diff --git a/tests/test_ch.py b/tests/test_ch.py
index 853a2f7184fc56ebd805ea278a4cc21f8ecbad21..510b1171741a9a4ea0176976a7aa45dddaa427fe 100644
--- a/tests/test_ch.py
+++ b/tests/test_ch.py
@@ -4,133 +4,194 @@
"""Tests for Shenzhen dataset."""
-import pytest
+import importlib
-from ptbench.data.shenzhen import dataset
+import pytest
def test_protocol_consistency():
# Default protocol
- subset = dataset.subsets("default")
+
+ datamodule = importlib.import_module(
+ "ptbench.data.shenzhen.default"
+ ).datamodule
+ subset = datamodule.database_split.subsets
+
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 422
for s in subset["train"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 107
for s in subset["validation"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "test" in subset
assert len(subset["test"]) == 133
for s in subset["test"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_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 folds 0-1
for f in range(2):
- subset = dataset.subsets("fold_" + str(f))
+ datamodule = importlib.import_module(
+ f"ptbench.data.shenzhen.fold_{str(f)}"
+ ).datamodule
+ subset = datamodule.database_split.subsets
+
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 476
for s in subset["train"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 119
for s in subset["validation"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "test" in subset
assert len(subset["test"]) == 67
for s in subset["test"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_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 folds 2-9
for f in range(2, 10):
- subset = dataset.subsets("fold_" + str(f))
+ datamodule = importlib.import_module(
+ f"ptbench.data.shenzhen.fold_{str(f)}"
+ ).datamodule
+ subset = datamodule.database_split.subsets
+
assert len(subset) == 3
assert "train" in subset
assert len(subset["train"]) == 476
for s in subset["train"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "validation" in subset
assert len(subset["validation"]) == 120
for s in subset["validation"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_0")
assert "test" in subset
assert len(subset["test"]) == 66
for s in subset["test"]:
- assert s.key.startswith("CXR_png/CHNCXR_0")
+ assert s[0].startswith("CXR_png/CHNCXR_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.shenzhen")
def test_loading():
- def _check_size(size):
- if (
- size[0] >= 1130
- and size[0] <= 3001
- and size[1] >= 948
- and size[1] <= 3001
- ):
+ import torch
+ import torchvision.transforms
+
+ from ptbench.data.datamodule import _DelayedLoadingDataset
+
+ def _check_size(shape):
+ if shape[0] == 1 and shape[1] == 512 and shape[2] == 512:
return True
return False
def _check_sample(s):
- data = s.data
- assert isinstance(data, dict)
- assert len(data) == 2
+ assert len(s) == 2
- assert "data" in data
- assert _check_size(data["data"].size) # Check size
- assert data["data"].mode == "L" # Check colors
+ data = s[0]
+ metadata = s[1]
- assert "label" in data
- assert data["label"] in [0, 1] # Check labels
+ assert isinstance(data, torch.Tensor)
+
+ print(data.shape)
+ assert _check_size(data.shape) # Check size
+
+ 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
- subset = dataset.subsets("default")
- for s in subset["train"][:limit]:
+ datamodule = importlib.import_module(
+ "ptbench.data.shenzhen.default"
+ ).datamodule
+ subset = datamodule.database_split.subsets
+ 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.shenzhen")
def test_check():
- assert dataset.check() == 0
+ 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.shenzhen.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.shenzhen.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
+ )