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train.py
4
train.py
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@ -240,12 +240,14 @@ def train():
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# Hyperparameter Burn-in
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n_burn = 200 # number of burn-in batches
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if ni <= n_burn:
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# g = ni / n_burn # gain
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# g = (ni / n_burn) ** 2 # gain
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for x in model.named_modules(): # initial stats may be poor, wait to track
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if x[0].endswith('BatchNorm2d'):
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x[1].track_running_stats = ni == n_burn
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# for x in optimizer.param_groups:
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# x['lr'] = x['initial_lr'] * lf(epoch) * g # gain rises from 0 - 1
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# if 'momentum' in x:
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# x['momentum'] = hyp['momentum'] * g
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# Plot images with bounding boxes
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if ni < 1:
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