文章起源：华为云论坛_云计算论坛_开发者论坛_技术论坛-华为云1 概述本文将介绍如何将PyTorch源码转换成MindSpore低阶API代码,并在Ascend芯片上实现单机单卡训练。下图展现了MindSpore高阶API、低阶API和PyTorch的训练流程的区别。

与MindSpore高阶API雷同，低阶API训练也须要进行：配置运行信息、数据读取和预处理、网络定义、定义损失函数和优化器。具体步骤同高阶API。2 结构模型(低阶API)结构模型时，首先将网络原型与损失函数封装，再将组合的模型与优化器封装，最终组合成一个可用于训练的网络。 因为训练并验证中，需计算在训练集上的精度 ，因而返回值中需蕴含网络的输入值。import mindsporefrom mindspore import Modelimport mindspore.nn as nnfrom mindspore.ops import functional as Ffrom mindspore.ops import operations as P
class BuildTrainNetwork(nn.Cell):

'''Build train network.'''def __init__(self, my_network, my_criterion, train_batch_size, class_num):    super(BuildTrainNetwork, self).__init__()    self.network = my_network    self.criterion = my_criterion    self.print = P.Print()    # Initialize self.output    self.output = mindspore.Parameter(Tensor(np.ones((train_batch_size,                     class_num)), mindspore.float32), requires_grad=False)def construct(self, input_data, label):    output = self.network(input_data)    # Get the network output and assign it to self.output    self.output = output    loss0 = self.criterion(output, label)    return loss0

class TrainOneStepCellV2(TrainOneStepCell):

'''Build train network.'''def __init__(self, network, optimizer, sens=1.0):    super(TrainOneStepCellV2, self).__init__(network, optimizer, sens=1.0)def construct(self, *inputs):    weights = self.weights    loss = self.network(*inputs)    # Obtain self.network from BuildTrainNetwork    output = self.network.output    sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)    # Get the gradient of the network parameters    grads = self.grad(self.network, weights)(*inputs, sens)    grads = self.grad_reducer(grads)    # Optimize model parameters    loss = F.depend(loss, self.optimizer(grads))    return loss, output# Construct model

model_constructed = BuildTrainNetwork(net, loss_function, TRAIN_BATCH_SIZE, CLASS_NUM)
model_constructed = TrainOneStepCellV2(model_constructed, opt)3 训练并验证（低阶API）和PyTorch中相似，采纳低阶API进行网络训练并验证。具体步骤如下：class CorrectLabelNum(nn.Cell):

def __init__(self):    super(CorrectLabelNum, self).__init__()    self.print = P.Print()    self.argmax = mindspore.ops.Argmax(axis=1)    self.sum = mindspore.ops.ReduceSum()def construct(self, output, target):    output = self.argmax(output)    correct = self.sum((output == target).astype(mindspore.dtype.float32))    return correct

def train_net(model, network, criterion,

epoch_max, train_path, val_path,train_batch_size, val_batch_size,repeat_size):"""define the training method"""# Create datasetds_train, steps_per_epoch_train = create_dataset(train_path,    do_train=True, batch_size=train_batch_size, repeat_num=repeat_size)ds_val, steps_per_epoch_val = create_dataset(val_path, do_train=False,            batch_size=val_batch_size, repeat_num=repeat_size)# CheckPoint CallBack definitionconfig_ck = CheckpointConfig(save_checkpoint_steps=steps_per_epoch_train,                            keep_checkpoint_max=epoch_max)ckpoint_cb = ModelCheckpoint(prefix="train_resnet_cifar10",                            directory="./", config=config_ck)# Create dict to save internal callback object's parameterscb_params = _InternalCallbackParam()cb_params.train_network = modelcb_params.epoch_num = epoch_maxcb_params.batch_num = steps_per_epoch_traincb_params.cur_epoch_num = 0cb_params.cur_step_num = 0run_context = RunContext(cb_params)ckpoint_cb.begin(run_context)print("============== Starting Training ==============")correct_num = CorrectLabelNum()correct_num.set_train(False)for epoch in range(epoch_max):    print("

Epoch:", epoch+1, "/", epoch_max)

    train_loss = 0    train_correct = 0    train_total = 0      for _, (data, gt_classes) in enumerate(ds_train):        model.set_train()        loss, output = model(data, gt_classes)        train_loss += loss        correct = correct_num(output, gt_classes)        correct = correct.asnumpy()        train_correct += correct.sum()        # Update current step number        cb_params.cur_step_num += 1        # Check whether to save checkpoint or not        ckpoint_cb.step_end(run_context)            cb_params.cur_epoch_num += 1    my_train_loss = train_loss/steps_per_epoch_train    my_train_accuracy = 100*train_correct/(train_batch_size*                            steps_per_epoch_train)    print('Train Loss:', my_train_loss)    print('Train Accuracy:', my_train_accuracy, '%')        print('evaluating {}/{} ...'.format(epoch + 1, epoch_max))    val_loss = 0    val_correct = 0    for _, (data, gt_classes) in enumerate(ds_val):        network.set_train(False)        output = network(data)        loss = criterion(output, gt_classes)        val_loss += loss        correct = correct_num(output, gt_classes)        correct = correct.asnumpy()        val_correct += correct.sum()    my_val_loss = val_loss/steps_per_epoch_val    my_val_accuracy = 100*val_correct/(val_batch_size*steps_per_epoch_val)    print('Validation Loss:', my_val_loss)    print('Validation Accuracy:', my_val_accuracy, '%')print("--------- trains out ---------")4 运行脚本启动命令：python MindSpore_1P_low_API.py --data_path=xxx --epoch_num=xxx在开发环境的Terminal中运行脚本，能够看到网络输入后果：


注：因为高阶API采纳数据下沉模式进行训练，而低阶API不反对数据下沉训练，因而高阶API比低阶API训练速度快。性能比照：低阶API: 2000 imgs/sec ；高阶API: 2200 imgs/sec具体代码请返回MindSpore论坛进行下载：华为云论坛_云计算论坛_开发者论坛_技术论坛-华为云