Formatting

Signed-off-by: Eric Kerfoot <[email protected]>

Author: ericspod

monai/engines/evaluator.py

@@ -492,7 +492,7 @@ def _iteration(self, engine: EnsembleEvaluator, batchdata: dict[str, torch.Tenso
         for idx, network in enumerate(engine.networks):
             with engine.mode(network):
                 if engine.amp:
-                    with torch.autocast("cuda",**engine.amp_kwargs):
+                    with torch.autocast("cuda", **engine.amp_kwargs):
                         if isinstance(engine.state.output, dict):
                             engine.state.output.update(
                                 {engine.pred_keys[idx]: engine.inferer(inputs, network, *args, **kwargs)}

monai/engines/trainer.py

@@ -255,7 +255,7 @@ def _compute_pred_loss():
         engine.optimizer.zero_grad(set_to_none=engine.optim_set_to_none)
 
         if engine.amp and engine.scaler is not None:
-            with torch.autocast("cuda",**engine.amp_kwargs):
+            with torch.autocast("cuda", **engine.amp_kwargs):
                 _compute_pred_loss()
             engine.scaler.scale(engine.state.output[Keys.LOSS]).backward()
             engine.fire_event(IterationEvents.BACKWARD_COMPLETED)
@@ -689,7 +689,7 @@ def _compute_generator_loss() -> None:
         engine.state.g_optimizer.zero_grad(set_to_none=engine.optim_set_to_none)
 
         if engine.amp and engine.state.g_scaler is not None:
-            with torch.autocast("cuda",**engine.amp_kwargs):
+            with torch.autocast("cuda", **engine.amp_kwargs):
                 _compute_generator_loss()
 
             engine.state.output[Keys.LOSS] = (
@@ -737,7 +737,7 @@ def _compute_discriminator_loss() -> None:
         engine.state.d_network.zero_grad(set_to_none=engine.optim_set_to_none)
 
         if engine.amp and engine.state.d_scaler is not None:
-            with torch.autocast("cuda",**engine.amp_kwargs):
+            with torch.autocast("cuda", **engine.amp_kwargs):
                 _compute_discriminator_loss()
 
             engine.state.d_scaler.scale(engine.state.output[AdversarialKeys.DISCRIMINATOR_LOSS]).backward()

monai/networks/layers/vector_quantizer.py

@@ -100,7 +100,7 @@ def quantize(self, inputs: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, to
             torch.Tensor: Quantization indices of shape [B,H,W,D,1]
 
         """
-        with torch.autocast("cuda",enabled=False):
+        with torch.autocast("cuda", enabled=False):
             encoding_indices_view = list(inputs.shape)
             del encoding_indices_view[1]
 
@@ -138,7 +138,7 @@ def embed(self, embedding_indices: torch.Tensor) -> torch.Tensor:
         Returns:
             torch.Tensor: Quantize space representation of encoding_indices in channel first format.
         """
-        with torch.autocast("cuda",enabled=False):
+        with torch.autocast("cuda", enabled=False):
             embedding: torch.Tensor = (
                 self.embedding(embedding_indices).permute(self.quantization_permutation).contiguous()
             )