forked from mindspore-Ecosystem/mindspore
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
# Copyright 2022 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 mindspore as ms
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from mindspore import ops as P
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from mindspore import Tensor, nn
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from mindspore.common.initializer import One
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import numpy as np
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import pytest
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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.ss = P.StridedSlice()
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self.neg = P.Neg()
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def construct(self, x, y, z):
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x = self.ss(x, y, z, (1, 1))
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x = self.neg(x)
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return x
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_strided_slice_feed_input_dynamic():
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"""
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Feature: Test StridedSlice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the output is dynamic caused by tensor input with begin/end.
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Expectation: Assert the result is equal the numpy result.
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"""
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net = Net()
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x = Tensor(np.ones((4, 5)), ms.float32)
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y = Tensor((1, 1), ms.int64)
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z = Tensor((4, 5), ms.int64)
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dyn_x = Tensor(shape=[4, None], dtype=ms.float32)
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dyn_y = Tensor(shape=[2], dtype=ms.int64, init=One())
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dyn_z = Tensor(shape=[2], dtype=ms.int64, init=One())
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net.set_inputs(dyn_x, dyn_y, dyn_z)
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expect = np.negative(np.ones([3, 4]))
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out = net(x, y, z)
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tol = 1e-6
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assert (np.abs(out.asnumpy() - expect) < tol).all()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_strided_slice_feed_output_dynamic():
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"""
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Feature: Test StridedSlice for dynamic shape in feed mode.
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Description: The input shape is static but the output is dynamic caused by tensor input with begin/end.
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Expectation: Assert the result is equal the numpy result.
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"""
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net = Net()
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x = Tensor(np.ones((4, 5)), ms.float32)
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y = Tensor((1, 1), ms.int64)
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z = Tensor((4, 5), ms.int64)
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expect = np.negative(np.ones([3, 4]))
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out = net(x, y, z)
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tol = 1e-6
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assert (np.abs(out.asnumpy() - expect) < tol).all()
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