Fix for ValueError: continuous format is not supported

in the indad/model.py line 78

the mask must be convert to int type, otherwise, it will get ValueError: continuous format is not supported

Here is the solution: I changed the mask type from float to int

pixel_labels.extend(mask.flatten().numpy().astype(int))

The initial function is like below

	def evaluate(self, test_ds: VisionDataset) -> Tuple[float, float]:
		"""Calls predict step for each test sample."""
		image_preds = []
		image_labels = []
		pixel_preds = []
		pixel_labels = []

		for sample, mask, label in tqdm(test_ds, **TQDM_PARAMS):
			z_score, fmap = self.predict(sample.unsqueeze(0))
			
			image_preds.append(z_score.numpy())
			image_labels.append(label)
			
			pixel_preds.extend(fmap.flatten().numpy())
			pixel_labels.extend(mask.flatten().numpy())
			
		image_preds = np.stack(image_preds)

		image_rocauc = roc_auc_score(image_labels, image_preds)

		pixel_rocauc = roc_auc_score(pixel_labels, pixel_preds)

		return image_rocauc, pixel_rocauc

[rvorias]

FYI, the reduced datasets are not supposed to be run with the evaluate function. You better choose just “hazelnut” or “transistor” (the data is automatically downloaded)

[rvorias]

The evaluation function comes in handy as it can give you a threshold for a certain desired recall. If you want to visualize your predictions, it’s easier to call

# get predictions
img_lvl_anom_score, pxl_lvl_anom_score = model.predict(test_ds[0].unsqueeze(0))

Or something like that. You can then use the threshold that you got from your aurocs to threshold the faulty pixels. Right now, you’ll have to extract the best threshold yourself.