def train(args):
'''Training. Model will be saved after several iterations.
Args:
dataset_dir: string, directory of dataset
workspace: string, directory of workspace
subtask: 'a' | 'b' | 'c', corresponds to 3 subtasks in DCASE2019 Task1
data_type: 'development' | 'evaluation'
holdout_fold: '1' | 'none', set 1 for development and none for training
on all data without validation
model_type: string, e.g. 'Cnn_9layers_AvgPooling'
batch_size: int
cuda: bool
mini_data: bool, set True for debugging on a small part of data
'''
# Arugments & parameters
dataset_dir = args.dataset_dir
workspace = args.workspace
subtask = args.subtask
data_type = args.data_type
holdout_fold = args.holdout_fold
model_type = args.model_type
batch_size = args.batch_size
cuda = args.cuda and torch.cuda.is_available()
mini_data = args.mini_data
filename = args.filename
fixed = args.fixed
finetune = args.finetune
ite_train = args.ite_train
ite_eva = args.ite_eva
ite_store = args.ite_store
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
max_iteration = None # Number of mini-batches to evaluate on training data
reduce_lr = True
sources_to_evaluate = get_sources(subtask)
in_domain_classes_num = len(config.labels) - 1
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
sub_dir = get_subdir(subtask, data_type)
train_csv = os.path.join(dataset_dir, sub_dir, 'meta.csv')
validate_csv = os.path.join(dataset_dir, sub_dir, 'evaluation_setup',
'fold1_evaluate.csv')
feature_hdf5_path = os.path.join(workspace, 'features',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
scalar_path = os.path.join(workspace, 'scalars',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
checkpoints_dir = os.path.join(workspace, 'checkpoints', filename,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}'.format(sub_dir), 'holdout_fold={}'.format(holdout_fold),
model_type)
create_folder(checkpoints_dir)
validate_statistics_path = os.path.join(workspace, 'statistics', filename,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}'.format(sub_dir), 'holdout_fold={}'.format(holdout_fold),
model_type, 'validate_statistics.pickle')
create_folder(os.path.dirname(validate_statistics_path))
logs_dir = os.path.join(workspace, 'logs', filename, args.mode,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}'.format(sub_dir), 'holdout_fold={}'.format(holdout_fold), model_type)
create_logging(logs_dir, 'w')
logging.info(args)
if cuda:
logging.info('Using GPU.')
else:
logging.info('Using CPU. Set --cuda flag to use GPU.')
# Load scalar
scalar = load_scalar(scalar_path)
# Model
Model = eval(model_type)
if subtask in ['a', 'b']:
if fixed=='True':
model = Model(in_domain_classes_num, activation='logsoftmax', fixed=True)
else :
model = Model(in_domain_classes_num, activation='logsoftmax', fixed=False)
loss_func = nll_loss
elif subtask == 'c':
model = Model(in_domain_classes_num, activation='sigmoid')
loss_func = F.binary_cross_entropy
if cuda:
model.cuda()
# Optimizer
if fixed=='True':
optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3, betas=(0.9, 0.999),
eps=1e-08, weight_decay=0., amsgrad=True)
else :
optimizer = optim.Adam(model.parameters(), lr=1e-3, betas=(0.9, 0.999),
eps=1e-08, weight_decay=0., amsgrad=True)
if finetune=='True':
model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/'+model_type+'/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_Res38/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_Cnn14/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_Cnn10/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_MobileNetV2/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_MobileNetV1/2000_iterations.pth'
#model_path='/home/cdd/code2/dcase2020_task1/workspace/checkpoints/main/logmel_86frames_40melbins/TAU-urban-acoustic-scenes-2020-mobile-development/holdout_fold=1/Logmel_Wavegram_Cnn14/2000_iterations.pth'
device = torch.device('cuda')
checkpoint = torch.load(model_path, map_location=device)
model.load_state_dict(checkpoint['model'])
# Data generator
data_generator = DataGenerator(
feature_hdf5_path=feature_hdf5_path,
train_csv=train_csv,
validate_csv=validate_csv,
holdout_fold=holdout_fold,
scalar=scalar,
batch_size=batch_size)
# Evaluator
evaluator = Evaluator(
model=model,
data_generator=data_generator,
subtask=subtask,
cuda=cuda)
# Statistics
validate_statistics_container = StatisticsContainer(validate_statistics_path)
train_bgn_time = time.time()
iteration = 0
# Train on mini batches
for batch_data_dict in data_generator.generate_train():
# Evaluate
#1800
if iteration % 200 == 0 and iteration > ite_eva:
logging.info('------------------------------------')
logging.info('Iteration: {}'.format(iteration))
train_fin_time = time.time()
for source in sources_to_evaluate:
train_statistics = evaluator.evaluate(
data_type='train',
source=source,
max_iteration=None,
verbose=False)
if holdout_fold != 'none':
for source in sources_to_evaluate:
validate_statistics = evaluator.evaluate(
data_type='validate',
source=source,
max_iteration=None,
verbose=False)
validate_statistics_container.append_and_dump(
iteration, source, validate_statistics)
train_time = train_fin_time - train_bgn_time
validate_time = time.time() - train_fin_time
logging.info(
'Train time: {:.3f} s, validate time: {:.3f} s'
''.format(train_time, validate_time))
train_bgn_time = time.time()
# Save model
if iteration % 200 == 0 and iteration > ite_store:
checkpoint = {
'iteration': iteration,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}
checkpoint_path = os.path.join(
checkpoints_dir, '{}_iterations.pth'.format(iteration))
torch.save(checkpoint, checkpoint_path)
logging.info('Model saved to {}'.format(checkpoint_path))
# Reduce learning rate
if reduce_lr and iteration % 200 == 0 and iteration > 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.93
# Move data to GPU
for key in batch_data_dict.keys():
if key in ['feature', 'feature_gamm', 'feature_mfcc', 'feature_panns', 'target']:
batch_data_dict[key] = move_data_to_gpu(batch_data_dict[key], cuda)
# Train
# batch_output,batch_loss = model(batch_data_dict['feature'], batch_data_dict['feature_gamm'], batch_data_dict['feature_mfcc'], batch_data_dict['feature_panns'])
# loss = loss_func(batch_output, batch_data_dict['target'])
# Using Mixup
model.train()
mixed_x1, mixed_x2, mixed_x3, mixed_x4, y_a, y_b, lam = mixup_data(x1=batch_data_dict['feature'], x2=batch_data_dict['feature_gamm'], x3=batch_data_dict['feature_mfcc'], x4=batch_data_dict['feature_panns'], y=batch_data_dict['target'], alpha=0.2)
batch_output,batch_loss = model(mixed_x1, mixed_x2, mixed_x3, mixed_x4)
if batch_output.shape[1] == 10: # single scale models
loss = mixup_criterion(loss_func, batch_output, y_a, y_b, lam)
else: # multi scale models
losses = []
for ite in range(batch_output.shape[1]-1):
loss = mixup_criterion(loss_func, batch_output[:,ite,:], y_a, y_b, lam)
losses.append(loss)
loss = sum(losses)
# Backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Stop learning
# 12000 for scratch
if iteration == ite_train:
break
iteration += 1
def inference_validation(args):
'''Inference and calculate metrics on validation data.
Args:
dataset_dir: string, directory of dataset
subtask: 'a' | 'b' | 'c', corresponds to 3 subtasks in DCASE2019 Task1
data_type: 'development'
workspace: string, directory of workspace
model_type: string, e.g. 'Cnn_9layers'
iteration: int
batch_size: int
cuda: bool
mini_data: bool, set True for debugging on a small part of data
visualize: bool
'''
# Arugments & parameters
dataset_dir = args.dataset_dir
subtask = args.subtask
data_type = args.data_type
workspace = args.workspace
model_type = args.model_type
holdout_fold = args.holdout_fold
iteration = args.iteration
batch_size = args.batch_size
cuda = args.cuda and torch.cuda.is_available()
mini_data = args.mini_data
visualize = args.visualize
filename = args.filename
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
sources = get_sources(subtask)
in_domain_classes_num = len(config.labels) - 1
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
sub_dir = get_subdir(subtask, data_type)
train_csv = os.path.join(dataset_dir, sub_dir, 'evaluation_setup',
'fold1_train.csv')
validate_csv = os.path.join(dataset_dir, sub_dir, 'evaluation_setup',
'fold1_evaluate.csv')
feature_hdf5_path = os.path.join(workspace, 'features',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
scalar_path = os.path.join(workspace, 'scalars',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}.h5'.format(sub_dir))
checkpoint_path = os.path.join(workspace, 'checkpoints', filename,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}'.format(sub_dir), 'holdout_fold={}'.format(holdout_fold),
model_type, '{}_iterations.pth'.format(iteration))
logs_dir = os.path.join(workspace, 'logs', filename, args.mode,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins),
'{}'.format(sub_dir), 'holdout_fold={}'.format(holdout_fold),
model_type)
create_logging(logs_dir, 'w')
logging.info(args)
# Load scalar
scalar = load_scalar(scalar_path)
# Load model
Model = eval(model_type)
if subtask in ['a', 'b']:
model = Model(in_domain_classes_num, activation='logsoftmax')
loss_func = nll_loss
elif subtask == 'c':
model = Model(in_domain_classes_num, activation='sigmoid')
loss_func = F.binary_cross_entropy
#checkpoint = torch.load(checkpoint_path)
#model.load_state_dict(checkpoint['model'])
if cuda:
model.cuda()
# Data generator
data_generator = DataGenerator(
feature_hdf5_path=feature_hdf5_path,
train_csv=train_csv,
validate_csv=validate_csv,
holdout_fold=holdout_fold,
scalar=scalar,
batch_size=batch_size)
# Evaluator
evaluator = Evaluator(
model=model,
data_generator=data_generator,
subtask=subtask,
cuda=cuda)
if subtask in ['a', 'c']:
evaluator.evaluate(data_type='validate', source='a', verbose=True)
elif subtask == 'b':
evaluator.evaluate(data_type='validate', source='a', verbose=True)
evaluator.evaluate(data_type='validate', source='b', verbose=True)
evaluator.evaluate(data_type='validate', source='c', verbose=True)
# Visualize log mel spectrogram
if visualize:
evaluator.visualize(data_type='validate', source='a')
def train(args):
'''Training. Model will be saved after several iterations.
Args:
dataset_dir: string, directory of dataset
workspace: string, directory of workspace
subtask: 'a' | 'b' | 'c', corresponds to 3 subtasks in DCASE2019 Task1
data_type: 'development' | 'evaluation'
holdout_fold: '1' | 'none', set 1 for development and none for training
on all data without validation
model_type: string, e.g. 'Cnn_9layers_AvgPooling'
batch_size: int
cuda: bool
mini_data: bool, set True for debugging on a small part of data
'''
# Arugments & parameters
dataset_dir = args.dataset_dir
workspace = args.workspace
subtask = args.subtask
data_type = args.data_type
holdout_fold = args.holdout_fold
model_type = args.model_type
batch_size = args.batch_size
cuda = args.cuda and torch.cuda.is_available()
mini_data = args.mini_data
filename = args.filename
mel_bins = config.mel_bins
frames_per_second = config.frames_per_second
max_iteration = None # Number of mini-batches to evaluate on training data
reduce_lr = True
sources_to_evaluate = get_sources(subtask)
in_domain_classes_num = len(config.labels) - 1
# Paths
if mini_data:
prefix = 'minidata_'
else:
prefix = ''
sub_dir = get_subdir(subtask, data_type)
train_csv = os.path.join(dataset_dir, sub_dir, 'evaluation_setup',
'fold1_train.csv')
validate_csv = os.path.join(dataset_dir, sub_dir, 'evaluation_setup',
'fold1_evaluate.csv')
feature_hdf5_path = os.path.join(
workspace, 'features',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
feature_hdf5_path_left = os.path.join(
workspace, 'features_left',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
feature_hdf5_path_right = os.path.join(
workspace, 'features_right',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
feature_hdf5_path_side = os.path.join(
workspace, 'features_side',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
scalar_path = os.path.join(
workspace, 'scalars',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
scalar_path_left = os.path.join(
workspace, 'scalars_left',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
scalar_path_right = os.path.join(
workspace, 'scalars_right',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
scalar_path_side = os.path.join(
workspace, 'scalars_side',
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins),
'{}.h5'.format(sub_dir))
checkpoints_dir = os.path.join(
workspace, 'checkpoints', filename,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins), '{}'.format(sub_dir),
'holdout_fold={}'.format(holdout_fold), model_type)
create_folder(checkpoints_dir)
validate_statistics_path = os.path.join(
workspace, 'statistics', filename,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins), '{}'.format(sub_dir),
'holdout_fold={}'.format(holdout_fold), model_type,
'validate_statistics.pickle')
create_folder(os.path.dirname(validate_statistics_path))
logs_dir = os.path.join(
workspace, 'logs', filename, args.mode,
'{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second,
mel_bins), '{}'.format(sub_dir),
'holdout_fold={}'.format(holdout_fold), model_type)
create_logging(logs_dir, 'w')
logging.info(args)
# Load scalar
scalar = load_scalar(scalar_path)
scalar_left = load_scalar(scalar_path_left)
scalar_right = load_scalar(scalar_path_right)
scalar_side = load_scalar(scalar_path_side)
# Model
Model = eval(model_type)
if subtask in ['a', 'b']:
model = Model(in_domain_classes_num, activation='logsoftmax')
loss_func = nll_loss
elif subtask == 'c':
model = Model(in_domain_classes_num, activation='sigmoid')
loss_func = F.binary_cross_entropy
if cuda:
model.cuda()
# Optimizer
optimizer = optim.Adam(model.parameters(),
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0.,
amsgrad=True)
# Data generator
data_generator = DataGenerator(
feature_hdf5_path=feature_hdf5_path,
feature_hdf5_path_left=feature_hdf5_path_left,
feature_hdf5_path_right=feature_hdf5_path_right,
feature_hdf5_path_side=feature_hdf5_path_side,
train_csv=train_csv,
validate_csv=validate_csv,
scalar=scalar,
scalar_left=scalar_left,
scalar_right=scalar_right,
scalar_side=scalar_side,
batch_size=batch_size)
# Evaluator
evaluator = Evaluator(model=model,
data_generator=data_generator,
subtask=subtask,
cuda=cuda)
# Statistics
validate_statistics_container = StatisticsContainer(
validate_statistics_path)
train_bgn_time = time.time()
iteration = 0
# Train on mini batches
for batch_data_dict, batch_data_dict_left, batch_data_dict_right, batch_data_dict_side in data_generator.generate_train(
):
# Evaluate
if iteration % 200 == 0:
logging.info('------------------------------------')
logging.info('Iteration: {}'.format(iteration))
train_fin_time = time.time()
for source in sources_to_evaluate:
train_statistics = evaluator.evaluate(data_type='train',
source=source,
max_iteration=None,
verbose=False)
for source in sources_to_evaluate:
validate_statistics = evaluator.evaluate(data_type='validate',
source=source,
max_iteration=None,
verbose=False)
validate_statistics_container.append_and_dump(
iteration, source, validate_statistics)
train_time = train_fin_time - train_bgn_time
validate_time = time.time() - train_fin_time
logging.info('Train time: {:.3f} s, validate time: {:.3f} s'
''.format(train_time, validate_time))
train_bgn_time = time.time()
# Save model
if iteration % 1000 == 0 and iteration > 0:
checkpoint = {
'iteration': iteration,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
}
checkpoint_path = os.path.join(
checkpoints_dir, '{}_iterations.pth'.format(iteration))
torch.save(checkpoint, checkpoint_path)
logging.info('Model saved to {}'.format(checkpoint_path))
# Reduce learning rate
if reduce_lr and iteration % 200 == 0 and iteration > 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.9
# Move data to GPU
for key in batch_data_dict.keys():
if key in ['feature', 'target']:
batch_data_dict[key] = move_data_to_gpu(
batch_data_dict[key], cuda)
for key in batch_data_dict_left.keys():
if key in ['feature_left', 'target']:
batch_data_dict_left[key] = move_data_to_gpu(
batch_data_dict_left[key], cuda)
for key in batch_data_dict_right.keys():
if key in ['feature_right', 'target']:
batch_data_dict_right[key] = move_data_to_gpu(
batch_data_dict_right[key], cuda)
for key in batch_data_dict_side.keys():
if key in ['feature_side', 'target']:
batch_data_dict_side[key] = move_data_to_gpu(
batch_data_dict_side[key], cuda)
# Train
model.train()
batch_output = model(data=batch_data_dict['feature'],
data_left=batch_data_dict_left['feature_left'],
data_right=batch_data_dict_right['feature_right'],
data_side=batch_data_dict_side['feature_side'])
# loss
loss = loss_func(batch_output, batch_data_dict['target'])
# Backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Stop learning
if iteration == 15000:
break
iteration += 1