diff --git a/tests/conftest.py b/tests/conftest.py
index 75b802a252994df5e2b5793bcd2c3422f5b9ad15..6e992cdd04092d04ff40a0efdb6293c8cff7dd7c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -162,7 +162,9 @@ class DatabaseCheckers:
assert len(split[k]) == lengths[k]
for s in split[k]:
- assert any([s[0].startswith(k) for k in prefixes])
+ assert any(
+ [s[0].startswith(k) for k in prefixes]
+ ), f"Sample with name {s[0]} does not start with any of the prefixes in {prefixes}"
assert s[1] in possible_labels
@staticmethod
diff --git a/tests/test_hivtb.py b/tests/test_hivtb.py
index 920f1574ccabe924c9609d1fd1533a0524fb83e4..eb6513f0046d70a7846e310a7e47a837c7222d8e 100644
--- a/tests/test_hivtb.py
+++ b/tests/test_hivtb.py
@@ -3,127 +3,89 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""Tests for HIV-TB dataset."""
-import pytest
-import torch
-
-from ptbench.data.hivtb.datamodule import make_split
-
-
-def _check_split(
- split_filename: str,
- lengths: dict[str, int],
- prefix: str = "HIV-TB_Algorithm_study_X-rays/",
- extension: str = ".BMP",
- possible_labels: list[int] = [0, 1],
-):
- """Runs a simple consistence check on the data split.
-
- Parameters
- ----------
-
- split_filename
- This is the split we will check
-
- lenghts
- A dictionary that contains keys matching those of the split (this will
- be checked). The values of the dictionary should correspond to the
- sizes of each of the datasets in the split.
+import importlib
- prefix
- Each file named in a split should start with this prefix.
-
- extension
- Each file named in a split should end with this extension.
-
- possible_labels
- These are the list of possible labels contained in any split.
- """
-
- split = make_split(split_filename)
-
- assert len(split) == len(lengths)
-
- for k in lengths.keys():
- # dataset must have been declared
- assert k in split
-
- assert len(split[k]) == lengths[k]
- for s in split[k]:
- assert s[0].startswith(prefix)
- assert s[0].endswith(extension)
- assert s[1] in possible_labels
+import pytest
-def _check_loaded_batch(
- batch,
- size: int = 1,
- prefix: str = "HIV-TB_Algorithm_study_X-rays/",
- extension: str = ".BMP",
- possible_labels: list[int] = [0, 1],
+def id_function(val):
+ if isinstance(val, dict):
+ return str(val)
+ return repr(val)
+
+
+@pytest.mark.parametrize(
+ "split,lenghts",
+ [
+ ("fold-0", dict(train=174, validation=44, test=25)),
+ ("fold-1", dict(train=174, validation=44, test=25)),
+ ("fold-2", dict(train=174, validation=44, test=25)),
+ ("fold-3", dict(train=175, validation=44, test=24)),
+ ("fold-4", dict(train=175, validation=44, test=24)),
+ ("fold-5", dict(train=175, validation=44, test=24)),
+ ("fold-6", dict(train=175, validation=44, test=24)),
+ ("fold-7", dict(train=175, validation=44, test=24)),
+ ("fold-8", dict(train=175, validation=44, test=24)),
+ ("fold-9", dict(train=175, validation=44, test=24)),
+ ],
+ ids=id_function, # just changes how pytest prints it
+)
+def test_protocol_consistency(
+ database_checkers, split: str, lenghts: dict[str, int]
):
- """Checks the consistence of an individual (loaded) batch.
+ from ptbench.data.hivtb.datamodule import make_split
- Parameters
- ----------
-
- batch
- The loaded batch to be checked.
-
- prefix
- Each file named in a split should start with this prefix.
-
- extension
- Each file named in a split should end with this extension.
-
- possible_labels
- These are the list of possible labels contained in any split.
- """
-
- assert len(batch) == 2 # data, metadata
-
- assert isinstance(batch[0], torch.Tensor)
- assert batch[0].shape[0] == size # mini-batch size
- assert batch[0].shape[1] == 1 # grayscale images
- assert batch[0].shape[2] == batch[0].shape[3] # image is square
-
- assert isinstance(batch[1], dict) # metadata
- assert len(batch[1]) == 2 # label and name
-
- assert "label" in batch[1]
- assert all([k in possible_labels for k in batch[1]["label"]])
-
- assert "name" in batch[1]
- assert all([k.startswith(prefix) for k in batch[1]["name"]])
- assert all([k.endswith(extension) for k in batch[1]["name"]])
-
-
-def test_protocol_consistency():
- # Cross-validation fold 0-2
- for k in range(3):
- _check_split(
- f"fold-{k}.json",
- lengths=dict(train=174, validation=44, test=25),
- )
-
- # Cross-validation fold 3-9
- for k in range(3, 10):
- _check_split(
- f"fold-{k}.json",
- lengths=dict(train=175, validation=44, test=24),
- )
+ database_checkers.check_split(
+ make_split(f"{split}.json"),
+ lengths=lenghts,
+ prefixes=("HIV-TB_Algorithm_study_X-rays",),
+ possible_labels=(0, 1),
+ )
@pytest.mark.skip_if_rc_var_not_set("datadir.hivtb")
-def test_loading():
- from ptbench.data.hivtb.fold_0 import datamodule
+@pytest.mark.parametrize(
+ "dataset",
+ [
+ "train",
+ "validation",
+ "test",
+ ],
+)
+@pytest.mark.parametrize(
+ "name",
+ [
+ "fold_0",
+ "fold_1",
+ "fold_2",
+ "fold_3",
+ "fold_4",
+ "fold_5",
+ "fold_6",
+ "fold_7",
+ "fold_8",
+ "fold_9",
+ ],
+)
+def test_loading(database_checkers, name: str, dataset: str):
+ datamodule = importlib.import_module(
+ f".{name}", "ptbench.data.hivtb"
+ ).datamodule
datamodule.model_transforms = [] # should be done before setup()
datamodule.setup("predict") # sets up all datasets
- for loader in datamodule.predict_dataloader().values():
- limit = 5 # limit load checking
- for batch in loader:
- if limit == 0:
- break
- _check_loaded_batch(batch)
- limit -= 1
+ loader = datamodule.predict_dataloader()[dataset]
+
+ limit = 3 # limit load checking
+ for batch in loader:
+ if limit == 0:
+ break
+ database_checkers.check_loaded_batch(
+ batch,
+ batch_size=1,
+ color_planes=1,
+ prefixes=("HIV-TB_Algorithm_study_X-rays",),
+ possible_labels=(0, 1),
+ )
+ limit -= 1
diff --git a/tests/test_tbpoc.py b/tests/test_tbpoc.py
index ee34d8d09d14379dfc08dcc4adebc33ffedd18b9..7125efb195a058f36c785268c110c765f0890d65 100644
--- a/tests/test_tbpoc.py
+++ b/tests/test_tbpoc.py
@@ -3,127 +3,95 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""Tests for TB-POC dataset."""
-import pytest
-import torch
-
-from ptbench.data.tbpoc.datamodule import make_split
-
-
-def _check_split(
- split_filename: str,
- lengths: dict[str, int],
- prefix: str = "TBPOC_CXR/",
- extension: str = ".jpeg",
- possible_labels: list[int] = [0, 1],
-):
- """Runs a simple consistence check on the data split.
-
- Parameters
- ----------
-
- split_filename
- This is the split we will check
-
- lenghts
- A dictionary that contains keys matching those of the split (this will
- be checked). The values of the dictionary should correspond to the
- sizes of each of the datasets in the split.
-
- prefix
- Each file named in a split should start with this prefix.
+import importlib
- extension
- Each file named in a split should end with this extension.
-
- possible_labels
- These are the list of possible labels contained in any split.
- """
-
- split = make_split(split_filename)
-
- assert len(split) == len(lengths)
-
- for k in lengths.keys():
- # dataset must have been declared
- assert k in split
-
- assert len(split[k]) == lengths[k]
- for s in split[k]:
- # assert s[0].startswith(prefix)
- assert s[0].endswith(extension)
- assert s[1] in possible_labels
+import pytest
-def _check_loaded_batch(
- batch,
- size: int = 1,
- prefix: str = "TBPOC_CXR/",
- extension: str = ".jpeg",
- possible_labels: list[int] = [0, 1],
+def id_function(val):
+ if isinstance(val, dict):
+ return str(val)
+ return repr(val)
+
+
+@pytest.mark.parametrize(
+ "split,lenghts",
+ [
+ ("fold-0", dict(train=292, validation=74, test=41)),
+ ("fold-1", dict(train=292, validation=74, test=41)),
+ ("fold-2", dict(train=292, validation=74, test=41)),
+ ("fold-3", dict(train=292, validation=74, test=41)),
+ ("fold-4", dict(train=292, validation=74, test=41)),
+ ("fold-5", dict(train=292, validation=74, test=41)),
+ ("fold-6", dict(train=292, validation=74, test=41)),
+ ("fold-7", dict(train=293, validation=74, test=40)),
+ ("fold-8", dict(train=293, validation=74, test=40)),
+ ("fold-9", dict(train=293, validation=74, test=40)),
+ ],
+ ids=id_function, # just changes how pytest prints it
+)
+def test_protocol_consistency(
+ database_checkers, split: str, lenghts: dict[str, int]
):
- """Checks the consistence of an individual (loaded) batch.
-
- Parameters
- ----------
-
- batch
- The loaded batch to be checked.
-
- prefix
- Each file named in a split should start with this prefix.
-
- extension
- Each file named in a split should end with this extension.
-
- possible_labels
- These are the list of possible labels contained in any split.
- """
-
- assert len(batch) == 2 # data, metadata
-
- assert isinstance(batch[0], torch.Tensor)
- assert batch[0].shape[0] == size # mini-batch size
- assert batch[0].shape[1] == 1 # grayscale images
- assert batch[0].shape[2] == batch[0].shape[3] # image is square
-
- assert isinstance(batch[1], dict) # metadata
- assert len(batch[1]) == 2 # label and name
-
- assert "label" in batch[1]
- assert all([k in possible_labels for k in batch[1]["label"]])
-
- assert "name" in batch[1]
- # assert all([k.startswith(prefix) for k in batch[1]["name"]])
- assert all([k.endswith(extension) for k in batch[1]["name"]])
-
-
-def test_protocol_consistency():
- # Cross-validation fold 0-6
- for k in range(7):
- _check_split(
- f"fold-{k}.json",
- lengths=dict(train=292, validation=74, test=41),
- )
-
- # Cross-validation fold 7-9
- for k in range(7, 10):
- _check_split(
- f"fold-{k}.json",
- lengths=dict(train=293, validation=74, test=40),
- )
-
-
-@pytest.mark.skip_if_rc_var_not_set("datadir.hivtb")
-def test_loading():
- from ptbench.data.tbpoc.fold_0 import datamodule
+ from ptbench.data.tbpoc.datamodule import make_split
+
+ database_checkers.check_split(
+ make_split(f"{split}.json"),
+ lengths=lenghts,
+ prefixes=(
+ "TBPOC_CXR/TBPOC-",
+ "TBPOC_CXR/tbpoc-",
+ ),
+ possible_labels=(0, 1),
+ )
+
+
+@pytest.mark.skip_if_rc_var_not_set("datadir.tbpoc")
+@pytest.mark.parametrize(
+ "dataset",
+ [
+ "train",
+ "validation",
+ "test",
+ ],
+)
+@pytest.mark.parametrize(
+ "name",
+ [
+ "fold_0",
+ "fold_1",
+ "fold_2",
+ "fold_3",
+ "fold_4",
+ "fold_5",
+ "fold_6",
+ "fold_7",
+ "fold_8",
+ "fold_9",
+ ],
+)
+def test_loading(database_checkers, name: str, dataset: str):
+ datamodule = importlib.import_module(
+ f".{name}", "ptbench.data.tbpoc"
+ ).datamodule
datamodule.model_transforms = [] # should be done before setup()
datamodule.setup("predict") # sets up all datasets
- for loader in datamodule.predict_dataloader().values():
- limit = 5 # limit load checking
- for batch in loader:
- if limit == 0:
- break
- _check_loaded_batch(batch)
- limit -= 1
+ loader = datamodule.predict_dataloader()[dataset]
+
+ limit = 3 # limit load checking
+ for batch in loader:
+ if limit == 0:
+ break
+ database_checkers.check_loaded_batch(
+ batch,
+ batch_size=1,
+ color_planes=1,
+ prefixes=(
+ "TBPOC_CXR/TBPOC-",
+ "TBPOC_CXR/tbpoc-",
+ ),
+ possible_labels=(0, 1),
+ )
+ limit -= 1