def test_concat():
element_type = Type.f32
A = Parameter(element_type, Shape([1, 2]))
B = Parameter(element_type, Shape([1, 2]))
C = Parameter(element_type, Shape([1, 2]))
parameter_list = [A, B, C]
axis = 0
function = Function(NodeVector([Concat(NodeVector([A, B, C]), axis)]),
parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, Shape([1, 2]))
b = backend.create_tensor(element_type, Shape([1, 2]))
c = backend.create_tensor(element_type, Shape([1, 2]))
result = backend.create_tensor(element_type, Shape([3, 2]))
a.write(util.numpy_to_c(np.array([1, 2], dtype=np.float32)), 0, 8)
b.write(util.numpy_to_c(np.array([5, 6], dtype=np.float32)), 0, 8)
c.write(util.numpy_to_c(np.array([7, 8], dtype=np.float32)), 0, 8)
result_arr = np.zeros(6, dtype=np.float32).reshape(3, 2)
result.write(util.numpy_to_c(result_arr), 0, 24)
backend.call(backend.compile(function), [result], [a, b, c])
result.read(util.numpy_to_c(result_arr), 0, 24)
a_arr = np.array([[1, 2]], dtype=np.float32)
b_arr = np.array([[5, 6]], dtype=np.float32)
c_arr = np.array([[7, 8]], dtype=np.float32)
result_arr_ref = np.concatenate((a_arr, b_arr, c_arr), axis)
assert np.allclose(result_arr, result_arr_ref)
def test_add_with_mul():
element_type = Type.f32
shape = Shape([2, 2])
A = Parameter(element_type, shape)
B = Parameter(element_type, shape)
C = Parameter(element_type, shape)
parameter_list = [A, B, C]
function = Function(NodeVector([Multiply(Add(A, B), C)]), parameter_list,
'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, shape)
b = backend.create_tensor(element_type, shape)
c = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, shape)
a.write(util.numpy_to_c(np.array([1, 2, 3, 4], dtype=np.float32)), 0, 16)
b.write(util.numpy_to_c(np.array([5, 6, 7, 8], dtype=np.float32)), 0, 16)
c.write(util.numpy_to_c(np.array([9, 10, 11, 12], dtype=np.float32)), 0,
16)
result_arr = np.array([0, 0, 0, 0], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 16)
backend.call(backend.compile(function), [result], [a, b, c])
result.read(util.numpy_to_c(result_arr), 0, 16)
a_arr = np.array([1, 2, 3, 4], dtype=np.float32)
b_arr = np.array([5, 6, 7, 8], dtype=np.float32)
c_arr = np.array([9, 10, 11, 12], dtype=np.float32)
result_arr_ref = (a_arr + b_arr) * c_arr
assert np.allclose(result_arr, result_arr_ref)
def test_reduce():
float_element_type = Type.f32
AddParam1 = Parameter(float_element_type, Shape([]))
AddParam2 = Parameter(float_element_type, Shape([]))
constant_op = Constant(float_element_type, Shape([]), [0.])
reduce_function = Function(NodeVector([Add(AddParam1, AddParam2)]),
[AddParam1, AddParam2], 'add')
A = Parameter(float_element_type, Shape([2, 2, 2]))
parameter_list = [A]
function = Function(
NodeVector([Reduce(A, constant_op, reduce_function, AxisSet({0}))]),
parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(float_element_type, Shape([2, 2, 2]))
result = backend.create_tensor(float_element_type, Shape([2, 2]))
a.write(util.numpy_to_c(np.arange(8, dtype=np.float32).reshape(2, 2, 2)),
0, 32)
result_arr = np.zeros((2, 2), dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 16)
backend.call(backend.compile(function), [result], [a])
result.read(util.numpy_to_c(result_arr), 0, 16)
a_arr = np.arange(8).reshape(2, 2, 2)
result_arr_ref = np.add.reduce(a_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_convolution_with_strides():
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 = [2, 2]
function = Function(NodeVector([Convolution(A, B, Strides(strides))]),
parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, image_shape)
b = backend.create_tensor(element_type, filter_shape)
a.write(util.numpy_to_c(image_arr), 0, 10 * 10 * 4)
b.write(util.numpy_to_c(filter_arr), 0, 3 * 3 * 4)
result_arr = np.zeros(16, dtype=np.float32).reshape(1, 1, 4, 4)
result = backend.create_tensor(element_type, Shape([1, 1, 4, 4]))
result.write(util.numpy_to_c(result_arr), 0, 4 * 4 * 4)
backend.call(backend.compile(function), [result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 4 * 4 * 4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0],
strides).reshape(1, 1, 4, 4)
assert np.allclose(result_arr, result_arr_ref)
def binary_op_comparison(op_str):
element_type = Type.f32
shape = Shape([2, 2])
A = Parameter(element_type, shape)
B = Parameter(element_type, shape)
parameter_list = [A, B]
function = Function(NodeVector([binary_op(op_str, A, B)]), parameter_list,
'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, shape)
b = backend.create_tensor(element_type, shape)
result = backend.create_tensor(Type.boolean, shape)
a.write(util.numpy_to_c(np.array([[1, 5], [3, 2]], dtype=np.float32)), 0,
16)
b.write(util.numpy_to_c(np.array([[2, 4], [3, 1]], dtype=np.float32)), 0,
16)
result_arr = np.array([[False, False], [False, False]], dtype=np.bool)
result.write(util.numpy_to_c(result_arr), 0, 4)
backend.call(backend.compile(function), [result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 4)
a_arr = np.array([[1, 5], [3, 2]], dtype=np.float32)
b_arr = np.array([[2, 4], [3, 1]], dtype=np.float32)
result_arr_ref = binary_op_ref(op_str, a_arr, b_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_convolution_with_filter_dilation():
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)
strides = [1, 1]
dilation = [2, 2]
function = Function(
[Convolution(A, B, Strides(strides), Strides(dilation))],
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), 0, 10 * 10 * 4)
b.write(util.numpy_to_c(filter_arr), 0, 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), 0, 6 * 6 * 4)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 6 * 6 * 4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0], strides,
dilation).reshape(1, 1, 6, 6)
assert np.allclose(result_arr, result_arr_ref)
def binary_op_exec(op_str):
element_type = Type.f32
shape = Shape([2, 2])
A = Parameter(element_type, shape)
B = Parameter(element_type, shape)
parameter_list = [A, B]
function = Function([binary_op(op_str, A, B)], parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
b = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, shape)
a.write(util.numpy_to_c(np.array([[1, 6], [7, 4]], dtype=np.float32)), 0,
16)
b.write(util.numpy_to_c(np.array([[5, 2], [3, 8]], dtype=np.float32)), 0,
16)
result_arr = np.array([[0, 0], [0, 0]], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 16)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 16)
a_arr = np.array([[1, 6], [7, 4]], dtype=np.float32)
b_arr = np.array([[5, 2], [3, 8]], dtype=np.float32)
result_arr_ref = binary_op_ref(op_str, a_arr, b_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_convolution_with_non_zero_padding():
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
data = Parameter(element_type, image_shape)
filters = Parameter(element_type, filter_shape)
parameter_list = [data, filters]
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]
dilations = [2, 2]
pads_begin = [2, 1]
pads_end = [1, 2]
model = ng.convolution(data, filters, strides, pads_begin, pads_end,
dilations)
function = Function([model], parameter_list, "test")
runtime = get_runtime()
computation = runtime.computation(function, *parameter_list)
result = computation(image_arr, filter_arr)[0]
expected = convolution2d(image_arr[0][0], filter_arr[0][0], strides,
dilations, pads_begin,
pads_end).reshape([1, 1, 9, 9])
assert np.allclose(result, expected)
def test_select():
element_type = Type.f32
A = Parameter(Type.boolean, Shape([1, 2]))
B = Parameter(element_type, Shape([1, 2]))
C = Parameter(element_type, Shape([1, 2]))
parameter_list = [A, B, C]
function = Function(NodeVector([Select(A, B, C)]), parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(Type.boolean, Shape([1, 2]))
b = backend.create_tensor(element_type, Shape([1, 2]))
c = backend.create_tensor(element_type, Shape([1, 2]))
result = backend.create_tensor(element_type, Shape([1, 2]))
a.write(util.numpy_to_c(np.array([[True, False]], dtype=np.bool)), 0, 2)
b.write(util.numpy_to_c(np.array([[5, 6]], dtype=np.float32)), 0, 8)
c.write(util.numpy_to_c(np.array([[7, 8]], dtype=np.float32)), 0, 8)
result_arr = np.array([[0, 0]], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 8)
backend.call(backend.compile(function), [result], [a, b, c])
result.read(util.numpy_to_c(result_arr), 0, 8)
result_arr_ref = np.array([[5, 8]])
assert np.allclose(result_arr, result_arr_ref)
def test_convolution():
element_type = Type.f32
image_shape = Shape([1, 1, 16, 16])
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(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
filter_arr = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
filter_arr[0][0][0][0] = -1
filter_arr[0][0][1][1] = -1
filter_arr[0][0][2][2] = -1
filter_arr[0][0][0][2] = -1
filter_arr[0][0][2][0] = -1
result_arr = np.zeros(196, dtype=np.float32).reshape(1, 1, 14, 14)
function = Function(NodeVector([Convolution(A, B)]), parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, image_shape)
b = backend.make_primary_tensor_view(element_type, filter_shape)
a.write(util.numpy_to_c(image_arr), 0, 16*16*4)
b.write(util.numpy_to_c(filter_arr), 0, 3*3*4)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 14, 14]))
result.write(util.numpy_to_c(result_arr), 0, 14*14*4)
cf.call([a, b], [result])
result.read(util.numpy_to_c(result_arr), 0, 14*14*4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0]).reshape(1, 1, 14, 14)
assert np.allclose(result_arr, result_arr_ref)
def reduce(
node, # type: Node
initial_value, # type: ScalarData
reduction_function, # type: Union[Callable, Function]
reduction_axes=None, # type: List[int]
name=None, # type: str
):
# type: (...) -> Node
"""Perform general tensor reduction operation.
:param node: The node providing data for reduction operation.
:param initial_value: The initial value for reduction operation.
:param reduction_function: The function performing binary reduction operation or a nGraph
Function object. The operation must accept two nodes providing scalar
operands and return a node which produces a scalar result.
:param reduction_axes: The list of axes indices to be reduced. Default to reduce all axes.
:param name: The new name for output node.
:return: The node performing reduction operation with provided reduction node.
"""
if reduction_axes is None:
reduction_axes = list(range(len(node.shape)))
init_val_node = constant(initial_value)
if not isinstance(reduction_function, Function):
# wrap reduction function into Function object
param1 = Parameter(node.get_element_type(), Shape([]))
param2 = Parameter(node.get_element_type(), Shape([]))
reduction_operation = Function(
NodeVector([reduction_function(param1, param2)]), [param1, param2],
'reduction_operation')
else:
reduction_operation = reduction_function
return Reduce(node, init_val_node, reduction_operation,
AxisSet(set(reduction_axes)))
def test_convolution_simple():
element_type = Type.f32
image_shape = Shape([1, 1, 16, 16])
filter_shape = Shape([1, 1, 3, 3])
data = Parameter(element_type, image_shape)
filters = Parameter(element_type, filter_shape)
parameter_list = [data, filters]
image_arr = np.arange(-128, 128, 1, dtype=np.float32).reshape(1, 1, 16, 16)
filter_arr = np.ones(9, dtype=np.float32).reshape(1, 1, 3, 3)
filter_arr[0][0][0][0] = -1
filter_arr[0][0][1][1] = -1
filter_arr[0][0][2][2] = -1
filter_arr[0][0][0][2] = -1
filter_arr[0][0][2][0] = -1
strides = [1, 1]
pads_begin = [0, 0]
pads_end = [0, 0]
dilations = [1, 1]
model = ng.convolution(data, filters, strides, pads_begin, pads_end,
dilations)
function = Function([model], parameter_list, "test")
runtime = get_runtime()
computation = runtime.computation(function, *parameter_list)
result = computation(image_arr, filter_arr)[0]
expected = convolution2d(image_arr[0][0],
filter_arr[0][0]).reshape(1, 1, 14, 14)
assert np.allclose(result, expected)
def test_convolution_with_filter_dilation():
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)
strides = [1, 1]
dilation = [2, 2]
function = Function(NodeVector([Convolution(A, B, Strides(strides), Strides(dilation))]), parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, image_shape)
b = backend.make_primary_tensor_view(element_type, filter_shape)
a.write(util.numpy_to_c(image_arr), 0, 10*10*4)
b.write(util.numpy_to_c(filter_arr), 0, 3*3*4)
result_arr = np.zeros(36, dtype=np.float32).reshape(1, 1, 6, 6)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 6, 6]))
result.write(util.numpy_to_c(result_arr), 0, 6*6*4)
cf.call([a, b], [result])
result.read(util.numpy_to_c(result_arr), 0, 6*6*4)
result_arr_ref = convolution2d(image_arr[0][0], filter_arr[0][0], strides,
dilation).reshape(1, 1, 6, 6)
assert np.allclose(result_arr, result_arr_ref)
def test_convolutionBackpropData():
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
output_shape = Shape([1, 1, 17, 17])
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)
window_strides = [1, 1]
window_dilation = [1, 1]
padding_below = [0, 0]
padding_above = [0, 0]
data_dilation = [2, 2]
output_arr = convolution2d(image_arr[0][0], filter_arr[0][0],
window_strides, window_dilation, padding_below,
padding_above,
data_dilation).reshape(1, 1, 17, 17)
A = Parameter(element_type, filter_shape)
B = Parameter(element_type, output_shape)
parameter_list = [A, B]
function = Function(
NodeVector([
ConvolutionBackpropData(image_shape, A, B, Strides(window_strides),
Strides(window_dilation),
CoordinateDiff(padding_below),
CoordinateDiff(padding_above),
Strides(data_dilation))
]), parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, filter_shape)
b = backend.make_primary_tensor_view(element_type, output_shape)
a.write(util.numpy_to_c(filter_arr), 0, 3 * 3 * 4)
b.write(util.numpy_to_c(output_arr), 0, 17 * 17 * 4)
result_arr = np.zeros(10 * 10, dtype=np.float32).reshape(1, 1, 10, 10)
result = backend.make_primary_tensor_view(element_type,
Shape([1, 1, 10, 10]))
result.write(util.numpy_to_c(result_arr), 0, 10 * 10 * 4)
cf.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 10 * 10 * 4)
result_arr_ref = np.array(
[[[[22, 60, 70, 80, 90, 100, 110, 120, 130, 54.],
[105, 275, 300, 325, 350, 375, 400, 425, 450, 185.],
[205, 525, 550, 575, 600, 625, 650, 675, 700, 285.],
[305, 775, 800, 825, 850, 875, 900, 925, 950, 385.],
[405, 1025, 1050, 1075, 1100, 1125, 1150, 1175, 1200, 485.],
[505, 1275, 1300, 1325, 1350, 1375, 1400, 1425, 1450, 585.],
[605, 1525, 1550, 1575, 1600, 1625, 1650, 1675, 1700, 685.],
[705, 1775, 1800, 1825, 1850, 1875, 1900, 1925, 1950, 785.],
[805, 2025, 2050, 2075, 2100, 2125, 2150, 2175, 2200, 885.],
[342, 860, 870, 880, 890, 900, 910, 920, 930, 374.]]]])
assert np.allclose(result_arr, result_arr_ref)
def test_replace_slice():
element_type = Type.f32
A = Parameter(element_type, Shape([6, 4]))
B = Parameter(element_type, Shape([3, 2]))
parameter_list = [A, B]
input_arr_a = np.zeros(24, dtype=np.float32).reshape(6, 4)
input_arr_b = np.ones(6, dtype=np.float32).reshape(3, 2)
lower_bounds = [0, 1]
upper_bounds = [3, 3]
function = Function(
NodeVector([
ReplaceSlice(A, B, Coordinate(lower_bounds),
Coordinate(upper_bounds))
]), parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, Shape([6, 4]))
b = backend.create_tensor(element_type, Shape([3, 2]))
result = backend.create_tensor(element_type, Shape([6, 4]))
a.write(util.numpy_to_c(input_arr_a), 0, 24 * 4)
b.write(util.numpy_to_c(input_arr_b), 0, 6 * 4)
result_arr = np.zeros(24, dtype=np.float32).reshape(6, 4)
result.write(util.numpy_to_c(result_arr), 0, 24 * 4)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 24 * 4)
result_arr_ref = np.copy(input_arr_a)
result_arr_ref[lower_bounds[0]:upper_bounds[0],
lower_bounds[1]:upper_bounds[1]] = input_arr_b
assert np.allclose(result_arr, result_arr_ref)
#test with strides
lower_bounds = [0, 0]
upper_bounds = [5, 3]
strides = [2, 2]
function = Function(
NodeVector([
ReplaceSlice(A, B, Coordinate(lower_bounds),
Coordinate(upper_bounds), Strides(strides))
]), parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
handle = backend.compile(function)
handle.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 24 * 4)
result_arr_ref = np.copy(input_arr_a)
result_arr_ref[::strides[0], ::strides[1]] = input_arr_b
assert np.allclose(result_arr, result_arr_ref)
def test_convolutionBackpropFilters():
element_type = Type.f32
image_shape = Shape([1, 1, 10, 10])
filter_shape = Shape([1, 1, 3, 3])
output_shape = Shape([1, 1, 17, 17])
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)
window_strides = [1, 1]
window_dilation = [1, 1]
padding_below = [0, 0]
padding_above = [0, 0]
data_dilation = [2, 2]
output_arr = convolution2d(image_arr[0][0], filter_arr[0][0],
window_strides, window_dilation, padding_below,
padding_above,
data_dilation).reshape(1, 1, 17, 17)
A = Parameter(element_type, image_shape)
B = Parameter(element_type, output_shape)
parameter_list = [A, B]
function = Function(
NodeVector([
ConvolutionBackpropFilters(A, filter_shape, B,
Strides(window_strides),
Strides(window_dilation),
CoordinateDiff(padding_below),
CoordinateDiff(padding_above),
Strides(data_dilation))
]), parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, image_shape)
b = backend.make_primary_tensor_view(element_type, output_shape)
a.write(util.numpy_to_c(image_arr), 0, 10 * 10 * 4)
b.write(util.numpy_to_c(output_arr), 0, 17 * 17 * 4)
result_arr = np.zeros(3 * 3, dtype=np.float32).reshape(1, 1, 3, 3)
result = backend.make_primary_tensor_view(element_type, Shape([1, 1, 3,
3]))
result.write(util.numpy_to_c(result_arr), 0, 3 * 3 * 4)
cf.call([result], [a, b])
result.read(util.numpy_to_c(result_arr), 0, 3 * 3 * 4)
result_arr_ref = np.array([[[[923832, 413952, 939870.],
[425832, 190752, 432960.],
[1084212, 485232, 1100250.]]]])
assert np.allclose(result_arr, result_arr_ref)
def test_reshape():
element_type = Type.f32
shape = Shape([2, 3])
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function(
NodeVector([Reshape(A, AxisVector([0, 1]), Shape([3, 2]))]),
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([3, 2]))
a.write(
util.numpy_to_c(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)), 0,
24)
result_arr = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 24)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 24)
a_arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
result_arr_ref = np.reshape(a_arr, (3, 2))
assert np.allclose(result_arr, result_arr_ref)
def unary_op_exec(op_str, input_list):
"""
input_list needs to have deep length of 4
"""
element_type = Type.f32
shape = Shape(np.array(input_list).shape)
shape_np = np.array(input_list).shape
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function(NodeVector([unary_op(op_str, A)]), 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)
a.write(util.numpy_to_c(np.array(input_list, dtype=np.float32)), 0, 16)
result_arr = np.zeros(shape_np, dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 16)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 16)
a_arr = np.array(input_list, dtype=np.float32)
result_arr_ref = unary_op_ref(op_str, a_arr)
assert np.allclose(result_arr, result_arr_ref)
def parameter(shape, dtype=np.float32, name=None):
# type: (TensorShape, NumericType, str) -> Parameter
"""Return an ngraph Parameter object."""
assert_list_of_ints(shape,
'Parameter shape must be a list of integer values.')
element_type = get_element_type(dtype)
return Parameter(element_type, Shape(shape))
def test_broadcast():
element_type = Type.f32
A = Parameter(element_type, Shape([3]))
parameter_list = [A]
function = Function(
NodeVector([Broadcast(A, Shape([3, 3]), AxisSet({0}))]),
parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, Shape([3]))
result = backend.create_tensor(element_type, Shape([3, 3]))
a.write(util.numpy_to_c(np.array([1, 2, 3], dtype=np.float32)), 0, 12)
result_arr = np.zeros((3, 3), dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 36)
backend.call(backend.compile(function), [result], [a])
result.read(util.numpy_to_c(result_arr), 0, 36)
a_arr = np.array([[0], [0], [0]], dtype=np.float32)
b_arr = np.array([[1, 2, 3]], dtype=np.float32)
result_arr_ref = np.add(a_arr, b_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_sum():
element_type = Type.f32
shape = Shape([1, 4])
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function([Sum(A, AxisSet({1}))], parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, Shape([1]))
a.write(util.numpy_to_c(np.array([1, 2, 3, 4], dtype=np.float32)), 16)
result_arr = np.array([0], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 4)
handle = backend.compile(function)
handle.get_performance_data()
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 4)
a_arr = np.array([1, 2, 3, 4], dtype=np.float32)
result_arr_ref = np.sum(a_arr)
assert np.allclose(result_arr[0], result_arr_ref)
def unary_op_exec(op_str, input_list):
"""
input_list needs to have deep length of 4
"""
element_type = Type.f32
shape = Shape(np.array(input_list).shape)
shape_np = np.array(input_list).shape
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function(NodeVector([unary_op(op_str, A)]), parameter_list,
'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, shape)
a.write(util.numpy_to_c(np.array(input_list, dtype=np.float32)), 0, 16)
result_arr = np.zeros(shape_np, dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 16)
backend.call(function, [result], [a])
result.read(util.numpy_to_c(result_arr), 0, 16)
a_arr = np.array(input_list, dtype=np.float32)
result_arr_ref = unary_op_ref(op_str, a_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_broadcast():
element_type = Type.f32
A = Parameter(element_type, Shape([3]))
parameter_list = [A]
function = Function(
NodeVector([Broadcast(A, Shape([3, 3]), AxisSet({0}))]),
parameter_list, 'test')
backend, cf = make_backend_call_frame(function)
a = backend.make_primary_tensor_view(element_type, Shape([3]))
result = backend.make_primary_tensor_view(element_type, Shape([3, 3]))
a.write(util.numpy_to_c(np.array([1, 2, 3], dtype=np.float32)), 0, 12)
result_arr = np.zeros((3, 3), dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 36)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 36)
a_arr = np.array([[0], [0], [0]], dtype=np.float32)
b_arr = np.array([[1, 2, 3]], dtype=np.float32)
result_arr_ref = np.add(a_arr, b_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_broadcast():
element_type = Type.f32
A = Parameter(element_type, Shape([3]))
parameter_list = [A]
function = Function([Broadcast(A, Shape([3, 3]), AxisSet({0}))],
parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, Shape([3]))
result = backend.create_tensor(element_type, Shape([3, 3]))
a.write(util.numpy_to_c(np.array([1, 2, 3], dtype=np.float32)), 12)
result_arr = np.zeros((3, 3), dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 36)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 36)
a_arr = np.array([[0], [0], [0]], dtype=np.float32)
b_arr = np.array([[1, 2, 3]], dtype=np.float32)
result_arr_ref = np.add(a_arr, b_arr)
assert np.allclose(result_arr, result_arr_ref)
def test_reshape():
element_type = Type.f32
shape = Shape([2, 3])
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function(
NodeVector([Reshape(A, AxisVector([0, 1]), Shape([3, 2]))]),
parameter_list, 'test')
backend = Backend.create(pytest.config.getoption('backend'))
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, Shape([3, 2]))
a.write(
util.numpy_to_c(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)), 0,
24)
result_arr = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 0, 24)
backend.call(function, [result], [a])
result.read(util.numpy_to_c(result_arr), 0, 24)
a_arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
result_arr_ref = np.reshape(a_arr, (3, 2))
assert np.allclose(result_arr, result_arr_ref)
def test_reshape():
element_type = Type.f32
shape = Shape([2, 3])
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function([Reshape(A, AxisVector([0, 1]), Shape([3, 2]))],
parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, Shape([3, 2]))
a.write(
util.numpy_to_c(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)),
24)
result_arr = np.array([[0, 0], [0, 0], [0, 0]], dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 24)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 24)
a_arr = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
result_arr_ref = np.reshape(a_arr, (3, 2))
assert np.allclose(result_arr, result_arr_ref)
def unary_op_exec(op_str, input_list):
"""
input_list needs to have deep length of 4
"""
element_type = Type.f32
shape = Shape(np.array(input_list).shape)
shape_np = np.array(input_list).shape
A = Parameter(element_type, shape)
parameter_list = [A]
function = Function([unary_op(op_str, A)], parameter_list, 'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(element_type, shape)
a.write(util.numpy_to_c(np.array(input_list, dtype=np.float32)), 16)
result_arr = np.zeros(shape_np, dtype=np.float32)
result.write(util.numpy_to_c(result_arr), 16)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 16)
a_arr = np.array(input_list, dtype=np.float32)
result_arr_ref = unary_op_ref(op_str, a_arr)
assert np.allclose(result_arr, result_arr_ref)
def binary_op_comparison(op_str):
element_type = Type.f32
shape = Shape([2, 2])
A = Parameter(element_type, shape)
B = Parameter(element_type, shape)
parameter_list = [A, B]
function = Function([binary_op(op_str, A, B)], parameter_list, "test")
a_arr = np.array([[1, 5], [3, 2]], dtype=np.float32)
b_arr = np.array([[2, 4], [3, 1]], dtype=np.float32)
runtime = get_runtime()
computation = runtime.computation(function, A, B)
result = computation(a_arr, b_arr)[0]
expected = binary_op_ref(op_str, a_arr, b_arr)
assert np.allclose(result, expected)
def get_test_cnnnetwork():
element_type = Type.f32
param = Parameter(element_type, Shape([1, 3, 22, 22]))
relu = ng.relu(param)
func = Function([relu], [param], 'test')
caps = Function.to_capsule(func)
cnnNetwork = IENetwork(caps)
assert cnnNetwork != None
return cnnNetwork
def test_select():
element_type = Type.f32
A = Parameter(Type.boolean, Shape([1, 2]))
B = Parameter(element_type, Shape([1, 2]))
C = Parameter(element_type, Shape([1, 2]))
parameter_list = [A, B, C]
function = Function([ng.select(A, B, C)], parameter_list, "test")
runtime = get_runtime()
computation = runtime.computation(function, *parameter_list)
result = computation(
np.array([[True, False]], dtype=np.bool),
np.array([[5, 6]], dtype=np.float32),
np.array([[7, 8]], dtype=np.float32),
)[0]
expected = np.array([[5, 8]])
assert np.allclose(result, expected)
