MLIA-522 No 'perf' in mode 'all' for TFLite files

Fix the issue that no performance information is shown for TFLite files
when the mode 'all_tests' is used.

Change-Id: I8b4df4ab84ba9783b582ad449a34bf6177037e14

5 files changed, 33 insertions, 30 deletions

diff --git a/src/mlia/api.py b/src/mlia/api.py
index 53ea4c8..0f950db 100644
--- a/src/mlia/api.py
+++ b/src/mlia/api.py
@@ -93,8 +93,7 @@ def get_advice(
     event_handlers = _get_event_handlers(output)
 
     if context is not None:
-        if context.advice_category is None:
-            context.advice_category = advice_category
+        context.advice_category = advice_category
 
         if context.config_parameters is None:
             context.config_parameters = config_parameters
diff --git a/src/mlia/core/common.py b/src/mlia/core/common.py
index 5fbad42..a11bf9a 100644
--- a/src/mlia/core/common.py
+++ b/src/mlia/core/common.py
@@ -7,7 +7,8 @@ core module.
 """
 from abc import ABC
 from abc import abstractmethod
-from enum import Enum
+from enum import auto
+from enum import Flag
 from typing import Any
 
 # This type is used as type alias for the items which are being passed around
@@ -17,16 +18,16 @@ from typing import Any
 DataItem = Any
 
 
-class AdviceCategory(Enum):
+class AdviceCategory(Flag):
     """Advice category.
 
     Enumeration of advice categories supported by ML Inference Advisor.
     """
 
-    OPERATORS = 1
-    PERFORMANCE = 2
-    OPTIMIZATION = 3
-    ALL = 4
+    OPERATORS = auto()
+    PERFORMANCE = auto()
+    OPTIMIZATION = auto()
+    ALL = OPERATORS | PERFORMANCE | OPTIMIZATION
 
     @classmethod
     def from_string(cls, value: str) -> "AdviceCategory":
diff --git a/src/mlia/core/context.py b/src/mlia/core/context.py
index 8b3dd2c..83d2f7c 100644
--- a/src/mlia/core/context.py
+++ b/src/mlia/core/context.py
@@ -55,7 +55,7 @@ class Context(ABC):
 
     @property
     @abstractmethod
-    def advice_category(self) -> Optional[AdviceCategory]:
+    def advice_category(self) -> AdviceCategory:
         """Return advice category."""
 
     @property
@@ -97,7 +97,7 @@ class ExecutionContext(Context):
     def __init__(
         self,
         *,
-        advice_category: Optional[AdviceCategory] = None,
+        advice_category: AdviceCategory = AdviceCategory.ALL,
         config_parameters: Optional[Mapping[str, Any]] = None,
         working_dir: Optional[Union[str, Path]] = None,
         event_handlers: Optional[List[EventHandler]] = None,
@@ -141,7 +141,7 @@ class ExecutionContext(Context):
         self._action_resolver = action_resolver or APIActionResolver()
 
     @property
-    def advice_category(self) -> Optional[AdviceCategory]:
+    def advice_category(self) -> AdviceCategory:
         """Return advice category."""
         return self._advice_category
 
diff --git a/src/mlia/devices/ethosu/advice_generation.py b/src/mlia/devices/ethosu/advice_generation.py
index 7a818c9..0b1352b 100644
--- a/src/mlia/devices/ethosu/advice_generation.py
+++ b/src/mlia/devices/ethosu/advice_generation.py
@@ -175,9 +175,6 @@ class EthosUStaticAdviceProducer(ContextAwareAdviceProducer):
 
     def get_advice(self) -> Union[Advice, List[Advice]]:
         """Return predefined advice based on category."""
-        if self.context.advice_category is None:
-            return []
-
         advice_per_category = {
             AdviceCategory.PERFORMANCE: [
                 Advice(
diff --git a/src/mlia/devices/ethosu/advisor.py b/src/mlia/devices/ethosu/advisor.py
index 802826b..e93858f 100644
--- a/src/mlia/devices/ethosu/advisor.py
+++ b/src/mlia/devices/ethosu/advisor.py
@@ -23,6 +23,7 @@ from mlia.devices.ethosu.data_collection import EthosUOperatorCompatibility
 from mlia.devices.ethosu.data_collection import EthosUOptimizationPerformance
 from mlia.devices.ethosu.data_collection import EthosUPerformance
 from mlia.devices.ethosu.events import EthosUAdvisorStartedEvent
+from mlia.nn.tensorflow.utils import is_tflite_model
 
 
 class EthosUInferenceAdvisor(InferenceAdvisor, ParameterResolverMixin):
@@ -63,25 +64,30 @@ class EthosUInferenceAdvisor(InferenceAdvisor, ParameterResolverMixin):
         """Get collectors."""
         collectors: List[DataCollector] = []
 
-        if context.any_category_enabled(
-            AdviceCategory.OPERATORS,
-            AdviceCategory.ALL,
-        ):
+        if AdviceCategory.OPERATORS in context.advice_category:
             collectors.append(EthosUOperatorCompatibility(model, device))
 
-        if context.category_enabled(AdviceCategory.PERFORMANCE):
-            collectors.append(EthosUPerformance(model, device, backends))
-
-        if context.any_category_enabled(
-            AdviceCategory.OPTIMIZATION,
-            AdviceCategory.ALL,
-        ):
-            optimization_settings = self._get_optimization_settings(context)
-            collectors.append(
-                EthosUOptimizationPerformance(
-                    model, device, optimization_settings, backends
+        # Performance and optimization are mutually exclusive.
+        # Decide which one to use (taking into account the model format).
+        if is_tflite_model(model):
+            # TFLite models do not support optimization (only performance)!
+            if context.advice_category == AdviceCategory.OPTIMIZATION:
+                raise Exception(
+                    "Command 'optimization' is not supported for TFLite files."
                 )
-            )
+            if AdviceCategory.PERFORMANCE in context.advice_category:
+                collectors.append(EthosUPerformance(model, device, backends))
+        else:
+            # Keras/SavedModel: Prefer optimization
+            if AdviceCategory.OPTIMIZATION in context.advice_category:
+                optimization_settings = self._get_optimization_settings(context)
+                collectors.append(
+                    EthosUOptimizationPerformance(
+                        model, device, optimization_settings, backends
+                    )
+                )
+            elif AdviceCategory.PERFORMANCE in context.advice_category:
+                collectors.append(EthosUPerformance(model, device, backends))
 
         return collectors