Comments (4)
XuecaiHu
commented on July 4, 2024
If you want to test our Meta-RDN on very large LR image (Since the RDN is very big model, the size of input image is limtied ), you may need to change the test function in trainer.py by dividing the image into small patches.
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Myzhencai
commented on July 4, 2024
thank you @XuecaiHu
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suke27
commented on July 4, 2024
that is excepted, the memory is depend on input size, hwinchaneloutchannel3*3.
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PonderK
commented on July 4, 2024
I have the same problem, But I don't konw how to divide the image into small patches in trainer.py. Could you please share your code with me? Thanks you very much! @Myzhencai
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Related Issues (20)
- Have you debugged it yet？
- Meta-Upscale Module
- meta-upscale
- meta-upscale的输入
- 请问输入矩阵为什么需要mask
- Meta-upscale的实现 HOT 3
- RuntimeError: cuda runtime error (2) HOT 5
- Trying to train Meta-RCAN but failed HOT 2
- Testing directories HOT 3
- rewrite dataloader for more recnt Pytorch
- meta-learning for weight prediction
- dataloader error, help plz~
- Higher PSNR when i use pretrained model?
- 请问怎样运行 geberate_LR_metasr_X1_X4.m 文件？
- Pretrained models
- 如何将MetaUpSampler 改成适用于3d图像的上采样？
- 请问，想改成 针对3d数据，该怎么改？ 比如（batch，C， h, w, d），超分到（batch, C, H, W, D）。 HOT 2
- 你好，能帮忙指点下吗? 改成3d 后 pos_mat_small 维度不是Scale x Scale x Scale x 3的维度？ h_offset这需要改吗？ HOT 3
- 你好，cols = nn.functional.unfold(up_x.permute(0, 2, 3, 1), self.kernel_size, padding=1)，该咋改呀？ HOT 1
- Pre-training model selection for testing
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