Comments (5)
When I set print_freq
to 1, we can check that there's some negligible difference in loss
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I do not understand your question. But you can refer to the original training log of HAT for SRx4 on DF2K.
train_416_train_Final_Model_SRx4_scratch_DF2K_CR3W16P64_500k_B4G8_20220301_002049.log
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@chxy95 Thanks for quick response.
When I run same yaml file twice on my custom dataset, loss is not same. Maybe this is caused by some non-deterministic algorhitm in HAT. Anyway I want to reproduce same result on same yaml file, but I do not know how to do it.
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@Dongwoo-Im I'm not sure the reason for this either. The ONLY random factor in HAT appears to be drop_path_rate
. It should have no effect on the performance. You can set it to 0 and observe the training process. If it does not work, there may be some random factors that the current version of BasicSR does not take into account.
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@chxy95 Thanks your advice. I haven't solved yet, but I'll re-open when I get some insights or find solution.
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Related Issues (20)
- fine tuning Real_HAT_mse model with paired dataset HOT 2
- Scale by 2 or 8?
- 单通道训练问题 HOT 3
- RuntimeError: NCCL invalid usage HOT 1
- Having problem while Training on low resolution images
- How to use the package in Mac with M1/M2 chip
- How you calculate macs and params?
- Inquiry on HAT Application for Same-Size Super Resolution HOT 5
- How to modify basicsr code and apply to HAT model? HOT 2
- Can't Reproduce the Results of SRx3
- How can I calculate PSNR&SSIM with only LR images in HAT_SRX4_ImageNet-LR.yml?Thank you!
- Does the Real-HAT have a small version like HAT-S? HOT 6
- Half precision (fp16) Training and Inference of the HAT models HOT 3
- TypeError: calculate_psnr() missing 1 required positional argument: 'img2' HOT 2
- Tại bài hát mới
- Question about the forward method HOT 3
- 1x模型权重
- How to limit the range of the super-resolved image HOT 2
- LAM可视化
- Value of RPI in hat_arch.py value
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