基于PaddlePaddle搭建儿童X光胸部肺炎分类项目

该项目基于ResNet50网络对儿童胸片进行肺炎和正常二分类。先解压数据并生成训练、验证、测试列表，创建数据集并可视化。修改网络以获取最后卷积层输出用于类激活图。用Paddle高层API训练，评估得测试集准确率96.39%，并绘制混淆矩阵、ROC曲线（Normal AUC和Pneumonia AUC）及类激活图展示模型效果。

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背景

这个项目是一个典型二分类任务，只是一个和医疗有关的项目。是基于resenet50网络进行分类。 这个项目增加训练完成后一些指标计算和展示，例如ACC,混淆矩阵，AUC，类激活图等(类激活图是参考大佬FutureSI的项目)

解压数据，生成数据列表

这个数据是一个公开数据，每张图片都是儿童的胸片。里面有两个类别一个是肺炎，一个正常的胸片。

In [ ]

!unzip -o data/data103760/data.zip -d /home/aistudio/work

In [80]

label = {'pneumonia_':1,'normal_':0}import osfrom PIL import Imageimport randomrandom.seed(2021)dataset_path = '/home/aistudio/work/data'trainf = open(os.path.join(dataset_path, 'train_list.txt'), 'w')valf = open(os.path.join(dataset_path, 'val_list.txt'), 'w')testf = open(os.path.join(dataset_path, 'test_list.txt'), 'w')for key,value in label.items():    img_dir = os.path.join(dataset_path, key)    imgs_name = os.listdir(img_dir)    random.shuffle(imgs_name)    for idx, name in enumerate(imgs_name):        img_path = os.path.join(img_dir, name)        if idx % 10 == 0:            valf.write((img_path + ' ' + str(value) + 'n'))        elif idx % 9 == 0:            testf.write((img_path + ' ' + str(value) + 'n'))        else:            trainf.write((img_path + ' ' + str(value) + 'n'))trainf.close()valf.close()testf.close()print('finished!')

finished!

创建Dataset 并可视化数据

In [81]

from paddle.vision.transforms import Compose,Transpose, BrightnessTransform,Resize,Normalize,RandomHorizontalFlip,RandomRotation,ContrastTransform,RandomCropfrom paddle.io import DataLoader, Datasetimport cv2import numpy as nptrain_transform = Compose([RandomRotation(degrees=10),#随机旋转0到10度                    RandomHorizontalFlip(),#随机翻转                    ContrastTransform(0.1),#随机调整图片的对比度                    BrightnessTransform(0.1),#随机调整图片的亮度                    Resize(size=(240,240)),#调整图片大小为240,240                    RandomCrop(size=(224,224)),#从240大小中随机裁剪出224                    Normalize(mean=[127.5, 127.5, 127.5],std=[127.5, 127.5, 127.5],data_format='HWC'),#归一化                    Transpose()])#对‘HWC’转换成‘CHW’val_transform = Compose([                    Resize(size=(224,224)),                    Normalize(mean=[127.5, 127.5, 127.5],std=[127.5, 127.5, 127.5],data_format='HWC'),                    Transpose()])# 定义DataSetclass XChestDateset(Dataset):    def __init__(self, txt_path, transform=None,mode='train'):        super(XChestDateset, self).__init__()        self.mode = mode        self.data_list = []        self.transform = transform        if mode == 'train':            self.data_list = np.loadtxt(txt_path, dtype='str')        elif mode == 'valid':            self.data_list = np.loadtxt(txt_path, dtype='str')        elif mode == 'test':            self.data_list = np.loadtxt(txt_path, dtype='str')    def __getitem__(self, idx):        img_path = self.data_list[idx][0]        img = cv2.imread(img_path)        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)        if self.transform:            img = self.transform(img)        return img, int(self.data_list[idx][1])    def __len__(self):        return self.data_list.shape[0]train_txt = 'work/data/train_list.txt'val_txt = 'work/data/val_list.txt'BATCH_SIZE = 16trn_dateset = XChestDateset(train_txt,train_transform, 'train')train_loader = DataLoader(trn_dateset, shuffle=True, batch_size=BATCH_SIZE  )val_dateset = XChestDateset(val_txt, val_transform,'valid')valid_loader = DataLoader(val_dateset, shuffle=False, batch_size=BATCH_SIZE)

In [82]

#可视化经过 数据增强后的胸片import matplotlib.pyplot as plt def imshow(img):    img = np.transpose(img, (1,2,0))    img = img*127.5 + 127.5  #反归一化，还原图片    img = img.astype(np.int32)    plt.imshow(img)dataiter = iter(train_loader)images, labels = dataiter.next()num = images.shape[0]row = 4fig = plt.figure(figsize=(14,14))for idx in range(num):    ax = fig.add_subplot(row,int(num/row), idx+1, xticks=[], yticks=[])    imshow(images[idx])    if labels[idx]:        ax.set_title('pneumonia')    else:        ax.set_title('normal')

创建RestNet50 网络

这个restNet50 网络代码是飞桨官方教程提供的代码

但是要最后生成类激活图就要稍微修改代码。

根据大佬FutureSI提到Grad-CAM 梯度加权的类激活热图原理中，需要获取网络最后一个卷积层输出的特征图，然后计算它的梯度。所以网络设计的时候需要提供一个方法，用来获取最后一个卷积层的输出

代码修改部分如下:

        """        以全局池化层为界，self.conv_layer用来存放所有卷积层。self.last_layer 用来全局池化层后面的全连接层        """        self.conv_layer = nn.Sequential(self.conv, self.pool2d_max,*self.bottleneck_block_list)        last_layer = [            paddle.nn.AdaptiveAvgPool2D(output_size=1),            nn.Flatten(1, -1),            nn.Linear(in_features=2048, out_features=class_dim),        ]        self.last_layer = nn.Sequential(*last_layer)        """        这是原来的代码，用以上的代码代替。其实就是单纯组合了一下，没有增加新的层        """        # 在c5的输出特征图上使用全局池化        # self.pool2d_avg = paddle.nn.AdaptiveAvgPool2D(output_size=1)        # 创建全连接层，输出大小为类别数目，经过残差网络的卷积和全局池化后，        # 卷积特征的维度是[B,2048,1,1]，故最后一层全连接的输入维度是2048        # self.out = nn.Linear(in_features=2048, out_features=class_dim)    def forward(self, inputs):        # y = self.conv(inputs)        # y = self.pool2d_max(y)        # for bottleneck_block in self.bottleneck_block_list:        #     y = bottleneck_block(y)        # y = self.pool2d_avg(y)        # y = paddle.reshape(y, [y.shape[0], -1])        # y = self.out(y)        """        上面是原来的代码，用下面的两行代替(就是上面的代码组装起来而已。。。)        """        conv = self.conv_layer(inputs)        y = self.last_layer(conv)        return y

In [83]

# -*- coding:utf-8 -*-# ResNet模型代码import numpy as npimport paddleimport paddle.nn as nnimport paddle.nn.functional as F# ResNet中使用了BatchNorm层，在卷积层的后面加上BatchNorm以提升数值稳定性# 定义卷积批归一化块class ConvBNLayer(paddle.nn.Layer):    def __init__(self,                 num_channels,                 num_filters,                 filter_size,                 stride=1,                 groups=1,                 act=None):               """        num_channels, 卷积层的输入通道数        num_filters, 卷积层的输出通道数        stride, 卷积层的步幅        groups, 分组卷积的组数，默认groups=1不使用分组卷积        """        super(ConvBNLayer, self).__init__()        # 创建卷积层        self._conv = nn.Conv2D(            in_channels=num_channels,            out_channels=num_filters,            kernel_size=filter_size,            stride=stride,            padding=(filter_size - 1) // 2,            groups=groups,            bias_attr=False)        # 创建BatchNorm层        self._batch_norm = paddle.nn.BatchNorm2D(num_filters)                self.act = act    def forward(self, inputs):        y = self._conv(inputs)        y = self._batch_norm(y)        if self.act == 'leaky':            y = F.leaky_relu(x=y, negative_slope=0.1)        elif self.act == 'relu':            y = F.relu(x=y)        return y# 定义残差块# 每个残差块会对输入图片做三次卷积，然后跟输入图片进行短接# 如果残差块中第三次卷积输出特征图的形状与输入不一致，则对输入图片做1x1卷积，将其输出形状调整成一致class BottleneckBlock(paddle.nn.Layer):    def __init__(self,                 num_channels,                 num_filters,                 stride,                 shortcut=True):        super(BottleneckBlock, self).__init__()        # 创建第一个卷积层 1x1        self.conv0 = ConvBNLayer(            num_channels=num_channels,            num_filters=num_filters,            filter_size=1,            act='relu')        # 创建第二个卷积层 3x3        self.conv1 = ConvBNLayer(            num_channels=num_filters,            num_filters=num_filters,            filter_size=3,            stride=stride,            act='relu')        # 创建第三个卷积 1x1，但输出通道数乘以4        self.conv2 = ConvBNLayer(            num_channels=num_filters,            num_filters=num_filters * 4,            filter_size=1,            act=None)        # 如果conv2的输出跟此残差块的输入数据形状一致，则shortcut=True        # 否则shortcut = False，添加1个1x1的卷积作用在输入数据上，使其形状变成跟conv2一致        if not shortcut:            self.short = ConvBNLayer(                num_channels=num_channels,                num_filters=num_filters * 4,                filter_size=1,                stride=stride)        self.shortcut = shortcut        self._num_channels_out = num_filters * 4    def forward(self, inputs):        y = self.conv0(inputs)        conv1 = self.conv1(y)        conv2 = self.conv2(conv1)        # 如果shortcut=True，直接将inputs跟conv2的输出相加        # 否则需要对inputs进行一次卷积，将形状调整成跟conv2输出一致        if self.shortcut:            short = inputs        else:            short = self.short(inputs)        y = paddle.add(x=short, y=conv2)        y = F.relu(y)        return y# 定义ResNet模型class ResNet(paddle.nn.Layer):    def __init__(self, layers=50, class_dim=2):        """                layers, 网络层数，可以是50, 101或者152        class_dim，分类标签的类别数        """        super(ResNet, self).__init__()        self.layers = layers        supported_layers = [50, 101, 152]        assert layers in supported_layers,             "supported layers are {} but input layer is {}".format(supported_layers, layers)        if layers == 50:            #ResNet50包含多个模块，其中第2到第5个模块分别包含3、4、6、3个残差块            depth = [3, 4, 6, 3]        elif layers == 101:            #ResNet101包含多个模块，其中第2到第5个模块分别包含3、4、23、3个残差块            depth = [3, 4, 23, 3]        elif layers == 152:            #ResNet152包含多个模块，其中第2到第5个模块分别包含3、8、36、3个残差块            depth = [3, 8, 36, 3]                # 残差块中使用到的卷积的输出通道数        num_filters = [64, 128, 256, 512]        # ResNet的第一个模块，包含1个7x7卷积，后面跟着1个最大池化层        self.conv = ConvBNLayer(            num_channels=3,            num_filters=64,            filter_size=7,            stride=2,            act='relu')        self.pool2d_max = nn.MaxPool2D(            kernel_size=3,            stride=2,            padding=1)        # ResNet的第二到第五个模块c2、c3、c4、c5        self.bottleneck_block_list = []        num_channels = 64        for block in range(len(depth)):            shortcut = False            for i in range(depth[block]):                bottleneck_block = self.add_sublayer(                    'bb_%d_%d' % (block, i),                    BottleneckBlock(                        num_channels=num_channels,                        num_filters=num_filters[block],                        stride=2 if i == 0 and block != 0 else 1, # c3、c4、c5将会在第一个残差块使用stride=2；其余所有残差块stride=1                        shortcut=shortcut))                num_channels = bottleneck_block._num_channels_out                self.bottleneck_block_list.append(bottleneck_block)                shortcut = True        """        以全局池化层为界，self.conv_layer用来存放所有卷积层。self.last_layer 用来全局池化层后面的全连接层        """        self.conv_layer = nn.Sequential(self.conv, self.pool2d_max,*self.bottleneck_block_list)        last_layer = [            paddle.nn.AdaptiveAvgPool2D(output_size=1),            nn.Flatten(1, -1),            nn.Linear(in_features=2048, out_features=class_dim),        ]        self.last_layer = nn.Sequential(*last_layer)        """        这是原来的代码，用以上的代码代替。其实就是单纯组合了一下，没有增加新的层        """        # 在c5的输出特征图上使用全局池化        # self.pool2d_avg = paddle.nn.AdaptiveAvgPool2D(output_size=1)        # 创建全连接层，输出大小为类别数目，经过残差网络的卷积和全局池化后，        # 卷积特征的维度是[B,2048,1,1]，故最后一层全连接的输入维度是2048        # self.out = nn.Linear(in_features=2048, out_features=class_dim)    def forward(self, inputs):        # y = self.conv(inputs)        # y = self.pool2d_max(y)        # for bottleneck_block in self.bottleneck_block_list:        #     y = bottleneck_block(y)        # y = self.pool2d_avg(y)        # y = paddle.reshape(y, [y.shape[0], -1])        # y = self.out(y)        """        上面是原来的代码，用下面的两行代替(就是上面的代码组装起来而已。。。)        """        conv = self.conv_layer(inputs)        y = self.last_layer(conv)        return y

使用Paddle 高层API 进行网络训练

In [84]

from paddle.regularizer import L2Decayfrom paddle.nn import CrossEntropyLossfrom paddle.metric import AccuracyBATCH_SIZE = 16 EPOCHS = 30 #训练次数decay_steps = int(len(trn_dateset)/BATCH_SIZE * EPOCHS)train_loader = DataLoader(trn_dateset, shuffle=True, batch_size=BATCH_SIZE  )valid_loader = DataLoader(val_dateset, shuffle=False, batch_size=BATCH_SIZE)model = paddle.Model(ResNet( class_dim=2))base_lr = 0.0125lr = paddle.optimizer.lr.PolynomialDecay(base_lr, power=0.9, decay_steps=decay_steps, end_lr=0.0)# 定义优化器optimizer = paddle.optimizer.Momentum(learning_rate=lr,                     momentum=0.9,                     weight_decay=L2Decay(1e-4),                     parameters=model.parameters())# 进行训练前准备model.prepare(optimizer, CrossEntropyLoss(), Accuracy(topk=(1, 5)))# 启动训练model.fit(train_loader,          valid_loader,          epochs=EPOCHS,          batch_size=BATCH_SIZE,          eval_freq =5,#多少epoch 进行验证          save_freq = 5,#多少epoch 进行模型保存          log_freq =30,#多少steps 打印训练信息          save_dir='/home/aistudio/checkpoint')

In [85]

#对验证集进行验证，查看验证集得分model.evaluate(valid_loader, log_freq=30, verbose=2)

Eval begin...step 30/32 - loss: 0.1032 - acc_top1: 0.9563 - acc_top5: 1.0000 - 64ms/stepstep 32/32 - loss: 0.8956 - acc_top1: 0.9559 - acc_top5: 1.0000 - 63ms/stepEval samples: 499

{'loss': [0.8956491], 'acc_top1': 0.9559118236472945, 'acc_top5': 1.0}

对测试集进行预测，计算准确率等指标并绘制混淆矩阵和ROC曲线

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import cv2from PIL import Imageimport matplotlib.pyplot as pltimport numpy as npfrom sklearn.preprocessing import label_binarizeimport paddlefrom sklearn.metrics import accuracy_score,recall_score,precision_score,f1_score,confusion_matrixmodel_path = 'checkpoint/final.pdparams'model = ResNet( class_dim=2)para_state_dict = paddle.load(model_path)model.set_dict(para_state_dict)model.eval()test_txt = 'work/data/test_list.txt'test_dataset = XChestDateset(test_txt,val_transform, 'test')test_loader = DataLoader(test_dataset, shuffle=True, batch_size=1  )dataiter = iter(test_loader)y_score = list()pre_label = list()true_label = list()for images, labels in dataiter:    true_label.append(labels.numpy()[0])    out = model(images)    y_score.append(out.numpy()[0])    out = np.argmax(out.numpy())    pre_label.append(out)

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#绘制混淆矩阵from sklearn.metrics import confusion_matrixfrom sklearn.metrics import classification_reportimport seaborn as snsconfusion = confusion_matrix(true_label, pre_label)#计算混淆矩阵plt.figure(figsize=(7,7))sns.heatmap(confusion,cmap='Blues_r',annot=True,fmt='.20g',annot_kws={'size':20,'weight':'bold', })#绘制混淆矩阵plt.xlabel('Predict')plt.ylabel('True')plt.show()print("混淆矩阵为：n{}".format(confusion))print("n计算各项指标：")print(classification_report(true_label, pre_label,digits=4))

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/seaborn/matrix.py:69: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  mask = np.zeros(data.shape, np.bool)/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/seaborn/matrix.py:79: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations  dtype=np.bool)/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:101: DeprecationWarning: np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead  ret = np.asscalar(ex)

混淆矩阵为：[[124  11] [  7 356]]计算各项指标：              precision    recall  f1-score   support           0     0.9466    0.9185    0.9323       135           1     0.9700    0.9807    0.9753       363    accuracy                         0.9639       498   macro avg     0.9583    0.9496    0.9538       498weighted avg     0.9637    0.9639    0.9637       498

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#绘制ROC曲线from sklearn.metrics import roc_curve, roc_auc_score,aucplt.figure(figsize=(8,8))kind = {"normal":0,'pneumonia':1}y_score = np.array(y_score)fpr , tpr ,threshold = roc_curve(true_label, y_score[:,kind['normal']], pos_label=kind['normal'])roc_auc = auc(fpr,tpr) ###计算auc的fpr1 , tpr1 ,threshold = roc_curve(true_label, y_score[:,kind['pneumonia']], pos_label=kind['pneumonia'])roc_auc1 = auc(fpr1,tpr1) ###计算auc的plt.plot(fpr, tpr,marker='o', markersize=5,label='Normal')plt.plot(fpr1, tpr1,marker='*', markersize=5,label='Pneumonia')plt.title("Normal AUC:{:.4f}, Pneumonia AUC:{:.4f}".format(    roc_auc,roc_auc1))plt.xlabel('FPR')plt.ylabel('TPR')plt.legend(loc=4)plt.show()

绘制类激活图，可视化模型效果

In [86]

# 获取 Grad-CAM 类激活热图def get_gradcam(model, data, label, class_dim=2):    conv = model.conv_layer(data) # 得到模型最后一个卷积层的特征图    predict = model.last_layer(conv) # 得到前向计算的结果    label = paddle.reshape(label, [-1])    predict_one_hot = paddle.nn.functional.one_hot(label, class_dim) * predict # 将模型输出转化为one-hot向量    score = paddle.mean(predict_one_hot) # 得到预测结果中概率最高的那个分类的值    score.backward() # 反向传播计算梯度    grad_map = conv.grad # 得到目标类别的loss对最后一个卷积层输出的特征图的梯度    grad = paddle.mean(paddle.to_tensor(grad_map), (2, 3), keepdim=True) # 对特征图的梯度进行GAP（全局平局池化）    gradcam = paddle.sum(grad * conv, axis=1) # 将最后一个卷积层输出的特征图乘上从梯度求得权重进行各个通道的加和    gradcam = paddle.maximum(gradcam, paddle.to_tensor(0.)) # 进行ReLU操作，小于0的值设为0    for j in range(gradcam.shape[0]):        gradcam[j] = gradcam[j] / paddle.max(gradcam[j]) # 分别归一化至[0, 1]    return gradcam# 将 Grad-CAM 叠加在原图片上显示激活热图的效果def show_gradcam(model, data, label, class_dim=2, pic_size=224):    gradcams = get_gradcam(model, data, label,class_dim=class_dim)    for i in range(data.shape[0]):        img = (data[i].numpy() *127.5 +127.5).astype('uint8').transpose([1, 2, 0]) # 归一化至[0,255]区间，形状：[h,w,c]        heatmap = cv2.resize(gradcams[i].numpy() * 255., (data.shape[2], data.shape[3])).astype('uint8') # 调整热图尺寸与图片一致、归一化        heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) # 将热图转化为“伪彩热图”显示模式        superimposed_img = cv2.addWeighted(heatmap, .2, img, .8, 1.) # 将特图叠加到原图片上    return superimposed_imgmodel_path = 'checkpoint/final.pdparams'model = ResNet( class_dim=2)para_state_dict = paddle.load(model_path)model.set_dict(para_state_dict)model.eval()test_txt = 'work/data/test_list.txt'test_dataset = XChestDateset(test_txt,val_transform, 'test')test_loader = DataLoader(test_dataset, shuffle=True, batch_size=16  )dataiter = iter(test_loader)images, labels = dataiter.next()num = images.shape[0]row = 4fig = plt.figure(figsize=(14,14))for idx in range(num):    ax = fig.add_subplot(row,int(num/row), idx+1, xticks=[], yticks=[])    image = paddle.unsqueeze(images[idx], axis=0)    heat_map = show_gradcam(model, image, labels[idx], class_dim=2)    heat_map = cv2.cvtColor(heat_map, cv2.COLOR_BGR2RGB)    plt.imshow(heat_map)    if labels[idx]:        ax.set_title('pneumonia')    else:        ax.set_title('normal')

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/varbase_patch_methods.py:373: UserWarning: Warning:tensor.grad will return the tensor value of the gradient.   warnings.warn(warning_msg)

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