forked from mindspore-Ecosystem/mindspore
106 lines
3.5 KiB
Python
106 lines
3.5 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 os
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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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from mindspore import Tensor, nn
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from mindspore.common import dtype as mstype
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from mindspore.train.serialization import export, load
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class CaseNet(nn.Cell):
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def __init__(self):
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super(CaseNet, self).__init__()
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self.conv = nn.Conv2d(1, 1, 3)
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self.relu = nn.ReLU()
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self.relu1 = nn.ReLU()
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self.softmax = nn.Softmax()
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self.layers1 = (self.relu, self.softmax)
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self.layers2 = (self.conv, self.relu1)
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def construct(self, x, index1, index2):
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x = self.layers1[index1](x)
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x = self.layers2[index2](x)
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return x
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_switch_layer():
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context.set_context(mode=context.GRAPH_MODE)
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net = CaseNet()
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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export(net, data, idx, idx2, file_name=file_name, file_format='MINDIR')
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assert os.path.exists(mindir_name)
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(data, idx, idx2)
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relu = nn.ReLU()
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true_value = relu(data)
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ret = np.allclose(outputs_after_load.asnumpy(), true_value.asnumpy())
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assert ret
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@pytest.mark.skip(reason="depend on export")
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_export():
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context.set_context(mode=context.GRAPH_MODE)
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net = CaseNet()
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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export(net, data, idx, idx2, file_name=file_name, file_format='MINDIR')
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assert os.path.exists(mindir_name)
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@pytest.mark.skip(reason="depend on export")
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@pytest.mark.level1
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_mindir_load():
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context.set_context(mode=context.GRAPH_MODE)
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data = Tensor(np.ones((1, 1, 224, 224)), mstype.float32)
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idx = Tensor(0, mstype.int32)
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idx2 = Tensor(-1, mstype.int32)
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file_name = "switch_layer_net"
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mindir_name = file_name + ".mindir"
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graph = load(mindir_name)
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loaded_net = nn.GraphCell(graph)
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outputs_after_load = loaded_net(data, idx, idx2)
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relu = nn.ReLU()
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true_value = relu(data)
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ret = np.allclose(outputs_after_load.asnumpy(), true_value.asnumpy())
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assert ret
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