parser.add_argument("--resume", action="store_true")
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
if config['trainer'] == 'LipTrainer':
trainer = LipTrainer(config)
else:
sys.exit('Train option not supported')
trainer.cuda()
train_loader = get_data_loader_list(config, train=True)
model_name = config['trainer']
train_writer = tensorboardX.SummaryWriter(
os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory = prepare_sub_folder(output_directory)
shutil.copy(opts.config, os.path.join(output_directory, 'config.yaml'))
iterations = trainer.resume(checkpoint_directory,
param=config) if opts.resume else 0
while True:
for id, data in enumerate(train_loader):
trainer.update_learning_rate()
audio = data['AU'].cuda(async=True).detach()
parameter = data['PM'].cuda(async=True).detach()
trainer = GanimationTrainer(config)
trainer.cuda()
# Load audio2landmark module
state_dict_lstm = torch.load(opts.checkpoint_lstm)
trainer.audio2exp.load_state_dict(state_dict_lstm['audio2exp'])
# Load neutral2emotion module
state_dict_gan = torch.load(opts.checkpoint_n2e)
trainer.gan.load_state_dict(state_dict_gan['gan'])
# Load refinement module
state_dict_gan = torch.load(opts.checkpoint_ref)
trainer.encdec.load_state_dict(state_dict_gan['gan'])
trainer.eval()
test_loader = get_data_loader_list(config, train=False, demo=True)
model_name = config['trainer']
train_writer = tensorboardX.SummaryWriter(
os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory, image_directory = prepare_sub_folder(output_directory)
with torch.no_grad():
for id, data in enumerate(test_loader):
video_ref = data['VR'].cuda().detach()
audio = data['AU'].cuda().detach()
em = data['EM'][0]
level = data['LV'][0]
# Main training code
tkfc = trainer.forward(video_ref, audio, em, level) #[0].cpu()
output_directory = os.path.join(opts.output_path + "/outputs",
model_name + '_' + opts.tag)
checkpoint_directory, image_directory, landmark_directory = prepare_sub_folder(
output_directory, is_test=True)
landmark_directory = 'datasets/face/test_keypoints/keypoints'
shutil.copy(opts.config, os.path.join(output_directory, 'config.yaml'))
f = open(os.path.join(root_dir, config['pca_path']), 'rb')
pca = pickle.load(f)
trainer = LipTrainer(config, is_train=False)
trainer.to(config['device'])
state_dict_lstm = torch.load(opts.checkpoint_lstm)
trainer.audio2exp.load_state_dict(state_dict_lstm['audio2exp'])
test_loader = get_data_loader_list(config, split='test')
iterations = trainer.resume(checkpoint_directory,
param=config) if opts.resume else 0
loss_test = 0
cnt = 0
for id, data in enumerate(test_loader):
audio = data[0].to(config['device']).detach()
target_kp = data[1].to(config['device']).detach()
items = len(audio)
N = data[2][0] # (-, N, theta, mean, ...)
theta = data[3][0]
mean = data[4][0].numpy()
all_ldmk = data[5][0].numpy()
frame_id = data[6][0]
img = data[7][0].numpy()
type=str,
default='.',
help="outputs path")
parser.add_argument("--resume", action="store_true")
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
trainer = GanimationTrainer(config)
trainer.cuda()
train_loader = get_data_loader_list(config, train=True, demo=False)
model_name = config['trainer']
train_writer = tensorboardX.SummaryWriter(
os.path.join(opts.output_path + "/logs", model_name))
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory, image_directory = prepare_sub_folder(output_directory)
shutil.copy(opts.config, os.path.join(output_directory, 'config.yaml'))
iterations = trainer.resume(checkpoint_directory,
param=config) if opts.resume else 0
while True:
for id, data in enumerate(train_loader):
trainer.update_learning_rate()
# label data
emc_label = data['EL'].cuda().detach()
int_label = data['IL'].cuda().detach()
default='a2l',
help="outputs path")
parser.add_argument("--resume", action="store_true")
opts = parser.parse_args()
cudnn.benchmark = True
# Load experiment setting
config = get_config(opts.config)
max_iter = config['max_iter']
trainer = LipTrainer(config)
trainer.to(config['device'])
train_loader = get_data_loader_list(config, split='train')
eval_loader = get_data_loader_list(config, split='eval')
model_name = config['trainer']
# train_writer = tensorboardX.SummaryWriter(os.path.join(opts.output_path + "/logs", model_name))
logging.basicConfig(filename=os.path.join(opts.output_path + "/logs",
model_name + '.log'),
level=logging.DEBUG)
logging.debug('This message should go to the log file')
logging.info('So should this')
output_directory = os.path.join(opts.output_path + "/outputs", model_name)
checkpoint_directory = os.path.join(prepare_sub_folder(output_directory), '..')
shutil.copy(opts.config, os.path.join(output_directory, 'config.yaml'))
iterations = trainer.resume(checkpoint_directory,
param=config) if opts.resume else 0