"{:.3f}".format(average_latent_loss0), "latent1=",
"{:.3f}".format(average_latent_loss1))
if (epoch % save_epoch == 0) or (epoch == training_epochs - 1):
torch.save(scan.state_dict(),
'{}/scan_epoch_{}.pth'.format(exp, epoch))
data_manager = DataManager()
data_manager.prepare()
dae = DAE()
vae = VAE()
scan = SCAN()
if use_cuda:
dae.load_state_dict(torch.load('save/dae/dae_epoch_2999.pth'))
vae.load_state_dict(torch.load('save/vae/vae_epoch_2999.pth'))
scan.load_state_dict(torch.load('save/scan/scan_epoch_1499.pth'))
dae, vae, scan = dae.cuda(), vae.cuda(), scan.cuda()
else:
dae.load_state_dict(
torch.load('save/dae/dae_epoch_2999.pth',
map_location=lambda storage, loc: storage))
vae.load_state_dict(
torch.load('save/vae/vae_epoch_2999.pth',
map_location=lambda storage, loc: storage))
scan.load_state_dict(
torch.load(exp + '/' + opt.load,
map_location=lambda storage, loc: storage))
if opt.train:
target = torch.transpose(target, 1, 3)
if use_cuda: hsv_image_t = target.data.cpu().numpy()
else: hsv_image_t = target.data.numpy()
rgb_image_t = utils.convert_hsv_to_rgb(hsv_image_t[0])
utils.save_image(rgb_image_t,
"{}/target_epoch_{}.png".format(exp, epoch))
# Save to checkpoint
if (epoch % save_epoch == 0) or (epoch == training_epochs - 1):
torch.save(dae.state_dict(),
'{}/dae_epoch_{}.pth'.format(exp, epoch))
data_manager = DataManager()
data_manager.prepare()
dae = DAE()
if opt.load != '':
print('loading {}'.format(opt.load))
if use_cuda:
dae.load_state_dict(torch.load())
else:
dae.load_state_dict(
torch.load(exp + '/' + opt.load,
map_location=lambda storage, loc: storage))
if use_cuda: dae = dae.cuda()
if opt.train:
dae_optimizer = optim.Adam(dae.parameters(), lr=1e-4, eps=1e-8)
train_dae(dae, data_manager, dae_optimizer)
parser.add_argument('--output-path',
default=None,
type=str,
help="Where to save raw acoustic output")
parser = add_decoder_args(parser)
parser.add_argument('--save-output',
action="store_true",
help="Saves output of model from test")
args = parser.parse_args()
if __name__ == '__main__':
torch.set_grad_enabled(False)
device = torch.device("cuda" if args.cuda else "cpu")
model = load_model(device, args.model_path, args.cuda)
denoiser = DAE()
denoiser.load_state_dict(
torch.load('./models/denoiser_deepspeech_final.pth'))
denoiser = denoiser.to(device)
denoiser.eval()
if args.decoder == "beam":
from decoder import BeamCTCDecoder
decoder = BeamCTCDecoder(model.labels,
lm_path=args.lm_path,
alpha=args.alpha,
beta=args.beta,
cutoff_top_n=args.cutoff_top_n,
cutoff_prob=args.cutoff_prob,
beam_width=args.beam_width,
num_processes=args.lm_workers)
elif args.decoder == "greedy":