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import logging |
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import os |
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import datasets as ds |
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import pytest |
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logging.basicConfig( |
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.INFO |
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) |
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@pytest.fixture |
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def dataset_path() -> str: |
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return "cocostuff.py" |
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@pytest.mark.skipif( |
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bool(os.environ.get("CI", False)), |
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reason="Because this test downloads a large data set, we will skip running it on CI.", |
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) |
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def test_load_stuff_thing_dataset(dataset_path: str): |
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dataset = ds.load_dataset(path=dataset_path, name="stuff-thing") |
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expected_features = [ |
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"image", |
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"image_id", |
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"image_filename", |
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"width", |
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"height", |
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"stuff_map", |
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"objects", |
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] |
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for expected_feature in expected_features: |
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assert expected_feature in dataset["train"].features.keys() |
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assert expected_feature in dataset["validation"].features.keys() |
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assert dataset["train"].num_rows == 118280 |
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assert dataset["validation"].num_rows == 5000 |
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@pytest.mark.skipif( |
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bool(os.environ.get("CI", False)), |
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reason="Because this test downloads a large data set, we will skip running it on CI.", |
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) |
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def test_load_stuff_only_dataset(dataset_path: str): |
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dataset = ds.load_dataset(path=dataset_path, name="stuff-only") |
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expected_features = [ |
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"image", |
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"image_id", |
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"image_filename", |
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"width", |
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"height", |
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"objects", |
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] |
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for expected_feature in expected_features: |
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assert expected_feature in dataset["train"].features.keys() |
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assert expected_feature in dataset["validation"].features.keys() |
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assert dataset["train"].num_rows == 118280 |
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assert dataset["validation"].num_rows == 5000 |
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