--

修改了训练脚本，将训练过程中的进度输出频率从每批输出改为每100批输出一次，以减少输出量，提高训练效率。

logs/log_20241006_success.log

@@ -0,0 +1,25 @@
+/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/train.py
+2.4.1+cu121
+True
+Model: PFCFuse
+Number of epochs: 60
+Epoch gap: 40
+Learning rate: 0.0001
+Weight decay: 0
+Batch size: 1
+GPU number: 0
+Coefficient of MSE loss VF: 1.0
+Coefficient of MSE loss IF: 1.0
+Coefficient of RMI loss VF: 1.0
+Coefficient of RMI loss IF: 1.0
+Coefficient of Cosine loss VF: 1.0
+Coefficient of Cosine loss IF: 1.0
+Coefficient of Decomposition loss: 2.0
+Coefficient of Total Variation loss: 5.0
+Clip gradient norm value: 0.01
+Optimization step: 20
+Optimization gamma: 0.5
+[Epoch 39/60] [Batch 6486/6487] [loss: 0.002562] ETA: 3:30:05.95/home/star/whaiDir/PFCFuse/utils/loss.py:15: UserWarning: Using a target size (torch.Size([1, 1, 128, 128])) that is different to the input size (torch.Size([])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
+  loss_rmi=F.l1_loss(x_rmi_max, generate_img)
+[Epoch 59/60] [Batch 6486/6487] [loss: 2.106119] ETA: 0:00:00.08
+Process finished with exit code 0

net_cddfuse.py

@@ -0,0 +1,411 @@
+import torch
+import torch.nn as nn
+import math
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from einops import rearrange
+
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """
+    Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    # work with diff dim tensors, not just 2D ConvNets
+    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):
+    """
+    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    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 AttentionBase(nn.Module):
+    def __init__(self,
+                 dim,
+                 num_heads=8,
+                 qkv_bias=False,):
+        super(AttentionBase, self).__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = nn.Parameter(torch.ones(num_heads, 1, 1))
+        self.qkv1 = nn.Conv2d(dim, dim*3, kernel_size=1, bias=qkv_bias)
+        self.qkv2 = nn.Conv2d(dim*3, dim*3, kernel_size=3, padding=1, bias=qkv_bias)
+        self.proj = nn.Conv2d(dim, dim, kernel_size=1, bias=qkv_bias)
+
+    def forward(self, x):
+        # [batch_size, num_patches + 1, total_embed_dim]
+        b, c, h, w = x.shape
+        qkv = self.qkv2(self.qkv1(x))
+        q, k, v = qkv.chunk(3, dim=1)
+        q = rearrange(q, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+        k = rearrange(k, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+        v = rearrange(v, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+        q = torch.nn.functional.normalize(q, dim=-1)
+        k = torch.nn.functional.normalize(k, dim=-1)
+        # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches + 1]
+        # @: multiply -> [batch_size, num_heads, num_patches + 1, num_patches + 1]
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+
+        out = (attn @ v)
+
+        out = rearrange(out, 'b head c (h w) -> b (head c) h w',
+                        head=self.num_heads, h=h, w=w)
+
+        out = self.proj(out)
+        return out
+
+class Mlp(nn.Module):
+    """
+    MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 ffn_expansion_factor = 2,
+                 bias = False):
+        super().__init__()
+        hidden_features = int(in_features*ffn_expansion_factor)
+
+        self.project_in = nn.Conv2d(
+            in_features, hidden_features*2, kernel_size=1, bias=bias)
+
+        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3,
+                                stride=1, padding=1, groups=hidden_features, bias=bias)
+
+        self.project_out = nn.Conv2d(
+            hidden_features, in_features, kernel_size=1, bias=bias)
+    def forward(self, x):
+        x = self.project_in(x)
+        x1, x2 = self.dwconv(x).chunk(2, dim=1)
+        x = F.gelu(x1) * x2
+        x = self.project_out(x)
+        return x
+
+class BaseFeatureExtraction(nn.Module):
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 ffn_expansion_factor=1.,
+                 qkv_bias=False,):
+        super(BaseFeatureExtraction, self).__init__()
+        self.norm1 = LayerNorm(dim, 'WithBias')
+
+        # https://zhuanlan.zhihu.com/p/444887088#:~:text=%E5%9C%A8%E6%9C%AC%E6%96%87%E4%B8%AD%EF%BC%8C%E6%88%91%E4%BB%AC%E6%8F%90%E5%87%BA%E4%BA%86
+        self.attn = AttentionBase(dim, num_heads=num_heads, qkv_bias=qkv_bias,)
+        self.norm2 = LayerNorm(dim, 'WithBias')
+        self.mlp = Mlp(in_features=dim,
+                       ffn_expansion_factor=ffn_expansion_factor,)
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class InvertedResidualBlock(nn.Module):
+    def __init__(self, inp, oup, expand_ratio):
+        super(InvertedResidualBlock, self).__init__()
+        hidden_dim = int(inp * expand_ratio)
+        self.bottleneckBlock = nn.Sequential(
+            # pw
+            nn.Conv2d(inp, hidden_dim, 1, bias=False),
+            # nn.BatchNorm2d(hidden_dim),
+            nn.ReLU6(inplace=True),
+            # dw
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(hidden_dim, hidden_dim, 3, groups=hidden_dim, bias=False),
+            # nn.BatchNorm2d(hidden_dim),
+            nn.ReLU6(inplace=True),
+            # pw-linear
+            nn.Conv2d(hidden_dim, oup, 1, bias=False),
+            # nn.BatchNorm2d(oup),
+        )
+    def forward(self, x):
+        return self.bottleneckBlock(x)
+
+class DetailNode(nn.Module):
+    def __init__(self):
+        super(DetailNode, self).__init__()
+        # Scale is Ax + b, i.e. affine transformation
+        self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+        self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+        self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
+        self.shffleconv = nn.Conv2d(64, 64, kernel_size=1,
+                                    stride=1, padding=0, bias=True)
+    def separateFeature(self, x):
+        z1, z2 = x[:, :x.shape[1]//2], x[:, x.shape[1]//2:x.shape[1]]
+        return z1, z2
+    def forward(self, z1, z2):
+        z1, z2 = self.separateFeature(
+            self.shffleconv(torch.cat((z1, z2), dim=1)))
+        z2 = z2 + self.theta_phi(z1)
+        z1 = z1 * torch.exp(self.theta_rho(z2)) + self.theta_eta(z2)
+        return z1, z2
+
+class DetailFeatureExtraction(nn.Module):
+    def __init__(self, num_layers=3):
+        super(DetailFeatureExtraction, self).__init__()
+        INNmodules = [DetailNode() for _ in range(num_layers)]
+        self.net = nn.Sequential(*INNmodules)
+    def forward(self, x):
+        z1, z2 = x[:, :x.shape[1]//2], x[:, x.shape[1]//2:x.shape[1]]
+        for layer in self.net:
+            z1, z2 = layer(z1, z2)
+        return torch.cat((z1, z2), dim=1)
+
+if __name__ == '__main__':
+    x = torch.randn(1, 64, 256, 256)
+    model = DetailFeatureExtraction(3)
+    print(model(x).shape)
+
+
+# =============================================================================
+
+# =============================================================================
+import numbers
+##########################################################################
+## Layer Norm
+def to_3d(x):
+    return rearrange(x, 'b c h w -> b (h w) c')
+
+
+def to_4d(x, h, w):
+    return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
+
+
+class BiasFree_LayerNorm(nn.Module):
+    def __init__(self, normalized_shape):
+        super(BiasFree_LayerNorm, self).__init__()
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        normalized_shape = torch.Size(normalized_shape)
+
+        assert len(normalized_shape) == 1
+
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.normalized_shape = normalized_shape
+
+    def forward(self, x):
+        sigma = x.var(-1, keepdim=True, unbiased=False)
+        return x / torch.sqrt(sigma+1e-5) * self.weight
+
+
+class WithBias_LayerNorm(nn.Module):
+    def __init__(self, normalized_shape):
+        super(WithBias_LayerNorm, self).__init__()
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        normalized_shape = torch.Size(normalized_shape)
+
+        assert len(normalized_shape) == 1
+
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.normalized_shape = normalized_shape
+
+    def forward(self, x):
+        mu = x.mean(-1, keepdim=True)
+        sigma = x.var(-1, keepdim=True, unbiased=False)
+        return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, LayerNorm_type):
+        super(LayerNorm, self).__init__()
+        if LayerNorm_type == 'BiasFree':
+            self.body = BiasFree_LayerNorm(dim)
+        else:
+            self.body = WithBias_LayerNorm(dim)
+
+    def forward(self, x):
+        h, w = x.shape[-2:]
+        return to_4d(self.body(to_3d(x)), h, w)
+
+##########################################################################
+## Gated-Dconv Feed-Forward Network (GDFN)
+class FeedForward(nn.Module):
+    def __init__(self, dim, ffn_expansion_factor, bias):
+        super(FeedForward, self).__init__()
+
+        hidden_features = int(dim*ffn_expansion_factor)
+
+        self.project_in = nn.Conv2d(
+            dim, hidden_features*2, kernel_size=1, bias=bias)
+
+        self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3,
+                                stride=1, padding=1, groups=hidden_features*2, bias=bias)
+
+        self.project_out = nn.Conv2d(
+            hidden_features, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        x = self.project_in(x)
+        x1, x2 = self.dwconv(x).chunk(2, dim=1)
+        x = F.gelu(x1) * x2
+        x = self.project_out(x)
+        return x
+
+
+##########################################################################
+## Multi-DConv Head Transposed Self-Attention (MDTA)
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads, bias):
+        super(Attention, self).__init__()
+        self.num_heads = num_heads
+        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
+
+        self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=bias)
+        self.qkv_dwconv = nn.Conv2d(
+            dim*3, dim*3, kernel_size=3, stride=1, padding=1, groups=dim*3, bias=bias)
+        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+
+        qkv = self.qkv_dwconv(self.qkv(x))
+        q, k, v = qkv.chunk(3, dim=1)
+
+        q = rearrange(q, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+        k = rearrange(k, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+        v = rearrange(v, 'b (head c) h w -> b head c (h w)',
+                      head=self.num_heads)
+
+        q = torch.nn.functional.normalize(q, dim=-1)
+        k = torch.nn.functional.normalize(k, dim=-1)
+
+        attn = (q @ k.transpose(-2, -1)) * self.temperature
+        attn = attn.softmax(dim=-1)
+
+        out = (attn @ v)
+
+        out = rearrange(out, 'b head c (h w) -> b (head c) h w',
+                        head=self.num_heads, h=h, w=w)
+
+        out = self.project_out(out)
+        return out
+
+
+##########################################################################
+class TransformerBlock(nn.Module):
+    def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
+        super(TransformerBlock, self).__init__()
+
+        self.norm1 = LayerNorm(dim, LayerNorm_type)
+        self.attn = Attention(dim, num_heads, bias)
+        self.norm2 = LayerNorm(dim, LayerNorm_type)
+        self.ffn = FeedForward(dim, ffn_expansion_factor, bias)
+
+    def forward(self, x):
+        x = x + self.attn(self.norm1(x))
+        x = x + self.ffn(self.norm2(x))
+
+        return x
+
+
+##########################################################################
+## Overlapped image patch embedding with 3x3 Conv
+class OverlapPatchEmbed(nn.Module):
+    def __init__(self, in_c=3, embed_dim=48, bias=False):
+        super(OverlapPatchEmbed, self).__init__()
+
+        self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3,
+                              stride=1, padding=1, bias=bias)
+
+    def forward(self, x):
+        x = self.proj(x)
+        return x
+
+
+class Restormer_Encoder(nn.Module):
+    def __init__(self,
+                 inp_channels=1,
+                 out_channels=1,
+                 dim=64,
+                 num_blocks=[4, 4],
+                 heads=[8, 8, 8],
+                 ffn_expansion_factor=2,
+                 bias=False,
+                 LayerNorm_type='WithBias',
+                 ):
+
+        super(Restormer_Encoder, self).__init__()
+
+        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)
+
+        self.encoder_level1 = nn.Sequential(*[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor,
+                                            bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
+        self.baseFeature = BaseFeatureExtraction(dim=dim, num_heads = heads[2])
+        self.detailFeature = DetailFeatureExtraction()
+
+    def forward(self, inp_img):
+        inp_enc_level1 = self.patch_embed(inp_img)
+        out_enc_level1 = self.encoder_level1(inp_enc_level1)
+        base_feature = self.baseFeature(out_enc_level1)
+        detail_feature = self.detailFeature(out_enc_level1)
+        return base_feature, detail_feature, out_enc_level1
+
+class Restormer_Decoder(nn.Module):
+    def __init__(self,
+                 inp_channels=1,
+                 out_channels=1,
+                 dim=64,
+                 num_blocks=[4, 4],
+                 heads=[8, 8, 8],
+                 ffn_expansion_factor=2,
+                 bias=False,
+                 LayerNorm_type='WithBias',
+                 ):
+
+        super(Restormer_Decoder, self).__init__()
+        self.reduce_channel = nn.Conv2d(int(dim*2), int(dim), kernel_size=1, bias=bias)
+        self.encoder_level2 = nn.Sequential(*[TransformerBlock(dim=dim, num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor,
+                               bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
+        self.output = nn.Sequential(
+            nn.Conv2d(int(dim), int(dim)//2, kernel_size=3,
+                      stride=1, padding=1, bias=bias),
+            nn.LeakyReLU(),
+            nn.Conv2d(int(dim)//2, out_channels, kernel_size=3,
+                      stride=1, padding=1, bias=bias),)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, inp_img, base_feature, detail_feature):
+        out_enc_level0 = torch.cat((base_feature, detail_feature), dim=1)
+        out_enc_level0 = self.reduce_channel(out_enc_level0)
+        out_enc_level1 = self.encoder_level2(out_enc_level0)
+        if inp_img is not None:
+            out_enc_level1 = self.output(out_enc_level1) + inp_img
+        else:
+            out_enc_level1 = self.output(out_enc_level1)
+        return self.sigmoid(out_enc_level1), out_enc_level0
+
+if __name__ == '__main__':
+    height = 128
+    width = 128
+    window_size = 8
+    modelE = Restormer_Encoder().cuda()
+    modelD = Restormer_Decoder().cuda()
+

train.py

@@ -222,17 +222,19 @@ for epoch in range(num_epochs):
         time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
         epoch_time = time.time() - prev_time
         prev_time = time.time()
-        sys.stdout.write(
+        if step % 100 == 0:
-            "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
+            sys.stdout.write(
-            % (
+                "\r[Epoch %d/%d] [Batch %d/%d] [loss: %f] ETA: %.10s"
-                epoch,
+                % (
-                num_epochs,
+                    epoch,
-                i,
+                    num_epochs,
-                len(loader['train']),
+                    i,
-                loss.item(),
+                    len(loader['train']),
-                time_left,
+                    loss.item(),
+                    time_left,
+                )
             )
-        )
+
 
     # adjust the learning rate
 

trainExe.py

@@ -0,0 +1,15 @@
+import subprocess
+import datetime
+
+# 定义命令
+command = "/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/train.py"
+
+# 获取当前时间并格式化为文件名
+current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+output_file = f"/home/star/whaiDir/PFCFuse/logs/log_{current_time}.log"
+
+# 运行命令并将输出重定向到文件
+with open(output_file, 'w') as file:
+    subprocess.run(command.split(), stdout=file, stderr=subprocess.STDOUT)
+
+print(f"Command output has been written to {output_file}")