MLIA-607 Update documentation and comments

Use "TensorFlow Lite" instead of "TFLite" in
documentation and comments

Change-Id: Ie4450d72fb2e5261d152d72ab8bd94c3da914c46

17 files changed, 47 insertions, 45 deletions

diff --git a/src/mlia/cli/commands.py b/src/mlia/cli/commands.py
index 5dd39f9..e044e1a 100644
--- a/src/mlia/cli/commands.py
+++ b/src/mlia/cli/commands.py
@@ -50,7 +50,7 @@ def all_tests(
     This command runs a series of tests in order to generate a
     comprehensive report/advice:
 
-        - converts the input Keras model into TFLite format
+        - converts the input Keras model into TensorFlow Lite format
         - checks the model for operator compatibility on the specified device
         - applies optimizations to the model and estimates the resulting performance
           on both the original and the optimized models
@@ -112,14 +112,14 @@ def operators(
     :param ctx: execution context
     :param target_profile: target profile identifier. Will load appropriate parameters
             from the profile.json file based on this argument.
-    :param model: path to the model, which can be TFLite or Keras
+    :param model: path to the model, which can be TensorFlow Lite or Keras
     :param output: path to the file where the report will be saved
     :param supported_ops_report: if True then generates supported operators
            report in current directory and exits
 
     Example:
         Run command for the target profile ethos-u55-256 and the provided
-        TFLite model and print report on the standard output
+        TensorFlow Lite model and print report on the standard output
 
         >>> from mlia.api import ExecutionContext
         >>> from mlia.cli.logging import setup_logging
@@ -161,13 +161,13 @@ def performance(
     :param ctx: execution context
     :param target_profile: target profile identifier. Will load appropriate parameters
             from the profile.json file based on this argument.
-    :param model: path to the model, which can be TFLite or Keras
+    :param model: path to the model, which can be TensorFlow Lite or Keras
     :param output: path to the file where the report will be saved
     :param evaluate_on: list of the backends to use for evaluation
 
     Example:
         Run command for the target profile ethos-u55-256 and
-        the provided TFLite model and print report on the standard output
+        the provided TensorFlow Lite model and print report on the standard output
 
         >>> from mlia.api import ExecutionContext
         >>> from mlia.cli.logging import setup_logging
@@ -205,7 +205,7 @@ def optimization(
     :param ctx: execution context
     :param target: target profile identifier. Will load appropriate parameters
             from the profile.json file based on this argument.
-    :param model: path to the TFLite model
+    :param model: path to the TensorFlow Lite model
     :param optimization_type: list of the optimization techniques separated
            by comma, e.g. 'pruning,clustering'
     :param optimization_target: list of the corresponding targets for
@@ -217,7 +217,7 @@ def optimization(
 
     Example:
         Run command for the target profile ethos-u55-256 and
-        the provided TFLite model and print report on the standard output
+        the provided TensorFlow Lite model and print report on the standard output
 
         >>> from mlia.cli.logging import setup_logging
         >>> setup_logging()
diff --git a/src/mlia/cli/options.py b/src/mlia/cli/options.py
index f7f95c0..e5e85f0 100644
--- a/src/mlia/cli/options.py
+++ b/src/mlia/cli/options.py
@@ -62,15 +62,17 @@ def add_multi_optimization_options(parser: argparse.ArgumentParser) -> None:
 
 def add_optional_tflite_model_options(parser: argparse.ArgumentParser) -> None:
     """Add optional model specific options."""
-    model_group = parser.add_argument_group("TFLite model options")
+    model_group = parser.add_argument_group("TensorFlow Lite model options")
     # make model parameter optional
-    model_group.add_argument("model", nargs="?", help="TFLite model (optional)")
+    model_group.add_argument(
+        "model", nargs="?", help="TensorFlow Lite model (optional)"
+    )
 
 
 def add_tflite_model_options(parser: argparse.ArgumentParser) -> None:
     """Add model specific options."""
-    model_group = parser.add_argument_group("TFLite model options")
-    model_group.add_argument("model", help="TFLite model")
+    model_group = parser.add_argument_group("TensorFlow Lite model options")
+    model_group.add_argument("model", help="TensorFlow Lite model")
 
 
 def add_output_options(parser: argparse.ArgumentParser) -> None:
diff --git a/src/mlia/devices/ethosu/advice_generation.py b/src/mlia/devices/ethosu/advice_generation.py
index dee1650..8a38d2c 100644
--- a/src/mlia/devices/ethosu/advice_generation.py
+++ b/src/mlia/devices/ethosu/advice_generation.py
@@ -196,7 +196,7 @@ class EthosUStaticAdviceProducer(ContextAwareAdviceProducer):
                 Advice(
                     [
                         "For better performance, make sure that all the operators "
-                        "of your final TFLite model are supported by the NPU.",
+                        "of your final TensorFlow Lite model are supported by the NPU.",
                     ]
                     + self.context.action_resolver.operator_compatibility_details()
                 )
diff --git a/src/mlia/devices/ethosu/advisor.py b/src/mlia/devices/ethosu/advisor.py
index be58de7..2c25f6c 100644
--- a/src/mlia/devices/ethosu/advisor.py
+++ b/src/mlia/devices/ethosu/advisor.py
@@ -52,10 +52,10 @@ class EthosUInferenceAdvisor(DefaultInferenceAdvisor):
         # 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)!
+            # TensorFlow Lite models do not support optimization (only performance)!
             if context.advice_category == AdviceCategory.OPTIMIZATION:
                 raise Exception(
-                    "Command 'optimization' is not supported for TFLite files."
+                    "Command 'optimization' is not supported for TensorFlow Lite files."
                 )
             if AdviceCategory.PERFORMANCE in context.advice_category:
                 collectors.append(EthosUPerformance(model, device, backends))
diff --git a/src/mlia/nn/tensorflow/config.py b/src/mlia/nn/tensorflow/config.py
index 6ee32e7..03d1d0f 100644
--- a/src/mlia/nn/tensorflow/config.py
+++ b/src/mlia/nn/tensorflow/config.py
@@ -31,7 +31,7 @@ class ModelConfiguration:
     def convert_to_tflite(
         self, tflite_model_path: str | Path, quantized: bool = False
     ) -> TFLiteModel:
-        """Convert model to TFLite format."""
+        """Convert model to TensorFlow Lite format."""
         raise NotImplementedError()
 
     def convert_to_keras(self, keras_model_path: str | Path) -> KerasModel:
@@ -52,8 +52,8 @@ class KerasModel(ModelConfiguration):
     def convert_to_tflite(
         self, tflite_model_path: str | Path, quantized: bool = False
     ) -> TFLiteModel:
-        """Convert model to TFLite format."""
-        logger.info("Converting Keras to TFLite ...")
+        """Convert model to TensorFlow Lite format."""
+        logger.info("Converting Keras to TensorFlow Lite ...")
 
         converted_model = convert_to_tflite(self.get_keras_model(), quantized)
         logger.info("Done\n")
@@ -71,7 +71,7 @@ class KerasModel(ModelConfiguration):
 
 
 class TFLiteModel(ModelConfiguration):  # pylint: disable=abstract-method
-    """TFLite model configuration."""
+    """TensorFlow Lite model configuration."""
 
     def input_details(self) -> list[dict]:
         """Get model's input details."""
@@ -81,7 +81,7 @@ class TFLiteModel(ModelConfiguration):  # pylint: disable=abstract-method
     def convert_to_tflite(
         self, tflite_model_path: str | Path, quantized: bool = False
     ) -> TFLiteModel:
-        """Convert model to TFLite format."""
+        """Convert model to TensorFlow Lite format."""
         return self
 
 
@@ -94,7 +94,7 @@ class TfModel(ModelConfiguration):  # pylint: disable=abstract-method
     def convert_to_tflite(
         self, tflite_model_path: str | Path, quantized: bool = False
     ) -> TFLiteModel:
-        """Convert model to TFLite format."""
+        """Convert model to TensorFlow Lite format."""
         converted_model = convert_tf_to_tflite(self.model_path, quantized)
         save_tflite_model(converted_model, tflite_model_path)
 
@@ -114,12 +114,12 @@ def get_model(model: str | Path) -> ModelConfiguration:
 
     raise Exception(
         "The input model format is not supported"
-        "(supported formats: TFLite, Keras, TensorFlow saved model)!"
+        "(supported formats: TensorFlow Lite, Keras, TensorFlow saved model)!"
     )
 
 
 def get_tflite_model(model: str | Path, ctx: Context) -> TFLiteModel:
-    """Convert input model to TFLite and returns TFLiteModel object."""
+    """Convert input model to TensorFlow Lite and returns TFLiteModel object."""
     tflite_model_path = ctx.get_model_path("converted_model.tflite")
     converted_model = get_model(model)
 
diff --git a/src/mlia/nn/tensorflow/tflite_metrics.py b/src/mlia/nn/tensorflow/tflite_metrics.py
index 0af7500..d7ae2a4 100644
--- a/src/mlia/nn/tensorflow/tflite_metrics.py
+++ b/src/mlia/nn/tensorflow/tflite_metrics.py
@@ -1,7 +1,7 @@
 # SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
 # SPDX-License-Identifier: Apache-2.0
 """
-Contains class TFLiteMetrics to calculate metrics from a TFLite file.
+Contains class TFLiteMetrics to calculate metrics from a TensorFlow Lite file.
 
 These metrics include:
 * Sparsity (per layer and overall)
@@ -102,7 +102,7 @@ class ReportClusterMode(Enum):
 
 
 class TFLiteMetrics:
-    """Helper class to calculate metrics from a TFLite file.
+    """Helper class to calculate metrics from a TensorFlow Lite file.
 
     Metrics include:
     * sparsity (per-layer and overall)
@@ -111,12 +111,12 @@ class TFLiteMetrics:
     """
 
     def __init__(self, tflite_file: str, ignore_list: list[str] | None = None) -> None:
-        """Load the TFLite file and filter layers."""
+        """Load the TensorFlow Lite file and filter layers."""
         self.tflite_file = tflite_file
         if ignore_list is None:
             ignore_list = DEFAULT_IGNORE_LIST
         self.ignore_list = [ignore.casefold() for ignore in ignore_list]
-        # Initialize the TFLite interpreter with the model file
+        # Initialize the TensorFlow Lite interpreter with the model file
         self.interpreter = tf.lite.Interpreter(
             model_path=tflite_file, experimental_preserve_all_tensors=True
         )
@@ -218,7 +218,7 @@ class TFLiteMetrics:
         """Print a summary of all the model information."""
         print(f"Model file: {self.tflite_file}")
         print("#" * 80)
-        print(" " * 28 + "### TFLITE SUMMARY ###")
+        print(" " * 28 + "### TENSORFLOW LITE SUMMARY ###")
         print(f"File: {os.path.abspath(self.tflite_file)}")
         print("Input(s):")
         self._print_in_outs(self.interpreter.get_input_details(), verbose)
diff --git a/src/mlia/nn/tensorflow/utils.py b/src/mlia/nn/tensorflow/utils.py
index 6250f56..7970329 100644
--- a/src/mlia/nn/tensorflow/utils.py
+++ b/src/mlia/nn/tensorflow/utils.py
@@ -63,7 +63,7 @@ def representative_tf_dataset(model: str) -> Callable:
 
 
 def convert_to_tflite(model: tf.keras.Model, quantized: bool = False) -> Interpreter:
-    """Convert Keras model to TFLite."""
+    """Convert Keras model to TensorFlow Lite."""
     if not isinstance(model, tf.keras.Model):
         raise Exception("Invalid model type")
 
@@ -83,7 +83,7 @@ def convert_to_tflite(model: tf.keras.Model, quantized: bool = False) -> Interpr
 
 
 def convert_tf_to_tflite(model: str, quantized: bool = False) -> Interpreter:
-    """Convert TensorFlow model to TFLite."""
+    """Convert TensorFlow model to TensorFlow Lite."""
     if not isinstance(model, str):
         raise Exception("Invalid model type")
 
@@ -109,15 +109,15 @@ def save_keras_model(model: tf.keras.Model, save_path: str | Path) -> None:
 
 
 def save_tflite_model(model: tf.lite.TFLiteConverter, save_path: str | Path) -> None:
-    """Save TFLite model at provided path."""
+    """Save TensorFlow Lite model at provided path."""
     with open(save_path, "wb") as file:
         file.write(model)
 
 
 def is_tflite_model(model: str | Path) -> bool:
-    """Check if model type is supported by TFLite API.
+    """Check if model type is supported by TensorFlow Lite API.
 
-    TFLite model is indicated by the model file extension .tflite
+    TensorFlow Lite model is indicated by the model file extension .tflite
     """
     model_path = Path(model)
     return model_path.suffix == ".tflite"
diff --git a/src/mlia/resources/backend_configs/systems/corstone-300-vht/backend-config.json b/src/mlia/resources/backend_configs/systems/corstone-300-vht/backend-config.json
index 5c44ebc..7bc12c7 100644
--- a/src/mlia/resources/backend_configs/systems/corstone-300-vht/backend-config.json
+++ b/src/mlia/resources/backend_configs/systems/corstone-300-vht/backend-config.json
@@ -16,7 +16,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
@@ -52,7 +52,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
diff --git a/src/mlia/resources/backend_configs/systems/corstone-300/backend-config.json b/src/mlia/resources/backend_configs/systems/corstone-300/backend-config.json
index 41d2fd0..c27c6f5 100644
--- a/src/mlia/resources/backend_configs/systems/corstone-300/backend-config.json
+++ b/src/mlia/resources/backend_configs/systems/corstone-300/backend-config.json
@@ -16,7 +16,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
@@ -52,7 +52,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
diff --git a/src/mlia/resources/backend_configs/systems/corstone-310-vht/backend-config.json b/src/mlia/resources/backend_configs/systems/corstone-310-vht/backend-config.json
index 3ea9a6a..bbcadfd 100644
--- a/src/mlia/resources/backend_configs/systems/corstone-310-vht/backend-config.json
+++ b/src/mlia/resources/backend_configs/systems/corstone-310-vht/backend-config.json
@@ -16,7 +16,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
diff --git a/src/mlia/resources/backend_configs/systems/corstone-310/backend-config.json b/src/mlia/resources/backend_configs/systems/corstone-310/backend-config.json
index d043a2d..e0b1d1d 100644
--- a/src/mlia/resources/backend_configs/systems/corstone-310/backend-config.json
+++ b/src/mlia/resources/backend_configs/systems/corstone-310/backend-config.json
@@ -16,7 +16,7 @@
       "run": [
         {
           "name": "--data",
-          "description": "Full file name for a custom model. Model must be in TFLite format compiled with Vela.",
+          "description": "Full file name for a custom model. Model must be in TensorFlow Lite format compiled with Vela.",
           "values": [],
           "alias": "input_file"
         },
diff --git a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
index 7ee5e00..4856d27 100644
--- a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
+++ b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "Generic Inference Runner: Ethos-U55/65 Shared SRAM",
-    "description": "This application allows running inferences using custom NN TFLite models on Ethos-U. No data pre-/post-processing is executed.",
+    "description": "This application allows running inferences using custom NN TensorFlow Lite models on Ethos-U. No data pre-/post-processing is executed.",
     "supported_systems": [
       {
         "name": "Corstone-300: Cortex-M55+Ethos-U55"
diff --git a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
index 51ff429..3b512ff 100644
--- a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
+++ b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "Generic Inference Runner: Ethos-U55 SRAM",
-    "description": "This application allows running inferences using custom NN TFLite models on Ethos-U. No data pre-/post-processing is executed.",
+    "description": "This application allows running inferences using custom NN TensorFlow Lite models on Ethos-U. No data pre-/post-processing is executed.",
     "supported_systems": [
       {
         "name": "Corstone-300: Cortex-M55+Ethos-U55"
diff --git a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U65-Dedicated_Sram-TA/backend-config.json b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U65-Dedicated_Sram-TA/backend-config.json
index b59c85e..fc0569f 100644
--- a/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U65-Dedicated_Sram-TA/backend-config.json
+++ b/src/mlia/resources/backends/applications/inference_runner-sse-300-22.05.01-ethos-U65-Dedicated_Sram-TA/backend-config.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "Generic Inference Runner: Ethos-U65 Dedicated SRAM",
-    "description": "This application allows running inferences using custom NN TFLite models on Ethos-U. No data pre-/post-processing is executed.",
+    "description": "This application allows running inferences using custom NN TensorFlow Lite models on Ethos-U. No data pre-/post-processing is executed.",
     "supported_systems": [
       {
         "name": "Corstone-300: Cortex-M55+Ethos-U65"
diff --git a/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json b/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
index 69c5e60..0bdbd27 100644
--- a/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
+++ b/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Shared_Sram-TA/backend-config.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "Generic Inference Runner: Ethos-U55/65 Shared SRAM",
-    "description": "This application allows running inferences using custom NN TFLite models on Ethos-U. No data pre-/post-processing is executed.",
+    "description": "This application allows running inferences using custom NN TensorFlow Lite models on Ethos-U. No data pre-/post-processing is executed.",
     "supported_systems": [
       {
         "name": "Corstone-310: Cortex-M85+Ethos-U55"
diff --git a/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json b/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
index fbe4a16..a56e49d 100644
--- a/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
+++ b/src/mlia/resources/backends/applications/inference_runner-sse-310-22.05.01-ethos-U55-Sram_Only-TA/backend-config.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "Generic Inference Runner: Ethos-U55 SRAM",
-    "description": "This application allows running inferences using custom NN TFLite models on Ethos-U. No data pre-/post-processing is executed.",
+    "description": "This application allows running inferences using custom NN TensorFlow Lite models on Ethos-U. No data pre-/post-processing is executed.",
     "supported_systems": [
       {
         "name": "Corstone-310: Cortex-M85+Ethos-U55"
diff --git a/src/mlia/tools/vela_wrapper.py b/src/mlia/tools/vela_wrapper.py
index 47c15e9..00d2f2c 100644
--- a/src/mlia/tools/vela_wrapper.py
+++ b/src/mlia/tools/vela_wrapper.py
@@ -275,7 +275,7 @@ class VelaCompiler:  # pylint: disable=too-many-instance-attributes
 
     @staticmethod
     def _read_model(model: str | Path) -> tuple[Graph, NetworkType]:
-        """Read TFLite model."""
+        """Read TensorFlow Lite model."""
         try:
             model_path = str(model) if isinstance(model, Path) else model
 
@@ -448,7 +448,7 @@ def run_on_npu(operator: Op) -> NpuSupported:
     a particular operator is supported to run on NPU.
 
     There are two groups of checks:
-      - general TFLite constraints
+      - general TensorFlow Lite constraints
       - operator specific constraints
 
     If an operator is not supported on NPU then this function