66 lines
2.3 KiB
Python
66 lines
2.3 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 sys
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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.context as context
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import mindspore.common.dtype as mstype
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _grad_ops as G
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class ConcatOffsetNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.unique = P.Unique()
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self.concat_offset = G.ConcatOffset(3, 0)
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self.reshape = P.Reshape()
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def construct(self, x, y, z):
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x = self.reshape(self.unique(x)[0], (-1, 1, 2, 1))
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y = self.reshape(self.unique(y)[0], (-1, 1, 2, 1))
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z = self.reshape(self.unique(z)[0], (-1, 1, 2, 1))
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out = self.concat_offset((x, y, z))
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return out
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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_concat_offset_dynamic_gpu():
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"""
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/// Feature: Concatoffset op dynamic shape
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/// Description: Concatoffset forward with dynamic shape
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/// Expectation: Euqal to expected value
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"""
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if sys.platform != 'linux':
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return
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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x = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x2 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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x3 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.float32)
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net = ConcatOffsetNet()
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out = net(x, x2, x3)
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expect = np.array([[0, 0, 0, 0],
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[3, 0, 0, 0],
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[6, 0, 0, 0]])
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if isinstance(out, tuple):
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assert (np.array(out) == expect).all()
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else:
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assert (out.asnumpy() == expect).all()
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