Comments (7)
ilkarman
commented on August 20, 2024
5
Freddie, also the create_patches = false in the config natually increases the memory usage.
With create_patches = true; I can run batch-size 32 with min_l and n_views = 16, which matches your memory usage predictions table. So the below works on our tiny 16GB GPUs:
"training": {
"num_epochs": 400,
"batch_size": 32,
"min_L": 16,
"n_views": 16,
"n_workers": 4,
"crop": 3,
"lr": 0.0007,
"lr_step": 2,
"lr_decay": 0.97,
"load_lr_maps": false,
"beta": 50.0,
"create_patches": true,
"patch_size": 64,
"val_proportion": 0.10,
"lambda": 0.000001
}
}
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alkalait
commented on August 20, 2024
3
That's right.
-
min_Lacts as a lower-bound on the views always present. If the input has views fewer thanmin_Lthen it will be padded tomin_Lwith dummy views (0-padding). -
n_views: the number of LR views randomly* selected from the LR pool of a site.
For instance, what you want to avoid is n_views = 16 and min_L = 32 as that would always 0-pad your views to 32.
* views with larger clearance are more likely to be selected
If I were you, I'd try min_L = n_views = 16 first, then min_L = n_views = 8.
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alkalait
commented on August 20, 2024
Have you tried reducing the number of views?
Have a look at the table in the README
Increased batch size will buy you more performance, whereas 32 views has diminished returns compared to 16.
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jqueguiner
commented on August 20, 2024
Yes, already tried that :
"training": {
"num_epochs": 400,
"batch_size": 16,
"min_L": 32,
"n_views": 4,
"n_workers": 4,
"crop": 3,
"lr": 0.0007,
"lr_step": 2,
"lr_decay": 0.97,
"load_lr_maps": false,
"beta": 50.0,
"create_patches": false,
"patch_size": 64,
"val_proportion": 0.10,
"lambda": 0.000001
}gives
root@8b530a18a524:/data# python src/train.py --config config/config.json
0%| | 0/400 [00:00<?, ?it/s]
Traceback (most recent call last): | 0/66 [00:00<?, ?it/s]
File "src/train.py", line 307, in <module>
main(config)
File "src/train.py", line 293, in main
trainAndGetBestModel(fusion_model, regis_model, optimizer, dataloaders, baseline_cpsnrs, config)
File "src/train.py", line 174, in trainAndGetBestModel
srs = fusion_model(lrs, alphas) # fuse multi frames (B, 1, 3*W, 3*H)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/data/src/DeepNetworks/HRNet.py", line 207, in forward
layer1 = self.encode(stacked_input) # encode input tensor
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/data/src/DeepNetworks/HRNet.py", line 74, in forward
x = self.res_layers(x)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/data/src/DeepNetworks/HRNet.py", line 34, in forward
residual = self.block(x)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/activation.py", line 961, in forward
return F.prelu(input, self.weight)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1121, in prelu
return torch.prelu(input, weight)
RuntimeError: CUDA out of memory. Tried to allocate 2.00 GiB (GPU 0; 15.75 GiB total capacity; 14.27 GiB already allocated; 433.56 MiB free; 1.16 MiB cached)
while GPU mem was flushed just before the experiment
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ilkarman
commented on August 20, 2024
Would you also decrease min_L (minimum number of views) to match n_views? On a 16GB P100 I can just about squeeze into memory: batch=8, min_L=16 n_views=16 (but not if min_L=32)
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alkalait
commented on August 20, 2024
Indeed. That flag flew under our radar!
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jqueguiner
commented on August 20, 2024
GG @ilkarman
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Related Issues (10)
- Shared representation in fusion block
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