forked from mindspore-Ecosystem/mindspore
97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
# 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.common.dtype as mstype
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import mindspore.context as context
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from mindspore.common.tensor import Tensor
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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class LinSpaceNet(Cell):
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def __init__(self, num):
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super(LinSpaceNet, self).__init__()
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self.ls_op = P.LinSpace()
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self.num = num
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def construct(self, start, stop):
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output = self.ls_op(start, stop, self.num)
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return output
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('start_np, stop_np', [(5, 150), (-25, 147), (-25.3, -147)])
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@pytest.mark.parametrize('num_np', [12, 10, 20])
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def test_lin_space(start_np, stop_np, num_np):
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"""
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Feature: ALL To ALL
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Description: test cases for LinSpace
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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start = Tensor(start_np, dtype=mstype.float32)
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stop = Tensor(stop_np, dtype=mstype.float32)
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num = num_np
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ls_op = P.LinSpace()
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result_ms = ls_op(start, stop, num).asnumpy()
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result_np = np.linspace(start_np, stop_np, num_np, axis=-1)
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assert np.allclose(result_ms, result_np)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('start_np, stop_np', [(5, 150), (-25, 147), (-25.3, -147)])
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@pytest.mark.parametrize('num_np', [10, 20, 36])
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def test_lin_space_net(start_np, stop_np, num_np):
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"""
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Feature: ALL To ALL
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Description: test cases for LinSpace Net
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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start = Tensor(start_np, dtype=mstype.float32)
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stop = Tensor(stop_np, dtype=mstype.float32)
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net = LinSpaceNet(num_np)
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result_ms = net(start, stop).asnumpy()
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result_np = np.linspace(start_np, stop_np, num_np, axis=-1)
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assert np.allclose(result_ms, result_np)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('start_np, stop_np', [[[2, 3, 5], [4, 6, 8]], [[-4, 7, -2], [-10, 26, 18]]])
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@pytest.mark.parametrize('num_np', [10, 20, 36])
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@pytest.mark.parametrize('dtype', [mstype.float32, mstype.float64])
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def test_lin_space_batched(start_np, stop_np, num_np, dtype):
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"""
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Feature: ALL To ALL
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Description: test cases for LinSpace Net
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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start = Tensor(start_np, dtype=mstype.float32)
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stop = Tensor(stop_np, dtype=mstype.float32)
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net = LinSpaceNet(num_np)
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result_ms = net(start, stop).asnumpy()
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result_np = np.linspace(start_np, stop_np, num_np, axis=-1)
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assert np.allclose(result_ms, result_np)
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