updates
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10
train.py
10
train.py
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@ -62,11 +62,9 @@ def train():
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# Initialize
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init_seeds()
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multi_scale = opt.multi_scale
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if multi_scale:
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img_sz_min = round(img_size / 32 / 1.5)
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img_sz_max = round(img_size / 32 * 1.5)
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if opt.multi_scale:
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img_sz_min = round(img_size / 32 / 1.5) - 1
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img_sz_max = round(img_size / 32 * 1.5) + 1
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img_size = img_sz_max * 32 # initiate with maximum multi_scale size
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print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
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@ -241,7 +239,7 @@ def train():
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targets = targets.to(device)
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# Multi-Scale training
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if multi_scale:
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if opt.multi_scale:
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if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
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sf = img_size / max(imgs.shape[2:]) # scale factor
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