def test_maxpool2d(self):
# TODO merge into test_maxpool2d_strided when backward() is implemented
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
with self.subTest(kernel_size=ksz):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
lambda x: Tensor.max_pool2d(x, kernel_size=ksz), gpu=self.gpu, forward_only=self.gpu)
def test_maxpool_sizes(self):
for sz in [(2, 2), (3, 3), (3, 2), (5, 5), (5, 1)]:
helper_test_op(
[(32, 2, 110, 28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=sz),
lambda x: Tensor.max_pool2d(x, kernel_size=sz),
gpu=self.gpu)
def test_maxpool2d(self):
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
with self.subTest(kernel_size=ksz):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz),
# TODO: why is this tolerance so high?
lambda x: Tensor.max_pool2d(x, kernel_size=ksz), grad_atol=1e-4)
def test_maxpool2d(self):
for ksz in [(2, 2), (3, 3), (3, 2), (5, 5), (5, 1)]:
with self.subTest(kernel_size=ksz):
helper_test_op([(32, 2, 110, 28)],
lambda x: torch.nn.functional.max_pool2d(
x, kernel_size=ksz),
lambda x: Tensor.max_pool2d(x, kernel_size=ksz),
device=self.device)
def test_maxpool2d(self):
for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
for strd in [(1,1), (2,1), (2,2), (4,2)]:
# TODO Grad tolerance for CPU implementation needs to be slightly relaxed; why?
with self.subTest(kernel_size=ksz, stride=strd):
helper_test_op([(32,2,110,28)],
lambda x: torch.nn.functional.max_pool2d(x, kernel_size=ksz, stride=strd),
lambda x: Tensor.max_pool2d(x, kernel_size=ksz, stride=strd), gpu=self.gpu, forward_only=self.gpu, grad_atol=1e-3)
def test_maxpool2d_strided_fwd(self):
for ksz in [(2, 2), (3, 3), (3, 2), (5, 5), (5, 1)]:
for strd in [(1, 1), (2, 1), (2, 2), (4, 2)]:
with self.subTest(kernel_size=ksz, stride=strd):
helper_test_op([(32, 2, 110, 28)],
lambda x: torch.nn.functional.max_pool2d(
x, kernel_size=ksz, stride=strd),
lambda x: Tensor.max_pool2d(
x, kernel_size=ksz, stride=strd),
gpu=self.gpu,
forward_only=True)
