Update test.py profiling (#3555)
* Update test.py profiling * half_precision to half * inplace
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@@ -74,7 +74,7 @@ def train(hyp, opt, device, tb_writer=None):
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loggers['wandb'] = wandb_logger.wandb
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data_dict = wandb_logger.data_dict
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if wandb_logger.wandb:
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # may update weights, epochs if resuming
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nc = 1 if single_cls else int(data_dict['nc']) # number of classes
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names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
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@@ -354,18 +354,18 @@ def train(hyp, opt, device, tb_writer=None):
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final_epoch = epoch + 1 == epochs
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if not opt.notest or final_epoch: # Calculate mAP
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wandb_logger.current_epoch = epoch + 1
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results, maps, times = test.test(data_dict,
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batch_size=batch_size * 2,
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imgsz=imgsz_test,
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model=ema.ema,
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single_cls=single_cls,
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dataloader=testloader,
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save_dir=save_dir,
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save_json=is_coco and final_epoch,
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verbose=nc < 50 and final_epoch,
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plots=plots and final_epoch,
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wandb_logger=wandb_logger,
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compute_loss=compute_loss)
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results, maps, _ = test.test(data_dict,
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batch_size=batch_size * 2,
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imgsz=imgsz_test,
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model=ema.ema,
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single_cls=single_cls,
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dataloader=testloader,
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save_dir=save_dir,
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save_json=is_coco and final_epoch,
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verbose=nc < 50 and final_epoch,
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plots=plots and final_epoch,
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wandb_logger=wandb_logger,
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compute_loss=compute_loss)
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# Write
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with open(results_file, 'a') as f:
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