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import os |
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import datasets as ds |
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import pytest |
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from MSCOCO import CATEGORIES, SUPER_CATEGORIES |
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@pytest.fixture |
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def dataset_path() -> str: |
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return "MSCOCO.py" |
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@pytest.mark.skipif( |
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condition=bool(os.environ.get("CI", False)), |
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reason=( |
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"Because this loading script downloads a large dataset, " |
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"we will skip running it on CI." |
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), |
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) |
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@pytest.mark.parametrize( |
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argnames="decode_rle,", |
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argvalues=( |
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True, |
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False, |
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), |
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) |
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@pytest.mark.parametrize( |
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argnames=( |
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"dataset_year", |
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"coco_task", |
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"expected_num_train", |
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"expected_num_validation", |
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), |
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argvalues=( |
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(2014, "captions", 82783, 40504), |
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(2017, "captions", 118287, 5000), |
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(2014, "instances", 82081, 40137), |
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(2017, "instances", 117266, 4952), |
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(2014, "person_keypoints", 45174, 21634), |
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(2017, "person_keypoints", 64115, 2693), |
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), |
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) |
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def test_load_dataset( |
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dataset_path: str, |
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dataset_year: int, |
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coco_task: str, |
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decode_rle: bool, |
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expected_num_train: int, |
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expected_num_validation: int, |
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): |
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dataset = ds.load_dataset( |
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path=dataset_path, |
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year=dataset_year, |
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coco_task=coco_task, |
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decode_rle=decode_rle, |
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) |
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assert dataset["train"].num_rows == expected_num_train |
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assert dataset["validation"].num_rows == expected_num_validation |
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def test_consts(): |
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assert len(CATEGORIES) == 80 |
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assert len(SUPER_CATEGORIES) == 12 |
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