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Merged
constantinpape merged 3 commits into from
Nov 27, 2025
Merged

Add prepocess to pred func #149

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3fecc1f
added torch_em load_model to supervised training
lufre1 Nov 10, 2025
6263ed3
added docs for chekcpoint_path
lufre1 Nov 10, 2025
586623c
added preprocess to predcition function so that input for predict_wit…
lufre1 Nov 27, 2025
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8 changes: 6 additions & 2 deletions synapse_net/inference/mitochondria.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
import time
from typing import Dict, List, Optional, Tuple, Union
from typing import Callable, Dict, List, Optional, Tuple, Union

import elf.parallel as parallel
import numpy as np
Expand Down Expand Up @@ -65,6 +65,7 @@ def segment_mitochondria(
ws_halo: Tuple[int, ...] = (48, 48, 48),
boundary_threshold: float = 0.25,
area_threshold: int = 5000,
preprocess: Callable = None,
) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
"""Segment mitochondria in an input volume.

Expand Down Expand Up @@ -99,7 +100,10 @@ def segment_mitochondria(
# Rescale the mask if it was given and run prediction.
if mask is not None:
mask = scaler.scale_input(mask, is_segmentation=True)
pred = get_prediction(input_volume, model_path=model_path, model=model, tiling=tiling, mask=mask, verbose=verbose)
pred = get_prediction(
input_volume, model_path=model_path, model=model, tiling=tiling, mask=mask, verbose=verbose,
preprocess=preprocess
)

# Run segmentation and rescale the result if necessary.
foreground, boundaries = pred[:2]
Expand Down
9 changes: 7 additions & 2 deletions synapse_net/inference/util.py
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Expand Up @@ -126,6 +126,7 @@ def get_prediction(
mask: Optional[ArrayLike] = None,
prediction: Optional[ArrayLike] = None,
devices: Optional[List[str]] = None,
preprocess: Optional[callable] = None,
) -> ArrayLike:
"""Run prediction on a given volume.

Expand Down Expand Up @@ -192,7 +193,7 @@ def get_prediction(
# print(f"updated_tiling {updated_tiling}")
prediction = get_prediction_torch_em(
input_volume, updated_tiling, model_path, model, verbose, with_channels,
mask=mask, prediction=prediction, devices=devices,
mask=mask, prediction=prediction, devices=devices, preprocess=preprocess,
)

return prediction
Expand All @@ -208,6 +209,7 @@ def get_prediction_torch_em(
mask: Optional[ArrayLike] = None,
prediction: Optional[ArrayLike] = None,
devices: Optional[List[str]] = None,
preprocess: Optional[callable] = None,
) -> np.ndarray:
"""Run prediction using torch-em on a given volume.

Expand Down Expand Up @@ -258,7 +260,10 @@ def get_prediction_torch_em(
print("Run prediction with mask.")
mask = mask.astype("bool")

preprocess = None if isinstance(input_volume, np.ndarray) else torch_em.transform.raw.standardize
if preprocess is None:
preprocess = None if isinstance(input_volume, np.ndarray) else torch_em.transform.raw.standardize
else:
preprocess = preprocess
prediction = predict_with_halo(
input_volume, model, gpu_ids=devices,
block_shape=block_shape, halo=halo,
Expand Down
5 changes: 5 additions & 0 deletions synapse_net/training/supervised_training.py
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Expand Up @@ -201,6 +201,7 @@ def supervised_training(
in_channels: int = 1,
out_channels: int = 2,
mask_channel: bool = False,
checkpoint_path: Optional[str] = None,
**loader_kwargs,
):
"""Run supervised segmentation training.
Expand Down Expand Up @@ -243,6 +244,7 @@ def supervised_training(
out_channels: The number of output channels of the UNet.
mask_channel: Whether the last channels in the labels should be used for masking the loss.
This can be used to implement more complex masking operations and is not compatible with `ignore_label`.
checkpoint_path: Path to the directory where 'best.pt' resides; continue training this model.
loader_kwargs: Additional keyword arguments for the dataloader.
"""
train_loader = get_supervised_loader(train_paths, raw_key, label_key, patch_shape, batch_size,
Expand All @@ -265,6 +267,9 @@ def supervised_training(
model = get_2d_model(out_channels=out_channels, in_channels=in_channels)
else:
model = get_3d_model(out_channels=out_channels, in_channels=in_channels)

if checkpoint_path:
model = torch_em.util.load_model(checkpoint=checkpoint_path)

loss, metric = None, None
# No ignore label -> we can use default loss.
Expand Down