|
- # 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.
- # ============================================================================
-
- import numpy as np
- import pytest
-
- import mindspore.context as context
- from mindspore.common.tensor import Tensor
- from mindspore.nn import Cell
- from mindspore.ops import operations as P
-
-
- class Net(Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.lessequal = P.LessEqual()
-
- def construct(self, x, y):
- return self.lessequal(x, y)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_lessequal():
- x = Tensor(np.array([[1, 2, 3]]).astype(np.float32))
- y = Tensor(np.array([[2, 2, 2]]).astype(np.float32))
- expect = np.array([[True, True, False]])
- x1 = Tensor(np.array([[1, 2, 3]]).astype(np.int16))
- y1 = Tensor(np.array([[2]]).astype(np.int16))
- expect1 = np.array([[True, True, False]])
- x2 = Tensor(np.array([[1, 2, 3]]).astype(np.uint8))
- y2 = Tensor(np.array([[2]]).astype(np.uint8))
- expect2 = np.array([[True, True, False]])
- x3 = Tensor(np.array([[1, 2, 3]]).astype(np.float64))
- y3 = Tensor(np.array([[2]]).astype(np.float64))
- expect3 = np.array([[True, True, False]])
- x4 = Tensor(np.array([[1, 2, 3]]).astype(np.float16))
- y4 = Tensor(np.array([[2]]).astype(np.float16))
- expect4 = np.array([[True, True, False]])
- x5 = Tensor(np.array([[1, 2, 3]]).astype(np.int64))
- y5 = Tensor(np.array([[2]]).astype(np.int64))
- expect5 = np.array([[True, True, False]])
- x6 = Tensor(np.array([[1, 2, 3]]).astype(np.int32))
- y6 = Tensor(np.array([[2, 2, 2]]).astype(np.int32))
- expect6 = np.array([[True, True, False]])
- x7 = Tensor(np.array([[1, 2, 3]]).astype(np.int8))
- y7 = Tensor(np.array([[2]]).astype(np.int8))
- expect7 = np.array([[True, True, False]])
-
- x = [x, x1, x2, x3, x4, x5, x6, x7]
- y = [y, y1, y2, y3, y4, y5, y6, y7]
- expect = [expect, expect1, expect2, expect3, expect4, expect5, expect6, expect7]
-
- context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
- lessequal = Net()
- for i, xi in enumerate(x):
- output = lessequal(xi, y[i])
- assert np.all(output.asnumpy() == expect[i])
- assert output.shape == expect[i].shape
- print('test [%d/%d] passed!' % (i, len(x)))
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- lessequal = Net()
- for i, xi in enumerate(x):
- output = lessequal(xi, y[i])
- assert np.all(output.asnumpy() == expect[i])
- assert output.shape == expect[i].shape
- print('test [%d/%d] passed!' % (i, len(x)))
|
