PaddlePaddle/PaddleOCR · error · TypeError

The input data is inconsistent with expectations.

Error message

The input data is inconsistent with expectations.

What it means

Raised by the ocr_rec hubserving module's predict method when the images/paths input contract is violated. The method requires exactly one non-empty list: either `images` (list of numpy image arrays) or `paths` (list of image file paths). Any other combination raises TypeError before any inference runs.

Source

Thrown at deploy/hubserving/ocr_rec/module.py:112

            images.append(img)
        return images

    def predict(self, images=[], paths=[]):
        """
        Get the text box in the predicted images.
        Args:
            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
            paths (list[str]): The paths of images. If paths not images
        Returns:
            res (list): The result of text detection box and save path of images.
        """

        if images != [] and isinstance(images, list) and paths == []:
            predicted_data = images
        elif images == [] and isinstance(paths, list) and paths != []:
            predicted_data = self.read_images(paths)
        else:
            raise TypeError("The input data is inconsistent with expectations.")

        assert (
            predicted_data != []
        ), "There is not any image to be predicted. Please check the input data."

        img_list = []
        for img in predicted_data:
            if img is None:
                continue
            img_list.append(img)

        rec_res_final = []
        try:
            rec_res, predict_time = self.text_recognizer(img_list)
            for dno in range(len(rec_res)):
                text, score = rec_res[dno]
                rec_res_final.append(
                    {

View on GitHub (pinned to 2661c7c0ef)

Solutions

  1. Send exactly one key as a non-empty array: {"images": [...]} xor {"paths": [...]}.
  2. Wrap scalar inputs in a one-element list.
  3. Validate the payload client-side before POSTing to the served endpoint.

Example fix

# before
res = mod.predict(images=arr, paths=[])  # ndarray, not list -> TypeError

# after
res = mod.predict(images=[arr], paths=[])
Defensive patterns

Strategy: validation

Validate before calling

def valid_rec_request(images, paths) -> bool:
    return (isinstance(images, list) and images and paths == []) or (
        isinstance(paths, list) and paths and images == []
    )

Type guard

def as_list(v):
    if isinstance(v, list):
        return v
    return [v]  # wrap scalars/arrays so predict never sees a bare value

Try / catch

try:
    res = mod.predict(images=images, paths=paths)
except TypeError as e:
    if "inconsistent" in str(e):
        return [], 400  # bad request, log payload for triage
    raise

Prevention

When it happens

Trigger: Both images and paths supplied; both empty; either argument not a list (e.g. paths as a bare string or images as a single ndarray).

Common situations: Sending a recognition request with {"paths": "img.jpg"} instead of an array; batching scripts that pass images and paths together for logging purposes; empty request bodies from upstream services.

Related errors

AI-assisted analysis of PaddlePaddle/PaddleOCR@2661c7c0ef (2026-08-14). Data as JSON: /api/errors/1af5d6b1dfe5e8ad. Report an issue: GitHub.