add test for TimmBackbone

1. Split `test_functional` into `test_engine` and `test_full_inference`
2. In `test_full_inference` use `@pytest.mark.parametrize` for `CNNBackbone` and `TimmBackbone` instead of making 2 copies of the function

tests/models/test_feature_extractor.py

@@ -5,9 +5,10 @@
 from typing import Callable
 
 import numpy as np
+import pytest
 import torch
 
-from tiatoolbox.models.architecture.vanilla import CNNBackbone
+from tiatoolbox.models.architecture.vanilla import CNNBackbone, TimmBackbone
 from tiatoolbox.models.engine.semantic_segmentor import (
     DeepFeatureExtractor,
     IOSegmentorConfig,
@@ -22,8 +23,8 @@
 # -------------------------------------------------------------------------------------
 
 
-def test_functional(remote_sample: Callable, tmp_path: Path) -> None:
-    """Test for feature extraction."""
+def test_engine(remote_sample: Callable, tmp_path: Path) -> None:
+    """Test feature extraction with DeepFeatureExtractor engine."""
     save_dir = tmp_path / "output"
     # # convert to pathlib Path to prevent wsireader complaint
     mini_wsi_svs = Path(remote_sample("wsi4_1k_1k_svs"))
@@ -41,12 +42,27 @@ def test_functional(remote_sample: Callable, tmp_path: Path) -> None:
     wsi_0_root_path = output_list[0][1]
     positions = np.load(f"{wsi_0_root_path}.position.npy")
     features = np.load(f"{wsi_0_root_path}.features.0.npy")
+    assert len(positions.shape) == 2
     assert len(features.shape) == 4
 
     # * test same output between full infer and engine
     # pre-emptive clean up
     shutil.rmtree(save_dir, ignore_errors=True)  # default output dir test
 
+
+@pytest.mark.parametrize(
+    "model", [CNNBackbone("resnet50"), TimmBackbone("efficientnet_b0", pretrained=True)]
+)
+def test_full_inference(
+    remote_sample: Callable, tmp_path: Path, model: Callable
+) -> None:
+    """Test full inference with CNNBackbone and TimmBackbone models."""
+    save_dir = tmp_path / "output"
+    # pre-emptive clean up
+    shutil.rmtree(save_dir, ignore_errors=True)  # default output dir test
+
+    mini_wsi_svs = Path(remote_sample("wsi4_1k_1k_svs"))
+
     ioconfig = IOSegmentorConfig(
         input_resolutions=[
             {"units": "mpp", "resolution": 0.25},
@@ -60,7 +76,6 @@ def test_functional(remote_sample: Callable, tmp_path: Path) -> None:
         save_resolution={"units": "mpp", "resolution": 8.0},
     )
 
-    model = CNNBackbone("resnet50")
     extractor = DeepFeatureExtractor(batch_size=4, model=model)
     # should still run because we skip exception
     output_list = extractor.predict(