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📝 Update 05-patch-prediction notebook for the New API #977

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📝 Update 05-patch-prediction notebook for the New API #977

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5db61eb
📝 Make changes to 05-patch-prediction notebook
gozdeg Dec 15, 2025
e7273b9
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 15, 2025
ede9dfe
🐛 Bug fix for model/module assumption - implement helper logic to acc…
gozdeg Dec 16, 2025
407b6f3
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 16, 2025
6da5741
📝 Update notebook-5 & fix engine_abc _get_model_attr() type checking
gozdeg Dec 17, 2025
7d47b2b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 17, 2025
cffae15
:bug: Fix pre-commit errors
shaneahmed Dec 17, 2025
a8d28c1
:white_check_mark: Fix rendering issues
shaneahmed Dec 17, 2025
709813b
:rewind: Revert changes to multi_task_segmentor.py and nucleus_instan…
shaneahmed Dec 17, 2025
cfe0d5e
:art: Improve Jupyter Notebook output
shaneahmed Dec 17, 2025
e1afce2
📝 Update-notebook-5
gozdeg Dec 18, 2025
267133e
🐛 Small fix
gozdeg Dec 18, 2025
c960baf
Merge branch 'dev-define-engines-abc' into dev-update-example-notebooks
shaneahmed Dec 19, 2025
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597 changes: 403 additions & 194 deletions examples/05-patch-prediction.ipynb
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3 changes: 2 additions & 1 deletion pre-commit/notebook_markdown_format.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,8 @@ def main(files: list[Path]) -> None:

"""
for path in files:
notebook = json.loads(path.read_text())
with Path.open(path, encoding="utf-8", errors="ignore") as f:
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@shaneahmed shaneahmed Dec 19, 2025

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Suggested change
with Path.open(path, encoding="utf-8", errors="ignore") as f:
notebook = json.loads(path.read_text())

notebook = json.load(f)
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Suggested change
notebook = json.load(f)

formatted_notebook = format_notebook(copy.deepcopy(notebook))
changed = any(
cell != formatted_cell
Expand Down
1 change: 1 addition & 0 deletions requirements/requirements.txt
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Expand Up @@ -10,6 +10,7 @@ flask-cors>=4.0.0
glymur>=0.12.7
huggingface_hub>=0.33.3
imagecodecs>=2022.9.26
ipywidgets>=8.1.7
joblib>=1.1.1
jupyterlab>=3.5.2
matplotlib>=3.6.2
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3 changes: 2 additions & 1 deletion tiatoolbox/models/engine/deep_feature_extractor.py
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Expand Up @@ -302,8 +302,9 @@ def infer_wsi(
probabilities_zarr, coordinates_zarr = None, None

probabilities_used_percent = 0
infer_batch = self._get_model_attr("infer_batch")
for batch_data in tqdm_loop:
batch_output = self.model.infer_batch(
batch_output = infer_batch(
self.model,
batch_data["image"],
device=self.device,
Expand Down
16 changes: 13 additions & 3 deletions tiatoolbox/models/engine/engine_abc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,6 +64,7 @@

if TYPE_CHECKING: # pragma: no cover
import os
from collections.abc import Callable

from torch.utils.data import DataLoader

Expand Down Expand Up @@ -374,6 +375,14 @@ def _initialize_model_ioconfig(

return model, None

def _get_model_attr(self: EngineABC, attr_name: str) -> Callable:
"""Return a model attribute, unwrapping DataParallel if required."""
try:
return getattr(self.model, attr_name)
except AttributeError:
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Suggested change
except AttributeError:
except AttributeError: # pragma: no cover

module = getattr(self.model, "module", None)
return getattr(module, attr_name)

def get_dataloader(
self: EngineABC,
images: str | Path | list[str | Path] | np.ndarray,
Expand Down Expand Up @@ -427,7 +436,7 @@ def get_dataloader(
auto_get_mask=auto_get_mask,
)

dataset.preproc_func = self.model.preproc_func
dataset.preproc_func = self._get_model_attr("preproc_func")

# preprocessing must be defined with the dataset
return torch.utils.data.DataLoader(
Expand All @@ -443,7 +452,7 @@ def get_dataloader(
inputs=images, labels=labels, patch_input_shape=ioconfig.patch_input_shape
)

dataset.preproc_func = self.model.preproc_func
dataset.preproc_func = self._get_model_attr("preproc_func")

# preprocessing must be defined with the dataset
return torch.utils.data.DataLoader(
Expand Down Expand Up @@ -528,8 +537,9 @@ def infer_patches(
else self.dataloader
)

infer_batch = self._get_model_attr("infer_batch")
for batch_data in tqdm_loop:
batch_output = self.model.infer_batch(
batch_output = infer_batch(
self.model,
batch_data["image"],
device=self.device,
Expand Down
3 changes: 2 additions & 1 deletion tiatoolbox/models/engine/patch_predictor.py
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Expand Up @@ -381,7 +381,8 @@ def post_process_patches(
_ = kwargs.get("return_probabilities")
_ = prediction_shape
_ = prediction_dtype
raw_predictions = self.model.postproc_func(raw_predictions)
postproc_func = self._get_model_attr("postproc_func")
raw_predictions = postproc_func(raw_predictions)
return cast_to_min_dtype(raw_predictions)

def post_process_wsi(
Expand Down
5 changes: 3 additions & 2 deletions tiatoolbox/models/engine/semantic_segmentor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -361,7 +361,7 @@ def get_dataloader(
auto_get_mask=auto_get_mask,
)

dataset.preproc_func = self.model.preproc_func
dataset.preproc_func = self._get_model_attr("preproc_func")
self.output_locations = dataset.outputs

# preprocessing must be defined with the dataset
Expand Down Expand Up @@ -477,8 +477,9 @@ def infer_wsi(
else dataloader.dataset.outputs
)

infer_batch = self._get_model_attr("infer_batch")
for batch_idx, batch_data in enumerate(tqdm_loop):
batch_output = self.model.infer_batch(
batch_output = infer_batch(
self.model,
batch_data["image"],
device=self.device,
Expand Down
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