| # Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| """Contains helper functions that can be used across the example apps.""" |
| |
| import os |
| import errno |
| from pathlib import Path |
| |
| import numpy as np |
| import datetime |
| |
| |
| def dict_labels(labels_file_path: str, include_rgb=False) -> dict: |
| """Creates a dictionary of labels from the input labels file. |
| |
| Args: |
| labels_file: Path to file containing labels to map model outputs. |
| include_rgb: Adds randomly generated RGB values to the values of the |
| dictionary. Used for plotting bounding boxes of different colours. |
| |
| Returns: |
| Dictionary with classification indices for keys and labels for values. |
| |
| Raises: |
| FileNotFoundError: |
| Provided `labels_file_path` does not exist. |
| """ |
| labels_file = Path(labels_file_path) |
| if not labels_file.is_file(): |
| raise FileNotFoundError( |
| errno.ENOENT, os.strerror(errno.ENOENT), labels_file_path |
| ) |
| |
| labels = {} |
| with open(labels_file, "r") as f: |
| for idx, line in enumerate(f, 0): |
| if include_rgb: |
| labels[idx] = line.strip("\n"), tuple(np.random.random(size=3) * 255) |
| else: |
| labels[idx] = line.strip("\n") |
| return labels |
| |
| |
| 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_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_data: The prepared input data |
| """ |
| |
| 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_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") |
| |
| 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(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] / input_quant_scale) + input_quant_offset |
| data[row, col] = np.clip(data[row, col], d_min, d_max) |
| data = data.astype(input_data_type) |
| return data |
| |
| |
| def dequantize_output(data, is_output_quantized, output_quant_scale, output_quant_offset): |
| """Dequantize the (u)int8 output to float""" |
| |
| if is_output_quantized: |
| if data.ndim != 2: |
| raise RuntimeError("Data must have 2 dimensions for quantization") |
| |
| data = data.astype(float) |
| for row in range(data.shape[0]): |
| for col in range(data.shape[1]): |
| 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 |