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.
79 lines
2.4 KiB
79 lines
2.4 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.
|
|
# ============================================================================
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.operations import _grad_ops as G
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
|
|
|
|
class NetSigmoidGrad(nn.Cell):
|
|
def __init__(self):
|
|
super(NetSigmoidGrad, self).__init__()
|
|
self.sigmoid_grad = G.SigmoidGrad()
|
|
|
|
def construct(self, y, dy):
|
|
return self.sigmoid_grad(y, dy)
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.ops = P.Sigmoid()
|
|
|
|
def construct(self, x):
|
|
return self.ops(x)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_net():
|
|
x = np.random.randn(2, 3, 3, 4).astype(np.float32)
|
|
y_expect = 1 / (1 + np.exp(-x))
|
|
net = Net()
|
|
out = net(Tensor(x))
|
|
diff = out.asnumpy() - y_expect
|
|
err = np.ones(shape=y_expect.shape) * 1.0e-5
|
|
assert np.all(diff < err)
|
|
assert out.shape == y_expect.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_sigmoid_grad():
|
|
y = Tensor(np.array([[[[-1, 1, 2],
|
|
[1, -1, 1],
|
|
[2, 1, -1]]]]).astype(np.float32))
|
|
dy = Tensor(np.array([[[[-11, 2, 4],
|
|
[-1, 1, -1],
|
|
[-4, 4, -4]]]]).astype(np.float32))
|
|
|
|
expect = np.array([[[[22, 0, -8],
|
|
[0, -2, 0],
|
|
[8, 0, 8]]]]).astype(np.float32)
|
|
|
|
error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6
|
|
|
|
sigmoid_grad = NetSigmoidGrad()
|
|
output = sigmoid_grad(y, dy)
|
|
diff = np.abs(output.asnumpy() - expect)
|
|
assert np.all(abs(diff) < error)
|