改进的注意力多尺度特征融合卷积神经网络

本文改进注意力多尺度特征融合卷积神经网络，加入基于style的重新校准模块（SRM），通过样式池提取特征图通道样式信息，经通道无关的style集成估计权重，增强CNN表示能力且参数少。用Caltech101的16类数据集，对比VGG19、ResNet50等模型，改进模型性能提升较明显。

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① 项目背景

本文改进了注意力多尺度特征融合卷积神经网络，加入了一种基于style的重新校准模块（SRM)，可以通过利用其style自适应地重新校准中间特征图。 SRM首先通过样式池从特征图的每个通道中提取样式信息，然后通过与通道无关的style集成来估计每个通道的重新校准权重。通过将各个style的相对重要性纳入特征图，SRM有效地增强了CNN的表示能力。重点是轻量级，引入的参数非常少，同时效果还不错。

论文地址：https://arxiv.org/pdf/1903.10829.pdf

② 数据准备

2.1 解压缩数据集

我们将网上获取的数据集以压缩包的方式上传到aistudio数据集中，并加载到我们的项目内。

在使用之前我们进行数据集压缩包的一个解压。

In [1]

!unzip -oq /home/aistudio/data/data69664/Images.zip -d work/dataset

In [2]

import paddleimport numpy as npfrom typing import Callable#参数配置config_parameters = {    "class_dim": 16,  #分类数    "target_path":"/home/aistudio/work/",                         'train_image_dir': '/home/aistudio/work/trainImages',    'eval_image_dir': '/home/aistudio/work/evalImages',    'epochs':100,    'batch_size': 32,    'lr': 0.01}

2.2 划分数据集

接下来我们使用标注好的文件进行数据集类的定义，方便后续模型训练使用。

In [3]

import osimport shutiltrain_dir = config_parameters['train_image_dir']eval_dir = config_parameters['eval_image_dir']paths = os.listdir('work/dataset/Images')if not os.path.exists(train_dir):    os.mkdir(train_dir)if not os.path.exists(eval_dir):    os.mkdir(eval_dir)for path in paths:    imgs_dir = os.listdir(os.path.join('work/dataset/Images', path))    target_train_dir = os.path.join(train_dir,path)    target_eval_dir = os.path.join(eval_dir,path)    if not os.path.exists(target_train_dir):        os.mkdir(target_train_dir)    if not os.path.exists(target_eval_dir):        os.mkdir(target_eval_dir)    for i in range(len(imgs_dir)):        if ' ' in imgs_dir[i]:            new_name = imgs_dir[i].replace(' ', '_')        else:            new_name = imgs_dir[i]        target_train_path = os.path.join(target_train_dir, new_name)        target_eval_path = os.path.join(target_eval_dir, new_name)             if i % 5 == 0:            shutil.copyfile(os.path.join(os.path.join('work/dataset/Images', path), imgs_dir[i]), target_eval_path)        else:            shutil.copyfile(os.path.join(os.path.join('work/dataset/Images', path), imgs_dir[i]), target_train_path)print('finished train val split!')

finished train val split!

2.3 数据集定义与数据集展示

2.3.1 数据集展示

我们先看一下解压缩后的数据集长成什么样子,对比分析经典模型在Caltech101抽取16类mini版数据集上的效果

In [5]

import osimport randomfrom matplotlib import pyplot as pltfrom PIL import Imageimgs = []paths = os.listdir('work/dataset/Images')for path in paths:       img_path = os.path.join('work/dataset/Images', path)    if os.path.isdir(img_path):        img_paths = os.listdir(img_path)        img = Image.open(os.path.join(img_path, random.choice(img_paths)))        imgs.append((img, path))f, ax = plt.subplots(4, 4, figsize=(12,12))for i, img in enumerate(imgs[:16]):    ax[i//4, i%4].imshow(img[0])    ax[i//4, i%4].axis('off')    ax[i//4, i%4].set_title('label: %s' % img[1])plt.show()

2.3.2 导入数据集的定义实现

In [6]

#数据集的定义class Dataset(paddle.io.Dataset):    """    步骤一：继承paddle.io.Dataset类    """    def __init__(self, transforms: Callable, mode: str ='train'):        """        步骤二：实现构造函数，定义数据读取方式        """        super(Dataset, self).__init__()                self.mode = mode        self.transforms = transforms        train_image_dir = config_parameters['train_image_dir']        eval_image_dir = config_parameters['eval_image_dir']        train_data_folder = paddle.vision.DatasetFolder(train_image_dir)        eval_data_folder = paddle.vision.DatasetFolder(eval_image_dir)                if self.mode  == 'train':            self.data = train_data_folder        elif self.mode  == 'eval':            self.data = eval_data_folder    def __getitem__(self, index):        """        步骤三：实现__getitem__方法，定义指定index时如何获取数据，并返回单条数据（训练数据，对应的标签）        """        data = np.array(self.data[index][0]).astype('float32')        data = self.transforms(data)        label = np.array([self.data[index][1]]).astype('int64')                return data, label            def __len__(self):        """        步骤四：实现__len__方法，返回数据集总数目        """        return len(self.data)

In [7]

from paddle.vision import transforms as T#数据增强transform_train =T.Compose([T.Resize((256,256)),                            #T.RandomVerticalFlip(10),                            #T.RandomHorizontalFlip(10),                            T.RandomRotation(10),                            T.Transpose(),                            T.Normalize(mean=[0, 0, 0],                           # 像素值归一化                                        std =[255, 255, 255]),                    # transforms.ToTensor(), # transpose操作 + (img / 255),并且数据结构变为PaddleTensor                            T.Normalize(mean=[0.50950350, 0.54632660, 0.57409690],# 减均值 除标准差                                            std= [0.26059777, 0.26041326, 0.29220656])# 计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]                            ])transform_eval =T.Compose([ T.Resize((256,256)),                            T.Transpose(),                            T.Normalize(mean=[0, 0, 0],                           # 像素值归一化                                        std =[255, 255, 255]),                    # transforms.ToTensor(), # transpose操作 + (img / 255),并且数据结构变为PaddleTensor                            T.Normalize(mean=[0.50950350, 0.54632660, 0.57409690],# 减均值 除标准差                                            std= [0.26059777, 0.26041326, 0.29220656])# 计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]                            ])

2.3.3 实例化数据集类

根据所使用的数据集需求实例化数据集类，并查看总样本量。

In [8]

train_dataset =Dataset(mode='train',transforms=transform_train)eval_dataset  =Dataset(mode='eval', transforms=transform_eval )#数据异步加载train_loader = paddle.io.DataLoader(train_dataset,                                     places=paddle.CUDAPlace(0),                                     batch_size=32,                                     shuffle=True,                                    #num_workers=2,                                    #use_shared_memory=True                                    )eval_loader = paddle.io.DataLoader (eval_dataset,                                     places=paddle.CUDAPlace(0),                                     batch_size=32,                                    #num_workers=2,                                    #use_shared_memory=True                                    )print('训练集样本量: {}，验证集样本量: {}'.format(len(train_loader), len(eval_loader)))

训练集样本量: 45，验证集样本量: 12

③ 模型选择和开发

3.1 对比网络构建

本次我们选取了经典的卷积神经网络resnet50，vgg19,mobilenet_v2来进行实验比较。

In [ ]

network = paddle.vision.models.vgg19(num_classes=16)#模型封装model = paddle.Model(network)#模型可视化model.summary((-1, 3,256 , 256))

In [ ]

network = paddle.vision.models.resnet50(num_classes=16)#模型封装model2 = paddle.Model(network)#模型可视化model2.summary((-1, 3,256 , 256))

3.2 对比网络训练

In [12]

#优化器选择class SaveBestModel(paddle.callbacks.Callback):    def __init__(self, target=0.5, path='work/best_model', verbose=0):        self.target = target        self.epoch = None        self.path = path    def on_epoch_end(self, epoch, logs=None):        self.epoch = epoch    def on_eval_end(self, logs=None):        if logs.get('acc') > self.target:            self.target = logs.get('acc')            self.model.save(self.path)            print('best acc is {} at epoch {}'.format(self.target, self.epoch))callback_visualdl = paddle.callbacks.VisualDL(log_dir='work/vgg19')callback_savebestmodel = SaveBestModel(target=0.5, path='work/best_model')callbacks = [callback_visualdl, callback_savebestmodel]base_lr = config_parameters['lr']epochs = config_parameters['epochs']def make_optimizer(parameters=None):    momentum = 0.9    learning_rate= paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=base_lr, T_max=epochs, verbose=False)    weight_decay=paddle.regularizer.L2Decay(0.0001)    optimizer = paddle.optimizer.Momentum(        learning_rate=learning_rate,        momentum=momentum,        weight_decay=weight_decay,        parameters=parameters)    return optimizeroptimizer = make_optimizer(model.parameters())model.prepare(optimizer,              paddle.nn.CrossEntropyLoss(),              paddle.metric.Accuracy())model.fit(train_loader,          eval_loader,          epochs=100,          batch_size=1,           # 是否打乱样本集               callbacks=callbacks,           verbose=1)   # 日志展示格式

3.3 改进的注意力多尺度特征融合卷积神经网络SRM-Inception-Net

3.3.1 SRM模块的介绍

SRM首先通过样式池从特征图的每个通道中提取样式信息，然后通过与通道无关的style集成来估计每个通道的重新校准权重。通过将各个style的相对重要性纳入特征图，SRM有效地增强了CNN的表示能力。重点是轻量级，引入的参数非常少，其中Style Pooling是avgpool和stdpool拼接，Style Intergration就是一个自适应加权融合.

图1 SRM模块细节示意图

In [9]

import paddle.nn as nnclass srm_layer(nn.Layer):    def __init__(self, channel):        super(srm_layer, self).__init__()        self.cfc = self.create_parameter(shape=[channel, 2], default_initializer=nn.initializer.Assign(paddle.zeros([channel, 2])))        self.bn = nn.BatchNorm2D(channel)        self.activation = nn.Sigmoid()        setattr(self.cfc, 'srm_param', True)        setattr(self.bn.weight, 'srm_param', True)        setattr(self.bn.bias, 'srm_param', True)    def _style_pooling(self, x, eps=1e-5):        N, C, _, _ = x.shape        channel_mean = paddle.mean(paddle.reshape(x, [N, C, -1]), axis=2, keepdim=True)        channel_var = paddle.var(paddle.reshape(x, [N, C, -1]), axis=2, keepdim=True) + eps        channel_std = paddle.sqrt(channel_var)        t = paddle.concat((channel_mean, channel_std), axis=2)        return t         def _style_integration(self, t):        z = t*paddle.reshape(self.cfc, [-1, self.cfc.shape[0], self.cfc.shape[1]])        tmp = paddle.sum(z, axis=2)        z = paddle.reshape(tmp, [tmp.shape[0], tmp.shape[1], 1, 1]) # B x C x 1 x 1        z_hat = self.bn(z)        g = self.activation(z_hat)        return g    def forward(self, x):        # B x C x 2        t = self._style_pooling(x)        # B x C x 1 x 1        g = self._style_integration(t)        return x * g

3.3.2 注意力多尺度特征融合卷积神经网络的搭建

In [10]

import paddle.nn.functional as F# 构建模型（Inception层）class Inception(paddle.nn.Layer):    def __init__(self, in_channels, c1, c2, c3, c4):        super(Inception, self).__init__()        # 路线1，卷积核1x1        self.route1x1_1 = paddle.nn.Conv2D(in_channels, c1, kernel_size=1)        # 路线2，卷积层1x1、卷积层3x3        self.route1x1_2 = paddle.nn.Conv2D(in_channels, c2[0], kernel_size=1)        self.route3x3_2 = paddle.nn.Conv2D(c2[0], c2[1], kernel_size=3, padding=1)        # 路线3，卷积层1x1、卷积层5x5        self.route1x1_3 = paddle.nn.Conv2D(in_channels, c3[0], kernel_size=1)        self.route5x5_3 = paddle.nn.Conv2D(c3[0], c3[1], kernel_size=5, padding=2)        # 路线4，池化层3x3、卷积层1x1        self.route3x3_4 = paddle.nn.MaxPool2D(kernel_size=3, stride=1, padding=1)        self.route1x1_4 = paddle.nn.Conv2D(in_channels, c4, kernel_size=1)    def forward(self, x):        route1 = F.relu(self.route1x1_1(x))        route2 = F.relu(self.route3x3_2(F.relu(self.route1x1_2(x))))        route3 = F.relu(self.route5x5_3(F.relu(self.route1x1_3(x))))        route4 = F.relu(self.route1x1_4(self.route3x3_4(x)))        out = [route1, route2, route3, route4]        return paddle.concat(out, axis=1)  # 在通道维度(axis=1)上进行连接# 构建 BasicConv2d 层def BasicConv2d(in_channels, out_channels, kernel, stride=1, padding=0):    layer = paddle.nn.Sequential(                paddle.nn.Conv2D(in_channels, out_channels, kernel, stride, padding),                 paddle.nn.BatchNorm2D(out_channels, epsilon=1e-3),                paddle.nn.ReLU())    return layer# 搭建网络class TowerNet(paddle.nn.Layer):    def __init__(self, in_channel, num_classes):        super(TowerNet, self).__init__()        self.b1 = paddle.nn.Sequential(                    BasicConv2d(in_channel, out_channels=64, kernel=3, stride=2, padding=1),                    paddle.nn.MaxPool2D(2, 2))        self.b2 = paddle.nn.Sequential(                    BasicConv2d(64, 128, kernel=3, padding=1),                    paddle.nn.MaxPool2D(2, 2))        self.b3 = paddle.nn.Sequential(                    BasicConv2d(128, 256, kernel=3, padding=1),                    paddle.nn.MaxPool2D(2, 2),                    srm_layer(256))        self.b4 = paddle.nn.Sequential(                    BasicConv2d(256, 256, kernel=3, padding=1),                    paddle.nn.MaxPool2D(2, 2),                    srm_layer(256))        self.b5 = paddle.nn.Sequential(                    Inception(256, 64, (64, 128), (16, 32), 32),                    paddle.nn.MaxPool2D(2, 2),                    srm_layer(256),                    Inception(256, 64, (64, 128), (16, 32), 32),                    paddle.nn.MaxPool2D(2, 2),                    srm_layer(256),                    Inception(256, 64, (64, 128), (16, 32), 32))        self.AvgPool2D=paddle.nn.AvgPool2D(2)        self.flatten=paddle.nn.Flatten()        self.b6 = paddle.nn.Linear(256, num_classes)    def forward(self, x):        x = self.b1(x)        x = self.b2(x)        x = self.b3(x)        x = self.b4(x)        x = self.b5(x)        x = self.AvgPool2D(x)        x = self.flatten(x)        x = self.b6(x)        return x

In [11]

model = paddle.Model(TowerNet(3, config_parameters['class_dim']))model.summary((-1, 3, 256, 256))

④改进模型的训练和优化器的选择

In [12]

#优化器选择class SaveBestModel(paddle.callbacks.Callback):    def __init__(self, target=0.5, path='work/best_model', verbose=0):        self.target = target        self.epoch = None        self.path = path    def on_epoch_end(self, epoch, logs=None):        self.epoch = epoch    def on_eval_end(self, logs=None):        if logs.get('acc') > self.target:            self.target = logs.get('acc')            self.model.save(self.path)            print('best acc is {} at epoch {}'.format(self.target, self.epoch))callback_visualdl = paddle.callbacks.VisualDL(log_dir='work/SRM_Inception_Net')callback_savebestmodel = SaveBestModel(target=0.5, path='work/best_model')callbacks = [callback_visualdl, callback_savebestmodel]base_lr = config_parameters['lr']epochs = config_parameters['epochs']def make_optimizer(parameters=None):    momentum = 0.9    learning_rate= paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=base_lr, T_max=epochs, verbose=False)    weight_decay=paddle.regularizer.L2Decay(0.0002)    optimizer = paddle.optimizer.Momentum(        learning_rate=learning_rate,        momentum=momentum,        weight_decay=weight_decay,        parameters=parameters)    return optimizeroptimizer = make_optimizer(model.parameters())

In [13]

model.prepare(optimizer,              paddle.nn.CrossEntropyLoss(),              paddle.metric.Accuracy())

In [14]

model.fit(train_loader,          eval_loader,          epochs=100,          batch_size=1,           # 是否打乱样本集               callbacks=callbacks,           verbose=1)   # 日志展示格式

⑤模型训练效果展示

绿色曲线为本次改进模型训练曲线，在增加了SRM模块的注意力机制后，性能和其他经典网络有了较大幅度的提升，但相较于SA注意力机制还稍差些。

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