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Not enough memory available to process your request #126
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Same problem. Is there any solution? |
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I trying to use test part and in .yml files there is not batch size option. Please can you help with that case |
I thought train and test are not run each other and if change train it will have no action on test commands. |
Paste log:
Traceback (most recent call last):
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/cog/server/worker.py", line 217, in _predict
result = predict(**payload)
File "predict.py", line 79, in predict
single_image_inference(model, inp, str(out_path))
File "predict.py", line 101, in single_image_inference
model.test()
File "/src/basicsr/models/image_restoration_model.py", line 247, in test
pred = self.net_g(self.lq[i:j])
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/src/basicsr/models/archs/NAFNet_arch.py", line 141, in forward
x = encoder(x)
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward
input = module(input)
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/src/basicsr/models/archs/NAFNet_arch.py", line 62, in forward
x = self.norm1(x)
File "/root/.pyenv/versions/3.9.12/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
return forward_call(*input, **kwargs)
File "/src/basicsr/models/archs/arch_util.py", line 300, in forward
return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
File "/src/basicsr/models/archs/arch_util.py", line 271, in forward
var = (x - mu).pow(2).mean(1, keepdim=True)
RuntimeError: CUDA out of memory. Tried to allocate 4.27 GiB (GPU 0; 14.58 GiB total capacity; 10.46 GiB already allocated; 213.31 MiB free; 13.40 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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