forked from OSSInnovation/mindspore
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
# 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.
|
|
# ============================================================================
|
|
"""
|
|
train step wrap
|
|
"""
|
|
import mindspore.nn as nn
|
|
from mindspore import ParameterTuple
|
|
from mindspore.ops import composite as C
|
|
|
|
|
|
class TrainStepWrap(nn.Cell):
|
|
"""
|
|
TrainStepWrap definition
|
|
"""
|
|
|
|
def __init__(self, network):
|
|
super(TrainStepWrap, self).__init__()
|
|
self.network = network
|
|
self.network.set_train()
|
|
self.weights = ParameterTuple(network.trainable_params())
|
|
self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
|
|
self.hyper_map = C.HyperMap()
|
|
self.grad = C.GradOperation(get_by_list=True)
|
|
|
|
def construct(self, x, label):
|
|
weights = self.weights
|
|
grads = self.grad(self.network, weights)(x, label)
|
|
return self.optimizer(grads)
|
|
|
|
|
|
class NetWithLossClass(nn.Cell):
|
|
"""
|
|
NetWithLossClass definition
|
|
"""
|
|
|
|
def __init__(self, network):
|
|
super(NetWithLossClass, self).__init__(auto_prefix=False)
|
|
self.loss = nn.SoftmaxCrossEntropyWithLogits()
|
|
self.network = network
|
|
|
|
def construct(self, x, label):
|
|
predict = self.network(x)
|
|
return self.loss(predict, label)
|
|
|
|
|
|
def train_step_with_loss_warp(network):
|
|
return TrainStepWrap(NetWithLossClass(network))
|
|
|
|
|
|
class TrainStepWrap2(nn.Cell):
|
|
"""
|
|
TrainStepWrap2 definition
|
|
"""
|
|
|
|
def __init__(self, network, sens):
|
|
super(TrainStepWrap2, self).__init__()
|
|
self.network = network
|
|
self.network.set_train()
|
|
self.weights = ParameterTuple(network.get_parameters())
|
|
self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
|
|
self.hyper_map = C.HyperMap()
|
|
self.grad = C.GradOperation(get_by_list=True, sens_param=True)
|
|
self.sens = sens
|
|
|
|
def construct(self, x):
|
|
weights = self.weights
|
|
grads = self.grad(self.network, weights)(x, self.sens)
|
|
return self.optimizer(grads)
|
|
|
|
|
|
def train_step_with_sens(network, sens):
|
|
return TrainStepWrap2(network, sens)
|
|
|
|
|
|
class TrainStepWrapWithoutOpt(nn.Cell):
|
|
"""
|
|
TrainStepWrapWithoutOpt definition
|
|
"""
|
|
|
|
def __init__(self, network):
|
|
super(TrainStepWrapWithoutOpt, self).__init__()
|
|
self.network = network
|
|
self.weights = ParameterTuple(network.trainable_params())
|
|
self.grad = C.GradOperation(get_by_list=True)
|
|
|
|
def construct(self, x, label):
|
|
grads = self.grad(self.network, self.weights)(x, label)
|
|
return grads
|
|
|
|
|
|
def train_step_without_opt(network):
|
|
return TrainStepWrapWithoutOpt(NetWithLossClass(network))
|