def experiment():
E_net = Encoder().double()
T_net = Transition().double()
optimizer_predict = torch.optim.RMSprop(itertools.chain(
E_net.parameters(), T_net.parameters()),
lr=HP_DICT['learning_rate'])
data = RectsData(HP_DICT)
if HP_DICT['GPU']:
E_net = E_net.to('cuda')
T_net = T_net.to('cuda')
data = data.to('cuda')
plt.ion()
return train(E_net, T_net, data, optimizer_predict)
if args.use_cuda and not torch.cuda.is_available():
raise Exception('No GPU found, please run without --cuda')
device = torch.device('cuda' if args.use_cuda else 'cpu')
dataset = DatasetFromFolder(args.input, 'file', params.sr, params.length,
params.frame_length, params.hop, params.n_mels,
'valid', None)
data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1)
encoder = Encoder(params.upscale_factors,
params.n_wavenets * params.n_layers * params.n_loops,
params.r, params.n_mels)
wavenet = UniWaveNet(params.n_wavenets, params.n_layers, params.n_loops,
params.a, params.r, params.s)
encoder = encoder.to(device)
wavenet = wavenet.to(device)
encoder.load_state_dict(torch.load(args.encoder_model))
wavenet.load_state_dict(torch.load(args.wavenet_model))
print(torch.load(args.wavenet_model)['wavenet_list.0.conv_in.weight'][0][0][0])
print(torch.load(args.wavenet_model)['wavenet_list.0.conv1x1.weight'][0][0][0])
print(
torch.load(args.wavenet_model)['wavenet_list.0.conv_out.weight'][0][0][0])
print(torch.load(args.wavenet_model)['wavenet_list.1.conv_in.weight'][0][0][0])
print(torch.load(args.wavenet_model)['wavenet_list.1.conv1x1.weight'][0][0][0])
print(
torch.load(args.wavenet_model)['wavenet_list.1.conv_out.weight'][0][0][0])
