The __getitem__ function may have some problem.

[qqwqqw689]

the original __getitem__ function.

    def __getitem__(self, batch_index: int) -> [torch.Tensor, int]:
        image_dir, image_name = self.image_file_paths[batch_index].split(self.delimiter)[-2:]
        # Read a batch of image data
        if image_name.split(".")[-1].lower() in IMG_EXTENSIONS:
            image = cv2.imread(self.image_file_paths[batch_index])
            target = self.class_to_idx[image_dir]
        else:
            raise ValueError(f"Unsupported image extensions, Only support `{IMG_EXTENSIONS}`, "
                             "please check the image file extensions.")

        # BGR to RGB
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # OpenCV convert PIL
        image = Image.fromarray(image)

        # Data preprocess
        image = self.pre_transform(image)

        # Convert image data into Tensor stream format (PyTorch).
        # Note: The range of input and output is between [0, 1]
        tensor = imgproc.image_to_tensor(image, False, False)

        # Data postprocess
        tensor = self.post_transform(tensor)

        return {"image": tensor, "target": target}

But according to pytorch doc,

the accurate form should be:

    def __getitem__(self, batch_index: int) :
        image_dir, image_name = self.image_file_paths[batch_index].split(self.delimiter)[-2:]
        # Read a batch of image data
        if image_name.split(".")[-1].lower() in IMG_EXTENSIONS:
            image = cv2.imread(self.image_file_paths[batch_index])
            target = self.class_to_idx[image_dir]
        else:
            raise ValueError(f"Unsupported image extensions, Only support `{IMG_EXTENSIONS}`, "
                             "please check the image file extensions.")

        # BGR to RGB
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

        # OpenCV convert PIL
        image = Image.fromarray(image)

        # Data preprocess
        image = self.pre_transform(image)

        # Convert image data into Tensor stream format (PyTorch).
        # Note: The range of input and output is between [0, 1]
        tensor = imgproc.image_to_tensor(image, False, False)

        # Data postprocess
        tensor = self.post_transform(tensor)

        return tensor, target

PS: I didn't run the code to experiment this idea.