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- # Copyright 2019 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 NetEqual(Cell):
- def __init__(self):
- super(NetEqual, self).__init__()
- self.Equal = P.Equal()
-
- def construct(self, x, y):
- return self.Equal(x, y)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_equal():
- x0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
- x0 = Tensor(x0_np)
- y0_np = np.arange(24).reshape((4, 3, 2)).astype(np.float32)
- y0 = Tensor(y0_np)
- expect0 = np.equal(x0_np, y0_np)
- x1_np = np.array([0, 1, 3]).astype(np.float32)
- x1 = Tensor(x1_np)
- y1_np = np.array([0, 1, -3]).astype(np.float32)
- y1 = Tensor(y1_np)
- expect1 = np.equal(x1_np, y1_np)
-
- context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
- equal = NetEqual()
- output0 = equal(x0, y0)
- assert np.all(output0.asnumpy() == expect0)
- assert output0.shape() == expect0.shape
- output1 = equal(x1, y1)
- assert np.all(output1.asnumpy() == expect1)
- assert output1.shape() == expect1.shape
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- equal = NetEqual()
- output0 = equal(x0, y0)
- assert np.all(output0.asnumpy() == expect0)
- assert output0.shape() == expect0.shape
- output1 = equal(x1, y1)
- assert np.all(output1.asnumpy() == expect1)
- assert output1.shape() == expect1.shape
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