def generate_lyrics_wrapper(genres, keywords):
args = init_args()
device, n_gpu = U.get_device(logger)
# Reload the model and the tokenizer
model = GPT2LMHeadModel.from_pretrained(args.load_model_dir)
enc = GPT2Tokenizer.from_pretrained(args.load_model_dir)
model.eval()
U.set_seed(np.random.randint(0, 100))
# U.set_seed(2)
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# @ GENERATE FROM FINE-TUNED GPT2
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
tags = genres
keywords = keywords
context = "[s:tags]" + tags + "[e:tags]" + \
"[s:keywords]" + keywords + "[e:keywords]" + \
"[s:lyrics]"
end_token = "[e:lyrics]"
context = enc.encode(context)
sequence_batch = generate_lyrics(model, enc, args, context, end_token,
device)
for seq in sequence_batch:
print(enc.decode(seq))
print("\n---------------\n")
return seq[0]
def main(context):
# args = init_args()
load_model_dir = "/Users/aw678/PycharmProjects/lyrics_generator_flask_app/tuned_models/genius_lyrics_v2/gpt2_13-11-2019@18'25/model_epoch_20"
gen_batch = 2
device, n_gpu = U.get_device(logger)
# Reload the model and the tokenizer
model = GPT2LMHeadModel.from_pretrained(load_model_dir)
enc = GPT2Tokenizer.from_pretrained(load_model_dir)
model.eval()
U.set_seed(np.random.randint(0, 100))
# U.set_seed(2)
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# @ GENERATE FROM FINE-TUNED GPT2
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# genre = "Pop"
# artist = "Justin Bieber"
# year = "2015"
# album = "Purpose"
# song_name = "Love Yourself"
#
# context = "[s:genre]" + genre + "[e:genre]" + \
# "[s:artist]" + artist + "[e:artist]" + \
# "[s:year]" + year + "[e:year]" + \
# "[s:album]" + album + "[e:album]" + \
# "[s:song_name]" + song_name + "[e:song_name]" + \
# "[s:lyrics]"
#
# context = "[s:genre]" + genre + "[e:genre]" + \
# "[s:artist]" + artist + "[e:artist]" + \
# "[s:lyrics]"
end_token = "[e:lyrics]"
context = enc.encode(context)
sequence_batch = generate_lyrics(model, enc, gen_batch, context, end_token,
device)
lyrics_list = []
for indx, seq in enumerate(sequence_batch):
lyrics_list.append(enc.decode(seq))
return lyrics_list
def main():
args = init_args()
device, n_gpu = U.get_device(logger)
# Reload the model and the tokenizer
model = GPT2LMHeadModel.from_pretrained(args.load_model_dir)
enc = GPT2Tokenizer.from_pretrained(args.load_model_dir)
model.eval()
U.set_seed(np.random.randint(0, 100))
# U.set_seed(2)
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# @ GENERATE FROM FINE-TUNED GPT2
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
genre = "Pop"
artist = "Justin Bieber"
year = "2015"
album = "Purpose"
song_name = "Love Yourself"
context = "[s:genre]" + genre + "[e:genre]" + \
"[s:artist]" + artist + "[e:artist]" + \
"[s:year]" + year + "[e:year]" + \
"[s:album]" + album + "[e:album]" + \
"[s:song_name]" + song_name + "[e:song_name]" + \
"[s:lyrics]"
context = "[s:genre]" + genre + "[e:genre]" + \
"[s:artist]" + artist + "[e:artist]" + \
"[s:lyrics]"
end_token = "[e:lyrics]"
context = enc.encode(context)
sequence_batch = generate_lyrics(model, enc, args, context, end_token,
device)
for seq in sequence_batch:
print(enc.decode(seq))
print("\n---------------\n")
def run(args):
set_seed(args.seed)
set_logging(ROOT_DIR, args)
import pprint
logging.info(
pprint.pformat(vars(args)) if not isinstance(args, dict) else pprint.
pformat(args))
# set up cuda device
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.device)
device = torch.device('cuda')
loader = Dev_Loader(is_divide_variance=args.is_divide_variance)
train_loader = loader.train(batch_size=args.batch_size)
val_loader = loader.val(batch_size=args.batch_size)
# model = getattr(net_archs, args.net)(args).cuda()
from xception import ModifiedXception
model = ModifiedXception(num_classes=args.nb_class,
drop_rate=args.drop_rate,
decay=args.decay).cuda()
if args.optimizer == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.init_lr,
momentum=0.9,
nesterov=True)
elif args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.init_lr,
weight_decay=args.l2)
if args.lr_factor < 1.0:
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
verbose=True,
factor=args.lr_factor,
patience=args.lr_patience)
train_hist = History(name='train')
test_list = ['a', 'bc', 'abc']
val_hist = dict()
for d in test_list:
val_hist[d] = History(name='val/{}'.format(d))
if args.continue_run:
ckpt_file = Reporter(exp=args.exp).select_last(
args.ckpt_prefix[0:5]).selected_ckpt
logging.info('continue training from {}'.format(ckpt_file))
ckpt_dicts = torch.load(ckpt_file)
model.load_state_dict(ckpt_dicts['model_state_dict'])
model.cuda()
optimizer.load_state_dict(ckpt_dicts['optimizer_state_dict'])
start_epoch = ckpt_dicts['epoch'] + 1
else:
start_epoch = 1
# checkpoint after new History, order matters
ckpter = CheckPoint(model=model,
optimizer=optimizer,
path='{}/ckpt/{}'.format(ROOT_DIR, args.exp),
prefix=args.ckpt_prefix,
interval=1,
save_num=1)
for epoch in range(start_epoch, args.run_epochs):
train_mixup_all(train_loader,
model,
optimizer,
device,
mix_alpha=args.mix_alpha)
train_hist.add(logs=eval_model(train_loader, model, device),
epoch=epoch)
a_logs = eval_model(val_loader['a'], model, device)
bc_logs = eval_model(val_loader['bc'], model, device)
avg_loss = (a_logs['loss'] + bc_logs['loss']) / 2
avg_acc = (a_logs['acc'] + bc_logs['acc']) / 2
avg_logs = {'loss': avg_loss, 'acc': avg_acc}
val_hist['a'].add(logs=a_logs, epoch=epoch)
val_hist['bc'].add(logs=bc_logs, epoch=epoch)
val_hist['abc'].add(logs=avg_logs, epoch=epoch)
if args.lr_factor < 1.0:
scheduler.step(val_hist['abc'].recent['acc'])
# plotting
if args.plot:
train_hist.clc_plot()
for d in test_list:
val_hist[d].plot()
# logging
logging.info("Epoch{:04d},{:6},{}".format(epoch, train_hist.name,
str(train_hist.recent)))
for d in test_list:
logging.info("Epoch{:04d},{:6},{}".format(epoch, val_hist[d].name,
str(val_hist[d].recent)))
ckpter.check_on(epoch=epoch,
monitor='acc',
loss_acc=val_hist['abc'].recent)
# explicitly save last
ckpter.save(epoch=args.run_epochs - 1,
monitor='acc',
loss_acc=val_hist['abc'].recent)