forked from mindspore-Ecosystem/mindspore
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# Copyright 2020-2021 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.nn as nn
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from mindspore import Tensor
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import mindspore.ops as ops
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from mindspore.ops import operations as P
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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.select = P.Select()
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def construct(self, cond_op, input_x, input_y):
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return self.select(cond_op, input_x, input_y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_select():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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select = Net()
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cond = np.array([[True, False], [True, False]]).astype(np.bool)
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x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
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y = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
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output = select(Tensor(cond), Tensor(x), Tensor(y))
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expect = [[1.2, 2], [1, 4.0]]
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error = np.ones(shape=[2, 2]) * 1.0e-6
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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assert np.all(-diff < error)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = np.array([[1, 0], [1, 0]]).astype(np.bool)
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y = np.array([[0, 0], [1, 1]]).astype(np.bool)
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output = select(Tensor(cond), Tensor(x), Tensor(y))
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expect = np.array([[1, 0], [1, 1]]).astype(np.bool)
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assert np.all(output.asnumpy() == expect)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = np.array([[1, 0], [1, 0]]).astype(np.bool)
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y = np.array([[0, 0], [1, 1]]).astype(np.bool)
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output = select(Tensor(cond), Tensor(x), Tensor(y))
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expect = np.array([[1, 0], [1, 1]]).astype(np.bool)
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assert np.all(output.asnumpy() == expect)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_functional_select_scalar():
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"""
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Feature: Test functional select operator. Support x or y is a int/float.
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Description: Operator select's input `x` is a Tensor with int32 type, input `y` is a int.
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Expectation: Assert result.
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"""
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context.set_context(device_target="GPU")
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cond = np.array([[True, False], [True, False]]).astype(np.bool)
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x = np.array([[12, 1], [1, 0]]).astype(np.int32)
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y = 2
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output = ops.select(Tensor(cond), Tensor(x), y)
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expect = [[12, 2], [1, 2]]
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error = np.ones(shape=[2, 2]) * 1.0e-6
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diff = output.asnumpy() - expect
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assert np.all(diff < error)
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assert np.all(-diff < error)
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