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a-little-bit-more's Issues

About train

Hello, I'd like to train with my own dataset, but after just one epoch, I encountered the following results. I'm not sure why. Could you help me understand? Thank you.

loss: 0.0063 - binary_accuracy: 0.9994 - val_loss: 1.6513e-05 - val_binary_accuracy: 1.0000

Testing code does not work well

Hi! I'm interested in this work, but I find the testing code does not work well. The output images are totally dark. Just like this:

image

I create a new conda environment following the README.

Here is the output of command line. Would you mind having a look at it and pointing out the problem? Thanks!

$ python test.py --set_names Kodak --type_8_or_16 0 --quant 3 --quant_end 8 --dep 16 --save_result 1
Using TensorFlow backend.
2022-02-28 19:25:15.720844: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA
2022-02-28 19:25:15.924086: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties: 
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:1a:00.0
totalMemory: 23.70GiB freeMemory: 23.44GiB
2022-02-28 19:25:16.048250: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 1 with properties: 
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:3d:00.0
totalMemory: 23.70GiB freeMemory: 23.44GiB
2022-02-28 19:25:16.153546: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 2 with properties: 
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:89:00.0
totalMemory: 23.70GiB freeMemory: 23.44GiB
2022-02-28 19:25:16.275457: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 3 with properties: 
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:b1:00.0
totalMemory: 23.70GiB freeMemory: 23.44GiB
2022-02-28 19:25:16.391234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 4 with properties: 
name: GeForce RTX 3090 major: 8 minor: 6 memoryClockRate(GHz): 1.695
pciBusID: 0000:b2:00.0
totalMemory: 23.70GiB freeMemory: 23.44GiB
2022-02-28 19:25:16.391292: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0, 1, 2, 3, 4
2022-02-28 19:28:48.279998: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-02-28 19:28:48.280047: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0 1 2 3 4 
2022-02-28 19:28:48.280054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N N N N N 
2022-02-28 19:28:48.280058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 1:   N N N N N 
2022-02-28 19:28:48.280062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 2:   N N N N N 
2022-02-28 19:28:48.280066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 3:   N N N N N 
2022-02-28 19:28:48.280069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 4:   N N N N N 
2022-02-28 19:28:48.280303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 22726 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3090, pci bus id: 0000:1a:00.0, compute capability: 8.6)
2022-02-28 19:28:48.280689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 14885 MB memory) -> physical GPU (device: 1, name: GeForce RTX 3090, pci bus id: 0000:3d:00.0, compute capability: 8.6)
2022-02-28 19:28:48.280933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:2 with 19706 MB memory) -> physical GPU (device: 2, name: GeForce RTX 3090, pci bus id: 0000:89:00.0, compute capability: 8.6)
2022-02-28 19:28:48.281121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:3 with 19706 MB memory) -> physical GPU (device: 3, name: GeForce RTX 3090, pci bus id: 0000:b1:00.0, compute capability: 8.6)
2022-02-28 19:28:48.281313: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:4 with 19706 MB memory) -> physical GPU (device: 4, name: GeForce RTX 3090, pci bus id: 0000:b2:00.0, compute capability: 8.6)
2022-02-28 19:28:52: load trained model - 4 - 030
2022-02-28 19:28:57: load trained model - 5 - 030
2022-02-28 19:29:01: load trained model - 6 - 030
2022-02-28 19:29:07: load trained model - 7 - 030
2022-02-28 19:29:13: load trained model - 8 - 030
     Kodak : kodim04.png : 1055.9769 second
     Kodak : kodim01.png : 0.5756 second
     Kodak : kodim20.png : 0.5917 second
     Kodak : kodim21.png : 0.5345 second
     Kodak : kodim10.png : 0.5409 second
     Kodak : kodim08.png : 0.5639 second
     Kodak : kodim14.png : 0.5364 second
     Kodak : kodim23.png : 0.5551 second
     Kodak : kodim16.png : 0.5398 second
     Kodak : kodim19.png : 0.5437 second
     Kodak : kodim07.png : 0.5365 second
     Kodak : kodim15.png : 0.5465 second
     Kodak : kodim11.png : 0.5364 second
     Kodak : kodim17.png : 0.5362 second
     Kodak : kodim02.png : 0.5558 second
     Kodak : kodim24.png : 0.5336 second
     Kodak : kodim13.png : 0.5503 second
     Kodak : kodim03.png : 0.5627 second
     Kodak : kodim18.png : 0.5432 second
     Kodak : kodim06.png : 0.5517 second
     Kodak : kodim22.png : 0.5383 second
     Kodak : kodim12.png : 0.5451 second
     Kodak : kodim05.png : 0.5566 second
     Kodak : kodim09.png : 0.5336 second
2022-02-28 19:47:29: Dataset: Kodak      
  PSNR = 8.4058dB, SSIM = 0.0673

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