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"""The GQA dataset preprocessed for LXMERT.""" |
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import base64 |
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import csv |
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import json |
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import os |
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import sys |
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import datasets |
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import numpy as np |
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csv.field_size_limit(sys.maxsize) |
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_CITATION = """\ |
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@inproceedings{hudson2019gqa, |
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title={Gqa: A new dataset for real-world visual reasoning and compositional question answering}, |
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author={Hudson, Drew A and Manning, Christopher D}, |
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booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition}, |
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pages={6700--6709}, |
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year={2019} |
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} |
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""" |
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_DESCRIPTION = """\ |
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GQA is a new dataset for real-world visual reasoning and compositional question answering, |
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seeking to address key shortcomings of previous visual question answering (VQA) datasets. |
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""" |
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_URLS = { |
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"train": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/train.json", |
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"valid": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/valid.json", |
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"trainval_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/vg_gqa_obj36.zip", |
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"testdev": "https://nlp.cs.unc.edu/data/lxmert_data/gqa/testdev.json", |
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"test_feat": "https://nlp.cs.unc.edu/data/lxmert_data/vg_gqa_imgfeat/gqa_testdev_obj36.zip", |
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"ans2label": "https://raw.githubusercontent.com/airsplay/lxmert/master/data/gqa/trainval_ans2label.json", |
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} |
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TRAINVAL_FEAT_PATH = "vg_gqa_imgfeat/vg_gqa_obj36.tsv" |
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TEST_FEAT_PATH = "vg_gqa_imgfeat/gqa_testdev_obj36.tsv" |
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FIELDNAMES = [ |
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"img_id", "img_h", "img_w", "objects_id", "objects_conf", "attrs_id", "attrs_conf", "num_boxes", "boxes", "features" |
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] |
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_SHAPE_FEATURES = (36, 2048) |
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_SHAPE_BOXES = (36, 4) |
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class GqaLxmert(datasets.GeneratorBasedBuilder): |
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"""The GQA dataset preprocessed for LXMERT, with the objects features detected by a Faster RCNN replacing the |
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raw images.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="gqa", version=datasets.Version("1.0.0"), description="GQA dataset."), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"question": datasets.Value("string"), |
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"question_id": datasets.Value("int32"), |
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"image_id": datasets.Value("string"), |
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"features": datasets.Array2D(_SHAPE_FEATURES, dtype="float32"), |
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"normalized_boxes": datasets.Array2D(_SHAPE_BOXES, dtype="float32"), |
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"label": datasets.ClassLabel(num_classes=1842), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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dl_dir = dl_manager.download_and_extract(_URLS) |
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trainval_features_path = os.path.join(dl_dir["trainval_feat"], TRAINVAL_FEAT_PATH) |
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test_features_path = os.path.join(dl_dir["test_feat"], TEST_FEAT_PATH) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": dl_dir["train"], |
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"ans2label_path": dl_dir["ans2label"], |
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"features_path": trainval_features_path, |
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"testset": False, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": dl_dir["valid"], |
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"ans2label_path": dl_dir["ans2label"], |
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"features_path": trainval_features_path, |
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"testset": False, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": dl_dir["testdev"], |
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"ans2label_path": dl_dir["ans2label"], |
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"features_path": test_features_path, |
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"testset": True, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, ans2label_path, features_path, testset=False): |
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""" Yields examples as (key, example) tuples.""" |
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if not hasattr(self, "ans2label"): |
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with open(ans2label_path, encoding="utf-8") as f: |
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self.ans2label = json.load(f) |
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if testset: |
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self.test_id2features = self._load_features(features_path) |
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else: |
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if not hasattr(self, "trainval_id2features"): |
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self.trainval_id2features = self._load_features(features_path) |
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with open(filepath, encoding="utf-8") as f: |
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gqa = json.load(f) |
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for id_, d in enumerate(gqa): |
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if testset: |
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try: |
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img_features = self.test_id2features[d["img_id"]] |
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except KeyError: |
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continue |
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label_key = next(iter(d["label"])) |
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if label_key not in self.ans2label: |
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print ("Ignored one sample because of unseen label.") |
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continue |
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label = self.ans2label[label_key] |
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else: |
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img_features = self.trainval_id2features[d["img_id"]] |
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label = self.ans2label[next(iter(d["label"]))] |
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yield id_, { |
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"question": d["sent"], |
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"question_id": d["question_id"], |
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"image_id": d["img_id"], |
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"features": img_features["features"], |
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"normalized_boxes": img_features["normalized_boxes"], |
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"label": label, |
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} |
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def _load_features(self, filepath): |
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"""Returns a dictionary mapping an image id to the corresponding image's objects features.""" |
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id2features = {} |
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with open(filepath) as f: |
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reader = csv.DictReader(f, FIELDNAMES, delimiter="\t") |
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for i, item in enumerate(reader): |
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features = {} |
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img_h = int(item["img_h"]) |
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img_w = int(item["img_w"]) |
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num_boxes = int(item["num_boxes"]) |
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features["features"] = np.frombuffer(base64.b64decode(item["features"]), dtype=np.float32).reshape( |
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(num_boxes, -1) |
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) |
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boxes = np.frombuffer(base64.b64decode(item["boxes"]), dtype=np.float32).reshape((num_boxes, 4)) |
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features["normalized_boxes"] = self._normalize_boxes(boxes, img_h, img_w) |
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id2features[item["img_id"]] = features |
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return id2features |
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def _normalize_boxes(self, boxes, img_h, img_w): |
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""" Normalizes the input boxes given the original image size.""" |
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normalized_boxes = boxes.copy() |
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normalized_boxes[:, (0, 2)] /= img_w |
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normalized_boxes[:, (1, 3)] /= img_h |
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return normalized_boxes |
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