Upload ACT-Thor.py

ACT-Thor.py

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+# Adapted from the dataset builders for the Winoground Dataset (https://huggingface.co/datasets/facebook/winoground)
+import os
+import ast
+
+import datasets
+import json
+import pandas as pd
+# from huggingface_hub import hf_hub_url
+
+
+_CITATION = """\
+@inproceedings{hanna-etal-2022-act,
+    title = "ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments",
+    author = "Hanna, Michael  and
+      Pedeni, Federico  and
+      Suglia, Alessandro and
+      Testoni, Alberto and
+      Bernardi, Raffaella",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, South Korea",
+    publisher = "International Committee on Computational Linguistics",
+}
+"""
+
+_URL = "https://huggingface.co/datasets/mwhanna/ACT-Thor"
+
+_DESCRIPTION = """\
+ACT-Thor is a dataset intended for evaluating models' understanding of actions.
+"""
+
+
+class ACTThorConfig(datasets.BuilderConfig):
+    """BuilderConfig for ACT-Thor."""
+
+    def __init__(self, split_type, **kwargs):
+        """BuilderConfig for ACT-Thor.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ACTThorConfig, self).__init__(**kwargs)
+        self.split_type = split_type
+
+
+
+class ACTThor(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIG_CLASS = ACTThorConfig
+
+    BUILDER_CONFIGS = [
+        ACTThorConfig('sample',
+            name="sample",
+        ),
+        ACTThorConfig('object',
+            name="object",
+        ),
+        ACTThorConfig('scene',
+            name="scene",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "sample"
+
+    IMAGE_EXTENSION = ".png"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "before_image": datasets.Image(),
+                    "after_image_0": datasets.Image(),
+                    "after_image_1": datasets.Image(),
+                    "after_image_2": datasets.Image(),
+                    "after_image_3": datasets.Image(),
+                    "action": datasets.Value("string"),
+                    "action_id": datasets.Value("int32"),
+                    "label": datasets.Value("int32"),
+                    "object": datasets.Value("string"),
+                    "scene": datasets.Value("string"),
+                }
+            ),
+            homepage=_URL,
+            citation=_CITATION,
+            task_templates=[],
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        # hf_auth_token = dl_manager.download_config.use_auth_token
+        # if hf_auth_token is None:
+        #     raise ConnectionError(
+        #         "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
+        #     )
+
+        downloaded_files = dl_manager.download_and_extract({
+            "examples_csv": 'https://www.dropbox.com/s/4xdlimis1lv17x4/dataset_hf.csv?dl=1', # hf_hub_url("datasets/facebook/winoground", filename="data/examples.jsonl"),
+            "images_dir": 'https://www.dropbox.com/s/odkkrtvogi8go76/images.zip?dl=1', # hf_hub_url("datasets/facebook/winoground", filename="data/images.zip")
+        })
+
+        split_type = self.config.split_type
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'split_type': split_type, 'split':'train', **downloaded_files}),
+        datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={'split_type': split_type, 'split':'valid', **downloaded_files}),
+        datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'split_type': split_type, 'split':'test', **downloaded_files})]
+
+    def _generate_examples(self, examples_csv, images_dir, split_type, split):
+        """Yields examples."""
+        #print('The examples csv is stored in ')
+        #print(examples_csv)
+        df = pd.read_csv(examples_csv)
+        df = df[df[f'{split_type}_split'] == split]
+        df = df.drop(['sample_split', 'object_split', 'scene_split'], axis='columns')
+        for example in df.to_dict('records'):
+            order = ast.literal_eval(example['order'])
+            example["before_image"] = os.path.join(images_dir, "images", "before_images", example["before_image"] + self.IMAGE_EXTENSION)
+            example["after_image_0"] = os.path.join(images_dir, "images", "after_images", example[f"after_image_{order[0]}"] + self.IMAGE_EXTENSION)
+            example["after_image_1"] = os.path.join(images_dir, "images", "after_images", example[f"after_image_{order[1]}"] + self.IMAGE_EXTENSION)
+            example["after_image_2"] = os.path.join(images_dir, "images", "after_images", example[f"after_image_{order[2]}"] + self.IMAGE_EXTENSION)
+            example["after_image_3"] = os.path.join(images_dir, "images", "after_images", example[f"after_image_{order[3]}"] + self.IMAGE_EXTENSION)
+            id_ = example["id"]
+            del example['order']
+            yield id_, example