| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Visual Attributes in the Wild (VAW) dataset""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @InProceedings{Pham_2021_CVPR, | |
| author = {Pham, Khoi and Kafle, Kushal and Lin, Zhe and Ding, Zhihong and Cohen, Scott and Tran, Quan and Shrivastava, Abhinav}, | |
| title = {Learning To Predict Visual Attributes in the Wild}, | |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| month = {June}, | |
| year = {2021}, | |
| pages = {13018-13028} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| Visual Attributes in the Wild (VAW) dataset: https://github.com/adobe-research/vaw_dataset#dataset-setup | |
| Raw annotations and configs such as attrubte_types can be found at: https://github.com/adobe-research/vaw_dataset/tree/main/data | |
| Note: The train split loaded from this hf dataset is a concatenation of the train_part1.json and train_part2.json. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "http://vawdataset.com/" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "https://github.com/adobe-research/vaw_dataset/blob/main/LICENSE.md" | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| # _URLS = { | |
| # # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", | |
| # # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", | |
| # } | |
| # _URL = "https://github.com/adobe-research/vaw_dataset/blob/main/data/" | |
| _URL = "https://raw.githubusercontent.com/adobe-research/vaw_dataset/main/data/" | |
| _URLS = { | |
| "train": { | |
| "part1": _URL + "train_part1.json", | |
| "part2": _URL + "train_part2.json" | |
| }, | |
| "val": _URL + "val.json", | |
| "test": _URL + "test.json" | |
| } | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class VAW(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| # BUILDER_CONFIGS = [ | |
| # datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), | |
| # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| # if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option1": datasets.Value("string"), | |
| # "answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| # else: # This is an example to show how to have different features for "first_domain" and "second_domain" | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option2": datasets.Value("string"), | |
| # "second_domain_answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| features = datasets.Features( | |
| { | |
| "image_id": datasets.Value("string"), # int (Image ids correspond to respective Visual Genome image ids) | |
| "instance_id": datasets.Value("string"), # int (Unique instance ID) | |
| "instance_bbox": datasets.features.Sequence(datasets.Value("float")), # [x, y, width, height] (Bounding box co-ordinates for the instance) | |
| "instance_polygon": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Sequence(datasets.Value("float")))) , # list of [x y] (List of vertices for segmentation polygon if exists else None) | |
| "object_name": datasets.Value("string"), # str (Name of the object for the instance) | |
| "positive_attributes": datasets.features.Sequence(datasets.Value("string")) , # list of str (Explicitly labeled positive attributes for the instance) | |
| "negative_attributes": datasets.features.Sequence(datasets.Value("string")) # list of str (Explicitly labeled negative attributes for the instance) | |
| } | |
| ) | |
| 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, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # 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): | |
| # TODO: 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 | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| # urls = _URLS[self.config.name] | |
| # data_dir = dl_manager.download_and_extract(urls) | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| print("downloaded_files: ", downloaded_files) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["train"], | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["val"], | |
| "split": "val", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["test"], | |
| "split": "test" | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| # with open(filepath, encoding="utf-8") as f: | |
| # for key, row in enumerate(f): | |
| # data = json.loads(row) | |
| # if self.config.name == "first_domain": | |
| # # Yields examples as (key, example) tuples | |
| # yield key, { | |
| # "sentence": data["sentence"], | |
| # "option1": data["option1"], | |
| # "answer": "" if split == "test" else data["answer"], | |
| # } | |
| # else: | |
| # yield key, { | |
| # "sentence": data["sentence"], | |
| # "option2": data["option2"], | |
| # "second_domain_answer": "" if split == "test" else data["second_domain_answer"], | |
| # } | |
| if split == "train": | |
| # concat part1 and part 2 files | |
| part1_data = json.load(open(filepath['part1'], encoding="utf-8")) | |
| part2_data = json.load(open(filepath['part2'], encoding="utf-8")) | |
| data = part1_data + part2_data | |
| else: | |
| data = json.load(open(filepath, encoding="utf-8")) | |
| for key, row in enumerate(data): | |
| yield key, { | |
| "image_id": row["image_id"], | |
| "instance_id": row["instance_id"], | |
| "instance_bbox": row["instance_bbox"], | |
| "instance_polygon": row["instance_polygon"], | |
| "object_name": row["object_name"], | |
| "positive_attributes": row["positive_attributes"], | |
| "negative_attributes": row["negative_attributes"] | |
| } | |