def test_convert():
element_type = Type.f32
shape = Shape([1, 3])
A = Parameter(element_type, shape)
parameter_list = [A]
# f32 to boolean
function = Function(NodeVector([Convert(A, Type.boolean)]), parameter_list,
'test')
backend = Backend.create(test.BACKEND_NAME)
a = backend.create_tensor(element_type, shape)
result = backend.create_tensor(Type.boolean, shape)
a.write(util.numpy_to_c(np.array([1, 5, 3], dtype=np.float32)), 0, 12)
result_arr = np.array([False, False, False], dtype=np.bool)
result.write(util.numpy_to_c(result_arr), 0, 3)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 3)
a_arr = np.array([1, 5, 3], dtype=np.float32)
result_arr_ref = a_arr.astype(bool)
assert np.allclose(result_arr, result_arr_ref)
# f32 to i32
function = Function(NodeVector([Convert(A, Type.i32)]), parameter_list,
'test')
backend = Backend.create(test.BACKEND_NAME)
result = backend.create_tensor(Type.i32, shape)
a.write(util.numpy_to_c(np.array([1.4, 5.5, 3.9], dtype=np.float32)), 0,
12)
result_arr = np.array([0, 0, 0], dtype=np.int32)
result.write(util.numpy_to_c(result_arr), 0, 12)
handle = backend.compile(function)
handle.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 12)
a_arr = np.array([1.4, 5.4, 3.9], dtype=np.float32)
result_arr_ref = a_arr.astype(int)
assert np.allclose(result_arr, result_arr_ref)
def test_convert():
element_type = Type.f32
shape = Shape([1, 3])
A = Parameter(element_type, shape)
parameter_list = [A]
# f32 to boolean
function = Function(NodeVector([Convert(A, Type.boolean)]), 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(Type.boolean, shape)
a.write(util.numpy_to_c(np.array([1, 5, 3], dtype=np.float32)), 0, 12)
result_arr = np.array([False, False, False], dtype=np.bool)
result.write(util.numpy_to_c(result_arr), 0, 3)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 3)
a_arr = np.array([1, 5, 3], dtype=np.float32)
result_arr_ref = a_arr.astype(bool)
assert np.allclose(result_arr, result_arr_ref)
# f32 to i32
function = Function(NodeVector([Convert(A, Type.i32)]), parameter_list,
'test')
backend, cf = make_backend_call_frame(function)
result = backend.make_primary_tensor_view(Type.i32, shape)
a.write(util.numpy_to_c(np.array([1.4, 5.5, 3.9], dtype=np.float32)), 0,
12)
result_arr = np.array([0, 0, 0], dtype=np.int32)
result.write(util.numpy_to_c(result_arr), 0, 12)
cf.call([result], [a])
result.read(util.numpy_to_c(result_arr), 0, 12)
a_arr = np.array([1.4, 5.4, 3.9], dtype=np.float32)
result_arr_ref = a_arr.astype(int)
assert np.allclose(result_arr, result_arr_ref)
def convert(node,
new_type,
name=None): # type: (Node, NumericType, str) -> Node
"""Return node which casts input node values to specified type."""
new_element_type = get_element_type(new_type)
return Convert(node, new_element_type)
from ngraph.impl import Node, Shape, AxisVector, AxisSet
from ngraph.impl.op import Parameter, Maximum, Reshape, Dot, Broadcast
from ngraph.impl.op import Constant, Exp, Log, Sum
from ngraph.impl.op import Greater, Convert, Reduce
from ngraph.impl.op import OneHot
from typing import List, Dict, Set
float_element_type = Type.f32
int_element_type = Type.i32
bz = 53
lr = 0.2
Input = Parameter(float_element_type, Shape([bz, 28, 28]))
Label = Parameter(int_element_type, Shape([bz]))
LabelOneHot = Convert((OneHot(Label, Shape([bz, 10]), 1)), float_element_type)
MaxParam1 = Parameter(float_element_type, Shape([]))
MaxParam2 = Parameter(float_element_type, Shape([]))
MaxFn = Function(Maximum(MaxParam1, MaxParam2), [MaxParam1, MaxParam2],
'mnist')
def make_scalar_constant(elem_type, scalar, shape=None, axis_set=None):
# type: (int, float, List[int], Set[int]) -> float
"""Create a Constant node for scalar value."""
if shape is None:
shape = Shape([])
if axis_set is None:
axis_set = AxisSet(set())
scalar_shape = Shape([]) # type: List[int]
