_dropout(O4) if emb4 is not None else None
+        return O1,O2,O3,O4, weights
+
+
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_channel, mlp_channel):
+        super(Mlp, self).__init__()
+        self.fc1 = nn.Linear(in_channel, mlp_channel)
+        self.fc2 = nn.Linear(mlp_channel, in_channel)
+        self.act_fn = nn.GELU()
+        self.dropout = Dropout(0.0)
+        self._init_weights()
+
+    def _init_weights(self):
+        nn.init.xavier_uniform_(self.fc1.weight)
+        nn.init.xavier_uniform_(self.fc2.weight)
+        nn.init.normal_(self.fc1.bias, std=1e-6)
+        nn.init.normal_(self.fc2.bias, std=1e-6)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = self.dropout(x)
+        return x
+
+class Block_ViT(nn.Module):
+    def __init__(self, vis, channel_num, expand_ratio=4,KV_size=480):
+        super(Block_ViT, self).__init__()
+        expand_ratio = 4
+        self.attn_norm1 = LayerNorm(channel_num[0],eps=1e-6)
+        self.attn_norm2 = LayerNorm(channel_num[1],eps=1e-6)
+        self.attn_norm3 = LayerNorm(channel_num[2],eps=1e-6)
+        self.attn_norm4 = LayerNorm(channel_num[3],eps=1e-6)
+        self.attn_norm =  LayerNorm(KV_size,eps=1e-6)
+        self.channel_attn = Attention_org(vis, channel_num)
+
+        self.ffn_norm1 = LayerNorm(channel_num[0],eps=1e-6)
+        self.ffn_norm2 = LayerNorm(channel_num[1],eps=1e-6)
+        self.ffn_norm3 = LayerNorm(channel_num[2],eps=1e-6)
+        self.ffn_norm4 = LayerNorm(channel_num[3],eps=1e-6)
+        self.ffn1 = Mlp(channel_num[0],channel_num[0]*expand_ratio)
+        self.ffn2 = Mlp(channel_num[1],channel_num[1]*expand_ratio)
+        self.ffn3 = Mlp(channel_num[2],channel_num[2]*expand_ratio)
+        self.ffn4 = Mlp(channel_num[3],channel_num[3]*expand_ratio)
+
+
+    def forward(self, emb1,emb2,emb3,emb4):
+        embcat = []
+        org1 = emb1
+        org2 = emb2
+        org3 = emb3
+        org4 = emb4
+        for i in range(4):
+            var_name = "emb"+str(i+1)  #emb1,emb2,emb3,emb4
+            tmp_var = locals()[var_name]
+            if tmp_var is not None:
+                embcat.append(tmp_var)
+
+        emb_all = torch.cat(embcat,dim=2)
+        cx1 = self.attn_norm1(emb1) if emb1 is not None else None
+        cx2 = self.attn_norm2(emb2) if emb2 is not None else None
+        cx3 = self.attn_norm3(emb3) if emb3 is not None else None
+        cx4 = self.attn_norm4(emb4) if emb4 is not None else None
+        emb_all = self.attn_norm(emb_all)
+        cx1,cx2,cx3,cx4, weights = self.channel_attn(cx1,cx2,cx3,cx4,emb_all)
+        #残差
+        cx1 = org1 + cx1 if emb1 is not None else None
+        cx2 = org2 + cx2 if emb2 is not None else None
+        cx3 = org3 + cx3 if emb3 is not None else None
+        cx4 = org4 + cx4 if emb4 is not None else None
+
+        org1 = cx1
+        org2 = cx2
+        org3 = cx3
+        org4 = cx4
+        x1 = self.ffn_norm1(cx1) if emb1 is not None else None
+        x2 = self.ffn_norm2(cx2) if emb2 is not None else None
+        x3 = self.ffn_norm3(cx3) if emb3 is not None else None
+        x4 = self.ffn_norm4(cx4) if emb4 is not None else None
+        x1 = self.ffn1(x1) if emb1 is not None else None
+        x2 = self.ffn2(x2) if emb2 is not None else None
+        x3 = self.ffn3(x3) if emb3 is not None else None
+        x4 = self.ffn4(x4) if emb4 is not None else None
+        #残差
+        x1 = x1 + org1 if emb1 is not None else None
+        x2 = x2 + org2 if emb2 is not None else None
+        x3 = x3 + org3 if emb3 is not None else None
+        x4 = x4 + org4 if emb4 is not None else None
+
+        return x1, x2, x3, x4, weights
+
+
+class Encoder(nn.Module):
+    def __init__(self, vis, channel_num, num_layers=4):
+        super(Encoder, self).__init__()
+        self.vis = vis
+        self.layer = nn.ModuleList()
+        self.encoder_norm1 = LayerNorm(channel_num[0],eps=1e-6)
+        self.encoder_norm2 = LayerNorm(channel_num[1],eps=1e-6)
+        self.encoder_norm3 = LayerNorm(channel_num[2],eps=1e-6)
+        self.encoder_norm4 = LayerNorm(channel_num[3],eps=1e-6)
+        for _ in range(num_layers):
+            layer = Block_ViT(vis, channel_num)
+            self.layer.append(copy.deepcopy(layer))
+
+    def forward(self, emb1,emb2,emb3,emb4):
+        attn_weights = []
+        for layer_block in self.layer:
+            emb1,emb2,emb3,emb4, weights = layer_block(emb1,emb2,emb3,emb4)
+            if self.vis:
+                attn_weights.append(weights)
+        emb1 = self.encoder_norm1(emb1) if emb1 is not None else None
+        emb2 = self.encoder_norm2(emb2) if emb2 is not None else None
+        emb3 = self.encoder_norm3(emb3) if emb3 is not None else None
+        emb4 = self.encoder_norm4(emb4) if emb4 is not None else None
+        return emb1,emb2,emb3,emb4, attn_weights
+
+
+class ChannelTransformer(nn.Module):
+    def __init__(self,  vis=False, img_size=256, channel_num=[64, 128, 256, 512], patchSize=[32, 16, 8, 4]):
+        super().__init__()
+
+        self.patchSize_1 = patchSize[0]
+        self.patchSize_2 = patchSize[1]
+        self.patchSize_3 = patchSize[2]
+        self.patchSize_4 = patchSize[3]
+        self.embeddings_1 = Channel_Embeddings(self.patchSize_1, img_size=img_size,    in_channels=channel_num[0])
+        self.embeddings_2 = Channel_Embeddings(self.patchSize_2, img_size=img_size//2, in_channels=channel_num[1])
+        self.embeddings_3 = Channel_Embeddings(self.patchSize_3, img_size=img_size//4, in_channels=channel_num[2])
+        self.embeddings_4 = Channel_Embeddings(self.patchSize_4, img_size=img_size//8, in_channels=channel_num[3])
+        self.encoder = Encoder( vis, channel_num)
+
+        self.reconstruct_1 = Reconstruct(channel_num[0], channel_num[0], kernel_size=1,scale_factor=(self.patchSize_1,self.patchSize_1))
+        self.reconstruct_2 = Reconstruct(channel_num[1], channel_num[1], kernel_size=1,scale_factor=(self.patchSize_2,self.patchSize_2))
+        self.reconstruct_3 = Reconstruct(channel_num[2], channel_num[2], kernel_size=1,scale_factor=(self.patchSize_3,self.patchSize_3))
+        self.reconstruct_4 = Reconstruct(channel_num[3], channel_num[3], kernel_size=1,scale_factor=(self.patchSize_4,self.patchSize_4))
+
+    def forward(self,en1,en2,en3,en4):
+
+        emb1 = self.embeddings_1(en1)
+        emb2 = self.embeddings_2(en2)
+        emb3 = self.embeddings_3(en3)
+        emb4 = self.embeddings_4(en4)
+
+        encoded1, encoded2, encoded3, encoded4, attn_weights = self.encoder(emb1,emb2,emb3,emb4)  # (B, n_patch, hidden)
+        x1 = self.reconstruct_1(encoded1) if en1 is not None else None
+        x2 = self.reconstruct_2(encoded2) if en2 is not None else None
+        x3 = self.reconstruct_3(encoded3) if en3 is not None else None
+        x4 = self.reconstruct_4(encoded4) if en4 is not None else None
+
+        x1 = x1 + en1  if en1 is not None else None
+        x2 = x2 + en2  if en2 is not None else None
+        x3 = x3 + en3  if en3 is not None else None
+        x4 = x4 + en4  if en4 is not None else None
+
+        return x1, x2, x3, x4, attn_weights
+