求百度AI Studio眼疾识别LeNet网络模型训练好后的模型测试代码

Question

以下是LeNet网络模型及训练：

from PIL import Image
import cv2
import os
import random
import paddle
import numpy as np
from paddle.nn import Conv2D, MaxPool2D, Linear, Dropout
import paddle.nn.functional as F

DATADIR = 'F:\pythonProject2\yanji\PALM-Training400\PALM-Training400'
# 文件名以N开头的是正常眼底图片，以P开头的是病变眼底图片
file1 = 'N0012.jpg'
file2 = 'P0095.jpg'

# 读取图片
img1 = Image.open(os.path.join(DATADIR, file1))
img1 = np.array(img1)
img2 = Image.open(os.path.join(DATADIR, file2))
img2 = np.array(img2)


# 对读入的图像数据进行预处理
def transform_img(img):
    # 将图片尺寸缩放道 224x224
    img = cv2.resize(img, (224, 224))
    # 读入的图像数据格式是[H, W, C]
    # 使用转置操作将其变成[C, H, W]
    img = np.transpose(img, (2, 0, 1))
    img = img.astype('float32')
    # 将数据范围调整到[-1.0, 1.0]之间
    img = img / 255.
    img = img * 2.0 - 1.0
    return img


# 定义训练集数据读取器
def data_loader(datadir, batch_size=10, mode='train'):
    # 将datadir目录下的文件列出来，每条文件都要读入
    filenames = os.listdir(datadir)

    def reader():
        if mode == 'train':
            # 训练时随机打乱数据顺序
            random.shuffle(filenames)
        batch_imgs = []
        batch_labels = []
        for name in filenames:
            filepath = os.path.join(datadir, name)
            img = cv2.imread(filepath)
            img = transform_img(img)
            if name[0] == 'H' or name[0] == 'N':
                # H开头的文件名表示高度近似，N开头的文件名表示正常视力
                # 高度近视和正常视力的样本，都不是病理性的，属于负样本，标签为0
                label = 0
            elif name[0] == 'P':
                # P开头的是病理性近视，属于正样本，标签为1
                label = 1
            else:
                raise ('Not excepted file name')
            # 每读取一个样本的数据，就将其放入数据列表中
            batch_imgs.append(img)
            batch_labels.append(label)
            if len(batch_imgs) == batch_size:
                # 当数据列表的长度等于batch_size的时候，
                # 把这些数据当作一个mini-batch，并作为数据生成器的一个输出
                imgs_array = np.array(batch_imgs).astype('float32')
                labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)
                yield imgs_array, labels_array
                batch_imgs = []
                batch_labels = []

        if len(batch_imgs) > 0:
            # 剩余样本数目不足一个batch_size的数据，一起打包成一个mini-batch
            imgs_array = np.array(batch_imgs).astype('float32')
            labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)
            yield imgs_array, labels_array

    return reader


# 定义验证集数据读取器
def valid_data_loader(datadir, csvfile, batch_size=10, mode='valid'):
    # 训练集读取时通过文件名来确定样本标签，验证集则通过csvfile来读取每个图片对应的标签
    # 请查看解压后的验证集标签数据，观察csvfile文件里面所包含的内容
    # csvfile文件所包含的内容格式如下，每一行代表一个样本，
    # 其中第一列是图片id，第二列是文件名，第三列是图片标签，
    # 第四列和第五列是Fovea的坐标，与分类任务无关
    # ID,imgName,Label,Fovea_X,Fovea_Y
    # 1,V0001.jpg,0,1157.74,1019.87
    # 2,V0002.jpg,1,1285.82,1080.47
    # 打开包含验证集标签的csvfile，并读入其中的内容
    filelists = open(csvfile).readlines()

    # valid_loader = valid_data_loader(DATADIR2, CSVFILE)

    def reader():
        batch_imgs = []
        batch_labels = []
        for line in filelists[1:]:
            line = line.strip().split(',')
            name = line[1]
            label = int(float(line[2]))
            # 根据图片文件名加载图片，并对图像数据作预处理
            filepath = os.path.join(datadir, name)
            img = cv2.imread(filepath)
            img = transform_img(img)
            # 每读取一个样本的数据，就将其放入数据列表中
            batch_imgs.append(img)
            batch_labels.append(label)
            if len(batch_imgs) == batch_size:
                # 当数据列表的长度等于batch_size的时候，
                # 把这些数据当作一个mini-batch，并作为数据生成器的一个输出
                imgs_array = np.array(batch_imgs).astype('float32')
                labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)
                yield imgs_array, labels_array
                batch_imgs = []
                batch_labels = []

        if len(batch_imgs) > 0:
            # 剩余样本数目不足一个batch_size的数据，一起打包成一个mini-batch
            imgs_array = np.array(batch_imgs).astype('float32')
            labels_array = np.array(batch_labels).astype('float32').reshape(-1, 1)
            yield imgs_array, labels_array

    return reader


train_loader = data_loader(DATADIR,
                           batch_size=10, mode='train')
data_reader = train_loader()
data = next(data_reader)
# print(data[0].shape, data[1].shape)

eval_loader = data_loader(DATADIR,
                          batch_size=10, mode='eval')
data_reader = eval_loader()
data = next(data_reader)
# print(data[0].shape, data[1].shape)


DATADIR2 = 'F:\pythonProject2\yanji\PALM-Validation400'
CSVFILE = 'F:\pythonProject2\yanji\PALM-Validation-GT\labels.csv'
# 设置迭代轮数
EPOCH_NUM = 5


# 定义训练过程
def train_pm(model, optimizer):
    # 开启0号GPU训练
    # use_gpu = True
    # paddle.set_device('gpu:0') if use_gpu else paddle.set_device('cpu')

    print('start training ... ')
    model.train()
    # 定义数据读取器，训练数据读取器和验证数据读取器
    train_loader = data_loader(DATADIR, batch_size=10, mode='train')
    valid_loader = valid_data_loader(DATADIR2, CSVFILE)
    for epoch in range(EPOCH_NUM):
        for batch_id, data in enumerate(train_loader()):
            x_data, y_data = data
            img = paddle.to_tensor(x_data)
            label = paddle.to_tensor(y_data)
            # 运行模型前向计算，得到预测值
            logits = model(img)
            loss = F.binary_cross_entropy_with_logits(logits, label)
            avg_loss = paddle.mean(loss)

            if batch_id % 20 == 0:
                print("epoch: {}, batch_id: {}, loss is: {:.4f}".format(epoch, batch_id, float(avg_loss.numpy())))
            # 反向传播，更新权重，清除梯度
            avg_loss.backward()
            optimizer.step()
            optimizer.clear_grad()

        model.eval()
        accuracies = []
        losses = []
        for batch_id, data in enumerate(valid_loader()):
            x_data, y_data = data
            img = paddle.to_tensor(x_data)
            label = paddle.to_tensor(y_data)
            # 运行模型前向计算，得到预测值
            logits = model(img)
            # 二分类，sigmoid计算后的结果以0.5为阈值分两个类别
            # 计算sigmoid后的预测概率，进行loss计算
            pred = F.sigmoid(logits)
            loss = F.binary_cross_entropy_with_logits(logits, label)
            # 计算预测概率小于0.5的类别
            pred2 = pred * (-1.0) + 1.0
            # 得到两个类别的预测概率，并沿第一个维度级联
            pred = paddle.concat([pred2, pred], axis=1)
            acc = paddle.metric.accuracy(pred, paddle.cast(label, dtype='int64'))

            accuracies.append(acc.numpy())
            losses.append(loss.numpy())
        print("[validation] accuracy/loss: {:.4f}/{:.4f}".format(np.mean(accuracies), np.mean(losses)))
        model.train()

        paddle.save(model.state_dict(), 'palm.pdparams')
        paddle.save(optimizer.state_dict(), 'palm.pdopt')


def evaluation(model, params_file_path):
    # 开启0号GPU预估
    # use_gpu = True
    # paddle.set_device('gpu:0') if use_gpu else paddle.set_device('cpu')

    print('start evaluation .......')

    # 加载模型参数
    model_state_dict = paddle.load(params_file_path)
    model.load_dict(model_state_dict)

    model.eval()
    eval_loader = data_loader(DATADIR,
                              batch_size=10, mode='eval')

    acc_set = []
    avg_loss_set = []
    for batch_id, data in enumerate(eval_loader()):
        x_data, y_data = data
        img = paddle.to_tensor(x_data)
        label = paddle.to_tensor(y_data)
        y_data = y_data.astype(np.int64)
        label_64 = paddle.to_tensor(y_data)
        # 计算预测和精度
        prediction, acc = model(img, label_64)
        # 计算损失函数值
        loss = F.binary_cross_entropy_with_logits(prediction, label)
        avg_loss = paddle.mean(loss)
        acc_set.append(float(acc.numpy()))
        avg_loss_set.append(float(avg_loss.numpy()))
    # 求平均精度
    acc_val_mean = np.array(acc_set).mean()
    avg_loss_val_mean = np.array(avg_loss_set).mean()

    print('loss={:.4f}, acc={:.4f}'.format(avg_loss_val_mean, acc_val_mean))


# 定义 LeNet 网络结构
class LeNet(paddle.nn.Layer):
    def __init__(self, num_classes=1):
        super(LeNet, self).__init__()

        # 创建卷积和池化层块，每个卷积层使用Sigmoid激活函数，后面跟着一个2x2的池化
        self.conv1 = Conv2D(in_channels=3, out_channels=6, kernel_size=5)
        self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)
        self.conv2 = Conv2D(in_channels=6, out_channels=16, kernel_size=5)
        self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)
        # 创建第3个卷积层
        self.conv3 = Conv2D(in_channels=16, out_channels=120, kernel_size=4)
        # 创建全连接层，第一个全连接层的输出神经元个数为64
        self.fc1 = Linear(in_features=300000, out_features=64)
        # 第二个全连接层输出神经元个数为分类标签的类别数
        self.fc2 = Linear(in_features=64, out_features=num_classes)

    # 网络的前向计算过程
    def forward(self, x, label=None):
        x = self.conv1(x)
        x = F.sigmoid(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.sigmoid(x)
        x = self.max_pool2(x)
        x = self.conv3(x)
        x = F.sigmoid(x)
        x = paddle.reshape(x, [x.shape[0], -1])
        x = self.fc1(x)
        x = F.sigmoid(x)
        x = self.fc2(x)
        if label is not None:
            acc = paddle.metric.accuracy(input=x, label=label)
            return x, acc
        else:
            return x


# 创建模型
model = LeNet(num_classes=1)
# # 启动训练过程
# opt = paddle.optimizer.Momentum(learning_rate=0.001, momentum=0.9, parameters=model.parameters())
# train_pm(model, optimizer=opt)
# evaluation(model, params_file_path="palm.pdparams")

求模型测试代码