113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
from network import *
|
|
from tensorboardX import SummaryWriter
|
|
import os
|
|
from tqdm import tqdm
|
|
from pathlib import Path
|
|
import jsonargparse
|
|
from parse import add_config_options_to_parser
|
|
from pprint import pprint
|
|
from parakeet.models.dataloader.jlspeech import LJSpeechLoader
|
|
|
|
class MyDataParallel(dg.parallel.DataParallel):
|
|
"""
|
|
A data parallel proxy for model.
|
|
"""
|
|
|
|
def __init__(self, layers, strategy):
|
|
super(MyDataParallel, self).__init__(layers, strategy)
|
|
|
|
def __getattr__(self, key):
|
|
if key in self.__dict__:
|
|
return object.__getattribute__(self, key)
|
|
elif key is "_layers":
|
|
return object.__getattribute__(self, "_sub_layers")["_layers"]
|
|
else:
|
|
return getattr(
|
|
object.__getattribute__(self, "_sub_layers")["_layers"], key)
|
|
|
|
def load_checkpoint(step, model_path):
|
|
model_dict, opti_dict = fluid.dygraph.load_dygraph(os.path.join(model_path, step))
|
|
return model_dict, opti_dict
|
|
|
|
def main(cfg):
|
|
|
|
local_rank = dg.parallel.Env().local_rank if cfg.use_data_parallel else 0
|
|
nranks = dg.parallel.Env().nranks if cfg.use_data_parallel else 1
|
|
|
|
if local_rank == 0:
|
|
# Print the whole config setting.
|
|
pprint(jsonargparse.namespace_to_dict(cfg))
|
|
|
|
global_step = 0
|
|
place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
|
|
if cfg.use_data_parallel else fluid.CUDAPlace(0)
|
|
if cfg.use_gpu else fluid.CPUPlace())
|
|
|
|
if not os.path.exists(cfg.log_dir):
|
|
os.mkdir(cfg.log_dir)
|
|
path = os.path.join(cfg.log_dir,'postnet')
|
|
|
|
writer = SummaryWriter(path) if local_rank == 0 else None
|
|
|
|
with dg.guard(place):
|
|
model = ModelPostNet(cfg)
|
|
|
|
model.train()
|
|
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=dg.NoamDecay(1/(cfg.warm_up_step *( cfg.lr ** 2)), cfg.warm_up_step),
|
|
parameter_list=model.parameters())
|
|
|
|
|
|
if cfg.checkpoint_path is not None:
|
|
model_dict, opti_dict = load_checkpoint(str(cfg.postnet_step), os.path.join(cfg.checkpoint_path, "postnet"))
|
|
model.set_dict(model_dict)
|
|
optimizer.set_dict(opti_dict)
|
|
global_step = cfg.postnet_step
|
|
print("load checkpoint!!!")
|
|
|
|
if cfg.use_data_parallel:
|
|
strategy = dg.parallel.prepare_context()
|
|
model = MyDataParallel(model, strategy)
|
|
|
|
reader = LJSpeechLoader(cfg, nranks, local_rank, is_vocoder=True).reader()
|
|
|
|
for epoch in range(cfg.epochs):
|
|
pbar = tqdm(reader)
|
|
for i, data in enumerate(pbar):
|
|
pbar.set_description('Processing at epoch %d'%epoch)
|
|
mel, mag = data
|
|
mag = dg.to_variable(mag.numpy())
|
|
mel = dg.to_variable(mel.numpy())
|
|
global_step += 1
|
|
|
|
mag_pred = model(mel)
|
|
loss = layers.mean(layers.abs(layers.elementwise_sub(mag_pred, mag)))
|
|
|
|
if cfg.use_data_parallel:
|
|
loss = model.scale_loss(loss)
|
|
loss.backward()
|
|
model.apply_collective_grads()
|
|
else:
|
|
loss.backward()
|
|
optimizer.minimize(loss, grad_clip = fluid.dygraph_grad_clip.GradClipByGlobalNorm(cfg.grad_clip_thresh))
|
|
model.clear_gradients()
|
|
|
|
if local_rank==0:
|
|
writer.add_scalars('training_loss',{
|
|
'loss':loss.numpy(),
|
|
}, global_step)
|
|
|
|
if global_step % cfg.save_step == 0:
|
|
if not os.path.exists(cfg.save_path):
|
|
os.mkdir(cfg.save_path)
|
|
save_path = os.path.join(cfg.save_path,'postnet/%d' % global_step)
|
|
dg.save_dygraph(model.state_dict(), save_path)
|
|
dg.save_dygraph(optimizer.state_dict(), save_path)
|
|
|
|
if local_rank==0:
|
|
writer.close()
|
|
|
|
if __name__ == '__main__':
|
|
parser = jsonargparse.ArgumentParser(description="Train postnet model", formatter_class='default_argparse')
|
|
add_config_options_to_parser(parser)
|
|
cfg = parser.parse_args('-c ./config/train_postnet.yaml'.split())
|
|
main(cfg) |