vaw.py

# 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"]
            }