Deep-Learning-with-TensorFlow-book/ch15-自定义数据集/resnet.py

import  os
import  tensorflow as tf
import  numpy as np
from    tensorflow import keras
from    tensorflow.keras import layers



tf.random.set_seed(22)
np.random.seed(22)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')



class ResnetBlock(keras.Model):

    def __init__(self, channels, strides=1):
        super(ResnetBlock, self).__init__()

        self.channels = channels
        self.strides = strides

        self.conv1 = layers.Conv2D(channels, 3, strides=strides,
                                   padding=[[0,0],[1,1],[1,1],[0,0]])
        self.bn1 = keras.layers.BatchNormalization()
        self.conv2 = layers.Conv2D(channels, 3, strides=1,
                                   padding=[[0,0],[1,1],[1,1],[0,0]])
        self.bn2 = keras.layers.BatchNormalization()

        if strides!=1:
            self.down_conv = layers.Conv2D(channels, 1, strides=strides, padding='valid')
            self.down_bn = tf.keras.layers.BatchNormalization()

    def call(self, inputs, training=None):
        residual = inputs

        x = self.conv1(inputs)
        x = tf.nn.relu(x)
        x = self.bn1(x, training=training)
        x = self.conv2(x)
        x = tf.nn.relu(x)
        x = self.bn2(x, training=training)

        # 残差连接
        if self.strides!=1:
            residual = self.down_conv(inputs)
            residual = tf.nn.relu(residual)
            residual = self.down_bn(residual, training=training)

        x = x + residual
        x = tf.nn.relu(x)
        return x


class ResNet(keras.Model):

    def __init__(self, num_classes, initial_filters=16, **kwargs):
        super(ResNet, self).__init__(**kwargs)

        self.stem = layers.Conv2D(initial_filters, 3, strides=3, padding='valid')

        self.blocks = keras.models.Sequential([
            ResnetBlock(initial_filters * 2, strides=3),
            ResnetBlock(initial_filters * 2, strides=1),
            # layers.Dropout(rate=0.5),

            ResnetBlock(initial_filters * 4, strides=3),
            ResnetBlock(initial_filters * 4, strides=1),

            ResnetBlock(initial_filters * 8, strides=2),
            ResnetBlock(initial_filters * 8, strides=1),

            ResnetBlock(initial_filters * 16, strides=2),
            ResnetBlock(initial_filters * 16, strides=1),
        ])

        self.final_bn = layers.BatchNormalization()
        self.avg_pool = layers.GlobalMaxPool2D()
        self.fc = layers.Dense(num_classes)

    def call(self, inputs, training=None):
        # print('x:',inputs.shape)
        out = self.stem(inputs)
        out = tf.nn.relu(out)

        # print('stem:',out.shape)

        out = self.blocks(out, training=training)
        # print('res:',out.shape)

        out = self.final_bn(out, training=training)
        # out = tf.nn.relu(out)

        out = self.avg_pool(out)

        # print('avg_pool:',out.shape)
        out = self.fc(out)

        # print('out:',out.shape)

        return out



def main():
    num_classes = 5

    resnet18 = ResNet(5)
    resnet18.build(input_shape=(4,224,224,3))
    resnet18.summary()






if __name__ == '__main__':
    main()