**conf['model_parameter'], **conf['training_parameter'], shuffle=False,drop_last=True)
idx2char = {}
with open(conf['meta_variable']['data_path']+'/idx2chap.csv','r') as f:
for line in f:
if 'idx' in line:continue
idx2char[int(line.split(',')[0])] = line[:-1].split(',')[1]
# Load pre-trained model if needed
if conf['training_parameter']['use_pretrained']:
global_step = conf['training_parameter']['pretrained_step']
listener = torch.load(conf['training_parameter']['pretrained_listener_path'])
speller = torch.load(conf['training_parameter']['pretrained_speller_path'])
else:
global_step = 0
listener = Listener(**conf['model_parameter'])
speller = Speller(**conf['model_parameter'])
optimizer = torch.optim.Adam([{'params':listener.parameters()}, {'params':speller.parameters()}],
lr=conf['training_parameter']['learning_rate'])
best_ler = 1.0
record_gt_text = False
log_writer = SummaryWriter(conf['meta_variable']['training_log_dir']+conf['meta_variable']['experiment_name'])
# Training
print('Training starts...',flush=True)
while global_step<total_steps:
# Teacher forcing rate linearly decay
tf_rate = tf_rate_upperbound - (tf_rate_upperbound-tf_rate_lowerbound)*min((float(global_step)/tf_decay_step),1)
idx2char = {}
with open(conf['meta_variable']['data_path'] + '/idx2chap.csv', 'r') as f:
for line in f:
if 'idx' in line: continue
idx2char[int(line.split(',')[0])] = line[:-1].split(',')[1]
# Load pre-trained model if needed
if conf['training_parameter']['use_pretrained']:
global_step = conf['training_parameter']['pretrained_step']
listener = torch.load(
conf['training_parameter']['pretrained_listener_path'])
speller = torch.load(conf['training_parameter']['pretrained_speller_path'])
else:
global_step = 0
listener = Listener(**conf['model_parameter'])
speller = Speller(**conf['model_parameter'])
#las_module = LAS()
#las_module.set_submodules(listener, speller)
# make module run parallel
#las_module = torch.nn.DataParallel(las_module, device_ids=[0, 1, 2 ,3])
optimizer = torch.optim.Adam([{
'params': listener.parameters()
}, {
'params': speller.parameters()
}],
lr=conf['training_parameter']['learning_rate'])
def main(args):
# Tensorboard logging
# Writer will output to ./runs/ directory by default
writer = SummaryWriter(comment=args.experiment_name)
# Fix seed
seed = 17
np.random.seed(seed)
torch.manual_seed(seed)
random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
print("---------------------------------------")
print("Loading Config...", flush=True)
# Load config file for experiment
config_path = args.config_path
print("Loading configure file at", config_path)
with open(config_path, "r") as f:
params = yaml.load(f, Loader=yaml.FullLoader)
data_name = params["data"]["name"]
tf_rate_upperbound = params["training"]["tf_rate_upperbound"]
tf_rate_lowerbound = params["training"]["tf_rate_lowerbound"]
tf_decay_step = params["training"]["tf_decay_step"]
epochs = params["training"]["epochs"]
# Load datasets
print("---------------------------------------")
print("Processing datasets...", flush=True)
train_dataset = AudioDataset(params, "train")
train_loader = AudioDataLoader(
train_dataset, shuffle=True,
num_workers=params["data"]["num_works"]).loader
dev_dataset = AudioDataset(params, "test")
dev_loader = AudioDataLoader(
dev_dataset, num_workers=params["data"]["num_works"]).loader
print("---------------------------------------")
print("Creating model architecture...", flush=True)
# Create listener and speller
listener = Listener(**params["model"]["listener"])
speller = Speller(**params["model"]["speller"])
las = LAS(listener, speller)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
las = nn.DataParallel(las)
print(las)
las.cuda()
# Create optimizer
optimizer = torch.optim.Adam(
params=las.parameters(),
lr=params["training"]["lr"],
)
if params["training"]["continue_from"]:
print("Loading checkpoint model %s" %
params["training"]["continue_from"])
package = torch.load(params["training"]["continue_from"])
las.load_state_dict(package["state_dict"])
optimizer.load_state_dict(package["optim_dict"])
start_epoch = int(package.get("epoch", 1))
else:
start_epoch = 0
print("---------------------------------------")
print("Training...", flush=True)
# import pdb
# pdb.set_trace()
global_step = 0 + (len(train_loader) * start_epoch)
best_cv_loss = 10e5
my_fields = {"loss": 0}
for epoch in tqdm(range(start_epoch, epochs), desc="Epoch training"):
epoch_step = 0
train_loss = []
train_ler = []
batch_loss = 0
for i, (data) in tqdm(enumerate(train_loader),
total=len(train_loader),
leave=False,
desc=f"Epoch number {epoch}"):
# print(
# f"Current Epoch: {epoch} Loss {np.round(batch_loss, 3)} | Epoch step: {epoch_step}/{len(train_loader)}",
# end="\r",
# flush=True,
# )
my_fields["loss"] = batch_loss
# Adjust LR
tf_rate = tf_rate_upperbound - (
tf_rate_upperbound - tf_rate_lowerbound) * min(
(float(global_step) / tf_decay_step), 1)
inputs = data[1]["inputs"].cuda()
labels = data[2]["targets"].cuda()
print(f"INPUT SHAPE {inputs.shape} LABELS SHAPE: {labels.shape}")
batch_loss, batch_ler = batch_iterator(
batch_data=inputs,
batch_label=labels,
las_model=las,
optimizer=optimizer,
tf_rate=tf_rate,
is_training=True,
max_label_len=params["model"]["speller"]["max_label_len"],
label_smoothing=params["training"]["label_smoothing"],
vocab_dict=train_dataset.char2idx,
)
if i % 100 == 0:
torch.cuda.empty_cache()
train_loss.append(batch_loss)
train_ler.extend(batch_ler)
global_step += 1
epoch_step += 1
# print(batch_ler)
writer.add_scalar("loss/train-step", batch_loss, global_step)
writer.add_scalar("ler/train-step",
np.array([sum(train_ler) / len(train_ler)]),
global_step)
train_loss = np.array([sum(train_loss) / len(train_loss)])
train_ler = np.array([sum(train_ler) / len(train_ler)])
writer.add_scalar("loss/train-epoch", train_loss, epoch)
writer.add_scalar("ler/train-epoch", train_ler, epoch)
# Validation
val_loss = []
val_ler = []
val_step = 0
for i, (data) in tqdm(enumerate(dev_loader),
total=len(dev_loader),
leave=False,
desc="Validation"):
# print(
# f"Current Epoch: {epoch} | Epoch step: {epoch_step}/{len(train_loader)} Validating step: {val_step}/{len(dev_loader)}",
# end="\r",
# flush=True,
# )
inputs = data[1]["inputs"].cuda()
labels = data[2]["targets"].cuda()
batch_loss, batch_ler = batch_iterator(
batch_data=inputs,
batch_label=labels,
las_model=las,
optimizer=optimizer,
tf_rate=tf_rate,
is_training=False,
max_label_len=params["model"]["speller"]["vocab_size"],
label_smoothing=params["training"]["label_smoothing"],
vocab_dict=dev_dataset.char2idx,
)
if i % 100 == 0:
torch.cuda.empty_cache()
val_loss.append(batch_loss)
val_ler.extend(batch_ler)
val_step += 1
val_loss = np.array([sum(val_loss) / len(val_loss)])
val_ler = np.array([sum(val_ler) / len(val_ler)])
writer.add_scalar("loss/dev", val_loss, epoch)
writer.add_scalar("ler/dev", val_ler, epoch)
# Checkpoint saving model each epoch and keeping only last 10 epochs
if params["training"]["checkpoint"]:
# Check if epoch-10 file exits, if so we delete it
file_path_old = os.path.join(
params["training"]["save_folder"],
f"{data_name}-epoch{epoch - 10}.pth.tar")
if os.path.exists(file_path_old):
os.remove(file_path_old)
file_path = os.path.join(params["training"]["save_folder"],
f"{data_name}-epoch{epoch}.pth.tar")
torch.save(
las.serialize(optimizer=optimizer,
epoch=epoch,
tr_loss=val_loss,
val_loss=val_loss),
file_path,
)
print()
print("Saving checkpoint model to %s" % file_path)
if val_loss < best_cv_loss: # We found a best model, lets save it too
file_path = os.path.join(
params["training"]["save_folder"],
f"{data_name}-BEST_LOSS-epoch{epoch}.pth.tar")
# purge(params["training"]["save_folder"], "*BEST_LOSS*") # Remove
# previous best models
torch.save(
las.serialize(optimizer=optimizer,
epoch=epoch,
tr_loss=val_loss,
val_loss=val_loss),
file_path,
)
print("Saving BEST model to %s" % file_path)
# writer.add_scalars("cer", {"train": np.array([np.array(batch_ler).mean()])}, global_step)
# pdb.set_trace()
# print(inputs.size())
print()