updates
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train.py
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train.py
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@ -138,15 +138,11 @@ def train():
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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# lf = lambda x: 1 - x / epochs # linear ramp to zero
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# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
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# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
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lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.99 + 0.01 # cosine https://arxiv.org/pdf/1812.01187.pdf
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lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.99 + 0.01 # cosine https://arxiv.org/pdf/1812.01187.pdf
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf, last_epoch=start_epoch - 1)
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# scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(epochs * x) for x in [0.8, 0.9]], gamma=0.1)
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# scheduler = lr_scheduler.MultiStepLR(optimizer, [round(epochs * x) for x in [0.8, 0.9]], 0.1, start_epoch - 1)
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scheduler.last_epoch = start_epoch
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# # Plot lr schedule
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# Plot lr schedule
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# y = []
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# y = []
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# for _ in range(epochs):
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# for _ in range(epochs):
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# scheduler.step()
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# scheduler.step()
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