| import datasets |
| import json |
| import numpy |
| import torch |
|
|
| _DESCRIPTION = """\ |
| Dataset of pre-processed samples from a small portion of the \ |
| Waymo Open Motion Data for our risk-biased prediction task. |
| """ |
|
|
| _CITATION = """\ |
| @InProceedings{NiMe:2022, |
| author = {Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan McAllister}, |
| title = {RAP: Risk-Aware Prediction for Robust Planning}, |
| booktitle = {Proceedings of the 2022 IEEE International Conference on Robot Learning (CoRL)}, |
| month = {December}, |
| year = {2022}, |
| address = {Grafton Road, Auckland CBD, Auckland 1010}, |
| url = {}, |
| } |
| """ |
|
|
| _URL = "https://huggingface.co/datasets/jmercat/risk_biased_dataset/resolve/main/" |
| _URLS = { |
| "test": _URL + "data.json", |
| } |
|
|
| class RiskBiasedDataset(datasets.GeneratorBasedBuilder): |
| """Dataset of pre-processed samples from a portion of the |
| Waymo Open Motion Data for the risk-biased prediction task.""" |
| |
| VERSION = datasets.Version("0.0.0") |
| |
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name="risk_biased_dataset", version=VERSION, description="Dataset of pre-processed samples from a portion of the Waymo Open Motion Data for the risk-biased prediction task."), |
| ] |
| |
| DEFAULT_CONFIG_NAME = "risk_biased_dataset" |
| |
| def _info(self): |
| return datasets.DatasetInfo( |
| description= _DESCRIPTION, |
| features=datasets.Features( |
| {"x": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))), |
| "mask_x": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))), |
| "y": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))), |
| "mask_y": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))), |
| "mask_loss": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))), |
| "map_data": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))), |
| "mask_map": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("bool")))), |
| "offset": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))), |
| "x_ego": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))), |
| "y_ego": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32"))))), |
| } |
| ), |
| supervised_keys=None, |
| homepage="https://sites.google.com/view/corl-risk/home", |
| citation=_CITATION, |
| ) |
| |
| def _split_generators(self, dl_manager): |
| urls_to_download = _URLS |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
| |
| return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"], "split": "test"}),] |
| |
| def _generate_examples(self, filepath, split): |
| """Yields examples.""" |
| assert split == "test" |
| with open(filepath, "r") as f: |
| data = json.load(f) |
| |
| x = torch.from_numpy(numpy.array(data["x"]).astype(numpy.float32)) |
| mask_x = torch.from_numpy(numpy.array(data["mask_x"]).astype(numpy.bool_)) |
| y = torch.from_numpy(numpy.array(data["y"]).astype(numpy.float32)) |
| mask_y = torch.from_numpy(numpy.array(data["mask_y"]).astype(numpy.bool_)) |
| mask_loss = torch.from_numpy( numpy.array(data["mask_loss"]).astype(numpy.bool_)) |
| map_data = torch.from_numpy(numpy.array(data["map_data"]).astype(numpy.float32)) |
| mask_map = torch.from_numpy(numpy.array(data["mask_map"]).astype(numpy.bool_)) |
| offset = torch.from_numpy(numpy.array(data["offset"]).astype(numpy.float32)) |
| x_ego = torch.from_numpy(numpy.array(data["x_ego"]).astype(numpy.float32)) |
| y_ego = torch.from_numpy(numpy.array(data["y_ego"]).astype(numpy.float32)) |
| |
| batch_size = x.shape[0] |
| |
| for i in range(batch_size): |
| |
| |
| |
| |
| |
| |
| yield i, {"x": x[i:i+1], "mask_x": mask_x[i:i+1], |
| "y": y[i:i+1], "mask_y": mask_y[i:i+1], "mask_loss": mask_loss[i:i+1], |
| "map_data": map_data[i:i+1], "mask_map": mask_map[i:i+1], |
| "offset": offset[i:i+1], |
| "x_ego": x_ego[i:i+1], |
| "y_ego": y_ego[i:i+1]} |
| |
| |
|
|
