bat_detect/train/train_utils.py

import numpy as np
import random
import os
import glob
import json


def write_notes_file(file_name, text):
    with open(file_name, 'a') as da:
        da.write(text + '\n')


def get_blank_dataset_dict(dataset_name, is_test, ann_path, wav_path):
    ddict = {'dataset_name': dataset_name, 'is_test': is_test, 'is_binary': False,
            'ann_path': ann_path, 'wav_path': wav_path}
    return ddict


def get_short_class_names(class_names, str_len=3):
    class_names_short = []
    for cc in class_names:
        class_names_short.append(' '.join([sp[:str_len] for sp in cc.split(' ')]))
    return class_names_short


def remove_dupes(data_train, data_test):
    test_ids = [dd['id'] for dd in data_test]
    data_train_prune = []
    for aa in data_train:
        if aa['id'] not in test_ids:
            data_train_prune.append(aa)
    diff = len(data_train) - len(data_train_prune)
    if diff != 0:
        print(diff, 'items removed from train set')
    return data_train_prune


def get_genus_mapping(class_names):
    genus_names, genus_mapping = np.unique([cc.split(' ')[0] for cc in class_names], return_inverse=True)
    return genus_names.tolist(), genus_mapping.tolist()


def standardize_low_freq(data, class_of_interest):
    # address the issue of highly variable low frequency annotations
    # this often happens for contstant frequency calls
    # for the class of interest sets the low and high freq to be the dataset mean
    low_freqs = []
    high_freqs = []
    for dd in data:
        for aa in dd['annotation']:
            if aa['class'] == class_of_interest:
                low_freqs.append(aa['low_freq'])
                high_freqs.append(aa['high_freq'])

    low_mean = np.mean(low_freqs)
    high_mean = np.mean(high_freqs)
    assert(low_mean < high_mean)

    print('\nStandardizing low and high frequency for:')
    print(class_of_interest)
    print('low:  ', round(low_mean, 2))
    print('high: ', round(high_mean, 2))

    # only set the low freq, high stays the same
    # assumes that low_mean < high_mean
    for dd in data:
        for aa in dd['annotation']:
            if aa['class'] == class_of_interest:
                aa['low_freq'] = low_mean
                if aa['high_freq'] < low_mean:
                    aa['high_freq'] = high_mean

    return data


def load_set_of_anns(data, classes_to_ignore=[], events_of_interest=None,
                     convert_to_genus=False, verbose=True, list_of_anns=False,
                     filter_issues=False, name_replace=False):

    # load the annotations
    anns = []
    if list_of_anns:
        # path to list of individual json files
        anns.extend(load_anns_from_path(data['ann_path'], data['wav_path']))
    else:
        # dictionary of datasets
        for dd in data:
            anns.extend(load_anns(dd['ann_path'], dd['wav_path']))

    # discarding unannoated files
    anns = [aa for aa in anns if aa['annotated'] is True]

    # filter files that have annotation issues - is the input is a dictionary of
    # datasets, this will lilely have already been done
    if filter_issues:
        anns = [aa for aa in anns if aa['issues'] is False]

    # check for some basic formatting errors with class names
    for ann in anns:
        for aa in ann['annotation']:
            aa['class'] = aa['class'].strip()

    # only load specified events - i.e. types of calls
    if events_of_interest is not None:
        for ann in anns:
            filtered_events = []
            for aa in ann['annotation']:
                if aa['event'] in events_of_interest:
                    filtered_events.append(aa)
            ann['annotation'] = filtered_events

    # change class names
    # replace_names will be a dictionary mapping input name to output
    if type(name_replace) is dict:
        for ann in anns:
            for aa in ann['annotation']:
                if aa['class'] in name_replace:
                    aa['class'] = name_replace[aa['class']]

    # convert everything to genus name
    if convert_to_genus:
        for ann in anns:
            for aa in ann['annotation']:
                    aa['class'] = aa['class'].split(' ')[0]

    # get unique class names
    class_names_all = []
    for ann in anns:
        for aa in ann['annotation']:
            if aa['class'] not in classes_to_ignore:
                class_names_all.append(aa['class'])

    class_names, class_cnts = np.unique(class_names_all, return_counts=True)
    class_inv_freq = (class_cnts.sum() / (len(class_names) * class_cnts.astype(np.float32)))

    if verbose:
        print('Class count:')
        str_len = np.max([len(cc) for cc in class_names]) + 5
        for cc in range(len(class_names)):
            print(str(cc).ljust(5) + class_names[cc].ljust(str_len) + str(class_cnts[cc]))

    if len(classes_to_ignore) == 0:
        return anns
    else:
        return anns, class_names.tolist(), class_inv_freq.tolist()


def load_anns(ann_file_name, raw_audio_dir):
    with open(ann_file_name) as da:
        anns = json.load(da)

    for aa in anns:
        aa['file_path'] = raw_audio_dir + aa['id']

    return anns


def load_anns_from_path(ann_file_dir, raw_audio_dir):
    files = glob.glob(ann_file_dir + '*.json')
    anns = []
    for ff in files:
        with open(ff) as da:
            ann = json.load(da)
        ann['file_path'] = raw_audio_dir + ann['id']
        anns.append(ann)

    return anns


class AverageMeter(object):
  """Computes and stores the average and current value"""
  def __init__(self):
    self.reset()

  def reset(self):
    self.val = 0
    self.avg = 0
    self.sum = 0
    self.count = 0

  def update(self, val, n=1):
    self.val = val
    self.sum += val * n
    self.count += n
    self.avg = self.sum / self.count