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added cristae model and single channel transfrom #100

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Merged
merged 4 commits into from
Feb 28, 2025
Merged

added cristae model and single channel transfrom #100

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466c733
added cristae model and single channel transfrom
lufre1 Feb 21, 2025
4334d7d
modified normalization function so it handles different channel sizes
lufre1 Feb 27, 2025
3cfeda4
changed to torch_em standardize
lufre1 Feb 28, 2025
f033882
removed debug relic
lufre1 Feb 28, 2025
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8 changes: 6 additions & 2 deletions synapse_net/inference/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import time
import warnings
from glob import glob
from typing import Dict, Optional, Tuple, Union
from typing import Dict, List, Optional, Tuple, Union

# # Suppress annoying import warnings.
# with warnings.catch_warnings():
Expand Down Expand Up @@ -85,6 +85,7 @@ def get_prediction(
model: Optional[torch.nn.Module] = None,
verbose: bool = True,
with_channels: bool = False,
channels_to_normalize: Optional[List[int]] = [0],
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@constantinpape constantinpape Feb 27, 2025 •
edited
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Optional implies that this value can be None. What happens if it is None?
I think in this case all channels should be normalized (independently).
I would also set this as the default, and for the cristae model pass channels_to_normalize=[0].

mask: Optional[np.ndarray] = None,
) -> np.ndarray:
"""Run prediction on a given volume.
Expand All @@ -99,6 +100,7 @@ def get_prediction(
tiling: The tiling configuration for the prediction.
verbose: Whether to print timing information.
with_channels: Whether to predict with channels.
channels_to_normalize: List of channels to normalize. Defaults to 0.
mask: Optional binary mask. If given, the prediction will only be run in
the foreground region of the mask.

Expand All @@ -120,8 +122,10 @@ def get_prediction(
# We standardize the data for the whole volume beforehand.
# If we have channels then the standardization is done independently per channel.
if with_channels:
input_volume = input_volume.astype(np.float32, copy=False)
# TODO Check that this is the correct axis.
input_volume = np.stack([torch_em.transform.raw.normalize(input_volume[0]), input_volume[1]], axis=0)
for ch in channels_to_normalize:
input_volume[ch] = torch_em.transform.raw.normalize(input_volume[ch])
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@constantinpape constantinpape Feb 27, 2025

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Let's leave this at normalize.

else:
input_volume = torch_em.transform.raw.standardize(input_volume)

Expand Down