def main(conf):
train_set = LibriMix(
csv_dir=conf["data"]["train_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["data"]["n_src"],
segment=conf["data"]["segment"],
)
val_set = LibriMix(
csv_dir=conf["data"]["valid_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["data"]["n_src"],
segment=conf["data"]["segment"],
)
train_loader = DataLoader(
train_set,
shuffle=True,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
val_loader = DataLoader(
val_set,
shuffle=False,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
conf["masknet"].update({"n_src": conf["data"]["n_src"]})
model = DCCRNet(**conf["filterbank"],
**conf["masknet"],
sample_rate=conf["data"]["sample_rate"])
optimizer = make_optimizer(model.parameters(), **conf["optim"])
# Define scheduler
scheduler = None
if conf["training"]["half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir = conf["main_args"]["exp_dir"]
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, "conf.yml")
with open(conf_path, "w") as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")
system = System(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Define callbacks
callbacks = []
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
callbacks.append(checkpoint)
if conf["training"]["early_stop"]:
callbacks.append(
EarlyStopping(monitor="val_loss",
mode="min",
patience=30,
verbose=True))
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
distributed_backend = "ddp" if torch.cuda.is_available() else None
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=5.0,
)
trainer.fit(system)
best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(best_k, f, indent=0)
state_dict = torch.load(checkpoint.best_model_path)
system.load_state_dict(state_dict=state_dict["state_dict"])
system.cpu()
to_save = system.model.serialize()
to_save.update(train_set.get_infos())
torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))
def main(conf):
# train_set = WhamDataset(
# conf["data"]["train_dir"],
# conf["data"]["task"],
# sample_rate=conf["data"]["sample_rate"],
# segment=conf["data"]["segment"],
# nondefault_nsrc=conf["data"]["nondefault_nsrc"],
# )
# val_set = WhamDataset(
# conf["data"]["valid_dir"],
# conf["data"]["task"],
# sample_rate=conf["data"]["sample_rate"],
# nondefault_nsrc=conf["data"]["nondefault_nsrc"],
# )
train_set = LibriMix(
csv_dir=conf["data"]["train_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["masknet"]["n_src"],
segment=conf["data"]["segment"],
)
val_set = LibriMix(
csv_dir=conf["data"]["valid_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["masknet"]["n_src"],
segment=conf["data"]["segment"],
)
train_loader = DataLoader(
train_set,
shuffle=True,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
val_loader = DataLoader(
val_set,
shuffle=False,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
# Update number of source values (It depends on the task)
# TODO: redundant
conf["masknet"].update({"n_src": train_set.n_src})
model = DPRNNTasNet(**conf["filterbank"],
**conf["masknet"],
sample_rate=conf['data']['sample_rate'])
# from torchsummary import summary
# model.cuda()
# summary(model, (24000,))
# import pdb
# pdb.set_trace()
optimizer = make_optimizer(model.parameters(), **conf["optim"])
# Define scheduler
scheduler = None
if conf["training"]["half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir = conf["main_args"]["exp_dir"]
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, "conf.yml")
with open(conf_path, "w") as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")
system = System(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Define callbacks
callbacks = []
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
checkpoint = ModelCheckpoint(checkpoint_dir,
monitor="val_loss",
mode="min",
save_top_k=5,
verbose=True)
callbacks.append(checkpoint)
if conf["training"]["early_stop"]:
callbacks.append(
EarlyStopping(monitor="val_loss",
mode="min",
patience=30,
verbose=True))
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
distributed_backend = "ddp" if torch.cuda.is_available() else None
if conf["training"]["cont"]:
from glob import glob
ckpts = glob('%s/*.ckpt' % checkpoint_dir)
ckpts.sort()
latest_ckpt = ckpts[-1]
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=conf["training"]["gradient_clipping"],
resume_from_checkpoint=latest_ckpt)
else:
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=conf["training"]["gradient_clipping"],
)
trainer.fit(system)
best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(best_k, f, indent=0)
# Save best model (next PL version will make this easier)
# best_path = [b for b, v in best_k.items() if v == min(best_k.values())][0]
# state_dict = torch.load(best_path)
state_dict = torch.load(checkpoint.best_model_path)
# state_dict = torch.load('exp/train_dprnn_130d5f9a/checkpoints/epoch=154.ckpt')
system.load_state_dict(state_dict=state_dict["state_dict"])
system.cpu()
to_save = system.model.serialize()
to_save.update(train_set.get_infos())
torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))
def main(conf):
# train_set = WhamDataset(
# conf["data"]["train_dir"],
# conf["data"]["task"],
# sample_rate=conf["data"]["sample_rate"],
# segment=conf["data"]["segment"],
# nondefault_nsrc=conf["data"]["nondefault_nsrc"],
# )
# val_set = WhamDataset(
# conf["data"]["valid_dir"],
# conf["data"]["task"],
# sample_rate=conf["data"]["sample_rate"],
# nondefault_nsrc=conf["data"]["nondefault_nsrc"],
# )
train_set = LibriMix(
csv_dir=conf["data"]["train_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["masknet"]["n_src"],
segment=conf["data"]["segment"],
)
print(conf["data"]["train_dir"])
val_set = LibriMix(
csv_dir=conf["data"]["valid_dir"],
task=conf["data"]["task"],
sample_rate=conf["data"]["sample_rate"],
n_src=conf["masknet"]["n_src"],
segment=conf["data"]["segment"],
)
train_loader = DataLoader(
train_set,
shuffle=True,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
val_loader = DataLoader(
val_set,
shuffle=False,
batch_size=conf["training"]["batch_size"],
num_workers=conf["training"]["num_workers"],
drop_last=True,
)
# Update number of source values (It depends on the task)
conf["masknet"].update({"n_src": train_set.n_src})
# TODO params
# model = TransMask(**conf["filterbank"], **conf["masknet"])
model = DPTrans(**conf["filterbank"], **conf["masknet"], sample_rate=conf['data']['sample_rate'])
# from torchsummary import summary
# model.cuda()
# summary(model, (24000,))
# import pdb
# pdb.set_trace()
optimizer = make_optimizer(model.parameters(), **conf["optim"])
# Define scheduler
scheduler = None
if conf["training"]["half_lr"]:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5)
# # TODO warmup for transformer
# from asteroid.engine.schedulers import DPTNetScheduler
# schedulers = {
# "scheduler": DPTNetScheduler(
# # optimizer, len(train_loader) // conf["training"]["batch_size"], 64
# # optimizer, len(train_loader), 64,
# optimizer, len(train_loader), 128,
# stride=2,
# # exp_max=0.0004 * 16,
# # warmup_steps=1000
# ),
# "interval": "batch",
# }
# from torch.optim.lr_scheduler import ReduceLROnPlateau
# if conf["training"]["half_lr"]:
# print('Use ReduceLROnPlateau halflr...........')
# schedulers = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir = conf["main_args"]["exp_dir"]
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, "conf.yml")
with open(conf_path, "w") as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx")
system = System(
model=model,
loss_func=loss_func,
optimizer=optimizer,
train_loader=train_loader,
val_loader=val_loader,
scheduler=scheduler,
config=conf,
)
# Define callbacks
callbacks = []
checkpoint_dir = os.path.join(exp_dir, "checkpoints/")
# checkpoint_dir = os.path.join(exp_dir)
checkpoint = ModelCheckpoint(
checkpoint_dir, monitor="val_loss", mode="min", save_top_k=5, verbose=True
)
callbacks.append(checkpoint)
if conf["training"]["early_stop"]:
callbacks.append(EarlyStopping(monitor="val_loss", mode="min", patience=30, verbose=True))
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
distributed_backend = "ddp" if torch.cuda.is_available() else None
if conf["training"]["cont"]:
from glob import glob
ckpts = glob('%s/*.ckpt' % checkpoint_dir)
ckpts.sort()
latest_ckpt = ckpts[-1]
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=conf["training"]["gradient_clipping"],
resume_from_checkpoint=latest_ckpt
)
else:
trainer = pl.Trainer(
max_epochs=conf["training"]["epochs"],
callbacks=callbacks,
default_root_dir=exp_dir,
gpus=gpus,
distributed_backend=distributed_backend,
limit_train_batches=1.0, # Useful for fast experiment
gradient_clip_val=conf["training"]["gradient_clipping"],
)
trainer.fit(system)
best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(best_k, f, indent=0)
state_dict = torch.load(checkpoint.best_model_path)
# state_dict = torch.load('exp/train_transmask_rnn_acous_gelu_6layer_peconv_stride2_batch6/_ckpt_epoch_208.ckpt')
system.load_state_dict(state_dict=state_dict["state_dict"])
system.cpu()
to_save = system.model.serialize()
to_save.update(train_set.get_infos())
torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))
def main(conf):
train_set = LibriMix(csv_dir=conf['data']['train_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
val_set = LibriMix(csv_dir=conf['data']['valid_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
train_loader = DataLoader(train_set, shuffle=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
val_loader = DataLoader(val_set, shuffle=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
conf['masknet'].update({'n_src': conf['data']['n_src']})
# Define model and optimizer in a local function (defined in the recipe).
# Two advantages to this : re-instantiating the model and optimizer
# for retraining and evaluating is straight-forward.
model, optimizer = make_model_and_optimizer(conf)
# Define scheduler
scheduler = None
if conf['training']['half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir = conf['main_args']['exp_dir']
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, 'conf.yml')
with open(conf_path, 'w') as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')
system = System(model=model, loss_func=loss_func, optimizer=optimizer,
train_loader=train_loader, val_loader=val_loader,
scheduler=scheduler, config=conf)
# Define callbacks
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss',
mode='min', save_top_k=5, verbose=1)
early_stopping = False
if conf['training']['early_stop']:
early_stopping = EarlyStopping(monitor='val_loss', patience=10,
verbose=1)
# Don't ask GPU if they are not available.
if not torch.cuda.is_available():
print('No available GPU were found, set gpus to None')
conf['main_args']['gpus'] = None
trainer = pl.Trainer(max_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=conf['main_args']['gpus'],
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.)
trainer.fit(system)
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(checkpoint.best_k_models, f, indent=0)
def main(conf):
train_set = LibriMix(csv_dir=conf['data']['train_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
val_set = LibriMix(csv_dir=conf['data']['valid_dir'],
task=conf['data']['task'],
sample_rate=conf['data']['sample_rate'],
n_src=conf['data']['n_src'],
segment=conf['data']['segment'])
train_loader = DataLoader(train_set, shuffle=True,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
val_loader = DataLoader(val_set, shuffle=False,
batch_size=conf['training']['batch_size'],
num_workers=conf['training']['num_workers'],
drop_last=True)
conf['masknet'].update({'n_src': conf['data']['n_src']})
model = ConvTasNet(**conf['filterbank'], **conf['masknet'])
optimizer = make_optimizer(model.parameters(), **conf['optim'])
# Define scheduler
scheduler = None
if conf['training']['half_lr']:
scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5,
patience=5)
# Just after instantiating, save the args. Easy loading in the future.
exp_dir = conf['main_args']['exp_dir']
os.makedirs(exp_dir, exist_ok=True)
conf_path = os.path.join(exp_dir, 'conf.yml')
with open(conf_path, 'w') as outfile:
yaml.safe_dump(conf, outfile)
# Define Loss function.
loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx')
system = System(model=model, loss_func=loss_func, optimizer=optimizer,
train_loader=train_loader, val_loader=val_loader,
scheduler=scheduler, config=conf)
# Define callbacks
checkpoint_dir = os.path.join(exp_dir, 'checkpoints/')
checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss',
mode='min', save_top_k=5, verbose=1)
early_stopping = False
if conf['training']['early_stop']:
early_stopping = EarlyStopping(monitor='val_loss', patience=30,
verbose=1)
# Don't ask GPU if they are not available.
gpus = -1 if torch.cuda.is_available() else None
trainer = pl.Trainer(max_epochs=conf['training']['epochs'],
checkpoint_callback=checkpoint,
early_stop_callback=early_stopping,
default_save_path=exp_dir,
gpus=gpus,
distributed_backend='dp',
train_percent_check=1.0, # Useful for fast experiment
gradient_clip_val=5.)
trainer.fit(system)
best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()}
with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f:
json.dump(best_k, f, indent=0)
# Save best model (next PL version will make this easier)
best_path = [b for b, v in best_k.items() if v == min(best_k.values())][0]
state_dict = torch.load(best_path)
system.load_state_dict(state_dict=state_dict['state_dict'])
system.cpu()
to_save = system.model.serialize()
to_save.update(train_set.get_infos())
torch.save(to_save, os.path.join(exp_dir, 'best_model.pth'))