fix LR bug
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762b06de4b
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7
train.py
7
train.py
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@ -101,6 +101,9 @@ def train(hyp):
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optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
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optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
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optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
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# Scheduler https://arxiv.org/pdf/1812.01187.pdf
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lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
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del pg0, pg1, pg2
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@ -144,9 +147,7 @@ def train(hyp):
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if mixed_precision:
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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# Scheduler https://arxiv.org/pdf/1812.01187.pdf
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lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler.last_epoch = start_epoch - 1 # do not move
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# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
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# plot_lr_scheduler(optimizer, scheduler, epochs)
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