昇思25天学习打卡营第16天|ShuffleNet图像分类

标签：25 nn 16 self dataset channels 打卡 out size

ShuffleNet网络介绍

ShuffleNetV1是由旷视科技提出的一种高效计算的卷积神经网络（CNN）模型，主要用于移动设备。与MobileNet和SqueezeNet类似，ShuffleNetV1的设计目标是利用有限的计算资源达到最佳模型精度。其核心设计是引入了Pointwise Group Convolution和Channel Shuffle，这两种操作在保持精度的同时大大降低了模型的计算量。

模型架构

ShuffleNet最显著的特点是在ResNet的基础上，通过对通道进行重排解决了Group Convolution带来的弊端。具体来说，ShuffleNet对ResNet的Bottleneck单元进行了改进，在较小的计算量情况下实现了较高的准确率。

Pointwise Group Convolution

分组卷积（Group Convolution）将卷积核分组，减少了参数量和计算量。每个卷积核只处理输入特征图的一部分通道，虽然参数量减少了，但这种方法也限制了不同组别之间的信息交流。

Channel Shuffle

分组卷积的一个主要问题是不同组别的通道无法交流。为了解决这个问题，ShuffleNet引入了Channel Shuffle机制，通过重排通道，确保不同组别的通道信息能够相互交流。

模型构建

ShuffleNet的网络结构如下所示，以输入图像224×224，组数3（g = 3）为例：

import mindspore as ms
from mindspore import nn, ops, Tensor

class GroupConv(nn.Cell):
    def __init__(self, in_channels, out_channels, kernel_size, stride, pad_mode="pad", pad=0, groups=1, has_bias=False):
        super(GroupConv, self).__init__()
        self.groups = groups
        self.convs = nn.CellList()
        for _ in range(groups):
            self.convs.append(nn.Conv2d(in_channels // groups, out_channels // groups, kernel_size=kernel_size, stride=stride, has_bias=has_bias, padding=pad, pad_mode=pad_mode, group=1, weight_init='xavier_uniform'))

    def construct(self, x):
        features = ops.split(x, split_size_or_sections=int(len(x[0]) // self.groups), axis=1)
        outputs = ()
        for i in range(self.groups):
            outputs = outputs + (self.convs[i](features[i].astype("float32")),)
        out = ops.cat(outputs, axis=1)
        return out

class ShuffleV1Block(nn.Cell):
    def __init__(self, inp, oup, group, first_group, mid_channels, ksize, stride):
        super(ShuffleV1Block, self).__init__()
        self.stride = stride
        pad = ksize // 2
        self.group = group
        if stride == 2:
            outputs = oup - inp
        else:
            outputs = oup
        self.relu = nn.ReLU()
        branch_main_1 = [
            GroupConv(in_channels=inp, out_channels=mid_channels, kernel_size=1, stride=1, pad_mode="pad", pad=0, groups=1 if first_group else group),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(),
        ]
        branch_main_2 = [
            nn.Conv2d(mid_channels, mid_channels, kernel_size=ksize, stride=stride, pad_mode='pad', padding=pad, group=mid_channels, weight_init='xavier_uniform', has_bias=False),
            nn.BatchNorm2d(mid_channels),
            GroupConv(in_channels=mid_channels, out_channels=outputs, kernel_size=1, stride=1, pad_mode="pad", pad=0, groups=group),
            nn.BatchNorm2d(outputs),
        ]
        self.branch_main_1 = nn.SequentialCell(branch_main_1)
        self.branch_main_2 = nn.SequentialCell(branch_main_2)
        if self.stride == 2:
            self.branch_proj = nn.AvgPool2d(kernel_size=3, stride=2, pad_mode='same')

    def construct(self, old_x):
        left = old_x
        right = old_x
        out = old_x
        right = self.branch_main_1(right)
        if self.group > 1:
            right = self.channel_shuffle(right)
        right = self.branch_main_2(right)
        if self.stride == 1:
            out = self.relu(left + right)
        elif self.stride == 2:
            left = self.branch_proj(left)
            out = ops.cat((left, right), 1)
            out = self.relu(out)
        return out

    def channel_shuffle(self, x):
        batchsize, num_channels, height, width = ops.shape(x)
        group_channels = num_channels // self.group
        x = ops.reshape(x, (batchsize, group_channels, self.group, height, width))
        x = ops.transpose(x, (0, 2, 1, 3, 4))
        x = ops.reshape(x, (batchsize, num_channels, height, width))
        return x

class ShuffleNetV1(nn.Cell):
    def __init__(self, n_class=1000, model_size='2.0x', group=3):
        super(ShuffleNetV1, self).__init__()
        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

模型训练

使用CIFAR-10数据集进行训练。首先，准备数据集并进行数据增强处理。

from mindspore.dataset import Cifar10Dataset
from mindspore.dataset import vision, transforms

def get_dataset(train_dataset_path, batch_size, usage):
    image_trans = []
    if usage == "train":
        image_trans = [
            vision.RandomCrop((32, 32), (4, 4, 4, 4)),
            vision.RandomHorizontalFlip(prob=0.5),
            vision.Resize((224, 224)),
            vision.Rescale(1.0 / 255.0, 0.0),
            vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
            vision.HWC2CHW()
        ]
    elif usage == "test":
        image_trans = [
            vision.Resize((224, 224)),
            vision.Rescale(1.0 / 255.0, 0.0),
            vision.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
            vision.HWC2CHW()
        ]
    label_trans = transforms.TypeCast(ms.int32)
    dataset = Cifar10Dataset(train_dataset_path, usage=usage, shuffle=True, num_samples=2000)
    dataset = dataset.map(image_trans, 'image')
    dataset = dataset.map(label_trans, 'label')
    dataset = dataset.batch(batch_size, drop_remainder=True)
    return dataset

train_dataset = get_dataset("./dataset/cifar-10-batches-bin", 32, "train")
batches_per_epoch = train_dataset.get_dataset_size()

定义训练过程，包括损失函数、优化器和训练步骤。

import time
from mindspore import Model, nn
from mindspore.train import ModelCheckpoint, CheckpointConfig, TimeMonitor, LossMonitor
from mindspore.nn import Momentum

def train():
    ms.set_context(mode=ms.PYNATIVE_MODE, device_target="CPU")
    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 = nn.cosine_decay_lr(min_lr, base_lr, batches_per_epoch * 2, batches_per_epoch, decay_epoch=2)
    lr = Tensor(lr_scheduler[-1])
    optimizer = 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()
    model.train(1, train_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 ==============")

if __name__ == '__main__':
    train()

模型评估

在CIFAR-10测试集上评估模型性能。

from mindspore import load_checkpoint, load_param_into_net

def test():
    ms.set_context(mode=ms.PYNATIVE_MODE, device_target="CPU")
    test_dataset = get_dataset("./dataset/cifar-10-batches-bin", 32, "test")
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    param_dict = load_checkpoint("shufflenetv1-1_500.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': nn.Top1CategoricalAccuracy(), 'Top_5_Acc': nn.Top5CategoricalAccuracy()}
    model = Model(net, loss_fn=loss, metrics=eval_metrics)
    start_time = time.time()
    res = model.eval(test_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:'" + "./shufflenetv1-1_500.ckpt" + "', time: " + hour + "h " + minute + "m " + second + "s"
    print(log)
    with open('./eval_log.txt', 'a') as file_object:
        file_object.write(log + '\n')

if __name__ == '__main__':
    test()

模型预测

在CIFAR-10测试集上进行模型预测，并将预测结果可视化。

import matplotlib.pyplot as plt
import numpy as np

def predict():
    net = ShuffleNetV1(model_size="2.0x", n_class=10)
    param_dict = load_checkpoint("shufflenetv1-1_500.ckpt")
    load_param_into_net(net, param_dict)
    model = Model(net)
    predict_dataset = get_dataset("./dataset/cifar-10-batches-bin", 32, "test")
    
    class_dict = {0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
    plt.figure(figsize=(16, 5))
    for i, data in enumerate(predict_dataset.create_dict_iterator(), 1):
        images = data['image']
        labels = data['label']
        output = model.predict(Tensor(images))
        pred = np.argmax(output.asnumpy(), axis=1)
        for j in range(len(images)):
            plt.subplot(4, 8, i * 8 + j + 1)
            plt.title(f'{class_dict[pred[j]]}')
            plt.imshow(images[j].transpose(1, 2, 0).asnumpy())
            plt.axis("off")
        if i == 3:  # 只展示前三批次结果
            break
    plt.show()

if __name__ == '__main__':
    predict()

结果

学习心得：学习ShuffleNet的过程中，我对高效计算的卷积神经网络有了更深入的理解。ShuffleNet作为一种轻量级模型，通过引入Pointwise Group Convolution和Channel Shuffle，实现了在有限计算资源下的高效图像分类。在学习过程中，我深入研究了分组卷积和通道重排机制。分组卷积通过将卷积核分组，减少了计算量，但也带来了信息交流的问题。Channel Shuffle机制通过重排通道，解决了不同组别通道信息交流的问题，使得模型在保证计算效率的同时，也能有效地提取图像特征。

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标签：25,nn,16,self,dataset,channels,打卡,out,size
From： https://blog.csdn.net/ljd939952281/article/details/140193443