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ultralytics 8.0.100 add Mosaic9() augmentation (#2605)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Tommy in Tongji <36354458+TommyZihao@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: BIGBOSS-FOX <47949596+BIGBOSS-FOX@users.noreply.github.com>
Co-authored-by: xbkaishui <xxkaishui@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-14 20:43:35 +02:00
committed by GitHub
parent db1c5885d5
commit dce4efce48
23 changed files with 351 additions and 64 deletions
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2
ultralytics/yolo/utils/checks.py
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@ -233,7 +233,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
n += 1
if s and install and AUTOINSTALL: # check environment variable
LOGGER.info(f"{prefix} YOLOv8 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
LOGGER.info(f"{prefix} Ultralytics requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
try:
assert is_online(), 'AutoUpdate skipped (offline)'
LOGGER.info(subprocess.check_output(f'pip install --no-cache {s} {cmds}', shell=True).decode())

2
ultralytics/yolo/utils/loss.py
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@ -34,7 +34,7 @@ class BboxLoss(nn.Module):
def forward(self, pred_dist, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask):
"""IoU loss."""
weight = torch.masked_select(target_scores.sum(-1), fg_mask).unsqueeze(-1)
weight = target_scores.sum(-1)[fg_mask]
iou = bbox_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask], xywh=False, CIoU=True)
loss_iou = ((1.0 - iou) * weight).sum() / target_scores_sum

1
ultralytics/yolo/utils/tal.py
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@ -189,6 +189,7 @@ class TaskAlignedAssigner(nn.Module):
for k in range(self.topk):
# Expand topk_idxs for each value of k and add 1 at the specified positions
count_tensor.scatter_add_(-1, topk_idxs[:, :, k:k + 1], ones)
# count_tensor.scatter_add_(-1, topk_idxs, torch.ones_like(topk_idxs, dtype=torch.int8, device=topk_idxs.device))
# filter invalid bboxes
count_tensor.masked_fill_(count_tensor > 1, 0)

5
ultralytics/yolo/utils/torch_utils.py
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@ -165,14 +165,15 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace('module_list.', '')
LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g' %
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g %10s' %
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype))
flops = get_flops(model, imgsz)
fused = ' (fused)' if model.is_fused() else ''
fs = f', {flops:.1f} GFLOPs' if flops else ''
m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
LOGGER.info(f'{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}')
return n_p, flops
def get_num_params(model):