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This commit is contained in:
wanyiming 2021-11-25 14:54:03 +08:00
parent a92d97adb2
commit ab65f4ac98
5 changed files with 321 additions and 12 deletions
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27
tests/st/initializer/test_initializer.py
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@ -24,7 +24,8 @@ def test_sparse():
Description: Initialize a 2 dimension sparse matrix to fill the input tensor.
Expectation: The Tensor is initialized with a 2 dimension sparse matrix.
"""
initializer(Sparse(sparsity=0.1, sigma=0.01), [5, 8], mindspore.float32)
tensor1 = initializer(Sparse(sparsity=0.1, sigma=0.01), [5, 8], mindspore.float32)
tensor1.init_data()
def test_orthogonal():
@ -33,8 +34,10 @@ def test_orthogonal():
Description: Initialize a (semi) orthogonal matrix to fill the input tensor.
Expectation: The Tensor is initialized with values from orthogonal matrix.
"""
initializer(Orthogonal(gain=2.), [2, 3, 4], mindspore.float32)
initializer('orthogonal', [2, 3, 4], mindspore.float32)
tensor1 = initializer(Orthogonal(gain=2.), [2, 3, 4], mindspore.float32)
tensor2 = initializer('orthogonal', [2, 3, 4], mindspore.float32)
tensor1.init_data()
tensor2.init_data()
def test_variancescaling():
@ -43,13 +46,17 @@ def test_variancescaling():
Description: Randomly initialize an array with scaling to fill the input tensor.
Expectation: The Tensor is initialized successfully.
"""
initializer('varianceScaling', [2, 3], mindspore.float32)
initializer(VarianceScaling(scale=1.0, mode='fan_out', distribution='untruncated_normal'), [2, 3],
mindspore.float32)
initializer(VarianceScaling(scale=2.0, mode='fan_in', distribution='truncated_normal'), [2, 3],
mindspore.float32)
initializer(VarianceScaling(scale=3.0, mode='fan_avg', distribution='uniform'), [2, 3],
mindspore.float32)
tensor1 = initializer('varianceScaling', [2, 3], mindspore.float32)
tensor2 = initializer(VarianceScaling(scale=1.0, mode='fan_out', distribution='untruncated_normal'), [2, 3],
mindspore.float32)
tensor3 = initializer(VarianceScaling(scale=2.0, mode='fan_in', distribution='truncated_normal'), [2, 3],
mindspore.float32)
tensor4 = initializer(VarianceScaling(scale=3.0, mode='fan_avg', distribution='uniform'), [2, 3],
mindspore.float32)
tensor1.init_data()
tensor2.init_data()
tensor3.init_data()
tensor4.init_data()
def test_identity():

2
tests/st/optimizer/test_asgd_cpu.py
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@ -45,7 +45,7 @@ def test_no_default_asgd_graph():
no_default_fc1_bias_asgd, no_default_fc2_weight_asgd, no_default_fc2_bias_asgd
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
config = {'name': 'ASGD', 'lr': 0.001, 'lambd': 1e-3, 'alpha': 0.8, 't0': 50., 'weight_decay': 0.001}
loss, cells = build_network(config, is_group=True)
loss, cells = build_network(config)
assert np.allclose(cells.ax[0].asnumpy(), no_default_fc1_weight_asgd, atol=1.e-5)
assert np.allclose(cells.ax[1].asnumpy(), no_default_fc1_bias_asgd, atol=1.e-5)
assert np.allclose(cells.ax[2].asnumpy(), no_default_fc2_weight_asgd, atol=1.e-5)

102
tests/ut/python/nn/optim/test_asgd.py Normal file
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@ -0,0 +1,102 @@
# 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"Optimizer got an empty parameters list"):
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"Optimizer got an empty parameters list"):
ASGD(net.get_parameters())

125
tests/ut/python/nn/optim/test_rprop.py Normal file
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@ -0,0 +1,125 @@
# 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 Rprop """
import numpy as np
import pytest
import mindspore.nn as nn
from mindspore import Tensor, Parameter
from mindspore.nn.optim import Rprop
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_rpropwithoutparam():
"""
Feature: Test Rprop 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"Optimizer got an empty parameters list"):
Rprop(net.trainable_params(), learning_rate=0.1)
def test_rprop_tuple():
"""
Feature: Test Rprop optimizer.
Description: Test if error is raised when the type of etas and step_sizes is not correct.
Expectation: TypeError is raised.
"""
net = Net()
with pytest.raises(TypeError):
Rprop(net.get_parameters(), etas=[0.5, 1.2], learning_rate=0.1)
with pytest.raises(TypeError):
Rprop(net.get_parameters(), step_sizes=[1e-6, 50.], learning_rate=0.1)
def test_rprop_size():
"""
Feature: Test Rprop optimizer.
Description: Test if error is raised when the size of etas and step_sizes is not correct.
Expectation: ValueError is raised.
"""
net = Net()
with pytest.raises(ValueError):
Rprop(net.get_parameters(), etas=(0.5, 1.2, 1.3), learning_rate=0.1)
with pytest.raises(ValueError):
Rprop(net.get_parameters(), step_sizes=(1e-6, 50., 60.), learning_rate=0.1)
def test_rprop_stepsize():
"""
Feature: Test Rprop optimizer.
Description: Test if error is raised when the value of step_sizes is not correct.
Expectation: ValueError is raised.
"""
net = Net()
with pytest.raises(ValueError):
Rprop(net.get_parameters(), step_sizes=(50., 1e-6), learning_rate=0.1)
def test_rprop_etas():
"""
Feature: Test Rprop optimizer.
Description: Test if error is raised when the value range of etas is not correct.
Expectation: ValueError is raised.
"""
net = Net()
with pytest.raises(ValueError):
Rprop(net.get_parameters(), etas=(0.5, 0.9), learning_rate=0.1)
with pytest.raises(ValueError):
Rprop(net.get_parameters(), etas=(1., 1.2), learning_rate=0.1)
with pytest.raises(ValueError):
Rprop(net.get_parameters(), etas=(-0.1, 1.2), learning_rate=0.1)
def test_rprop_mindspore_with_empty_params():
"""
Feature: Test Rprop 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"Optimizer got an empty parameters list"):
Rprop(net.get_parameters())

77
tests/ut/python/utils/test_initializer.py
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@ -56,7 +56,12 @@ def _check_uniform(tensor, boundary_a, boundary_b):
return p > 0.0001
def test_init_Initializer():
def test_init_initializer():
"""
Feature: Test initializer.
Description: Test initializer.
Expectation: Shape and value is initialized successfully..
"""
tensor = init.initializer(InitTwo(), [2, 2], ms.int32)
assert tensor.shape == (2, 2)
_check_value(tensor.init_data(), 2, 2)
@ -203,6 +208,76 @@ def test_init_he_uniform_error():
init.initializer(init.HeUniform(), [6], ms.float32).init_data()
def test_init_identity():
"""
Feature: Test identity initializer.
Description: Test if error is raised when the shape of the initialized tensor is not correct.
Expectation: ValueError is raised.
"""
with py.raises(ValueError):
tensor = init.initializer(init.Identity(), [5, 4, 6], ms.float32)
tensor.init_data()
def test_init_sparse():
"""
Feature: Test sparse initializer.
Description: Test if error is raised when the shape of the initialized tensor is not correct.
Expectation: ValueError is raised.
"""
with py.raises(ValueError):
tensor = init.initializer(init.Sparse(sparsity=0.1), [5, 4, 6], ms.float32)
tensor.init_data()
def test_init_dirac():
"""
Feature: Test dirac initializer.
Description: Test if error is raised when the shape of the initialized tensor is not correct.
or shape[0] is not divisible by group.
Expectation: ValueError is raised.
"""
with py.raises(ValueError):
tensor1 = init.initializer(init.Dirac(groups=2), [5, 4, 6], ms.float32)
tensor1.init_data()
with py.raises(ValueError):
tensor2 = init.initializer(init.Dirac(groups=1), [5, 4], ms.float32)
tensor2.init_data()
with py.raises(ValueError):
tensor3 = init.initializer(init.Dirac(groups=1), [5, 4, 6, 7, 8, 9], ms.float32)
tensor3.init_data()
def test_init_orthogonal():
"""
Feature: Test orthogonal initializer.
Description: Test if error is raised when the shape of the initialized tensor is not correct.
Expectation: ValueError is raised.
"""
with py.raises(ValueError):
tensor = init.initializer(init.Orthogonal(), [5,], ms.float32)
tensor.init_data()
def test_init_variancescaling():
"""
Feature: Test orthogonal initializer.
Description: Test if error is raised when scale is less than 0 or mode and distribution are not correct.
Expectation: ValueError is raised.
"""
with py.raises(ValueError):
init.initializer(init.VarianceScaling(scale=-0.1), [5, 4, 6], ms.float32)
with py.raises(ValueError):
init.initializer(init.VarianceScaling(scale=0.1, mode='fans'), [5, 4, 6], ms.float32)
with py.raises(ValueError):
init.initializer(init.VarianceScaling(scale=0.1, mode='fan_in',
distribution='uniformal'), [5, 4, 6], ms.float32)
def test_conv2d_abnormal_kernel_negative():
kernel = np.random.randn(64, 3, 7, 7).astype(np.float32)
with py.raises(ValueError):