First version of the Visual attributes in the wild (VAW) dataset.

README.md

@@ -1,3 +1,29 @@
----
-license: other
----
+# Dataset Card for Visual Attributes in the Wild (VAW)
+
+## Dataset Description
+
+- **Homepage:** http://vawdataset.com/
+- **Repository:** https://github.com/adobe-research/vaw_dataset;
+    Note: The raw dataset files will be downloaded from: https://github.com/adobe-research/vaw_dataset/tree/main/data, where one can also find additional metadata files such as attribute types. The train split loaded from this hf dataset is a concatenation of the train_part1.json and train_part2.json.
+- **Paper 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}
+    }
+    ```
+- **LICENSE:** https://github.com/adobe-research/vaw_dataset/blob/main/LICENSE.md
+
+
+### Dataset Summary
+A large scale visual attributes dataset with explicitly labelled positive and negative attributes.
+
+- 620 Unique Attributes including color, shape, texture, posture and many others
+- 260,895 Instances of different objects
+- 2260 Unique Objects observed in the wild
+- 72,274 Images from the Visual Genome Dataset
+- 4 different evaluation metrics for measuring multi-faceted performance metrics

vaw.py

@@ -0,0 +1,220 @@
+# 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 NewDataset(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"]
+            }