1428 update to the latest usage SaveImage (Project-MONAI#1429)

Fixes Project-MONAI#1428



### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Avoid including large-size files in the PR.
- [x] Clean up long text outputs from code cells in the notebook.
- [x] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [x] Ensure (1) hyperlinks and markdown anchors are working (2) use
relative paths for tutorial repo files (3) put figure and graphs in the
`./figure` folder
- [x] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

Signed-off-by: Wenqi Li <[email protected]>

3d_segmentation/challenge_baseline/run_net.py

@@ -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
@@ -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),
@@ -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']}")
@@ -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")