Fix auto3dseg swin template infer bug and sigmoid bug (#256)

* Update algo docs and remove disclaimer

Signed-off-by: heyufan1995 <heyufan1995@gmail.com>

* fix infer cuda error and sigmoid

Signed-off-by: heyufan1995 <heyufan1995@gmail.com>

* [MONAI] code formatting

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

---------

Signed-off-by: heyufan1995 <heyufan1995@gmail.com>
Signed-off-by: monai-bot <monai.miccai2019@gmail.com>
Co-authored-by: monai-bot <monai.miccai2019@gmail.com>

Author: heyufan1995

auto3dseg/algorithm_templates/segresnet2d/scripts/segmenter.py

@@ -324,7 +324,6 @@ def get_crop_transforms(self):
                 max_samples_per_class = None
             indices_key = None
 
-
             if cache_class_indices:
                 ts.append(
                     ClassesToIndicesd(

auto3dseg/algorithm_templates/swinunetr/scripts/infer.py

@@ -15,6 +15,7 @@
 from typing import Optional, Sequence, Union
 
 import torch
+import torch.distributed as dist
 
 import monai
 from monai import transforms
@@ -85,7 +86,11 @@ def __init__(self, config_file: Optional[Union[str, Sequence[str]]] = None, **ov
             infer_ds = monai.data.Dataset(data=self.infer_files, transform=self.infer_transforms)
             self.infer_loader = ThreadDataLoader(infer_ds, num_workers=8, batch_size=1, shuffle=False)
 
-        self.device = torch.device("cuda:0")
+        try:
+            device = f"cuda:{dist.get_rank()}"
+        except BaseException:
+            device = f"cuda:0"
+        self.device = device
 
         self.model = parser.get_parsed_content("network")
         self.model = self.model.to(self.device)
@@ -141,6 +146,7 @@ def infer(self, image_file, save_mask=False):
         device_list_output = [self.device, "cpu", "cpu"]
         for _device_in, _device_out in zip(device_list_input, device_list_output):
             try:
+                logger.debug(f"Working on {image_file} on device {_device_in}/{_device_out} in/out.")
                 with torch.cuda.amp.autocast(enabled=self.amp):
                     batch_data["pred"] = sliding_window_inference(
                         inputs=batch_data["image"].to(_device_in),
@@ -165,6 +171,7 @@ def infer(self, image_file, save_mask=False):
                 break
         if not finished:
             raise RuntimeError("Infer not finished due to OOM.")
+        logger.debug(f"{image_file} fininshed.")
         return batch_data[0]["pred"]
 
     @torch.no_grad()

auto3dseg/algorithm_templates/swinunetr/scripts/train.py

@@ -366,7 +366,7 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
             post_transforms += [transforms.AsDiscreted(keys="pred", argmax=True)]
         else:
             post_transforms += [
-                transforms.Activations(sigmoid=True),
+                transforms.Activationsd(keys="pred", sigmoid=True),
                 transforms.AsDiscreted(keys="pred", threshold=0.5),
             ]
         post_transforms = transforms.Compose(post_transforms)

auto3dseg/algorithm_templates/swinunetr/scripts/validate.py

@@ -124,7 +124,10 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
     if softmax:
         post_transforms += [transforms.AsDiscreted(keys="pred", argmax=True)]
     else:
-        post_transforms += [transforms.Activations(sigmoid=True), transforms.AsDiscreted(keys="pred", threshold=0.5)]
+        post_transforms += [
+            transforms.Activationsd(keys="pred", sigmoid=True),
+            transforms.AsDiscreted(keys="pred", threshold=0.5),
+        ]
 
     if save_mask:
         post_transforms += [