File size: 5,302 Bytes
463f42a 85a1d8a 463f42a 85a1d8a 463f42a 85a1d8a 463f42a 85a1d8a 463f42a 85a1d8a 463f42a 85a1d8a 463f42a 85a1d8a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import json
import os
import datasets
import numpy as np
_DESCRIPTION = """
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding
"""
_HOMEPAGE = "https://github.com/llvm-ad/maplm"
_LICENSE = "https://github.com/LLVM-AD/MAPLM/blob/main/LICENSE"
_CITATION = """\
@inproceedings{cao_maplm_2024,
title = {{MAPLM}: {A} {Real}-{World} {Large}-{Scale} {Vision}-{Language} {Dataset} for {Map} and {Traffic} {Scene} {Understanding}},
booktitle = {{CVPR}},
author = {Cao, Xu and Zhou, Tong and Ma, Yunsheng and Ye, Wenqian and Cui, Can and Tang, Kun and Cao, Zhipeng and Liang, Kaizhao and Wang, Ziran and Rehg, James M. and Zheng, Chao},
year = {2024},
}
"""
class MapLMBuilderConfig(datasets.BuilderConfig):
"""BuilderConfig for MapLM dataset."""
def __init__(self, name, splits):
super(MapLMBuilderConfig, self).__init__(name=name)
self.splits = splits
class MapLMDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = MapLMBuilderConfig
BUILDER_CONFIGS = [
MapLMBuilderConfig(
name="v2.0",
splits=["train", "val", "test"],
)
]
DEFAULT_CONFIG_NAME = "v2.0"
def _info(self):
# info stores information about your dataset like its description, license, and features.
feature_dict = {
"frame_id": datasets.Value("string"),
"images": datasets.Sequence(datasets.Value("string")),
"question": datasets.Sequence(datasets.Value("string")),
"options": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
"answer": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
"tag": datasets.Sequence(datasets.Value("string")),
}
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=datasets.Features(feature_dict),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
splits = []
data_root = dl_manager.download("data/")
for split in self.config.splits:
annotation_file = os.path.join(data_root, f"{split}_v2.json")
annotations = json.load(open(annotation_file))
if split == "test":
generator = datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"annotations": annotations},
)
elif split == "train":
generator = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"annotations": annotations},
)
elif split == "val":
generator = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"annotations": annotations},
)
else:
continue
splits.append(generator)
return splits
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, annotations):
for i, anno_key in enumerate(annotations):
data_item = {}
data_item["frame_id"] = annotations[anno_key]["id"]
data_item["images"] = list(annotations[anno_key]["image_paths"].values())
data_item["question"] = []
data_item["options"] = []
data_item["answer"] = []
data_item["tag"] = []
for perception_key in annotations[anno_key]["QA"]["perception"]:
data_item["question"].append(
annotations[anno_key]["QA"]["perception"][perception_key][
"question"
]
)
data_item["options"].append(
annotations[anno_key]["QA"]["perception"][perception_key]["option"]
)
anno_answer = annotations[anno_key]["QA"]["perception"][perception_key][
"answer"
]
if isinstance(anno_answer, list):
data_item["answer"].append(anno_answer)
else:
data_item["answer"].append([anno_answer])
data_item["tag"].append(
annotations[anno_key]["QA"]["perception"][perception_key]["tag"]
)
for behavior_key in annotations[anno_key]["QA"]["behavior"]:
data_item["question"].append(
annotations[anno_key]["QA"]["behavior"][behavior_key]["question"]
)
data_item["options"].append(
annotations[anno_key]["QA"]["behavior"][behavior_key]["option"]
)
data_item["answer"].append(
annotations[anno_key]["QA"]["behavior"][behavior_key]["answer"]
)
data_item["tag"].append(
annotations[anno_key]["QA"]["behavior"][behavior_key]["tag"]
)
yield i, data_item
|