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Commit 5e6d38bc authored by Maxime DELITROZ's avatar Maxime DELITROZ Committed by André Anjos
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updated tests for Montgomery dataset

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1 merge request!6Making use of LightningDataModule and simplification of data loading
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tests/test_mc.py
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"""Tests for Montgomery dataset."""
import importlib
import pytest
def test_protocol_consistency():
from ptbench.data.montgomery import dataset
# Default protocol
subset = dataset.subsets("default")
datamodule = importlib.import_module(
"ptbench.data.montgomery.default"
).datamodule
subset = datamodule.dataset_split.subsets
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 88
for s in subset["train"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 22
for s in subset["validation"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "test" in subset
assert len(subset["test"]) == 28
for s in subset["test"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
# Check labels
for s in subset["train"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["validation"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["test"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
# Cross-validation fold 0-7
for f in range(8):
subset = dataset.subsets("fold_" + str(f))
datamodule = importlib.import_module(
f"ptbench.data.montgomery.fold_{str(f)}"
).datamodule
subset = datamodule.database_split.subsets
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 99
for s in subset["train"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 25
for s in subset["validation"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "test" in subset
assert len(subset["test"]) == 14
for s in subset["test"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
# Check labels
for s in subset["train"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["validation"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["test"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
# Cross-validation fold 8-9
for f in range(8, 10):
subset = dataset.subsets("fold_" + str(f))
datamodule = importlib.import_module(
f"ptbench.data.montgomery.fold_{str(f)}"
).datamodule
subset = datamodule.database_split.subsets
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 100
for s in subset["train"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 25
for s in subset["validation"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
assert "test" in subset
assert len(subset["test"]) == 13
for s in subset["test"]:
assert s.key.startswith("CXR_png/MCUCXR_0")
assert s[0].startswith("CXR_png/MCUCXR_0")
# Check labels
for s in subset["train"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["validation"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
for s in subset["test"]:
assert s.label in [0.0, 1.0]
assert s[1] in [0.0, 1.0]
@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
def test_loading():
from ptbench.data.montgomery import dataset
import torch
import torchvision.transforms
from ptbench.data.datamodule import _DelayedLoadingDataset
def _check_sample(s):
data = s.data
assert isinstance(data, dict)
assert len(data) == 2
assert "data" in data
assert data["data"].size in (
(4020, 4892), # portrait
(4892, 4020), # landscape
(512, 512), # test database @ CI
data = s[0]
metadata = s[1]
assert isinstance(data, torch.Tensor)
assert data.size in (
(1, 4020, 4892), # portrait
(1, 4892, 4020), # landscape
(1, 512, 512), # test database @ CI
)
assert data["data"].mode == "L" # Check colors
assert (
torchvision.transforms.ToPILImage()(data).mode == "L"
) # Check colors
assert "label" in data
assert data["label"] in [0, 1] # Check labels
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
subset = dataset.subsets("default")
for s in subset["train"][:limit]:
datamodule = importlib.import_module(
"ptbench.data.montgomery.default"
).datamodule
subset = datamodule.database_split.subsetss
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_if_rc_var_not_set("datadir.montgomery")
def test_check():
from ptbench.data.montgomery import dataset
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.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.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
)
assert dataset.check() == 0
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