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ultralytics 8.0.108 add Meituan YOLOv6 models (#2811)

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Co-authored-by: Michael Currie <mcurrie@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Hicham Talaoubrid <98521878+HichTala@users.noreply.github.com>
Co-authored-by: Zlobin Vladimir <vladimir.zlobin@intel.com>
Co-authored-by: Szymon Mikler <sjmikler@gmail.com>
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Glenn Jocher 2023-05-25 00:43:32 +02:00 • committed by GitHub
parent 07b57c03c8
commit ffc0e8ccf7
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18 changed files with 233 additions and 45 deletions
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17
ultralytics/yolo/utils/torch_utils.py
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@ -162,8 +162,9 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
"""Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]."""
if not verbose:
return
n_p = get_num_params(model)
n_g = get_num_gradients(model) # number gradients
n_p = get_num_params(model) # number of parameters
n_g = get_num_gradients(model) # number of gradients
n_l = len(list(model.modules())) # number of layers
if detailed:
LOGGER.info(
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
@ -173,11 +174,12 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
(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 ''
fused = ' (fused)' if getattr(model, 'is_fused', lambda: False)() 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
yaml_file = getattr(model, 'yaml_file', '') or getattr(model, 'yaml', {}).get('yaml_file', '')
model_name = Path(yaml_file).stem.replace('yolo', 'YOLO') or 'Model'
LOGGER.info(f'{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}')
return n_l, n_p, n_g, flops
def get_num_params(model):
@ -199,8 +201,7 @@ def get_flops(model, imgsz=640):
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1E9 * 2 if thop else 0 # stride GFLOPs
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
return flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
except Exception:
return 0