updates
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@ -194,11 +194,14 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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for i, file in enumerate(iter):
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for i, file in enumerate(iter):
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try:
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try:
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with open(file, 'r') as f:
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with open(file, 'r') as f:
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self.labels[i] = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
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assert self.labels[i].shape[1] == 5, 'corrupted labels file: %s' % file
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if l.shape[0]:
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assert l.shape[1] == 5, '> 5 label columns: %s' % file
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assert (l >= 0).all(), 'negative labels: %s' % file
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
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self.labels[i] = l
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except:
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except:
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print('Warning: missing labels for %s' % self.img_files[i])
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print('Warning: missing labels for %s' % self.img_files[i]) # missing label file
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pass # missing label file
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assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
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assert len(np.concatenate(self.labels, 0)) > 0, 'No labels found. Incorrect label paths provided.'
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def __len__(self):
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def __len__(self):
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