idxStart = bi * batch_size
inputData_np = test_X_std[idxStart:(idxStart + batch_size), :, :]
inputData_np_ori = test_X[idxStart:(idxStart + batch_size), :, :]
# outputData_np = test_Y_std[idxStart:(idxStart+batch_size),:,:]
# outputData_np_ori = test_Y[idxStart:(idxStart+batch_size),:,:]
inputData = Variable(
torch.from_numpy(inputData_np)).cuda() #(batch, 73, frameNum)
# outputGT = Variable(torch.from_numpy(outputData_np)).cuda() #(batch, 73, frameNum)
#outputGT = Variable(torch.from_numpy(inputData_np)).cuda() #(batch, 73, frameNum)
# ===================forward=====================
if "vae" in model.__class__.__name__:
output, mu, logvar = model(inputData)
loss, recon_loss, kld_loss = modelZoo.vae_loss_function(
output, inputData, mu, logvar, criterion, args.weight_kld)
else:
output = model(inputData)
#loss = criterion(output, outputGT)
loss = criterion(output, inputData)
print('loss: {}'.format(loss))
#De-standardaize
output_np = output.data.cpu().numpy() #(batch, featureDim, frames)
output_np = output_np * Xstd + Xmean
output_np = np.swapaxes(output_np, 1, 2) #(batch, frames, featureDim)
output_np = np.reshape(output_np, (-1, featureDim))
output_np = np.swapaxes(output_np, 0, 1) #(featureDim, frames)
inputData_np_ori = np.swapaxes(inputData_np_ori, 1,
inputData = Variable(
torch.from_numpy(inputData_np)).cuda() #(batch, 73, frameNum)
inputData_speech_cuda = Variable(
torch.from_numpy(inputdata_speech[:, :, 0])).cuda()
#outputGT = Variable(torch.from_numpy(outputData_np)).cuda() #(batch, 73, frameNum)
#outputGT = Variable(torch.from_numpy(inputData_np)).cuda() #(batch, 73, frameNum)
#################### VAE Only ####################
# ===================forward=====================
output, mu, logvar = model(inputData, inputData_speech_cuda)
#loss = criterion(output, inputData)
#loss = modelZoo.vae_loss_function(output, inputData, mu, logvar,criterion)
#loss, recon_loss, kld_loss = modelZoo.vae_loss_function(output, inputData, mu, logvar,criterion,args.weight_kld)
loss, recon_loss, kld_loss = modelZoo.vae_loss_function(
output, inputData[:, :-1, :], mu, logvar, criterion,
args.weight_kld) #ignore label in the inputData
# ===================backward====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================log========================
# print('model: {}, epoch [{}/{}], loss:{:.4f} (recon: {:.4f}, kld {:.4f})'
# .format(checkpointFolder_base, epoch +pretrain_epoch, num_epochs, loss.item(), recon_loss.item(), kld_loss.item()))
avgLoss += loss.item() * batch_size
avgReconLoss += recon_loss.item() * batch_size
avgKLDLoss += kld_loss.item() * batch_size
if tensorboard_bLog: