深度图像先验算法pytorch代码

以下是一个基于PyTorch的深度图像先验算法的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class DepthPrior(nn.Module):
    def __init__(self, input_shape):
        super(DepthPrior, self).__init__()
        self.conv1 = nn.Conv2d(input_shape[0], 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 1, kernel_size=3, padding=1)
        self.relu = nn.ReLU()

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

# 训练数据和标签
train_data = torch.randn((100, 3, 256, 256))
train_labels = torch.randn((100, 1, 256, 256))

# 定义网络和优化器
net = DepthPrior((3, 256, 256))
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 训练网络
num_epochs = 10
batch_size = 10
num_batches = int(train_data.shape[0] / batch_size)

for epoch in range(num_epochs):
    for batch in range(num_batches):
        start_idx = batch * batch_size
        end_idx = (batch + 1) * batch_size
        batch_data = train_data[start_idx:end_idx]
        batch_labels = train_labels[start_idx:end_idx]

        optimizer.zero_grad()

        output = net(batch_data)
        loss = nn.MSELoss()(output, batch_labels)
        loss.backward()
        optimizer.step()

        print('Epoch [{}/{}], Batch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, batch+1, num_batches, loss.item()))

以上代码实现了一个简单的深度图像先验算法。它的输入是一个3通道的图像，输出是一个单通道的深度图像。该模型使用了三个卷积层和ReLU激活函数。我们使用MSE损失函数来优化模型，并使用Adam优化器进行优化。在训练过程中，我们将训练数据分成小批次进行训练，以便在每个小批次上计算损失，并使用反向传播更新权重。