MLIA-607 Update documentation and comments

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

Change-Id: Ie4450d72fb2e5261d152d72ab8bd94c3da914c46

2 files changed, 13 insertions, 11 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: