updates
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train.py
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train.py
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@ -207,7 +207,7 @@ def train(
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if opt.multi_scale:
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if (i + 1 + nb * epoch) % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
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print('multi_scale img_size = %g' % img_size)
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print('img_size = %g' % img_size)
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scale_factor = img_size / max(imgs.shape[-2:])
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imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False)
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