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.
203 lines
8.2 KiB
203 lines
8.2 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.
|
|
# ============================================================================
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.ops import operations as P
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
|
|
|
|
class NetCenteredRMSProp(nn.Cell):
|
|
def __init__(self, lr, decay, momentum, epsilon, var, g, mg, rms, mom):
|
|
super(NetCenteredRMSProp, self).__init__()
|
|
self.rms_opt = P.ApplyCenteredRMSProp()
|
|
self.lr = lr
|
|
self.decay = decay
|
|
self.momentum = momentum
|
|
self.epsilon = epsilon
|
|
self.var = var
|
|
self.g = g
|
|
self.mg = mg
|
|
self.rms = rms
|
|
self.mom = mom
|
|
|
|
def construct(self):
|
|
return self.rms_opt(self.var, self.mg, self.rms, self.mom, self.g, self.lr, self.decay, self.momentum,
|
|
self.epsilon)
|
|
|
|
|
|
class NetRMSProp(nn.Cell):
|
|
def __init__(self, lr, decay, momentum, epsilon, var, g, mg, rms, mom):
|
|
super(NetRMSProp, self).__init__()
|
|
self.lr = lr
|
|
self.decay = decay
|
|
self.momentum = momentum
|
|
self.epsilon = epsilon
|
|
self.var = var
|
|
self.g = g
|
|
self.mg = mg
|
|
self.rms = rms
|
|
self.mom = mom
|
|
self.rms_opt = P.ApplyRMSProp()
|
|
|
|
def construct(self):
|
|
return self.rms_opt(self.var, self.rms, self.mom, self.lr, self.g, self.decay, self.momentum, self.epsilon)
|
|
|
|
|
|
def rmsprop_numpy(variable, gradients, mean_square, moment,
|
|
learning_rate, decay, momentum, epsilon):
|
|
mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
|
|
moment = momentum * moment + learning_rate / np.sqrt(mean_square + epsilon) * gradients
|
|
variable = variable - moment
|
|
return variable, gradients, mean_square, moment
|
|
|
|
|
|
def rmspropcented_numpy(variable, gradients, mean_gradients, mean_square, moment,
|
|
learning_rate, decay, momentum, epsilon):
|
|
mean_gradients = mean_gradients * decay + (1.0 - decay) * gradients
|
|
mean_square = mean_square * decay + (1.0 - decay) * gradients * gradients
|
|
moment = momentum * moment + learning_rate / np.sqrt(
|
|
mean_square - mean_gradients * mean_gradients + epsilon) * gradients
|
|
variable = variable - moment
|
|
return variable, gradients, mean_gradients, mean_square, moment
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_rmsprop():
|
|
learning_rate, decay, momentum, epsilon, centered = [0.5, 0.8, 0.9, 1e-3, True]
|
|
|
|
variable_np = np.array([1.0, 2.0], dtype=np.float32)
|
|
gradients_np = np.array([0.1, 0.2], dtype=np.float32)
|
|
mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
|
|
mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
|
|
moment_np = np.array([0.0, 0.0], dtype=np.float32)
|
|
|
|
variable = Tensor(variable_np)
|
|
gradients = Tensor(gradients_np)
|
|
mean_gradients = Tensor(mean_gradients_np)
|
|
mean_square = Tensor(mean_square_np)
|
|
moment = Tensor(moment_np)
|
|
|
|
variable_ms = Parameter(initializer(variable, variable.shape), name='var')
|
|
gradients_ms = Parameter(initializer(gradients, gradients.shape), name='grad')
|
|
mean_gradients_ms = Parameter(initializer(mean_gradients, mean_gradients.shape), name='mg')
|
|
mean_square_ms = Parameter(initializer(mean_square, mean_square.shape), name='msr')
|
|
moment_ms = Parameter(initializer(moment, moment.shape), name='mom')
|
|
|
|
if centered:
|
|
variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np = \
|
|
rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
|
|
learning_rate, decay, momentum, epsilon)
|
|
net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
|
|
mean_square_ms, moment_ms)
|
|
_ = net()
|
|
|
|
else:
|
|
variable_np, gradients_np, mean_square_np, moment_np = \
|
|
rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
|
|
learning_rate, decay, momentum, epsilon)
|
|
net = NetRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
|
|
mean_square_ms, moment_ms)
|
|
_ = net()
|
|
|
|
error = np.ones(shape=variable_np.shape) * 10e-6
|
|
diff = variable_ms.asnumpy() - variable_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=gradients_np.shape) * 10e-6
|
|
diff = gradients_ms.asnumpy() - gradients_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=mean_gradients_np.shape) * 10e-6
|
|
diff = mean_gradients_ms.asnumpy() - mean_gradients_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=mean_square_np.shape) * 10e-6
|
|
diff = mean_square_ms.asnumpy() - mean_square_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=moment_np.shape) * 10e-6
|
|
diff = moment_ms.asnumpy() - moment_np
|
|
assert np.all(diff < error)
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_cpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_rmspropcenter():
|
|
learning_rate, decay, momentum, epsilon, centered = [0.1, 0.3, 0.9, 1.0, False]
|
|
|
|
variable_np = np.array([1.0, 2.0], dtype=np.float32)
|
|
gradients_np = np.array([0.1, 0.2], dtype=np.float32)
|
|
mean_gradients_np = np.array([0.0, 0.0], dtype=np.float32)
|
|
mean_square_np = np.array([epsilon, epsilon], dtype=np.float32)
|
|
moment_np = np.array([0.0, 0.0], dtype=np.float32)
|
|
|
|
variable = Tensor(variable_np)
|
|
gradients = Tensor(gradients_np)
|
|
mean_gradients = Tensor(mean_gradients_np)
|
|
mean_square = Tensor(mean_square_np)
|
|
moment = Tensor(moment_np)
|
|
|
|
variable_ms = Parameter(initializer(variable, variable.shape), name='var')
|
|
gradients_ms = Parameter(initializer(gradients, gradients.shape), name='grad')
|
|
mean_gradients_ms = Parameter(initializer(mean_gradients, mean_gradients.shape), name='mg')
|
|
mean_square_ms = Parameter(initializer(mean_square, mean_square.shape), name='msr')
|
|
moment_ms = Parameter(initializer(moment, moment.shape), name='mom')
|
|
|
|
if centered:
|
|
variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np = \
|
|
rmspropcented_numpy(variable_np, gradients_np, mean_gradients_np, mean_square_np, moment_np,
|
|
learning_rate, decay, momentum, epsilon)
|
|
net = NetCenteredRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
|
|
mean_square_ms, moment_ms)
|
|
_ = net()
|
|
else:
|
|
variable_np, gradients_np, mean_square_np, moment_np = \
|
|
rmsprop_numpy(variable_np, gradients_np, mean_square_np, moment_np,
|
|
learning_rate, decay, momentum, epsilon)
|
|
net = NetRMSProp(learning_rate, decay, momentum, epsilon, variable_ms, gradients_ms, mean_gradients_ms,
|
|
mean_square_ms, moment_ms)
|
|
_ = net()
|
|
|
|
error = np.ones(shape=variable_np.shape) * 10e-6
|
|
diff = variable_ms.asnumpy() - variable_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=gradients_np.shape) * 10e-6
|
|
diff = gradients_ms.asnumpy() - gradients_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=mean_gradients_np.shape) * 10e-6
|
|
diff = mean_gradients_ms.asnumpy() - mean_gradients_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=mean_square_np.shape) * 10e-6
|
|
diff = mean_square_ms.asnumpy() - mean_square_np
|
|
assert np.all(diff < error)
|
|
|
|
error = np.ones(shape=moment_np.shape) * 10e-6
|
|
diff = moment_ms.asnumpy() - moment_np
|
|
assert np.all(diff < error)
|