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.
54 lines
1.7 KiB
54 lines
1.7 KiB
# Copyright 2019 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.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.composite import GradOperation
|
|
|
|
context.set_context(device_target="Ascend")
|
|
|
|
|
|
class Grad(nn.Cell):
|
|
def __init__(self, network):
|
|
super(Grad, self).__init__()
|
|
self.grad = GradOperation(get_all=True, sens_param=True)
|
|
self.network = network
|
|
|
|
@ms_function
|
|
def construct(self, input_, output_grad):
|
|
return self.grad(self.network)(input_, output_grad)
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.relu = P.ReLU(strategy=None)
|
|
|
|
def construct(self, x):
|
|
return self.relu(x)
|
|
|
|
|
|
def test_net():
|
|
x = np.random.randn(2, 3, 3, 4).astype(np.float32)
|
|
sens = np.random.randn(2, 3, 3, 4).astype(np.float32)
|
|
net = Grad(Net())
|
|
output = net(Tensor(x), Tensor(sens))
|
|
print(len(output))
|
|
print(output[0].asnumpy())
|