resolves unused variables (Project-MONAI#436)

* resolves unused variables

* fixes issue: 'torch' is imported with both 'import' and 'import from'

* less strict check in tests/test_simulatedelayd.py for crossplatform unit testing

examples/classification_3d_ignite/densenet_evaluation_array.py

@@ -84,6 +84,7 @@ def prepare_batch(batch, device=None, non_blocking=False):
     val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
 
     state = evaluator.run(val_loader)
+    print(state)
 
 
 if __name__ == "__main__":

examples/classification_3d_ignite/densenet_evaluation_dict.py

@@ -92,6 +92,7 @@ def prepare_batch(batch, device=None, non_blocking=False):
     val_loader = DataLoader(val_ds, batch_size=2, num_workers=4, pin_memory=torch.cuda.is_available())
 
     state = evaluator.run(val_loader)
+    print(state)
 
 
 if __name__ == "__main__":

examples/classification_3d_ignite/densenet_training_array.py

@@ -134,6 +134,7 @@ def run_validation(engine):
 
     train_epochs = 30
     state = trainer.run(train_loader, train_epochs)
+    print(state)
 
 
 if __name__ == "__main__":

examples/classification_3d_ignite/densenet_training_dict.py

@@ -156,6 +156,7 @@ def run_validation(engine):
 
     train_epochs = 30
     state = trainer.run(train_loader, train_epochs)
+    print(state)
 
 
 if __name__ == "__main__":

examples/segmentation_3d_ignite/unet_evaluation_array.py

@@ -106,6 +106,7 @@ def _sliding_window_processor(engine, batch):
     # sliding window inference for one image at every iteration
     loader = DataLoader(ds, batch_size=1, num_workers=1, pin_memory=torch.cuda.is_available())
     state = evaluator.run(loader)
+    print(state)
     shutil.rmtree(tempdir)
 
 

examples/segmentation_3d_ignite/unet_evaluation_dict.py

@@ -112,6 +112,7 @@ def _sliding_window_processor(engine, batch):
         val_ds, batch_size=1, num_workers=4, collate_fn=list_data_collate, pin_memory=torch.cuda.is_available()
     )
     state = evaluator.run(val_loader)
+    print(state)
     shutil.rmtree(tempdir)
 
 

examples/segmentation_3d_ignite/unet_training_array.py

@@ -153,6 +153,7 @@ def run_validation(engine):
 
     train_epochs = 30
     state = trainer.run(train_loader, train_epochs)
+    print(state)
     shutil.rmtree(tempdir)
 
 

examples/segmentation_3d_ignite/unet_training_dict.py

@@ -193,6 +193,7 @@ def run_validation(engine):
 
     train_epochs = 5
     state = trainer.run(train_loader, train_epochs)
+    print(state)
     shutil.rmtree(tempdir)
 
 

monai/metrics/rocauc.py

@@ -86,7 +86,6 @@ def compute_roc_auc(y_pred, y, to_onehot_y=False, add_softmax=False, average="ma
         y_pred_ndim = 1
     if y_ndim == 2 and y.shape[1] == 1:
         y = y.squeeze(dim=-1)
-        y_ndim = 1
 
     if y_pred_ndim == 1:
         if to_onehot_y:

monai/visualize/img2tensorboard.py

@@ -14,14 +14,13 @@
 import PIL
 from PIL.GifImagePlugin import Image as GifImage
 from torch.utils.tensorboard import SummaryWriter
-from torch import Tensor
 from tensorboard.compat.proto import summary_pb2
 from monai.transforms.utils import rescale_array
 
 from typing import Sequence, Union
 
 
-def _image3_animated_gif(tag: str, image: Union[np.array, Tensor], scale_factor: float = 1):
+def _image3_animated_gif(tag: str, image: Union[np.array, torch.Tensor], scale_factor: float = 1):
     """Function to actually create the animated gif.
     Args:
         tag (str): Data identifier
@@ -48,7 +47,7 @@ def _image3_animated_gif(tag: str, image: Union[np.array, Tensor], scale_factor:
 
 def make_animated_gif_summary(
     tag: str,
-    image: Union[np.array, Tensor],
+    image: Union[np.array, torch.Tensor],
     max_out: int = 3,
     animation_axes: Sequence[int] = (3,),
     image_axes: Sequence[int] = (1, 2),
@@ -86,7 +85,7 @@ def make_animated_gif_summary(
     image = image[tuple(slicing)]
 
     for it_i in range(min(max_out, list(image.shape)[0])):
-        one_channel_img: Union[Tensor, np.array] = image[it_i, :, :, :].squeeze(dim=0) if torch.is_tensor(
+        one_channel_img: Union[torch.Tensor, np.array] = image[it_i, :, :, :].squeeze(dim=0) if torch.is_tensor(
             image
         ) else image[it_i, :, :, :]
         summary_op = _image3_animated_gif(tag + suffix.format(it_i), one_channel_img, scale_factor)
@@ -96,7 +95,7 @@ def make_animated_gif_summary(
 def add_animated_gif(
     writer: SummaryWriter,
     tag: str,
-    image_tensor: Union[np.array, Tensor],
+    image_tensor: Union[np.array, torch.Tensor],
     max_out: int,
     scale_factor: float,
     global_step: int = None,
@@ -123,7 +122,7 @@ def add_animated_gif(
 def add_animated_gif_no_channels(
     writer: SummaryWriter,
     tag: str,
-    image_tensor: Union[np.array, Tensor],
+    image_tensor: Union[np.array, torch.Tensor],
     max_out: int,
     scale_factor: float,
     global_step: int = None,

tests/test_simulatedelayd.py

@@ -25,7 +25,7 @@ def test_value(self, delay_test_time: float):
         _ = resize({"imgd": self.imt[0]})
         stop: float = time.time()
         measured_approximate: float = stop - start
-        np.testing.assert_array_less(delay_test_time, measured_approximate)
+        np.testing.assert_allclose(delay_test_time, measured_approximate, rtol=1e-1)
 
 
 if __name__ == "__main__":