facebookresearch/detectron2 · error · ValueError

Unsupported query remaining: f{queries}, orginal filename: {

Error message

Unsupported query remaining: f{queries}, orginal filename: {parsed_url.geturl()}

What it means

DetectionCheckpointer._load_file supports only the 'matching_heuristics=True' URL query parameter. After popping it, any remaining query string in the checkpoint path causes this ValueError because the loader cannot interpret other options.

Source

Thrown at detectron2/checkpoint/detection_checkpoint.py:108

                "model_state" in data
            ), f"Cannot load .pyth file {filename}; pycls checkpoints must contain 'model_state'."
            model_state = {
                k: v
                for k, v in data["model_state"].items()
                if not k.endswith("num_batches_tracked")
            }
            return {"model": model_state, "__author__": "pycls", "matching_heuristics": True}

        loaded = self._torch_load(filename)
        if "model" not in loaded:
            loaded = {"model": loaded}
        assert self._parsed_url_during_load is not None, "`_load_file` must be called inside `load`"
        parsed_url = self._parsed_url_during_load
        queries = parse_qs(parsed_url.query)
        if queries.pop("matching_heuristics", "False") == ["True"]:
            loaded["matching_heuristics"] = True
        if len(queries) > 0:
            raise ValueError(
                f"Unsupported query remaining: f{queries}, orginal filename: {parsed_url.geturl()}"
            )
        return loaded

    def _torch_load(self, f):
        return super()._load_file(f)

    def _load_model(self, checkpoint):
        if checkpoint.get("matching_heuristics", False):
            self._convert_ndarray_to_tensor(checkpoint["model"])
            # convert weights by name-matching heuristics
            checkpoint["model"] = align_and_update_state_dicts(
                self.model.state_dict(),
                checkpoint["model"],
                c2_conversion=checkpoint.get("__author__", None) == "Caffe2",
            )
        # for non-caffe2 models, use standard ways to load it
        incompatible = super()._load_model(checkpoint)

View on GitHub (pinned to a2f4a8771a)

Solutions

1. Remove unsupported query parameters from the path and configure those options in code instead
2. Pass only ?matching_heuristics=True if you need suffix matching
3. For options like device mapping, subclass DetectionCheckpointer and override _load_file/_torch_load

Example fix

# before
ckpt.load("model.pth?matching_heuristics=True&strict=False")
# after
ckpt.load("model.pth?matching_heuristics=True")  # strictness handled via model.load_state_dict yourself

from urllib.parse import urlparse, parse_qs
q = parse_qs(urlparse(path).query)
assert set(q) <= {"matching_heuristics"}, f"unsupported queries: {set(q) - {'matching_heuristics'}}"

def is_supported_ckpt_path(path: str) -> bool:
    return set(parse_qs(urlparse(path).query)) <= {"matching_heuristics"}

try:
    ckpt.load(path)
except ValueError as e:
    if "Unsupported query" in str(e):
        ckpt.load(urlparse(path).path + "?matching_heuristics=True")
    else:
        raise

When it happens

Trigger: Loading a checkpoint with a URL like 'model.pth?matching_heuristics=True&strict=False' or any path containing '?' followed by unrecognized key=value pairs.

Common situations: Trying to pass torch.load options (e.g. map_location, strict) through the checkpoint filename; copying example URLs with extra parameters; hand-crafting query strings assuming generic support.

Related errors

• Cannot match one checkpoint key to multiple keys in the mode
• File {path} not found on main worker.

AI-assisted analysis of facebookresearch/detectron2@a2f4a8771a (2026-08-27). Data as JSON: /api/errors/d34d0d0e4985e07d. Report an issue: GitHub.