def run(args):
nnet = Nnet(**nnet_conf)
trainer = PermutationTrainer(nnet,
gpuid=args.gpu,
checkpoint=args.checkpoint,
**trainer_conf)
data_conf = {"train_data": train_data, "dev_data": dev_data}
confs = [nnet_conf, feats_conf, trainer_conf, data_conf]
names = ["mdl.json", "feats.json", "trainer.json", "data.conf"]
for conf, fname in zip(confs, names):
dump_json(conf, args.checkpoint, fname)
feats_conf["shuf"] = True
train_loader = make_pitloader(train_data["linear_x"],
feats_conf,
train_data,
batch_size=args.batch_size,
cache_size=args.cache_size)
feats_conf["shuf"] = False
dev_loader = make_pitloader(dev_data["linear_x"],
feats_conf,
dev_data,
batch_size=args.batch_size,
cache_size=args.cache_size)
trainer.run(train_loader, dev_loader, num_epochs=args.epochs)
def run(args):
gpuids = tuple(map(int, args.gpus.split(",")))
nnet = ConvTasNet(**nnet_conf)
trainer = SiSnrTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
resume=args.resume,
**trainer_conf)
data_conf = {
"train": train_data,
"dev": dev_data,
"chunk_size": chunk_size
}
for conf, fname in zip([nnet_conf, trainer_conf, data_conf],
["mdl.json", "trainer.json", "data.json"]):
dump_json(conf, args.checkpoint, fname)
train_loader = make_dataloader(train=True,
data_kwargs=train_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
dev_loader = make_dataloader(train=False,
data_kwargs=dev_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
trainer.run(train_loader, dev_loader, num_epochs=args.epochs)
def run(args):
train_data["knownPercent"] = args.known_percent
dev_data["knownPercent"] = args.known_percent
gpuids = tuple(map(int, args.gpus.split(",")))
nnet = ConvTasNet(**nnet_conf)
if args.mixofmix == 0:
logger.info("SisSnrTrainer")
trainer = SiSnrTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
resume=args.resume,
comment=args.comment,
**trainer_conf)
else:
logger.info("MixtureOfMixturesTrainer")
trainer = MixtureOfMixturesTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
resume=args.resume,
comment=args.comment,
**trainer_conf)
logger.info("Known pecents " + str(dev_data["knownPercent"]))
data_conf = {
"train": train_data,
"dev": dev_data,
"chunk_size": chunk_size
}
for conf, fname in zip([nnet_conf, trainer_conf, data_conf],
["mdl.json", "trainer.json", "data.json"]):
dump_json(conf, args.checkpoint, fname)
if args.mixofmix == 0:
train_loader = make_dataloader(train=True,
data_kwargs=train_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
dev_loader = make_dataloader(train=False,
data_kwargs=dev_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
else:
train_loader = make_dataloader(train=True,
data_kwargs=train_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers,
mixofmix=True)
dev_loader = make_dataloader(train=False,
data_kwargs=dev_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers,
mixofmix=True)
trainer.run(train_loader, dev_loader, num_epochs=args.epochs)
def run(args):
gpuids = tuple(map(int, args.gpus.split(",")))
logger.info("Create ConvTasNet ...")
nnet = ConvTasNet(**nnet_conf)
if args.loss == "nr_loss":
logger.info("Create NoiseReconstructTrainer ...")
trainer = NoiseReconstructTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
resume=args.resume,
**trainer_conf)
else:
logger.info("Create SiSnrTrainer ...")
trainer = SiSnrTrainer(nnet,
gpuid=gpuids,
checkpoint=args.checkpoint,
resume=args.resume,
**trainer_conf)
logger.info("Finish ConvTasNet.")
logger.info("Prepare data {} {}.".format(train_data, dev_data))
data_conf = {
"train": train_data,
"dev": dev_data,
"chunk_size": chunk_size
}
for conf, fname in zip([nnet_conf, trainer_conf, data_conf],
["mdl.json", "trainer.json", "data.json"]):
dump_json(conf, args.checkpoint, fname)
logger.info("make_dataloader for train.")
train_loader = make_dataloader(train=True,
data_kwargs=train_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
logger.info("make_dataloader for dev.")
dev_loader = make_dataloader(train=False,
data_kwargs=dev_data,
batch_size=args.batch_size,
chunk_size=chunk_size,
num_workers=args.num_workers)
logger.info("runing...")
trainer.run(train_loader, dev_loader, num_epochs=args.epochs)