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import os
import shutil
import tempfile
import h5py
import numpy as np
import pytest
import torch
from pathml.datasets.datasets import EntityDataset, TileDataset
# Assuming TileDataset is in pathml.ml, adjust the import as necessary
from pathml.graph import Graph
@pytest.fixture
def create_test_h5_file():
"""
Fixture to create a temporary h5 file simulating the output of SlideData processing.
This file will serve as input for testing TileDataset.
"""
tmp_dir = tempfile.mkdtemp()
h5_path = os.path.join(tmp_dir, "test_tile_dataset.h5")
with h5py.File(h5_path, "w") as f:
tiles = f.create_group("tiles")
tiles.attrs["tile_shape"] = "(224, 224, 3)"
for i in range(5):
tile = tiles.create_group(str(i))
array_data = np.random.rand(224, 224, 3).astype(
"float32"
) # Ensure data type matches expected torch.Tensor type
tile.create_dataset("array", data=array_data)
if i % 2 == 0: # Add masks to some tiles
masks = tile.create_group("masks")
masks.create_dataset(
"mask1",
data=np.random.randint(2, size=(224, 224)).astype("float32"),
)
labels = tile.create_group("labels").attrs
labels["example_label"] = "label_value"
fields = f.create_group("fields")
labels = fields.create_group("labels")
labels.attrs["slide_label"] = "slide_value"
yield h5_path
os.remove(h5_path)
os.rmdir(tmp_dir)
def test_tile_dataset_initialization(create_test_h5_file):
h5_path = create_test_h5_file
dataset = TileDataset(h5_path)
assert len(dataset) == 5
assert dataset.tile_shape == (224, 224, 3)
assert dataset.slide_level_labels["slide_label"] == "slide_value"
def test_tile_dataset_getitem(create_test_h5_file):
h5_path = create_test_h5_file
dataset = TileDataset(h5_path)
for i in range(len(dataset)):
im, masks, lab_tile, lab_slide = dataset[i]
# Image tensor shape should match expected (C, H, W) after transpose
assert im.shape == (
3,
224,
224,
), "Image tensor shape should match expected (C, H, W)"
if masks is not None:
assert masks.shape[0] > 0 and masks.shape[1:] == (
224,
224,
), "Mask shape should be (n_masks, H, W)"
assert "example_label" in lab_tile, "Tile labels should include 'example_label'"
assert (
lab_slide["slide_label"] == "slide_value"
), "Slide label should match expected value"
def test_tile_dataset_unsupported_shape_explicit_check(create_test_h5_file):
h5_path = create_test_h5_file
dataset = TileDataset(h5_path)
with h5py.File(h5_path, "r+") as f:
# Create an unsupported shape explicitly
del f["tiles"]["0"]["array"]
f["tiles"]["0"].create_dataset(
"array", data=np.random.rand(10, 10)
) # 2D array, unsupported
try:
_ = dataset[0]
assert False, "NotImplementedError was expected but not raised."
except NotImplementedError:
pass # This is the expected outcome
# Additional test cases can be added here to cover more scenarios, such as different image shapes (e.g., 5D images),
# testing with actual mask data, and ensuring that custom collate_fn behavior is as expected.
def test_tile_dataset_with_masks(create_test_h5_file):
h5_path = create_test_h5_file
dataset = TileDataset(h5_path)
# Assuming the first item has masks
_, masks, _, _ = dataset[0]
assert masks is not None, "Masks should be present"
assert masks.shape[0] > 0, "There should be at least one mask"
def fake_graph_inputs():
edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long)
node_centroids = torch.randn(3, 2)
node_features = torch.randn(3, 2)
target = torch.tensor([1])
graph_obj = Graph(
edge_index=edge_index,
node_centroids=node_centroids,
node_features=node_features,
target=target,
)
assignment = assignment = torch.randint(low=0, high=3, size=(3, 2)).long()
return graph_obj, graph_obj, assignment
@pytest.fixture
def create_test_graph_file():
"""
Fixture to create a temporary h5 file simulating the output of SlideData processing.
This file will serve as input for testing TileDataset.
"""
graphs_path = tempfile.mkdtemp()
os.makedirs(os.path.join(graphs_path, "cell_graphs", "train"), exist_ok=True)
os.makedirs(os.path.join(graphs_path, "tissue_graphs", "train"), exist_ok=True)
os.makedirs(
os.path.join(graphs_path, "assignment_matrices", "train"), exist_ok=True
)
cell_graph, tissue_graph, assignment = fake_graph_inputs()
torch.save(
cell_graph, os.path.join(graphs_path, "cell_graphs", "train", "example.pt")
)
torch.save(
tissue_graph, os.path.join(graphs_path, "tissue_graphs", "train", "example.pt")
)
torch.save(
assignment,
os.path.join(graphs_path, "assignment_matrices", "train", "example.pt"),
)
yield graphs_path
os.remove(os.path.join(graphs_path, "cell_graphs", "train", "example.pt"))
os.remove(os.path.join(graphs_path, "tissue_graphs", "train", "example.pt"))
os.remove(os.path.join(graphs_path, "assignment_matrices", "train", "example.pt"))
shutil.rmtree(graphs_path)
def test_entity_dataset(create_test_graph_file):
graphs_path = create_test_graph_file
train_dataset = EntityDataset(
os.path.join(graphs_path, "cell_graphs/train/"),
os.path.join(graphs_path, "tissue_graphs/train/"),
os.path.join(graphs_path, "assignment_matrices/train/"),
)
batch = train_dataset[0]
assert batch.x_cell.shape == (3, 2)
assert batch.x_tissue.shape == (3, 2)
assert batch.edge_index_cell.shape == (2, 4)
assert batch.edge_index_tissue.shape == (2, 4)
assert len(train_dataset) == 1
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