inference -> evaluation, as requested

Signed-off-by: Tomasz Kornuta <[email protected]>

nemo/backends/pytorch/actions.py

@@ -423,7 +423,7 @@ def __nm_graph_forward_pass(
                 # if module.is_trainable():
                 if isinstance(pmodule, nn.Module):
                     pmodule.train()
-            elif mode == OperationMode.inference:
+            elif mode == OperationMode.evaluation:
                 # if module.is_trainable():
                 if isinstance(pmodule, nn.Module):
                     pmodule.eval()
@@ -584,7 +584,7 @@ def _eval(self, tensors_2_evaluate, callback, step, verbose=False):
                     t.unique_name: d for t, d in zip(call_chain[0][2].values(), tensors) if t is not None
                 }
                 self.__nm_graph_forward_pass(
-                    call_chain=call_chain, registered_tensors=registered_e_tensors, mode=OperationMode.inference,
+                    call_chain=call_chain, registered_tensors=registered_e_tensors, mode=OperationMode.evaluation,
                 )
 
                 if not is_distributed or self.global_rank == 0:
@@ -766,7 +766,7 @@ def _infer(
                 self.__nm_graph_forward_pass(
                     call_chain=call_chain,
                     registered_tensors=registered_e_tensors,
-                    mode=OperationMode.inference,
+                    mode=OperationMode.evaluation,
                     use_cache=use_cache,
                 )
 

nemo/core/neural_graph.py

@@ -95,16 +95,16 @@ def __call__(self, **kwargs):
         outer_mode = self._app_state.active_graph.operation_mode
         inner_mode = self.operation_mode
 
-        if inner_mode == OperationMode.inference and outer_mode == OperationMode.training:
+        if inner_mode == OperationMode.evaluation and outer_mode == OperationMode.training:
             raise TypeError("Cannot nest 'inference' graph into 'training'")
 
-        if inner_mode == OperationMode.training and outer_mode == OperationMode.inference:
+        if inner_mode == OperationMode.training and outer_mode == OperationMode.evaluation:
             raise TypeError("Cannot nest 'training' graph into 'inference'")
 
         if inner_mode == OperationMode.training and outer_mode == OperationMode.both:
             raise TypeError("Cannot nest 'training' graph into 'both'")
 
-        if inner_mode == OperationMode.inference and outer_mode == OperationMode.both:
+        if inner_mode == OperationMode.evaluation and outer_mode == OperationMode.both:
             raise TypeError("Cannot nest 'inference' graph into 'both'")
 
         # Check inputs: iterate through all inputs passed to the "self".
@@ -525,7 +525,7 @@ def __serialize_header(self) -> Dict[str, Any]:
         # Add operation mode.
         if self._operation_mode == OperationMode.training:
             header["operation_mode"] = "training"
-        elif self._operation_mode == OperationMode.inference:
+        elif self._operation_mode == OperationMode.evaluation:
             header["operation_mode"] = "inference"
         else:
             header["operation_mode"] = "both"
@@ -717,7 +717,7 @@ def __deserialize_header(cls, serialized_header: Dict[str, Any]):
         if serialized_header["operation_mode"] == "training":
             operation_mode = OperationMode.training
         elif serialized_header["operation_mode"] == "inference":
-            operation_mode = OperationMode.inference
+            operation_mode = OperationMode.evaluation
         else:
             operation_mode = OperationMode.both
 

tests/unit/core/neural_graph/test_neural_graph_nesting.py

@@ -45,9 +45,9 @@ def test_module_nesting1_change_operation_modes(self):
             _, _ = dl()
             assert dl.operation_mode == OperationMode.training
 
-        with NeuralGraph(operation_mode=OperationMode.inference):
+        with NeuralGraph(operation_mode=OperationMode.evaluation):
             _, _ = dl()
-            assert dl.operation_mode == OperationMode.inference
+            assert dl.operation_mode == OperationMode.evaluation
 
     @pytest.mark.unit
     def test_graph_nesting2_possible_operation_modes(self):
@@ -63,7 +63,7 @@ def test_graph_nesting2_possible_operation_modes(self):
         with NeuralGraph(operation_mode=OperationMode.training) as training:
             _, _ = dl()
 
-        with NeuralGraph(operation_mode=OperationMode.inference) as inference:
+        with NeuralGraph(operation_mode=OperationMode.evaluation) as inference:
             _, _ = dl()
 
         # Allowed operations.
@@ -72,15 +72,15 @@ def test_graph_nesting2_possible_operation_modes(self):
             _, _ = both()
 
         # Can nest 'both' into 'inference'.
-        with NeuralGraph(operation_mode=OperationMode.inference):
+        with NeuralGraph(operation_mode=OperationMode.evaluation):
             _, _ = both()
 
         # Can nest 'training' into 'training'.
         with NeuralGraph(operation_mode=OperationMode.training):
             _, _ = training()
 
         # Can nest 'inference' into 'inference'.
-        with NeuralGraph(operation_mode=OperationMode.inference):
+        with NeuralGraph(operation_mode=OperationMode.evaluation):
             _, _ = inference()
 
         # Can nest 'both' into 'both'.
@@ -95,7 +95,7 @@ def test_graph_nesting2_possible_operation_modes(self):
 
         # Cannot nest 'training' into 'inference'.
         with pytest.raises(TypeError):
-            with NeuralGraph(operation_mode=OperationMode.inference):
+            with NeuralGraph(operation_mode=OperationMode.evaluation):
                 _, _ = training()
 
         # Cannot nest 'training' into 'both'.

tests/unit/core/neural_graph/test_neural_graph_serialization.py

@@ -81,7 +81,7 @@ def test_graph_serialization_2_simple_graph_output_binding(self):
         loss = MSELoss(name="tgs2_loss")
 
         # Create the graph.
-        with NeuralGraph(operation_mode=OperationMode.inference) as g1:
+        with NeuralGraph(operation_mode=OperationMode.evaluation) as g1:
             x, t = dl()
             prediction1 = tn(x=x)
             _ = loss(predictions=prediction1, target=t)