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train.py
5
train.py
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@ -119,7 +119,7 @@ def train(
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# plt.savefig('LR.png', dpi=300)
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, image_weighting=False)
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dataset = LoadImagesAndLabels(train_path, img_size, batch_size, augment=True, image_weights=False)
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# Initialize distributed training
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if torch.cuda.device_count() > 1:
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@ -167,7 +167,8 @@ def train(
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p.requires_grad = False if epoch == 0 else True
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# Update image weights (optional)
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image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=1 - maps)
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w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
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image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
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dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
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mloss = torch.zeros(5).to(device) # mean losses
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