1 files changed, 68 insertions, 1 deletions
diff --git a/python/pyarmnn/examples/common/utils.py b/python/pyarmnn/examples/common/utils.py
index cf09fdefb8..d4dadf80a4 100644
--- a/python/pyarmnn/examples/common/utils.py
+++ b/python/pyarmnn/examples/common/utils.py
@@ -1,4 +1,4 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
 # SPDX-License-Identifier: MIT
 
 """Contains helper functions that can be used across the example apps."""
@@ -8,6 +8,7 @@ import errno
 from pathlib import Path
 
 import numpy as np
+import pyarmnn as ann
 
 
 def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
@@ -39,3 +40,69 @@ def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
             else:
                 labels[idx] = line.strip("\n")
         return labels
+
+
+def prepare_input_tensors(audio_data, input_binding_info, 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
+    Returns:
+        input_tensors: the prepared input tensors, ready to be consumed by the ArmNN NetworkExecutor
+    """
+
+    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
+
+
+def quantize_input(data, input_binding_info):
+    """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:
+        raise ValueError("Could not quantize data to required data type")
+
+    d_min = np.iinfo(data_type).min
+    d_max = np.iinfo(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] = np.clip(data[row, col], d_min, d_max)
+    data = data.astype(data_type)
+    return data
+
+
+def dequantize_output(data, output_binding_info):
+    """Dequantize the (u)int8 output to float"""
+
+    if output_binding_info[1].IsQuantized():
+        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
+    return data