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118 lines
4.0 KiB
118 lines
4.0 KiB
# Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
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# SPDX-License-Identifier: MIT
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"""Contains helper functions that can be used across the example apps."""
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import os
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import errno
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from pathlib import Path
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import numpy as np
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import datetime
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def dict_labels(labels_file_path: str, include_rgb=False) -> dict:
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"""Creates a dictionary of labels from the input labels file.
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Args:
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labels_file: Path to file containing labels to map model outputs.
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include_rgb: Adds randomly generated RGB values to the values of the
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dictionary. Used for plotting bounding boxes of different colours.
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Returns:
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Dictionary with classification indices for keys and labels for values.
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Raises:
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FileNotFoundError:
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Provided `labels_file_path` does not exist.
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"""
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labels_file = Path(labels_file_path)
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if not labels_file.is_file():
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raise FileNotFoundError(
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errno.ENOENT, os.strerror(errno.ENOENT), labels_file_path
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)
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labels = {}
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with open(labels_file, "r") as f:
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for idx, line in enumerate(f, 0):
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if include_rgb:
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labels[idx] = line.strip("\n"), tuple(np.random.random(size=3) * 255)
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else:
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labels[idx] = line.strip("\n")
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return labels
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def prepare_input_data(audio_data, input_data_type, input_quant_scale, input_quant_offset, mfcc_preprocessor):
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"""
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Takes a block of audio data, extracts the MFCC features, quantizes the array, and uses ArmNN to create the
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input tensors.
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Args:
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audio_data: The audio data to process
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mfcc_instance: The mfcc class instance
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input_data_type: The model's input data type
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input_quant_scale: The model's quantization scale
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input_quant_offset: The model's quantization offset
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mfcc_preprocessor: The mfcc preprocessor instance
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Returns:
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input_data: The prepared input data
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"""
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input_data = mfcc_preprocessor.extract_features(audio_data)
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if input_data_type != np.float32:
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input_data = quantize_input(input_data, input_data_type, input_quant_scale, input_quant_offset)
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return input_data
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def quantize_input(data, input_data_type, input_quant_scale, input_quant_offset):
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"""Quantize the float input to (u)int8 ready for inputting to model."""
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if data.ndim != 2:
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raise RuntimeError("Audio data must have 2 dimensions for quantization")
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if (input_data_type != np.int8) and (input_data_type != np.uint8):
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raise ValueError("Could not quantize data to required data type")
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d_min = np.iinfo(input_data_type).min
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d_max = np.iinfo(input_data_type).max
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for row in range(data.shape[0]):
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for col in range(data.shape[1]):
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data[row, col] = (data[row, col] / input_quant_scale) + input_quant_offset
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data[row, col] = np.clip(data[row, col], d_min, d_max)
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data = data.astype(input_data_type)
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return data
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def dequantize_output(data, is_output_quantized, output_quant_scale, output_quant_offset):
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"""Dequantize the (u)int8 output to float"""
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if is_output_quantized:
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if data.ndim != 2:
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raise RuntimeError("Data must have 2 dimensions for quantization")
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data = data.astype(float)
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for row in range(data.shape[0]):
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for col in range(data.shape[1]):
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data[row, col] = (data[row, col] - output_quant_offset)*output_quant_scale
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return data
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class Profiling:
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def __init__(self, enabled: bool):
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self.m_start = 0
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self.m_end = 0
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self.m_enabled = enabled
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def profiling_start(self):
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if self.m_enabled:
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self.m_start = datetime.datetime.now()
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def profiling_stop_and_print_us(self, msg):
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if self.m_enabled:
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self.m_end = datetime.datetime.now()
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period = self.m_end - self.m_start
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period_us = period.seconds * 1_000_000 + period.microseconds
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print(f'Profiling: {msg} : {period_us:,} microSeconds')
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return period_us
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return 0
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