在使用Stable Difussion的时候, free memory 和 already allocated memory 相加小于所需内存 但是显示CUDA out of
memory,在网上看了下,在python.exe里输入以下代码 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:32" 现在是free memory很小,但是reserved memory 很大,请大神指点一下
@Override
Traceback (most recent call last):
File "basicsr/train.py", line 244, in <module>
train_pipeline(root_path)
File "basicsr/train.py", line 237, in train_pipeline
model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
File "/home/iecy/pycharm/Openbayes/QuanTexSR-main/basicsr/models/base_model.py", line 48, in validation
self.nondist_validation(dataloader, current_iter, tb_logger, save_img, save_as_dir)
File "/home/iecy/pycharm/Openbayes/QuanTexSR-main/basicsr/models/qsr_model.py", line 289, in nondist_validation
self.test()
File "/home/iecy/pycharm/Openbayes/QuanTexSR-main/basicsr/models/qsr_model.py", line 253, in test
self.output = net_g.test(lq_input)
File "/home/iecy/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/home/iecy/pycharm/Openbayes/QuanTexSR-main/basicsr/archs/quantsr_arch.py", line 417, in test
input = self.content_model(input)
File "/home/iecy/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/iecy/pycharm/Openbayes/QuanTexSR-main/basicsr/archs/rrdbnet_arch.py", line 117, in forward
feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
File "/home/iecy/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py", line 3690, in interpolate
return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
}
其中上面有一行就是问题所在。我将它修改之后,就没有再出现cuda out of memory.
@Override
Test 0801_s001: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:10<00:00, 10.36s/image]
2022-04-21 18:43:44,104 INFO: Validation General_Image_Valid
# psnr: 8.9772 Best: 8.9772 @ 0 iter
# ssim: 0.0348 Best: 0.0348 @ 0 iter
# lpips: 0.9741 Best: 0.9741 @ 0 iter
}