def max_pool(x, # type: Node
window_shape, # type: TensorShape
strides=None, # type: List[int]
padding_above=None, # type: List[int]
padding_below=None, # type: List[int]
name=None, # type: str
):
# type: (...) -> Node
"""Return max pooling node."""
if strides is None:
strides = [1] * len(window_shape) # Default to as many 1s as spatial dimensions of input.
if padding_above is None:
padding_above = [0] * len(window_shape)
if padding_below is None:
padding_below = [0] * len(window_shape)
return MaxPool(x, Shape(window_shape), Strides(strides),
Shape(padding_above), Shape(padding_below))
def test_max_pool():
#test 1d
element_type = Type.f32
shape = Shape([1, 1, 10])
A = Parameter(element_type, shape)
parameter_list = [A]
input_arr = np.arange(10, dtype=np.float32).reshape(1, 1, 10)
window_shape = [3]
function = Function([MaxPool(A, Shape(window_shape))], parameter_list,
'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, Shape([1, 1, 8]))
a.write(util.numpy_to_c(input_arr), 10 * 4)
result_arr = np.zeros(8, dtype=np.float32).reshape(1, 1, 8)
result.write(util.numpy_to_c(result_arr), 8 * 4)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 32)
result_arr_ref = (np.arange(8) + 2).reshape(1, 1, 8)
assert np.allclose(result_arr, result_arr_ref)
#test 1d with strides
strides = [2]
function = Function([MaxPool(A, Shape(window_shape), Strides(strides))],
parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
size = 4
result = backend.create_tensor(element_type, Shape([1, 1, size]))
result_arr = np.zeros(size, dtype=np.float32).reshape(1, 1, size)
result.write(util.numpy_to_c(result_arr), size * 4)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), size * 4)
result_arr_ref = ((np.arange(size) + 1) * 2).reshape(1, 1, size)
assert np.allclose(result_arr, result_arr_ref)
#test 2d
element_type = Type.f32
shape = Shape([1, 1, 10, 10])
A = Parameter(element_type, shape)
parameter_list = [A]
input_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
window_shape = [3, 3]
function = Function([MaxPool(A, Shape(window_shape))], parameter_list,
'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, Shape([1, 1, 8, 8]))
a.write(util.numpy_to_c(input_arr), 10 * 10 * 4)
result_arr = np.zeros(64, dtype=np.float32).reshape(1, 1, 8, 8)
result.write(util.numpy_to_c(result_arr), 8 * 8 * 4)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 8 * 8 * 4)
result_arr_ref = ((np.arange(100).reshape(10,
10))[2:,
2:]).reshape(1, 1, 8, 8)
assert np.allclose(result_arr, result_arr_ref)
#test 2d with strides
strides = [2, 2]
function = Function([MaxPool(A, Shape(window_shape), Strides(strides))],
parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
size = 4
result = backend.create_tensor(element_type, Shape([1, 1, size, size]))
result_arr = np.zeros(size * size,
dtype=np.float32).reshape(1, 1, size, size)
result.write(util.numpy_to_c(result_arr), size * size * 4)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), size * size * 4)
result_arr_ref = ((np.arange(100).reshape(10, 10))[2::2, 2::2]).reshape(
1, 1, size, size)
assert np.allclose(result_arr, result_arr_ref)
def test_max_pool():
#test 1d
element_type = Type.f32
shape = Shape([1, 1, 10])
A = Parameter(element_type, shape)
parameter_list = [A]
input_arr = np.arange(10, dtype=np.float32).reshape(1, 1, 10)
window_shape = [3]
function = Function(NodeVector([MaxPool(A, Shape(window_shape))]),
parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, shape)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 8]))
a.write(util.numpy_to_c(input_arr), 0, 10 * 4)
result_arr = np.zeros(8, dtype=np.float32).reshape(1, 1, 8)
result.write(util.numpy_to_c(result_arr), 0, 8 * 4)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 32)
result_arr_ref = (np.arange(8) + 2).reshape(1, 1, 8)
assert np.allclose(result_arr, result_arr_ref)
#test 1d with strides
strides = [2]
function = Function(
NodeVector([MaxPool(A, Shape(window_shape), Strides(strides))]),
parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
size = 4
result = backend.make_primary_tensor_view(element_type, Shape([1, 1,
size]))
result_arr = np.zeros(size, dtype=np.float32).reshape(1, 1, size)
result.write(util.numpy_to_c(result_arr), 0, size * 4)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, size * 4)
result_arr_ref = ((np.arange(size) + 1) * 2).reshape(1, 1, size)
assert np.allclose(result_arr, result_arr_ref)
#test 2d
element_type = Type.f32
shape = Shape([1, 1, 10, 10])
A = Parameter(element_type, shape)
parameter_list = [A]
input_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
window_shape = [3, 3]
function = Function(NodeVector([MaxPool(A, Shape(window_shape))]),
parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, shape)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 8,
8]))
a.write(util.numpy_to_c(input_arr), 0, 10 * 10 * 4)
result_arr = np.zeros(64, dtype=np.float32).reshape(1, 1, 8, 8)
result.write(util.numpy_to_c(result_arr), 0, 8 * 8 * 4)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 8 * 8 * 4)
result_arr_ref = ((np.arange(100).reshape(10,
10))[2:,
2:]).reshape(1, 1, 8, 8)
assert np.allclose(result_arr, result_arr_ref)
#test 2d with strides
strides = [2, 2]
function = Function(
NodeVector([MaxPool(A, Shape(window_shape), Strides(strides))]),
parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
size = 4
result = backend.make_primary_tensor_view(element_type,
Shape([1, 1, size, size]))
result_arr = np.zeros(size * size,
dtype=np.float32).reshape(1, 1, size, size)
result.write(util.numpy_to_c(result_arr), 0, size * size * 4)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, size * size * 4)
result_arr_ref = ((np.arange(100).reshape(10, 10))[2::2, 2::2]).reshape(
1, 1, size, size)
assert np.allclose(result_arr, result_arr_ref)