Support multi-GPU evaluation #50
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Attempting to evaluate any trained models with multiple GPUs will throw errors indicating the model being used does not contain a 'module' attribute, preventing the code from running concurrently on multiple GPUs. Following the layout of the training scripts, the evaluation code should instead leverage PyTorch's parallel neural network library when instantiating the model, and deploy the model on the requested device (preferably GPUs if applicable). Signed-Off-By: Robert Clark <[email protected]>
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BTW, I know you are incredibly busy @Tramac, but I believe this should resolve a few outstanding issues in the repository which might make life a little easier. Otherwise, let me know if there's anything you would like me to update! Thanks! |
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Happy to help out! Thank YOU for your awesome work on getting this repository together! This is by far the most complete collection of semantic segmentation scripts/tools that I've found on GitHub. |
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Attempting to evaluate any trained models with multiple GPUs will throw errors indicating the model being used does not contain a 'module' attribute, preventing the code from running concurrently on multiple GPUs. Following the layout of the training scripts, the evaluation code should instead leverage PyTorch's parallel neural network library when instantiating the model, and deploy the model on the requested device
(preferably GPUs if applicable).
Signed-Off-By: Robert Clark [email protected]