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Diffstat (limited to 'python/pyarmnn/examples/common/utils.py')
-rw-r--r-- | python/pyarmnn/examples/common/utils.py | 69 |
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 |