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import argparse
import logging
from pathlib import Path
import torch
import torch.optim as optim
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.utils.data import DataLoader
from s3prl.nn import S3PRLUpstream, UpstreamDownstreamModel
from s3prl.sampler import DistributedBatchSamplerWrapper
from s3prl.superb import sv as problem
from s3prl.wrapper import LightningModuleSimpleWrapper
device = "cuda" if torch.cuda.is_available() else "cpu"
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--voxceleb1",
type=str,
default="/work/jason410/PublicData/Voxceleb1",
help="The root directory of VoxCeleb1",
)
parser.add_argument(
"--save_to",
type=str,
default="lightning_result/sv",
help="The directory to save checkpoint",
)
parser.add_argument("--total_steps", type=int, default=200000)
parser.add_argument("--log_step", type=int, default=100)
parser.add_argument("--eval_step", type=int, default=1000)
parser.add_argument("--save_step", type=int, default=100)
parser.add_argument(
"--not_resume",
action="store_true",
help="Don't resume from the last checkpoint",
)
# for debugging
parser.add_argument("--limit_train_batches", type=int)
parser.add_argument("--limit_val_batches", type=int)
parser.add_argument("--fast_dev_run", action="store_true")
parser.add_argument("--backbone", type=str, default="XVector")
parser.add_argument("--pooling_type", type=str, default="TAP")
parser.add_argument("--loss_type", type=str, default="softmax")
parser.add_argument("--spk_embd_dim", type=int, default=1500)
args = parser.parse_args()
return args
def main():
logging.basicConfig()
logger.setLevel(logging.INFO)
args = parse_args()
voxceleb1 = Path(args.voxceleb1)
assert voxceleb1.is_dir()
save_to = Path(args.save_to)
save_to.mkdir(exist_ok=True, parents=True)
logger.info("Preparing preprocessor")
preprocessor = problem.Preprocessor(voxceleb1)
logger.info("Preparing train dataloader")
train_dataset = problem.TrainDataset(**preprocessor.train_data())
train_sampler = problem.TrainSampler(
train_dataset, max_timestamp=16000 * 1000, shuffle=True
)
train_sampler = DistributedBatchSamplerWrapper(
train_sampler, num_replicas=1, rank=0
)
train_dataloader = DataLoader(
train_dataset,
batch_sampler=train_sampler,
num_workers=6,
collate_fn=train_dataset.collate_fn,
)
logger.info("Preparing valid dataloader")
valid_dataset = problem.ValidDataset(
**preprocessor.valid_data(),
**train_dataset.statistics(),
)
valid_dataset.save_checkpoint(save_to / "valid_dataset.ckpt")
# valid_dataset_reload = Object.load_checkpoint(save_to / "valid_dataset.ckpt")
valid_sampler = problem.TrainSampler(
valid_dataset, max_timestamp=16000 * 1000, shuffle=True
)
valid_sampler = DistributedBatchSamplerWrapper(
valid_sampler, num_replicas=1, rank=0
)
valid_dataloader = DataLoader(
valid_dataset,
batch_sampler=valid_sampler,
num_workers=6,
collate_fn=valid_dataset.collate_fn,
)
logger.info("Preparing test dataloader")
test_dataset = problem.TestDataset(
**preprocessor.test_data(),
**train_dataset.statistics(),
)
test_dataset.save_checkpoint(save_to / "test_dataset.ckpt")
test_sampler = problem.TestSampler(test_dataset, 8)
test_sampler = DistributedBatchSamplerWrapper(test_sampler, num_replicas=1, rank=0)
test_dataloader = DataLoader(
test_dataset, batch_size=1, num_workers=6, collate_fn=test_dataset.collate_fn
)
upstream = S3PRLUpstream("apc")
# Have to specify the backbone, pooling_type, spk_embd_dim
downstream = problem.speaker_embedding_extractor(
backbone=args.backbone,
pooling_type=args.pooling_type,
input_size=upstream.output_size,
output_size=args.spk_embd_dim,
)
model = UpstreamDownstreamModel(upstream, downstream)
# Have to specify the loss_type
task = problem.Task(
model=model,
categories=preprocessor.statistics().category,
loss_type=args.loss_type,
trials=test_dataset.statistics().label,
)
optimizer = optim.Adam(task.parameters(), lr=1e-3)
lightning_task = LightningModuleSimpleWrapper(task, optimizer)
# The above is the usage of our library
# The below is pytorch-lightning specific usage, which can be very simple
# or very sophisticated, depending on how much you want to customized your
# training loop
checkpoint_callback = ModelCheckpoint(
dirpath=str(save_to),
filename="superb-sv-{step:02d}-{valid_0_accuracy:.2f}",
monitor="valid_0_accuracy", # since might have multiple valid dataloaders
save_last=True,
save_top_k=3, # top 3 best ckpt on valid
mode="max", # higher, better
every_n_train_steps=args.save_step,
)
trainer = Trainer(
callbacks=[checkpoint_callback],
accelerator="gpu",
gpus=1,
max_steps=args.total_steps,
log_every_n_steps=args.log_step,
val_check_interval=args.eval_step,
limit_val_batches=args.limit_val_batches or 1.0,
limit_train_batches=args.limit_train_batches or 1.0,
fast_dev_run=args.fast_dev_run,
)
last_ckpt = save_to / "last.ckpt"
if args.not_resume or not last_ckpt.is_file():
last_ckpt = None
trainer.fit(
lightning_task,
train_dataloader,
val_dataloaders=valid_dataloader,
ckpt_path=last_ckpt,
)
trainer.test(
lightning_task,
dataloaders=test_dataloader,
ckpt_path=last_ckpt,
)
if __name__ == "__main__":
main()
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