# control_points_batch = control_points_l * batch_size
# print(torch.Tensor(np.array(control_points_batch)).size())
control_points_t = Variable(torch.Tensor(np.array(control_points_l)),
requires_grad=True)
elapsed_fw, elapsed_bw = 0, 0
passes = args.passes
crit = torch.nn.L1Loss().cuda()
tic_total = time()
memory_cached, memory_allocated = 0, 0
for i in range(passes):
tic = time()
curve = net.forward(control_points_t)
if use_cuda:
torch.cuda.synchronize()
elapsed_fw += time() - tic
# print('{}: Total.'.format(time() - tic))
loss = crit(curve, curve.clone().detach())
tic = time()
# print('{}: Loss.'.format(time() - tic))
loss.backward()
if use_cuda:
torch.cuda.synchronize()
elapsed_bw += time() - tic
if use_cuda:
memory_allocated = torch.cuda.max_memory_allocated()
memory_cached = torch.cuda.max_memory_cached()
