deeplabv3代码

DeepLabv3是一个语义分割模型，它主要包括两个部分：骨干网络和ASPP模块。骨干网络通常采用ResNet或Xception，ASPP模块可以有效地捕捉多尺度信息。以下是DeepLabv3的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, DepthwiseConv2D, BatchNormalization, Activation, Add, Dropout, UpSampling2D, concatenate
from tensorflow.keras.models import Model

def ASPP(inputs, output_stride):
    # ASPP模块
    if output_stride == 16:
        dilations = [1, 6, 12, 18]
    elif output_stride == 8:
        dilations = [1, 12, 24, 36]

    # 分支1:全局池化
    b0 = tf.keras.layers.GlobalAveragePooling2D()(inputs)
    b0 = tf.keras.layers.Reshape((1, 1, inputs.shape[3]))(b0)
    b0 = tf.keras.layers.Conv2D(256, (1, 1), padding='same')(b0)
    b0 = tf.keras.layers.BatchNormalization()(b0)
    b0 = tf.keras.layers.ReLU()(b0)
    b0 = tf.keras.layers.UpSampling2D(size=(inputs.shape[1] // 4, inputs.shape[2] // 4), interpolation='bilinear')(b0)

    # 分支2:1x1卷积
    b1 = tf.keras.layers.Conv2D(256, (1, 1), padding='same')(inputs)
    b1 = tf.keras.layers.BatchNormalization()(b1)
    b1 = tf.keras.layers.ReLU()(b1)

    # 分支3:空洞卷积
    b2 = tf.keras.layers.Conv2D(256, (3, 3), dilation_rate=dilations[0], padding='same')(inputs)
    b2 = tf.keras.layers.BatchNormalization()(b2)
    b2 = tf.keras.layers.ReLU()(b2)

    # 分支4:空洞卷积
    b3 = tf.keras.layers.Conv2D(256, (3, 3), dilation_rate=dilations[1], padding='same')(inputs)
    b3 = tf.keras.layers.BatchNormalization()(b3)
    b3 = tf.keras.layers.ReLU()(b3)

    # 分支5:空洞卷积
    b4 = tf.keras.layers.Conv2D(256, (3, 3), dilation_rate=dilations[2], padding='same')(inputs)
    b4 = tf.keras.layers.BatchNormalization()(b4)
    b4 = tf.keras.layers.ReLU()(b4)

    # 分支6:空洞卷积
    b5 = tf.keras.layers.Conv2D(256, (3, 3), dilation_rate=dilations[3], padding='same')(inputs)
    b5 = tf.keras.layers.BatchNormalization()(b5)
    b5 = tf.keras.layers.ReLU()(b5)

    # 将分支输出连接在一起
    out = tf.keras.layers.concatenate([b0, b1, b2, b3, b4, b5], axis=3)
    out = tf.keras.layers.Conv2D(256, (1, 1), padding='same')(out)
    out = tf.keras.layers.BatchNormalization()(out)
    out = tf.keras.layers.ReLU()(out)
    out = tf.keras.layers.Dropout(0.5)(out)

    return out

def DeepLabv3(input_shape=(512, 512, 3), output_stride=16, num_classes=21):
    # 骨干网络
    inputs = Input(shape=input_shape)
    resnet = tf.keras.applications.ResNet50(include_top=False, weights='imagenet', input_tensor=inputs)

    # ASPP模块
    x = ASPP(resnet.output, output_stride)

    # 1x1卷积
    x = tf.keras.layers.Conv2D(256, (1, 1), padding='same')(resnet.output)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.ReLU()(x)

    # 上采样
    x = UpSampling2D(size=(4, 4), interpolation='bilinear')(x)
    x = concatenate([x, x], axis=3)

    # 分类器
    x = Conv2D(256, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Dropout(0.5)(x)

    x = Conv2D(num_classes, (1, 1), padding='same')(x)
    x = UpSampling2D(size=(4, 4), interpolation='bilinear')(x)

    # 输出
    outputs = Activation('softmax')(x)

    model = Model(inputs=inputs, outputs=outputs)

    return model