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)