Open savokiiihjm opened 4 years ago
in train.py
, change the argument baseroot
to your data path; for details, please refer to my dataset.py
part
I have a new question, why do I report an error when I change the image in “noisy_img_examples” to my own image. Is there a requirement for training data? Thank you for your reply.
------------------ 原始邮件 ------------------ 发件人: "Yuzhi ZHAO"<notifications@github.com>; 发送时间: 2019年12月26日(星期四) 晚上10:16 收件人: "zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising"<Self-Guided-Network-for-Fast-Image-Denoising@noreply.github.com>; 抄送: "wefw"<1315897148@qq.com>;"Author"<author@noreply.github.com>; 主题: Re: [zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising] About training data (#2)
in train.py, change the argument baseroot to your data path; for details, please refer to my dataset.py part
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Could you please pose your bug log?
I found my mistake through inspection, thank you for your help
Sorry to bother you again. This error occurred when I tested the model. How to solve it.
recon_img = model(noisy_img) TypeError: 'collections.OrderedDict' object is not callable
Hi, it is my mistake. It returns TypeError
because your saved pth file is just weights, not the model.
please change the line in test.py
model = utils.create_generator(opt)
or you can just download the new test.py
Regards, Zhao
Thank you, I will change it and test my model.
---Original--- From: "Yuzhi ZHAO"<notifications@github.com> Date: Sun, Dec 29, 2019 15:42 PM To: "zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising"<Self-Guided-Network-for-Fast-Image-Denoising@noreply.github.com>; Cc: "savokiiihjm"<1315897148@qq.com>;"Author"<author@noreply.github.com>; Subject: Re: [zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising] About training data (#2)
Hi, it is my mistake. It returns TypeError because your saved pth file is just weights, not the model. please change the line in test.py model = utils.create_generator(opt)
or you can just download the new test.py
Regards, Zhao
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When I changed the test set, the program reported an error:RuntimeError: Calculated padded input size per channel: (32 x 1). Kernel size: (2 x 2). Kernel size can't be greater than actual input size
recon_img = model(noisy_img) in this sentence
How large is the size?
My data set is 6464 , I can change it into 256256. But I want to know how to make the network detect other sizes of data
It is a fully-convolutional network so it works well for any resolution. However, for this model, it exists down-sample operation. Therefore, you should ensure the least resolution >=1. You may print the shape at the shallowest part of SGN.
I'll try as you say,Thank you
------------------ 原始邮件 ------------------ 发件人: "Yuzhi ZHAO"<notifications@github.com>; 发送时间: 2020年1月2日(星期四) 下午5:35 收件人: "zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising"<Self-Guided-Network-for-Fast-Image-Denoising@noreply.github.com>; 抄送: "wefw"<1315897148@qq.com>;"Author"<author@noreply.github.com>; 主题: Re: [zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising] About training data (#2)
It is a fully-convolutional network so it works well for any resolution. However, for this model, it exists down-sample operation. Therefore, you should ensure the least resolution >=1. You may print the shape at the shallowest part of SGN.
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Sorry to bother you again. How can I modify the code so that I can test the 64 * 64 test set.
decrease down-sample time
Where should I change the parameters
network.py you define a new network to fit your situation.
Can you tell me how to modify network.py in more detail? I didn't successfully test the dataset after I modified it. Thank you!
Why can't we test with the test picture given in the code
Have you check image resolution? In the paper it is 256 256. If you still use 64 64, modify network architecture to fit your condition.
I used the test image in the code. The size of the image is 256 * 256, but when I run the test function, I still get an error: RuntimeError: Calculated padded input size per channel: (128 x 1). Kernel size: (2 x 2). Kernel size can't be greater than actual input size
What is the function of pixel_ Unsuffle
inverse of pixel_shuffle
I've tried many times to test data that still doesn't work. Can you give me a script to test 256 * 256 data. Thank you
When I set pre_train = false, the following problem occurs: attributeerror: 'collections. Ordereddict' object has no attribute 'state_dict'
Can you give me a test.py that can run directly? The test data is 256 * 256
In network.py, 'X1 = pixel unshuffle. Pixel_unsuffle (x, 2)' x should be a torch. Size ([1, 3, 256, 256]) But I found that x is torch. Size ([1, 3, 256, 2]), This is a problem at present. What is the cause of this problem and how should I solve it.
Please give me an answer when you are free.Thank you
ok, I will train one for testing. I may be busy this days
Hey, you may have a look at new issue: https://github.com/zhaoyuzhi/Self-Guided-Network-for-Fast-Image-Denoising/issues/3
Can you teach me how to use my own training data