ResNet18-TensorFlow

Dear 丶 2023-02-16 05:55 27阅读 0赞

1、ResNet代码

# -*- coding: utf-8 -*-
    """
    Created on Wed Feb 26 19:38:01 2020
    
    @author: HongyongHan
    """
    
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras import layers,Sequential
    
    class BasicBlock(layers.Layer):
        #初始化函数
        #filter_num:理解为卷积核通道的数目，也就是channel的通道数
        #stride = 1意味着对图片不进行采样
        def __init__(self,filter_num,strides=1):
            #调用母类的初始化方法
            super(BasicBlock,self).__init__()
            #filter_num：卷积核通道的数目.(3,3):卷积核的size
            #padding='same'如果stride等于1，那么输出等于输入。
            #如果stride大于等于2的话，padding=same,会自动补全，
            # 如果等于2的话，输入是32x32,可能输出是14x14,那么如果padding=same
            #会padding输入的大小，使得输出是16x16
    
    
            self.conv1=layers.Conv2D(filter_num,(3,3),strides=strides,padding='same')
            self.bn1=layers.BatchNormalization()
            #非线性激活函数
            self.relu=layers.Activation('relu')
    
            #那么这里设置stride=1,就始终保持一样
            self.conv2=layers.Conv2D(filter_num,(3,3),strides=1,padding='same')
            self.bn2=layers.BatchNormalization()
    
            if strides != 1:
                #下采样
                self.downsample=Sequential()
                self.downsample.add(layers.Conv2D(filter_num,(1,1),strides=strides))
            else:
                self.downsample=lambda x:x
    
    
    
        def call(self,inputs,training=None):
            #[b,h,w,c]
            out=self.conv1(inputs)
            out=self.bn1(out)
            out=self.relu(out)
    
            out=self.conv2(out)
            out=self.bn2(out)
    
            identify=self.downsample(inputs)
            output=layers.add([out,identify])
            #使用tf的函数功能
            output=tf.nn.relu(output)
    
            return output
    
    
    class ResNet(keras.Model):
        def __init__(self,layer_dims,num_classes=100):
            #layer_dims:resnet18里面有[2,2,2,2]，也就是四个resblock
            #这里指定了一共有多少个resblock层，每个层有多少个basicblock
            #后面在设置blocks的数量的时候，就是用的这里的层的个数
          #一个resblock里面包含了两层basicblock
            #num_classes = 100:就是我们设置的输出的类的个数
            super(ResNet, self).__init__()
    
            #实现预处理层
            self.stem=Sequential([layers.Conv2D(64,(3,3),strides=(1,1)),
                                  layers.BatchNormalization(),
                                  layers.Activation('relu'),
                                  layers.MaxPool2D(pool_size=(2,2),strides=(1,1),padding='same')
                                  ])
            #创建4个res_block
            #这里blocks的数量是layer_dims[0]
            #这里创建的四个res_block与前面的layer_dims:[2,2,2,2]对应
            #将stride设置为2是为了让feature_size越来越小
            self.layer1=self.build_resblock(64,layer_dims[0])
            self.layer2=self.build_resblock(128,layer_dims[1],strides=2)
            self.layer3=self.build_resblock(256,layer_dims[2],strides=2)
            self.layer4=self.build_resblock(512,layer_dims[3],strides=2)
    
    
            #out:[b,512,h,w]
            #经过运算之后不能得到h和w的值，
            #使用自适应的方法得到h,w
            #GlobalAveragePooling2D:就是不管你的长和宽是多少
            #会在某个channel上面的长和宽加起来，取均值
            self.avgpool=layers.GlobalAveragePooling2D()
            #创建全连接层
            #这里的Dense是用来分类的,这里输出是之前输出的类别，num_classes
            self.fc=layers.Dense(num_classes)
    
    
    
        def call(self,inputs,training=None):
            #完成前向运算过程
            x = self.stem(inputs)
            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)
            #这里已经变成[b,c]的shape,不需要reshape了
            x=self.avgpool(x)
            #这里输出是[b,100]
            x=self.fc(x)
    
            return x
    
    
    
        def build_resblock(self,filter_num,blocks,strides=1):
            res_blocks=Sequential()
            #添加第一层basicblock
            #可能有下采样的功能的
            res_blocks.add(BasicBlock(filter_num,strides))
            #但是对于后面的basicblock不让有下采样功能
            #从1开始，一直到blocks个
            for _ in range(1,blocks):
                #这样只会在第一个下采样，后面的不在下采样，保持shape不变
                res_blocks.add(BasicBlock(filter_num,strides=1))
            return res_blocks
    
    def resnet18():
        return ResNet([2,2,2,2])
    
    def resnet34():
        return ResNet([3,4,6,3])

2、resnet18\_train

# -*- coding: utf-8 -*-
    """
    Created on Wed Feb 26 19:38:40 2020
    
    @author: HongyongHan
    """
    
    import tensorflow as tf
    from tensorflow.keras import layers,optimizers,datasets,Sequential
    import os
    from ResNet import resnet18
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    tf.random.set_seed(2345)
    
    
    
    #预处理函数
    def preprocess(x,y):
        #【-1，1】
        x=2 * tf.cast(x,dtype=tf.float32)/255.-1
        y=tf.cast(y,dtype=tf.int32)
        return x,y
    
    (x,y),(x_test,y_test)=datasets.cifar100.load_data()
    #因为y的维度是(64,1)的，需要squeeze掉。
    y=tf.squeeze(y,axis=1)
    y_test=tf.squeeze(y_test,axis=1)
    print(x.shape,y.shape,x_test.shape,y_test.shape)
    
    train_db=tf.data.Dataset.from_tensor_slices((x,y))
    train_db=train_db.shuffle(1000).map(preprocess).batch(64)
    
    test_db=tf.data.Dataset.from_tensor_slices((x_test,y_test))
    test_db=test_db.map(preprocess).batch(64)
    
    sample=next(iter(train_db))
    print('sample',sample[0].shape,sample[1].shape,
          tf.reduce_min(sample[0]),tf.reduce_max(sample[1]))
    
    def main():
        #[b,32,32,3] => [b,1,1,512]
        model=resnet18()
        model.build(input_shape=(None,32,32,3))
        optimizer=optimizers.Adam(lr=1e-3)
        for epoch in range(50):
            for step,(x,y) in enumerate(train_db):
                #这里做一个前向循环,将需要求解梯度放进来
                with tf.GradientTape() as tape:
                    #[b,32,32,3] => [b,100]
                    logits=model(x)
                    #[b] => [b,100]
                    y_onehot=tf.one_hot(y,depth=100)
                    #compute loss
                    loss=tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True)
                    #计算均值，对每个batch的均值进行计算
                    loss=tf.reduce_mean(loss)
                #计算gradient
                grads=tape.gradient(loss,model.trainable_variables)
                #传给优化器两个参数：grads和variable，完成梯度更新
                optimizer.apply_gradients(zip(grads,model.trainable_variables))
    
                if step % 100 == 0:
                    print(epoch,step,'losses:',float(loss))
            total_num=0
            total_correct=0
            for x,y in test_db:
                logits=model(x)
                prob=tf.nn.softmax(logits,axis=1)
                pred=tf.argmax(prob,axis=1)
                pred=tf.cast(pred,dtype=tf.int32)
                correct=tf.cast(tf.equal(pred,y),dtype=tf.int32)
                correct=tf.reduce_sum(correct)
    
                total_num += x.shape[0]
                total_correct += int(correct)
            acc=total_correct / total_num
            print(epoch,'acc:',acc)
    
    if __name__ == '__main__':
        main()