From 245d64c60d0ea30f5080ff53225b5169927e24d6 Mon Sep 17 00:00:00 2001
From: Matthew Bentham <Matthew.Bentham@arm.com>
Date: Mon, 2 Dec 2019 12:59:43 +0000
Subject: Work in progress of python bindings for Arm NN

Not built or tested in any way

Signed-off-by: Matthew Bentham <Matthew.Bentham@arm.com>
Change-Id: Ie7f92b529aa5087130f0c5cc8c17db1581373236
---
 .../src/pyarmnn/_utilities/profiling_helper.py     | 95 ++++++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 python/pyarmnn/src/pyarmnn/_utilities/profiling_helper.py

(limited to 'python/pyarmnn/src/pyarmnn/_utilities/profiling_helper.py')

diff --git a/python/pyarmnn/src/pyarmnn/_utilities/profiling_helper.py b/python/pyarmnn/src/pyarmnn/_utilities/profiling_helper.py
new file mode 100644
index 0000000000..d10c28915e
--- /dev/null
+++ b/python/pyarmnn/src/pyarmnn/_utilities/profiling_helper.py
@@ -0,0 +1,95 @@
+# Copyright © 2019 Arm Ltd. All rights reserved.
+# SPDX-License-Identifier: MIT
+import json
+from collections import namedtuple
+
+ProfilerData = namedtuple('ProfilerData', ['inference_data', 'per_workload_execution_data'])
+ProfilerData.__doc__ = """Container to hold the profiling inference data, and the profiling data per workload.
+
+Contains:
+    inference_data (dict): holds end-to-end inference performance data. Keys:
+                           'time_unit' - timer units.
+                           'execution_time' - list of total inference execution times for each inference run.                            
+    per_workload_execution_data (dict): holds per operation performance data, key is a operation name
+                                        Each operation has 
+                                        'time_unit' - timer units.   
+                                        'execution_time' - list of total execution times for each inference run.
+                                        'backend' - backend used for this operation.
+                                        
+Example:
+    
+    >>> data = get_profiling_data(profiler)
+    >>> print(data)
+    >>> ProfilerData(inference_data={'time_unit': 'us', 
+                                     'execution_time': [8901372.972]}, 
+                    per_workload_execution_data={'CopyMemGeneric_Execute_#3': {'time_unit': 'us', 
+                                                                               'execution_time': [28.941], 
+                                                                               'backend': 'Unknown'}, 
+                                                 'RefConvolution2dWorkload_Execute_#5': {'time_unit': 'us', 
+                                                                                         'execution_time': [126838.071], 
+                                                                                         'backend': 'CpuRef'}, 
+                                                 'RefDepthwiseConvolution2dWorkload_Execute_#6': {'time_unit': 'us', 
+                                                                                                  'execution_time': [49886.208], 
+                                                                                                  'backend': 'CpuRef'}
+                                                 ...etc
+                                                 }
+                    )  
+"""
+
+
+def get_profiling_data(profiler: 'IProfiler') -> ProfilerData:
+    """Reads IProfiler object passed in, extracts the relevant data
+        and returns it in a ProfilerData container.
+
+        Args:
+            profile_log (IProfiler): The IProfiler object to be parsed.
+
+        Returns:
+            ProfilerData: A container containing the relevant data extracted from the Profiler output.
+    """
+
+    top_level_dict = json.loads(profiler.as_json())
+    armnn_data = top_level_dict["ArmNN"]
+    inference_measurements = armnn_data["inference_measurements_#1"]
+    execution_data = inference_measurements["Execute_#2"]
+
+    workload_data = {}
+    inference_data = {}
+    for exec_key, exec_value in execution_data.items():
+        # Check all items with a type.
+        if "type" in exec_value and exec_value["type"] == "Event":
+            for event_key, event_value in exec_value.items():
+                if event_key.startswith("Wall clock time_#") and event_value["type"] == "Measurement":
+                    time_data = __get_wall_clock_times__(event_value)
+                    time_data["backend"] = __get_backend(exec_key)
+                    workload_data[exec_key] = time_data
+        # This is the total inference time map
+        if exec_key.startswith("Wall clock time_#") and exec_value["type"] == "Measurement":
+            time_data = __get_wall_clock_times__(exec_value)
+            inference_data.update(time_data)
+    return ProfilerData(inference_data=inference_data, per_workload_execution_data=workload_data)
+
+
+def __get_wall_clock_times__(wall_clock_item):
+    execution_times = wall_clock_item["raw"]
+    time_data = {}
+    raw_data = []
+    for time in execution_times:
+        raw_data.append(time)
+    time_data["time_unit"] = wall_clock_item["unit"]
+    time_data["execution_time"] = raw_data
+    return time_data
+
+
+def __get_backend(exec_key):
+    if "ref" in exec_key.lower():
+        return "CpuRef"
+    elif "neon" in exec_key.lower():
+        return "CpuAcc"
+    elif "cl" in exec_key.lower():
+        return "GpuAcc"
+    elif "npu" in exec_key.lower():
+        return "NpuAcc"
+    else:
+        return "Unknown"
+
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