Upload decoder.py

model/decoder.py

@@ -0,0 +1,301 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from model.utils import weight_init
+
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim1, dim2, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim1 // num_heads
+        self.scale = head_dim ** -0.5
+
+        self.q = nn.Linear(dim1, dim1, bias=qkv_bias)
+        self.kv = nn.Linear(dim2, dim1 * 2, bias=qkv_bias)
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim1, dim1)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, y):
+        B1, N1, C1 = x.shape
+        B2, N2, C2 = y.shape
+
+        q = self.q(x).reshape(B1, N1, self.num_heads, C1 // self.num_heads).permute(0, 2, 1, 3)
+        kv = self.kv(y).reshape(B2, N2, 2, self.num_heads, C1 // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        k, v = kv[0], kv[1]
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B1, N1, C1)
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+
+        return x
+
+
+
+class Block(nn.Module):
+    def __init__(self, dim1, dim2, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.norm1 = norm_layer(dim1)
+        self.norm2 = norm_layer(dim2)
+        self.attn = CrossAttention(dim1, dim2, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm3 = norm_layer(dim1)
+        mlp_hidden_dim = int(dim1 * mlp_ratio)
+        self.mlp = Mlp(in_features=dim1, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+    def forward(self, x, y):
+        x = x + self.drop_path(self.attn(self.norm1(x), self.norm2(y)))
+        x = x + self.drop_path(self.mlp(self.norm3(x)))
+        return x
+
+
+
+class ContentAwareAggregation(nn.Module):
+    def __init__(self, low_dim, high_dim):
+        super().__init__()
+        self.project = nn.Sequential(
+            nn.Conv2d(high_dim, low_dim, kernel_size=1),
+            nn.BatchNorm2d(low_dim),
+            nn.ReLU(inplace=True)
+        )
+
+        self.attn_gen = nn.Sequential(
+            nn.Conv2d(low_dim, low_dim, kernel_size=3, padding=1, groups=low_dim),
+            nn.BatchNorm2d(low_dim),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(low_dim, low_dim, kernel_size=1),
+            nn.Sigmoid()
+        )
+
+    def forward(self, low_feat, high_feat):
+        high_feat = F.interpolate(high_feat, size=low_feat.shape[2:], mode='bilinear', align_corners=False)
+        high_feat = self.project(high_feat)
+        attn = self.attn_gen(low_feat + high_feat)
+        out = attn * low_feat + high_feat
+        return out
+
+
+
+class FeatureInjector(nn.Module):
+    def __init__(self, dim1=384, dim2=[64, 128, 256], num_heads=8, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
+                    drop_path=0., act_layer=nn.ReLU, norm_layer=nn.LayerNorm):
+        super().__init__()
+
+        self.c2_c5 = Block(dim1, dim2[0], num_heads, mlp_ratio, qkv_bias, drop, attn_drop, drop_path, act_layer, norm_layer)
+        self.c3_c5 = Block(dim1, dim2[1], num_heads, mlp_ratio, qkv_bias, drop, attn_drop, drop_path, act_layer, norm_layer)
+        self.c4_c5 = Block(dim1, dim2[2], num_heads, mlp_ratio, qkv_bias, drop, attn_drop, drop_path, act_layer, norm_layer)
+
+        self.fuse = nn.Conv2d(dim1*3, dim1, 1, bias=False)
+        self.caa = ContentAwareAggregation(dim1, dim1)
+
+        weight_init(self)
+
+    def base_forward(self, c2, c3, c4, c5):
+        H, W = c5.shape[2:]
+
+        c2 = rearrange(c2, 'b c h w -> b (h w) c')
+        c3 = rearrange(c3, 'b c h w -> b (h w) c')
+        c4 = rearrange(c4, 'b c h w -> b (h w) c')
+        c5 = rearrange(c5, 'b c h w -> b (h w) c')
+
+        _c2 = self.c2_c5(c5, c2)
+        _c2 = rearrange(_c2, 'b (h w) c -> b c h w', h=H, w=W)
+
+        _c3 = self.c3_c5(c5, c3)
+        _c3 = rearrange(_c3, 'b (h w) c -> b c h w', h=H, w=W)
+
+        _c4 = self.c4_c5(c5, c4)
+        _c4 = rearrange(_c4, 'b (h w) c -> b c h w', h=H, w=W)
+
+        _c5 = self.fuse(torch.cat([_c2, _c3, _c4], dim=1))
+
+        return _c5
+
+    def forward(self, fx, fy):
+        _c5x = self.base_forward(fx[0], fx[1], fx[2], fx[3])
+        _c5y = self.base_forward(fy[0], fy[1], fy[2], fy[3])
+
+
+        _c5x = self.caa(_c5x, _c5y)
+        _c5y = self.caa(_c5y, _c5x)
+
+        return _c5x, _c5y
+
+
+class DualAttentionGate(nn.Module):
+    def __init__(self, channels, ratio=8):
+        super().__init__()
+        self.channel_att = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),  # [B,C,1,1]
+            nn.Conv2d(channels, channels // ratio, 1, bias=False),  # [B,C/8,1,1]
+            nn.ReLU(),
+            nn.Conv2d(channels // ratio, channels, 1, bias=False),  # [B,C,1,1]
+            nn.Sigmoid()
+        )
+
+        self.spatial_att = nn.Sequential(
+            nn.Conv2d(2, 1, 7, padding=3, bias=False),  # 输入2通道(mean+std)
+            nn.Sigmoid()  # 输出[B,1,H,W]
+        )
+
+    def forward(self, x):
+
+        c_att = self.channel_att(x)
+        mean = torch.mean(x, dim=1, keepdim=True)
+        std = torch.std(x, dim=1, keepdim=True)
+        s_att = self.spatial_att(torch.cat([mean, std], dim=1))
+
+
+        return x * c_att * s_att
+
+
+class SimplifiedFGFM(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super().__init__()
+        self.down = nn.Conv2d(in_channels, out_channels, 1, bias=False)
+        self.flow_make = nn.Conv2d(out_channels * 2, 4, 3, padding=1, bias=False)
+        self.dual_att = DualAttentionGate(out_channels)
+
+    def flow_warp(self, input, flow, size):
+
+        out_h, out_w = size
+        n, c, h, w = input.size()
+
+        norm = torch.tensor([[[[out_w, out_h]]]]).type_as(input).to(input.device)
+        grid = torch.meshgrid(
+            torch.linspace(-1.0, 1.0, out_h),
+            torch.linspace(-1.0, 1.0, out_w),
+            indexing='ij'
+        )
+        grid = torch.stack((grid[1], grid[0]), 2).repeat(n, 1, 1, 1).type_as(input)
+        grid = grid + flow.permute(0, 2, 3, 1) / norm
+
+        return F.grid_sample(input, grid, align_corners=True)
+
+    def forward(self, lowres_feature, highres_feature):
+
+        l_feature = self.down(lowres_feature)
+        l_feature_up = F.interpolate(l_feature, size=highres_feature.shape[2:], mode='bilinear', align_corners=True)
+
+        flow = self.flow_make(torch.cat([l_feature_up, highres_feature], dim=1))
+        flow_l, flow_h = flow[:, :2, :, :], flow[:, 2:, :, :]
+
+        l_warp = self.flow_warp(l_feature, flow_l, highres_feature.shape[2:])
+        h_warp = self.flow_warp(highres_feature, flow_h, highres_feature.shape[2:])
+
+
+        fused = self.dual_att(l_warp + h_warp)
+        return fused
+
+
+
+class Decoder(nn.Module):
+    def __init__(self, in_dim=[64, 128, 256, 384], decay=4, num_class=1):
+        super().__init__()
+        c2_channel, c3_channel, c4_channel, c5_channel = in_dim
+
+        self.structure_enhance = FeatureInjector(dim1=c5_channel)
+
+
+        self.fgfm_c4 = SimplifiedFGFM(in_channels=c5_channel, out_channels=c4_channel)
+        self.fgfm_c3 = SimplifiedFGFM(in_channels=c4_channel, out_channels=c3_channel)
+        self.fgfm_c2 = SimplifiedFGFM(in_channels=c3_channel, out_channels=c2_channel)
+
+
+        self.classfier = nn.Sequential(
+            nn.ConvTranspose2d(c2_channel, c2_channel, kernel_size=4, stride=2, padding=1),
+            nn.Conv2d(c2_channel, num_class, 3, 1, padding=1, bias=False)
+        )
+
+
+        self.mlp = nn.ModuleList([
+            nn.Sequential(
+                nn.Conv2d(dim * 3, dim // decay, 1, bias=False),
+                nn.BatchNorm2d(dim // decay),
+                nn.ReLU(),
+                nn.Conv2d(dim // decay, dim // decay, 3, 1, padding=1, bias=False),
+                nn.ReLU(),
+                nn.Conv2d(dim // decay, dim // decay, 3, 1, padding=1, bias=False),
+                nn.ReLU(),
+                nn.Conv2d(dim // decay, dim, 3, 1, padding=1, bias=False)
+            ) for dim in in_dim
+        ])
+
+    def difference_modeling(self, x, y, block):
+        f = torch.cat([x, y, torch.abs(x - y)], dim=1)
+        return block(f)
+
+    def forward(self, fx, fy):
+        c2x, c3x, c4x = fx[:-1]
+        c2y, c3y, c4y = fy[:-1]
+
+
+        c5x, c5y = self.structure_enhance(fx, fy)
+
+
+        c2 = self.difference_modeling(c2x, c2y, self.mlp[0])
+        c3 = self.difference_modeling(c3x, c3y, self.mlp[1])
+        c4 = self.difference_modeling(c4x, c4y, self.mlp[2])
+        c5 = self.difference_modeling(c5x, c5y, self.mlp[3])
+
+
+        c4f = self.fgfm_c4(c5, c4)
+        c3f = self.fgfm_c3(c4f, c3)
+        c2f = self.fgfm_c2(c3f, c2)
+
+
+        pred = self.classfier(c2f)
+        pred_mask = torch.sigmoid(pred)
+
+        return pred_mask