def update_plot2D(samples_per_class, x_select, y_select, active_tab, fold_ix,
model_path, dataset_ix, sr):
global X
global X_pca
global Y
global file_names
global feature_extractor
print('start visualization')
if (active_tab == 'tab_visualization'):
fold_name = dataset.fold_list[fold_ix]
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
scaler = load_pickle(scaler_path)
dataset_name = options_datasets[dataset_ix]['label']
params_dataset = params['datasets'][dataset_name]
folds_train, folds_val, _ = evaluation_setup(
fold_name, dataset.fold_list, params_dataset['evaluation_mode'])
print(feature_extractor)
data_gen_train = DataGenerator(
dataset,
feature_extractor,
folds=folds_train,
batch_size=params['train']['batch_size'],
shuffle=True,
train=False,
scaler=scaler)
X_list, Y_list = data_gen_train.get_data()
file_names = data_gen_train.audio_file_list
# file_names = data_gen_train.convert_features_path_to_audio_path(
# file_names, sr=sr)
Xt = []
Yt = []
for j in range(len(X_list)):
ix = int(len(X_list[j]) / 2) if len(X_list[j]) > 1 else 0
Xj = np.expand_dims(X_list[j][ix], 0)
Yj = np.expand_dims(Y_list[j][ix], 0)
Xt.append(Xj)
Yt.append(Yj)
X = np.concatenate(Xt, axis=0)
Yt = np.concatenate(Yt, axis=0)
with graph.as_default():
model_container.load_model_weights(exp_folder_fold)
X_emb = model_container.get_intermediate_output(-2, X)
# output_select
pca = PCA(n_components=4)
pca.fit(X_emb)
X_pca = pca.transform(X_emb)
print('pca', X_pca.shape, Yt.shape)
figure2D = generate_figure2D(X_pca,
Yt,
dataset.label_list,
pca_components=[x_select, y_select],
samples_per_class=samples_per_class)
return [figure2D]
def test_evaluation_setup():
fold_list = ["fold1", "fold2", "fold3", "fold4"]
fold = "fold1"
evaluation_mode = "cross-validation"
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=True)
assert folds_test == ["fold1"]
assert folds_val == ["fold2"]
assert folds_train == ["fold3", "fold4"]
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=False)
assert folds_test == ["fold1"]
assert folds_val == folds_train
assert folds_train == ["fold2", "fold3", "fold4"]
evaluation_mode = "train-validate-test"
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=True)
assert folds_test == ["test"]
assert folds_val == ["validate"]
assert folds_train == ["train"]
evaluation_mode = "train-test"
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=True)
assert folds_test == ["test"]
assert folds_val == ["train"]
assert folds_train == ["train"]
evaluation_mode = "cross-validation-with-test"
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=True)
assert folds_test == ["test"]
assert folds_val == ["fold2"]
assert folds_train == ["fold1", "fold3", "fold4"]
evaluation_mode = "blablabla"
with pytest.raises(AttributeError):
folds_train, folds_val, folds_test = evaluation_setup(
fold, fold_list, evaluation_mode, use_validate_set=True)
def main():
# Parse arguments
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'-d',
'--dataset',
type=str,
help='dataset name (e.g. UrbanSound8k, ESC50, URBAN_SED, SONYC_UST)',
default='UrbanSound8k')
parser.add_argument(
'-f',
'--features',
type=str,
help='features name (e.g. Spectrogram, MelSpectrogram, Openl3)',
default='MelSpectrogram')
parser.add_argument('-p',
'--path',
type=str,
help='path to the parameters.json file',
default='../')
parser.add_argument(
'-m',
'--model',
type=str,
help='model name (e.g. MLP, SB_CNN, SB_CNN_SED, A_CRNN, VGGish)',
default='SB_CNN')
parser.add_argument('-fold',
'--fold_name',
type=str,
help='fold name',
default='fold1')
parser.add_argument('-s',
'--models_path',
type=str,
help='path to save the trained model',
default='../trained_models')
parser.add_argument('--aug', dest='augmentation', action='store_true')
parser.add_argument('--no-aug', dest='augmentation', action='store_false')
parser.set_defaults(augmentation=False)
args = parser.parse_args()
print(__doc__)
if args.dataset not in get_available_datasets():
raise AttributeError('Dataset not available')
if args.features not in get_available_features():
raise AttributeError('Features not available')
if args.model not in get_available_models():
raise AttributeError('Model not available')
# Get parameters
parameters_file = os.path.join(args.path, 'parameters.json')
params = load_json(parameters_file)
params_dataset = params['datasets'][args.dataset]
params_features = params['features']
params_model = params['models'][args.model]
# Get and init dataset class
dataset_class = get_available_datasets()[args.dataset]
dataset_path = os.path.join(args.path, params_dataset['dataset_path'])
dataset = dataset_class(dataset_path)
if args.fold_name not in dataset.fold_list:
raise AttributeError('Fold not available')
# Data augmentation
if args.augmentation:
# Define the augmentations
augmentations = params['data_augmentations']
# Initialize AugmentedDataset
dataset = AugmentedDataset(dataset, params['features']['sr'],
augmentations)
# Process all files
print('Doing data augmentation ...')
dataset.process()
print('Done!')
# Get and init feature class
features_class = get_available_features()[args.features]
features = features_class(
sequence_time=params_features['sequence_time'],
sequence_hop_time=params_features['sequence_hop_time'],
audio_win=params_features['audio_win'],
audio_hop=params_features['audio_hop'],
sr=params_features['sr'],
**params_features[args.features])
print('Features shape: ', features.get_shape())
# Check if features were extracted
if not features.check_if_extracted(dataset):
print('Extracting features ...')
features.extract(dataset)
print('Done!')
use_validate_set = True
if args.dataset in ['TUTSoundEvents2017', 'ESC50', 'ESC10']:
# When have less data, don't use validation set.
use_validate_set = False
folds_train, folds_val, _ = evaluation_setup(
args.fold_name,
dataset.fold_list,
params_dataset['evaluation_mode'],
use_validate_set=use_validate_set)
data_gen_train = DataGenerator(dataset,
features,
folds=folds_train,
batch_size=params['train']['batch_size'],
shuffle=True,
train=True,
scaler=None)
scaler = Scaler(normalizer=params_model['normalizer'])
print('Fitting scaler ...')
scaler.fit(data_gen_train)
print('Done!')
# Pass scaler to data_gen_train to be used when data
# loading
data_gen_train.set_scaler(scaler)
data_gen_val = DataGenerator(dataset,
features,
folds=folds_val,
batch_size=params['train']['batch_size'],
shuffle=False,
train=False,
scaler=scaler)
# Define model
features_shape = features.get_shape()
n_frames_cnn = features_shape[1]
n_freq_cnn = features_shape[2]
n_classes = len(dataset.label_list)
model_class = get_available_models()[args.model]
metrics = ['classification']
if args.dataset in sed_datasets:
metrics = ['sed']
if args.dataset in tagging_datasets:
metrics = ['tagging']
model_container = model_class(model=None,
model_path=None,
n_classes=n_classes,
n_frames_cnn=n_frames_cnn,
n_freq_cnn=n_freq_cnn,
metrics=metrics,
**params_model['model_arguments'])
model_container.model.summary()
# Set paths
model_folder = os.path.join(args.models_path, args.model, args.dataset)
exp_folder = os.path.join(model_folder, args.fold_name)
mkdir_if_not_exists(exp_folder, parents=True)
# Save model json and scaler
model_container.save_model_json(model_folder)
save_pickle(scaler, os.path.join(exp_folder, 'scaler.pickle'))
# data_train = data_gen_train.get_data()
# data_val = data_gen_val.get_data()
# Train model
model_container.train(
data_gen_train,
data_gen_val,
# data_train, data_val,
label_list=dataset.label_list,
weights_path=exp_folder,
**params['train'],
sequence_time_sec=params_features['sequence_hop_time'])
def main():
# Parse arguments
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter
)
parser.add_argument(
'-od', '--origin_dataset', type=str,
help='dataset name (e.g. UrbanSound8k, ESC50, URBAN_SED, SONYC_UST)',
default='UrbanSound8k'
)
parser.add_argument(
'-ofold', '--origin_fold_name', type=str,
help='origin fold name',
default='fold1')
parser.add_argument(
'-d', '--dataset', type=str,
help='dataset name (e.g. UrbanSound8k, ESC50, URBAN_SED, SONYC_UST)',
default='ESC50'
)
parser.add_argument(
'-fold', '--fold_name', type=str,
help='destination fold name',
default='fold1')
parser.add_argument(
'-f', '--features', type=str,
help='features name (e.g. Spectrogram, MelSpectrogram, Openl3)',
default='MelSpectrogram'
)
parser.add_argument(
'-p', '--path', type=str,
help='path to the parameters.json file',
default='../'
)
parser.add_argument(
'-m', '--model', type=str,
help='model name (e.g. MLP, SB_CNN, SB_CNN_SED, A_CRNN, VGGish)',
default='SB_CNN')
parser.add_argument(
'-s', '--models_path', type=str,
help='path to save the trained model',
default='../trained_models'
)
args = parser.parse_args()
print(__doc__)
if args.dataset not in get_available_datasets():
raise AttributeError('Dataset not available')
if args.features not in get_available_features():
raise AttributeError('Features not available')
if args.model not in get_available_models():
raise AttributeError('Model not available')
# Get parameters
parameters_file = os.path.join(args.path, 'parameters.json')
params = load_json(parameters_file)
params_dataset = params['datasets'][args.dataset]
params_features = params['features']
params_model = params['models'][args.model]
# Load origin model
model_path_origin = os.path.join(args.models_path, args.model,
args.origin_dataset)
model_class = get_available_models()[args.model]
metrics = ['accuracy']
if args.dataset in sed_datasets:
metrics = ['sed']
model_container = model_class(
model=None, model_path=model_path_origin,
metrics=metrics
)
model_container.load_model_weights(
os.path.join(model_path_origin, args.origin_fold_name))
kwargs = {}
if args.dataset in sed_datasets:
kwargs = {'sequence_hop_time': params_features['sequence_hop_time']}
# Get and init dataset class
dataset_class = get_available_datasets()[args.dataset]
dataset_path = os.path.join(args.path, params_dataset['dataset_path'])
dataset = dataset_class(dataset_path, **kwargs)
if args.fold_name not in dataset.fold_list:
raise AttributeError('Fold not available')
# Get and init feature class
features_class = get_available_features()[args.features]
features = features_class(
sequence_time=params_features['sequence_time'],
sequence_hop_time=params_features['sequence_hop_time'],
audio_win=params_features['audio_win'],
audio_hop=params_features['audio_hop'],
sr=params_features['sr'], **params_features[args.features]
)
print('Features shape: ', features.get_shape())
# Check if features were extracted
if not features.check_if_extracted(dataset):
print('Extracting features ...')
features.extract(dataset)
print('Done!')
use_validate_set = True
if args.dataset in ['TUTSoundEvents2017', 'ESC50', 'ESC10']:
# When have less data, don't use validation set.
use_validate_set = False
folds_train, folds_val, _ = evaluation_setup(
args.fold_name, dataset.fold_list,
params_dataset['evaluation_mode'],
use_validate_set=use_validate_set
)
data_gen_train = DataGenerator(
dataset, features, folds=folds_train,
batch_size=params['train']['batch_size'],
shuffle=True, train=True, scaler=None
)
scaler = Scaler(normalizer=params_model['normalizer'])
print('Fitting features ...')
scaler.fit(data_gen_train)
print('Done!')
data_gen_train.set_scaler(scaler)
data_gen_val = DataGenerator(
dataset, features, folds=folds_val,
batch_size=params['train']['batch_size'],
shuffle=False, train=False, scaler=scaler
)
# Fine-tune model
n_classes = len(dataset.label_list)
layer_where_to_cut = -2
model_container.fine_tuning(layer_where_to_cut,
new_number_of_classes=n_classes,
new_activation='sigmoid',
freeze_source_model=True)
model_container.model.summary()
# Set paths
model_folder = os.path.join(
args.models_path, args.model,
args.origin_dataset+'_ft_'+args.dataset)
exp_folder = os.path.join(model_folder, args.fold_name)
mkdir_if_not_exists(exp_folder, parents=True)
# Save model json and scaler
model_container.save_model_json(model_folder)
save_pickle(scaler, os.path.join(exp_folder, 'scaler.pickle'))
# Train model
model_container.train(
data_gen_train, data_gen_val,
label_list=dataset.label_list,
weights_path=exp_folder,
sequence_time_sec=params_features['sequence_hop_time'],
**params['train'])
def start_training(status, fold_ix, normalizer, model_path, epochs,
early_stopping, optimizer_ix, learning_rate, batch_size,
considered_improvement, n_clicks_train, dataset_ix):
global data_generator_train
global data_generator_val
if status == 'TRAINING':
if fold_ix is None:
return [True, 'Please select a Fold', 'danger', ""]
if optimizer_ix is None:
return [True, 'Please select an Optimizer', 'danger', ""]
dataset_name = options_datasets[dataset_ix]['label']
fold_name = dataset.fold_list[fold_ix]
params_dataset = params['datasets'][dataset_name]
optimizer = options_optimizers[optimizer_ix]['label']
use_validate_set = True
if dataset_name in ['TUTSoundEvents2017', 'ESC50', 'ESC10']:
# When have less data, don't use validation set.
use_validate_set = False
folds_train, folds_val, _ = evaluation_setup(
fold_name,
dataset.fold_list,
params_dataset['evaluation_mode'],
use_validate_set=use_validate_set)
data_generator_train = DataGenerator(
dataset,
feature_extractor,
folds=folds_train,
batch_size=params['train']['batch_size'],
shuffle=True,
train=True,
scaler=None)
scaler = Scaler(normalizer=normalizer)
print('Fitting scaler ...')
scaler.fit(data_generator_train)
print('Done!')
# Pass scaler to data_gen_train to be used when data
# loading
data_generator_train.set_scaler(scaler)
data_generator_val = DataGenerator(dataset,
feature_extractor,
folds=folds_val,
batch_size=batch_size,
shuffle=False,
train=False,
scaler=scaler)
exp_folder_fold = conv_path(os.path.join(model_path, fold_name))
mkdir_if_not_exists(exp_folder_fold, parents=True)
scaler_path = os.path.join(exp_folder_fold, 'scaler.pickle')
save_pickle(scaler, scaler_path)
train_arguments = {
'epochs': epochs,
'early_stopping': early_stopping,
'optimizer': optimizer,
'learning_rate': learning_rate,
'batch_size': batch_size,
'considered_improvement': considered_improvement
}
with graph.as_default():
model_container.train(data_generator_train,
data_generator_val,
weights_path=exp_folder_fold,
label_list=dataset.label_list,
**train_arguments)
model_container.load_model_weights(exp_folder_fold)
return [True, "Model trained", 'success', 'True']
else:
raise dash.exceptions.PreventUpdate