Fix(2d_segmentation): Restore the predicted value of seg image to the range of 0~225 before saving it (#1951)

### Description
To fix the problem that evaluation output images are all black because
`post_trans` scale their pixel values to 0~1.

### Checks
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [ ] Avoid including large-size files in the PR.
- [ ] Clean up long text outputs from code cells in the notebook.
- [ ] For security purposes, please check the contents and remove any
sensitive info such as user names and private key.
- [ ] 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
- [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>`

Signed-off-by: Rekawiia Lin <[email protected]>
Co-authored-by: YunLiu <[email protected]>

Author: Reqwey

2d_segmentation/torch/unet_evaluation_array.py

@@ -47,7 +47,7 @@ def main(tempdir):
     val_loader = DataLoader(val_ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
     post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
-    saver = SaveImage(output_dir="./output", output_ext=".png", output_postfix="seg")
+    saver = SaveImage(output_dir="./output", output_ext=".png", output_postfix="seg", scale=255)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model = UNet(
         spatial_dims=2,

2d_segmentation/torch/unet_evaluation_dict.py

@@ -61,7 +61,7 @@ def main(tempdir):
     val_loader = DataLoader(val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate)
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
     post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
-    saver = SaveImage(output_dir="./output", output_ext=".png", output_postfix="seg")
+    saver = SaveImage(output_dir="./output", output_ext=".png", output_postfix="seg", scale=255)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model = UNet(
         spatial_dims=2,