♻️ Update Changes from New Engine Design

.gitignore

@@ -116,3 +116,6 @@ ENV/
 
 # vim/vi generated
 *.swp
+
+# output zarr generated
+*.zarr

tests/models/test_abc.py

@@ -6,14 +6,15 @@
 
 import pytest
 import torch
+import torchvision.models as torch_models
 from torch import nn
 
-from tiatoolbox import rcParam
+from tiatoolbox import rcParam, utils
 from tiatoolbox.models.architecture import (
     fetch_pretrained_weights,
     get_pretrained_model,
 )
-from tiatoolbox.models.models_abc import ModelABC
+from tiatoolbox.models.models_abc import ModelABC, model_to
 from tiatoolbox.utils import env_detection as toolbox_env
 
 if TYPE_CHECKING:
@@ -149,3 +150,18 @@ def test_model_abc() -> None:
     weights_path = fetch_pretrained_weights("alexnet-kather100k")
     with pytest.raises(RuntimeError, match=r".*loading state_dict*"):
         _ = model.load_weights_from_file(weights_path)
+
+
+def test_model_to() -> None:
+    """Test for placing model on device."""
+    # Test on GPU
+    # no GPU on GitHub Actions so this will crash
+    if not utils.env_detection.has_gpu():
+        model = torch_models.resnet18()
+        with pytest.raises((AssertionError, RuntimeError)):
+            _ = model_to(device="cuda", model=model)
+
+    # Test on CPU
+    model = torch_models.resnet18()
+    model = model_to(device="cpu", model=model)
+    assert isinstance(model, nn.Module)

tests/models/test_arch_mapde.py

@@ -45,7 +45,7 @@ def test_functionality(remote_sample: Callable) -> None:
     model = _load_mapde(name="mapde-conic")
     patch = model.preproc(patch)
     batch = torch.from_numpy(patch)[None]
-    model = model.to(select_device(on_gpu=ON_GPU))
-    output = model.infer_batch(model, batch, on_gpu=ON_GPU)
+    model = model.to()
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))
     output = model.postproc(output[0])
     assert np.all(output[0:2] == [[19, 171], [53, 89]])

tests/models/test_arch_micronet.py

@@ -39,7 +39,7 @@ def test_functionality(
     model = model.to(map_location)
     pretrained = torch.load(weights_path, map_location=map_location)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=ON_GPU)
+    output = model.infer_batch(model, batch, device=map_location)
     output, _ = model.postproc(output[0])
     assert np.max(np.unique(output)) == 46
 

tests/models/test_arch_nuclick.py

@@ -10,6 +10,7 @@
 from tiatoolbox.models import NuClick
 from tiatoolbox.models.architecture import fetch_pretrained_weights
 from tiatoolbox.utils import imread
+from tiatoolbox.utils.misc import select_device
 
 ON_GPU = False
 
@@ -53,7 +54,7 @@ def test_functional_nuclick(
     model = NuClick(num_input_channels=5, num_output_channels=1)
     pretrained = torch.load(weights_path, map_location="cpu")
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=ON_GPU)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))
     postproc_masks = model.postproc(
         output,
         do_reconstruction=True,

tests/models/test_arch_sccnn.py

@@ -5,17 +5,18 @@
 import numpy as np
 import torch
 
-from tiatoolbox import utils
 from tiatoolbox.models import SCCNN
 from tiatoolbox.models.architecture import fetch_pretrained_weights
+from tiatoolbox.utils import env_detection
+from tiatoolbox.utils.misc import select_device
 from tiatoolbox.wsicore.wsireader import WSIReader
 
 
 def _load_sccnn(name: str) -> torch.nn.Module:
     """Loads SCCNN model with specified weights."""
     model = SCCNN()
     weights_path = fetch_pretrained_weights(name)
-    map_location = utils.misc.select_device(on_gpu=utils.env_detection.has_gpu())
+    map_location = select_device(on_gpu=env_detection.has_gpu())
     pretrained = torch.load(weights_path, map_location=map_location)
     model.load_state_dict(pretrained)
 
@@ -40,11 +41,19 @@ def test_functionality(remote_sample: Callable) -> None:
     )
     batch = torch.from_numpy(patch)[None]
     model = _load_sccnn(name="sccnn-crchisto")
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(
+        model,
+        batch,
+        device=select_device(on_gpu=env_detection.has_gpu()),
+    )
     output = model.postproc(output[0])
     assert np.all(output == [[8, 7]])
 
     model = _load_sccnn(name="sccnn-conic")
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(
+        model,
+        batch,
+        device=select_device(on_gpu=env_detection.has_gpu()),
+    )
     output = model.postproc(output[0])
     assert np.all(output == [[7, 8]])

tests/models/test_arch_unet.py

@@ -9,6 +9,7 @@
 
 from tiatoolbox.models.architecture import fetch_pretrained_weights
 from tiatoolbox.models.architecture.unet import UNetModel
+from tiatoolbox.utils.misc import select_device
 from tiatoolbox.wsicore.wsireader import WSIReader
 
 ON_GPU = False
@@ -48,7 +49,7 @@ def test_functional_unet(remote_sample: Callable) -> None:
     model = UNetModel(3, 2, encoder="resnet50", decoder_block=[3])
     pretrained = torch.load(pretrained_weights, map_location="cpu")
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=ON_GPU)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))
     _ = output[0]
 
     # run untrained network to test for architecture
@@ -60,4 +61,4 @@ def test_functional_unet(remote_sample: Callable) -> None:
         encoder_levels=[32, 64],
         skip_type="concat",
     )
-    _ = model.infer_batch(model, batch, on_gpu=ON_GPU)
+    _ = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))

tests/models/test_arch_vanilla.py

@@ -5,10 +5,11 @@
 import torch
 
 from tiatoolbox.models.architecture.vanilla import CNNModel, TimmModel
-from tiatoolbox.utils.misc import model_to
+from tiatoolbox.models.models_abc import model_to
 
 ON_GPU = False
 RNG = np.random.default_rng()  # Numpy Random Generator
+device = "cuda" if ON_GPU else "cpu"
 
 
 def test_functional() -> None:
@@ -43,8 +44,8 @@ def test_functional() -> None:
     try:
         for backbone in backbones:
             model = CNNModel(backbone, num_classes=1)
-            model_ = model_to(on_gpu=ON_GPU, model=model)
-            model.infer_batch(model_, samples, on_gpu=ON_GPU)
+            model_ = model_to(device=device, model=model)
+            model.infer_batch(model_, samples, device=device)
     except ValueError as exc:
         msg = f"Model {backbone} failed."
         raise AssertionError(msg) from exc
@@ -70,8 +71,8 @@ def test_timm_functional() -> None:
     try:
         for backbone in backbones:
             model = TimmModel(backbone=backbone, num_classes=1, pretrained=False)
-            model_ = model_to(on_gpu=ON_GPU, model=model)
-            model.infer_batch(model_, samples, on_gpu=ON_GPU)
+            model_ = model_to(device=device, model=model)
+            model.infer_batch(model_, samples, device=device)
     except ValueError as exc:
         msg = f"Model {backbone} failed."
         raise AssertionError(msg) from exc

tests/models/test_feature_extractor.py

@@ -14,6 +14,7 @@
     IOSegmentorConfig,
 )
 from tiatoolbox.utils import env_detection as toolbox_env
+from tiatoolbox.utils.misc import select_device
 from tiatoolbox.wsicore.wsireader import WSIReader
 
 ON_GPU = not toolbox_env.running_on_ci() and toolbox_env.has_gpu()
@@ -35,7 +36,7 @@ def test_engine(remote_sample: Callable, tmp_path: Path) -> None:
     output_list = extractor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -82,7 +83,7 @@ def test_full_inference(
         [mini_wsi_svs],
         mode="wsi",
         ioconfig=ioconfig,
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )

tests/models/test_hovernet.py

@@ -14,6 +14,7 @@
     ResidualBlock,
     TFSamepaddingLayer,
 )
+from tiatoolbox.utils.misc import select_device
 from tiatoolbox.wsicore.wsireader import WSIReader
 
 
@@ -34,7 +35,7 @@ def test_functionality(remote_sample: Callable) -> None:
     weights_path = fetch_pretrained_weights("hovernet_fast-pannuke")
     pretrained = torch.load(weights_path)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=False))
     output = [v[0] for v in output]
     output = model.postproc(output)
     assert len(output[1]) > 0, "Must have some nuclei."
@@ -51,7 +52,7 @@ def test_functionality(remote_sample: Callable) -> None:
     weights_path = fetch_pretrained_weights("hovernet_fast-monusac")
     pretrained = torch.load(weights_path)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=False))
     output = [v[0] for v in output]
     output = model.postproc(output)
     assert len(output[1]) > 0, "Must have some nuclei."
@@ -68,7 +69,7 @@ def test_functionality(remote_sample: Callable) -> None:
     weights_path = fetch_pretrained_weights("hovernet_original-consep")
     pretrained = torch.load(weights_path)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=False))
     output = [v[0] for v in output]
     output = model.postproc(output)
     assert len(output[1]) > 0, "Must have some nuclei."
@@ -85,7 +86,7 @@ def test_functionality(remote_sample: Callable) -> None:
     weights_path = fetch_pretrained_weights("hovernet_original-kumar")
     pretrained = torch.load(weights_path)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=False))
     output = [v[0] for v in output]
     output = model.postproc(output)
     assert len(output[1]) > 0, "Must have some nuclei."

tests/models/test_hovernetplus.py

@@ -7,6 +7,7 @@
 from tiatoolbox.models import HoVerNetPlus
 from tiatoolbox.models.architecture import fetch_pretrained_weights
 from tiatoolbox.utils import imread
+from tiatoolbox.utils.misc import select_device
 from tiatoolbox.utils.transforms import imresize
 
 
@@ -28,7 +29,7 @@ def test_functionality(remote_sample: Callable) -> None:
     weights_path = fetch_pretrained_weights("hovernetplus-oed")
     pretrained = torch.load(weights_path)
     model.load_state_dict(pretrained)
-    output = model.infer_batch(model, batch, on_gpu=False)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=False))
     assert len(output) == 4, "Must contain predictions for: np, hv, tp and ls branches."
     output = [v[0] for v in output]
     output = model.postproc(output)

tests/models/test_multi_task_segmentor.py

@@ -17,6 +17,7 @@
 from tiatoolbox.utils import env_detection as toolbox_env
 from tiatoolbox.utils import imwrite
 from tiatoolbox.utils.metrics import f1_detection
+from tiatoolbox.utils.misc import select_device
 
 ON_GPU = toolbox_env.has_gpu()
 BATCH_SIZE = 1 if not ON_GPU else 8  # 16
@@ -64,7 +65,7 @@ def test_functionality_local(remote_sample: Callable, tmp_path: Path) -> None:
     output = multi_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -83,7 +84,7 @@ def test_functionality_local(remote_sample: Callable, tmp_path: Path) -> None:
     output = multi_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -117,7 +118,7 @@ def test_functionality_hovernetplus(remote_sample: Callable, tmp_path: Path) ->
     output = multi_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -148,7 +149,7 @@ def test_functionality_hovernet(remote_sample: Callable, tmp_path: Path) -> None
     output = multi_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -195,7 +196,7 @@ def test_masked_segmentor(remote_sample: Callable, tmp_path: Path) -> None:
         masks=[sample_wsi_msk],
         mode="wsi",
         ioconfig=ioconfig,
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -230,7 +231,7 @@ def test_functionality_process_instance_predictions(
     output = semantic_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -268,7 +269,7 @@ def test_empty_image(tmp_path: Path) -> None:
     _ = multi_segmentor.predict(
         [sample_patch_path],
         mode="tile",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -284,7 +285,7 @@ def test_empty_image(tmp_path: Path) -> None:
     _ = multi_segmentor.predict(
         [sample_patch_path],
         mode="tile",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
     )
@@ -312,7 +313,7 @@ def test_empty_image(tmp_path: Path) -> None:
     _ = multi_segmentor.predict(
         [sample_patch_path],
         mode="tile",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
         ioconfig=bcc_wsi_ioconfig,
@@ -361,7 +362,7 @@ def test_functionality_semantic(remote_sample: Callable, tmp_path: Path) -> None
     output = multi_segmentor.predict(
         [mini_wsi_svs],
         mode="wsi",
-        on_gpu=ON_GPU,
+        device=select_device(on_gpu=ON_GPU),
         crash_on_exception=True,
         save_dir=save_dir,
         ioconfig=bcc_wsi_ioconfig,
@@ -413,7 +414,7 @@ def test_crash_segmentor(remote_sample: Callable, tmp_path: Path) -> None:
             masks=[sample_wsi_msk],
             mode="wsi",
             ioconfig=ioconfig,
-            on_gpu=ON_GPU,
+            device=select_device(on_gpu=ON_GPU),
             crash_on_exception=True,
             save_dir=save_dir,
         )