def do_job(iter_num):
model = get_model()
x = [12345, 54321, 00000, 1111]
y = [1, 1, 0, 0]
model.fit(x=x,
y=y,
validation_split=0.5,
epochs=10,
shuffle=True,
batch_size=2,
verbose=2)
print("Done process " + str(iter_num))
clear_session()
print("close")
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: job_id - (optional) all the output files will be uniquely represented with this. (default) 1
second input: task_id - (optional) To chose the system configuration in parameters.py.
(default) uses default parameters
"""
if len(argv) != 3:
print('\n\n')
print('-------------------------------------------------------------------------------------------------------')
print('The code expected two inputs')
print('\t>> python seld.py <job-id> <task-id>')
print('\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print('\t\t<task-id> is used to choose the user-defined parameter set from parameter.py')
print('Using default inputs for now')
print('-------------------------------------------------------------------------------------------------------')
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 3 else argv[-1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 2 else argv[1]
model_dir = 'models/'
utils.create_folder(model_dir)
unique_name = '{}_ov{}_split{}_{}{}_3d{}_{}'.format(
params['dataset'], params['overlap'], params['split'], params['mode'], params['weakness'],
int(params['cnn_3d']), job_id
)
unique_name = os.path.join(model_dir, unique_name)
print("unique_name: {}\n".format(unique_name))
data_gen_train = cls_data_generator.DataGenerator(
dataset=params['dataset'], ov=params['overlap'], split=params['split'], db=params['db'], nfft=params['nfft'],
batch_size=params['batch_size'], seq_len=params['sequence_length'], classifier_mode=params['mode'],
weakness=params['weakness'], datagen_mode='train', cnn3d=params['cnn_3d'], xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only']
)
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'], ov=params['overlap'], split=params['split'], db=params['db'], nfft=params['nfft'],
batch_size=params['batch_size'], seq_len=params['sequence_length'], classifier_mode=params['mode'],
weakness=params['weakness'], datagen_mode='test', cnn3d=params['cnn_3d'], xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'], shuffle=False
)
data_in, data_out = data_gen_train.get_data_sizes()
print(
'FEATURES:\n'
'\tdata_in: {}\n'
'\tdata_out: {}\n'.format(
data_in, data_out
)
)
gt = collect_test_labels(data_gen_test, data_out, params['mode'], params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print(
'MODEL:\n'
'\tdropout_rate: {}\n'
'\tCNN: nb_cnn_filt: {}, pool_size{}\n'
'\trnn_size: {}, fnn_size: {}\n'.format(
params['dropout_rate'],
params['nb_cnn3d_filt'] if params['cnn_3d'] else params['nb_cnn2d_filt'], params['pool_size'],
params['rnn_size'], params['fnn_size']
)
)
model = keras_model.get_model(data_in=data_in, data_out=data_out, dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'], pool_size=params['pool_size'],
rnn_size=params['rnn_size'], fnn_size=params['fnn_size'],
classification_mode=params['mode'], weights=params['loss_weights'], loader=False, loader2=False) # Change loader to True to enable transfer learning, Change loader2 to True to enable transfer learning with different labels
best_metric = 99999
conf_mat = None
best_conf_mat = None
best_epoch = -1
patience_cnt = 0
epoch_metric_loss = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_loss = np.zeros((params['nb_epochs'], 6))
sed_loss = np.zeros((params['nb_epochs'], 2))
nb_epoch = 2 if params['quick_test'] else params['nb_epochs']
for epoch_cnt in range(nb_epoch):
start = time.time()
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=2 if params['quick_test'] else data_gen_train.get_total_batches_in_data(),
validation_data=data_gen_test.generate(),
validation_steps=2 if params['quick_test'] else data_gen_test.get_total_batches_in_data(),
epochs=1,
verbose=0
)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
val_loss[epoch_cnt] = hist.history.get('val_loss')[-1]
pred = model.predict_generator(
generator=data_gen_test.generate(),
steps=2 if params['quick_test'] else data_gen_test.get_total_batches_in_data(),
verbose=2
)
if params['mode'] == 'regr':
sed_pred = evaluation_metrics.reshape_3Dto2D(pred[0]) > 0.5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
sed_loss[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(sed_pred, sed_gt, data_gen_test.nb_frames_1s())
if params['azi_only']:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xy(doa_pred, doa_gt,
sed_pred, sed_gt)
else:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xyz(doa_pred, doa_gt,
sed_pred, sed_gt)
epoch_metric_loss[epoch_cnt] = np.mean([
sed_loss[epoch_cnt, 0],
1-sed_loss[epoch_cnt, 1],
2*np.arcsin(doa_loss[epoch_cnt, 1]/2.0)/np.pi,
1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))]
)
plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss, epoch_metric_loss)
patience_cnt += 1
if epoch_metric_loss[epoch_cnt] < best_metric:
best_metric = epoch_metric_loss[epoch_cnt]
best_conf_mat = conf_mat
best_epoch = epoch_cnt
model.save('{}_model.h5'.format(unique_name))
patience_cnt = 0
print(
'epoch_cnt: %d, time: %.2fs, tr_loss: %.2f, val_loss: %.2f, '
'F1_overall: %.2f, ER_overall: %.2f, '
'doa_error_gt: %.2f, doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'error_metric: %.2f, best_error_metric: %.2f, best_epoch : %d' %
(
epoch_cnt, time.time() - start, tr_loss[epoch_cnt], val_loss[epoch_cnt],
sed_loss[epoch_cnt, 1], sed_loss[epoch_cnt, 0],
doa_loss[epoch_cnt, 1], doa_loss[epoch_cnt, 2], doa_loss[epoch_cnt, 5] / float(sed_gt.shape[0]),
epoch_metric_loss[epoch_cnt], best_metric, best_epoch
)
)
if patience_cnt > params['patience']:
break
print('best_conf_mat : {}'.format(best_conf_mat))
print('best_conf_mat_diag : {}'.format(np.diag(best_conf_mat)))
print('saved model for the best_epoch: {} with best_metric: {}, '.format(best_epoch, best_metric))
print('DOA Metrics: doa_loss_gt: {}, doa_loss_pred: {}, good_pks_ratio: {}'.format(
doa_loss[best_epoch, 1], doa_loss[best_epoch, 2], doa_loss[best_epoch, 5] / float(sed_gt.shape[0])))
print('SED Metrics: ER_overall: {}, F1_overall: {}'.format(sed_loss[best_epoch, 1], sed_loss[best_epoch, 0]))
print('unique_name: {} '.format(unique_name))
data = file_list_to_data(files,
msg="generate train_dataset",
n_mels=param["feature"]["n_mels"],
n_frames=param["feature"]["n_frames"],
n_hop_frames=param["feature"]["n_hop_frames"],
n_fft=param["feature"]["n_fft"],
hop_length=param["feature"]["hop_length"],
power=param["feature"]["power"])
# number of vectors for each wave file
n_vectors_ea_file = int(data.shape[0] / len(files))
# train model
print("============== MODEL TRAINING ==============")
model = keras_model.get_model(param["feature"]["n_mels"] * param["feature"]["n_frames"],
param["fit"]["lr"])
model.summary()
history = model.fit(x=data,
y=data,
epochs=param["fit"]["epochs"],
batch_size=param["fit"]["batch_size"],
shuffle=param["fit"]["shuffle"],
validation_split=param["fit"]["validation_split"],
verbose=param["fit"]["verbose"])
# calculate y_pred for fitting anomaly score distribution
y_pred = []
start_idx = 0
for file_idx in range(len(files)):
import numpy as np
import os
import argparse
import keras_model as models
import get_dataset as aww_data
import aww_util
num_classes = 12 # should probably draw this directly from the dataset.
# FLAGS = None
if __name__ == '__main__':
Flags, unparsed = aww_util.parse_command()
print('We will download data to {:}'.format(Flags.data_dir))
print('We will train for {:} epochs'.format(Flags.epochs))
ds_train, ds_test, ds_val = aww_data.get_training_data(Flags)
print("Done getting data")
model = models.get_model(model_name=Flags.model_architecture)
model.summary()
train_hist = model.fit(ds_train,
validation_data=ds_val,
epochs=Flags.epochs)
model.save(Flags.saved_model_path)
if Flags.run_test_set:
test_scores = model.evaluate(ds_test)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: task_id - (optional) To chose the system configuration in parameters.py.
(default) 1 - uses default parameters
second input: job_id - (optional) all the output files will be uniquely represented with this.
(default) 1
"""
print(argv)
if len(argv) != 3:
print('\n\n')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('The code expected two optional inputs')
print('\t>> python seld.py <task-id> <job-id>')
print(
'\t\t<task-id> is used to choose the user-defined parameter set from parameter.py'
)
print('Using default inputs for now')
print(
'\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 2 else argv[1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 3 else argv[-1]
feat_cls = cls_feature_class.FeatureClass(params)
train_splits, val_splits, test_splits = None, None, None
if params['mode'] == 'dev':
test_splits = [6]
val_splits = [5]
train_splits = [[1, 2, 3, 4]]
elif params['mode'] == 'eval':
test_splits = [[7, 8]]
val_splits = [[6]]
train_splits = [[1, 2, 3, 4, 5]]
for split_cnt, split in enumerate(test_splits):
print(
'\n\n---------------------------------------------------------------------------------------------------'
)
print(
'------------------------------------ SPLIT {} -----------------------------------------------'
.format(split))
print(
'---------------------------------------------------------------------------------------------------'
)
# Unique name for the run
cls_feature_class.create_folder(params['model_dir'])
unique_name = '{}_{}_{}_{}_split{}'.format(task_id, job_id,
params['dataset'],
params['mode'], split)
unique_name = os.path.join(params['model_dir'], unique_name)
model_name = '{}_model.h5'.format(unique_name)
print("unique_name: {}\n".format(unique_name))
# Load train and validation data
print('Loading training dataset:')
data_gen_train = cls_data_generator.DataGenerator(
params=params, split=train_splits[split_cnt])
print('Loading validation dataset:')
data_gen_val = cls_data_generator.DataGenerator(
params=params,
split=val_splits[split_cnt],
shuffle=False,
per_file=True,
is_eval=False)
# Collect the reference labels for validation data
data_in, data_out = data_gen_train.get_data_sizes()
print('FEATURES:\n\tdata_in: {}\n\tdata_out: {}\n'.format(
data_in, data_out))
nb_classes = data_gen_train.get_nb_classes()
print(
'MODEL:\n\tdropout_rate: {}\n\tCNN: nb_cnn_filt: {}, f_pool_size{}, t_pool_size{}\n\trnn_size: {}, fnn_size: {}\n\tdoa_objective: {}\n'
.format(params['dropout_rate'], params['nb_cnn2d_filt'],
params['f_pool_size'], params['t_pool_size'],
params['rnn_size'], params['fnn_size'],
params['doa_objective']))
print('Using loss weights : {}'.format(params['loss_weights']))
model = keras_model.get_model(data_in=data_in,
data_out=data_out,
dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'],
f_pool_size=params['f_pool_size'],
t_pool_size=params['t_pool_size'],
rnn_size=params['rnn_size'],
fnn_size=params['fnn_size'],
weights=params['loss_weights'],
doa_objective=params['doa_objective'],
is_accdoa=params['is_accdoa'])
# Dump results in DCASE output format for calculating final scores
dcase_output_val_folder = os.path.join(
params['dcase_output_dir'],
'{}_{}_{}_val'.format(task_id, params['dataset'], params['mode']))
cls_feature_class.delete_and_create_folder(dcase_output_val_folder)
print('Dumping recording-wise val results in: {}'.format(
dcase_output_val_folder))
# Initialize evaluation metric class
score_obj = ComputeSELDResults(params)
best_seld_metric = 99999
best_epoch = -1
patience_cnt = 0
nb_epoch = 2 if params['quick_test'] else params['nb_epochs']
tr_loss = np.zeros(nb_epoch)
seld_metric = np.zeros((nb_epoch, 5))
# start training
for epoch_cnt in range(nb_epoch):
start = time.time()
# train once per epoch
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=2 if params['quick_test'] else
data_gen_train.get_total_batches_in_data(),
epochs=params['epochs_per_fit'],
verbose=2,
)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
# predict once per epoch
pred = model.predict_generator(
generator=data_gen_val.generate(),
steps=2 if params['quick_test'] else
data_gen_val.get_total_batches_in_data(),
verbose=2)
if params['is_accdoa']:
sed_pred, doa_pred = get_accdoa_labels(pred, nb_classes)
sed_pred = reshape_3Dto2D(sed_pred)
doa_pred = reshape_3Dto2D(doa_pred)
else:
sed_pred = reshape_3Dto2D(pred[0]) > 0.5
doa_pred = reshape_3Dto2D(pred[1] if params['doa_objective'] is
'mse' else pred[1][:, :,
nb_classes:])
# Calculate the DCASE 2021 metrics - Location-aware detection and Class-aware localization scores
dump_DCASE2021_results(data_gen_val, feat_cls,
dcase_output_val_folder, sed_pred, doa_pred)
seld_metric[epoch_cnt, :] = score_obj.get_SELD_Results(
dcase_output_val_folder)
patience_cnt += 1
if seld_metric[epoch_cnt, -1] < best_seld_metric:
best_seld_metric = seld_metric[epoch_cnt, -1]
best_epoch = epoch_cnt
model.save(model_name)
patience_cnt = 0
print(
'epoch_cnt: {}, time: {:0.2f}s, tr_loss: {:0.2f}, '
'\n\t\t DCASE2021 SCORES: ER: {:0.2f}, F: {:0.1f}, LE: {:0.1f}, LR:{:0.1f}, seld_score (early stopping score): {:0.2f}, '
'best_seld_score: {:0.2f}, best_epoch : {}\n'.format(
epoch_cnt,
time.time() - start, tr_loss[epoch_cnt],
seld_metric[epoch_cnt, 0], seld_metric[epoch_cnt, 1] * 100,
seld_metric[epoch_cnt, 2], seld_metric[epoch_cnt, 3] * 100,
seld_metric[epoch_cnt, -1], best_seld_metric, best_epoch))
if patience_cnt > params['patience']:
break
print('\nResults on validation split:')
print('\tUnique_name: {} '.format(unique_name))
print('\tSaved model for the best_epoch: {}'.format(best_epoch))
print('\tSELD_score (early stopping score) : {}'.format(
best_seld_metric))
print('\n\tDCASE2021 scores')
print(
'\tClass-aware localization scores: Localization Error: {:0.1f}, Localization Recall: {:0.1f}'
.format(seld_metric[best_epoch, 2],
seld_metric[best_epoch, 3] * 100))
print(
'\tLocation-aware detection scores: Error rate: {:0.2f}, F-score: {:0.1f}'
.format(seld_metric[best_epoch, 0],
seld_metric[best_epoch, 1] * 100))
# ------------------ Calculate metric scores for unseen test split ---------------------------------
print(
'\nLoading the best model and predicting results on the testing split'
)
print('\tLoading testing dataset:')
data_gen_test = cls_data_generator.DataGenerator(
params=params,
split=split,
shuffle=False,
per_file=True,
is_eval=True if params['mode'] is 'eval' else False)
model = keras_model.load_seld_model('{}_model.h5'.format(unique_name),
params['doa_objective'])
pred_test = model.predict_generator(
generator=data_gen_test.generate(),
steps=2 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
verbose=2)
if params['is_accdoa']:
test_sed_pred, test_doa_pred = get_accdoa_labels(
pred_test, nb_classes)
test_sed_pred = reshape_3Dto2D(test_sed_pred)
test_doa_pred = reshape_3Dto2D(test_doa_pred)
else:
test_sed_pred = reshape_3Dto2D(pred_test[0]) > 0.5
test_doa_pred = reshape_3Dto2D(
pred_test[1] if params['doa_objective'] is 'mse' else
pred_test[1][:, :, nb_classes:])
# Dump results in DCASE output format for calculating final scores
dcase_output_test_folder = os.path.join(
params['dcase_output_dir'],
'{}_{}_{}_test'.format(task_id, params['dataset'], params['mode']))
cls_feature_class.delete_and_create_folder(dcase_output_test_folder)
print('Dumping recording-wise test results in: {}'.format(
dcase_output_test_folder))
dump_DCASE2021_results(data_gen_test, feat_cls,
dcase_output_test_folder, test_sed_pred,
test_doa_pred)
if params['mode'] is 'dev':
# Calculate DCASE2021 scores
test_seld_metric = score_obj.get_SELD_Results(
dcase_output_test_folder)
print('Results on test split:')
print('\tDCASE2021 Scores')
print(
'\tClass-aware localization scores: Localization Error: {:0.1f}, Localization Recall: {:0.1f}'
.format(test_seld_metric[2], test_seld_metric[3] * 100))
print(
'\tLocation-aware detection scores: Error rate: {:0.2f}, F-score: {:0.1f}'
.format(test_seld_metric[0], test_seld_metric[1] * 100))
print('\tSELD (early stopping metric): {:0.2f}'.format(
test_seld_metric[-1]))
# generate dataset
print("============== DATASET_GENERATOR ==============")
files = file_list_generator(target_dir)
train_data = list_to_vector_array(
files,
msg="generate train_dataset",
n_mels=param["feature"]["n_mels"],
frames=param["feature"]["frames"],
n_fft=param["feature"]["n_fft"],
hop_length=param["feature"]["hop_length"],
power=param["feature"]["power"])
# train model
print("============== MODEL TRAINING ==============")
model = keras_model.get_model(param["feature"]["n_mels"] *
param["feature"]["frames"])
model.summary()
model.compile(**param["fit"]["compile"])
history = model.fit(train_data,
train_data,
epochs=param["fit"]["epochs"],
batch_size=param["fit"]["batch_size"],
shuffle=param["fit"]["shuffle"],
validation_split=param["fit"]["validation_split"],
verbose=param["fit"]["verbose"])
visualizer.loss_plot(history.history["loss"],
history.history["val_loss"])
visualizer.save_figure(history_img)
model.save(model_file_path)
# train model
print("============== MODEL TRAINING ==============")
# checkpoint
model_checkpoint = ModelCheckpoint(best_model_filepath +
"{epoch:02d}.hdf5",
monitor='val_loss',
verbose=1,
save_best_only=True)
early = EarlyStopping(monitor='val_loss',
mode='min',
patience=10,
min_delta=0.0001)
# create model
model = keras_model.get_model((shape0_feat, shape1_feat),
param["autoencoder"]["latentDim"])
model.summary()
#train model
model.compile(**param["fit"]["compile"])
history = model.fit_generator(gen_train,
validation_data=gen_val,
epochs=param["fit"]["epochs"],
verbose=param["fit"]["verbose"],
callbacks=[model_checkpoint, early])
visualizer.loss_plot(history.history["loss"],
history.history["val_loss"])
visualizer.save_figure(history_img)
model.save(model_file_path)
com.logger.info("save_model -> {}".format(model_file_path))
num_classes = 12 # should probably draw this directly from the dataset.
# FLAGS = None
if __name__ == '__main__':
Flags, unparsed = kws_util.parse_command()
print('We will download data to {:}'.format(Flags.data_dir))
ds_train, ds_test, ds_val = kws_data.get_training_data(Flags)
print("Done getting data")
if Flags.model_init_path is None:
print(
"Starting with untrained model. Accuracy will be random-guess level at best."
)
model = models.get_model(args=Flags)
else:
print(f"Starting with pre-trained model from {Flags.model_init_path}")
model = keras.models.load_model(Flags.model_init_path)
model.summary()
test_scores = model.evaluate(ds_test, return_dict=True)
print("Test loss:", test_scores['loss'])
print("Test accuracy:", test_scores['sparse_categorical_accuracy'])
outputs = np.zeros((0, num_classes))
labels = np.array([])
for samples, batch_labels in ds_test:
outputs = np.vstack((outputs, model.predict(samples)))
labels = np.hstack((labels, batch_labels))
def main(args):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: job_id - (optional) all the output files will be uniquely represented with this. (default) 1
second input: task_id - (optional) To chose the system configuration in parameters.py.
(default) uses default parameters
"""
# use parameter set defined by user
task_id = args.params
params = parameter.get_params(task_id)
job_id = args.model_name
model_dir = 'models/' + args.author + '/' if args.author != "" else 'models/'
utils.create_folder(model_dir)
unique_name = '{}_ov{}_split{}_{}{}_3d{}_{}'.format(
params['dataset'], params['overlap'], params['split'], params['mode'],
params['weakness'], int(params['cnn_3d']), job_id)
model_name = unique_name
epoch_manager = JSON_Manager(args.author, unique_name)
logdir = "logs/" + args.author + "/" + unique_name
unique_name = os.path.join(model_dir, unique_name)
print("unique_name: {}\n".format(unique_name))
session = tf.InteractiveSession()
file_writer = tf.summary.FileWriter(logdir, session.graph)
data_gen_train = cls_data_generator.DataGenerator(
dataset=params['dataset'],
ov=params['overlap'],
split=params['split'],
db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
classifier_mode=params['mode'],
weakness=params['weakness'],
datagen_mode='train',
cnn3d=params['cnn_3d'],
xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'])
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'],
ov=params['overlap'],
split=params['split'],
db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
classifier_mode=params['mode'],
weakness=params['weakness'],
datagen_mode='test',
cnn3d=params['cnn_3d'],
xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'],
shuffle=False)
data_in, data_out = data_gen_train.get_data_sizes()
print('FEATURES:\n'
'\tdata_in: {}\n'
'\tdata_out: {}\n'.format(data_in, data_out))
gt = collect_test_labels(data_gen_test, data_out, params['mode'],
params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print('MODEL:\n'
'\tdropout_rate: {}\n'
'\tCNN: nb_cnn_filt: {}, pool_size{}\n'
'\trnn_size: {}, fnn_size: {}\n'.format(
params['dropout_rate'], params['nb_cnn3d_filt']
if params['cnn_3d'] else params['nb_cnn2d_filt'],
params['pool_size'], params['rnn_size'], params['fnn_size']))
model = keras_model.get_model(data_in=data_in,
data_out=data_out,
dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'],
pool_size=params['pool_size'],
rnn_size=params['rnn_size'],
fnn_size=params['fnn_size'],
classification_mode=params['mode'],
weights=params['loss_weights'])
initial = epoch_manager.get_epoch()
if initial != 0:
print(f"Resume training from epoch {initial}")
print("Loading already trained model...")
# In order to load custom layers we need to link the references to the custom objects
model = load_model(os.path.join(model_dir, model_name + "_model.h5"),
custom_objects={
'QuaternionConv2D': QuaternionConv2D,
'QuaternionGRU': QuaternionGRU,
'QuaternionDense': QuaternionDense
})
best_metric = epoch_manager.get_best_metric()
best_std = epoch_manager.get_best_std()
conf_mat = None
best_conf_mat = epoch_manager.get_best_conf_mat()
best_epoch = epoch_manager.get_best_epoch()
patience_cnt = epoch_manager.get_patience_cnt()
epoch_metric_loss = np.zeros(params['nb_epochs'])
sed_score = np.zeros(params['nb_epochs'])
std_score = np.zeros(params['nb_epochs'])
doa_score = np.zeros(params['nb_epochs'])
seld_score = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_loss = np.zeros((params['nb_epochs'], 6))
sed_loss = np.zeros((params['nb_epochs'], 2))
time_hold = tf.placeholder(tf.float32, shape=None, name='time_summary')
time_summ = tf.summary.scalar('time', time_hold)
tr_loss_hold = tf.placeholder(tf.float32,
shape=None,
name='tr_loss_summary')
tr_loss_summ = tf.summary.scalar('tr_loss', tr_loss_hold)
val_loss_hold = tf.placeholder(tf.float32,
shape=None,
name='val_loss_summary')
val_loss_summ = tf.summary.scalar('val_loss', val_loss_hold)
f1_hold = tf.placeholder(tf.float32, shape=None, name='f1_summary')
f1_summ = tf.summary.scalar('F1_overall', f1_hold)
er_hold = tf.placeholder(tf.float32, shape=None, name='er_summary')
er_summ = tf.summary.scalar('ER_overall', er_hold)
doa_error_gt_hold = tf.placeholder(tf.float32,
shape=None,
name='doa_error_gt_summary')
doa_error_gt_summ = tf.summary.scalar('doa_error_gt', doa_error_gt_hold)
doa_error_pred_hold = tf.placeholder(tf.float32,
shape=None,
name='doa_error_pred_summary')
doa_error_pred_summ = tf.summary.scalar('doa_error_pred',
doa_error_pred_hold)
good_pks_hold = tf.placeholder(tf.float32,
shape=None,
name='good_pks_summary')
good_pks_summ = tf.summary.scalar('good_pks_ratio', good_pks_hold)
sed_score_hold = tf.placeholder(tf.float32,
shape=None,
name='sed_score_summary')
sed_score_summ = tf.summary.scalar('sed_score', sed_score_hold)
doa_score_hold = tf.placeholder(tf.float32,
shape=None,
name='doa_score_summary')
doa_score_summ = tf.summary.scalar('doa_score', doa_score_hold)
seld_score_hold = tf.placeholder(tf.float32,
shape=None,
name='seld_score_summary')
seld_score_summ = tf.summary.scalar('seld_score', seld_score_hold)
std_score_hold = tf.placeholder(tf.float32,
shape=None,
name='std_score_summary')
std_score_summ = tf.summary.scalar('std_score', std_score_hold)
best_error_metric_hold = tf.placeholder(tf.float32,
shape=None,
name='best_error_metric_summary')
best_error_metric_summ = tf.summary.scalar('best_error_metric',
best_error_metric_hold)
best_epoch_hold = tf.placeholder(tf.float32,
shape=None,
name='best_epoch_summary')
best_epoch_summ = tf.summary.scalar('best_epoch', best_epoch_hold)
best_std_hold = tf.placeholder(tf.float32,
shape=None,
name='best_std_summary')
best_std_summ = tf.summary.scalar('best_std', best_std_hold)
merged = tf.summary.merge_all()
for epoch_cnt in range(initial, params['nb_epochs']):
start = time.time()
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=5 if params['quick_test'] else
data_gen_train.get_total_batches_in_data(),
validation_data=data_gen_test.generate(),
validation_steps=5 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
epochs=1,
verbose=1)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
val_loss[epoch_cnt] = hist.history.get('val_loss')[-1]
pred = model.predict_generator(
generator=data_gen_test.generate(),
steps=5 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
verbose=2)
print("pred:", pred[1].shape)
if params['mode'] == 'regr':
sed_pred = evaluation_metrics.reshape_3Dto2D(pred[0]) > 0.5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
sed_loss[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(
sed_pred, sed_gt, data_gen_test.nb_frames_1s())
if params['azi_only']:
doa_loss[
epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xy(
doa_pred, doa_gt, sed_pred, sed_gt)
else:
doa_loss[
epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xyz(
doa_pred, doa_gt, sed_pred, sed_gt)
sed_score[epoch_cnt] = np.mean(
[sed_loss[epoch_cnt, 0], 1 - sed_loss[epoch_cnt, 1]])
doa_score[epoch_cnt] = np.mean([
2 * np.arcsin(doa_loss[epoch_cnt, 1] / 2.0) / np.pi,
1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))
])
seld_score[epoch_cnt] = np.mean(
[sed_score[epoch_cnt], doa_score[epoch_cnt]])
# standard deviation
std_score[epoch_cnt] = np.std(
[sed_score[epoch_cnt], doa_score[epoch_cnt]])
plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss,
sed_score, doa_score)
patience_cnt += 1
epoch_manager.increase_patience_cnt()
model.save('{}_model.h5'.format(unique_name))
if seld_score[epoch_cnt] < best_metric:
best_metric = seld_score[epoch_cnt]
epoch_manager.set_best_metric(best_metric)
best_std = std_score[epoch_cnt]
epoch_manager.set_best_std(best_std)
best_conf_mat = conf_mat
epoch_manager.set_best_conf_mat(conf_mat)
best_epoch = epoch_cnt
epoch_manager.set_best_epoch(best_epoch)
model.save('{}_best_model.h5'.format(unique_name))
patience_cnt = 0
epoch_manager.reset_patience_cnt()
if patience_cnt > params['patience']:
print(
f"\n---- PATIENCE TRIGGERED AFTER {epoch_cnt} EPOCHS ----\n")
break
summary = session.run(merged,
feed_dict={
time_hold:
time.time() - start,
tr_loss_hold:
tr_loss[epoch_cnt],
val_loss_hold:
val_loss[epoch_cnt],
f1_hold:
sed_loss[epoch_cnt, 1],
er_hold:
sed_loss[epoch_cnt, 0],
doa_error_gt_hold:
doa_loss[epoch_cnt, 1],
doa_error_pred_hold:
doa_loss[epoch_cnt, 2],
good_pks_hold:
doa_loss[epoch_cnt, 5] /
float(sed_gt.shape[0]),
sed_score_hold:
sed_score[epoch_cnt],
doa_score_hold:
doa_score[epoch_cnt],
seld_score_hold:
seld_score[epoch_cnt],
std_score_hold:
std_score[epoch_cnt],
best_error_metric_hold:
best_metric,
best_epoch_hold:
best_epoch,
best_std_hold:
best_std
})
file_writer.add_summary(summary, epoch_cnt)
print(
'epoch_cnt: %d, time: %.2fs, tr_loss: %.2f, val_loss: %.2f, '
'F1_overall: %.2f, ER_overall: %.2f, '
'doa_error_gt: %.2f, doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'sed_score: %.2f, doa_score: %.2f, best_error_metric: %.2f, best_epoch : %d, best_std: %.2f'
% (epoch_cnt, time.time() - start, tr_loss[epoch_cnt],
val_loss[epoch_cnt], sed_loss[epoch_cnt, 1],
sed_loss[epoch_cnt, 0], doa_loss[epoch_cnt, 1],
doa_loss[epoch_cnt, 2], doa_loss[epoch_cnt, 5] /
float(sed_gt.shape[0]), sed_score[epoch_cnt],
doa_score[epoch_cnt], best_metric, best_epoch, best_std))
epoch_manager.increase_epoch()
lower_confidence, upper_confidence = evaluation_metrics.compute_confidence_interval(
best_metric, best_std, params['nb_epochs'],
confid_coeff=1.96) # 1.96 for a 95% CI
print("\n---- FINISHED TRAINING ----\n")
print('best_conf_mat : {}'.format(best_conf_mat))
print('best_conf_mat_diag : {}'.format(np.diag(best_conf_mat)))
print('saved model for the best_epoch: {} with best_metric: {}, '.format(
best_epoch, best_metric))
print(
'DOA Metrics: doa_loss_gt: {}, doa_loss_pred: {}, good_pks_ratio: {}'.
format(doa_loss[best_epoch, 1], doa_loss[best_epoch, 2],
doa_loss[best_epoch, 5] / float(sed_gt.shape[0])))
print('SED Metrics: ER_overall: {}, F1_overall: {}'.format(
sed_loss[best_epoch, 0], sed_loss[best_epoch, 1]))
print('Confidence Interval: lower_interval: {}, upper_inteval: {}'.format(
lower_confidence, upper_confidence))
print('unique_name: {} '.format(unique_name))
# set path
machine_type = os.path.split(target_dir)[1]
model_file_path = "{model}/model_{machine_type}.hdf5".format(model=param["model_directory"],
machine_type=machine_type)
history_img = "{model}/history_{machine_type}.png".format(model=param["model_directory"],
machine_type=machine_type)
if os.path.exists(model_file_path):
com.logger.info("model exists")
continue
# train model
print("============== MODEL CREATING ==============")
model = keras_model.get_model(param['feature']['n_mels'] * param["feature"]["frames"])
model.summary()
model.compile(**param["fit"]["compile"])
# generate dataset
for method_augm in param['feature']['augmentation']:
print("============== DATASET_", method_augm, "_GENERATOR ==============")
files = file_list_generator(target_dir)[:3000]
train_data = list_to_vector_array(files,
msg="generate train_dataset",
n_mels=param["feature"]["n_mels"],
frames=param["feature"]["frames"],
n_fft=param["feature"]["n_fft"],
hop_length=param["feature"]["hop_length"],
power=param["feature"]["power"],
train_data = list_to_vector_array(
files,
False,
msg="generate train_dataset",
n_mels=param["feature"]["n_mels"],
frames=param["feature"]["frames"],
n_fft=param["feature"]["n_fft"],
hop_length=param["feature"]["hop_length"],
power=param["feature"]["power"])
print("============== MODEL TRAINING ==============")
## Load pre-trained model
# model = keras_model.get_model(param["feature"]["n_mels"] * param["feature"]["frames"])
# model = keras.models.load_model("../dcase2020_task2_baseline/{model}/model_{machine_type}.hdf5".format(model=param["model_directory"], machine_type=machine_type))
model = keras_model.get_model(100)
# model.summary()
model.compile(**param["fit"]["compile"])
# model.compile(**param["fit"]["compile"])
history = model.fit(train_data,
train_data,
epochs=100,
batch_size=param["fit"]["batch_size"],
shuffle=param["fit"]["shuffle"],
validation_split=param["fit"]["validation_split"],
verbose=param["fit"]["verbose"])
visualizer.loss_plot(history.history["loss"],
history.history["val_loss"])
visualizer.save_figure(history_img)
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: job_id - (optional) all the output files will be uniquely represented with this. (default) 1
second input: task_id - (optional) To chose the system configuration in parameters.py.
(default) uses default parameters
"""
if len(argv) != 3:
print('\n\n')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('The code expected two inputs')
print('\t>> python seld.py <job-id> <task-id>')
print(
'\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print(
'\t\t<task-id> is used to choose the user-defined parameter set from parameter.py'
)
print('Using default inputs for now')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 3 else argv[-1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 2 else argv[1]
model_dir = 'models/'
utils.create_folder(model_dir)
unique_name = '{}_ov{}_split{}_{}{}_3d{}_{}'.format(
params['dataset'], params['overlap'], params['split'], params['mode'],
params['weakness'], int(params['cnn_3d']), job_id)
unique_name = os.path.join(model_dir, unique_name)
print("unique_name: {}\n".format(unique_name))
data_gen_train = cls_data_generator.DataGenerator(
dataset=params['dataset'],
ov=params['overlap'],
split=params['split'],
db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
classifier_mode=params['mode'],
weakness=params['weakness'],
datagen_mode='train',
cnn3d=params['cnn_3d'],
xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'])
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'],
ov=params['overlap'],
split=params['split'],
db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
classifier_mode=params['mode'],
weakness=params['weakness'],
datagen_mode='test',
cnn3d=params['cnn_3d'],
xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'],
shuffle=False)
data_in, data_out = data_gen_train.get_data_sizes()
print('FEATURES:\n'
'\tdata_in: {}\n'
'\tdata_out: {}\n'.format(data_in, data_out))
gt = collect_test_labels(data_gen_test, data_out, params['mode'],
params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print('MODEL:\n'
'\tdropout_rate: {}\n'
'\tCNN: nb_cnn_filt: {}, pool_size{}\n'
'\trnn_size: {}, fnn_size: {}\n'.format(
params['dropout_rate'], params['nb_cnn3d_filt']
if params['cnn_3d'] else params['nb_cnn2d_filt'],
params['pool_size'], params['rnn_size'], params['fnn_size']))
# TPU CODE FOR GOOGLE COLABORATORY
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.config.experimental_connect_to_cluster(resolver)
# This is the TPU initialization code that has to be at the beginning.
tf.tpu.experimental.initialize_tpu_system(resolver)
print("All devices: ", tf.config.list_logical_devices('TPU'))
strategy = tf.distribute.experimental.TPUStrategy(resolver)
with strategy.scope():
# Load or create model
model = utils.load_model(unique_name)
if model is None:
model = keras_model.get_model(
data_in=data_in,
data_out=data_out,
dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'],
pool_size=params['pool_size'],
rnn_size=params['rnn_size'],
fnn_size=params['fnn_size'],
classification_mode=params['mode'],
weights=params['loss_weights'])
model.summary()
best_metric = 99999
conf_mat = None
best_conf_mat = None
best_epoch = -1
patience_cnt = 0
epoch_metric_loss = np.zeros(params['nb_epochs'])
sed_score = np.zeros(params['nb_epochs'])
doa_score = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_loss = np.zeros((params['nb_epochs'], 6))
sed_loss = np.zeros((params['nb_epochs'], 2))
for epoch_cnt in range(params['nb_epochs']):
start = time.time()
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=5 if params['quick_test'] else
data_gen_train.get_total_batches_in_data(),
validation_data=data_gen_test.generate(),
validation_steps=5 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
epochs=1,
verbose=1)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
val_loss[epoch_cnt] = hist.history.get('val_loss')[-1]
pred = model.predict_generator(
generator=data_gen_test.generate(),
steps=5 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
verbose=2)
print("pred:", pred[1].shape)
if params['mode'] == 'regr':
sed_pred = evaluation_metrics.reshape_3Dto2D(pred[0]) > 0.5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
sed_loss[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(
sed_pred, sed_gt, data_gen_test.nb_frames_1s())
if params['azi_only']:
doa_loss[
epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xy(
doa_pred, doa_gt, sed_pred, sed_gt)
else:
doa_loss[
epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xyz(
doa_pred, doa_gt, sed_pred, sed_gt)
# epoch_metric_loss[epoch_cnt] = np.mean([
# sed_loss[epoch_cnt, 0],
# 1-sed_loss[epoch_cnt, 1],
# 2*np.arcsin(doa_loss[epoch_cnt, 1]/2.0)/np.pi,
# 1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))]
# )
sed_score[epoch_cnt] = np.mean(
[sed_loss[epoch_cnt, 0], 1 - sed_loss[epoch_cnt, 1]])
doa_score[epoch_cnt] = np.mean([
2 * np.arcsin(doa_loss[epoch_cnt, 1] / 2.0) / np.pi,
1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))
])
#plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss, epoch_metric_loss)
plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss,
sed_score, doa_score)
patience_cnt += 1
# if epoch_metric_loss[epoch_cnt] < best_metric:
# best_metric = epoch_metric_loss[epoch_cnt]
# best_conf_mat = conf_mat
# best_epoch = epoch_cnt
# model.save('{}_model.h5'.format(unique_name))
# patience_cnt = 0
if sed_score[epoch_cnt] < best_metric:
best_metric = sed_score[epoch_cnt]
best_conf_mat = conf_mat
best_epoch = epoch_cnt
model.save('{}_model.h5'.format(unique_name))
patience_cnt = 0
print(
'epoch_cnt: %d, time: %.2fs, tr_loss: %.2f, val_loss: %.2f, '
'F1_overall: %.2f, ER_overall: %.2f, '
'doa_error_gt: %.2f, doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'sed_score: %.2f, doa_score: %.2f, best_error_metric: %.2f, best_epoch : %d'
% (epoch_cnt, time.time() - start, tr_loss[epoch_cnt],
val_loss[epoch_cnt], sed_loss[epoch_cnt, 1],
sed_loss[epoch_cnt, 0], doa_loss[epoch_cnt, 1],
doa_loss[epoch_cnt, 2], doa_loss[epoch_cnt, 5] /
float(sed_gt.shape[0]), sed_score[epoch_cnt],
doa_score[epoch_cnt], best_metric, best_epoch))
#plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss, sed_score, doa_score, epoch_cnt)
print('best_conf_mat : {}'.format(best_conf_mat))
print('best_conf_mat_diag : {}'.format(np.diag(best_conf_mat)))
print('saved model for the best_epoch: {} with best_metric: {}, '.format(
best_epoch, best_metric))
print(
'DOA Metrics: doa_loss_gt: {}, doa_loss_pred: {}, good_pks_ratio: {}'.
format(doa_loss[best_epoch, 1], doa_loss[best_epoch, 2],
doa_loss[best_epoch, 5] / float(sed_gt.shape[0])))
print('SED Metrics: ER_overall: {}, F1_overall: {}'.format(
sed_loss[best_epoch, 0], sed_loss[best_epoch, 1]))
print('unique_name: {} '.format(unique_name))
def main(params, network_params):
print(
'\n\n----------------------------------------------------------------------------------------------------'
)
print(
'----------------------------------- Splitting data into {:0>2d} folds -----------------------------------'
.format(params['nb_folds']))
print(
'----------------------------------------------------------------------------------------------------'
)
evaluation_cls = evaluation_metrics(results_dir=params['results_dir'],
win_len=params['win_len'],
hop_len=params['hop_len'],
seq_len=params['seq_len'],
seq_hop_len=params['seq_hop_len'],
nb_channels=params['nb_channels'],
nb_classes=params['nb_classes'],
silent=True)
data_splitter_cls = data_splitter(data_dir=params['data_dir'],
win_len=params['win_len'],
hop_len=params['hop_len'],
seq_len=params['seq_len'],
seq_hop_len=params['seq_hop_len'],
nb_channels=params['nb_channels'],
nb_classes=params['nb_classes'],
validate=False,
nb_folds=params['nb_folds'],
silent=True)
data_splitter_cls.perform()
Path(os.path.join(params['log_dir'])).mkdir(parents=True, exist_ok=True)
Path(os.path.join(params['results_dir'])).mkdir(parents=True,
exist_ok=True)
for fold_cnt in range(params['nb_folds']):
print(
'\n\n----------------------------------------------------------------------------------------------------'
)
print(
'------------------------------------------ fold: {:0>2d} ------------------------------------------'
.format(fold_cnt + 1))
print(
'----------------------------------------------------------------------------------------------------'
)
model_name = os.path.join(params['log_dir'],
'fold_{}_best_model.h5'.format(fold_cnt + 1))
csv_logger = CSVLogger(filename=os.path.join(
params['log_dir'], 'fold_{}_log.csv'.format(fold_cnt + 1)))
early_stopper = EarlyStopping(monitor='loss', min_delta=0, mode='min')
data_splitter_cls.prepare(fold_n=fold_cnt, run_type='train')
data_splitter_cls.prepare(fold_n=fold_cnt, run_type='val')
data_splitter_cls.prepare(fold_n=fold_cnt, run_type='test')
print('Loading training dataset:')
train_data_gen = DataGenerator(data_dir=params['data_dir'],
shuffle=False,
win_len=params['win_len'],
hop_len=params['hop_len'],
seq_len=params['seq_len'],
seq_hop_len=params['seq_hop_len'],
nb_channels=params['nb_channels'],
nb_classes=params['nb_classes'],
n_fold=fold_cnt,
batch_size=params['batch_size'],
run_type='train')
val_data_gen = DataGenerator(data_dir=params['data_dir'],
shuffle=False,
win_len=params['win_len'],
hop_len=params['hop_len'],
seq_len=params['seq_len'],
seq_hop_len=params['seq_hop_len'],
nb_channels=params['nb_channels'],
nb_classes=params['nb_classes'],
n_fold=fold_cnt,
batch_size=params['batch_size'],
run_type='val')
test_data_gen = DataGenerator(data_dir=params['data_dir'],
shuffle=False,
win_len=params['win_len'],
hop_len=params['hop_len'],
seq_len=params['seq_len'],
seq_hop_len=params['seq_hop_len'],
nb_channels=params['nb_channels'],
nb_classes=params['nb_classes'],
n_fold=fold_cnt,
batch_size=params['batch_size'],
run_type='test')
model = get_model(params, network_params)
#for epoch_cnt in range(params['nb_epochs']):
#print('Epoch No. {}'.format(epoch_cnt+1))
hist = model.fit(x=train_data_gen,
validation_data=val_data_gen,
epochs=params['nb_epochs'],
workers=10,
use_multiprocessing=True,
callbacks=[csv_logger]) #early_stopper,csv_logger])
model.save(model_name)
print(
'\nLoading the best model and predicting results on the testing data'
)
model = load_model(model_name)
pred_test = model.predict(x=test_data_gen,
workers=2,
use_multiprocessing=False)
pred_test_logits = pred_test
pred_test = pred_test > 0.5
test_data_records = pd.read_csv(
os.path.join(
test_data_gen._data_dir, 'folds_metadata',
'{}_metadata_{}.csv'.format(test_data_gen._run_type,
fold_cnt + 1)))
test_h5 = h5py.File(
os.path.join(test_data_gen._data_dir, 'folds_h5',
'{}_metadata_{}.hdf5'.format('test', fold_cnt + 1)))
ref_test = np.asarray(test_h5['seq_labels'])
test_h5.close()
Path(
os.path.join(params['results_dir'],
'test_val_fold{:0>2d}'.format(fold_cnt + 1))).mkdir(
parents=True, exist_ok=True)
Path(
os.path.join(
params['results_dir'],
'test_val_logits_fold{:0>2d}'.format(fold_cnt + 1))).mkdir(
parents=True, exist_ok=True)
Path(
os.path.join(params['results_dir'],
'ref_val_fold{:0>2d}'.format(fold_cnt + 1))).mkdir(
parents=True, exist_ok=True)
evaluation_cls.combine_seqlogits(pred_test,
test_data_records,
run_type='test',
fold_n=fold_cnt)
evaluation_cls.combine_seqlogits(ref_test,
test_data_records,
run_type='ref',
fold_n=fold_cnt)
evaluation_cls.combine_seqlogitspro(pred_test_logits,
test_data_records,
fold_n=fold_cnt)
nb_test_files = os.listdir(
os.path.join(params['results_dir'],
'test_val_fold{:0>2d}'.format(fold_cnt + 1)))
acc = np.zeros((len(nb_test_files), 1))
sens = np.zeros((len(nb_test_files), 1))
spec = np.zeros((len(nb_test_files), 1))
fold_clogits = []
fold_cgtruth = []
print('Model evaluation for fold#{}:'.format(fold_cnt + 1))
for testfile_cnt, testfile in enumerate(nb_test_files):
print('Evaluating segmentation of file# {}: {}'.format(
testfile_cnt + 1, testfile))
acc[testfile_cnt], sens[testfile_cnt], spec[
testfile_cnt] = evaluation_cls.evaluate_sequences(
np.load(
os.path.join(
params['results_dir'],
'ref_val_fold{:0>2d}'.format(fold_cnt + 1),
testfile)),
np.load(
os.path.join(
params['results_dir'],
'test_val_fold{:0>2d}'.format(fold_cnt + 1),
testfile)))
fold_clogits = np.concatenate(
(fold_clogits,
np.load(
os.path.join(
params['results_dir'],
'test_val_logits_fold{:0>2d}'.format(fold_cnt + 1),
testfile))))
fold_cgtruth = np.concatenate(
(fold_cgtruth,
np.load(
os.path.join(params['results_dir'],
'ref_val_fold{:0>2d}'.format(fold_cnt + 1),
testfile))))
np.save(
os.path.join(
params['results_dir'],
'fold_{:0>2d}_aggregate_logits.npy'.format(fold_cnt + 1)),
fold_clogits)
np.save(
os.path.join(
params['results_dir'],
'fold_{:0>2d}_aggregate_gtruth.npy'.format(fold_cnt + 1)),
fold_cgtruth)
np.save(
os.path.join(params['results_dir'],
'fold_{:0>2d}_qmeasures_acc.npy'.format(fold_cnt +
1)), acc)
np.save(
os.path.join(
params['results_dir'],
'fold_{:0>2d}_qmeasures_sens.npy'.format(fold_cnt + 1)), sens)
np.save(
os.path.join(
params['results_dir'],
'fold_{:0>2d}_qmeasures_spec.npy'.format(fold_cnt + 1)), spec)
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: job_id - (optional) all the output files will be uniquely represented with this. (default) 1
second input: task_id - (optional) To chose the system configuration in parameters.py.
(default) uses default parameters
"""
if len(argv) != 3:
print('\n\n')
print('-------------------------------------------------------------------------------------------------------')
print('The code expected two inputs')
print('\t>> python seld.py <job-id> <task-id>')
print('\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print('\t\t<task-id> is used to choose the user-defined parameter set from parameter.py')
print('Using default inputs for now')
print('-------------------------------------------------------------------------------------------------------')
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 3 else argv[-1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 2 else argv[1]
model_dir = 'models/'
utils.create_folder(model_dir)
unique_name = '{}_train{}_validation{}_seq{}'.format(params['dataset'], params['train_split'], params['val_split'], params['sequence_length'])
unique_name = os.path.join(model_dir, unique_name)
print("unique_name: {}\n".format(unique_name))
# Cycling over overlaps
for ov in range(1, params['overlap']+1):
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'], ov=params['overlap'], ov_num=ov, split=params['test_split'], db=params['db'], nfft=params['nfft'],
batch_size=params['batch_size'], seq_len=params['sequence_length'], classifier_mode=params['mode'],
weakness=params['weakness'], datagen_mode='test', cnn3d=params['cnn_3d'], xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'], shuffle=False
)
data_in, data_out = data_gen_test.get_data_sizes()
n_classes = data_out[0][2]
print(
'FEATURES:\n'
'\tdata_in: {}\n'
'\tdata_out: {}\n'.format(
data_in, data_out
)
)
gt = collect_test_labels(data_gen_test, data_out, params['mode'], params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print("#### Saving DOA and SED GT Values ####")
f = open("models/doa_gt.txt", "w+")
for elem in doa_gt:
f.write(str(list(elem)) + "\n")
f.close()
f = open("models/sed_gt.txt", "w+")
for elem in sed_gt:
f.write(str(elem)+"\n")
f.close()
print("######################################")
print(
'MODEL:\n'
'\tdropout_rate: {}\n'
'\tCNN: nb_cnn_filt: {}, pool_size{}\n'
'\trnn_size: {}, fnn_size: {}\n'.format(
params['dropout_rate'],
params['nb_cnn3d_filt'] if params['cnn_3d'] else params['nb_cnn2d_filt'], params['pool_size'],
params['rnn_size'], params['fnn_size']
)
)
model = keras_model.get_model(data_in=data_in, data_out=data_out, dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'], pool_size=params['pool_size'],
rnn_size=params['rnn_size'], fnn_size=params['fnn_size'],
classification_mode=params['mode'], weights=params['loss_weights'], summary=False)
if(os.path.exists('{}_model.ckpt'.format(unique_name))):
print("Model found!")
model.load_weights('{}_model.ckpt'.format(unique_name))
for i in range(10):
print("###")
sed_score = np.zeros(params['nb_epochs'])
doa_score = np.zeros(params['nb_epochs'])
seld_score = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_loss = np.zeros((params['nb_epochs'], 6))
sed_loss = np.zeros((params['nb_epochs'], 2))
epoch_cnt = 0
start = time.time()
print("#### Prediction on validation split ####")
pred = model.predict_generator(
generator=data_gen_test.generate(),
steps=params['quick_test_steps'] if params['quick_test'] else data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
workers=1,
verbose=1,
)
print("##########################")
#print("pred:", pred[1].shape)
if params['mode'] == 'regr':
sed_pred = np.array(evaluation_metrics.reshape_3Dto2D(pred[0])) > .5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
print("#### Saving DOA and SED Pred Values ####")
f = open("models/doa_pred.txt", "w+")
for elem in doa_pred:
f.write(str(list(elem)) + "\n")
f.close()
f = open("models/sed_pred.txt", "w+")
for elem in sed_pred:
f.write(str(elem)+"\n")
f.close()
print("########################################")
# Old version of confidence intervals
'''
# Computing confidence intervals
sed_err = sed_gt - sed_pred
[sed_conf_low, sed_conf_up, sed_median] = compute_confidence(sed_err)
# print("Condidence Interval for SED error is [" + str(sed_conf_low) + ", " + str(sed_conf_up) + "]")
print("Confidence Interval for SED error is [ %f, %f ]" % (sed_conf_low, sed_conf_up))
# print("\tMedian is " + str(sed_median))
print("\tMedian is %f" % (sed_median))
# print("\tDisplacement: +/- " + str(sed_conf_up - sed_median))
print("\tDisplacement: +/- %f" % (sed_conf_up - sed_median))
doa_err = doa_gt - doa_pred
[doa_conf_low, doa_conf_up, doa_median] = compute_confidence(doa_err)
# print("Condidence Interval for DOA is [" + str(doa_conf_low) + ", " + str(doa_conf_up) + "]")
print("Confidence Interval for DOA is [ %f, %f ]" % (doa_conf_low, doa_conf_up))
# print("Median is " + str(doa_median))
print("\tMedian is %f" % (doa_median))
# print("Displacement: +/- " + str(doa_conf_up - doa_median))
print("\tDisplacement: +/- %f" % (doa_conf_up - doa_median))
# ------------------------------
'''
sed_loss[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(sed_pred, sed_gt, data_gen_test.nb_frames_1s())
if params['azi_only']:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xy(doa_pred, doa_gt,
sed_pred, sed_gt)
else:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xyz(doa_pred, doa_gt,
sed_pred, sed_gt)
sed_score[epoch_cnt] = np.mean([sed_loss[epoch_cnt, 0], 1 - sed_loss[epoch_cnt, 1]])
doa_score[epoch_cnt] = np.mean([2 * np.arcsin(doa_loss[epoch_cnt, 1] / 2.0) / np.pi,
1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))])
seld_score[epoch_cnt] = (sed_score[epoch_cnt] + doa_score[epoch_cnt]) / 2
if os.path.isdir('./models'):
plot.imshow(conf_mat, cmap='binary', interpolation='None')
plot.savefig('models/confusion_matrix.jpg')
# New confidence computation, differing doa and sed errors
sed_err = sed_loss[epoch_cnt, 0]
[sed_conf_low, sed_conf_up] = compute_confidence(sed_err, sed_pred.shape[0])
print("Confidence Interval for SED error is [ %f, %f ]" % (sed_conf_low, sed_conf_up))
doa_err = doa_gt - doa_pred
[x_err, y_err, z_err] = compute_doa_confidence(doa_err, n_classes)
print('epoch_cnt: %d, time: %.2fs, tr_loss: %.4f, val_loss: %.4f, '
'F1_overall: %.2f, ER_overall: %.2f, '
'doa_error_gt: %.2f, doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'sed_score: %.4f, doa_score: %.4f, seld_score: %.4f' %
(
epoch_cnt, time.time() - start, tr_loss[epoch_cnt], val_loss[epoch_cnt],
sed_loss[epoch_cnt, 1], sed_loss[epoch_cnt, 0],
doa_loss[epoch_cnt, 1], doa_loss[epoch_cnt, 2], doa_loss[epoch_cnt, 5] / float(sed_gt.shape[0]),
sed_score[epoch_cnt], doa_score[epoch_cnt], seld_score[epoch_cnt]
)
)
simple_plotter.plot_3d("models/doa_gt.txt", "models/doa_pred.txt", 0, 11, 200)
simple_plotter.plot_confidence(x_err, y_err, z_err, "ov"+str(ov))
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: task_id - (optional) To chose the system configuration in parameters.py.
(default) 1 - uses default parameters
second input: job_id - (optional) all the output files will be uniquely represented with this.
(default) 1
"""
if len(argv) != 3:
print('\n\n')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('The code expected two optional inputs')
print('\t>> python seld.py <task-id> <job-id>')
print(
'\t\t<task-id> is used to choose the user-defined parameter set from parameter.py'
)
print('Using default inputs for now')
print(
'\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 2 else argv[1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 3 else argv[-1]
train_splits, val_splits, test_splits = None, None, None
if params['mode'] == 'dev':
test_splits = [1, 2, 3, 4]
val_splits = [2, 3, 4, 1]
train_splits = [[3, 4], [4, 1], [1, 2], [2, 3]]
# SUGGESTION: Considering the long training time, major tuning of the method can be done on the first split.
# Once you finlaize the method you can evaluate its performance on the complete cross-validation splits
# test_splits = [1]
# val_splits = [2]
# train_splits = [[3, 4]]
elif params['mode'] == 'eval':
test_splits = [0]
val_splits = [1]
train_splits = [[2, 3, 4]]
avg_scores_val = []
avg_scores_test = []
for split_cnt, split in enumerate(test_splits):
print(
'\n\n---------------------------------------------------------------------------------------------------'
)
print(
'------------------------------------ SPLIT {} -----------------------------------------------'
.format(split))
print(
'---------------------------------------------------------------------------------------------------'
)
# Unique name for the run
cls_feature_class.create_folder(params['model_dir'])
unique_name = '{}_{}_{}_{}_split{}'.format(task_id, job_id,
params['dataset'],
params['mode'], split)
unique_name = os.path.join(params['model_dir'], unique_name)
model_name = '{}_model.h5'.format(unique_name)
print("unique_name: {}\n".format(unique_name))
# Load train and validation data
print('Loading training dataset:')
data_gen_train = cls_data_generator.DataGenerator(
dataset=params['dataset'],
split=train_splits[split_cnt],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
feat_label_dir=params['feat_label_dir'])
print('Loading validation dataset:')
data_gen_val = cls_data_generator.DataGenerator(
dataset=params['dataset'],
split=val_splits[split_cnt],
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
feat_label_dir=params['feat_label_dir'],
shuffle=False)
# Collect the reference labels for validation data
data_in, data_out = data_gen_train.get_data_sizes()
print('FEATURES:\n\tdata_in: {}\n\tdata_out: {}\n'.format(
data_in, data_out))
gt = collect_test_labels(data_gen_val, data_out, params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
# rescaling the reference elevation data from [-180 180] to [-def_elevation def_elevation] for scoring purpose
nb_classes = data_gen_train.get_nb_classes()
def_elevation = data_gen_train.get_default_elevation()
doa_gt[:,
nb_classes:] = doa_gt[:, nb_classes:] / (180. / def_elevation)
print(
'MODEL:\n\tdropout_rate: {}\n\tCNN: nb_cnn_filt: {}, pool_size{}\n\trnn_size: {}, fnn_size: {}\n'
.format(params['dropout_rate'], params['nb_cnn2d_filt'],
params['pool_size'], params['rnn_size'],
params['fnn_size']))
model = keras_model.get_model(data_in=data_in,
data_out=data_out,
dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'],
pool_size=params['pool_size'],
rnn_size=params['rnn_size'],
fnn_size=params['fnn_size'],
weights=params['loss_weights'])
best_seld_metric = 99999
best_epoch = -1
patience_cnt = 0
seld_metric = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_metric = np.zeros((params['nb_epochs'], 6))
sed_metric = np.zeros((params['nb_epochs'], 2))
nb_epoch = 2 if params['quick_test'] else params['nb_epochs']
# start training
for epoch_cnt in range(nb_epoch):
start = time.time()
# train once per epoch
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=2 if params['quick_test'] else
data_gen_train.get_total_batches_in_data(),
validation_data=data_gen_val.generate(),
validation_steps=2 if params['quick_test'] else
data_gen_val.get_total_batches_in_data(),
epochs=params['epochs_per_fit'],
verbose=2)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
val_loss[epoch_cnt] = hist.history.get('val_loss')[-1]
# predict once per peoch
pred = model.predict_generator(
generator=data_gen_val.generate(),
steps=2 if params['quick_test'] else
data_gen_val.get_total_batches_in_data(),
verbose=2)
# Calculate the metrics
sed_pred = evaluation_metrics.reshape_3Dto2D(pred[0]) > 0.5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
# rescaling the elevation data from [-180 180] to [-def_elevation def_elevation] for scoring purpose
doa_pred[:,
nb_classes:] = doa_pred[:, nb_classes:] / (180. /
def_elevation)
sed_metric[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(
sed_pred, sed_gt, data_gen_val.nb_frames_1s())
doa_metric[
epoch_cnt, :] = evaluation_metrics.compute_doa_scores_regr(
doa_pred, doa_gt, sed_pred, sed_gt)
seld_metric[epoch_cnt] = evaluation_metrics.compute_seld_metric(
sed_metric[epoch_cnt, :], doa_metric[epoch_cnt, :])
# Visualize the metrics with respect to epochs
plot_functions(unique_name, tr_loss, val_loss, sed_metric,
doa_metric, seld_metric)
patience_cnt += 1
if seld_metric[epoch_cnt] < best_seld_metric:
best_seld_metric = seld_metric[epoch_cnt]
best_epoch = epoch_cnt
model.save(model_name)
patience_cnt = 0
print(
'epoch_cnt: %d, time: %.2fs, tr_loss: %.2f, val_loss: %.2f, '
'ER_overall: %.2f, F1_overall: %.2f, '
'doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'seld_score: %.2f, best_seld_score: %.2f, best_epoch : %d\n' %
(epoch_cnt, time.time() - start, tr_loss[epoch_cnt],
val_loss[epoch_cnt], sed_metric[epoch_cnt,
0], sed_metric[epoch_cnt, 1],
doa_metric[epoch_cnt, 0], doa_metric[epoch_cnt, 1],
seld_metric[epoch_cnt], best_seld_metric, best_epoch))
if patience_cnt > params['patience']:
break
avg_scores_val.append([
sed_metric[best_epoch, 0], sed_metric[best_epoch, 1],
doa_metric[best_epoch, 0], doa_metric[best_epoch,
1], best_seld_metric
])
print('\nResults on validation split:')
print('\tUnique_name: {} '.format(unique_name))
print('\tSaved model for the best_epoch: {}'.format(best_epoch))
print('\tSELD_score: {}'.format(best_seld_metric))
print('\tDOA Metrics: DOA_error: {}, frame_recall: {}'.format(
doa_metric[best_epoch, 0], doa_metric[best_epoch, 1]))
print('\tSED Metrics: ER_overall: {}, F1_overall: {}\n'.format(
sed_metric[best_epoch, 0], sed_metric[best_epoch, 1]))
# ------------------ Calculate metric scores for unseen test split ---------------------------------
print('Loading testing dataset:')
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'],
split=split,
batch_size=params['batch_size'],
seq_len=params['sequence_length'],
feat_label_dir=params['feat_label_dir'],
shuffle=False,
per_file=params['dcase_output'],
is_eval=True if params['mode'] is 'eval' else False)
print(
'\nLoading the best model and predicting results on the testing split'
)
model = load_model('{}_model.h5'.format(unique_name))
pred_test = model.predict_generator(
generator=data_gen_test.generate(),
steps=2 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
verbose=2)
test_sed_pred = evaluation_metrics.reshape_3Dto2D(pred_test[0]) > 0.5
test_doa_pred = evaluation_metrics.reshape_3Dto2D(pred_test[1])
# rescaling the elevation data from [-180 180] to [-def_elevation def_elevation] for scoring purpose
test_doa_pred[:, nb_classes:] = test_doa_pred[:, nb_classes:] / (
180. / def_elevation)
if params['dcase_output']:
# Dump results in DCASE output format for calculating final scores
dcase_dump_folder = os.path.join(
params['dcase_dir'],
'{}_{}_{}'.format(task_id, params['dataset'], params['mode']))
cls_feature_class.create_folder(dcase_dump_folder)
print('Dumping recording-wise results in: {}'.format(
dcase_dump_folder))
test_filelist = data_gen_test.get_filelist()
# Number of frames for a 60 second audio with 20ms hop length = 3000 frames
max_frames_with_content = data_gen_test.get_nb_frames()
# Number of frames in one batch (batch_size* sequence_length) consists of all the 3000 frames above with
# zero padding in the remaining frames
frames_per_file = data_gen_test.get_frame_per_file()
for file_cnt in range(test_sed_pred.shape[0] // frames_per_file):
output_file = os.path.join(
dcase_dump_folder,
test_filelist[file_cnt].replace('.npy', '.csv'))
dc = file_cnt * frames_per_file
output_dict = evaluation_metrics.regression_label_format_to_output_format(
data_gen_test,
test_sed_pred[dc:dc + max_frames_with_content, :],
test_doa_pred[dc:dc + max_frames_with_content, :] * 180 /
np.pi)
evaluation_metrics.write_output_format_file(
output_file, output_dict)
if params['mode'] is 'dev':
test_data_in, test_data_out = data_gen_test.get_data_sizes()
test_gt = collect_test_labels(data_gen_test, test_data_out,
params['quick_test'])
test_sed_gt = evaluation_metrics.reshape_3Dto2D(test_gt[0])
test_doa_gt = evaluation_metrics.reshape_3Dto2D(test_gt[1])
# rescaling the reference elevation from [-180 180] to [-def_elevation def_elevation] for scoring purpose
test_doa_gt[:, nb_classes:] = test_doa_gt[:, nb_classes:] / (
180. / def_elevation)
test_sed_loss = evaluation_metrics.compute_sed_scores(
test_sed_pred, test_sed_gt, data_gen_test.nb_frames_1s())
test_doa_loss = evaluation_metrics.compute_doa_scores_regr(
test_doa_pred, test_doa_gt, test_sed_pred, test_sed_gt)
test_metric_loss = evaluation_metrics.compute_seld_metric(
test_sed_loss, test_doa_loss)
avg_scores_test.append([
test_sed_loss[0], test_sed_loss[1], test_doa_loss[0],
test_doa_loss[1], test_metric_loss
])
print('Results on test split:')
print('\tSELD_score: {}, '.format(test_metric_loss))
print('\tDOA Metrics: DOA_error: {}, frame_recall: {}'.format(
test_doa_loss[0], test_doa_loss[1]))
print('\tSED Metrics: ER_overall: {}, F1_overall: {}\n'.format(
test_sed_loss[0], test_sed_loss[1]))
print('\n\nValidation split scores per fold:\n')
for cnt in range(len(val_splits)):
print(
'\tSplit {} - SED ER: {} F1: {}; DOA error: {} frame recall: {}; SELD score: {}'
.format(cnt, avg_scores_val[cnt][0], avg_scores_val[cnt][1],
avg_scores_val[cnt][2], avg_scores_val[cnt][3],
avg_scores_val[cnt][4]))
if params['mode'] is 'dev':
print('\n\nTesting split scores per fold:\n')
for cnt in range(len(val_splits)):
print(
'\tSplit {} - SED ER: {} F1: {}; DOA error: {} frame recall: {}; SELD score: {}'
.format(cnt, avg_scores_test[cnt][0], avg_scores_test[cnt][1],
avg_scores_test[cnt][2], avg_scores_test[cnt][3],
avg_scores_test[cnt][4]))
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: task_id - (optional) To chose the system configuration in parameters.py.
(default) 1 - uses default parameters
second input: job_id - (optional) all the output files will be uniquely represented with this.
(default) 1
"""
print(argv)
if len(argv) != 3:
print('\n\n')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('The code expected two optional inputs')
print('\t>> python seld.py <task-id> <job-id>')
print(
'\t\t<task-id> is used to choose the user-defined parameter set from parameter.py'
)
print('Using default inputs for now')
print(
'\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print(
'-------------------------------------------------------------------------------------------------------'
)
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 2 else argv[1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 3 else argv[-1]
feat_cls = cls_feature_class.FeatureClass(params)
train_splits, val_splits, test_splits = None, None, None
if params['mode'] == 'dev':
test_splits = [1]
val_splits = [2]
train_splits = [[3, 4, 5, 6]]
elif params['mode'] == 'eval':
test_splits = [[7, 8]]
val_splits = [[1]]
train_splits = [[2, 3, 4, 5, 6]]
avg_scores_val = []
avg_scores_test = []
for split_cnt, split in enumerate(test_splits):
print(
'\n\n---------------------------------------------------------------------------------------------------'
)
print(
'------------------------------------ SPLIT {} -----------------------------------------------'
.format(split))
print(
'---------------------------------------------------------------------------------------------------'
)
# Unique name for the run
cls_feature_class.create_folder(params['model_dir'])
unique_name = '{}_{}_{}_{}_split{}'.format(task_id, job_id,
params['dataset'],
params['mode'], split)
unique_name = os.path.join(params['model_dir'], unique_name)
model_name = '{}_model.h5'.format(unique_name)
print("unique_name: {}\n".format(unique_name))
# Load train and validation data
print('Loading training dataset:')
data_gen_train = cls_data_generator.DataGenerator(
params=params, split=train_splits[split_cnt])
print('Loading validation dataset:')
data_gen_val = cls_data_generator.DataGenerator(
params=params, split=val_splits[split_cnt], shuffle=False)
# Collect the reference labels for validation data
data_in, data_out = data_gen_train.get_data_sizes()
print('FEATURES:\n\tdata_in: {}\n\tdata_out: {}\n'.format(
data_in, data_out))
nb_classes = data_gen_train.get_nb_classes()
gt = collect_test_labels(data_gen_val, data_out, nb_classes,
params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print(
'MODEL:\n\tdropout_rate: {}\n\tCNN: nb_cnn_filt: {}, f_pool_size{}, t_pool_size{}\n\trnn_size: {}, fnn_size: {}\n\tdoa_objective: {}\n'
.format(params['dropout_rate'], params['nb_cnn2d_filt'],
params['f_pool_size'], params['t_pool_size'],
params['rnn_size'], params['fnn_size'],
params['doa_objective']))
print('Using loss weights : {}'.format(params['loss_weights']))
model = keras_model.get_model(data_in=data_in,
data_out=data_out,
dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'],
f_pool_size=params['f_pool_size'],
t_pool_size=params['t_pool_size'],
rnn_size=params['rnn_size'],
fnn_size=params['fnn_size'],
weights=params['loss_weights'],
doa_objective=params['doa_objective'])
best_seld_metric = 99999
best_epoch = -1
patience_cnt = 0
nb_epoch = 2 if params['quick_test'] else params['nb_epochs']
seld_metric = np.zeros(nb_epoch)
new_seld_metric = np.zeros(nb_epoch)
tr_loss = np.zeros(nb_epoch)
doa_metric = np.zeros((nb_epoch, 6))
sed_metric = np.zeros((nb_epoch, 2))
new_metric = np.zeros((nb_epoch, 4))
# start training
for epoch_cnt in range(nb_epoch):
start = time.time()
# train once per epoch
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=2 if params['quick_test'] else
data_gen_train.get_total_batches_in_data(),
epochs=params['epochs_per_fit'],
verbose=2,
)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
# predict once per peoch
pred = model.predict_generator(
generator=data_gen_val.generate(),
steps=2 if params['quick_test'] else
data_gen_val.get_total_batches_in_data(),
verbose=2)
sed_pred = evaluation_metrics.reshape_3Dto2D(pred[0]) > 0.5
doa_pred = evaluation_metrics.reshape_3Dto2D(
pred[1]
if params['doa_objective'] is 'mse' else pred[1][:, :,
nb_classes:])
# Calculate the DCASE 2019 metrics - Detection-only and Localization-only scores
sed_metric[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(
sed_pred, sed_gt, data_gen_val.nb_frames_1s())
doa_metric[
epoch_cnt, :] = evaluation_metrics.compute_doa_scores_regr_xyz(
doa_pred, doa_gt, sed_pred, sed_gt)
seld_metric[epoch_cnt] = evaluation_metrics.early_stopping_metric(
sed_metric[epoch_cnt, :], doa_metric[epoch_cnt, :])
# Calculate the DCASE 2020 metrics - Location-aware detection and Class-aware localization scores
cls_new_metric = SELD_evaluation_metrics.SELDMetrics(
nb_classes=data_gen_val.get_nb_classes(),
doa_threshold=params['lad_doa_thresh'])
pred_dict = feat_cls.regression_label_format_to_output_format(
sed_pred, doa_pred)
gt_dict = feat_cls.regression_label_format_to_output_format(
sed_gt, doa_gt)
pred_blocks_dict = feat_cls.segment_labels(pred_dict,
sed_pred.shape[0])
gt_blocks_dict = feat_cls.segment_labels(gt_dict, sed_gt.shape[0])
cls_new_metric.update_seld_scores_xyz(pred_blocks_dict,
gt_blocks_dict)
new_metric[epoch_cnt, :] = cls_new_metric.compute_seld_scores()
new_seld_metric[
epoch_cnt] = evaluation_metrics.early_stopping_metric(
new_metric[epoch_cnt, :2], new_metric[epoch_cnt, 2:])
# Visualize the metrics with respect to epochs
plot_functions(unique_name, tr_loss, sed_metric, doa_metric,
seld_metric, new_metric, new_seld_metric)
patience_cnt += 1
if new_seld_metric[epoch_cnt] < best_seld_metric:
best_seld_metric = new_seld_metric[epoch_cnt]
best_epoch = epoch_cnt
model.save(model_name)
patience_cnt = 0
print(
'epoch_cnt: {}, time: {:0.2f}s, tr_loss: {:0.2f}, '
'\n\t\t DCASE2019 SCORES: ER: {:0.2f}, F: {:0.1f}, DE: {:0.1f}, FR:{:0.1f}, seld_score: {:0.2f}, '
'\n\t\t DCASE2020 SCORES: ER: {:0.2f}, F: {:0.1f}, DE: {:0.1f}, DE_F:{:0.1f}, seld_score (early stopping score): {:0.2f}, '
'best_seld_score: {:0.2f}, best_epoch : {}\n'.format(
epoch_cnt,
time.time() - start, tr_loss[epoch_cnt],
sed_metric[epoch_cnt, 0], sed_metric[epoch_cnt, 1] * 100,
doa_metric[epoch_cnt, 0], doa_metric[epoch_cnt, 1] * 100,
seld_metric[epoch_cnt], new_metric[epoch_cnt, 0],
new_metric[epoch_cnt, 1] * 100, new_metric[epoch_cnt, 2],
new_metric[epoch_cnt, 3] * 100, new_seld_metric[epoch_cnt],
best_seld_metric, best_epoch))
if patience_cnt > params['patience']:
break
avg_scores_val.append([
new_metric[best_epoch, 0], new_metric[best_epoch, 1],
new_metric[best_epoch, 2], new_metric[best_epoch,
3], best_seld_metric
])
print('\nResults on validation split:')
print('\tUnique_name: {} '.format(unique_name))
print('\tSaved model for the best_epoch: {}'.format(best_epoch))
print('\tSELD_score (early stopping score) : {}'.format(
best_seld_metric))
print('\n\tDCASE2020 scores')
print(
'\tClass-aware localization scores: DOA_error: {:0.1f}, F-score: {:0.1f}'
.format(new_metric[best_epoch, 2],
new_metric[best_epoch, 3] * 100))
print(
'\tLocation-aware detection scores: Error rate: {:0.2f}, F-score: {:0.1f}'
.format(new_metric[best_epoch, 0],
new_metric[best_epoch, 1] * 100))
print('\n\tDCASE2019 scores')
print(
'\tLocalization-only scores: DOA_error: {:0.1f}, Frame recall: {:0.1f}'
.format(doa_metric[best_epoch, 0],
doa_metric[best_epoch, 1] * 100))
print(
'\tDetection-only scores: Error rate: {:0.2f}, F-score: {:0.1f}\n'.
format(sed_metric[best_epoch, 0], sed_metric[best_epoch, 1] * 100))
# ------------------ Calculate metric scores for unseen test split ---------------------------------
print(
'\nLoading the best model and predicting results on the testing split'
)
print('\tLoading testing dataset:')
data_gen_test = cls_data_generator.DataGenerator(
params=params,
split=split,
shuffle=False,
per_file=params['dcase_output'],
is_eval=True if params['mode'] is 'eval' else False)
model = keras_model.load_seld_model('{}_model.h5'.format(unique_name),
params['doa_objective'])
pred_test = model.predict_generator(
generator=data_gen_test.generate(),
steps=2 if params['quick_test'] else
data_gen_test.get_total_batches_in_data(),
verbose=2)
test_sed_pred = evaluation_metrics.reshape_3Dto2D(pred_test[0]) > 0.5
test_doa_pred = evaluation_metrics.reshape_3Dto2D(
pred_test[1]
if params['doa_objective'] is 'mse' else pred_test[1][:, :,
nb_classes:])
if params['dcase_output']:
# Dump results in DCASE output format for calculating final scores
dcase_dump_folder = os.path.join(
params['dcase_dir'],
'{}_{}_{}'.format(task_id, params['dataset'], params['mode']))
cls_feature_class.create_folder(dcase_dump_folder)
print('Dumping recording-wise results in: {}'.format(
dcase_dump_folder))
test_filelist = data_gen_test.get_filelist()
# Number of frames for a 60 second audio with 20ms hop length = 3000 frames
max_frames_with_content = data_gen_test.get_nb_frames()
# Number of frames in one batch (batch_size* sequence_length) consists of all the 3000 frames above with
# zero padding in the remaining frames
frames_per_file = data_gen_test.get_frame_per_file()
for file_cnt in range(test_sed_pred.shape[0] // frames_per_file):
output_file = os.path.join(
dcase_dump_folder,
test_filelist[file_cnt].replace('.npy', '.csv'))
dc = file_cnt * frames_per_file
output_dict = feat_cls.regression_label_format_to_output_format(
test_sed_pred[dc:dc + max_frames_with_content, :],
test_doa_pred[dc:dc + max_frames_with_content, :])
data_gen_test.write_output_format_file(output_file,
output_dict)
if params['mode'] is 'dev':
test_data_in, test_data_out = data_gen_test.get_data_sizes()
test_gt = collect_test_labels(data_gen_test, test_data_out,
nb_classes, params['quick_test'])
test_sed_gt = evaluation_metrics.reshape_3Dto2D(test_gt[0])
test_doa_gt = evaluation_metrics.reshape_3Dto2D(test_gt[1])
# Calculate DCASE2019 scores
test_sed_loss = evaluation_metrics.compute_sed_scores(
test_sed_pred, test_sed_gt, data_gen_test.nb_frames_1s())
test_doa_loss = evaluation_metrics.compute_doa_scores_regr_xyz(
test_doa_pred, test_doa_gt, test_sed_pred, test_sed_gt)
test_metric_loss = evaluation_metrics.early_stopping_metric(
test_sed_loss, test_doa_loss)
# Calculate DCASE2020 scores
cls_new_metric = SELD_evaluation_metrics.SELDMetrics(
nb_classes=data_gen_test.get_nb_classes(), doa_threshold=20)
test_pred_dict = feat_cls.regression_label_format_to_output_format(
test_sed_pred, test_doa_pred)
test_gt_dict = feat_cls.regression_label_format_to_output_format(
test_sed_gt, test_doa_gt)
test_pred_blocks_dict = feat_cls.segment_labels(
test_pred_dict, test_sed_pred.shape[0])
test_gt_blocks_dict = feat_cls.segment_labels(
test_gt_dict, test_sed_gt.shape[0])
cls_new_metric.update_seld_scores_xyz(test_pred_blocks_dict,
test_gt_blocks_dict)
test_new_metric = cls_new_metric.compute_seld_scores()
test_new_seld_metric = evaluation_metrics.early_stopping_metric(
test_new_metric[:2], test_new_metric[2:])
avg_scores_test.append([
test_new_metric[0], test_new_metric[1], test_new_metric[2],
test_new_metric[3], test_new_seld_metric
])
print('Results on test split:')
print('\tDCASE2020 Scores')
print(
'\tClass-aware localization scores: DOA Error: {:0.1f}, F-score: {:0.1f}'
.format(test_new_metric[2], test_new_metric[3] * 100))
print(
'\tLocation-aware detection scores: Error rate: {:0.2f}, F-score: {:0.1f}'
.format(test_new_metric[0], test_new_metric[1] * 100))
print('\tSELD (early stopping metric): {:0.2f}'.format(
test_new_seld_metric))
print('\n\tDCASE2019 Scores')
print(
'\tLocalization-only scores: DOA Error: {:0.1f}, Frame recall: {:0.1f}'
.format(test_doa_loss[0], test_doa_loss[1] * 100))
print(
'\tDetection-only scores:Error rate: {:0.2f}, F-score: {:0.1f}'
.format(test_sed_loss[0], test_sed_loss[1] * 100))
def main(argv):
"""
Main wrapper for training sound event localization and detection network.
:param argv: expects two optional inputs.
first input: job_id - (optional) all the output files will be uniquely represented with this. (default) 1
second input: task_id - (optional) To chose the system configuration in parameters.py.
(default) uses default parameters
"""
if len(argv) != 3:
print('\n\n')
print('-------------------------------------------------------------------------------------------------------')
print('The code expected two inputs')
print('\t>> python seld.py <job-id> <task-id>')
print('\t\t<job-id> is a unique identifier which is used for output filenames (models, training plots). '
'You can use any number or string for this.')
print('\t\t<task-id> is used to choose the user-defined parameter set from parameter.py')
print('Using default inputs for now')
print('-------------------------------------------------------------------------------------------------------')
print('\n\n')
# use parameter set defined by user
task_id = '1' if len(argv) < 3 else argv[-1]
params = parameter.get_params(task_id)
job_id = 1 if len(argv) < 2 else argv[1]
model_dir = 'models/'
utils.create_folder(model_dir)
unique_name = '{}_train{}_validation{}_seq{}'.format(params['dataset'], params['train_split'], params['val_split'],
params['sequence_length'])
unique_name = os.path.join(model_dir, unique_name)
print("unique_name: {}\n".format(unique_name))
data_gen_train = cls_data_generator.DataGenerator(
dataset=params['dataset'], ov=params['overlap'], split=params['train_split'], db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'], seq_len=params['sequence_length'], classifier_mode=params['mode'],
weakness=params['weakness'], datagen_mode='train', cnn3d=params['cnn_3d'], xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only']
)
data_gen_test = cls_data_generator.DataGenerator(
dataset=params['dataset'], ov=params['overlap'], split=params['val_split'], db=params['db'],
nfft=params['nfft'],
batch_size=params['batch_size'], seq_len=params['sequence_length'], classifier_mode=params['mode'],
weakness=params['weakness'], datagen_mode='validation', cnn3d=params['cnn_3d'], xyz_def_zero=params['xyz_def_zero'],
azi_only=params['azi_only'], shuffle=False
)
data_in, data_out = data_gen_train.get_data_sizes()
#n_classes = data_out[0][2]
print(
'FEATURES:\n'
'\tdata_in: {}\n'
'\tdata_out: {}\n'.format(
data_in, data_out
)
)
gt = collect_test_labels(data_gen_test, data_out, params['mode'], params['quick_test'])
sed_gt = evaluation_metrics.reshape_3Dto2D(gt[0])
doa_gt = evaluation_metrics.reshape_3Dto2D(gt[1])
print(
'MODEL:\n'
'\tdropout_rate: {}\n'
'\tCNN: nb_cnn_filt: {}, pool_size{}\n'
'\trnn_size: {}, fnn_size: {}\n'.format(
params['dropout_rate'],
params['nb_cnn3d_filt'] if params['cnn_3d'] else params['nb_cnn2d_filt'], params['pool_size'],
params['rnn_size'], params['fnn_size']
)
)
model = keras_model.get_model(data_in=data_in, data_out=data_out, dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'], pool_size=params['pool_size'],
rnn_size=params['rnn_size'], fnn_size=params['fnn_size'],
classification_mode=params['mode'], weights=params['loss_weights'], summary=True)
if (os.path.exists('{}_model.ckpt'.format(unique_name))):
print("Model found!")
model.load_weights('{}_model.ckpt'.format(unique_name))
for i in range(10):
print("###")
best_metric = 99999
conf_mat = None
best_conf_mat = None
best_epoch = -1
patience_cnt = 0
epoch_metric_loss = np.zeros(params['nb_epochs'])
sed_score = np.zeros(params['nb_epochs'])
doa_score = np.zeros(params['nb_epochs'])
seld_score = np.zeros(params['nb_epochs'])
tr_loss = np.zeros(params['nb_epochs'])
val_loss = np.zeros(params['nb_epochs'])
doa_loss = np.zeros((params['nb_epochs'], 6))
sed_loss = np.zeros((params['nb_epochs'], 2))
for epoch_cnt in range(params['nb_epochs']):
start = time.time()
print("##### Training the model #####")
hist = model.fit_generator(
generator=data_gen_train.generate(),
steps_per_epoch=params['quick_test_steps'] if params[
'quick_test'] else data_gen_train.get_total_batches_in_data(),
validation_data=data_gen_test.generate(),
validation_steps=params['quick_test_steps'] if params[
'quick_test'] else data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
workers=1,
epochs=1,
verbose=1
)
tr_loss[epoch_cnt] = hist.history.get('loss')[-1]
val_loss[epoch_cnt] = hist.history.get('val_loss')[-1]
print("##########################")
# Save, get model and re-load weights for the predict_generator bug
print("##### Saving weights #####")
model.save_weights('{}_model.ckpt'.format(unique_name))
model = keras_model.get_model(data_in=data_in, data_out=data_out, dropout_rate=params['dropout_rate'],
nb_cnn2d_filt=params['nb_cnn2d_filt'], pool_size=params['pool_size'],
rnn_size=params['rnn_size'], fnn_size=params['fnn_size'],
classification_mode=params['mode'], weights=params['loss_weights'], summary=False)
model.load_weights('{}_model.ckpt'.format(unique_name))
print("##########################")
print("#### Prediction on validation split ####")
pred = model.predict_generator(
generator=data_gen_test.generate(),
steps=params['quick_test_steps'] if params['quick_test'] else data_gen_test.get_total_batches_in_data(),
use_multiprocessing=False,
workers=1,
verbose=1
)
print("########################################")
# print("pred:",pred[1].shape)
if params['mode'] == 'regr':
sed_pred = np.array(evaluation_metrics.reshape_3Dto2D(pred[0])) > .5
doa_pred = evaluation_metrics.reshape_3Dto2D(pred[1])
# Old confidence intervals
'''
sed_err = sed_gt - sed_pred
[sed_conf_low, sed_conf_up, sed_median] = compute_confidence(sed_err)
# print("Condidence Interval for SED error is [" + str(sed_conf_low) + ", " + str(sed_conf_up) + "]")
print("Confidence Interval for SED error is [ %.5f, %.5f ]" % (sed_conf_low, sed_conf_up))
# print("\tMedian is " + str(sed_median))
print("\tMedian is %.5f" % (sed_median))
# print("\tDisplacement: +/- " + str(sed_conf_up - sed_median))
print("\tDisplacement: +/- %.5f" % (sed_conf_up - sed_median))
doa_err = doa_gt - doa_pred
[doa_conf_low, doa_conf_up, doa_median] = compute_confidence(doa_err)
# print("Condidence Interval for DOA is [" + str(doa_conf_low) + ", " + str(doa_conf_up) + "]")
print("Confidence Interval for DOA is [ %.5f, %.5f ]" % (doa_conf_low, doa_conf_up))
# print("Median is " + str(doa_median))
print("\tMedian is %.5f" % (doa_median))
# print("Displacement: +/- " + str(doa_conf_up - doa_median))
print("\tDisplacement: +/- %.5f" % (doa_conf_up - doa_median))
'''
sed_loss[epoch_cnt, :] = evaluation_metrics.compute_sed_scores(sed_pred, sed_gt,
data_gen_test.nb_frames_1s())
if params['azi_only']:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xy(doa_pred, doa_gt,
sed_pred, sed_gt)
else:
doa_loss[epoch_cnt, :], conf_mat = evaluation_metrics.compute_doa_scores_regr_xyz(doa_pred, doa_gt,
sed_pred, sed_gt)
sed_score[epoch_cnt] = np.mean([sed_loss[epoch_cnt, 0], 1 - sed_loss[epoch_cnt, 1]])
doa_score[epoch_cnt] = np.mean([2 * np.arcsin(doa_loss[epoch_cnt, 1] / 2.0) / np.pi,
1 - (doa_loss[epoch_cnt, 5] / float(doa_gt.shape[0]))])
seld_score[epoch_cnt] = (sed_score[epoch_cnt] + doa_score[epoch_cnt]) / 2
if os.path.isdir('./models'):
plot.imshow(conf_mat, cmap='binary', interpolation='None')
plot.savefig('models/confusion_matrix.jpg')
# New confidence computation, differing doa and sed errors
sed_err = sed_loss[epoch_cnt, 0]
[sed_conf_low, sed_conf_up] = compute_confidence(sed_err, sed_pred.shape[0])
print("Confidence Interval for SED error is [ %f, %f ]" % (sed_conf_low, sed_conf_up))
#doa_err = doa_gt - doa_pred
#[x_err, y_err, z_err] = compute_doa_confidence(doa_err, n_classes)
plot_array = [tr_loss[epoch_cnt], # 0
val_loss[epoch_cnt], # 1
sed_loss[epoch_cnt][0], # 2 er
sed_loss[epoch_cnt][1], # 3 f1
doa_loss[epoch_cnt][0], # 4 avg_accuracy
doa_loss[epoch_cnt][1], # 5 doa_loss_gt
doa_loss[epoch_cnt][2], # 6 doa_loss_pred
doa_loss[epoch_cnt][3], # 7 doa_loss_gt_cnt
doa_loss[epoch_cnt][4], # 8 doa_loss_pred_cnt
doa_loss[epoch_cnt][5], # 9 good_frame_cnt
sed_score[epoch_cnt], # 10
doa_score[epoch_cnt],
seld_score[epoch_cnt],
#doa_conf_low, doa_median,
#doa_conf_up, sed_conf_low,
#sed_median, sed_conf_up]
sed_conf_low, sed_conf_up]
patience_cnt += 1
# model.save_weights('{}_model.ckpt'.format(unique_name))
simple_plotter.save_array_to_csv("{}_plot.csv".format(unique_name), plot_array)
#simple_plotter.plot_confidence(x_err, y_err, z_err, "ov")
print("##### Model and metrics saved! #####")
if seld_score[epoch_cnt] < best_metric:
best_metric = seld_score[epoch_cnt]
best_conf_mat = conf_mat
best_epoch = epoch_cnt
# Now we save the model at every iteration
model.save_weights('{}_BEST_model.ckpt'.format(unique_name))
patience_cnt = 0
print('epoch_cnt: %d, time: %.2fs, tr_loss: %.4f, val_loss: %.4f, '
'F1_overall: %.2f, ER_overall: %.2f, '
'doa_error_gt: %.2f, doa_error_pred: %.2f, good_pks_ratio:%.2f, '
'sed_score: %.4f, doa_score: %.4f, seld_score: %.4f, best_error_metric: %.2f, best_epoch : %d' %
(
epoch_cnt, time.time() - start, tr_loss[epoch_cnt], val_loss[epoch_cnt],
sed_loss[epoch_cnt, 1], sed_loss[epoch_cnt, 0],
doa_loss[epoch_cnt, 1], doa_loss[epoch_cnt, 2], doa_loss[epoch_cnt, 5] / float(sed_gt.shape[0]),
sed_score[epoch_cnt], doa_score[epoch_cnt], seld_score[epoch_cnt], best_metric, best_epoch
)
)
# plot_functions(unique_name, tr_loss, val_loss, sed_loss, doa_loss, sed_score, doa_score, epoch_cnt)
print('best_conf_mat : {}'.format(best_conf_mat))
print('best_conf_mat_diag : {}'.format(np.diag(best_conf_mat)))
print('saved model for the best_epoch: {} with best_metric: {}, '.format(best_epoch, best_metric))
print('DOA Metrics: doa_loss_gt: {}, doa_loss_pred: {}, good_pks_ratio: {}'.format(
doa_loss[best_epoch, 1], doa_loss[best_epoch, 2], doa_loss[best_epoch, 5] / float(sed_gt.shape[0])))
print('SED Metrics: ER_overall: {}, F1_overall: {}'.format(sed_loss[best_epoch, 0], sed_loss[best_epoch, 1]))
print('unique_name: {} '.format(unique_name))
