forked from mindspore-Ecosystem/mindspore
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
# 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.float32)
|
|
ONE = Tensor([1], mstype.float32)
|
|
|
|
|
|
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.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@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
|