forked from huawei/mindspore2022
103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
# Copyright 2021 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 ASGD """
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, Parameter
|
|
from mindspore.nn.optim import ASGD
|
|
from mindspore.ops import operations as P
|
|
|
|
class Net(nn.Cell):
|
|
""" Net definition """
|
|
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
|
|
self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
|
|
self.matmul = P.MatMul()
|
|
self.biasAdd = P.BiasAdd()
|
|
|
|
def construct(self, x):
|
|
x = self.biasAdd(self.matmul(x, self.weight), self.bias)
|
|
return x
|
|
|
|
|
|
class NetWithoutWeight(nn.Cell):
|
|
def __init__(self):
|
|
super(NetWithoutWeight, self).__init__()
|
|
self.matmul = P.MatMul()
|
|
|
|
def construct(self, x):
|
|
x = self.matmul(x, x)
|
|
return x
|
|
|
|
|
|
def test_asgdwithoutparam():
|
|
"""
|
|
Feature: Test ASGD optimizer.
|
|
Description: Test if error is raised when trainable_params is empty.
|
|
Expectation: ValueError is raised.
|
|
"""
|
|
net = NetWithoutWeight()
|
|
net.set_train()
|
|
with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
|
|
ASGD(net.trainable_params(), learning_rate=0.1)
|
|
|
|
|
|
def test_asgd_lambd():
|
|
"""
|
|
Feature: Test ASGD optimizer.
|
|
Description: Test if error is raised when the type of lambd is not correct.
|
|
Expectation: ValueError is raised.
|
|
"""
|
|
net = Net()
|
|
with pytest.raises(TypeError):
|
|
ASGD(net.get_parameters(), lambd=1, learning_rate=0.1)
|
|
|
|
|
|
def test_asgd_alpha():
|
|
"""
|
|
Feature: Test ASGD optimizer.
|
|
Description: Test if error is raised when the type of alpha is not correct.
|
|
Expectation: ValueError is raised.
|
|
"""
|
|
net = Net()
|
|
with pytest.raises(TypeError):
|
|
ASGD(net.get_parameters(), alpha=1, learning_rate=0.1)
|
|
|
|
|
|
def test_asgd_t0():
|
|
"""
|
|
Feature: Test ASGD optimizer.
|
|
Description: Test if error is raised when the type of t0 is not correct.
|
|
Expectation: ValueError is raised.
|
|
"""
|
|
net = Net()
|
|
with pytest.raises(TypeError):
|
|
ASGD(net.get_parameters(), t0=1, learning_rate=0.1)
|
|
|
|
|
|
def test_asgd_mindspore_with_empty_params():
|
|
"""
|
|
Feature: Test ASGD optimizer.
|
|
Description: Test if error is raised when there is no trainable_params.
|
|
Expectation: ValueError is raised.
|
|
"""
|
|
net = nn.Flatten()
|
|
with pytest.raises(ValueError, match=r"For 'Optimizer', the argument parameters must not be empty"):
|
|
ASGD(net.get_parameters())
|