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])