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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.bool8))
y = torch.from_numpy(numpy.array(data["y"]).astype(numpy.float32))
mask_y = torch.from_numpy(numpy.array(data["mask_y"]).astype(numpy.bool8))
mask_loss = torch.from_numpy( numpy.array(data["mask_loss"]).astype(numpy.bool8))
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.bool8))
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], "mask_x": mask_x[i],
# "y": y[i], "mask_y": mask_y[i], "mask_loss": mask_loss[i],
# "map_data": map_data[i], "mask_map": mask_map[i],
# "offset": offset[i],
# "x_ego": x_ego[i],
# "y_ego": y_ego[i]}
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]}
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