Add unit test and refactor TiledDataset

.github/workflows/format-lint.yml → .github/workflows/test-lint.yml

@@ -29,3 +29,6 @@ jobs:
       - name: Test formatting with black
         run: |
           black . --check
+      - name: pytest
+        run: |
+          pytest

.gitignore

@@ -168,4 +168,7 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
+
+# vscode
+.vscode/

pyproject.toml

@@ -1,2 +1,7 @@
 [tool.isort]
 profile = "black"
+
+[tool.pytest.ini_options]
+pythonpath = [
+  "src"
+]

requirements-dev.txt

@@ -2,3 +2,5 @@ black==24.3.0
 flake8==7.0.0
 isort==5.13.2
 pre-commit==3.6.2
+tiled[all]==0.1.0a114
+pytest

requirements.txt

@@ -1,5 +1,5 @@
-qlty==0.1.6
+qlty==0.1.7
 dvclive==3.44.0
-#dlsia==0.3.0
+dlsia==0.3.1
 pydantic==2.6.3
 tiled[client]==0.1.0a114

src/_tests/conftest.py

@@ -0,0 +1,38 @@
+import numpy as np
+import pytest
+from tiled.catalog import from_uri
+from tiled.client import Context, from_context
+from tiled.server.app import build_app
+
+
+@pytest.fixture
+def catalog(tmpdir):
+    adapter = from_uri(
+        f"sqlite+aiosqlite:///{tmpdir}/catalog.db",
+        writable_storage=str(tmpdir),
+        init_if_not_exists=True,
+    )
+    yield adapter
+
+
+@pytest.fixture
+def app(catalog):
+    app = build_app(catalog)
+    yield app
+
+
+@pytest.fixture
+def context(app):
+    with Context.from_app(app) as context:
+        yield context
+
+
+@pytest.fixture
+def client(context):
+    "Fixture for tests which only read data"
+    client = from_context(context)
+    recons_container = client.create_container("reconstructions")
+    recons_container.write_array(np.zeros((2, 3, 3), dtype=np.int8), key="recon1")
+    masks_container = client.create_container("uid0001", metadata={"mask_idx": ["0"]})
+    masks_container.write_array(np.zeros((1, 3, 3), dtype=np.int8), key="mask")
+    yield client

src/_tests/test_tiled_dataset.py

@@ -0,0 +1,16 @@
+from ..tiled_dataset import TiledDataset
+
+
+def test_tiled_dataset(client):
+    tiled_dataset = TiledDataset(
+        client["reconstructions"]["recon1"],
+    )
+    assert tiled_dataset
+    assert tiled_dataset[0].shape == (3, 3)
+
+
+def test_tiled_dataset_with_masks(client):
+    tiled_dataset = TiledDataset(
+        client["reconstructions"]["recon1"], mask_tiled_client=client["uid0001"]
+    )
+    assert tiled_dataset[0].shape == (3, 3)

src/segment.py

@@ -5,6 +5,7 @@
 import torch
 import yaml
 from qlty.qlty2D import NCYXQuilt
+from tiled.client import from_uri
 from torchvision import transforms
 
 from network import baggin_smsnet_ensemble, load_network
@@ -52,11 +53,17 @@
 
     print("Parameters loaded successfully.")
 
+    data_tiled_client = from_uri(
+        io_parameters.data_tiled_uri, api_key=io_parameters.data_tiled_api_key
+    )
+    mask_tiled_client = None
+    if io_parameters.mask_tiled_uri:
+        mask_tiled_client = from_uri(
+            io_parameters.mask_tiled_uri, api_key=io_parameters.mask_tiled_api_key
+        )
     dataset = TiledDataset(
-        data_tiled_uri=io_parameters.data_tiled_uri,
-        data_tiled_api_key=io_parameters.data_tiled_api_key,
-        mask_tiled_uri=io_parameters.mask_tiled_uri,
-        mask_tiled_api_key=io_parameters.mask_tiled_api_key,
+        data_tiled_client,
+        mask_tiled_client=mask_tiled_client,
         is_training=False,
         using_qlty=False,
         qlty_window=model_parameters.qlty_window,

src/tiled_dataset.py

@@ -1,16 +1,14 @@
 import torch
 from qlty import cleanup
 from qlty.qlty2D import NCYXQuilt
-from tiled.client import from_uri
 
 
 class TiledDataset(torch.utils.data.Dataset):
+
     def __init__(
         self,
-        data_tiled_uri,
-        data_tiled_api_key=None,
-        mask_tiled_uri=None,
-        mask_tiled_api_key=None,
+        data_tiled_client,
+        mask_tiled_client=None,
         is_training=None,
         using_qlty=False,
         qlty_window=50,
@@ -33,20 +31,16 @@ def __init__(
         Return:
             ml_data:        tuple, (data_tensor, mask_tensor)
         """
-        self.data_tiled_uri = data_tiled_uri
-        self.data_client = from_uri(data_tiled_uri, api_key=data_tiled_api_key)
-        self.mask_tiled_uri = mask_tiled_uri
-        if mask_tiled_uri:
-            self.mask_client_one_up = from_uri(
-                mask_tiled_uri, api_key=mask_tiled_api_key
-            )
-            self.mask_client = self.mask_client_one_up["mask"]
-            self.mask_idx = [
-                int(idx) for idx in self.mask_client_one_up.metadata["mask_idx"]
-            ]
+
+        self.data_client = data_tiled_client
+        self.mask_client = None
+        if mask_tiled_client:
+            self.mask_client = mask_tiled_client["mask"]
+            self.mask_idx = [int(idx) for idx in mask_tiled_client.metadata["mask_idx"]]
         else:
             self.mask_client = None
             self.mask_idx = None
+
         self.transform = transform
         if using_qlty:
             # this object handles unstitching and stitching

src/train.py

@@ -7,6 +7,7 @@
 import yaml
 from dlsia.core.train_scripts import Trainer
 from dvclive import Live
+from tiled.client import from_uri
 from torchvision import transforms
 
 from network import build_network
@@ -17,15 +18,12 @@
     TUNet3PlusParameters,
     TUNetParameters,
 )
-from seg_utils import crop_split_load, train_segmentation
+from seg_utils import crop_split_load
 from tiled_dataset import TiledDataset
 from utils import create_directory
 
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("yaml_path", type=str, help="path of yaml file for parameters")
-    args = parser.parse_args()
 
+def train(args):
     # Open the YAML file for all parameters
     with open(args.yaml_path, "r") as file:
         # Load parameters
@@ -59,11 +57,17 @@
     # Create Result Directory if not existed
     create_directory(model_dir)
 
+    data_tiled_client = from_uri(
+        io_parameters.data_tiled_uri, api_key=io_parameters.data_tiled_api_key
+    )
+    mask_tiled_client = None
+    if io_parameters.mask_tiled_uri:
+        mask_tiled_client = from_uri(
+            io_parameters.mask_tiled_uri, api_key=io_parameters.mask_tiled_api_key
+        )
     dataset = TiledDataset(
-        data_tiled_uri=io_parameters.data_tiled_uri,
-        data_tiled_api_key=io_parameters.data_tiled_api_key,
-        mask_tiled_uri=io_parameters.mask_tiled_uri,
-        mask_tiled_api_key=io_parameters.mask_tiled_api_key,
+        data_tiled_client=data_tiled_client,
+        mask_tiled_client=mask_tiled_client,
         is_training=True,
         using_qlty=False,
         qlty_window=model_parameters.qlty_window,
@@ -146,3 +150,10 @@
         torch.cuda.empty_cache()
 
     print(f"{network} trained successfully.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("yaml_path", type=str, help="path of yaml file for parameters")
+    args = parser.parse_args()
+    train(args)

src/utils.py

@@ -77,9 +77,13 @@ def allocate_array_space(
     # For now, only save image 1 by 1 regardless of the batch_size_inference.
     structure.chunks = ((1,) * array_shape[0], (array_shape[1],), (array_shape[2],))
 
+    mask_uri = None
+    if tiled_dataset.mask_client is not None:
+        mask_uri = tiled_dataset.mask_client.uri
+
     metadata = {
-        "data_uri": tiled_dataset.data_tiled_uri,
-        "mask_uri": tiled_dataset.mask_tiled_uri,
+        "data_uri": tiled_dataset.data_client.uri,
+        "mask_uri": mask_uri,
         "mask_idx": tiled_dataset.mask_idx,
         "uid": uid,
         "model": model,