batch_size = 4 num_workers = 12 mean = [-17.398721187929123, -10.020421713800838, -12.10841437771272] std = [6.290316422115964, 5.776936185931195, 5.795418280085563] max_value = 1.0 transforms = A.Compose( [A.Normalize(mean=mean, std=std, max_pixel_value=max_value), ToTensorV2()]) data_loader = get_inference_dataloader( test_dataset, transforms=transforms, batch_size=batch_size, num_workers=num_workers, pin_memory=True, ) prepare_batch = inference_prepare_batch_f32 # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) #################### Model #################### model = FPN(encoder_name='se_resnext50_32x4d', classes=2, encoder_weights=None) run_uuid = "30187583292246f6999d499642372da9" weights_filename = "best_model_43_val_miou_bg=0.7530081542328186.pth"
train_ds, val_ds, train_transforms=train_transforms, val_transforms=val_transforms, batch_size=batch_size // 2, num_workers=num_workers // 2, val_batch_size=val_batch_size, pin_memory=True, train_sampler=train_sampler, limit_train_num_samples=100 if debug else None, limit_val_num_samples=100 if debug else None) unsup_train_loader = get_inference_dataloader( test_dataset, transforms=train_transforms, batch_size=batch_size // 2, num_workers=num_workers // 2, pin_memory=True, ) # accumulation_steps = 2 prepare_batch = prepare_batch_fp32 # Image denormalization function to plot predictions with images img_denormalize = partial(denormalize, mean=mean, std=std) #################### Model #################### model = LWRefineNet(num_channels=3, num_classes=num_classes)