✅ Add test to cli.

tests/engines/test_patch_predictor.py

@@ -11,12 +11,14 @@
 
 import numpy as np
 import torch
+import yaml
 import zarr
 from click.testing import CliRunner
 
 from tests.conftest import timed
 from tiatoolbox import cli, logger, rcParam
 from tiatoolbox.models import IOPatchPredictorConfig
+from tiatoolbox.models.architecture import fetch_pretrained_weights
 from tiatoolbox.models.architecture.vanilla import CNNModel
 from tiatoolbox.models.engine.patch_predictor import PatchPredictor
 from tiatoolbox.utils import env_detection as toolbox_env
@@ -642,6 +644,17 @@ def test_cli_model_multiple_file_mask(remote_sample: Callable, tmp_path: Path) -
     dir_path_masks = tmp_path.joinpath("new_copies_masks")
     dir_path_masks.mkdir()
 
+    config = {
+        "input_resolutions": [{"units": "mpp", "resolution": 0.5}],
+        "patch_input_shape": [224, 224],
+    }
+
+    with Path.open(tmp_path.joinpath("config.yaml"), "w") as fptr:
+        yaml.dump(config, fptr)
+
+    model = "alexnet-kather100k"
+    weights = fetch_pretrained_weights(model)
+
     try:
         dir_path.joinpath("1_" + mini_wsi_svs.name).symlink_to(mini_wsi_svs)
         dir_path.joinpath("2_" + mini_wsi_svs.name).symlink_to(mini_wsi_svs)
@@ -671,6 +684,12 @@ def test_cli_model_multiple_file_mask(remote_sample: Callable, tmp_path: Path) -
             str(False),
             "--masks",
             str(dir_path_masks),
+            "--model",
+            model,
+            "--weights",
+            str(weights),
+            "--yaml-config-path",
+            tmp_path / "config.yaml",
             "--output-path",
             str(tmp_path / "output"),
             "--output-type",

tiatoolbox/cli/common.py

@@ -619,17 +619,17 @@ def prepare_model_cli(
 tiatoolbox_cli = TIAToolboxCLI()
 
 
-def prepare_ioconfig_seg(
-    segment_config_class: type[IOConfigABC],
+def prepare_ioconfig(
+    config_class: type[IOConfigABC],
     pretrained_weights: str | Path | None,
     yaml_config_path: str | Path,
 ) -> IOConfigABC | None:
-    """Prepare ioconfig for segmentation."""
+    """Prepare ioconfig for CLI."""
     import yaml
 
     if pretrained_weights is not None:
         with Path(yaml_config_path).open() as registry_handle:
             ioconfig = yaml.safe_load(registry_handle)
-        return segment_config_class(**ioconfig)
+        return config_class(**ioconfig)
 
     return None

tiatoolbox/cli/nucleus_instance_segment.py

@@ -19,7 +19,7 @@
     cli_pretrained_weights,
     cli_verbose,
     cli_yaml_config_path,
-    prepare_ioconfig_seg,
+    prepare_ioconfig,
     prepare_model_cli,
     tiatoolbox_cli,
 )
@@ -77,7 +77,7 @@ def nucleus_instance_segment(
         file_types=file_types,
     )
 
-    ioconfig = prepare_ioconfig_seg(
+    ioconfig = prepare_ioconfig(
         IOInstanceSegmentorConfig,
         pretrained_weights,
         yaml_config_path,

tiatoolbox/cli/patch_predictor.py

@@ -13,12 +13,12 @@
     cli_output_path,
     cli_output_type,
     cli_patch_mode,
-    cli_resolution,
     cli_return_labels,
     cli_return_probabilities,
-    cli_units,
     cli_verbose,
     cli_weights,
+    cli_yaml_config_path,
+    prepare_ioconfig,
     prepare_model_cli,
     tiatoolbox_cli,
 )
@@ -37,8 +37,7 @@
 @cli_weights()
 @cli_device(default="cpu")
 @cli_batch_size(default=1)
-@cli_resolution(default=0.5)
-@cli_units(default="mpp")
+@cli_yaml_config_path()
 @cli_masks(default=None)
 @cli_num_loader_workers(default=0)
 @cli_output_type(
@@ -56,8 +55,7 @@ def patch_predictor(
     masks: str | None,
     output_path: str,
     batch_size: int,
-    resolution: float,
-    units: str,
+    yaml_config_path: str,
     num_loader_workers: int,
     device: str,
     output_type: str,
@@ -68,6 +66,7 @@ def patch_predictor(
     verbose: bool,
 ) -> None:
     """Process an image/directory of input images with a patch classification CNN."""
+    from tiatoolbox.models.engine.io_config import IOPatchPredictorConfig
     from tiatoolbox.models.engine.patch_predictor import PatchPredictor
 
     files_all, masks_all, output_path = prepare_model_cli(
@@ -85,11 +84,17 @@ def patch_predictor(
         verbose=verbose,
     )
 
+    ioconfig = prepare_ioconfig(
+        IOPatchPredictorConfig,
+        pretrained_weights=weights,
+        yaml_config_path=yaml_config_path,
+    )
+
     _ = predictor.run(
         images=files_all,
         masks=masks_all,
         patch_mode=patch_mode,
-        input_resolutions=[{"units": units, "resolution": resolution}],
+        ioconfig=ioconfig,
         device=device,
         save_dir=output_path,
         output_type=output_type,

tiatoolbox/cli/semantic_segment.py

@@ -17,7 +17,7 @@
     cli_pretrained_weights,
     cli_verbose,
     cli_yaml_config_path,
-    prepare_ioconfig_seg,
+    prepare_ioconfig,
     prepare_model_cli,
     tiatoolbox_cli,
 )
@@ -71,7 +71,7 @@ def semantic_segment(
         file_types=file_types,
     )
 
-    ioconfig = prepare_ioconfig_seg(
+    ioconfig = prepare_ioconfig(
         IOSegmentorConfig,
         pretrained_weights,
         yaml_config_path,