Optimize train_CSL_graph_classification.py #84
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In both the training and evaluation loops, there are unnecessary calls to `loss.detach().item()`. You can calculate the loss without detaching it and only detach it if necessary at a later stage. In the train_epoch_dense function, you can remove the manual batch handling using `(iter % batch_size)` and instead rely on the `batch_size` parameter of the `data_loader`. The DataLoader automatically handles the batch iteration for you. In both the `train_epoch_sparse` and `train_epoch_dense` functions, you can move the `optimizer.zero_grad()` call outside the loop, just before the loop starts. This will avoid unnecessary repeated calls to `zero_grad()`.
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In both the training and evaluation loops, there are unnecessary calls to
loss.detach().item(). You can calculate the loss without detaching it and only detach it if necessary at a later stage. In the train_epoch_dense function, you can remove the manual batch handling using(iter % batch_size)and instead rely on thebatch_sizeparameter of thedata_loader. The DataLoader automatically handles the batch iteration for you. In both thetrain_epoch_sparseandtrain_epoch_densefunctions, you can move theoptimizer.zero_grad()call outside the loop, just before the loop starts. This will avoid unnecessary repeated calls tozero_grad().