在pytorch基于resnet50的图像能见度检测的代码

以下是基于PyTorch实现的图像能见度检测代码，使用的是ResNet50模型：

import torch
import torch.nn as nn
import torchvision.models as models

class VisibilityNet(nn.Module):
    def __init__(self):
        super(VisibilityNet, self).__init__()
        self.resnet = models.resnet50(pretrained=True)
        self.fc1 = nn.Linear(1000, 256)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(256, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.resnet(x)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return x

在训练数据集上训练模型时，可以使用以下代码：

import torch.optim as optim
import torch.utils.data as data
from torchvision import transforms

# Define transforms for data augmentation
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])

# Load the dataset using DataLoader
train_set = data.Dataset(train_data_path, transform=transform)
train_loader = data.DataLoader(train_set, batch_size=batch_size, shuffle=True)

# Initialize the model and optimizer
model = VisibilityNet()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train the model on the training set
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

其中，train_data_path是训练数据集的路径，batch_size是每个batch的大小，learning_rate是学习率，num_epochs是训练的轮数。在每个batch训练完后，通过计算损失和反向传播优化模型参数。