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110 lines
3.4 KiB
110 lines
3.4 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.ops = P.SquaredDifference()
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def construct(self, x, y):
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return self.ops(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_net01():
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net = Net()
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np.random.seed(1)
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x1 = np.random.randn(2, 3).astype(np.int32)
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y1 = np.random.randn(2, 3).astype(np.int32)
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output1 = net(Tensor(x1), Tensor(y1)).asnumpy()
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diff = x1 - y1
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expect1 = diff * diff
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assert np.all(expect1 == output1)
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assert output1.shape == expect1.shape
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x2 = np.random.randn(2, 3).astype(np.float32)
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y2 = np.random.randn(2, 3).astype(np.float32)
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output2 = net(Tensor(x2), Tensor(y2)).asnumpy()
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diff = x2 - y2
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expect2 = diff * diff
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assert np.all(expect2 == output2)
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assert output2.shape == expect2.shape
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x3 = np.random.randn(2, 3).astype(np.bool)
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y3 = np.random.randn(2, 3).astype(np.bool)
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try:
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net(Tensor(x3), Tensor(y3)).asnumpy()
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except TypeError:
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assert True
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_net02():
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net = Net()
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x1 = Tensor(1, mstype.float32)
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y1 = Tensor(np.array([[3, 3], [3, 3]]).astype(np.float32))
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expect1 = np.array([[4, 4], [4, 4]]).astype(np.float32)
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output1 = net(x1, y1).asnumpy()
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assert np.all(expect1 == output1)
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assert output1.shape == expect1.shape
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np.random.seed(1)
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x2 = np.random.randn(2, 3).astype(np.float32)
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y2 = np.random.randn(2, 2, 3).astype(np.float32)
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output2 = net(Tensor(x2), Tensor(y2)).asnumpy()
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diff = x2 - y2
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expect2 = diff * diff
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assert np.all(expect2 == output2)
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assert output2.shape == expect2.shape
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x3 = np.random.randn(1, 2).astype(np.float32)
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y3 = np.random.randn(3, 1).astype(np.float32)
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output3 = net(Tensor(x3), Tensor(y3)).asnumpy()
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diff = x3 - y3
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expect3 = diff * diff
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assert np.all(expect3 == output3)
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assert output3.shape == expect3.shape
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x4 = np.random.randn(2, 3).astype(np.float32)
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y4 = np.random.randn(1, 2).astype(np.float32)
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try:
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net(Tensor(x4), Tensor(y4)).asnumpy()
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except ValueError:
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assert True
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x5 = np.random.randn(2, 3, 2, 3, 4, 5, 6, 7).astype(np.float32)
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y5 = np.random.randn(2, 3, 2, 3, 4, 5, 6, 7).astype(np.float32)
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output5 = net(Tensor(x5), Tensor(y5)).asnumpy()
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diff = x5 - y5
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expect5 = diff * diff
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assert np.all(expect5 == output5)
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assert output5.shape == expect5.shape
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