def evaluate(epoch, ema):
avg_loss = 0.0
epoch_time = 0
# progbar = Progbar(len(val_loader.dataset) // c.eval_batch_size)
ema_model = FFTNetModel(hid_channels=256,
out_channels=256,
n_layers=c.num_quant,
cond_channels=80)
ema_model = ema.assign_ema_model(model, ema_model, use_cuda)
ema_model.eval()
with torch.no_grad():
for num_iter, batch in enumerate(train_loader):
start_time = time.time()
wav = batch[0].unsqueeze(1)
mel = batch[1].transpose(1, 2)
lens = batch[2]
target = batch[3]
if use_cuda:
wav = wav.cuda()
mel = mel.cuda()
target = target.cuda()
current_step = num_iter + epoch * len(train_loader) + 1
out = ema_model(wav, mel)
loss, fp, tp = criterion(out, target, lens)
step_time = time.time() - start_time
epoch_time += step_time
avg_loss += loss.item()
avg_loss /= num_iter
return avg_loss
from tqdm import tqdm
from model import FFTNet, FFTNetModel
from generic_utils import count_parameters
torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if use_cuda:
torch.backends.cudnn.benchmark = False
print(" ---- Test FFTNetModel step forward ----")
net = FFTNetModel(hid_channels=256,
out_channels=256,
n_layers=11,
cond_channels=80)
net.eval()
print(" > Number of model params: ", count_parameters(net))
x = torch.rand(1, 1, 1)
cx = torch.rand(1, 80, 1)
time_start = time.time()
with torch.no_grad():
for i in tqdm(range(20000)):
out = net.forward_step(x, cx)
time_avg = (time.time() - time_start) / 20000
print("> Avg time per step inference on CPU: {}".format(time_avg))
# on GPU
net = FFTNetModel(hid_channels=256,
out_channels=256,
n_layers=11,
cond_channels=80)