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mindspore/tests/st/pynative/test_implicit_conversion.py

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  1. # Copyright 2020 Huawei Technologies Co., Ltd

  2. #

  3. # Licensed under the Apache License, Version 2.0 (the "License");

  4. # you may not use this file except in compliance with the License.

  5. # You may obtain a copy of the License at

  6. #

  7. # http://www.apache.org/licenses/LICENSE-2.0

  8. #

  9. # Unless required by applicable law or agreed to in writing, software

  10. # distributed under the License is distributed on an "AS IS" BASIS,

  11. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

  12. # See the License for the specific language governing permissions and

  13. # limitations under the License.

  14. # ============================================================================

  15. """ test implicit conversion """

  16. import numpy as np

  17. 

  18. from mindspore import Tensor

  19. 

  20. 

  21. def test_float_tensor_and_int_add():

  22.     x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))

  23.     y = 2

  24.     ret_actual = x + y

  25.     ret_expect = Tensor(np.array([[2.1, 2.2, 2.3], [2.4, 2.5, 2.6]], dtype=np.float32))

  26.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  27. 

  28. 

  29. def test_bool_tensor_and_float_add():

  30.     x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))

  31.     y = 3.3

  32.     ret_actual = x + y

  33.     ret_expect = Tensor(np.array([[4.3, 3.3], [3.3, 4.3]], dtype=np.float32))

  34.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  35. 

  36. 

  37. def test_bool_tensor_and_int_add():

  38.     x = Tensor(np.array([[True, False], [False, True]], dtype=np.bool_))

  39.     y = 3

  40.     ret_actual = x + y

  41.     ret_expect = Tensor(np.array([[4, 3], [3, 4]], dtype=np.int32))

  42.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  43. 

  44. 

  45. def test_bool_and_int_tensor_add():

  46.     x = True

  47.     y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))

  48.     ret_actual = x + y

  49.     ret_expect = Tensor(np.array([[2, 3, 4], [5, 6, 7]], dtype=np.int32))

  50.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  51. 

  52. def test_float_tensor_and_int_tensor_add():

  53.     x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))

  54.     y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))

  55.     ret_actual = x + y

  56.     ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float32))

  57.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  58. 

  59. 

  60. def test_float_tensor_and_float_tensor_add():

  61.     x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float64))

  62.     y = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], dtype=np.float32))

  63.     ret_actual = x + y

  64.     ret_expect = Tensor(np.array([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6]], dtype=np.float64))

  65.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  66. 

  67. 

  68. def test_int_tensor_and_int_tensor_add():

  69.     x = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16))

  70.     y = Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32))

  71.     ret_actual = x + y

  72.     ret_expect = Tensor(np.array([[2, 4, 6], [8, 10, 12]], dtype=np.int32))

  73.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()

  74. 

  75. 

  76. def test_float_tensor_and_bool_tensors_add():

  77.     x = Tensor(np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]], dtype=np.float32))

  78.     y = Tensor(np.array([[True, True, True], [False, False, False]], dtype=np.bool_))

  79.     ret_actual = x + y

  80.     ret_expect = Tensor(np.array([[1.1, 1.2, 1.3], [0.4, 0.5, 0.6]], dtype=np.float32))

  81.     assert (ret_actual.asnumpy() == ret_expect.asnumpy()).all()