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import torch
import random
import numpy as np
import copy
import librosa
import torch.nn.functional as F
import torchaudio
import os
import sys
sys.path.append(os.path.join('..', '..'))
import bat_detect.utils.audio_utils as au
def generate_gt_heatmaps(spec_op_shape, sampling_rate, ann, params):
# spec may be resized on input into the network
num_classes = len(params['class_names'])
op_height = spec_op_shape[0]
op_width = spec_op_shape[1]
freq_per_bin = (params['max_freq'] - params['min_freq']) / op_height
# start and end times
x_pos_start = au.time_to_x_coords(ann['start_times'], sampling_rate,
params['fft_win_length'], params['fft_overlap'])
x_pos_start = (params['resize_factor']*x_pos_start).astype(np.int)
x_pos_end = au.time_to_x_coords(ann['end_times'], sampling_rate,
params['fft_win_length'], params['fft_overlap'])
x_pos_end = (params['resize_factor']*x_pos_end).astype(np.int)
# location on y axis i.e. frequency
y_pos_low = (ann['low_freqs'] - params['min_freq']) / freq_per_bin
y_pos_low = (op_height - y_pos_low).astype(np.int)
y_pos_high = (ann['high_freqs'] - params['min_freq']) / freq_per_bin
y_pos_high = (op_height - y_pos_high).astype(np.int)
bb_widths = x_pos_end - x_pos_start
bb_heights = (y_pos_low - y_pos_high)
valid_inds = np.where((x_pos_start >= 0) & (x_pos_start < op_width) &
(y_pos_low >= 0) & (y_pos_low < (op_height-1)))[0]
ann_aug = {}
ann_aug['x_inds'] = x_pos_start[valid_inds]
ann_aug['y_inds'] = y_pos_low[valid_inds]
keys = ['start_times', 'end_times', 'high_freqs', 'low_freqs', 'class_ids', 'individual_ids']
for kk in keys:
ann_aug[kk] = ann[kk][valid_inds]
# if the number of calls is only 1, then it is unique
# TODO would be better if we found these unique calls at the merging stage
if len(ann_aug['individual_ids']) == 1:
ann_aug['individual_ids'][0] = 0
y_2d_det = np.zeros((1, op_height, op_width), dtype=np.float32)
y_2d_size = np.zeros((2, op_height, op_width), dtype=np.float32)
# num classes and "background" class
y_2d_classes = np.zeros((num_classes+1, op_height, op_width), dtype=np.float32)
# create 2D ground truth heatmaps
for ii in valid_inds:
draw_gaussian(y_2d_det[0,:], (x_pos_start[ii], y_pos_low[ii]), params['target_sigma'])
#draw_gaussian(y_2d_det[0,:], (x_pos_start[ii], y_pos_low[ii]), params['target_sigma'], params['target_sigma']*2)
y_2d_size[0, y_pos_low[ii], x_pos_start[ii]] = bb_widths[ii]
y_2d_size[1, y_pos_low[ii], x_pos_start[ii]] = bb_heights[ii]
cls_id = ann['class_ids'][ii]
if cls_id > -1:
draw_gaussian(y_2d_classes[cls_id, :], (x_pos_start[ii], y_pos_low[ii]), params['target_sigma'])
#draw_gaussian(y_2d_classes[cls_id, :], (x_pos_start[ii], y_pos_low[ii]), params['target_sigma'], params['target_sigma']*2)
# be careful as this will have a 1.0 places where we have event but dont know gt class
# this will be masked in training anyway
y_2d_classes[num_classes, :] = 1.0 - y_2d_classes.sum(0)
y_2d_classes = y_2d_classes / y_2d_classes.sum(0)[np.newaxis, ...]
y_2d_classes[np.isnan(y_2d_classes)] = 0.0
return y_2d_det, y_2d_size, y_2d_classes, ann_aug
def draw_gaussian(heatmap, center, sigmax, sigmay=None):
# center is (x, y)
# this edits the heatmap inplace
if sigmay is None:
sigmay = sigmax
tmp_size = np.maximum(sigmax, sigmay) * 3
mu_x = int(center[0] + 0.5)
mu_y = int(center[1] + 0.5)
w, h = heatmap.shape[0], heatmap.shape[1]
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= h or ul[1] >= w or br[0] < 0 or br[1] < 0:
return False
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
#g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
g = np.exp(- ((x - x0) ** 2)/(2 * sigmax ** 2) - ((y - y0) ** 2)/(2 * sigmay ** 2))
g_x = max(0, -ul[0]), min(br[0], h) - ul[0]
g_y = max(0, -ul[1]), min(br[1], w) - ul[1]
img_x = max(0, ul[0]), min(br[0], h)
img_y = max(0, ul[1]), min(br[1], w)
heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]] = np.maximum(
heatmap[img_y[0]:img_y[1], img_x[0]:img_x[1]],
g[g_y[0]:g_y[1], g_x[0]:g_x[1]])
return True
def pad_aray(ip_array, pad_size):
return np.hstack((ip_array, np.ones(pad_size, dtype=np.int)*-1))
def warp_spec_aug(spec, ann, return_spec_for_viz, params):
# This is messy
# Augment spectrogram by randomly stretch and squeezing
# NOTE this also changes the start and stop time in place
# not taking care of spec for viz
if return_spec_for_viz:
assert False
delta = params['stretch_squeeze_delta']
op_size = (spec.shape[1], spec.shape[2])
resize_fract_r = np.random.rand()*delta*2 - delta + 1.0
resize_amt = int(spec.shape[2]*resize_fract_r)
if resize_amt >= spec.shape[2]:
spec_r = torch.cat((spec, torch.zeros((1, spec.shape[1], resize_amt-spec.shape[2]), dtype=spec.dtype)), 2)
else:
spec_r = spec[:, :, :resize_amt]
spec = F.interpolate(spec_r.unsqueeze(0), size=op_size, mode='bilinear', align_corners=False).squeeze(0)
ann['start_times'] *= (1.0/resize_fract_r)
ann['end_times'] *= (1.0/resize_fract_r)
return spec
def mask_time_aug(spec, params):
# Mask out a random block of time - repeat up to 3 times
# SpecAugment: A Simple Data Augmentation Methodfor Automatic Speech Recognition
fm = torchaudio.transforms.TimeMasking(int(spec.shape[1]*params['mask_max_time_perc']))
for ii in range(np.random.randint(1, 4)):
spec = fm(spec)
return spec
def mask_freq_aug(spec, params):
# Mask out a random frequncy range - repeat up to 3 times
# SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
fm = torchaudio.transforms.FrequencyMasking(int(spec.shape[1]*params['mask_max_freq_perc']))
for ii in range(np.random.randint(1, 4)):
spec = fm(spec)
return spec
def scale_vol_aug(spec, params):
return spec * np.random.random()*params['spec_amp_scaling']
def echo_aug(audio, sampling_rate, params):
sample_offset = int(params['echo_max_delay']*np.random.random()*sampling_rate) + 1
audio[:-sample_offset] += np.random.random()*audio[sample_offset:]
return audio
def resample_aug(audio, sampling_rate, params):
sampling_rate_old = sampling_rate
sampling_rate = np.random.choice(params['aug_sampling_rates'])
audio = librosa.resample(audio, sampling_rate_old, sampling_rate, res_type='polyphase')
audio = au.pad_audio(audio, sampling_rate, params['fft_win_length'],
params['fft_overlap'], params['resize_factor'],
params['spec_divide_factor'], params['spec_train_width'])
duration = audio.shape[0] / float(sampling_rate)
return audio, sampling_rate, duration
def resample_audio(num_samples, sampling_rate, audio2, sampling_rate2):
if sampling_rate != sampling_rate2:
audio2 = librosa.resample(audio2, sampling_rate2, sampling_rate, res_type='polyphase')
sampling_rate2 = sampling_rate
if audio2.shape[0] < num_samples:
audio2 = np.hstack((audio2, np.zeros((num_samples-audio2.shape[0]), dtype=audio2.dtype)))
elif audio2.shape[0] > num_samples:
audio2 = audio2[:num_samples]
return audio2, sampling_rate2
def combine_audio_aug(audio, sampling_rate, ann, audio2, sampling_rate2, ann2):
# resample so they are the same
audio2, sampling_rate2 = resample_audio(audio.shape[0], sampling_rate, audio2, sampling_rate2)
# # set mean and std to be the same
# audio2 = (audio2 - audio2.mean())
# audio2 = (audio2/audio2.std())*audio.std()
# audio2 = audio2 + audio.mean()
if ann['annotated'] and (ann2['annotated']) and \
(sampling_rate2 == sampling_rate) and (audio.shape[0] == audio2.shape[0]):
comb_weight = 0.3 + np.random.random()*0.4
audio = comb_weight*audio + (1-comb_weight)*audio2
inds = np.argsort(np.hstack((ann['start_times'], ann2['start_times'])))
for kk in ann.keys():
# when combining calls from different files, assume they come from different individuals
if kk == 'individual_ids':
if (ann[kk]>-1).sum() > 0:
ann2[kk][ann2[kk]>-1] += np.max(ann[kk][ann[kk]>-1]) + 1
if (kk != 'class_id_file') and (kk != 'annotated'):
ann[kk] = np.hstack((ann[kk], ann2[kk]))[inds]
return audio, ann
class AudioLoader(torch.utils.data.Dataset):
def __init__(self, data_anns_ip, params, dataset_name=None, is_train=False):
self.data_anns = []
self.is_train = is_train
self.params = params
self.return_spec_for_viz = False
for ii in range(len(data_anns_ip)):
dd = copy.deepcopy(data_anns_ip[ii])
# filter out unused annotation here
filtered_annotations = []
for ii, aa in enumerate(dd['annotation']):
if 'individual' in aa.keys():
aa['individual'] = int(aa['individual'])
# if only one call labeled it has to be from the same individual
if len(dd['annotation']) == 1:
aa['individual'] = 0
# convert class name into class label
if aa['class'] in self.params['class_names']:
aa['class_id'] = self.params['class_names'].index(aa['class'])
else:
aa['class_id'] = -1
if aa['class'] not in self.params['classes_to_ignore']:
filtered_annotations.append(aa)
dd['annotation'] = filtered_annotations
dd['start_times'] = np.array([aa['start_time'] for aa in dd['annotation']])
dd['end_times'] = np.array([aa['end_time'] for aa in dd['annotation']])
dd['high_freqs'] = np.array([float(aa['high_freq']) for aa in dd['annotation']])
dd['low_freqs'] = np.array([float(aa['low_freq']) for aa in dd['annotation']])
dd['class_ids'] = np.array([aa['class_id'] for aa in dd['annotation']]).astype(np.int)
dd['individual_ids'] = np.array([aa['individual'] for aa in dd['annotation']]).astype(np.int)
# file level class name
dd['class_id_file'] = -1
if 'class_name' in dd.keys():
if dd['class_name'] in self.params['class_names']:
dd['class_id_file'] = self.params['class_names'].index(dd['class_name'])
self.data_anns.append(dd)
ann_cnt = [len(aa['annotation']) for aa in self.data_anns]
self.max_num_anns = 2*np.max(ann_cnt) # x2 because we may be combining files during training
print('\n')
if dataset_name is not None:
print('Dataset : ' + dataset_name)
if self.is_train:
print('Split type : train')
else:
print('Split type : test')
print('Num files : ' + str(len(self.data_anns)))
print('Num calls : ' + str(np.sum(ann_cnt)))
def get_file_and_anns(self, index=None):
# if no file specified, choose random one
if index == None:
index = np.random.randint(0, len(self.data_anns))
audio_file = self.data_anns[index]['file_path']
sampling_rate, audio_raw = au.load_audio_file(audio_file, self.data_anns[index]['time_exp'],
self.params['target_samp_rate'], self.params['scale_raw_audio'])
# copy annotation
ann = {}
ann['annotated'] = self.data_anns[index]['annotated']
ann['class_id_file'] = self.data_anns[index]['class_id_file']
keys = ['start_times', 'end_times', 'high_freqs', 'low_freqs', 'class_ids', 'individual_ids']
for kk in keys:
ann[kk] = self.data_anns[index][kk].copy()
# if train then grab a random crop
if self.is_train:
nfft = int(self.params['fft_win_length']*sampling_rate)
noverlap = int(self.params['fft_overlap']*nfft)
length_samples = self.params['spec_train_width']*(nfft - noverlap) + noverlap
if audio_raw.shape[0] - length_samples > 0:
sample_crop = np.random.randint(audio_raw.shape[0] - length_samples)
else:
sample_crop = 0
audio_raw = audio_raw[sample_crop:sample_crop+length_samples]
ann['start_times'] = ann['start_times'] - sample_crop/float(sampling_rate)
ann['end_times'] = ann['end_times'] - sample_crop/float(sampling_rate)
# pad audio
if self.is_train:
op_spec_target_size = self.params['spec_train_width']
else:
op_spec_target_size = None
audio_raw = au.pad_audio(audio_raw, sampling_rate, self.params['fft_win_length'],
self.params['fft_overlap'], self.params['resize_factor'],
self.params['spec_divide_factor'], op_spec_target_size)
duration = audio_raw.shape[0] / float(sampling_rate)
# sort based on time
inds = np.argsort(ann['start_times'])
for kk in ann.keys():
if (kk != 'class_id_file') and (kk != 'annotated'):
ann[kk] = ann[kk][inds]
return audio_raw, sampling_rate, duration, ann
def __getitem__(self, index):
# load audio file
audio, sampling_rate, duration, ann = self.get_file_and_anns(index)
# augment on raw audio
if self.is_train and self.params['augment_at_train']:
# augment - combine with random audio file
if self.params['augment_at_train_combine'] and np.random.random() < self.params['aug_prob']:
audio2, sampling_rate2, duration2, ann2 = self.get_file_and_anns()
audio, ann = combine_audio_aug(audio, sampling_rate, ann, audio2, sampling_rate2, ann2)
# simulate echo by adding delayed copy of the file
if np.random.random() < self.params['aug_prob']:
audio = echo_aug(audio, sampling_rate, self.params)
# resample the audio
#if np.random.random() < self.params['aug_prob']:
# audio, sampling_rate, duration = resample_aug(audio, sampling_rate, self.params)
# create spectrogram
spec, spec_for_viz = au.generate_spectrogram(audio, sampling_rate, self.params, self.return_spec_for_viz)
rsf = self.params['resize_factor']
spec_op_shape = (int(self.params['spec_height']*rsf), int(spec.shape[1]*rsf))
# resize the spec
spec = torch.from_numpy(spec).unsqueeze(0).unsqueeze(0)
spec = F.interpolate(spec, size=spec_op_shape, mode='bilinear', align_corners=False).squeeze(0)
# augment spectrogram
if self.is_train and self.params['augment_at_train']:
if np.random.random() < self.params['aug_prob']:
spec = scale_vol_aug(spec, self.params)
if np.random.random() < self.params['aug_prob']:
spec = warp_spec_aug(spec, ann, self.return_spec_for_viz, self.params)
if np.random.random() < self.params['aug_prob']:
spec = mask_time_aug(spec, self.params)
if np.random.random() < self.params['aug_prob']:
spec = mask_freq_aug(spec, self.params)
outputs = {}
outputs['spec'] = spec
if self.return_spec_for_viz:
outputs['spec_for_viz'] = torch.from_numpy(spec_for_viz).unsqueeze(0)
# create ground truth heatmaps
outputs['y_2d_det'], outputs['y_2d_size'], outputs['y_2d_classes'], ann_aug =\
generate_gt_heatmaps(spec_op_shape, sampling_rate, ann, self.params)
# hack to get around requirement that all vectors are the same length in
# the output batch
pad_size = self.max_num_anns-len(ann_aug['individual_ids'])
outputs['is_valid'] = pad_aray(np.ones(len(ann_aug['individual_ids'])), pad_size)
keys = ['class_ids', 'individual_ids', 'x_inds', 'y_inds',
'start_times', 'end_times', 'low_freqs', 'high_freqs']
for kk in keys:
outputs[kk] = pad_aray(ann_aug[kk], pad_size)
# convert to pytorch
for kk in outputs.keys():
if type(outputs[kk]) != torch.Tensor:
outputs[kk] = torch.from_numpy(outputs[kk])
# scalars
outputs['class_id_file'] = ann['class_id_file']
outputs['annotated'] = ann['annotated']
outputs['duration'] = duration
outputs['sampling_rate'] = sampling_rate
outputs['file_id'] = index
return outputs
def __len__(self):
return len(self.data_anns)
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