feat(net): 替换 SMFA 为 SCSA 并调整相关配置

- 将 SMFA 模块替换为 SCSA 模块 - 更新项目配置，使用本地 Python 3.8 环境 -调整 SCSA 模块参数，如维度、头数等 - 优化注意力机制，提高模型性能
feat(net): 为 net.py 添加新的组件引用并优化前向传播逻辑
2024-11-08 12:04:52 +08:00 · 2024-11-05 14:09:59 +08:00 · 2024-10-26 19:18:01 +08:00 · 2024-10-26 19:04:12 +08:00 · 2024-10-26 18:37:15 +08:00 · 2024-10-09 12:04:46 +08:00
593 changed files with 1923 additions and 29 deletions
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    <option name="USE_PROJECT_PROFILE" value="false" />
    <version value="1.0" />
  </settings>
 </component>
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@ -0,0 +1,16 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="Black">
    <option name="sdkName" value="Python 3.9 (flaskTest)" />
  </component>
  <component name="MavenImportPreferences">
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        <option name="showDialogWithAdvancedSettings" value="true" />
        <option name="userSettingsFile" value="E:\maven\settings.xml" />
      </MavenGeneralSettings>
    </option>
  </component>
  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (pfcfuse)" project-jdk-type="Python SDK" />
 </project>
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@ -0,0 +1,8 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="ProjectModuleManager">
    <modules>
      <module fileurl="file://$PROJECT_DIR$/.idea/PFCFuse.iml" filepath="$PROJECT_DIR$/.idea/PFCFuse.iml" />
    </modules>
  </component>
 </project>
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@ -0,0 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="VcsDirectoryMappings">
    <mapping directory="" vcs="Git" />
  </component>
 </project>
--- a/componets/DynamicFilter(频域模块动态滤波器用于CV2维图像).py
+++ b/componets/DynamicFilter(频域模块动态滤波器用于CV2维图像).py
@ -0,0 +1,116 @@
 import torch
 import torch.nn as nn
 from timm.layers.helpers import to_2tuple
 """
 配备多头自注意力 （MHSA） 的模型在计算机视觉方面取得了显着的性能。它们的计算复杂度与输入特征图中的二次像素数成正比，导致处理速度缓慢，尤其是在处理高分辨率图像时。
 为了规避这个问题，提出了一种新型的代币混合器作为MHSA的替代方案：基于FFT的代币混合器涉及类似于MHSA的全局操作，但计算复杂度较低。
 在这里，我们提出了一种名为动态过滤器的新型令牌混合器以缩小上述差距。
 DynamicFilter 模块通过频域滤波和动态调整滤波器权重，能够对图像进行复杂的增强和处理。
 """
 class StarReLU(nn.Module):
    """
    StarReLU: s * relu(x) ** 2 + b
    """
    def __init__(self, scale_value=1.0, bias_value=0.0,
                 scale_learnable=True, bias_learnable=True,
                 mode=None, inplace=False):
        super().__init__()
        self.inplace = inplace
        self.relu = nn.ReLU(inplace=inplace)
        self.scale = nn.Parameter(scale_value * torch.ones(1),
                                  requires_grad=scale_learnable)
        self.bias = nn.Parameter(bias_value * torch.ones(1),
                                 requires_grad=bias_learnable)
    def forward(self, x):
        return self.scale * self.relu(x) ** 2 + self.bias
 class Mlp(nn.Module):
    """ MLP as used in MetaFormer models, eg Transformer, MLP-Mixer, PoolFormer, MetaFormer baslines and related networks.
    Mostly copied from timm.
    """
    def __init__(self, dim, mlp_ratio=4, out_features=None, act_layer=StarReLU, drop=0.,
                 bias=False, **kwargs):
        super().__init__()
        in_features = dim
        out_features = out_features or in_features
        hidden_features = int(mlp_ratio * in_features)
        drop_probs = to_2tuple(drop)
        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
        self.act = act_layer()
        self.drop1 = nn.Dropout(drop_probs[0])
        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
        self.drop2 = nn.Dropout(drop_probs[1])
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop1(x)
        x = self.fc2(x)
        x = self.drop2(x)
        return x
 class DynamicFilter(nn.Module):
    def __init__(self, dim, expansion_ratio=2, reweight_expansion_ratio=.25,
                 act1_layer=StarReLU, act2_layer=nn.Identity,
                 bias=False, num_filters=4, size=14, weight_resize=False,
                 **kwargs):
        super().__init__()
        size = to_2tuple(size)
        self.size = size[0]
        self.filter_size = size[1] // 2 + 1
        self.num_filters = num_filters
        self.dim = dim
        self.med_channels = int(expansion_ratio * dim)
        self.weight_resize = weight_resize
        self.pwconv1 = nn.Linear(dim, self.med_channels, bias=bias)
        self.act1 = act1_layer()
        self.reweight = Mlp(dim, reweight_expansion_ratio, num_filters * self.med_channels)
        self.complex_weights = nn.Parameter(
            torch.randn(self.size, self.filter_size, num_filters, 2,
                        dtype=torch.float32) * 0.02)
        self.act2 = act2_layer()
        self.pwconv2 = nn.Linear(self.med_channels, dim, bias=bias)
    def forward(self, x):
        B, H, W, _ = x.shape
        routeing = self.reweight(x.mean(dim=(1, 2))).view(B, self.num_filters,
                                                          -1).softmax(dim=1)
        x = self.pwconv1(x)
        x = self.act1(x)
        x = x.to(torch.float32)
        x = torch.fft.rfft2(x, dim=(1, 2), norm='ortho')
        if self.weight_resize:
            complex_weights = resize_complex_weight(self.complex_weights, x.shape[1],
                                                    x.shape[2])
            complex_weights = torch.view_as_complex(complex_weights.contiguous())
        else:
            complex_weights = torch.view_as_complex(self.complex_weights)
        routeing = routeing.to(torch.complex64)
        weight = torch.einsum('bfc,hwf->bhwc', routeing, complex_weights)
        if self.weight_resize:
            weight = weight.view(-1, x.shape[1], x.shape[2], self.med_channels)
        else:
            weight = weight.view(-1, self.size, self.filter_size, self.med_channels)
        x = x * weight
        x = torch.fft.irfft2(x, s=(H, W), dim=(1, 2), norm='ortho')
        x = self.act2(x)
        x = self.pwconv2(x)
        return x
 if __name__ == '__main__':
    block = DynamicFilter(32, size=64) # size==H,W
    input = torch.rand(3, 64, 64, 32)
    output = block(input)
    print(input.size())
    print(output.size())
--- a/componets/SCSA(CV2维图像).pdf
+++ b/componets/SCSA(CV2维图像).pdf
--- a/componets/SCSA.py
+++ b/componets/SCSA.py
@ -0,0 +1,156 @@
 import typing as t
 import torch
 import torch.nn as nn
 from einops.einops import rearrange
 from mmengine.model import BaseModule
 __all__ = ['SCSA']
 """SCSA：探索空间注意力和通道注意力之间的协同作用
 通道和空间注意力分别在为各种下游视觉任务提取特征依赖性和空间结构关系方面带来了显着的改进。
 虽然它们的结合更有利于发挥各自的优势，但通道和空间注意力之间的协同作用尚未得到充分探索，缺乏充分利用多语义信息的协同潜力来进行特征引导和缓解语义差异。
 我们的研究试图在多个语义层面揭示空间和通道注意力之间的协同关系，提出了一种新颖的空间和通道协同注意力模块（SCSA）。我们的SCSA由两部分组成：可共享的多语义空间注意力（SMSA）和渐进式通道自注意力（PCSA）。
 SMSA 集成多语义信息并利用渐进式压缩策略将判别性空间先验注入 PCSA 的通道自注意力中，有效地指导通道重新校准。此外，PCSA 中基于自注意力机制的稳健特征交互进一步缓解了 SMSA 中不同子特征之间多语义信息的差异。
 我们在七个基准数据集上进行了广泛的实验，包括 ImageNet-1K 上的分类、MSCOCO 2017 上的对象检测、ADE20K 上的分割以及其他四个复杂场景检测数据集。我们的结果表明，我们提出的 SCSA 不仅超越了当前最先进的注意力机制，
 而且在各种任务场景中表现出增强的泛化能力。
 """
 class SCSA(BaseModule):
    def __init__(
            self,
            dim: int,
            head_num: int,
            window_size: int = 7,
            group_kernel_sizes: t.List[int] = [3, 5, 7, 9],
            qkv_bias: bool = False,
            fuse_bn: bool = False,
            norm_cfg: t.Dict = dict(type='BN'),
            act_cfg: t.Dict = dict(type='ReLU'),
            down_sample_mode: str = 'avg_pool',
            attn_drop_ratio: float = 0.,
            gate_layer: str = 'sigmoid',
    ):
        super(SCSA, self).__init__()
        self.dim = dim
        self.head_num = head_num
        self.head_dim = dim // head_num
        self.scaler = self.head_dim ** -0.5
        self.group_kernel_sizes = group_kernel_sizes
        self.window_size = window_size
        self.qkv_bias = qkv_bias
        self.fuse_bn = fuse_bn
        self.down_sample_mode = down_sample_mode
        assert self.dim // 4, 'The dimension of input feature should be divisible by 4.'
        self.group_chans = group_chans = self.dim // 4
        self.local_dwc = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[0],
                                   padding=group_kernel_sizes[0] // 2, groups=group_chans)
        self.global_dwc_s = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[1],
                                      padding=group_kernel_sizes[1] // 2, groups=group_chans)
        self.global_dwc_m = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[2],
                                      padding=group_kernel_sizes[2] // 2, groups=group_chans)
        self.global_dwc_l = nn.Conv1d(group_chans, group_chans, kernel_size=group_kernel_sizes[3],
                                      padding=group_kernel_sizes[3] // 2, groups=group_chans)
        self.sa_gate = nn.Softmax(dim=2) if gate_layer == 'softmax' else nn.Sigmoid()
        self.norm_h = nn.GroupNorm(4, dim)
        self.norm_w = nn.GroupNorm(4, dim)
        self.conv_d = nn.Identity()
        self.norm = nn.GroupNorm(1, dim)
        self.q = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
        self.k = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
        self.v = nn.Conv2d(in_channels=dim, out_channels=dim, kernel_size=1, bias=qkv_bias, groups=dim)
        self.attn_drop = nn.Dropout(attn_drop_ratio)
        self.ca_gate = nn.Softmax(dim=1) if gate_layer == 'softmax' else nn.Sigmoid()
        if window_size == -1:
            self.down_func = nn.AdaptiveAvgPool2d((1, 1))
        else:
            if down_sample_mode == 'recombination':
                self.down_func = self.space_to_chans
                # dimensionality reduction
                self.conv_d = nn.Conv2d(in_channels=dim * window_size ** 2, out_channels=dim, kernel_size=1, bias=False)
            elif down_sample_mode == 'avg_pool':
                self.down_func = nn.AvgPool2d(kernel_size=(window_size, window_size), stride=window_size)
            elif down_sample_mode == 'max_pool':
                self.down_func = nn.MaxPool2d(kernel_size=(window_size, window_size), stride=window_size)
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        The dim of x is (B, C, H, W)
        """
        # Spatial attention priority calculation
        b, c, h_, w_ = x.size()
        # (B, C, H)
        x_h = x.mean(dim=3)
        l_x_h, g_x_h_s, g_x_h_m, g_x_h_l = torch.split(x_h, self.group_chans, dim=1)
        # (B, C, W)
        x_w = x.mean(dim=2)
        l_x_w, g_x_w_s, g_x_w_m, g_x_w_l = torch.split(x_w, self.group_chans, dim=1)
        x_h_attn = self.sa_gate(self.norm_h(torch.cat((
            self.local_dwc(l_x_h),
            self.global_dwc_s(g_x_h_s),
            self.global_dwc_m(g_x_h_m),
            self.global_dwc_l(g_x_h_l),
        ), dim=1)))
        x_h_attn = x_h_attn.view(b, c, h_, 1)
        x_w_attn = self.sa_gate(self.norm_w(torch.cat((
            self.local_dwc(l_x_w),
            self.global_dwc_s(g_x_w_s),
            self.global_dwc_m(g_x_w_m),
            self.global_dwc_l(g_x_w_l)
        ), dim=1)))
        x_w_attn = x_w_attn.view(b, c, 1, w_)
        x = x * x_h_attn * x_w_attn
        # Channel attention based on self attention
        # reduce calculations
        y = self.down_func(x)
        y = self.conv_d(y)
        _, _, h_, w_ = y.size()
        # normalization first, then reshape -> (B, H, W, C) -> (B, C, H * W) and generate q, k and v
        y = self.norm(y)
        q = self.q(y)
        k = self.k(y)
        v = self.v(y)
        # (B, C, H, W) -> (B, head_num, head_dim, N)
        q = rearrange(q, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
                      head_dim=int(self.head_dim))
        k = rearrange(k, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
                      head_dim=int(self.head_dim))
        v = rearrange(v, 'b (head_num head_dim) h w -> b head_num head_dim (h w)', head_num=int(self.head_num),
                      head_dim=int(self.head_dim))
        # (B, head_num, head_dim, head_dim)
        attn = q @ k.transpose(-2, -1) * self.scaler
        attn = self.attn_drop(attn.softmax(dim=-1))
        # (B, head_num, head_dim, N)
        attn = attn @ v
        # (B, C, H_, W_)
        attn = rearrange(attn, 'b head_num head_dim (h w) -> b (head_num head_dim) h w', h=int(h_), w=int(w_))
        # (B, C, 1, 1)
        attn = attn.mean((2, 3), keepdim=True)
        attn = self.ca_gate(attn)
        return attn * x
 if __name__ == '__main__':
    block = SCSA(
        dim=256,
        head_num=8,
    )
    input_tensor = torch.rand(1, 256, 32, 32)
    # 调用模块进行前向传播
    output_tensor = block(input_tensor)
    # 打印输入和输出张量的大小
    print("Input size:", input_tensor.size())
    print("Output size:", output_tensor.size())
--- a/componets/SMFA.py
+++ b/componets/SMFA.py
@ -0,0 +1,65 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 """ECCV2024(https://github.com/Zheng-MJ/SMFANet)
 基于Transformer的恢复方法取得了显著的效果，因为Transformer的自注意力机制（SA）可以探索非局部信息，从而实现更好的高分辨率图像重建。然而，关键的点积自注意力需要大量的计算资源，这限制了其在低功耗设备上的应用。
 此外，自注意力机制的低通滤波特性限制了其捕捉局部细节的能力，从而导致重建结果过于平滑。为了解决这些问题，我们提出了一种自调制特征聚合（SMFA）模块，协同利用局部和非局部特征交互，以实现更精确的重建。
 具体而言，SMFA模块采用了高效的自注意力近似（EASA）分支来建模非局部信息，并使用局部细节估计（LDE）分支来捕捉局部细节。此外，我们还引入了基于部分卷积的前馈网络（PCFN），以进一步优化从SMFA提取的代表性特征。
 大量实验表明，所提出的SMFANet系列在公共基准数据集上实现了更好的重建性能与计算效率的平衡。
 特别是，与SwinIR-light的×4放大相比，SMFANet+在五个公共测试集上的平均性能提高了0.14dB，运行速度提升了约10倍，且模型复杂度（如FLOPs）仅为其约43%。
 """
 class DMlp(nn.Module):
    def __init__(self, dim, growth_rate=2.0):
        super().__init__()
        hidden_dim = int(dim * growth_rate)
        self.conv_0 = nn.Sequential(
            nn.Conv2d(dim, hidden_dim, 3, 1, 1, groups=dim),
            nn.Conv2d(hidden_dim, hidden_dim, 1, 1, 0)
        )
        self.act = nn.GELU()
        self.conv_1 = nn.Conv2d(hidden_dim, dim, 1, 1, 0)
    def forward(self, x):
        x = self.conv_0(x)
        x = self.act(x)
        x = self.conv_1(x)
        return x
 class SMFA(nn.Module):
    def __init__(self, dim=36):
        super(SMFA, self).__init__()
        self.linear_0 = nn.Conv2d(dim, dim * 2, 1, 1, 0)
        self.linear_1 = nn.Conv2d(dim, dim, 1, 1, 0)
        self.linear_2 = nn.Conv2d(dim, dim, 1, 1, 0)
        self.lde = DMlp(dim, 2)
        self.dw_conv = nn.Conv2d(dim, dim, 3, 1, 1, groups=dim)
        self.gelu = nn.GELU()
        self.down_scale = 8
        self.alpha = nn.Parameter(torch.ones((1, dim, 1, 1)))
        self.belt = nn.Parameter(torch.zeros((1, dim, 1, 1)))
    def forward(self, f):
        _, _, h, w = f.shape
        y, x = self.linear_0(f).chunk(2, dim=1)
        x_s = self.dw_conv(F.adaptive_max_pool2d(x, (h // self.down_scale, w // self.down_scale)))
        x_v = torch.var(x, dim=(-2, -1), keepdim=True)
        x_l = x * F.interpolate(self.gelu(self.linear_1(x_s * self.alpha + x_v * self.belt)), size=(h, w),
                                mode='nearest')
        y_d = self.lde(y)
        return self.linear_2(x_l + y_d)
 if __name__ == '__main__':
    block = SMFA(dim=36)
    input = torch.randn(3, 36, 64, 64)
    output = block(input)
    print(input.size())
    print(output.size())
--- a/componets/TIAM.py
+++ b/componets/TIAM.py
@ -0,0 +1,110 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 """Elsevier2024
 变化检测 (CD) 是地球观测中一种重要的监测方法，尤其适用于土地利用分析、城市管理和灾害损失评估。然而，在星座互联和空天协作时代，感兴趣区域 (ROI) 的变化由于几何透视旋转和时间风格差异而导致许多错误检测。
 为了应对这些挑战，我们引入了 CDNeXt，该框架阐明了一种稳健而有效的方法，用于将基于预训练主干的 Siamese 网络与用于遥感图像的创新时空交互注意模块 (TIAM) 相结合。
 CDNeXt 可分为四个主要组件：编码器、交互器、解码器和检测器。值得注意的是，由 TIAM 提供支持的交互器从编码器提取的二进制时间特征中查询和重建空间透视依赖关系和时间风格相关性，以扩大 ROI 变化的差异。
 最后，检测器集成解码器生成的分层特征，随后生成二进制变化掩码。
 """
 class SpatiotemporalAttentionFullNotWeightShared(nn.Module):
    def __init__(self, in_channels, inter_channels=None, dimension=2, sub_sample=False):
        super(SpatiotemporalAttentionFullNotWeightShared, self).__init__()
        assert dimension in [2, ]
        self.dimension = dimension
        self.sub_sample = sub_sample
        self.in_channels = in_channels
        self.inter_channels = inter_channels
        if self.inter_channels is None:
            self.inter_channels = in_channels // 2
            if self.inter_channels == 0:
                self.inter_channels = 1
        self.g1 = nn.Sequential(
            nn.BatchNorm2d(self.in_channels),
            nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
                      kernel_size=1, stride=1, padding=0)
        )
        self.g2 = nn.Sequential(
            nn.BatchNorm2d(self.in_channels),
            nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
                      kernel_size=1, stride=1, padding=0),
        )
        self.W1 = nn.Sequential(
            nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
                      kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(self.in_channels)
        )
        self.W2 = nn.Sequential(
            nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
                      kernel_size=1, stride=1, padding=0),
            nn.BatchNorm2d(self.in_channels)
        )
        self.theta = nn.Sequential(
            nn.BatchNorm2d(self.in_channels),
            nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
                      kernel_size=1, stride=1, padding=0),
        )
        self.phi = nn.Sequential(
            nn.BatchNorm2d(self.in_channels),
            nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
                      kernel_size=1, stride=1, padding=0),
        )
    def forward(self, x1, x2):
        """
        :param x: (b, c, h, w)
        :param return_nl_map: if True return z, nl_map, else only return z.
        :return:
        """
        batch_size = x1.size(0)
        g_x11 = self.g1(x1).reshape(batch_size, self.inter_channels, -1)
        g_x12 = g_x11.permute(0, 2, 1)
        g_x21 = self.g2(x2).reshape(batch_size, self.inter_channels, -1)
        g_x22 = g_x21.permute(0, 2, 1)
        theta_x1 = self.theta(x1).reshape(batch_size, self.inter_channels, -1)
        theta_x2 = theta_x1.permute(0, 2, 1)
        phi_x1 = self.phi(x2).reshape(batch_size, self.inter_channels, -1)
        phi_x2 = phi_x1.permute(0, 2, 1)
        energy_time_1 = torch.matmul(theta_x1, phi_x2)
        energy_time_2 = energy_time_1.permute(0, 2, 1)
        energy_space_1 = torch.matmul(theta_x2, phi_x1)
        energy_space_2 = energy_space_1.permute(0, 2, 1)
        energy_time_1s = F.softmax(energy_time_1, dim=-1)
        energy_time_2s = F.softmax(energy_time_2, dim=-1)
        energy_space_2s = F.softmax(energy_space_1, dim=-2)
        energy_space_1s = F.softmax(energy_space_2, dim=-2)
        #  C1*S(C2) energy_time_1s * C1*H1W1 g_x12 * energy_space_1s S(H2W2)*H1W1 -> C1*H1W1
        y1 = torch.matmul(torch.matmul(energy_time_2s, g_x11), energy_space_2s).contiguous()  # C2*H2W2
        #  C2*S(C1) energy_time_2s * C2*H2W2 g_x21 * energy_space_2s S(H1W1)*H2W2 -> C2*H2W2
        y2 = torch.matmul(torch.matmul(energy_time_1s, g_x21), energy_space_1s).contiguous()  # C1*H1W1
        y1 = y1.reshape(batch_size, self.inter_channels, *x2.size()[2:])
        y2 = y2.reshape(batch_size, self.inter_channels, *x1.size()[2:])
        return x1 + self.W1(y1), x2 + self.W2(y2)
 if __name__ == '__main__':
    in_channels = 64
    batch_size = 8
    height = 32
    width = 32
    block = SpatiotemporalAttentionFullNotWeightShared(in_channels=in_channels)
    input1 = torch.rand(batch_size, in_channels, height, width)
    input2 = torch.rand(batch_size, in_channels, height, width)
    output1, output2 = block(input1, input2)
    print(f"Input1 size: {input1.size()}")
    print(f"Input2 size: {input2.size()}")
    print(f"Output1 size: {output1.size()}")
    print(f"Output2 size: {output2.size()}")
--- a/componets/UFFC(CV2维任务).pdf
+++ b/componets/UFFC(CV2维任务).pdf
--- a/componets/UFFC.py
+++ b/componets/UFFC.py
@ -0,0 +1,123 @@
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 """ICCV2023
 最近提出的图像修复方法 LaMa 以快速傅里叶卷积 (FFC) 为基础构建了其网络，该网络最初是为图像分类等高级视觉任务而提出的。
 FFC 使全卷积网络在其早期层中拥有全局感受野。得益于 FFC 模块的独特特性，LaMa 能够生成稳健的重复纹理，
 这是以前的修复方法无法实现的。但是，原始 FFC 模块是否适合图像修复等低级视觉任务？
 在本文中，我们分析了在图像修复中使用 FFC 的基本缺陷，即 1) 频谱偏移、2) 意外的空间激活和 3) 频率感受野有限。
 这些缺陷使得基于 FFC 的修复框架难以生成复杂纹理并执行完美重建。
 基于以上分析，我们提出了一种新颖的无偏快速傅里叶卷积 (UFFC) 模块，该模块通过
 1) 范围变换和逆变换、2) 绝对位置嵌入、3) 动态跳过连接和 4) 自适应剪辑对原始 FFC 模块进行了修改，以克服这些缺陷，
 实现更好的修复效果。在多个基准数据集上进行的大量实验证明了我们方法的有效性，在纹理捕捉能力和表现力方面均优于最先进的方法。
 """
 class FourierUnit_modified(nn.Module):
    def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
                 spectral_pos_encoding=False, use_se=False, ffc3d=False, fft_norm='ortho'):
        # bn_layer not used
        super(FourierUnit_modified, self).__init__()
        self.groups = groups
        self.input_shape = 32  # change!!!!!it!!!!!!manually!!!!!!
        self.in_channels = in_channels
        self.locMap = nn.Parameter(torch.rand(self.input_shape, self.input_shape // 2 + 1))
        self.lambda_base = nn.Parameter(torch.tensor(0.), requires_grad=True)
        self.conv_layer_down55 = torch.nn.Conv2d(in_channels=in_channels * 2 + 1,  # +1 for locmap
                                                 out_channels=out_channels * 2,
                                                 kernel_size=1, stride=1, padding=0, dilation=1, groups=self.groups,
                                                 bias=False, padding_mode='reflect')
        self.conv_layer_down55_shift = torch.nn.Conv2d(in_channels=in_channels * 2 + 1,  # +1 for locmap
                                                       out_channels=out_channels * 2,
                                                       kernel_size=3, stride=1, padding=2, dilation=2,
                                                       groups=self.groups, bias=False, padding_mode='reflect')
        self.norm = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.spatial_scale_factor = spatial_scale_factor
        self.spatial_scale_mode = spatial_scale_mode
        self.spectral_pos_encoding = spectral_pos_encoding
        self.ffc3d = ffc3d
        self.fft_norm = fft_norm
        self.img_freq = None
        self.distill = None
    def forward(self, x):
        batch = x.shape[0]
        if self.spatial_scale_factor is not None:
            orig_size = x.shape[-2:]
            x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode,
                              align_corners=False)
        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
        ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
        ffted = ffted.view((batch, -1,) + ffted.size()[3:])
        locMap = self.locMap.expand_as(ffted[:, :1, :, :])  # B 1 H' W'
        ffted_copy = ffted.clone()
        cat_img_mask_freq = torch.cat((ffted[:, :self.in_channels, :, :],
                                       ffted[:, self.in_channels:, :, :],
                                       locMap), dim=1)
        ffted = self.conv_layer_down55(cat_img_mask_freq)
        ffted = torch.fft.fftshift(ffted, dim=-2)
        ffted = self.relu(ffted)
        locMap_shift = torch.fft.fftshift(locMap, dim=-2)  ## ONLY IF NOT SHIFT BACK
        # REPEAT CONV
        cat_img_mask_freq1 = torch.cat((ffted[:, :self.in_channels, :, :],
                                        ffted[:, self.in_channels:, :, :],
                                        locMap_shift), dim=1)
        ffted = self.conv_layer_down55_shift(cat_img_mask_freq1)
        ffted = torch.fft.fftshift(ffted, dim=-2)
        lambda_base = torch.sigmoid(self.lambda_base)
        ffted = ffted_copy * lambda_base + ffted * (1 - lambda_base)
        # irfft
        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)
        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
        output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
        if self.spatial_scale_factor is not None:
            output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
        epsilon = 0.5
        output = output - torch.mean(output) + torch.mean(x)
        output = torch.clip(output, float(x.min() - epsilon), float(x.max() + epsilon))
        self.distill = output  # for self perc
        return output
 if __name__ == '__main__':
    in_channels = 16
    out_channels = 16
    block = FourierUnit_modified(in_channels=in_channels, out_channels=out_channels)
    input_tensor = torch.rand(8, in_channels, 32, 32)
    output = block(input_tensor)
    print("Input size:", input_tensor.size())
    print("Output size:", output.size())
--- a/componets/whaiutil.py
+++ b/componets/whaiutil.py
@ -0,0 +1,42 @@
 import os
 from PIL import Image
 def transfer(input_path, quality=20, resize_factor=0.1):
    # 打开TIFF图像
    # img = Image.open(input_path)
    #
    # # 保存为JPEG，并设置压缩质量
    # img.save(output_path, 'JPEG', quality=quality)
    # input_path = os.path.join(input_folder, filename)
    # 获取input_path的文件名
    # 使用os.path.splitext获取文件名和后缀的元组
    # 使用os.path.basename获取文件名（包含后缀）
    filename_with_extension = os.path.basename(input_path)
    filename, file_extension = os.path.splitext(filename_with_extension)
    # 使用os.path.dirname获取文件所在的目录路径
    output_folder = os.path.dirname(input_path)
    output_path = os.path.join(output_folder, filename + '.jpg')
    img = Image.open(input_path)
    # 将图像缩小到原来的一半
    new_width = int(img.width * resize_factor)
    new_height = int(img.height * resize_factor)
    resized_img = img.resize((new_width, new_height))
    # 保存为JPEG，并设置压缩质量
    # 转换为RGB模式，丢弃透明通道
    rgb_img = resized_img.convert('RGB')
    # 保存为JPEG，并设置压缩质量
    # 压缩
    rgb_img.save(output_path, 'JPEG', quality=quality)
    print(f'{output_path} 转换完成')
    return output_path
--- a/logs/20241005.log
+++ b/logs/20241005.log
@ -0,0 +1,35 @@
 /home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
 # base pcffuse
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	2.39	33.82	11.32	0.81	0.8	0.12	0.07	0.11
 ================================================================================
 Process finished with exit code 0
--- a/logs/20241007_whai.log
+++ b/logs/20241007_whai.log
@ -0,0 +1,33 @@
 /home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.01	40.67	15.39	1.53	1.76	0.64	0.53	0.95
 ================================================================================
--- a/logs/ans_log_20241009_115306.log
+++ b/logs/ans_log_20241009_115306.log
@ -0,0 +1,89 @@
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.14	46.48	13.18	2.22	1.76	0.79	0.56	1.02
 ================================================================================
 ================================================================================
 The test result of RoadScene :
 FLIR_07206.jpg
 FLIR_08202.jpg
 FLIR_05893.jpg
 FLIR_06974.jpg
 FLIR_04424.jpg
 FLIR_08284.jpg
 FLIR_07786.jpg
 FLIR_08021.jpg
 FLIR_07968.jpg
 FLIR_01130.jpg
 FLIR_06993.jpg
 FLIR_07190.jpg
 FLIR_06570.jpg
 FLIR_07809.jpg
 FLIR_06430.jpg
 FLIR_08592.jpg
 FLIR_00211.jpg
 FLIR_08721.jpg
 FLIR_05955.jpg
 FLIR_04688.jpg
 FLIR_07732.jpg
 FLIR_06392.jpg
 FLIR_00977.jpg
 FLIR_05105.jpg
 FLIR_04269.jpg
 FLIR_07970.jpg
 FLIR_05005.jpg
 FLIR_07209.jpg
 FLIR_07555.jpg
 FLIR_06325.jpg
 FLIR_04943.jpg
 FLIR_video_02829.jpg
 FLIR_08248.jpg
 FLIR_04484.jpg
 FLIR_08058.jpg
 FLIR_06795.jpg
 FLIR_06995.jpg
 FLIR_05879.jpg
 FLIR_04593.jpg
 FLIR_08094.jpg
 FLIR_08526.jpg
 FLIR_08858.jpg
 FLIR_09465.jpg
 FLIR_05064.jpg
 FLIR_05857.jpg
 FLIR_05914.jpg
 FLIR_04722.jpg
 FLIR_06506.jpg
 FLIR_06282.jpg
 FLIR_04512.jpg
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.41	52.99	15.81	2.37	1.78	0.71	0.55	0.96
 ================================================================================
--- a/logs/log_20241006_164707.log
+++ b/logs/log_20241006_164707.log
--- a/logs/log_20241006_164911.log
+++ b/logs/log_20241006_164911.log
--- a/logs/log_20241006_165611.log
+++ b/logs/log_20241006_165611.log
@ -0,0 +1,24 @@
 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 0/60] [Batch 0/6487] [loss: 10.193036] ETA: 9 days, 21
[Epoch 0/60] [Batch 1/6487] [loss: 4.166963] ETA: 11:49:56.5
[Epoch 0/60] [Batch 2/6487] [loss: 10.681509] ETA: 10:23:19.1
[Epoch 0/60] [Batch 3/6487] [loss: 6.257133] ETA: 10:31:48.3
[Epoch 0/60] [Batch 4/6487] [loss: 13.018341] ETA: 10:32:54.2
[Epoch 0/60] [Batch 5/6487] [loss: 11.268185] ETA: 10:27:32.2
[Epoch 0/60] [Batch 6/6487] [loss: 6.920656] ETA: 10:34:01.5
[Epoch 0/60] [Batch 7/6487] [loss: 4.666215] ETA: 10:32:45.3
[Epoch 0/60] [Batch 8/6487] [loss: 10.787085] ETA: 10:26:01.9
[Epoch 0/60] [Batch 9/6487] [loss: 5.754866] ETA: 10:34:34.2
[Epoch 0/60] [Batch 10/6487] [loss: 28.760792] ETA: 10:36:32.6
[Epoch 0/60] [Batch 11/6487] [loss: 8.672796] ETA: 10:25:11.9
[Epoch 0/60] [Batch 12/6487] [loss: 14.300608] ETA: 10:28:19.9
[Epoch 0/60] [Batch 13/6487] [loss: 11.821722] ETA: 10:34:18.6
[Epoch 0/60] [Batch 14/6487] [loss: 7.627745] ETA: 10:31:44.8
[Epoch 0/60] [Batch 15/6487] [loss: 5.722600] ETA: 10:34:17.4
[Epoch 0/60] [Batch 16/6487] [loss: 10.423873] ETA: 11:33:27.1
[Epoch 0/60] [Batch 17/6487] [loss: 4.454098] ETA: 9:37:13.67
[Epoch 0/60] [Batch 18/6487] [loss: 3.820719] ETA: 9:33:57.42
[Epoch 0/60] [Batch 19/6487] [loss: 6.564124] ETA: 9:41:22.09
[Epoch 0/60] [Batch 20/6487] [loss: 5.406681] ETA: 9:47:30.11
[Epoch 0/60] [Batch 21/6487] [loss: 25.275440] ETA: 9:39:29.91
[Epoch 0/60] [Batch 22/6487] [loss: 4.228334] ETA: 9:42:15.45
[Epoch 0/60] [Batch 23/6487] [loss: 22.508118] ETA: 9:38:18.10
[Epoch 0/60] [Batch 24/6487] [loss: 5.062001] ETA: 9:46:09.29
[Epoch 0/60] [Batch 25/6487] [loss: 3.157355] ETA: 9:41:30.09
[Epoch 0/60] [Batch 26/6487] [loss: 6.438435] ETA: 10:02:51.9
[Epoch 0/60] [Batch 27/6487] [loss: 7.430470] ETA: 9:18:12.94
[Epoch 0/60] [Batch 28/6487] [loss: 3.783903] ETA: 10:41:13.9
[Epoch 0/60] [Batch 29/6487] [loss: 2.954306] ETA: 9:44:25.10
[Epoch 0/60] [Batch 30/6487] [loss: 5.863827] ETA: 9:35:13.84
[Epoch 0/60] [Batch 31/6487] [loss: 6.467144] ETA: 9:46:19.80
[Epoch 0/60] [Batch 32/6487] [loss: 4.801052] ETA: 9:32:17.18
[Epoch 0/60] [Batch 33/6487] [loss: 5.658401] ETA: 9:31:10.28
[Epoch 0/60] [Batch 34/6487] [loss: 2.085633] ETA: 9:36:47.39
[Epoch 0/60] [Batch 35/6487] [loss: 15.402915] ETA: 9:40:51.43
[Epoch 0/60] [Batch 36/6487] [loss: 3.181264] ETA: 9:33:06.65
[Epoch 0/60] [Batch 37/6487] [loss: 3.883055] ETA: 9:42:29.60
[Epoch 0/60] [Batch 38/6487] [loss: 3.342676] ETA: 10:07:02.2
[Epoch 0/60] [Batch 39/6487] [loss: 2.589705] ETA: 9:36:43.32
[Epoch 0/60] [Batch 40/6487] [loss: 3.742121] ETA: 9:42:57.54
[Epoch 0/60] [Batch 41/6487] [loss: 2.732829] ETA: 9:36:54.65
[Epoch 0/60] [Batch 42/6487] [loss: 6.655626] ETA: 9:42:20.71
[Epoch 0/60] [Batch 43/6487] [loss: 1.822412] ETA: 9:38:02.02
[Epoch 0/60] [Batch 44/6487] [loss: 2.875143] ETA: 9:41:02.96
[Epoch 0/60] [Batch 45/6487] [loss: 2.319836] ETA: 9:38:16.23
[Epoch 0/60] [Batch 46/6487] [loss: 2.354790] ETA: 9:39:08.93
[Epoch 0/60] [Batch 47/6487] [loss: 1.986412] ETA: 9:52:11.40
[Epoch 0/60] [Batch 48/6487] [loss: 2.154071] ETA: 10:08:20.0
[Epoch 0/60] [Batch 49/6487] [loss: 1.425418] ETA: 9:54:04.42
[Epoch 0/60] [Batch 50/6487] [loss: 1.988360] ETA: 9:30:25.08
[Epoch 0/60] [Batch 51/6487] [loss: 4.090429] ETA: 9:43:53.52
[Epoch 0/60] [Batch 52/6487] [loss: 1.924778] ETA: 9:46:19.38
[Epoch 0/60] [Batch 53/6487] [loss: 2.191964] ETA: 9:46:59.93
[Epoch 0/60] [Batch 54/6487] [loss: 2.032799] ETA: 9:46:14.01
[Epoch 0/60] [Batch 55/6487] [loss: 1.923933] ETA: 9:44:21.65
[Epoch 0/60] [Batch 56/6487] [loss: 1.656838] ETA: 9:56:15.90
[Epoch 0/60] [Batch 57/6487] [loss: 1.656845] ETA: 10:21:26.1
[Epoch 0/60] [Batch 58/6487] [loss: 1.157820] ETA: 10:44:47.3
[Epoch 0/60] [Batch 59/6487] [loss: 1.652715] ETA: 10:46:39.9
[Epoch 0/60] [Batch 60/6487] [loss: 1.633865] ETA: 10:23:34.7
[Epoch 0/60] [Batch 61/6487] [loss: 1.290259] ETA: 9:24:06.12Traceback (most recent call last):
  File "/home/star/whaiDir/PFCFuse/train.py", line 232, in <module>
    loss.item(),
 KeyboardInterrupt
--- a/logs/log_20241006_165643.log
+++ b/logs/log_20241006_165643.log
--- a/logs/log_20241006_170059.log
+++ b/logs/log_20241006_170059.log
--- a/logs/log_20241006_221548.log
+++ b/logs/log_20241006_221548.log
--- a/logs/log_20241008_162000.log
+++ b/logs/log_20241008_162000.log
--- a/logs/log_20241008_success.log
+++ b/logs/log_20241008_success.log
@ -0,0 +1,38 @@
 /home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.01	40.4	15.51	1.55	1.75	0.66	0.54	0.96
 ================================================================================
 Process finished with exit code 0
--- a/models/PFCFusion10-05-18-13.pth
+++ b/models/PFCFusion10-05-18-13.pth
--- a/models/PFCFusion10-05-20-46.pth
+++ b/models/PFCFusion10-05-20-46.pth
--- a/models/whaiFusion10-06-22-17.pth
+++ b/models/whaiFusion10-06-22-17.pth
--- a/models/whaiFusion10-08-16-20.pth
+++ b/models/whaiFusion10-08-16-20.pth
--- a/net.py
+++ b/net.py
@ -6,6 +6,9 @@ import torch.utils.checkpoint as checkpoint
 from timm.models.layers import DropPath, to_2tuple, trunc_normal_
 from einops import rearrange
 from componets.SCSA import SCSA
 from componets.TIAM import SpatiotemporalAttentionFullNotWeightShared
 from componets.WTConvCV2 import WTConv2d
@ -157,7 +160,10 @@ class BaseFeatureExtraction(nn.Module):
        self.WTConv2d = WTConv2d(dim, dim)
        self.norm1 = LayerNorm(dim, 'WithBias')
-        self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
+        # self.token_mixer  = SMFA(dim=dim)
        self.token_mixer = SCSA(dim=dim,head_num=8)
        # self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
        self.norm2 = LayerNorm(dim, 'WithBias')
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
@ -178,11 +184,12 @@ class BaseFeatureExtraction(nn.Module):
    def forward(self, x): # 1 64 128 128
        if self.use_layer_scale:
            #  self.layer_scale_1(64,)
            wtConvX = self.WTConv2d(x)
            tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
            normal = self.norm1(x) # 1 64 128 128
            token_mix = self.token_mixer(normal) # 1 64 128 128
            x = self.WTConv2d(x)
            x = (x +
                 self.drop_path(
@ -190,9 +197,11 @@ class BaseFeatureExtraction(nn.Module):
                 )
                 # 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
            )
-            x = x + self.drop_path(
+            pol = self.poolmlp(self.norm2(x))
            x = wtConvX + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
-                * self.poolmlp(self.norm2(x)))
+                * pol)
        else:
            x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse
            x = x + self.drop_path(self.poolmlp(self.norm2(x)))
@ -248,22 +257,20 @@ class DetailFeatureExtraction(nn.Module):
        super(DetailFeatureExtraction, self).__init__()
        INNmodules = [DetailNode() for _ in range(num_layers)]
        self.net = nn.Sequential(*INNmodules)
-        self.enhancement_module = nn.Sequential(
+        self.enhancement_module = WTConv2d(32, 32)
            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 32, kernel_size=3, padding=1, bias=True),
        )
    def forward(self, x): # 1 64 128 128
        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
        # 增强并添加残差连接
        enhanced_z1 = self.enhancement_module(z1)
        enhanced_z2 = self.enhancement_module(z2)
        for layer in self.net:
            z1, z2 = layer(z1, z2)
        # 残差连接
        z1 = z1 + enhanced_z1
        z2 = z2 + enhanced_z2
        for layer in self.net:
            z1, z2 = layer(z1, z2)
        return torch.cat((z1, z2), dim=1)
 # =============================================================================
@ -428,6 +435,81 @@ class OverlapPatchEmbed(nn.Module):
        return x
 class BaseFeatureExtractionSAR(nn.Module):
    def __init__(self, dim, pool_size=3, mlp_ratio=4.,
                 act_layer=nn.GELU,
                 # norm_layer=nn.LayerNorm,
                 drop=0., drop_path=0.,
                 use_layer_scale=True, layer_scale_init_value=1e-5):
        super().__init__()
        self.WTConv2d = WTConv2d(dim, dim)
        self.norm1 = LayerNorm(dim, 'WithBias')
        self.token_mixer = Pooling(kernel_size=pool_size)  # vits是msa，MLPs是mlp，这个用pool来替代
        self.norm2 = LayerNorm(dim, 'WithBias')
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
                               act_layer=act_layer, drop=drop)
        # The following two techniques are useful to train deep PoolFormers.
        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()
        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
            self.layer_scale_2 = nn.Parameter(
                torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
    def forward(self, x): # 1 64 128 128
        if self.use_layer_scale:
            #  self.layer_scale_1(64,)
            tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
            normal = self.norm1(x) # 1 64 128 128
            token_mix = self.token_mixer(normal) # 1 64 128 128
            x = self.WTConv2d(x)
            x = (x +
                 self.drop_path(
                    tmp1 * token_mix
                 )
                 # 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
            )
            x = x + self.drop_path(
                self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
                * self.poolmlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse
            x = x + self.drop_path(self.poolmlp(self.norm2(x)))
        return x
 class DetailFeatureExtractionSAR(nn.Module):
    def __init__(self, num_layers=3):
        super(DetailFeatureExtractionSAR, self).__init__()
        INNmodules = [DetailNode() for _ in range(num_layers)]
        self.net = nn.Sequential(*INNmodules)
        self.enhancement_module = WTConv2d(32, 32)
    def forward(self, x): # 1 64 128 128
        z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
        # 增强并添加残差连接
        enhanced_z1 = self.enhancement_module(z1)
        enhanced_z2 = self.enhancement_module(z2)
        # 残差连接
        z1 = z1 + enhanced_z1
        z2 = z2 + enhanced_z2
        for layer in self.net:
            z1, z2 = layer(z1, z2)
        return torch.cat((z1, z2), dim=1)
 class Restormer_Encoder(nn.Module):
    def __init__(self,
                 inp_channels=1,
@ -441,21 +523,31 @@ class Restormer_Encoder(nn.Module):
                 ):
        super(Restormer_Encoder, self).__init__()
-        self.patch_embed = OverlapPatchEmbed(inp_channels, dim)
+        # 区分
        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)
        self.detailFeature = DetailFeatureExtraction()
-    def forward(self, inp_img):
+        self.baseFeature_sar = BaseFeatureExtractionSAR(dim=dim)
        self.detailFeature_sar = DetailFeatureExtractionSAR()
    def forward(self, inp_img,is_sar = False):
        inp_enc_level1 = self.patch_embed(inp_img)
        out_enc_level1 = self.encoder_level1(inp_enc_level1)
-        base_feature = self.baseFeature(out_enc_level1)
+        if is_sar:
-        detail_feature = self.detailFeature(out_enc_level1)
+            base_feature = self.baseFeature_sar(out_enc_level1)  # 1 64 128 128
-        return base_feature, detail_feature, out_enc_level1
+            detail_feature = self.detailFeature_sar(out_enc_level1)  # 1 64 128 128
            return base_feature, detail_feature, out_enc_level1  # 1 64 128 128
        else:
            base_feature = self.baseFeature(out_enc_level1)   # 1 64 128 128
            detail_feature = self.detailFeature(out_enc_level1)  # 1 64 128 128
            return base_feature, detail_feature, out_enc_level1 #  1 64 128 128
 class Restormer_Decoder(nn.Module):
@ -481,8 +573,12 @@ class Restormer_Decoder(nn.Module):
            nn.Conv2d(int(dim) // 2, out_channels, kernel_size=3,
                      stride=1, padding=1, bias=bias), )
        self.sigmoid = nn.Sigmoid()
        self.spatiotemporalAttentionFullNotWeightShared = SpatiotemporalAttentionFullNotWeightShared(in_channels=dim)
    def forward(self, inp_img, base_feature, detail_feature):
        base_feature, detail_feature =self.spatiotemporalAttentionFullNotWeightShared(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)
--- a/status.md
+++ b/status.md
@ -0,0 +1,136 @@
 PFCFuse
 ```angular2html
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.14	46.48	13.18	2.22	1.76	0.79	0.56	1.02
 ================================================================================
 ================================================================================
 The test result of RoadScene :
 FLIR_07206.jpg
 FLIR_08202.jpg
 FLIR_05893.jpg
 FLIR_06974.jpg
 FLIR_04424.jpg
 FLIR_08284.jpg
 FLIR_07786.jpg
 FLIR_08021.jpg
 FLIR_07968.jpg
 FLIR_01130.jpg
 FLIR_06993.jpg
 FLIR_07190.jpg
 FLIR_06570.jpg
 FLIR_07809.jpg
 FLIR_06430.jpg
 FLIR_08592.jpg
 FLIR_00211.jpg
 FLIR_08721.jpg
 FLIR_05955.jpg
 FLIR_04688.jpg
 FLIR_07732.jpg
 FLIR_06392.jpg
 FLIR_00977.jpg
 FLIR_05105.jpg
 FLIR_04269.jpg
 FLIR_07970.jpg
 FLIR_05005.jpg
 FLIR_07209.jpg
 FLIR_07555.jpg
 FLIR_06325.jpg
 FLIR_04943.jpg
 FLIR_video_02829.jpg
 FLIR_08248.jpg
 FLIR_04484.jpg
 FLIR_08058.jpg
 FLIR_06795.jpg
 FLIR_06995.jpg
 FLIR_05879.jpg
 FLIR_04593.jpg
 FLIR_08094.jpg
 FLIR_08526.jpg
 FLIR_08858.jpg
 FLIR_09465.jpg
 FLIR_05064.jpg
 FLIR_05857.jpg
 FLIR_05914.jpg
 FLIR_04722.jpg
 FLIR_06506.jpg
 FLIR_06282.jpg
 FLIR_04512.jpg
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.41	52.99	15.81	2.37	1.78	0.71	0.55	0.96
 ================================================================================
 ```
 20241008 
 ```
 /home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py 
 ================================================================================
 The test result of TNO :
 19.png
 05.png
 21.png
 18.png
 15.png
 22.png
 14.png
 13.png
 08.png
 01.png
 02.png
 03.png
 25.png
 17.png
 11.png
 16.png
 06.png
 07.png
 09.png
 10.png
 12.png
 23.png
 24.png
 20.png
 04.png
 		 EN	 SD	 SF	 MI	SCD	VIF	Qabf	SSIM
 PFCFuse    	7.01	40.4	15.51	1.55	1.75	0.66	0.54	0.96
 ================================================================================
 Process finished with exit code 0
 ```
--- a/test_IVF.py
+++ b/test_IVF.py
@ -1,3 +1,5 @@
 import datetime
 import cv2
 from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
 import os
@ -11,16 +13,18 @@ import logging
 warnings.filterwarnings("ignore")
 logging.basicConfig(level=logging.CRITICAL)
 current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
 os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-ckpt_path= r"/home/star/whaiDir/PFCFuse/models/PFCFusion10-05-20-46.pth"
+ckpt_path= r"/home/star/whaiDir/PFCFuse/models/whaiFusion10-08-16-20.pth"
-for dataset_name in ["TNO"]:
+for dataset_name in ["TNO","RoadScene"]:
    print("\n"*2+"="*80)
    model_name="PFCFuse    "
    print("The test result of "+dataset_name+' :')
-    test_folder=os.path.join('/home/star/whaiDir/CDDFuse/test_img/',dataset_name)
+    test_folder = os.path.join('test_img', dataset_name)
-    test_out_folder=os.path.join('test_result',dataset_name)
+    test_out_folder=os.path.join('test_result',current_time,dataset_name)
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
@ -39,7 +43,6 @@ for dataset_name in ["TNO"]:
    with torch.no_grad():
        for img_name in os.listdir(os.path.join(test_folder,"ir")):
            print(img_name)
            data_IR=image_read_cv2(os.path.join(test_folder,"ir",img_name),mode='GRAY')[np.newaxis,np.newaxis, ...]/255.0
            data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0
--- a/test_img/MRI_CT/CT/11.png
+++ b/test_img/MRI_CT/CT/11.png
--- a/test_img/MRI_CT/CT/12.png
+++ b/test_img/MRI_CT/CT/12.png
--- a/test_img/MRI_CT/CT/13.png
+++ b/test_img/MRI_CT/CT/13.png
--- a/test_img/MRI_CT/CT/14.png
+++ b/test_img/MRI_CT/CT/14.png
--- a/test_img/MRI_CT/CT/15.png
+++ b/test_img/MRI_CT/CT/15.png
--- a/test_img/MRI_CT/CT/16.png
+++ b/test_img/MRI_CT/CT/16.png
--- a/test_img/MRI_CT/CT/17.png
+++ b/test_img/MRI_CT/CT/17.png
--- a/test_img/MRI_CT/CT/18.png
+++ b/test_img/MRI_CT/CT/18.png
--- a/test_img/MRI_CT/CT/19.png
+++ b/test_img/MRI_CT/CT/19.png
--- a/test_img/MRI_CT/CT/20.png
+++ b/test_img/MRI_CT/CT/20.png
--- a/test_img/MRI_CT/CT/21.png
+++ b/test_img/MRI_CT/CT/21.png
--- a/test_img/MRI_CT/CT/22.png
+++ b/test_img/MRI_CT/CT/22.png
--- a/test_img/MRI_CT/CT/23.png
+++ b/test_img/MRI_CT/CT/23.png
--- a/test_img/MRI_CT/CT/24.png
+++ b/test_img/MRI_CT/CT/24.png
--- a/test_img/MRI_CT/CT/25.png
+++ b/test_img/MRI_CT/CT/25.png
--- a/test_img/MRI_CT/CT/26.png
+++ b/test_img/MRI_CT/CT/26.png
--- a/test_img/MRI_CT/CT/27.png
+++ b/test_img/MRI_CT/CT/27.png
--- a/test_img/MRI_CT/CT/28.png
+++ b/test_img/MRI_CT/CT/28.png
--- a/test_img/MRI_CT/CT/29.png
+++ b/test_img/MRI_CT/CT/29.png
--- a/test_img/MRI_CT/CT/30.png
+++ b/test_img/MRI_CT/CT/30.png
--- a/test_img/MRI_CT/CT/31.png
+++ b/test_img/MRI_CT/CT/31.png
--- a/test_img/MRI_CT/MRI/11.png
+++ b/test_img/MRI_CT/MRI/11.png
--- a/test_img/MRI_CT/MRI/12.png
+++ b/test_img/MRI_CT/MRI/12.png
--- a/test_img/MRI_CT/MRI/13.png
+++ b/test_img/MRI_CT/MRI/13.png
--- a/test_img/MRI_CT/MRI/14.png
+++ b/test_img/MRI_CT/MRI/14.png
--- a/test_img/MRI_CT/MRI/15.png
+++ b/test_img/MRI_CT/MRI/15.png
--- a/test_img/MRI_CT/MRI/16.png
+++ b/test_img/MRI_CT/MRI/16.png
--- a/test_img/MRI_CT/MRI/17.png
+++ b/test_img/MRI_CT/MRI/17.png
--- a/test_img/MRI_CT/MRI/18.png
+++ b/test_img/MRI_CT/MRI/18.png
--- a/test_img/MRI_CT/MRI/19.png
+++ b/test_img/MRI_CT/MRI/19.png
--- a/test_img/MRI_CT/MRI/20.png
+++ b/test_img/MRI_CT/MRI/20.png
--- a/test_img/MRI_CT/MRI/21.png
+++ b/test_img/MRI_CT/MRI/21.png
--- a/test_img/MRI_CT/MRI/22.png
+++ b/test_img/MRI_CT/MRI/22.png
--- a/test_img/MRI_CT/MRI/23.png
+++ b/test_img/MRI_CT/MRI/23.png
--- a/test_img/MRI_CT/MRI/24.png
+++ b/test_img/MRI_CT/MRI/24.png
--- a/test_img/MRI_CT/MRI/25.png
+++ b/test_img/MRI_CT/MRI/25.png
--- a/test_img/MRI_CT/MRI/26.png
+++ b/test_img/MRI_CT/MRI/26.png
--- a/test_img/MRI_CT/MRI/27.png
+++ b/test_img/MRI_CT/MRI/27.png
--- a/test_img/MRI_CT/MRI/28.png
+++ b/test_img/MRI_CT/MRI/28.png
--- a/test_img/MRI_CT/MRI/29.png
+++ b/test_img/MRI_CT/MRI/29.png
--- a/test_img/MRI_CT/MRI/30.png
+++ b/test_img/MRI_CT/MRI/30.png
--- a/test_img/MRI_CT/MRI/31.png
+++ b/test_img/MRI_CT/MRI/31.png
--- a/test_img/MRI_PET/MRI/11.png
+++ b/test_img/MRI_PET/MRI/11.png
--- a/test_img/MRI_PET/MRI/12.png
+++ b/test_img/MRI_PET/MRI/12.png
--- a/test_img/MRI_PET/MRI/13.png
+++ b/test_img/MRI_PET/MRI/13.png
--- a/test_img/MRI_PET/MRI/14.png
+++ b/test_img/MRI_PET/MRI/14.png
--- a/test_img/MRI_PET/MRI/15.png
+++ b/test_img/MRI_PET/MRI/15.png
--- a/test_img/MRI_PET/MRI/16.png
+++ b/test_img/MRI_PET/MRI/16.png
--- a/test_img/MRI_PET/MRI/17.png
+++ b/test_img/MRI_PET/MRI/17.png
--- a/test_img/MRI_PET/MRI/18.png
+++ b/test_img/MRI_PET/MRI/18.png
--- a/test_img/MRI_PET/MRI/19.png
+++ b/test_img/MRI_PET/MRI/19.png
--- a/test_img/MRI_PET/MRI/20.png
+++ b/test_img/MRI_PET/MRI/20.png
--- a/test_img/MRI_PET/MRI/21.png
+++ b/test_img/MRI_PET/MRI/21.png
--- a/test_img/MRI_PET/MRI/22.png
+++ b/test_img/MRI_PET/MRI/22.png
--- a/test_img/MRI_PET/MRI/23.png
+++ b/test_img/MRI_PET/MRI/23.png
--- a/test_img/MRI_PET/MRI/24.png
+++ b/test_img/MRI_PET/MRI/24.png
--- a/test_img/MRI_PET/MRI/25.png
+++ b/test_img/MRI_PET/MRI/25.png
--- a/test_img/MRI_PET/MRI/26.png
+++ b/test_img/MRI_PET/MRI/26.png
--- a/test_img/MRI_PET/MRI/27.png
+++ b/test_img/MRI_PET/MRI/27.png
--- a/test_img/MRI_PET/MRI/28.png
+++ b/test_img/MRI_PET/MRI/28.png
--- a/test_img/MRI_PET/MRI/29.png
+++ b/test_img/MRI_PET/MRI/29.png
--- a/test_img/MRI_PET/MRI/30.png
+++ b/test_img/MRI_PET/MRI/30.png
--- a/test_img/MRI_PET/MRI/31.png
+++ b/test_img/MRI_PET/MRI/31.png
--- a/test_img/MRI_PET/MRI/32.png
+++ b/test_img/MRI_PET/MRI/32.png
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
zjut	e1a339e04b	feat(net): 替换 SMFA 为 SCSA 并调整相关配置 - 将 SMFA 模块替换为 SCSA 模块 - 更新项目配置，使用本地 Python 3.8 环境 -调整 SCSA 模块参数，如维度、头数等 - 优化注意力机制，提高模型性能	2024-11-08 12:04:52 +08:00
zjut	41b2ea1ff9	feat(net): 为 net.py 添加新的组件引用并优化前向传播逻辑 - 在 net.py 中引入 SMFA 组件 - 优化 BasicLayer 类的前向传播逻辑 - 添加 SMFA、DynamicFilter 和 UFFC 组件的实现 - 使用SMFA替代Pooling self.WTConv2d = WTConv2d(dim, dim) self.norm1 = LayerNorm(dim, 'WithBias') self.token_mixer = SMFA(dim=dim) # self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代 self.norm2 = LayerNorm(dim, 'WithBias') mlp_hidden_dim = int(dim * mlp_ratio) self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)	2024-11-05 14:09:59 +08:00
whaifree	85dc7a92ed	feat(net): 添加时空注意力机制- 在 net.py 中引入 SpatiotemporalAttentionFullNotWeightShared 模块 - 在 Restormer_Decoder 类中添加时空注意力机制处理基础特征和细节特征	2024-10-26 19:18:01 +08:00
zjut	f4b3a933bf	train:调整训练日志输出频率 - 将训练日志的输出频率从每100 个批次调整为每个批次 - 此修改可以提供更频繁的训练进度反馈，便于实时监控训练状态	2024-10-26 19:04:12 +08:00
whaifree	b6486dbaf4	添加 .idea/ 和 status.md到 .gitignore 文件，避免个人配置和状态文件被跟踪。在测试脚本中移除了不必要的打印语句。新增了测试日志和成功运行的日志文件。	2024-10-26 18:37:15 +08:00
whaifree	7d6d629786	添加 .idea/ 和 status.md到 .gitignore 文件，避免个人配置和状态文件被跟踪。在测试脚本中移除了不必要的打印语句。新增了测试日志和成功运行的日志文件。	2024-10-09 12:04:46 +08:00
whaifree	15eb10b512	添加 .idea/ 和 status.md到 .gitignore 文件，避免个人配置和状态文件被跟踪。在测试脚本中移除了不必要的打印语句。新增了测试日志和成功运行的日志文件。	2024-10-09 11:57:57 +08:00
whaifree	5e561ab6f7	修改代码结构，提高可读性和可维护性；调整训练输出频率。改进 self.enhancement_module 为 self.enhancement_module = WTConv2d(32, 32)	2024-10-09 11:35:06 +08:00
whaifree	96ce7d5fda	修改代码结构，提高可读性和可维护性；调整训练输出频率。改进 self.enhancement_module 为 self.enhancement_module = WTConv2d(32, 32)	2024-10-08 16:50:11 +08:00
whaifree	afd55abe9e	模型结构 DetailFeatureExtraction增加了一个增强残差 BaseFeatureExtraction增加了 x = self.WTConv2d(x)	2024-10-07 15:24:33 +08:00