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 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 transpose(op, order): # type: (Node, List[int]) -> Node
"""Transpose data via reshape."""
v = []
for i in range(len(order)):
v.append(op.get_shape()[order[i]])
new_shape = v
return Reshape(op, AxisVector(order), Shape(new_shape))
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 reshape(node, input_order, output_shape, name=None):
# type: (Node, List[int], List[int], str) -> None
"""Return reshaped node according to provided parameters.
:param node: The tensor we want to reshape.
:param input_order: The order in which to iterate over input axes of input tensor.
:param output_shape: The new shape for input tensor.
"""
return Reshape(node, AxisVector(input_order), Shape(output_shape))
def transpose(op, order): # type: (Node, List[int]) -> Node
"""Transpose data via reshape."""
v = []
for i in range(len(order)):
v.append(op.get_shape()[order[i]])
new_shape = v
return Reshape(op, AxisVector(order), Shape(new_shape))
def relu(op): # type: (Node) -> Node
"""Relu operator."""
return Maximum(op, make_float32_constant_like(0., op))
# Flatten
X1 = Reshape(Input, AxisVector([0, 1, 2]), Shape([bz, 784]))
# Normalize
X2 = X1 / make_float32_constant_like(255., X1)
# Affine 1
W1 = Parameter(float_element_type, Shape([784, 100]))
b1 = Parameter(float_element_type, Shape([100]))
X3 = Dot(X2, W1) + Broadcast(b1, Shape([bz, 100]), AxisSet({0}))
X4 = relu(X3)
# Affine 2
W2 = Parameter(float_element_type, Shape([100, 10]))
b2 = Parameter(float_element_type, Shape([10]))
X5 = Dot(X4, W2) + Broadcast(b2, Shape([bz, 10]), AxisSet({0}))
