forked from OSSInnovation/mindspore
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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train step wrap
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"""
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import mindspore.nn as nn
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from mindspore import ParameterTuple
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from mindspore.ops import composite as C
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class TrainStepWrap(nn.Cell):
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"""
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TrainStepWrap definition
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"""
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def __init__(self, network):
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super(TrainStepWrap, self).__init__()
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self.network = network
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self.network.set_train()
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self.weights = ParameterTuple(network.trainable_params())
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self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
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self.hyper_map = C.HyperMap()
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self.grad = C.GradOperation('grad', get_by_list=True)
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def construct(self, x, label):
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weights = self.weights
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grads = self.grad(self.network, weights)(x, label)
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return self.optimizer(grads)
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class NetWithLossClass(nn.Cell):
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"""
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NetWithLossClass definition
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"""
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def __init__(self, network):
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super(NetWithLossClass, self).__init__(auto_prefix=False)
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self.loss = nn.SoftmaxCrossEntropyWithLogits()
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self.network = network
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def construct(self, x, label):
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predict = self.network(x)
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return self.loss(predict, label)
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def train_step_with_loss_warp(network):
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return TrainStepWrap(NetWithLossClass(network))
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class TrainStepWrap2(nn.Cell):
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"""
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TrainStepWrap2 definition
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"""
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def __init__(self, network, sens):
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super(TrainStepWrap2, self).__init__()
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self.network = network
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self.network.set_train()
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self.weights = ParameterTuple(network.get_parameters())
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self.optimizer = nn.Momentum(self.weights, 0.1, 0.9)
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self.hyper_map = C.HyperMap()
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self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
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self.sens = sens
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def construct(self, x):
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weights = self.weights
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grads = self.grad(self.network, weights)(x, self.sens)
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return self.optimizer(grads)
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def train_step_with_sens(network, sens):
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return TrainStepWrap2(network, sens)
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class TrainStepWrapWithoutOpt(nn.Cell):
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"""
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TrainStepWrapWithoutOpt definition
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"""
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def __init__(self, network):
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super(TrainStepWrapWithoutOpt, self).__init__()
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self.network = network
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self.weights = ParameterTuple(network.trainable_params())
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self.grad = C.GradOperation('grad', get_by_list=True)
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def construct(self, x, label):
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grads = self.grad(self.network, self.weights)(x, label)
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return grads
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def train_step_without_opt(network):
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return TrainStepWrapWithoutOpt(NetWithLossClass(network))
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