昇思25天学习打卡营第15天|应用实践之ShuffleNet图像分类

基本介绍

今天的应用实践的领域是计算机视觉领域，更确切的说是图像分类任务，不过，与昨日不同的是，今天所使用的模型是ShuffleNet模型。ShuffleNetV1是旷视科技提出的一种计算高效的CNN模型，和MobileNet, SqueezeNet等一样主要应用在移动端，所以模型的设计目标就是利用有限的计算资源来达到最好的模型精度。今天会简单介绍一些ShuffleNet模型，并使用CIFAR-10数据集进行训练与评估，最后进行模型预测

ShuffleNet模型简介

ShuffleNetV1的设计核心是引入了两种操作：Pointwise Group Convolution和Channel Shuffle，这在保持精度的同时大大降低了模型的计算量。因此，ShuffleNetV1和MobileNet类似，都是通过设计更高效的网络结构来实现模型的压缩和加速

Pointwise Group Convolution

Group Convolution（分组卷积）原理如下图所示，相比于普通的卷积操作，分组卷积的情况下，每一组的卷积核大小为in_channels/g*k*k，一共有g组，所有组共有(in_channels/g*k*k)*out_channels个参数，是正常卷积参数的1/g。分组卷积中，每个卷积核只处理输入特征图的一部分通道，其优点在于参数量会有所降低，但输出通道数仍等于卷积核的数量

Channel Shuffle

Group Convolution的弊端在于不同组别的通道无法进行信息交流，堆积GConv层后一个问题是不同组之间的特征图是不通信的，这就好像分成了g个互不相干的道路，每一个人各走各的，这可能会降低网络的特征提取能力。这也是Xception，MobileNet等网络采用密集的1x1卷积（Dense Pointwise Convolution）的原因。为了解决不同组别通道“近亲繁殖”的问题，ShuffleNet优化了大量密集的1x1卷积（在使用的情况下计算量占用率达到了惊人的93.4%），引入Channel Shuffle机制（通道重排）。这项操作直观上表现为将不同分组通道均匀分散重组，使网络在下一层能处理不同组别通道的信息。

以上两个结构就是ShuffleNet的主要结构，ShuffleNet的模型代码（MindSpore版）如下：

class ShuffleNetV1(nn.Cell):
    def __init__(self, n_class=1000, model_size='2.0x', group=3):
        super(ShuffleNetV1, self).__init__()
        print('model size is ', model_size)
        self.stage_repeats = [4, 8, 4]
        self.model_size = model_size
        if group == 3:
            if model_size == '0.5x':
                self.stage_out_channels = [-1, 12, 120, 240, 480]
            elif model_size == '1.0x':
                self.stage_out_channels = [-1, 24, 240, 480, 960]
            elif model_size == '1.5x':
                self.stage_out_channels = [-1, 24, 360, 720, 1440]
            elif model_size == '2.0x':
                self.stage_out_channels = [-1, 48, 480, 960, 1920]
            else:
                raise NotImplementedError
        elif group == 8:
            if model_size == '0.5x':
                self.stage_out_channels = [-1, 16, 192, 384, 768]
            elif model_size == '1.0x':
                self.stage_out_channels = [-1, 24, 384, 768, 1536]
            elif model_size == '1.5x':
                self.stage_out_channels = [-1, 24, 576, 1152, 2304]
            elif model_size == '2.0x':
                self.stage_out_channels = [-1, 48, 768, 1536, 3072]
            else:
                raise NotImplementedError
        input_channel = self.stage_out_channels[1]
        self.first_conv = nn.SequentialCell(
            nn.Conv2d(3, input_channel, 3, 2, 'pad', 1, weight_init='xavier_uniform', has_bias=False),
            nn.BatchNorm2d(input_channel),
            nn.ReLU(),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
        features = []
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]
            for i in range(numrepeat):
                stride = 2 if i == 0 else 1
                first_group = idxstage == 0 and i == 0
                features.append(ShuffleV1Block(input_channel, output_channel,
                                               group=group, first_group=first_group,
                                               mid_channels=output_channel // 4, ksize=3, stride=stride))
                input_channel = output_channel
        self.features = nn.SequentialCell(features)
        self.globalpool = nn.AvgPool2d(7)
        self.classifier = nn.Dense(self.stage_out_channels[-1], n_class)

    def construct(self, x):
        x = self.first_conv(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.globalpool(x)
        x = ops.reshape(x, (-1, self.stage_out_channels[-1]))
        x = self.classifier(x)
        return x

数据集准备

采用CIFAR-10数据集对ShuffleNet进行预训练。CIFAR-10共有60000张32*32的彩色图像，均匀地分为10个类别，其中50000张图片作为训练集，10000图片作为测试集。可直接使用mindspore.dataset.Cifar10Dataset接口下载并加载CIFAR-10的训练集。这部分的操作和昨天几乎一样，就不进行展示

模型训练与评估

采用随机初始化的参数做预训练。首先调用ShuffleNetV1定义网络，参数量选择"2.0x"，并定义损失函数为交叉熵损失，学习率经过4轮的warmup后采用余弦退火，优化器采用Momentum，总共训练5轮。最后用train.model中的Model接口将模型、损失函数、优化器封装在model中，并用model.train()对网络进行训练。将ModelCheckpoint、CheckpointConfig、TimeMonitor和LossMonitor传入回调函数中，将会打印训练的轮数、损失和时间，并将ckpt文件保存在当前目录下。具体训练代码如下：

def train():
    mindspore.set_context(mode=mindspore.PYNATIVE_MODE, device_target="Ascend")
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)
    min_lr = 0.0005
    base_lr = 0.05
    lr_scheduler = mindspore.nn.cosine_decay_lr(min_lr,
                                                base_lr,
                                                batches_per_epoch*250,
                                                batches_per_epoch,
                                                decay_epoch=250)
    lr = Tensor(lr_scheduler[-1])
    optimizer = nn.Momentum(params=net.trainable_params(), learning_rate=lr, momentum=0.9, weight_decay=0.00004, loss_scale=1024)
    loss_scale_manager = ms.amp.FixedLossScaleManager(1024, drop_overflow_update=False)
    model = Model(net, loss_fn=loss, optimizer=optimizer, amp_level="O3", loss_scale_manager=loss_scale_manager)
    callback = [TimeMonitor(), LossMonitor()]
    save_ckpt_path = "./"
    config_ckpt = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=5)
    ckpt_callback = ModelCheckpoint("shufflenetv1", directory=save_ckpt_path, config=config_ckpt)
    callback += [ckpt_callback]

    print("============== Starting Training ==============")
    start_time = time.time()
    # 由于时间原因，epoch = 5，可根据需求进行调整
    model.train(5, dataset, callbacks=callback)
    use_time = time.time() - start_time
    hour = str(int(use_time // 60 // 60))
    minute = str(int(use_time // 60 % 60))
    second = str(int(use_time % 60))
    print("total time:" + hour + "h " + minute + "m " + second + "s")
    print("============== Train Success ==============")

评估的时候直接使用model.eval()进行评估，具体代码如下：

def test():
    mindspore.set_context(mode=mindspore.GRAPH_MODE, device_target="Ascend")
    dataset = get_dataset("./dataset/cifar-10-batches-bin", 128, "test")
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    param_dict = load_checkpoint("shufflenetv1-5_390.ckpt")
    load_param_into_net(net, param_dict)
    net.set_train(False)
    loss = nn.CrossEntropyLoss(weight=None, reduction='mean', label_smoothing=0.1)
    eval_metrics = {'Loss': nn.Loss(), 'Top_1_Acc': Top1CategoricalAccuracy(),
                    'Top_5_Acc': Top5CategoricalAccuracy()}
    model = Model(net, loss_fn=loss, metrics=eval_metrics)
    start_time = time.time()
    res = model.eval(dataset, dataset_sink_mode=False)
    use_time = time.time() - start_time
    hour = str(int(use_time // 60 // 60))
    minute = str(int(use_time // 60 % 60))
    second = str(int(use_time % 60))
    log = "result:" + str(res) + ", ckpt:'" + "./shufflenetv1-5_390.ckpt" \
        + "', time: " + hour + "h " + minute + "m " + second + "s"
    print(log)
    filename = './eval_log.txt'
    with open(filename, 'a') as file_object:
        file_object.write(log + '\n')