def show_results( model: nn.Module, dataset: Dataset, class_map: Optional[ClassMap] = None, detection_threshold: float = 0.5, mask_threshold: float = 0.5, num_samples: int = 6, ncols: int = 3, denormalize_fn: Optional[callable] = denormalize_imagenet, show: bool = True, device: Optional[torch.device] = None, ) -> None: return base_show_results( predict_fn=predict, build_infer_batch_fn=build_infer_batch, model=model, dataset=dataset, class_map=class_map, num_samples=num_samples, ncols=ncols, denormalize_fn=denormalize_fn, show=show, detection_threshold=detection_threshold, mask_threshold=mask_threshold, device=device, )
def show_results( model: nn.Module, dataset: Dataset, class_map: Optional[ClassMap] = None, num_samples: int = 6, ncols: int = 3, denormalize_fn: Optional[callable] = denormalize_imagenet, show: bool = True, ) -> None: return base_show_results( predict_fn=predict, build_infer_batch_fn=build_infer_batch, model=model, dataset=dataset, class_map=class_map, num_samples=num_samples, ncols=ncols, denormalize_fn=denormalize_fn, show=show, )
def show_results( model: nn.Module, dataset: Dataset, detection_threshold: float = 0.5, num_samples: int = 6, ncols: int = 3, denormalize_fn: Optional[callable] = denormalize_imagenet, show: bool = True, device: Optional[torch.device] = None, ) -> None: return base_show_results( predict_fn=predict, model=model, dataset=dataset, num_samples=num_samples, ncols=ncols, denormalize_fn=denormalize_fn, show=show, detection_threshold=detection_threshold, device=device, )