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ultralytics 8.0.179 base Model class from nn.Module (#4911)

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Glenn Jocher 2023-09-15 02:39:09 +02:00 • committed by GitHub
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7 changed files with 101 additions and 56 deletions
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22
ultralytics/utils/benchmarks.py
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@ -100,10 +100,10 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
# Export
if format == '-':
filename = model.ckpt_path or model.cfg
export = model # PyTorch format
exported_model = model # PyTorch format
else:
filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device, verbose=False)
export = YOLO(filename, task=model.task)
exported_model = YOLO(filename, task=model.task)
assert suffix in str(filename), 'export failed'
emoji = '❎' # indicates export succeeded
@ -111,19 +111,19 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
assert model.task != 'pose' or i != 7, 'GraphDef Pose inference is not supported'
assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
export.predict(ASSETS / 'bus.jpg', imgsz=imgsz, device=device, half=half)
exported_model.predict(ASSETS / 'bus.jpg', imgsz=imgsz, device=device, half=half)
# Validate
data = data or TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
key = TASK2METRIC[model.task] # task to metric, i.e. metrics/mAP50-95(B) for task=detect
results = export.val(data=data,
batch=1,
imgsz=imgsz,
plots=False,
device=device,
half=half,
int8=int8,
verbose=False)
results = exported_model.val(data=data,
batch=1,
imgsz=imgsz,
plots=False,
device=device,
half=half,
int8=int8,
verbose=False)
metric, speed = results.results_dict[key], results.speed['inference']
y.append([name, '✅', round(file_size(filename), 1), round(metric, 4), round(speed, 2)])
except Exception as e: