forked from mindspore-Ecosystem/mindspore
139 lines
4.6 KiB
Python
139 lines
4.6 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.ops import functional as F
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from mindspore import Parameter, ParameterTuple
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run_opt = C.MultitypeFuncGraph("run_opt")
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# pylint: disable=unused-argument
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@run_opt.register("Function", "Int", "Number", "Number",
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"Tensor", "Tensor", "Tensor")
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def tensor_run_opt(opt, iterator, learning_rate, momentum,
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gradient, variable, moment):
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success = True
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new_weight = opt(gradient, moment, variable, learning_rate, momentum)
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success = F.depend(success, P.Assign()(variable, new_weight))
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return success
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class OptimizerByMomentum(nn.Cell):
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"""
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OptimizerByMomentum definition
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"""
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# list of tensor
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def __init__(self, weights):
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super(OptimizerByMomentum, self).__init__()
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self.learning_rate = Parameter(0.1, name="learning_rate")
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self.momentum = Parameter(0.05, name="momentum")
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self.iter = Parameter(0, name="iter")
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self.weights = weights
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self.moments = weights.clone(prefix="moments", init='zeros')
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self.hyper_map = C.HyperMap()
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self.opt = P.ApplyMomentum()
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def construct(self, grads):
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success = True
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weights = self.weights
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moments = self.moments
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success = self.hyper_map(
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F.partial(run_opt, self.opt, self.iter,
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self.learning_rate, self.momentum), grads, weights, moments)
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# self.learning_rate = updata_lr(self.learning_rate, self.momentum)
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return success
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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 = OptimizerByMomentum(self.weights)
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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 = OptimizerByMomentum(self.weights)
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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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