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Change upsample method in ResnetGenerator class #157
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Oh, I guess the reason was the argument to the Upsamle2 function (ngf * mult) is a very big number so it raised an error. Changing it to scale_factor of 2 resolved the issue.
and changing it to Upsample2d method must decrease the number of trainable parameters, right? |
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When I tried to change the Upsample method in the ResnetGenerator class to Upsample2, it suddenly requires more than 500GB and raises an error. What is the reason for this?
Here is the code (in models/networks.py):
And this is the error:
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 GiB (GPU 0; 39.59 GiB total capacity; 885.03 MiB already allocated; 20.78 GiB free; 1.89 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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