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ultralytics 8.0.32 HUB and TensorFlow fixes (#870)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-02-09 01:47:34 +04:00 • committed by GitHub
parent f5d003d05a
commit c9893810c7
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14 changed files with 118 additions and 85 deletions
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20
ultralytics/yolo/data/utils.py
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@ -61,7 +61,7 @@ def exif_size(img):
def verify_image_label(args):
# Verify one image-label pair
im_file, lb_file, prefix, keypoint = args
im_file, lb_file, prefix, keypoint, num_cls = args
# number (missing, found, empty, corrupt), message, segments, keypoints
nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None
try:
@ -97,16 +97,20 @@ def verify_image_label(args):
assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
kpts = np.zeros((lb.shape[0], 39))
for i in range(len(lb)):
kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5,
3)) # remove the occlusion parameter from the GT
kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5, 3)) # remove occlusion param from GT
kpts[i] = np.hstack((lb[i, :5], kpt))
lb = kpts
assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter"
else:
assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
assert (lb[:, 1:] <=
1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
assert (lb[:, 1:] <= 1).all(), \
f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
# All labels
max_cls = int(lb[:, 0].max()) # max label count
assert max_cls <= num_cls, \
f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \
f'Possible class labels are 0-{num_cls - 1}'
assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
_, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check
lb = lb[i] # remove duplicates
@ -192,8 +196,8 @@ def check_det_dataset(dataset, autodownload=True):
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)
data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
extract_dir, autodownload = data.parent, False
# Read yaml (optional)