def test_gpu(self): timer_ = timer.get_timer(cuda.cupy) self.assertIs(timer_.xp, cuda.cupy)
update_time = 0.0 print('iteration\tforward\tbackward\tupdate (in seconds)') for iteration in six.moves.range(start_iteration, args.iteration): if args.gpu >= 0: cache.clear_cache(args.cache_level) # data generation data = numpy.random.uniform(-1, 1, (args.batchsize, in_channels, in_flame, in_height, in_width)).astype(numpy.float32) data = chainer.Variable(xp.asarray(data)) label = numpy.zeros((args.batchsize, ), dtype=numpy.int32) label = chainer.Variable(xp.asarray(label)) # forward with timer.get_timer(xp) as t: loss = model(data, label) forward_time_one = t.total_time() # backward with timer.get_timer(xp) as t: loss.backward() backward_time_one = t.total_time() # parameter update with timer.get_timer(xp) as t: optimizer.update() update_time_one = t.total_time() if iteration < 0: print('Burn-in\t{}\t{}\t{}'.format(forward_time_one, backward_time_one,
def test_cpu(self): timer_ = timer.get_timer(numpy) self.assertIs(timer_.xp, numpy)