utils: tuning: libtuning: Implement extensible components of libtuning

Implement the extensible components of libtuning. This includes:
- Parsers, for supporting different types of input config file formats
- Generators, for supporting different types of output tuning file
  formats
- Modules, for supporting different tuning modules for different
  algorithms and platforms

No parsers, generators, or modules are actually implemented. Only the
base classes are implemented.

Signed-off-by: Paul Elder <paul.elder@ideasonboard.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>

1 files changed, 32 insertions, 0 deletions

diff --git a/utils/tuning/libtuning/modules/module.py b/utils/tuning/libtuning/modules/module.py
new file mode 100644
index 00000000..12e2fc7c
--- /dev/null
+++ b/utils/tuning/libtuning/modules/module.py
@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: GPL-2.0-or-later
+#
+# Copyright (C) 2022, Paul Elder <paul.elder@ideasonboard.com>
+#
+# module.py - Base class for algorithm-specific tuning modules
+
+
+# @var type Type of the module. Defined in the base module.
+# @var out_name The key that will be used for the algorithm in the algorithms
+#               dictionary in the tuning output file
+# @var hr_name Human-readable module name, mostly for debugging
+class Module(object):
+    type = 'base'
+    hr_name = 'Base Module'
+    out_name = 'GenericAlgorithm'
+
+    def __init__(self):
+        pass
+
+    def validate_config(self, config: dict) -> bool:
+        raise NotImplementedError
+
+    # @brief Do the module's processing
+    # @param config Full configuration from the input configuration file
+    # @param images List of images to process
+    # @param outputs The outputs of all modules that were executed before this
+    #                module. Note that this is an input parameter, and the
+    #                output of this module should be returned directly
+    # @return Result of the module's processing. It may be empty. None
+    #         indicates failure and that the result should not be used.
+    def process(self, config: dict, images: list, outputs: dict) -> dict:
+        raise NotImplementedError