torchbearer
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torchbearer: A model fitting library for PyTorch
Hi! I'm trying to fork the repo and add some functionality for an experiment. But that requires an addition in the training loop. I've read the documentation and the code...
 Please check the example of Learning Rate Schedulers.
When I insert this callback in the trial I get the following error. Is this some kind of bug? It seems like the gradients are not passed in the callback....
Native automatic mixed precision support (`torch.cuda.amp`) is now in master: https://pytorch.org/docs/master/amp.html https://pytorch.org/docs/master/notes/amp_examples.html Not sure if you ever tried Nvidia's (our) experimental Apex Amp, but I know it has many pain...
When y_pred is a tuple (i.e. model returns multiple outputs) the criterion now recieves the tuple unpacked. This should either be reverted or more clearly documented.
Should include most of the experimented modes https://arxiv.org/abs/1805.11272
For most callbacks we don't need to have a string representation, but for callbacks that modify the loss or callbacks that save files, it would be good to have one...
If you pass a 4D tensor (rows, columns, height, width) to TensorBoardImages then it would be good if it could detect this and infer the make_grid parameters: num_images, nrow. Would...
Would be nice if we could kill torchbearer training with a ping or similar