# 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"] }