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1428 update to the latest usage SaveImage #1429

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Jun 18, 2023
Merged

1428 update to the latest usage SaveImage #1429

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8 changes: 4 additions & 4 deletions 3d_segmentation/challenge_baseline/run_net.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@
import shutil
import sys

import numpy as np
import torch
import torch.nn as nn
from ignite.contrib.handlers import ProgressBar
Expand All @@ -42,7 +41,7 @@ def get_xforms(mode="train", keys=("image", "label")):
"""returns a composed transform for train/val/infer."""

xforms = [
LoadImaged(keys, ensure_channel_first=True),
LoadImaged(keys, ensure_channel_first=True, image_only=True),
Orientationd(keys, axcodes="LPS"),
Spacingd(keys, pixdim=(1.25, 1.25, 5.0), mode=("bilinear", "nearest")[: len(keys)]),
ScaleIntensityRanged(keys[0], a_min=-1000.0, a_max=500.0, b_min=0.0, b_max=1.0, clip=True),
Expand Down Expand Up @@ -239,7 +238,7 @@ def infer(data_folder=".", model_folder="runs", prediction_folder="output"):
)

inferer = get_inferer()
saver = monai.data.NiftiSaver(output_dir=prediction_folder, mode="nearest")
saver = monai.transforms.SaveImage(output_dir=prediction_folder, mode="nearest", resample=True)
with torch.no_grad():
for infer_data in infer_loader:
logging.info(f"segmenting {infer_data['image'].meta['filename_or_obj']}")
Expand All @@ -258,7 +257,8 @@ def infer(data_folder=".", model_folder="runs", prediction_folder="output"):
n = n + 1.0
preds = preds / n
preds = (preds.argmax(dim=1, keepdims=True)).float()
saver.save_batch(preds, infer_data["image"].meta)
for p in preds: # save each image+metadata in the batch respectively
saver(p)

# copy the saved segmentations into the required folder structure for submission
submission_dir = os.path.join(prediction_folder, "to_submit")
Expand Down