mindspore/tests/ut/ge/run_simple_net.py

72 lines
1.9 KiB
Python

# Copyright 2022 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 numpy as np
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.ops import operations as P
from mindspore.nn import Cell
context.set_context(mode=context.GRAPH_MODE)
class SeqNet(Cell):
def __init__(self):
super().__init__()
self.op_seq = (P.Sqrt(), P.Reciprocal(), P.Square())
def construct(self, x):
t = x
for op in self.op_seq:
t = op(t)
return t
def test_op_seq_net_ge():
"""
Feature: unify ge and vm backend
Description: test op seq with ge backend
Expectation: success
"""
net = SeqNet()
input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
input_me = Tensor(input_np)
net(input_me)
class TriuNet(nn.Cell):
def __init__(self):
super(TriuNet, self).__init__()
self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
def construct(self):
triu = nn.Triu()
return triu(self.value, 0)
def test_triu_ge():
"""
Feature: unify ge and vm backend
Description: test TriuNet with ge backend
Expectation: success
"""
net = TriuNet()
out = net()
assert np.sum(out.asnumpy()) == 26
if __name__ == "__main__":
test_op_seq_net_ge()
test_triu_ge()