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ubuntu_100
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.gitignore
vendored
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.idea/
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componets/whaiutil.py
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import os
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from PIL import Image
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def transfer(input_path, quality=20, resize_factor=0.1):
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# 打开TIFF图像
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# img = Image.open(input_path)
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#
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# # 保存为JPEG，并设置压缩质量
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# img.save(output_path, 'JPEG', quality=quality)
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# input_path = os.path.join(input_folder, filename)
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# 获取input_path的文件名
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# 使用os.path.splitext获取文件名和后缀的元组
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# 使用os.path.basename获取文件名（包含后缀）
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filename_with_extension = os.path.basename(input_path)
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filename, file_extension = os.path.splitext(filename_with_extension)
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# 使用os.path.dirname获取文件所在的目录路径
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output_folder = os.path.dirname(input_path)
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output_path = os.path.join(output_folder, filename + '.jpg')
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img = Image.open(input_path)
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# 将图像缩小到原来的一半
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new_width = int(img.width * resize_factor)
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new_height = int(img.height * resize_factor)
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resized_img = img.resize((new_width, new_height))
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# 保存为JPEG，并设置压缩质量
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# 转换为RGB模式，丢弃透明通道
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rgb_img = resized_img.convert('RGB')
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# 保存为JPEG，并设置压缩质量
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# 压缩
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rgb_img.save(output_path, 'JPEG', quality=quality)
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print(f'{output_path} 转换完成')
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return output_path
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logs/20241005.log
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/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
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# base pcffuse
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================================================================================
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The test result of TNO :
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04.png
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 2.39 33.82 11.32 0.81 0.8 0.12 0.07 0.11
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================================================================================
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Process finished with exit code 0
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logs/20241007_whai.log
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/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
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================================================================================
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The test result of TNO :
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04.png
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 7.01 40.67 15.39 1.53 1.76 0.64 0.53 0.95
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================================================================================
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logs/ans_log_20241009_115306.log
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================================================================================
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The test result of TNO :
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05.png
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04.png
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 7.14 46.48 13.18 2.22 1.76 0.79 0.56 1.02
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================================================================================
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================================================================================
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The test result of RoadScene :
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FLIR_07206.jpg
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 7.41 52.99 15.81 2.37 1.78 0.71 0.55 0.96
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================================================================================
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logs/log_20241006_164707.log
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logs/log_20241006_164911.log
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logs/log_20241006_165611.log
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2.4.1+cu121
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True
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Model: PFCFuse
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Number of epochs: 60
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Epoch gap: 40
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Learning rate: 0.0001
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Weight decay: 0
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Batch size: 1
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GPU number: 0
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Coefficient of MSE loss VF: 1.0
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Coefficient of MSE loss IF: 1.0
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Coefficient of RMI loss VF: 1.0
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Coefficient of RMI loss IF: 1.0
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Coefficient of Cosine loss VF: 1.0
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Coefficient of Cosine loss IF: 1.0
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Coefficient of Decomposition loss: 2.0
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Coefficient of Total Variation loss: 5.0
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Clip gradient norm value: 0.01
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Optimization step: 20
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Optimization gamma: 0.5
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[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):
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File "/home/star/whaiDir/PFCFuse/train.py", line 232, in <module>
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loss.item(),
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KeyboardInterrupt
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logs/log_20241006_165643.log
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/home/star/anaconda3/envs/pfcfuse/bin/python /home/star/whaiDir/PFCFuse/test_IVF.py
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================================================================================
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The test result of TNO :
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EN SD SF MI SCD VIF Qabf SSIM
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PFCFuse 7.01 40.4 15.51 1.55 1.75 0.66 0.54 0.96
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================================================================================
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Process finished with exit code 0
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logs/log_20241009_120135.log
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2.4.1+cu121
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True
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Model: PFCFuse
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Number of epochs: 60
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Epoch gap: 40
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Learning rate: 0.0001
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Weight decay: 0
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Batch size: 1
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GPU number: 0
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Coefficient of MSE loss VF: 1.0
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Coefficient of MSE loss IF: 1.0
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Coefficient of RMI loss VF: 1.0
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Coefficient of RMI loss IF: 1.0
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Coefficient of Cosine loss VF: 1.0
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Coefficient of Cosine loss IF: 1.0
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Coefficient of Decomposition loss: 2.0
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Coefficient of Total Variation loss: 5.0
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Clip gradient norm value: 0.01
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Optimization step: 20
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Optimization gamma: 0.5
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|
[Epoch 0/60] [Batch 0/6487] [loss: 10.616437] ETA: 10 days, 7
[Epoch 0/60] [Batch 100/6487] [loss: 1.343770] ETA: 12:06:09.6
[Epoch 0/60] [Batch 200/6487] [loss: 0.555045] ETA: 10:02:53.9Traceback (most recent call last):
|
||||||
|
File "/home/star/whaiDir/PFCFuse/train.py", line 154, in <module>
|
||||||
|
data_IR_hat, _ = DIDF_Decoder(data_IR, feature_I_B, feature_I_D)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
|
||||||
|
return self._call_impl(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
|
||||||
|
return forward_call(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 184, in forward
|
||||||
|
return self.module(*inputs[0], **module_kwargs[0])
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
|
||||||
|
return self._call_impl(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
|
||||||
|
return forward_call(*args, **kwargs)
|
||||||
|
File "/home/star/whaiDir/PFCFuse/net.py", line 568, in forward
|
||||||
|
out_enc_level1 = self.encoder_level2(out_enc_level0)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
|
||||||
|
return self._call_impl(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
|
||||||
|
return forward_call(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/container.py", line 219, in forward
|
||||||
|
input = module(input)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
|
||||||
|
return self._call_impl(*args, **kwargs)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
|
||||||
|
return forward_call(*args, **kwargs)
|
||||||
|
File "/home/star/whaiDir/PFCFuse/net.py", line 408, in forward
|
||||||
|
x = x + self.ffn(self.norm2(x))
|
||||||
|
KeyboardInterrupt
|
||||||
20
logs/log_20241009_120302.log
Normal file
@ -0,0 +1,20 @@
|
|||||||
|
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
|
||||||
30
logs/log_20241009_120341.log
Normal file
@ -0,0 +1,30 @@
|
|||||||
|
Traceback (most recent call last):
|
||||||
|
File "/home/star/whaiDir/PFCFuse/train.py", line 9, in <module>
|
||||||
|
from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
|
||||||
|
File "/home/star/whaiDir/PFCFuse/net.py", line 1, in <module>
|
||||||
|
import torch
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/__init__.py", line 2143, in <module>
|
||||||
|
from . import _meta_registrations
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_meta_registrations.py", line 9, in <module>
|
||||||
|
from torch._decomp import (
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_decomp/__init__.py", line 245, in <module>
|
||||||
|
import torch._decomp.decompositions
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_decomp/decompositions.py", line 12, in <module>
|
||||||
|
import torch._prims as prims
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_prims/__init__.py", line 844, in <module>
|
||||||
|
ndtri = _make_elementwise_unary_prim(
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_prims/__init__.py", line 472, in _make_elementwise_unary_prim
|
||||||
|
return _make_prim(
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_prims/__init__.py", line 320, in _make_prim
|
||||||
|
prim_def = torch.library.custom_op(
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_library/custom_ops.py", line 142, in custom_op
|
||||||
|
return inner(fn)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_library/custom_ops.py", line 123, in inner
|
||||||
|
result = CustomOpDef(namespace, opname, schema_str, fn)
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_library/custom_ops.py", line 169, in __init__
|
||||||
|
self._register_to_dispatcher()
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/_library/custom_ops.py", line 455, in _register_to_dispatcher
|
||||||
|
lib.define(
|
||||||
|
File "/home/star/anaconda3/envs/pfcfuse/lib/python3.8/site-packages/torch/library.py", line 118, in define
|
||||||
|
result = self.m.define(schema, alias_analysis, tuple(tags))
|
||||||
|
KeyboardInterrupt
|
||||||
1
logs/log_20241009_120404.log
Normal file
@ -0,0 +1 @@
|
|||||||
|
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
|
||||||
45
logs/status.md
Normal file
@ -0,0 +1,45 @@
|
|||||||
|
PFCFuse
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
```
|
||||||
BIN
models/PFCFusion10-05-18-13.pth
Normal file
BIN
models/PFCFusion10-05-20-46.pth
Normal file
BIN
models/whaiFusion10-06-22-17.pth
Normal file
BIN
models/whaiFusion10-08-16-20.pth
Normal file
102
net.py
@ -248,11 +248,7 @@ class DetailFeatureExtraction(nn.Module):
|
|||||||
super(DetailFeatureExtraction, self).__init__()
|
super(DetailFeatureExtraction, self).__init__()
|
||||||
INNmodules = [DetailNode() for _ in range(num_layers)]
|
INNmodules = [DetailNode() for _ in range(num_layers)]
|
||||||
self.net = nn.Sequential(*INNmodules)
|
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
|
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
|
z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
|
||||||
@ -428,6 +424,81 @@ class OverlapPatchEmbed(nn.Module):
|
|||||||
return x
|
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):
|
class Restormer_Encoder(nn.Module):
|
||||||
def __init__(self,
|
def __init__(self,
|
||||||
inp_channels=1,
|
inp_channels=1,
|
||||||
@ -441,21 +512,30 @@ class Restormer_Encoder(nn.Module):
|
|||||||
):
|
):
|
||||||
super(Restormer_Encoder, self).__init__()
|
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(
|
self.encoder_level1 = nn.Sequential(
|
||||||
*[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor,
|
*[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])])
|
bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
|
||||||
self.baseFeature = BaseFeatureExtraction(dim=dim)
|
self.baseFeature = BaseFeatureExtraction(dim=dim)
|
||||||
|
|
||||||
self.detailFeature = DetailFeatureExtraction()
|
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)
|
inp_enc_level1 = self.patch_embed(inp_img)
|
||||||
out_enc_level1 = self.encoder_level1(inp_enc_level1)
|
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):
|
class Restormer_Decoder(nn.Module):
|
||||||
|
|||||||
136
status.md
Normal file
@ -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
|
||||||
|
|
||||||
|
```
|
||||||
13
test_IVF.py
@ -1,3 +1,5 @@
|
|||||||
|
import datetime
|
||||||
|
|
||||||
import cv2
|
import cv2
|
||||||
from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
|
from net import Restormer_Encoder, Restormer_Decoder, BaseFeatureExtraction, DetailFeatureExtraction
|
||||||
import os
|
import os
|
||||||
@ -11,16 +13,18 @@ import logging
|
|||||||
warnings.filterwarnings("ignore")
|
warnings.filterwarnings("ignore")
|
||||||
logging.basicConfig(level=logging.CRITICAL)
|
logging.basicConfig(level=logging.CRITICAL)
|
||||||
|
|
||||||
|
current_time = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||||
|
|
||||||
|
|
||||||
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
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","sar"]:
|
||||||
print("\n"*2+"="*80)
|
print("\n"*2+"="*80)
|
||||||
model_name="PFCFuse "
|
model_name="PFCFuse "
|
||||||
print("The test result of "+dataset_name+' :')
|
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'
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||||
Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
|
Encoder = nn.DataParallel(Restormer_Encoder()).to(device)
|
||||||
@ -39,7 +43,6 @@ for dataset_name in ["TNO"]:
|
|||||||
|
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
for img_name in os.listdir(os.path.join(test_folder,"ir")):
|
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_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
|
data_VIS = cv2.split(image_read_cv2(os.path.join(test_folder, "vi", img_name), mode='YCrCb'))[0][np.newaxis, np.newaxis, ...] / 255.0
|
||||||
|
|||||||
BIN
test_img/MRI_CT/CT/11.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_CT/CT/12.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_CT/CT/13.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_CT/CT/14.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_CT/CT/15.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_CT/CT/16.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_CT/CT/17.png
Normal file
|
After Width: | Height: | Size: 39 KiB |
BIN
test_img/MRI_CT/CT/18.png
Normal file
|
After Width: | Height: | Size: 37 KiB |
BIN
test_img/MRI_CT/CT/19.png
Normal file
|
After Width: | Height: | Size: 34 KiB |
BIN
test_img/MRI_CT/CT/20.png
Normal file
|
After Width: | Height: | Size: 33 KiB |
BIN
test_img/MRI_CT/CT/21.png
Normal file
|
After Width: | Height: | Size: 38 KiB |
BIN
test_img/MRI_CT/CT/22.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_CT/CT/23.png
Normal file
|
After Width: | Height: | Size: 44 KiB |
BIN
test_img/MRI_CT/CT/24.png
Normal file
|
After Width: | Height: | Size: 44 KiB |
BIN
test_img/MRI_CT/CT/25.png
Normal file
|
After Width: | Height: | Size: 43 KiB |
BIN
test_img/MRI_CT/CT/26.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_CT/CT/27.png
Normal file
|
After Width: | Height: | Size: 40 KiB |
BIN
test_img/MRI_CT/CT/28.png
Normal file
|
After Width: | Height: | Size: 40 KiB |
BIN
test_img/MRI_CT/CT/29.png
Normal file
|
After Width: | Height: | Size: 40 KiB |
BIN
test_img/MRI_CT/CT/30.png
Normal file
|
After Width: | Height: | Size: 39 KiB |
BIN
test_img/MRI_CT/CT/31.png
Normal file
|
After Width: | Height: | Size: 37 KiB |
BIN
test_img/MRI_CT/MRI/11.png
Normal file
|
After Width: | Height: | Size: 59 KiB |
BIN
test_img/MRI_CT/MRI/12.png
Normal file
|
After Width: | Height: | Size: 58 KiB |
BIN
test_img/MRI_CT/MRI/13.png
Normal file
|
After Width: | Height: | Size: 60 KiB |
BIN
test_img/MRI_CT/MRI/14.png
Normal file
|
After Width: | Height: | Size: 66 KiB |
BIN
test_img/MRI_CT/MRI/15.png
Normal file
|
After Width: | Height: | Size: 63 KiB |
BIN
test_img/MRI_CT/MRI/16.png
Normal file
|
After Width: | Height: | Size: 61 KiB |
BIN
test_img/MRI_CT/MRI/17.png
Normal file
|
After Width: | Height: | Size: 60 KiB |
BIN
test_img/MRI_CT/MRI/18.png
Normal file
|
After Width: | Height: | Size: 57 KiB |
BIN
test_img/MRI_CT/MRI/19.png
Normal file
|
After Width: | Height: | Size: 54 KiB |
BIN
test_img/MRI_CT/MRI/20.png
Normal file
|
After Width: | Height: | Size: 52 KiB |
BIN
test_img/MRI_CT/MRI/21.png
Normal file
|
After Width: | Height: | Size: 51 KiB |
BIN
test_img/MRI_CT/MRI/22.png
Normal file
|
After Width: | Height: | Size: 53 KiB |
BIN
test_img/MRI_CT/MRI/23.png
Normal file
|
After Width: | Height: | Size: 54 KiB |
BIN
test_img/MRI_CT/MRI/24.png
Normal file
|
After Width: | Height: | Size: 56 KiB |
BIN
test_img/MRI_CT/MRI/25.png
Normal file
|
After Width: | Height: | Size: 55 KiB |
BIN
test_img/MRI_CT/MRI/26.png
Normal file
|
After Width: | Height: | Size: 52 KiB |
BIN
test_img/MRI_CT/MRI/27.png
Normal file
|
After Width: | Height: | Size: 50 KiB |
BIN
test_img/MRI_CT/MRI/28.png
Normal file
|
After Width: | Height: | Size: 50 KiB |
BIN
test_img/MRI_CT/MRI/29.png
Normal file
|
After Width: | Height: | Size: 49 KiB |
BIN
test_img/MRI_CT/MRI/30.png
Normal file
|
After Width: | Height: | Size: 48 KiB |
BIN
test_img/MRI_CT/MRI/31.png
Normal file
|
After Width: | Height: | Size: 46 KiB |
BIN
test_img/MRI_PET/MRI/11.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/12.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/13.png
Normal file
|
After Width: | Height: | Size: 43 KiB |
BIN
test_img/MRI_PET/MRI/14.png
Normal file
|
After Width: | Height: | Size: 43 KiB |
BIN
test_img/MRI_PET/MRI/15.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/16.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/17.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/18.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/19.png
Normal file
|
After Width: | Height: | Size: 43 KiB |
BIN
test_img/MRI_PET/MRI/20.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/21.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/22.png
Normal file
|
After Width: | Height: | Size: 41 KiB |
BIN
test_img/MRI_PET/MRI/23.png
Normal file
|
After Width: | Height: | Size: 39 KiB |
BIN
test_img/MRI_PET/MRI/24.png
Normal file
|
After Width: | Height: | Size: 38 KiB |
BIN
test_img/MRI_PET/MRI/25.png
Normal file
|
After Width: | Height: | Size: 37 KiB |
BIN
test_img/MRI_PET/MRI/26.png
Normal file
|
After Width: | Height: | Size: 36 KiB |
BIN
test_img/MRI_PET/MRI/27.png
Normal file
|
After Width: | Height: | Size: 34 KiB |
BIN
test_img/MRI_PET/MRI/28.png
Normal file
|
After Width: | Height: | Size: 32 KiB |
BIN
test_img/MRI_PET/MRI/29.png
Normal file
|
After Width: | Height: | Size: 29 KiB |
BIN
test_img/MRI_PET/MRI/30.png
Normal file
|
After Width: | Height: | Size: 26 KiB |
BIN
test_img/MRI_PET/MRI/31.png
Normal file
|
After Width: | Height: | Size: 21 KiB |
BIN
test_img/MRI_PET/MRI/32.png
Normal file
|
After Width: | Height: | Size: 39 KiB |
BIN
test_img/MRI_PET/MRI/33.png
Normal file
|
After Width: | Height: | Size: 40 KiB |
BIN
test_img/MRI_PET/MRI/34.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/35.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/36.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/37.png
Normal file
|
After Width: | Height: | Size: 42 KiB |
BIN
test_img/MRI_PET/MRI/38.png
Normal file
|
After Width: | Height: | Size: 44 KiB |
BIN
test_img/MRI_PET/MRI/39.png
Normal file
|
After Width: | Height: | Size: 46 KiB |
BIN
test_img/MRI_PET/MRI/40.png
Normal file
|
After Width: | Height: | Size: 44 KiB |
BIN
test_img/MRI_PET/MRI/41.png
Normal file
|
After Width: | Height: | Size: 44 KiB |
BIN
test_img/MRI_PET/MRI/42.png
Normal file
|
After Width: | Height: | Size: 43 KiB |
BIN
test_img/MRI_PET/MRI/43.png
Normal file
|
After Width: | Height: | Size: 41 KiB |