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# Dataloader |
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# Dataloader |
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if not training: |
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if not training: |
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if pt and not single_cls: # check --weights are trained on --data |
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ncm = model.model.yaml['nc'] |
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assert ncm == nc, f'{weights[0]} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ |
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f'classes). Pass correct combination of --weights and --data that are trained together.' |
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model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup |
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model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup |
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pad = 0.0 if task in ('speed', 'benchmark') else 0.5 |
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pad = 0.0 if task in ('speed', 'benchmark') else 0.5 |
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rect = False if task == 'benchmark' else pt # square inference for benchmarks |
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rect = False if task == 'benchmark' else pt # square inference for benchmarks |