MLIA-782 Remove --output parameter

* Remove --output parameter from argument parser
* Remove FormattedFilePath class and its presence across the codebase
* Move logging module from cli to core
* The output format is now injected in the execution context and used
  across MLIA
* Depending on the output format, TextReporter and JSONReporter have
  been created and used accordingly.
* The whole output to standard output and/or logfile is driven via the
  logging module: the only case where the print is used is when the
  --json parameter is specified. This is needed becase all output
  (including third party application as well) needs to be disabled
  otherwise it might corrupt the json output in the standard output.
* Debug information is logged into the log file and printed to stdout
  when the output format is plain_text.
* Update E2E test and config to cope with the new mechanism of
  outputting json data to standard output.

Change-Id: I4395800b0b1af4d24406a828d780bdeef98cd413

1 files changed, 5 insertions, 19 deletions

diff --git a/src/mlia/cli/commands.py b/src/mlia/cli/commands.py
index d2242ba..c17d571 100644
--- a/src/mlia/cli/commands.py
+++ b/src/mlia/cli/commands.py
@@ -7,10 +7,10 @@ functionality.
 
 Before running them from scripts 'logging' module should
 be configured. Function 'setup_logging' from module
-'mli.cli.logging' could be used for that, e.g.
+'mli.core.logging' could be used for that, e.g.
 
 >>> from mlia.api import ExecutionContext
->>> from mlia.cli.logging import setup_logging
+>>> from mlia.core.logging import setup_logging
 >>> setup_logging(verbose=True)
 >>> import mlia.cli.commands as mlia
 >>> mlia.check(ExecutionContext(), "ethos-u55-256",
@@ -27,7 +27,6 @@ from mlia.cli.command_validators import validate_backend
 from mlia.cli.command_validators import validate_check_target_profile
 from mlia.cli.config import get_installation_manager
 from mlia.cli.options import parse_optimization_parameters
-from mlia.cli.options import parse_output_parameters
 from mlia.utils.console import create_section_header
 
 logger = logging.getLogger(__name__)
@@ -41,8 +40,6 @@ def check(
     model: str | None = None,
     compatibility: bool = False,
     performance: bool = False,
-    output: Path | None = None,
-    json: bool = False,
     backend: list[str] | None = None,
 ) -> None:
     """Generate a full report on the input model.
@@ -61,7 +58,6 @@ def check(
     :param model: path to the Keras model
     :param compatibility: flag that identifies whether to run compatibility checks
     :param performance: flag that identifies whether to run performance checks
-    :param output: path to the file where the report will be saved
     :param backend: list of the backends to use for evaluation
 
     Example:
@@ -69,18 +65,15 @@ def check(
         and operator compatibility.
 
         >>> from mlia.api import ExecutionContext
-        >>> from mlia.cli.logging import setup_logging
+        >>> from mlia.core.logging import setup_logging
         >>> setup_logging()
         >>> from mlia.cli.commands import check
         >>> check(ExecutionContext(), "ethos-u55-256",
-                      "model.h5", compatibility=True, performance=True,
-                       output="report.json")
+                      "model.h5", compatibility=True, performance=True)
     """
     if not model:
         raise Exception("Model is not provided")
 
-    formatted_output = parse_output_parameters(output, json)
-
     # Set category based on checks to perform (i.e. "compatibility" and/or
     # "performance").
     # If no check type is specified, "compatibility" is the default category.
@@ -98,7 +91,6 @@ def check(
         target_profile,
         model,
         category,
-        output=formatted_output,
         context=ctx,
         backends=validated_backend,
     )
@@ -113,8 +105,6 @@ def optimize(  # pylint: disable=too-many-arguments
     pruning_target: float | None,
     clustering_target: int | None,
     layers_to_optimize: list[str] | None = None,
-    output: Path | None = None,
-    json: bool = False,
     backend: list[str] | None = None,
 ) -> None:
     """Show the performance improvements (if any) after applying the optimizations.
@@ -133,15 +123,13 @@ def optimize(  # pylint: disable=too-many-arguments
     :param pruning_target: pruning optimization target
     :param layers_to_optimize: list of the layers of the model which should be
            optimized, if None then all layers are used
-    :param output: path to the file where the report will be saved
-    :param json: set the output format to json
     :param backend: list of the backends to use for evaluation
 
     Example:
         Run command for the target profile ethos-u55-256 and
         the provided TensorFlow Lite model and print report on the standard output
 
-        >>> from mlia.cli.logging import setup_logging
+        >>> from mlia.core.logging import setup_logging
         >>> from mlia.api import ExecutionContext
         >>> setup_logging()
         >>> from mlia.cli.commands import optimize
@@ -161,7 +149,6 @@ def optimize(  # pylint: disable=too-many-arguments
         )
     )
 
-    formatted_output = parse_output_parameters(output, json)
     validated_backend = validate_backend(target_profile, backend)
 
     get_advice(
@@ -169,7 +156,6 @@ def optimize(  # pylint: disable=too-many-arguments
         model,
         {"optimization"},
         optimization_targets=opt_params,
-        output=formatted_output,
         context=ctx,
         backends=validated_backend,
     )