Autofix duplicate label handling (#5210)
* Autofix duplicate labels PR changes duplicate label handling from report error and ignore image-label pair to report warning and autofix image-label pair. This should fix this common issue for users and allow everyone to get started and get a model trained faster and easier than before. * sign fix * Cleanup * Increment cache version * all to any fixpull/5212/head
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fc3606420d
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991c654e81
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@ -375,7 +375,7 @@ def img2label_paths(img_paths):
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class LoadImagesAndLabels(Dataset):
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# YOLOv5 train_loader/val_loader, loads images and labels for training and validation
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cache_version = 0.5 # dataset labels *.cache version
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cache_version = 0.6 # dataset labels *.cache version
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def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
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cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
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@ -897,7 +897,7 @@ def verify_image_label(args):
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f.seek(-2, 2)
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if f.read() != b'\xff\xd9': # corrupt JPEG
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Image.open(im_file).save(im_file, format='JPEG', subsampling=0, quality=100) # re-save image
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msg = f'{prefix}WARNING: corrupt JPEG restored and saved {im_file}'
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msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
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# verify labels
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if os.path.isfile(lb_file):
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@ -909,11 +909,15 @@ def verify_image_label(args):
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segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...)
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l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
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l = np.array(l, dtype=np.float32)
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if len(l):
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assert l.shape[1] == 5, 'labels require 5 columns each'
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assert (l >= 0).all(), 'negative labels'
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
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assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
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nl = len(l)
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if nl:
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assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected'
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assert (l >= 0).all(), f'negative label values {l[l < 0]}'
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assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}'
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l = np.unique(l, axis=0) # remove duplicate rows
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if len(l) < nl:
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segments = np.unique(segments, axis=0)
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msg = f'{prefix}WARNING: {im_file}: {nl - len(l)} duplicate labels removed'
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else:
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ne = 1 # label empty
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l = np.zeros((0, 5), dtype=np.float32)
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@ -923,7 +927,7 @@ def verify_image_label(args):
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return im_file, l, shape, segments, nm, nf, ne, nc, msg
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except Exception as e:
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nc = 1
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msg = f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}'
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msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'
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return [None, None, None, None, nm, nf, ne, nc, msg]
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