updates
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15
train.py
15
train.py
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@ -167,6 +167,10 @@ def train(
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from apex import amp
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
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# Remove old results
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for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg') + 'results.txt':
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os.remove(f)
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# Start training
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model.hyp = hyp # attach hyperparameters to model
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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@ -175,8 +179,6 @@ def train(
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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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for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg'):
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os.remove(f)
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t, t0 = time.time(), time.time()
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for epoch in range(start_epoch, epochs):
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model.train()
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@ -185,7 +187,7 @@ def train(
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# Update scheduler
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scheduler.step()
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# Freeze backbone at epoch 0, unfreeze at epoch 1
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# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
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if freeze_backbone and epoch < 2:
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for name, p in model.named_parameters():
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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@ -200,7 +202,6 @@ def train(
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for i, (imgs, targets, _, _) in enumerate(dataloader):
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imgs = imgs.to(device)
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targets = targets.to(device)
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nt = len(targets)
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# Plot images with bounding boxes
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if epoch == 0 and i == 0:
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@ -233,13 +234,11 @@ def train(
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optimizer.step()
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optimizer.zero_grad()
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# Update running mean of tracked metrics
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mloss = (mloss * i + loss_items) / (i + 1)
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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) % (
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'%g/%g' % (epoch, epochs - 1),
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'%g/%g' % (i, nb - 1), *mloss, nt, time.time() - t)
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'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
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t = time.time()
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print(s)
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