how to show the predict result or test result?

[Phyonna]

like this image or figure 8 in the article. it's really hard for me .

[riverLaugh]

I have the same question.What does the "#BUG" means in main.py?

[Parkkkkk]

Edit your code

def train(self, training_data, test_data):

    state_dict = {}

    **ckpt_path = os.path.join(
        "your_ckpt_path")**
    if os.path.exists(ckpt_path):
        state_dict = torch.load(ckpt_path, map_location=self.device)
        if 'discriminator' in state_dict:
            self.discriminator.load_state_dict(state_dict['discriminator'])
            if "pre_projection" in state_dict:
                self.pre_projection.load_state_dict(state_dict["pre_projection"])
        else:
            self.load_state_dict(state_dict, strict=False)

        # self.predict(training_data, "train_")
        scores, segmentations, features, labels_gt, masks_gt = self.predict(test_data)
        auroc, full_pixel_auroc, anomaly_pixel_auroc = self._evaluate(test_data, scores, segmentations, features, labels_gt, masks_gt)
        **self.save_segmentation_images(test_data, segmentations, scores)**

        return auroc, full_pixel_auroc, anomaly_pixel_auroc

[Parkkkkk]

The code appears to have a limitation: it relies on the availability of ground truth masks for inference. Additionally, upon inspecting the figures, the mask images extracted seem to be identical to the Ground Truth (GT) masks. Therefore, I have concerns about the reliability of the inference results in this code.