Comments (4)
knazeri
commented on September 7, 2024
@1900zyh You are very welcome.
We tried exponential learning rate decay in the beginning; however, later we didn't find it much effective and instead stuck with our fixed decay policy as was explained in the paper. Having said that, we didn't try other decay methods and I'm interested to see how other decay policies affect the convergence!
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2018hello
commented on September 7, 2024
It is a great work! Thank you so much! @knazeri
And I have a question, how to plot the losses values in this project? Because I want to know when will the model become converge. Can you give some suggestions?
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knazeri
commented on September 7, 2024
@2018hello You can use tensorboardX which lets you visualize PyTorch code in Tensorboard. You can also write your custom code to plot loss values using the training log file. We save every loss value in a log file. you can change LOG_INTERVAL to set log saving interval!
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2018hello
commented on September 7, 2024
Thank you so much! I can plot the loss image!
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Related Issues (20)
- Test image is being filled in a lighter shade HOT 1
- Who can help me slove this error? (when I try to train ) HOT 5
- Run the program on CoLab
- Convergency of edge model HOT 10
- Hello, After reading your paper, may I have a question that why you choice 178 for the celebA dataset drop size.
- 如果对图像修复，edge-connect感兴趣，或者需要帮助，可以联系我
- Training on Google Colab immediately stops HOT 1
- Selection of dataset
- Canny sigma HOT 1
- how to implement the visualization for the learned edges? HOT 2
- Sizes of tensors must match except in dimension 1
- New easy to use inpanting method with transformers HOT 1
- When using edge=2, training has ValueError: operands could not be broadcast together with shapes (256,256,3) (256,256)
- Why is there an error when I train MODEL4: joint model/为什么我训练MODEL4 ：joint model会报错
- When I tried to start training, I got an error：RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 512, 4, 4]] is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). HOT 15
- About precision and recall during training HOT 1
- The loss function is abnormal when the edge network is trained
- RuntimeError: CUDA error: device-side assert triggered CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
- a question
- Edge Model Not converging
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