def main(args):
# Network Builders
builder = ModelBuilder()
net_sound = builder.build_sound(arch=args.arch_sound,
fc_dim=args.num_channels,
weights=args.weights_sound)
net_vision = builder.build_vision(arch=args.arch_vision,
fc_dim=args.num_channels,
weights=args.weights_vision)
net_synthesizer = builder.build_synthesizer(
arch=args.arch_synthesizer,
fc_dim=args.num_channels,
weights=args.weights_synthesizer)
nets = (net_sound, net_vision, net_synthesizer)
crit = builder.build_criterion(arch=args.loss)
# Dataset and Loader
dataset_train = MUSICMixDataset(args.list_train, args, split='train')
dataset_val = MUSICMixDataset(args.list_val,
args,
max_sample=args.num_val,
split='val')
loader_train = make_data_loader(dataset_train, args)
loader_val = make_data_loader(dataset_val, args)
args.epoch_iters = len(dataset_train) // args.batch_size
if args.mode == 'train':
print('1 Epoch = {} iters'.format(args.epoch_iters))
# Wrap networks
netWrapper = NetWrapper(nets, crit)
netWrapper = torch.nn.DataParallel(netWrapper,
device_ids=range(args.num_gpus))
netWrapper.to(args.device)
# Set up optimizer
optimizer = create_optimizer(nets, args)
# History of performance
history = {
'train': {
'epoch': [],
'err': []
},
'val': {
'epoch': [],
'err': [],
'sdr': [],
'sir': [],
'sar': []
}
}
# Eval mode
evaluate(netWrapper, loader_val, history, 0, args)
if args.mode == 'eval':
print('Evaluation Done!')
return
# Training loop
for epoch in range(1, args.num_epoch + 1):
train(netWrapper, loader_train, optimizer, history, epoch, args)
# Evaluation and visualization
if epoch % args.eval_epoch == 0:
evaluate(netWrapper, loader_val, history, epoch, args)
# checkpointing
checkpoint(nets, history, epoch, args)
print('Training Done!')
def main(args):
# Network Builders
builder = ModelBuilder()
net_sound_ground = builder.build_sound_ground(
arch=args.arch_sound_ground, weights=args.weights_sound_ground)
net_frame_ground = builder.build_frame_ground(
arch=args.arch_frame_ground,
pool_type=args.img_pool,
weights=args.weights_frame_ground)
net_sound = builder.build_sound(arch=args.arch_sound,
fc_dim=args.num_channels,
weights=args.weights_sound)
net_frame = builder.build_frame(arch=args.arch_frame,
fc_dim=args.num_channels,
pool_type=args.img_pool,
weights=args.weights_frame)
net_synthesizer = builder.build_synthesizer(
arch=args.arch_synthesizer,
fc_dim=args.num_channels,
weights=args.weights_synthesizer)
net_grounding = builder.build_grounding(arch=args.arch_grounding,
weights=args.weights_grounding)
nets = (net_sound_ground, net_frame_ground, net_sound, net_frame,
net_synthesizer, net_grounding)
crit = builder.build_criterion(arch=args.loss)
# Dataset and Loader
dataset_train = MUSICMixDataset(args.list_train, args, split='train')
dataset_val = MUSICMixDataset(args.list_val,
args,
max_sample=args.num_val,
split=args.split)
loader_train = torch.utils.data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
drop_last=True)
loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
drop_last=False)
args.epoch_iters = len(dataset_train) // args.batch_size
print('1 Epoch = {} iters'.format(args.epoch_iters))
# Wrap networks
netWrapper = NetWrapper(nets, crit)
netWrapper = torch.nn.DataParallel(netWrapper,
device_ids=range(args.num_gpus))
netWrapper.to(args.device)
# Set up optimizer
optimizer = create_optimizer(nets, args)
# History of peroformance
history = {
'train': {
'epoch': [],
'err': []
},
'val': {
'epoch': [],
'err': [],
'sdr': [],
'sir': [],
'sar': []
}
}
# Eval mode
if args.mode == 'eval':
args.testing = True
evaluate(netWrapper, loader_val, history, 0, args)
print('Evaluation Done!')
return
# Training loop
for epoch in range(1, args.num_epoch + 1):
train(netWrapper, loader_train, optimizer, history, epoch, args)
# Evaluation and visualization
if epoch % args.eval_epoch == 0:
args.testing = True
evaluate(netWrapper, loader_val, history, epoch, args)
args.testing = False
# checkpointing
checkpoint(nets, history, epoch, args)
# drop learning rate
if epoch in args.lr_steps:
adjust_learning_rate(optimizer, args)
print('Training Done!')
def main(args):
# Network Builders
builder = ModelBuilder()
net_sound = builder.build_sound(arch=args.arch_sound,
fc_dim=args.num_channels,
weights=args.weights_sound)
net_frame = builder.build_frame(arch=args.arch_frame,
fc_dim=args.num_channels,
pool_type=args.img_pool,
weights=args.weights_frame)
net_synthesizer = builder.build_synthesizer(
arch=args.arch_synthesizer,
fc_dim=args.num_channels,
weights=args.weights_synthesizer)
nets = (net_sound, net_frame, net_synthesizer)
crit = builder.build_criterion(arch=args.loss)
# Dataset and Loader
# dataset_train = MUSICMixDataset(
# args.list_train, args, split='train')
dataset_val = MUSICMixDataset(args.list_val,
args,
max_sample=args.num_val,
split='val')
# loader_train = torch.utils.data.DataLoader(
# dataset_train,
# batch_size=args.batch_size,
# shuffle=True,
# num_workers=int(args.workers),
# drop_last=True)
loader_val = torch.utils.data.DataLoader(dataset_val,
batch_size=args.batch_size,
shuffle=False,
num_workers=2,
drop_last=False)
# args.epoch_iters = len(dataset_train) // args.batch_size
# print('1 Epoch = {} iters'.format(args.epoch_iters))
# Wrap networks
netWrapper = NetWrapper(nets, crit)
netWrapper = torch.nn.DataParallel(netWrapper,
device_ids=range(args.num_gpus))
netWrapper.to(args.device)
# Set up optimizer
optimizer = create_optimizer(nets, args)
# History of peroformance
history = {
'train': {
'epoch': [],
'err': []
},
'val': {
'epoch': [],
'err': [],
'sdr': [],
'sir': [],
'sar': []
}
}
# Eval mode
evaluate(netWrapper, loader_val, history, 0, args)
if args.mode == 'eval':
print('Evaluation Done!')
return
