updates
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11
train.py
11
train.py
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@ -167,12 +167,11 @@ def train(cfg,
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
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n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
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t, t0 = time.time(), time.time()
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torch.cuda.empty_cache()
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for epoch in range(start_epoch, epochs):
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model.train()
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print(('\n%8s%12s' + '%10s' * 7) %
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('Epoch', 'Batch', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size'))
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print(('\n%8s' + '%10s' * 8) %
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('Epoch', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size', 'gpu_mem'))
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# Update scheduler
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scheduler.step()
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@ -236,10 +235,8 @@ def train(cfg,
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# Print batch results
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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# s = ('%8s%12s' + '%10.3g' * 7) % ('%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), img_size)
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t = time.time()
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mem = torch.cuda.memory_cached() if torch.cuda.is_available() else 0
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s = ('%8s' + '%10.3g' * 8) % ('%g/%g' % (epoch, epochs - 1), *mloss, len(targets), img_size, mem)
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pbar.set_description(s) # print(s)
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# Report time
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