Create vasr.py

vasr.py

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+# 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.
+""" VASR Loading Script """
+
+import json
+import os
+import pandas as pd
+import datasets
+from huggingface_hub import hf_hub_url
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """
+"""
+
+_DESCRIPTION = """\
+VASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy).
+"""
+
+_HOMEPAGE = "https://vasr-dataset.github.io/"
+
+_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
+
+_URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main"
+_URLS = {
+    "train": os.path.join(_URL, "train_gold.csv"),
+    "dev": os.path.join(_URL, "dev_gold.csv"),
+    "test": os.path.join(_URL, "test_gold.csv"),
+}
+
+
+class Vasr(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.1.0")
+
+    # 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('vasr', 'test')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="TEST", version=VERSION, description="vasr dataset gold test"),
+        datasets.BuilderConfig(name="VALIDATION", version=VERSION, description="vasr dataset gold validation"),
+        datasets.BuilderConfig(name="TRAIN", version=VERSION, description="vasr dataset gold train")
+    ]
+    IMAGE_EXTENSION = "jpg"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "A_img": [datasets.Value("string")],
+                "B_img": [datasets.Value("string")],
+                "C_img": [datasets.Value("string")],
+                "candidates": [datasets.Value("string")],
+                "label": datasets.Value("int64"),
+                "D_img": [datasets.Value("string")],
+                "A_verb": [datasets.Value("string")],
+                "B_verb": [datasets.Value("string")],
+                "C_verb": [datasets.Value("string")],
+                "D_verb": [datasets.Value("string")],
+                "diff_item_A": [datasets.Value("string")],
+                "diff_item_A_str_first": [datasets.Value("string")],
+                "diff_item_B": [datasets.Value("string")],
+                "diff_item_B_str_first": [datasets.Value("string")],
+            }
+        )
+        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):
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        # 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
+        data_dir = dl_manager.download_and_extract({
+            "images_dir": hf_hub_url("datasets/nlphuji/vasr", filename="vasr_images.zip")
+        })
+
+        # return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=data_dir)]
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST,
+                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["test"]}}),
+            datasets.SplitGenerator(name=datasets.Split.TRAIN,
+                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["train"]}}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION,
+                                    gen_kwargs={**data_dir, **{'filepath': downloaded_files["dev"]}}),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, examples_csv, images_dir):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+
+        df = pd.read_csv(examples_csv)
+
+        # columns_to_serialize = ['candidates', 'associations']
+        # for c in columns_to_serialize:
+        #   df[c] = df[c].apply(json.loads)
+
+        for r_idx, r in df.iterrows():
+            r_dict = r.to_dict()
+            r_dict['candidates'] = json.loads(r_dict['candidates'])
+            candidates_images = [os.path.join(images_dir, "vasr_images", f"{x}.{self.IMAGE_EXTENSION}") for x in
+                                 r_dict['candidates']]
+            r_dict['candidates_images'] = candidates_images
+            yield r_idx, r_dict