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UCTransNet.py

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+# -*- coding: utf-8 -*-
+# @Time    : 2024/2/17 11:06
+# @Author  : Haonan Wang
+# @File    : UCTransNet.py
+# @Software: PyCharm
+
+
+
+import torch.nn.functional as F
+import copy
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.nn import Dropout, Softmax, LayerNorm
+from torch.nn.modules.utils import _pair, _triple
+
+
+
+def get_activation(activation_type):
+    activation_type = activation_type.lower()
+    if hasattr(nn, activation_type):
+        return getattr(nn, activation_type)()
+    else:
+        return nn.ReLU()
+
+def _make_nConv(in_channels, out_channels, nb_Conv, activation='ReLU'):
+    layers = []
+    layers.append(ConvBatchNorm(in_channels, out_channels, activation))
+
+    for _ in range(nb_Conv - 1):
+        layers.append(ConvBatchNorm(out_channels, out_channels, activation))
+    return nn.Sequential(*layers)
+
+class ConvBatchNorm(nn.Module):
+    """(convolution => [BN] => ReLU)"""
+
+    def __init__(self, in_channels, out_channels, activation='ReLU'):
+        super(ConvBatchNorm, self).__init__()
+        self.conv = nn.Conv3d(in_channels, out_channels,
+                              kernel_size=3, padding=1)
+        self.norm = nn.BatchNorm3d(out_channels)
+        self.activation = get_activation(activation)
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        return self.activation(out)
+
+class DownBlock(nn.Module):
+    """Downscaling with maxpool convolution"""
+    def __init__(self, in_channels, out_channels, nb_Conv, activation='ReLU'):
+        super(DownBlock, self).__init__()
+        self.maxpool = nn.MaxPool3d(2)
+        self.nConvs = _make_nConv(in_channels, out_channels, nb_Conv, activation)
+
+    def forward(self, x):
+        out = self.maxpool(x)
+        return self.nConvs(out)
+
+class Flatten(nn.Module):
+    def forward(self, x):
+        return x.view(x.size(0), -1)
+
+class CCA(nn.Module):
+    """
+    CCA Block
+    """
+    def __init__(self, F_g, F_x):
+        super().__init__()
+        self.mlp_x = nn.Sequential(
+            Flatten(),
+            nn.Linear(F_x, F_x))
+        self.mlp_g = nn.Sequential(
+            Flatten(),
+            nn.Linear(F_g, F_x))
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, g, x):
+        # channel-wise attention
+        avg_pool_x = F.avg_pool3d( x, (x.size(2), x.size(3), x.size(4)), stride=(x.size(2), x.size(3), x.size(4)))
+        channel_att_x = self.mlp_x(avg_pool_x)
+        avg_pool_g = F.avg_pool3d( g, (g.size(2), g.size(3), g.size(4)), stride=(g.size(2), g.size(3), g.size(4)))
+        channel_att_g = self.mlp_g(avg_pool_g)
+        channel_att_sum = (channel_att_x + channel_att_g)/2.0
+        scale = torch.sigmoid(channel_att_sum).unsqueeze(2).unsqueeze(3).unsqueeze(4).expand_as(x)
+        x_after_channel = x * scale
+        out = self.relu(x_after_channel)
+        return out
+
+class UpBlock_attention(nn.Module):
+    def __init__(self, in_channels, out_channels, nb_Conv, activation='ReLU'):
+        super().__init__()
+        self.up = nn.Upsample(scale_factor=2)
+        self.coatt = CCA(F_g=in_channels//2, F_x=in_channels//2)
+        self.nConvs = _make_nConv(in_channels, out_channels, nb_Conv, activation)
+
+    def forward(self, x, skip_x):
+        up = self.up(x)
+        skip_x_att = self.coatt(g=up, x=skip_x)
+        x = torch.cat([skip_x_att, up], dim=1)  # dim 1 is the channel dimension
+        return self.nConvs(x)
+
+class UCTransNet(nn.Module):
+    def __init__(self, in_channels, out_channels, num_layers, KV_size, num_heads, attention_dropout_rate, mlp_dropout_rate,  feature_size, img_size, patch_sizes):
+        super().__init__()
+        self.inc = ConvBatchNorm(in_channels, feature_size)
+        self.down1 = DownBlock(feature_size, feature_size*2, nb_Conv=2)
+        self.down2 = DownBlock(feature_size*2, feature_size*4, nb_Conv=2)
+        self.down3 = DownBlock(feature_size*4, feature_size*8, nb_Conv=2)
+        self.down4 = DownBlock(feature_size*8, feature_size*8, nb_Conv=2)
+        self.mtc = ChannelTransformer(img_size, num_layers, KV_size, num_heads, attention_dropout_rate, mlp_dropout_rate,
+                                      channel_num=[feature_size, feature_size*2, feature_size*4, feature_size*8],
+                                      patchSize=patch_sizes)
+        self.up4 = UpBlock_attention(feature_size*16, feature_size*4, nb_Conv=2)
+        self.up3 = UpBlock_attention(feature_size*8, feature_size*2, nb_Conv=2)
+        self.up2 = UpBlock_attention(feature_size*4, feature_size, nb_Conv=2)
+        self.up1 = UpBlock_attention(feature_size*2, feature_size, nb_Conv=2)
+        self.outc = nn.Conv3d(feature_size, out_channels, kernel_size=(1, 1, 1), stride=(1, 1, 1))
+
+    def forward(self, x):
+        x = x.float()
+        x1 = self.inc(x)
+        x2 = self.down1(x1)
+        x3 = self.down2(x2)
+        x4 = self.down3(x3)
+        x5 = self.down4(x4)
+        x1,x2,x3,x4 = self.mtc(x1,x2,x3,x4)
+        x = self.up4(x5, x4)
+        x = self.up3(x, x3)
+        x = self.up2(x, x2)
+        x = self.up1(x, x1)
+
+        logits = self.outc(x) # if nusing BCEWithLogitsLoss or class>1
+
+        return logits
+
+
+
+
+
+
+
+
+
+class Channel_Embeddings(nn.Module):
+    """Construct the embeddings from patch, position embeddings.
+    """
+    def __init__(self, patchsize, img_size, in_channels, reduce_scale):
+        super().__init__()
+        patch_size = _triple(patchsize)
+        n_patches = (img_size[0] // reduce_scale // patch_size[0]) * (img_size[1] // reduce_scale // patch_size[1]) * (img_size[2] // reduce_scale // patch_size[2])
+
+        self.patch_embeddings = nn.Conv3d(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))
+        h, w, d = x.shape[-3:]
+        x = x.flatten(2)
+        x = x.transpose(-1, -2)  # (B, n_patches, hidden)
+        embeddings = x + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings, (h, w, d)
+
+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.Conv3d(in_channels, out_channels,kernel_size=kernel_size, padding=padding)
+        self.norm = nn.BatchNorm3d(out_channels)
+        self.activation = nn.ReLU(inplace=True)
+        self.scale_factor = scale_factor
+
+    def forward(self, x, shp):
+        if x is None:
+            return None
+
+        B, n_patch, hidden = x.size()  # reshape from (B, n_patch, hidden) to (B, h, w, hidden)
+        h, w, d = shp
+        x = x.permute(0, 2, 1)
+        x = x.contiguous().view(B, hidden, h, w, d)
+        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, KV_size, channel_num, num_heads, attention_dropout_rate):
+        super(Attention_org, self).__init__()
+        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)
+            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(attention_dropout_rate)
+        self.proj_dropout = Dropout(attention_dropout_rate)
+
+
+
+    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())
+
+        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
+
+
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_channel, mlp_channel, dropout_rate):
+        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(dropout_rate)
+        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, KV_size, channel_num, num_heads, attention_dropout_rate, mlp_dropout_rate):
+        super(Block_ViT, self).__init__()
+        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(KV_size, channel_num, num_heads, attention_dropout_rate)
+
+        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]*4, mlp_dropout_rate)
+        self.ffn2 = Mlp(channel_num[1],channel_num[1]*4, mlp_dropout_rate)
+        self.ffn3 = Mlp(channel_num[2],channel_num[2]*4, mlp_dropout_rate)
+        self.ffn4 = Mlp(channel_num[3],channel_num[3]*4, mlp_dropout_rate)
+
+
+    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)
+            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 = self.channel_attn(cx1,cx2,cx3,cx4,emb_all)
+        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
+        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
+
+
+class Encoder(nn.Module):
+    def __init__(self, num_layers, KV_size, channel_num, num_heads, attention_dropout_rate, mlp_dropout_rate):
+        super(Encoder, self).__init__()
+        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(KV_size, channel_num, num_heads, attention_dropout_rate, mlp_dropout_rate)
+            self.layer.append(copy.deepcopy(layer))
+
+    def forward(self, emb1,emb2,emb3,emb4):
+        for layer_block in self.layer:
+            emb1,emb2,emb3,emb4 = layer_block(emb1,emb2,emb3,emb4)
+        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
+
+
+class ChannelTransformer(nn.Module):
+    def __init__(self, img_size, num_layers, KV_size, num_heads, attention_dropout_rate, mlp_dropout_rate, 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, reduce_scale=1, in_channels=channel_num[0])
+        self.embeddings_2 = Channel_Embeddings(self.patchSize_2, img_size=img_size, reduce_scale=2, in_channels=channel_num[1])
+        self.embeddings_3 = Channel_Embeddings(self.patchSize_3, img_size=img_size, reduce_scale=4, in_channels=channel_num[2])
+        self.embeddings_4 = Channel_Embeddings(self.patchSize_4, img_size=img_size, reduce_scale=8, in_channels=channel_num[3])
+        self.encoder = Encoder(num_layers, KV_size, channel_num, num_heads, attention_dropout_rate, mlp_dropout_rate)
+
+        self.reconstruct_1 = Reconstruct(channel_num[0], channel_num[0], kernel_size=1,scale_factor=_triple(self.patchSize_1))
+        self.reconstruct_2 = Reconstruct(channel_num[1], channel_num[1], kernel_size=1,scale_factor=_triple(self.patchSize_2))
+        self.reconstruct_3 = Reconstruct(channel_num[2], channel_num[2], kernel_size=1,scale_factor=_triple(self.patchSize_3))
+        self.reconstruct_4 = Reconstruct(channel_num[3], channel_num[3], kernel_size=1,scale_factor=_triple(self.patchSize_4))
+
+    def forward(self, en1, en2, en3, en4):
+
+        emb1, shp1 = self.embeddings_1(en1)
+        emb2, shp2 = self.embeddings_2(en2)
+        emb3, shp3 = self.embeddings_3(en3)
+        emb4, shp4 = self.embeddings_4(en4)
+
+        encoded1, encoded2, encoded3, encoded4 = self.encoder(emb1,emb2,emb3,emb4)  # (B, n_patch, hidden)
+        x1 = self.reconstruct_1(encoded1, shp1) if en1 is not None else None
+        x2 = self.reconstruct_2(encoded2, shp2) if en2 is not None else None
+        x3 = self.reconstruct_3(encoded3, shp3) if en3 is not None else None
+        x4 = self.reconstruct_4(encoded4, shp4) 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

best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae3a4051a40de52f51db628ff7737501ecf043bfc11a8931829a9885c559766a
+size 816404132