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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- import os
- import numpy as np
- import pytest
-
- import mindspore.nn as nn
- from mindspore import context
- from mindspore.common.tensor import Tensor
- from mindspore.common import dtype as mstype
- from mindspore.train.serialization import export, load
-
- ZERO = Tensor([0], mstype.int32)
- ONE = Tensor([1], mstype.int32)
-
-
- class RecrusiveNet(nn.Cell):
- def construct(self, x, z):
- def f(x, z):
- y = ZERO
- if x < 0:
- y = ONE
- elif x < 3:
- y = x * f(x - 1, z)
- elif x < 5:
- y = x * f(x - 2, z)
- else:
- y = f(x - 4, z)
- z = y + 1 + z
- return z
-
- return f(x, z)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_ascend_training
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.env_onecard
- def test_recrusive():
- context.set_context(mode=context.GRAPH_MODE)
- network = RecrusiveNet()
-
- x = Tensor(np.array([1]).astype(np.float32))
- y = Tensor(np.array([2]).astype(np.float32))
- origin_out = network(x, y)
-
- file_name = "recrusive_net"
- export(network, x, y, file_name=file_name, file_format='MINDIR')
- mindir_name = file_name + ".mindir"
- assert os.path.exists(mindir_name)
-
- graph = load(mindir_name)
- loaded_net = nn.GraphCell(graph)
- outputs_after_load = loaded_net(x, y)
- assert origin_out == outputs_after_load
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