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_convolution_with_padding():
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
A = Parameter(element_type, image_shape)
B = Parameter(element_type, filter_shape)
parameter_list = [A, B]
image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
filter_arr = np.zeros(9, dtype=np.float32).reshape(1, 1, 3, 3)
filter_arr[0][0][1][1] = 1
strides = [1, 1]
dilation = [2, 2]
padding_below = [0, 0]
padding_above = [0, 0]
function = Function([
Convolution(A, B, Strides(strides), Strides(dilation),
CoordinateDiff(padding_below),
CoordinateDiff(padding_above))
], parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, image_shape)
b = backend.create_tensor(element_type, filter_shape)
a.write(util.numpy_to_c(image_arr), 10 * 10 * 4)
b.write(util.numpy_to_c(filter_arr), 3 * 3 * 4)
result_arr = np.zeros(36, dtype=np.float32).reshape(1, 1, 6, 6)
result = backend.create_tensor(element_type, Shape([1, 1, 6, 6]))
result.write(util.numpy_to_c(result_arr), 6 * 6 * 4)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 6 * 6 * 4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0], strides,
dilation, padding_below,
padding_above).reshape(1, 1, 6, 6)
assert np.allclose(result_arr, result_arr_ref)
# test with non-zero padding
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
A = Parameter(element_type, image_shape)
B = Parameter(element_type, filter_shape)
parameter_list = [A, B]
image_arr = np.arange(100, dtype=np.float32).reshape(1, 1, 10, 10)
filter_arr = (np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)) * -1
filter_arr[0][0][1][1] = 1
strides = [1, 1]
dilation = [2, 2]
padding_below = [2, 1]
padding_above = [1, 2]
function = Function([
Convolution(A, B, Strides(strides), Strides(dilation),
CoordinateDiff(padding_below),
CoordinateDiff(padding_above))
], parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, image_shape)
b = backend.create_tensor(element_type, filter_shape)
a.write(util.numpy_to_c(image_arr), 10 * 10 * 4)
b.write(util.numpy_to_c(filter_arr), 3 * 3 * 4)
result_arr = np.zeros(81, dtype=np.float32).reshape(1, 1, 9, 9)
result = backend.create_tensor(element_type, Shape([1, 1, 9, 9]))
result.write(util.numpy_to_c(result_arr), 9 * 9 * 4)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 9 * 9 * 4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0], strides,
dilation, padding_below,
padding_above).reshape(1, 1, 9, 9)
assert np.allclose(result_arr, result_arr_ref)
