0202备份 准备重构adair代码

.gitignore

@@ -158,3 +158,20 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+data/
+datasets/
+__pycache__/
+*.pth
+*.jpg
+*.png
+
+ckpt/
+checkpoints/
+*.pth
+*.pt
+*.ckpt
+*.safetensors
+
+/AdaIR
+/AdaIR_results
+

et --hard

@@ -0,0 +1,107 @@
+commit fc652196fb3870738bcae1a9214d09190f934e2a (HEAD -> cursor-adir, origin/cursor-adir)
+Author: Harry-yyq <1724017964@qq.com>
+Date:   Fri Feb 20 15:49:43 2026 +0800
+
+    0202备份 准备重构adair代码
+
+commit ccb8b98e49614e07badd0641e5163fa7635c2f02 (origin/main, origin/HEAD, main, adair-uie)
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Wed Dec 24 16:40:53 2025 +0800
+
+    Update README
+
+commit 211cd11813e0513b951f4860ec0e21220319c1d3
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Tue Dec 23 23:03:43 2025 +0800
+
+    Add hyperlink to BioIR project in README
+
+commit 23725b7b50106cd7a08b7dc6371992e992c6fa45
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Tue Dec 23 23:02:57 2025 +0800
+
+    Update README
+
+commit ab14c4b960e3fa7000eabd6ec102eee49fd24488
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Wed May 7 21:58:36 2025 +0200
+
+    Update README.md
+
+commit 7edc41b768c1a8df3567ae9f9e8948293fc23f48
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Sun Mar 2 09:33:35 2025 +0100
+
+    Update README.md
+
+commit 50a85577452bd5ef461bdf2da44b61b034ffd45f
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Sun Mar 2 09:32:30 2025 +0100
+
+    Update README.md
+
+commit 67cdc58e29f9a59064fe32132363cdff016b69c1
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Wed Jan 22 19:04:24 2025 +0100
+
+    Update README.md
+
+commit d46fb475af26b6eb8177079a458c1cb9c67dbbaa
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Thu Apr 25 13:56:16 2024 +0400
+
+    Update README.md
+
+commit 69e13fbdaa6dc5312c084adbca97a92caa6efc0f
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Fri Mar 22 17:34:35 2024 +0400
+
+    Update README.md
+
+commit c7b3ff2d701295bc591ff5351567a7c3b9d5e9a3
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Fri Mar 22 14:31:08 2024 +0400
+
+    Update README.md
+
+commit 6986022a6b2c18c49e216253c87de70e1437c249
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Fri Mar 22 11:52:54 2024 +0400
+
+    Update model.py
+
+commit 50f8cafe8841915ccbf23bdae6bf90e14b1b92d4
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Fri Mar 22 08:40:15 2024 +0400
+
+    Update README.md
+
+commit 4dd73536168f0b90142d5fd0c2081e4aa9fa3d0c
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Fri Mar 22 08:32:41 2024 +0400
+
+    Update README.md
+
+commit dab504eec37fc609fda8a2f8ed7d2714e14f2dcb
+Author: Yuning Cui <cuiyuning16@gmail.com>
+Date:   Wed Mar 20 14:17:41 2024 +0400
+
+    Code Release
+
+commit c9e7c96e7d8c880237b4c3e4da1bb78ef650dce8
+Author: Yuning Cui <cuiyuning16@gmail.com>
+Date:   Wed Mar 20 03:23:15 2024 +0400
+
+    Update README.md
+
+commit 9c86ec24bafb9948089c30d0cc9c324903f029fe
+Author: Yuning Cui <cuiyuning16@gmail.com>
+Date:   Wed Mar 20 03:18:24 2024 +0400
+
+    Code Release
+
+commit ab6eca623351e0dbe334feb3603944d2114d7d88
+Author: Yuning Cui <94985227+c-yn@users.noreply.github.com>
+Date:   Tue Mar 19 22:11:52 2024 +0400
+
+    Initial commit

net/model.py

@@ -366,6 +366,55 @@ def fft(self, x, n=128):
         return high, low
 
 
+##########################################################################
+## 频域色彩提亮模块 (FFT-based Color Block) — DarkIR 风格：仅修振幅、相位不变
+## 插入 Encoder 最深层（Bottleneck），用于水下等场景的全局亮度/色彩校正
+class FrequencyColorEnhanceBlock(nn.Module):
+    """在频域内仅对振幅进行缩放与特征变换，相位绝对保持不变，再逆变换回空域。"""
+
+    def __init__(self, dim, use_instruction=False, instruction_dim=None):
+        super().__init__()
+        self.dim = dim
+        self.use_instruction = use_instruction
+        # 仅对振幅做缩放的小型卷积网络（代表亮度和全局色彩调整）
+        self.amp_net = nn.Sequential(
+            nn.Conv2d(dim, max(dim // 4, 1), kernel_size=1),
+            nn.GELU(),
+            nn.Conv2d(max(dim // 4, 1), dim, kernel_size=1),
+            nn.Sigmoid(),
+        )
+        # 缩放因子范围 [0.5, 1.5]，避免振幅爆炸
+        self.amp_scale_bias = 0.5
+        self.amp_scale_range = 1.0
+
+    def forward(self, x, instruction=None):
+        """
+        x: (B, C, H, W) 编码器 Bottleneck 特征
+        instruction: 可选，与 AdaIR Instruction 结合时传入
+        """
+        # ---------- 防 NaN：AMP 混合精度下 FFT/IFFT 强制 float32 ----------
+        orig_dtype = x.dtype
+        x = x.float()
+
+        # 实部 FFT，输出形状 (B, C, H, W//2+1) 复数
+        fft = torch.fft.rfft2(x, norm="ortho")
+        # 振幅与相位解耦
+        amplitude = torch.abs(fft)
+        phase = torch.angle(fft)
+        # 防止振幅为 0 时梯度异常，做数值稳定
+        amplitude = amplitude.clamp(min=1e-8)
+
+        # 仅修振幅：小型网络输出 [0,1]，映射到缩放因子
+        scale = self.amp_net(amplitude) * self.amp_scale_range + self.amp_scale_bias
+        amplitude_new = amplitude * scale
+
+        # 绝对保护相位：用新振幅 + 原相位重构复数
+        fft_new = amplitude_new * torch.exp(1j * phase)
+        out = torch.fft.irfft2(fft_new, s=(x.size(-2), x.size(-1)), norm="ortho")
+
+        return out.to(orig_dtype)
+
+
 ##########################################################################
 ##---------- AdaIR -----------------------
 
@@ -405,7 +454,10 @@ def __init__(self,
 
         self.down3_4 = Downsample(int(dim*2**2)) ## From Level 3 to Level 4
         self.latent = nn.Sequential(*[TransformerBlock(dim=int(dim*2**3), num_heads=heads[3], ffn_expansion_factor=ffn_expansion_factor, bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[3])])
-
+        # Encoder 最深层：频域色彩提亮 + 辅助色彩输出（DarkIR 解耦：频域修色）
+        self.freq_color_block = FrequencyColorEnhanceBlock(int(dim*2**3))
+        self.color_head = nn.Conv2d(int(dim*2**3), 3, kernel_size=1, bias=bias)
+
         self.up4_3 = Upsample(int(dim*2**3)) ## From Level 4 to Level 3
         self.reduce_chan_level3 = nn.Conv2d(int(dim*2**3), int(dim*2**2), kernel_size=1, bias=bias)
 
@@ -437,8 +489,13 @@ def forward(self, inp_img,noise_emb = None):
 
         out_enc_level3 = self.encoder_level3(inp_enc_level3) 
 
-        inp_enc_level4 = self.down3_4(out_enc_level3)        
-        latent = self.latent(inp_enc_level4) 
+        inp_enc_level4 = self.down3_4(out_enc_level3)
+        latent = self.latent(inp_enc_level4)
+        # 频域色彩提亮（仅改振幅、相位不变）
+        latent = self.freq_color_block(latent)
+        # 辅助色彩输出：低分辨率 RGB，用于非对称监督 loss_color
+        I_low_res = self.color_head(latent)
+        I_low_res = torch.sigmoid(I_low_res)
 
         if self.decoder:
             latent = self.fre1(inp_img, latent)
@@ -470,6 +527,8 @@ def forward(self, inp_img,noise_emb = None):
         out_dec_level1 = self.refinement(out_dec_level1)
 
         out_dec_level1 = self.output(out_dec_level1) + inp_img
+        I_restored = out_dec_level1
 
-        return out_dec_level1
+        # latent 用于训练时记录 instruction_std，防止模式崩溃
+        return I_restored, I_low_res, latent