weight_decay fix
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31d807e589
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15
train.py
15
train.py
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@ -258,9 +258,6 @@ def train():
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# Compute loss
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# Compute loss
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loss, loss_items = compute_loss(pred, targets, model)
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loss, loss_items = compute_loss(pred, targets, model)
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if torch.isnan(loss):
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print('WARNING: nan loss detected, ending training', loss_items)
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return results
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# Scale loss by nominal batch_size of 64
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# Scale loss by nominal batch_size of 64
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loss *= batch_size / 64
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loss *= batch_size / 64
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@ -282,8 +279,14 @@ def train():
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mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
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mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
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s = ('%10s' * 2 + '%10.3g' * 6) % (
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s = ('%10s' * 2 + '%10.3g' * 6) % (
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'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
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'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
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pbar.set_description(s) # end batch -----------------------------------------------------------------------
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pbar.set_description(s)
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if torch.isnan(loss):
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print('WARNING: nan loss detected, ending training', loss_items)
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return results
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# end batch ------------------------------------------------------------------------------------------------
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# Process epoch results
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final_epoch = epoch + 1 == epochs
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final_epoch = epoch + 1 == epochs
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if opt.prebias:
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if opt.prebias:
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print_model_biases(model)
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print_model_biases(model)
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@ -342,7 +345,9 @@ def train():
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torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
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torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
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# Delete checkpoint
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# Delete checkpoint
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del chkpt # end epoch -------------------------------------------------------------------------------------
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del chkpt
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# end epoch ----------------------------------------------------------------------------------------------------
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# Report time
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# Report time
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plot_results() # save as results.png
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plot_results() # save as results.png
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