[tests] Make naming more explicit

tests/test_mc.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Tests for Montgomery dataset."""
-
-import importlib
-
-import pytest
-
-
-@pytest.mark.skip(reason="Test need to be updated")
-def test_protocol_consistency():
-    # Default protocol
-    datamodule = importlib.import_module(
-        "ptbench.data.montgomery.datamodules.default"
-    ).datamodule
-
-    subset = datamodule.splits
-
-    assert len(subset) == 3
-
-    assert "train" in subset
-    assert len(subset["train"]) == 88
-    for s in subset["train"]:
-        assert s[0].startswith("CXR_png/MCUCXR_0")
-
-    assert "validation" in subset
-    assert len(subset["validation"]) == 22
-    for s in subset["validation"]:
-        assert s[0].startswith("CXR_png/MCUCXR_0")
-
-    assert "test" in subset
-    assert len(subset["test"]) == 28
-    for s in subset["test"]:
-        assert s[0].startswith("CXR_png/MCUCXR_0")
-
-    # Check labels
-    for s in subset["train"]:
-        assert s[1] in [0.0, 1.0]
-
-    for s in subset["validation"]:
-        assert s[1] in [0.0, 1.0]
-
-    for s in subset["test"]:
-        assert s[1] in [0.0, 1.0]
-
-    # Cross-validation fold 0-7
-    for f in range(8):
-        datamodule = importlib.import_module(
-            f"ptbench.data.montgomery.datamodules.fold_{str(f)}"
-        ).datamodule
-        subset = datamodule.database_split
-
-        assert len(subset) == 3
-
-        assert "train" in subset
-        assert len(subset["train"]) == 99
-        for s in subset["train"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        assert "validation" in subset
-        assert len(subset["validation"]) == 25
-        for s in subset["validation"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        assert "test" in subset
-        assert len(subset["test"]) == 14
-        for s in subset["test"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        # Check labels
-        for s in subset["train"]:
-            assert s[1] in [0.0, 1.0]
-
-        for s in subset["validation"]:
-            assert s[1] in [0.0, 1.0]
-
-        for s in subset["test"]:
-            assert s[1] in [0.0, 1.0]
-
-    # Cross-validation fold 8-9
-    for f in range(8, 10):
-        datamodule = importlib.import_module(
-            f"ptbench.data.montgomery.datamodules.fold_{str(f)}"
-        ).datamodule
-        subset = datamodule.database_split
-
-        assert len(subset) == 3
-
-        assert "train" in subset
-        assert len(subset["train"]) == 100
-        for s in subset["train"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        assert "validation" in subset
-        assert len(subset["validation"]) == 25
-        for s in subset["validation"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        assert "test" in subset
-        assert len(subset["test"]) == 13
-        for s in subset["test"]:
-            assert s[0].startswith("CXR_png/MCUCXR_0")
-
-        # Check labels
-        for s in subset["train"]:
-            assert s[1] in [0.0, 1.0]
-
-        for s in subset["validation"]:
-            assert s[1] in [0.0, 1.0]
-
-        for s in subset["test"]:
-            assert s[1] in [0.0, 1.0]
-
-
-@pytest.mark.skip(reason="Test need to be updated")
-@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
-def test_loading():
-    import torch
-    import torchvision.transforms
-
-    from ptbench.data.datamodule import _DelayedLoadingDataset
-
-    def _check_sample(s):
-        assert len(s) == 2
-
-        data = s[0]
-        metadata = s[1]
-
-        assert isinstance(data, torch.Tensor)
-
-        assert data.size(0) == 1  # check single channel
-        assert data.size(1) == data.size(2)  # check square image
-
-        assert (
-            torchvision.transforms.ToPILImage()(data).mode == "L"
-        )  # Check colors
-
-        assert "label" in metadata
-        assert metadata["label"] in [0, 1]  # Check labels
-
-    limit = 30  # use this to limit testing to first images only, else None
-
-    datamodule = importlib.import_module(
-        "ptbench.data.montgomery.datamodules.default"
-    ).datamodule
-    subset = datamodule.database_split
-    raw_data_loader = datamodule.raw_data_loader
-
-    # Need to use private function so we can limit the number of samples to use
-    dataset = _DelayedLoadingDataset(subset["train"][:limit], raw_data_loader)
-
-    for s in dataset:
-        _check_sample(s)
-
-
-@pytest.mark.skip(reason="Test need to be updated")
-@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
-def test_check():
-    from ptbench.data.split import check_database_split_loading
-
-    limit = 30  # use this to limit testing to first images only, else 0
-
-    # Default protocol
-    datamodule = importlib.import_module(
-        "ptbench.data.montgomery.datamodules.default"
-    ).datamodule
-    database_split = datamodule.database_split
-    raw_data_loader = datamodule.raw_data_loader
-
-    assert (
-        check_database_split_loading(
-            database_split, raw_data_loader, limit=limit
-        )
-        == 0
-    )
-
-    # Folds
-    for f in range(10):
-        datamodule = importlib.import_module(
-            f"ptbench.data.montgomery.datamodules.fold_{f}"
-        ).datamodule
-        database_split = datamodule.database_split
-        raw_data_loader = datamodule.raw_data_loader
-
-        assert (
-            check_database_split_loading(
-                database_split, raw_data_loader, limit=limit
-            )
-            == 0
-        )