Delete model/decoder.py

model/decoder.py

@@ -1,309 +0,0 @@
-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__()
-        # 通道注意力分支
-        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()
-        )
-
-        # 空间注意力分支
-        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):
-        """
-        输入: x [B,C,H,W]
-        输出: 增强后的特征 [B,C,H,W]
-        """
-        # 通道注意力
-        c_att = self.channel_att(x)  # [B,C,1,1]
-
-        # 空间注意力
-        mean = torch.mean(x, dim=1, keepdim=True)  # [B,1,H,W]
-        std = torch.std(x, dim=1, keepdim=True)  # [B,1,H,W]
-        s_att = self.spatial_att(torch.cat([mean, std], dim=1))  # [B,1,H,W]
-
-        # 双重注意力融合
-        return x * c_att * s_att  # 逐元素相乘
-
-
-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):
-        # 保持原有光流变形实现
-        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):
-        # 1. 光流对齐
-        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:])
-
-        # 2. 双注意力融合
-        fused = self.dual_att(l_warp + h_warp)
-        return fused
-
-
-# Decoder 模块
-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)
-
-        # 使用改进的 SimplifiedFGFM 模块替换传统上采样
-        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)
-
-        # 最终分类器
-        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)
-        )
-
-        # 各层级的差异建模模块（MLP）
-        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]
-
-        # 融合后的高阶语义特征（c5）
-        c5x, c5y = self.structure_enhance(fx, fy)
-
-        # 各层特征差异建模
-        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])
-
-        # 使用改进的 FGFM 进行流引导特征融合
-        c4f = self.fgfm_c4(c5, c4)
-        c3f = self.fgfm_c3(c4f, c3)
-        c2f = self.fgfm_c2(c3f, c2)
-
-        # 输出变化掩码
-        pred = self.classfier(c2f)
-        pred_mask = torch.sigmoid(pred)
-
-        return pred_mask