83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
""" test implicit conversion """
|
|
import numpy as np
|
|
|
|
from mindspore import Tensor
|
|
|
|
|
|
def test_float_tensor_and_int_add():
|
|
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
|
y = 2
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_bool_tensor_and_float_add():
|
|
x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
|
|
y = 3.3
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_bool_tensor_and_int_add():
|
|
x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))
|
|
y = 3
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_bool_and_int_tensor_add():
|
|
x = True
|
|
y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_float_tensor_and_int_tensor_add():
|
|
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
|
y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_float_tensor_and_float_tensor_add():
|
|
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float64))
|
|
y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32))
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float64))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_int_tensor_and_int_tensor_add():
|
|
x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16))
|
|
y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|
|
|
|
|
|
def test_float_tensor_and_bool_tensors_add():
|
|
x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
|
y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))
|
|
ret_actual = x + y
|
|
ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))
|
|
assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()
|