点云focal loss多分类代码

### 实现点云多分类任务中使用Focal Loss 对于点云多分类任务，采用 Focal Loss 可以有效解决类别不平衡问题。下面展示了一个基于 PyTorch 的简单实现案例： ```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, Dataset class PointNet(nn.Module): def __init__(self, num_classes=40): super(PointNet, self).__init__() # 定义PointNet结构 self.conv1 = nn.Conv1d(3, 64, kernel_size=1) self.conv2 = nn.Conv1d(64, 128, kernel_size=1) self.fc1 = nn.Linear(128, 128) self.fc2 = nn.Linear(128, num_classes) def forward(self, x): batchsize = x.size()[0] x = torch.relu(self.conv1(x)) x = torch.relu(self.conv2(x)) x = torch.max(x, 2, keepdim=True)[0] x = x.view(-1, 128) x = torch.relu(self.fc1(x)) x = self.fc2(x) return x def focal_loss(input_values, gamma): """Compute the focal loss between `logits` and the golden `target` values. Args: input_values (Tensor): logits from model predictions. gamma (float): hyperparameter to adjust focus on hard examples. Returns: Tensor: computed focal loss value. """ p = torch.exp(-input_values) loss = (1 - p) ** gamma * input_values return loss.mean() class ModelTrainer(object): def train(model, dataloader, optimizer, device='cuda'): criterion = focal_loss for data in dataloader: points, target = data points, target = points.to(device), target.to(device) optimizer.zero_grad() pred = model(points.transpose(2, 1)) # 调整输入维度顺序 loss = criterion(pred, target) loss.backward() optimizer.step() model = PointNet(num_classes=40).to('cuda') optimizer = optim.Adam(model.parameters(), lr=0.001) trainer = ModelTrainer() ``` 此代码片段展示了如何构建一个简单的 PointNet 架构并应用 Focal Loss 进行训练[^1]。