1 files changed, 41 insertions, 32 deletions

diff --git a/python/pyarmnn/examples/common/utils.py b/python/pyarmnn/examples/common/utils.py
index d4dadf80a4..beca0d37a0 100644
--- a/python/pyarmnn/examples/common/utils.py
+++ b/python/pyarmnn/examples/common/utils.py
@@ -8,7 +8,7 @@ import errno
 from pathlib import Path
 
 import numpy as np
-import pyarmnn as ann
+import datetime
 
 
 def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
@@ -42,67 +42,76 @@ def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
         return labels
 
 
-def prepare_input_tensors(audio_data, input_binding_info, mfcc_preprocessor):
+def prepare_input_data(audio_data, input_data_type, input_quant_scale, input_quant_offset, mfcc_preprocessor):
     """
     Takes a block of audio data, extracts the MFCC features, quantizes the array, and uses ArmNN to create the
     input tensors.
 
     Args:
         audio_data: The audio data to process
-        mfcc_instance: the mfcc class instance
-        input_binding_info: the model input binding info
-        mfcc_preprocessor: the mfcc preprocessor instance
+        mfcc_instance: The mfcc class instance
+        input_data_type: The model's input data type
+        input_quant_scale: The model's quantization scale
+        input_quant_offset: The model's quantization offset
+        mfcc_preprocessor: The mfcc preprocessor instance
     Returns:
-        input_tensors: the prepared input tensors, ready to be consumed by the ArmNN NetworkExecutor
+        input_data: The prepared input data
     """
 
-    data_type = input_binding_info[1].GetDataType()
-    input_tensor = mfcc_preprocessor.extract_features(audio_data)
-    if data_type != ann.DataType_Float32:
-        input_tensor = quantize_input(input_tensor, input_binding_info)
-    input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor])
-    return input_tensors
+    input_data = mfcc_preprocessor.extract_features(audio_data)
+    if input_data_type != np.float32:
+        input_data = quantize_input(input_data, input_data_type, input_quant_scale, input_quant_offset)
+    return input_data
 
 
-def quantize_input(data, input_binding_info):
+def quantize_input(data, input_data_type, input_quant_scale, input_quant_offset):
     """Quantize the float input to (u)int8 ready for inputting to model."""
     if data.ndim != 2:
         raise RuntimeError("Audio data must have 2 dimensions for quantization")
 
-    quant_scale = input_binding_info[1].GetQuantizationScale()
-    quant_offset = input_binding_info[1].GetQuantizationOffset()
-    data_type = input_binding_info[1].GetDataType()
-
-    if data_type == ann.DataType_QAsymmS8:
-        data_type = np.int8
-    elif data_type == ann.DataType_QAsymmU8:
-        data_type = np.uint8
-    else:
+    if (input_data_type != np.int8) and (input_data_type != np.uint8):
         raise ValueError("Could not quantize data to required data type")
 
-    d_min = np.iinfo(data_type).min
-    d_max = np.iinfo(data_type).max
+    d_min = np.iinfo(input_data_type).min
+    d_max = np.iinfo(input_data_type).max
 
     for row in range(data.shape[0]):
         for col in range(data.shape[1]):
-            data[row, col] = (data[row, col] / quant_scale) + quant_offset
+            data[row, col] = (data[row, col] / input_quant_scale) + input_quant_offset
             data[row, col] = np.clip(data[row, col], d_min, d_max)
-    data = data.astype(data_type)
+    data = data.astype(input_data_type)
     return data
 
 
-def dequantize_output(data, output_binding_info):
+def dequantize_output(data, is_output_quantized, output_quant_scale, output_quant_offset):
     """Dequantize the (u)int8 output to float"""
 
-    if output_binding_info[1].IsQuantized():
+    if is_output_quantized:
         if data.ndim != 2:
             raise RuntimeError("Data must have 2 dimensions for quantization")
 
-        quant_scale = output_binding_info[1].GetQuantizationScale()
-        quant_offset = output_binding_info[1].GetQuantizationOffset()
-
         data = data.astype(float)
         for row in range(data.shape[0]):
             for col in range(data.shape[1]):
-                data[row, col] = (data[row, col] - quant_offset)*quant_scale
+                data[row, col] = (data[row, col] - output_quant_offset)*output_quant_scale
     return data
+
+
+class Profiling:
+    def __init__(self, enabled: bool):
+        self.m_start = 0
+        self.m_end = 0
+        self.m_enabled = enabled
+
+    def profiling_start(self):
+        if self.m_enabled:
+            self.m_start = datetime.datetime.now()
+
+    def profiling_stop_and_print_us(self, msg):
+        if self.m_enabled:
+            self.m_end = datetime.datetime.now()
+            period = self.m_end - self.m_start
+            period_us = period.seconds * 1_000_000 + period.microseconds
+            print(f'Profiling: {msg} : {period_us:,} microSeconds')
+            return period_us
+        return 0