From 0efca3cadbad5517a59884576ddb90cfe7ac30f8 Mon Sep 17 00:00:00 2001
From: Diego Russo <diego.russo@arm.com>
Date: Mon, 30 May 2022 13:34:14 +0100
Subject: Add MLIA codebase

Add MLIA codebase including sources and tests.

Change-Id: Id41707559bd721edd114793618d12ccd188d8dbd
---
 src/mlia/core/performance.py | 47 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 src/mlia/core/performance.py

(limited to 'src/mlia/core/performance.py')

diff --git a/src/mlia/core/performance.py b/src/mlia/core/performance.py
new file mode 100644
index 0000000..5433d5c
--- /dev/null
+++ b/src/mlia/core/performance.py
@@ -0,0 +1,47 @@
+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Module for performance estimation."""
+from abc import abstractmethod
+from typing import Callable
+from typing import Generic
+from typing import List
+from typing import TypeVar
+
+
+ModelType = TypeVar("ModelType")  # pylint: disable=invalid-name
+PerfMetricsType = TypeVar("PerfMetricsType")  # pylint: disable=invalid-name
+
+
+class PerformanceEstimator(Generic[ModelType, PerfMetricsType]):
+    """Base class for the performance estimation."""
+
+    @abstractmethod
+    def estimate(self, model: ModelType) -> PerfMetricsType:
+        """Estimate performance."""
+
+
+def estimate_performance(
+    original_model: ModelType,
+    estimator: PerformanceEstimator[ModelType, PerfMetricsType],
+    model_transformations: List[Callable[[ModelType], ModelType]],
+) -> List[PerfMetricsType]:
+    """Estimate performance impact.
+
+    This function estimates performance impact on model performance after
+    applying provided transformations/optimizations.
+
+    :param original_model: object that represents a model, could be
+           instance of the model or path to the model. This depends on
+           provided performance estimator.
+    :param estimator: performance estimator
+    :param model_transformations: list of the callables each of those
+           returns object that represents optimized model
+    """
+    original_metrics = estimator.estimate(original_model)
+
+    optimized_metrics = [
+        estimator.estimate(transform(original_model))
+        for transform in model_transformations
+    ]
+
+    return [original_metrics, *optimized_metrics]
-- 
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