Working clevr

README.md

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+Checks with https://visualqa.org/download.html:
+- Num train questions: 443,757
+- Num val questions: 214,354
+- Num test questions: 447,793
+
+- Num train answers: 4,437,570
+- Num val answers: 2,143,540
+
+-  Num train images: 82,783
+- Num val images: 40,504
+- Num test images: 81,434
+
+testdev is not mentionned:
+- Num questions: 107,394
+- Num images: 36,807

clevr.py

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+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""VQA v2 loading script."""
+
+
+import json
+from pathlib import Path
+import datasets
+
+
+_CITATION = """\
+@inproceedings{johnson2017clevr,
+  title={Clevr: A diagnostic dataset for compositional language and elementary visual reasoning},
+  author={Johnson, Justin and Hariharan, Bharath and Van Der Maaten, Laurens and Fei-Fei, Li and Lawrence Zitnick, C and Girshick, Ross},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={2901--2910},
+  year={2017}
+}
+"""
+
+_DESCRIPTION = """\
+CLEVR is a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires. We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.
+"""
+
+_HOMEPAGE = "https://cs.stanford.edu/people/jcjohns/clevr/"
+
+_LICENSE = "CC BY 4.0"  # TODO need to credit both ms coco and vqa authors!
+
+_URLS = "https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip"
+
+
+class ClevrDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question_index": datasets.Value("int64"),
+                "question_family_index": datasets.Value("int64"),
+                "image_filename": datasets.Value("string"),
+                "split": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "image": datasets.Image(),
+                "image_index": datasets.Value("int64"),
+                "program": datasets.Sequence({
+                    "inputs": datasets.Sequence(datasets.Value("int64")),
+                    "function": datasets.Value("string"),
+                    "value_inputs": datasets.Sequence(datasets.Value("string")),
+                }),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URLS)
+        gen_kwargs = {
+            split_name: {
+                "split": split_name,
+                "questions_path": Path(data_dir) / "CLEVR_v1.0" / "questions" / f"CLEVR_{split_name}_questions.json",
+                "image_folder": Path(data_dir) / "CLEVR_v1.0" / "images" / f"{split_name}",
+            }
+            for split_name in ["train", "val", "test"]
+        }
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs=gen_kwargs["train"],
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs=gen_kwargs["val"],
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs=gen_kwargs["test"],
+            ),
+        ]
+
+    def _generate_examples(self, split, questions_path, image_folder):
+        questions = json.load(open(questions_path, "r"))
+
+        for idx, question in enumerate(questions["questions"]):
+            question["image"] = str(image_folder / f"{question['image_filename']}")
+            if split == "test":
+                question["question_family_index"] = -1
+                question["answer"] = ""
+                question["program"] = [
+                    {
+                        "inputs": [],
+                        "function": "scene",
+                        "value_inputs": [],
+                    }
+                ]
+            yield idx, question
+

dataset_infos.json

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+{"default": {"description": "CLEVR is a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires. We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.\n", "citation": "@inproceedings{johnson2017clevr,\n  title={Clevr: A diagnostic dataset for compositional language and elementary visual reasoning},\n  author={Johnson, Justin and Hariharan, Bharath and Van Der Maaten, Laurens and Fei-Fei, Li and Lawrence Zitnick, C and Girshick, Ross},\n  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n  pages={2901--2910},\n  year={2017}\n}\n", "homepage": "https://cs.stanford.edu/people/jcjohns/clevr/", "license": "CC BY 4.0", "features": {"question_index": {"dtype": "int64", "id": null, "_type": "Value"}, "question_family_index": {"dtype": "int64", "id": null, "_type": "Value"}, "image_filename": {"dtype": "string", "id": null, "_type": "Value"}, "split": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "image": {"decode": true, "id": null, "_type": "Image"}, "image_index": {"dtype": "int64", "id": null, "_type": "Value"}, "program": {"feature": {"inputs": {"feature": {"dtype": "int64", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "function": {"dtype": "string", "id": null, "_type": "Value"}, "value_inputs": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "clevr", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 511632731, "num_examples": 699989, "dataset_name": "clevr"}, "validation": {"name": "validation", "num_bytes": 108498206, "num_examples": 149991, "dataset_name": "clevr"}, "test": {"name": "test", "num_bytes": 55113984, "num_examples": 149988, "dataset_name": "clevr"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip": {"num_bytes": 19021600724, "checksum": "5cd61cf1096ed20944df93c9adb31e74d189b8459a94f54ba00090e5c59936d1"}}, "download_size": 19021600724, "post_processing_size": null, "dataset_size": 675244921, "size_in_bytes": 19696845645}}