2022实时语义分割网络代码

抱歉，我不清楚您想要哪种实时语义分割网络的代码。但是，您可以在GitHub等代码托管平台上搜索相关项目并找到您需要的代码。以下是一些可能有帮助的链接：

- https://github.com/soeaver/Real-Time-Semantic-Segmentation
- https://github.com/NVIDIA/semantic-segmentation
- https://github.com/HiLab-git/Real-time-Semantic-Segmentation-on-Mobile
- https://github.com/xiaoyufenfei/Real-time-Semantic-Segmentation

请注意，这些代码可能需要特定的数据集和计算资源才能运行。在使用代码之前，请务必阅读相关文档并了解其使用方式和要求。

相关问题

语义分割网络代码

语义分割是计算机视觉领域的一个重要任务，下面是一个基于 U-Net 网络的语义分割代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D
from tensorflow.keras.models import Model

# 创建 U-Net 网络
def unet(input_size=(256, 256, 1)):
    inputs = Input(input_size)

    # 第一层卷积
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    # 第二层卷积
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    # 第三层卷积
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    # 第四层卷积
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    # 中间层
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    # 反卷积层
    up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)

    up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)

    up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)

    up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)

    # 输出层
    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)

    model = Model(inputs=inputs, outputs=conv10)

    return model

该代码使用了 TensorFlow 框架，创建了一个 U-Net 网络，可以用于二分类或多分类任务的语义分割。

fcn语义分割完整代码

FCN（Fully Convolutional Network）是一种用于语义分割深度学习模型，它通过将全连接层替为卷积层来实现像素级别分类。下面是FCN语义分割的完整代码示例：

import torch
import torch.nn as nn
import torchvision.models as modelsclass FCN(nn.Module):
    def __init__(self, num_classes):
        super(FCN, self).__init__()
        self.vgg = models.vgg16(pretrained=True)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(512, 4096, kernel_size=7)
        self.conv2 = nn.Conv2d(4096, 4096, kernel_size=1)
        self.conv3 = nn.Conv2d(4096, num_classes, kernel_size=1)
        self.upsample = nn.Upsample(scale_factor=32, mode='bilinear', align_corners=False)

    def forward(self, x):
        x = self.vgg.features(x)
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.conv3(x)
        x = self.upsample(x)
        return x

# 创建FCN模型实例
model = FCN(num_classes=21)

# 加载预训练权重
model.load_state_dict(torch.load('fcn_weights.pth'))

# 将模型设置为评估模式
model.eval()

上述代码中，我们使用了PyTorch框架，并借助`torchvision.models`中的VGG16模型作为基础网络。在模型的构造函数中，我们定义了卷积层和上采样层，并在前向传播函数中进行了相应的操作。最后，我们创建了一个FCN模型实例，并加载了预训练权重。需要注意的是，这里的`num_classes`参数表示语义分割任务中的类别数量。