updates
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train.py
8
train.py
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@ -193,10 +193,10 @@ def train(
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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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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w = model.class_weights.cpu().numpy() * (1 - maps) # 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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# # Update image weights (optional)
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# w = model.class_weights.cpu().numpy() * (1 - maps) # 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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for i, (imgs, targets, _, _) in enumerate(dataloader):
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@ -543,7 +543,6 @@ def kmeans_targets(path='./data/coco_64img.txt'): # from utils.utils import *;
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# Plotting functions ---------------------------------------------------------------------------------------------------
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1 # line thickness
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