forked from mindspore-Ecosystem/mindspore
77 lines
2.2 KiB
Python
77 lines
2.2 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 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.ops import operations as P
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from mindspore.common import dtype as mstype
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from mindspore import Tensor
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Net(nn.Cell):
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def __init__(self, shape, seed=0, seed2=0):
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super(Net, self).__init__()
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self.shape = shape
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self.seed = seed
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self.seed2 = seed2
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self.stdnormal = P.StandardNormal(seed, seed2)
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def construct(self):
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return self.stdnormal(self.shape)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net():
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seed = 10
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seed2 = 10
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shape = (3, 2, 4)
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net = Net(shape, seed, seed2)
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output = net()
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assert output.shape == (3, 2, 4)
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class DynamicShapeNet(nn.Cell):
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def __init__(self, seed=0, seed2=0):
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super(DynamicShapeNet, self).__init__()
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self.seed = seed
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self.seed2 = seed2
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self.stdnormal = P.StandardNormal(seed, seed2)
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def construct(self, input_shape):
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return self.stdnormal(input_shape)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_net_dynamic_shape():
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"""
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Feature: op dynamic shape
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Description: set input_shape None and input real tensor
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Expectation: success
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"""
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seed = 10
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seed2 = 10
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shape = Tensor((3, 2, 4), mstype.int64)
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shape_dyn = Tensor(shape=[None], dtype=shape.dtype)
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net = DynamicShapeNet(seed, seed2)
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net.set_inputs(shape_dyn)
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output = net(shape)
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assert output.shape == (3, 2, 4)
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