def train(args, logdir1, logdir2):
# model
model = Net2()
# dataflow
df = Net2DataFlow(hp.train2.data_path, hp.train2.batch_size)
# set logger for event and model saver
logger.set_logger_dir(logdir2)
session_conf = tf.ConfigProto(
# log_device_placement=True,
allow_soft_placement=True,
gpu_options=tf.GPUOptions(
# allow_growth=True,
per_process_gpu_memory_fraction=0.6,
),
)
session_inits = []
ckpt2 = '{}/{}'.format(logdir2, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
ckpt1 = tf.train.latest_checkpoint(logdir1)
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
train_conf = TrainConfig(
model=model,
data=QueueInput(df(n_prefetch=1000, n_thread=4)),
callbacks=[
# TODO save on prefix net2
ModelSaver(checkpoint_dir=logdir2),
# ConvertCallback(logdir2, hp.train2.test_per_epoch),
],
max_epoch=hp.train2.num_epochs,
steps_per_epoch=hp.train2.steps_per_epoch,
session_init=ChainInit(session_inits),
session_config=session_conf
)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_conf.nr_tower = len(args.gpu.split(','))
#trainer = SyncMultiGPUTrainerParameterServer(hp.train2.num_gpu)
trainer = SimpleTrainer()
launch_train_with_config(train_conf, trainer=trainer)
def train(args, logdir):
# model
print("####model")
model = Net1()
# dataflow
print("####dataflow")
df = Net1DataFlow(hp.Train1.data_path, hp.Train1.batch_size)
# set logger for event and model saver
print("####logger")
logger.set_logger_dir(logdir)
print("####session_conf")
session_conf = tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True, ),
allow_soft_placement=True)
print("####train_conf")
train_conf = TrainConfig(
model=model,
data=QueueInput(df(n_prefetch=1000, n_thread=5)),
callbacks=[
ModelSaver(checkpoint_dir=logdir),
# TODO EvalCallback()
],
max_epoch=hp.Train1.num_epochs,
steps_per_epoch=hp.Train1.steps_per_epoch,
session_config=session_conf)
print("####ckpt")
ckpt = '{}/{}'.format(
logdir, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir)
if ckpt:
train_conf.session_init = SaverRestore(ckpt)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_conf.nr_tower = len(args.gpu.split(','))
print("####trainer")
trainer = SyncMultiGPUTrainerReplicated(hp.Train1.num_gpu)
print("####launch_train_with_config")
launch_train_with_config(train_conf, trainer=trainer)
def train(args, logdir):
# model
model = Net()
# dataflow
df = NetDataFlow(hp.train.data_path, hp.train.batch_size)
# set logger for event and model saver
logger.set_logger_dir(logdir)
session_conf = tf.ConfigProto(
gpu_options=tf.GPUOptions(
allow_growth=True,
),)
session_conf.gpu_options.per_process_gpu_memory_fraction = 0.45 # 占用GPU90%的显存
train_conf = TrainConfig(
model=model,
data=QueueInput(df(n_prefetch=1000, n_thread=4)),
callbacks=[
ModelSaver(checkpoint_dir=logdir),
# TODO EvalCallback()
],
max_epoch=hp.train.num_epochs,
steps_per_epoch=hp.train.steps_per_epoch,
# session_config=session_conf
)
ckpt = '{}/{}'.format(logdir, args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir)
if ckpt:
train_conf.session_init = SaverRestore(ckpt)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_conf.nr_tower = len(args.gpu.split(','))
trainer = SyncMultiGPUTrainerReplicated(hp.train.num_gpu)
launch_train_with_config(train_conf, trainer=trainer)
def train(args, logdir2):
# model
model = Net2()
# dataflow
df = Net2DataFlow(hp.train2.mel_path, hp.train2.ppgs_path,
hp.train2.batch_size)
session_inits = []
ckpt2 = '{}/{}'.format(
logdir2,
args.ckpt) if args.ckpt else tf.train.latest_checkpoint(logdir2)
if ckpt2:
session_inits.append(SaverRestore(ckpt2))
'''
ckpt1 = tf.train.latest_checkpoint(logdir1)
if ckpt1:
session_inits.append(SaverRestore(ckpt1, ignore=['global_step']))
'''
train_conf = TrainConfig(
model=model,
data=QueueInput(df(n_prefetch=1000, n_thread=4)),
callbacks=[
# TODO save on prefix net2
ModelSaver(checkpoint_dir=logdir2),
# ConvertCallback(logdir2, hp.train2.test_per_epoch),
],
max_epoch=hp.train2.num_epochs,
steps_per_epoch=hp.train2.steps_per_epoch,
session_init=ChainInit(session_inits))
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_conf.nr_tower = len(args.gpu.split(','))
trainer = SyncMultiGPUTrainerReplicated(hp.train2.num_gpu)
print("strated trainer")
launch_train_with_config(train_conf, trainer=trainer)
# dataset
dataset = Dataset(hp.data_path,
hp.train.batch_size,
length=hp.signal.length)
print('dataset size is {}'.format(len(dataset.wav_files)))
# set logger for event and model saver
logger.set_logger_dir(hp.logdir)
train_conf = TrainConfig(
model=model,
data=TFDatasetInput(dataset()),
callbacks=[
ModelSaver(checkpoint_dir=hp.logdir),
RunUpdateOps() # for batch norm, exponential moving average
# TODO GenerateCallback()
],
max_epoch=hp.train.num_epochs,
steps_per_epoch=hp.train.steps_per_epoch,
)
ckpt = '{}/{}'.format(
hp.logdir, ckpt) if ckpt else tf.train.latest_checkpoint(hp.logdir)
if ckpt:
train_conf.session_init = SaverRestore(ckpt)
if gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(map(str, gpu))
train_conf.nr_tower = len(gpu)
if hp.train.num_gpu <= 1:
hp.train.tar_labels,
hp.train.ntar_labels,
length=hp.signal.length,
tar_ratio=hp.train.tar_ratio)
# set logger for event and model saver
logger.set_logger_dir(hp.logdir)
model = globals()[hp.model]()
print("Model name: {}".format(hp.model))
train_conf = TrainConfig(
model=model,
data=TFDatasetInput(dataset.train.get_dataset()),
callbacks=[
ModelSaver(checkpoint_dir=hp.logdir),
EvalCallback(),
# RunUpdateOps() # enable this when using batch normalization.
],
max_epoch=hp.train.num_epochs,
steps_per_epoch=hp.train.steps_per_epoch,
)
ckpt = '{}/{}'.format(
hp.logdir, ckpt) if ckpt else tf.train.latest_checkpoint(hp.logdir)
if ckpt:
train_conf.session_init = SaverRestore(ckpt)
if gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(map(str, gpu))
train_conf.nr_tower = len(gpu)
if hp.train.num_gpu <= 1:
# dataflow
audio_meta = AudioMeta(hp.train.data_path)
if args.remote:
df = get_remote_dataflow(args.port, hp.train.batch_size)
else:
df = DataLoader(audio_meta, hp.train.batch_size).dataflow(nr_prefetch=5000, nr_thread=int(multiprocessing.cpu_count() // 1.5))
# set logger for event and model saver
logger.set_logger_dir(hp.logdir)
if True:
train_conf = TrainConfig(
model=ClassificationModel(num_classes=audio_meta.num_speaker, **hp.model),
data=FlexibleQueueInput(df, capacity=500),
callbacks=[
ModelSaver(checkpoint_dir=hp.logdir),
EvalCallback()
],
steps_per_epoch=hp.train.steps_per_epoch,
# session_config=session_config
)
ckpt = args.ckpt if args.ckpt else tf.train.latest_checkpoint(hp.logdir)
if ckpt and not args.r:
train_conf.session_init = SaverRestore(ckpt)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
train_conf.nr_tower = len(args.gpu.split(','))
trainer = SyncMultiGPUTrainerReplicated(hp.train.num_gpu)