You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.2 KiB
71 lines
2.2 KiB
# 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.
|
|
# ============================================================================
|
|
""" test_parser_construct """
|
|
import pytest
|
|
import numpy as np
|
|
from mindspore import context
|
|
from mindspore.nn import Cell
|
|
from mindspore.common.tensor import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.composite import GradOperation
|
|
|
|
def setup_module():
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_parser_construct():
|
|
class ParentNet(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = P.ReLU()
|
|
|
|
def construct(self, x):
|
|
return self.relu(x)
|
|
|
|
class UncleNet(Cell):
|
|
def __init__(self):
|
|
super(UncleNet, self).__init__()
|
|
self.sigmoid = P.Sigmoid()
|
|
|
|
def construct(self, x):
|
|
return self.sigmoid(x)
|
|
|
|
class Net(UncleNet, ParentNet):
|
|
def __init__(self):
|
|
super().__init__()
|
|
super(UncleNet, self).__init__()
|
|
|
|
def construct(self, x):
|
|
return super(UncleNet, self).construct(x)
|
|
|
|
input_np_x = np.ones([2, 3, 4, 5]).astype(np.float32)
|
|
out_np = np.ones([2, 3, 4, 5]).astype(np.float32)
|
|
|
|
input_me = Tensor(input_np_x)
|
|
output_grad_me = Tensor(out_np)
|
|
net = Net()
|
|
out_me = net(input_me)
|
|
|
|
net1 = Net()
|
|
grad = GradOperation(sens_param=True)
|
|
grad_op = grad(net1)
|
|
grad_me = grad_op(input_me, output_grad_me)
|
|
|
|
assert np.allclose(input_np_x, out_me.asnumpy(), 0.001, 0.001)
|
|
assert np.allclose(input_np_x, grad_me.asnumpy(), 0.001, 0.001)
|