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import json
import os
import datasets
import torch
class COCOBuilderConfig(datasets.BuilderConfig):
def __init__(self, name, splits, **kwargs):
super().__init__(name, **kwargs)
self.splits = splits
# Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{DBLP:journals/corr/LinMBHPRDZ14,
author = {Tsung{-}Yi Lin and
Michael Maire and
Serge J. Belongie and
Lubomir D. Bourdev and
Ross B. Girshick and
James Hays and
Pietro Perona and
Deva Ramanan and
Piotr Doll{'{a} }r and
C. Lawrence Zitnick},
title = {Microsoft {COCO:} Common Objects in Context},
journal = {CoRR},
volume = {abs/1405.0312},
year = {2014},
url = {http://arxiv.org/abs/1405.0312},
archivePrefix = {arXiv},
eprint = {1405.0312},
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
# Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
COCO is a large-scale object detection, segmentation, and captioning dataset.
"""
# Add a link to an official homepage for the dataset here
_HOMEPAGE = "http://cocodataset.org/#home"
# Add the licence for the dataset here if you can find it
_LICENSE = ""
# Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# This script is supposed to work with local (downloaded) COCO dataset.
_URLs = {}
# Name of the dataset usually match the script name with CamelCase instead of snake_case
class COCODataset(datasets.GeneratorBasedBuilder):
"""An example dataset script to work with the local (downloaded) COCO dataset"""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIG_CLASS = COCOBuilderConfig
BUILDER_CONFIGS = [
COCOBuilderConfig(name='2017', splits=['train', 'valid', 'test']),
]
DEFAULT_CONFIG_NAME = "2017"
def _info(self):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
feature_dict = {
"image_id": datasets.Value("int64"),
"caption_id": datasets.Value("int64"),
"caption": datasets.Value("string"),
"height": datasets.Value("int64"),
"width": datasets.Value("int64"),
"file_name": datasets.Value("string"),
"coco_url": datasets.Value("string"),
"image_path": datasets.Value("string"),
"category_ids": datasets.Sequence(datasets.Value("int64")),
"category_one_hot": datasets.Sequence(datasets.Value("int64")),
}
features = datasets.Features(feature_dict)
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=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
data_dir = self.config.data_dir
if not data_dir:
raise ValueError(
"This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
)
_DL_URLS = {
"train": os.path.join(data_dir, "train2017.zip"),
"val": os.path.join(data_dir, "val2017.zip"),
"test": os.path.join(data_dir, "test2017.zip"),
"annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
"image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
}
splits = []
for split in self.config.splits:
if split == 'train':
dataset = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"captions_json_path": os.path.join(data_dir, "annotations", "captions_train2017.json"),
"instances_json_path": os.path.join(data_dir, "annotations", "instances_train2017.json"),
"image_dir": os.path.join(data_dir, "train2017"),
"split": "train",
}
)
elif split in ['val', 'valid', 'validation', 'dev']:
dataset = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"captions_json_path": os.path.join(data_dir, "annotations", "captions_val2017.json"),
"instances_json_path": os.path.join(data_dir, "annotations", "instances_val2017.json"),
"image_dir": os.path.join(data_dir, "val2017"),
"split": "valid",
},
)
elif split == 'test':
dataset = datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"captions_json_path": os.path.join(data_dir, "annotations", "image_info_test2017.json"),
"instances_json_path": os.path.join(data_dir, "annotations", "image_info_test2017.json"), # "instances_test2017.json
"image_dir": os.path.join(data_dir, "test2017"),
"split": "test",
},
)
else:
continue
splits.append(dataset)
return splits
# instances.json
# {
# "info": {
# "year": "2020",
# "version": "1",
# "description": "Exported from roboflow.ai",
# "contributor": "Roboflow",
# "url": "https://app.roboflow.ai/datasets/hard-hat-sample/1",
# "date_created": "2000-01-01T00:00:00+00:00"
# },
# "licenses": [
# {
# "id": 1,
# "url": "https://creativecommons.org/publicdomain/zero/1.0/",
# "name": "Public Domain"
# }
# ],
# "categories": [
# {
# "id": 0,
# "name": "Workers",
# "supercategory": "none"
# },
# {
# "id": 1,
# "name": "head",
# "supercategory": "Workers"
# },
# {
# "id": 2,
# "name": "helmet",
# "supercategory": "Workers"
# },
# {
# "id": 3,
# "name": "person",
# "supercategory": "Workers"
# }
# ],
# "images": [
# {
# "id": 0,
# "license": 1,
# "file_name": "0001.jpg",
# "height": 275,
# "width": 490,
# "date_captured": "2020-07-20T19:39:26+00:00"
# }
# ],
# "annotations": [
# {
# "id": 0,
# "image_id": 0,
# "category_id": 2,
# "bbox": [
# 45,
# 2,
# 85,
# 85
# ],
# "area": 7225,
# "segmentation": [],
# "iscrowd": 0
# },
# {
# "id": 1,
# "image_id": 0,
# "category_id": 2,
# "bbox": [
# 324,
# 29,
# 72,
# 81
# ],
# "area": 5832,
# "segmentation": [],
# "iscrowd": 0
# }
# ]
# }
def _generate_examples(
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
self, captions_json_path, instances_json_path, image_dir, split
):
""" Yields examples as (key, example, categories) tuples. """
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
_features = ["image_id", "caption_id", "caption", "height", "width", "file_name", "coco_url", "image_path", "id", "category_ids", "category_one_hot"]
features = list(_features)
if split in "valid":
split = "val"
with open(captions_json_path, 'r', encoding='UTF-8') as fp:
captions_data = json.load(fp)
with open(instances_json_path, 'r', encoding='UTF-8') as fp:
instances_data = json.load(fp)
# list of dict
images = captions_data["images"]
instances_annotations = instances_data["annotations"]
entries = images
self.classes = list(map(lambda x: {'id': x['id'], 'name': x['name']}, instances_data['categories']))
self.num_classes = len(self.classes)
# build a dict of image_id -> image info dict
d = {image["id"]: image for image in images}
# build a dict of image_id -> list of category_ids
cat_ids_dict = {}
for annotation in instances_annotations:
image_id = annotation["image_id"]
category_id = annotation["category_id"]
if image_id not in cat_ids_dict:
cat_ids_dict[image_id] = set([])
cat_ids_dict[image_id].add(category_id)
# list of dict
if split in ["train", "val"]:
annotations = captions_data["annotations"]
# build a dict of image_id ->
for annotation in annotations:
_id = annotation["id"]
image_id = annotation["image_id"]
image_info = d[image_id]
annotation.update(image_info)
annotation["id"] = _id
# Add the category_ids to the annotation
annotation["category_ids"] = cat_ids_dict[annotation["image_id"]] if annotation["image_id"] in cat_ids_dict else []
annotation['category_one_hot'] = torch.zeros(len(self.classes))
for category_id in annotation["category_ids"]:
# Get index of category_id in self.classes
index = next((index for (index, d) in enumerate(self.classes) if d["id"] == category_id), None)
annotation['category_one_hot'][index] = 1
entries = annotations
for id_, entry in enumerate(entries):
entry = {k: v for k, v in entry.items() if k in features}
if split == "test":
entry["image_id"] = entry["id"]
entry["id"] = -1
entry["caption"] = -1
entry["caption_id"] = entry.pop("id")
entry["image_path"] = os.path.join(image_dir, entry["file_name"])
entry = {k: entry[k] for k in _features if k in entry}
yield str((entry["image_id"], entry["caption_id"])), entry
from datasets import load_dataset
if __name__ == "__main__":
dataset = load_dataset(
"coco_dataset_multi_label_script/coco_dataset_multi_label_script.py",
"2017",
keep_in_memory=False,
splits=["valid"],
data_dir="/workspace/pixt/clip-training/data/mscoco",
)
print(dataset["validation"][0])
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