0a8e27413d721bf8d753e5e6061cc24f5bf6474f,train.py,,eval_model,#,489

Before Change

    model.eval()
    idx = np.random.randint(0, len(y))
    length = input_lengths[idx].data.cpu().numpy()[0]

    // (T,)
    y_target = y[idx].view(-1).data.cpu().numpy()[:length]

    if c is not None:
        c = c[idx, :, :length].unsqueeze(0)
        assert c.dim() == 3
        print("Shape of local conditioning features: {}".format(c.size()))
    if g is not None:
        // TODO: test
        g = g[idx]
        print("Shape of global conditioning features: {}".format(g.size()))

    // Dummy silence
    if is_mulaw_quantize(hparams.input_type):
        initial_value = P.mulaw_quantize(0, hparams.quantize_channels)
    elif is_mulaw(hparams.input_type):
        initial_value = P.mulaw(0.0, hparams.quantize_channels)
    else:
        initial_value = 0.0
    print("Intial value:", initial_value)

    // (C,)
    if is_mulaw_quantize(hparams.input_type):
        initial_input = np_utils.to_categorical(
            initial_value, num_classes=hparams.quantize_channels).astype(np.float32)
        initial_input = Variable(torch.from_numpy(initial_input)).view(
            1, 1, hparams.quantize_channels)
    else:
        initial_input = Variable(torch.zeros(1, 1, 1).fill_(initial_value))
    initial_input = initial_input.cuda() if use_cuda else initial_input

    // Run the model in fast eval mode
    y_hat = model.incremental_forward(
        initial_input, c=c, g=g, T=length, softmax=True, quantize=True, tqdm=tqdm,
        log_scale_min=hparams.log_scale_min)

    if is_mulaw_quantize(hparams.input_type):
        y_hat = y_hat.max(1)[1].view(-1).long().cpu().data.numpy()
        y_hat = P.inv_mulaw_quantize(y_hat, hparams.quantize_channels)
        y_target = P.inv_mulaw_quantize(y_target, hparams.quantize_channels)
    elif is_mulaw(hparams.input_type):
        y_hat = P.inv_mulaw(y_hat.view(-1).cpu().data.numpy(), hparams.quantize_channels)
        y_target = P.inv_mulaw(y_target, hparams.quantize_channels)
    else:
        y_hat = y_hat.view(-1).cpu().data.numpy()

    // Save audio
    os.makedirs(eval_dir, exist_ok=True)

After Change

    model.eval()
    idx = np.random.randint(0, len(y))
    length = input_lengths[idx].data.cpu().item()

    // (T,)
    y_target = y[idx].view(-1).data.cpu().numpy()[:length]

    if c is not None:
        c = c[idx, :, :length].unsqueeze(0)
        assert c.dim() == 3
        print("Shape of local conditioning features: {}".format(c.size()))
    if g is not None:
        // TODO: test
        g = g[idx]
        print("Shape of global conditioning features: {}".format(g.size()))

    // Dummy silence
    if is_mulaw_quantize(hparams.input_type):
        initial_value = P.mulaw_quantize(0, hparams.quantize_channels)
    elif is_mulaw(hparams.input_type):
        initial_value = P.mulaw(0.0, hparams.quantize_channels)
    else:
        initial_value = 0.0
    print("Intial value:", initial_value)

    // (C,)
    if is_mulaw_quantize(hparams.input_type):
        initial_input = np_utils.to_categorical(
            initial_value, num_classes=hparams.quantize_channels).astype(np.float32)
        initial_input = torch.from_numpy(initial_input).view(
            1, 1, hparams.quantize_channels)
    else:
        initial_input = torch.zeros(1, 1, 1).fill_(initial_value)
    initial_input = initial_input.to(device)

    // Run the model in fast eval mode
    y_hat = model.incremental_forward(
        initial_input, c=c, g=g, T=length, softmax=True, quantize=True, tqdm=tqdm,
        log_scale_min=hparams.log_scale_min)

    if is_mulaw_quantize(hparams.input_type):
        y_hat = y_hat.max(1)[1].view(-1).long().cpu().data.numpy()
        y_hat = P.inv_mulaw_quantize(y_hat, hparams.quantize_channels)
        y_target = P.inv_mulaw_quantize(y_target, hparams.quantize_channels)
    elif is_mulaw(hparams.input_type):
        y_hat = P.inv_mulaw(y_hat.view(-1).cpu().data.numpy(), hparams.quantize_channels)
        y_target = P.inv_mulaw(y_target, hparams.quantize_channels)
    else:
        y_hat = y_hat.view(-1).cpu().data.numpy()

    // Save audio
    os.makedirs(eval_dir, exist_ok=True)

Instances