我使用BiSeNetV2进行11分类的语义分割任务，代码中应该加入哪些评价指标，应该如何加入呢，给出全部的具体代码

常见的语义分割评价指标包括：pixel accuracy、mean accuracy、mean IoU、frequency weighted IoU等。下面是使用BiSeNetV2进行11分类语义分割任务时，加入评价指标的代码示例：

import torch
import numpy as np
from tqdm import tqdm

def evaluate(model, dataloader, device):
    '''
    model：语义分割模型
    dataloader：验证集数据加载器
    device：设备，如'cuda'或'cpu'
    '''
    model.eval()
    total_pixel_acc = 0.
    total_mean_acc = 0.
    total_mean_iou = 0.
    total_freq_iou = 0.
    with torch.no_grad():
        for inputs, labels in tqdm(dataloader):
            inputs = inputs.to(device)
            labels = labels.to(device)
            outputs = model(inputs)
            _, preds = torch.max(outputs, 1)
            preds = preds.detach().cpu().numpy()
            labels = labels.detach().cpu().numpy()
            total_pixel_acc += pixel_accuracy(preds, labels)
            total_mean_acc += mean_accuracy(preds, labels)
            total_mean_iou += mean_iou(preds, labels)
            total_freq_iou += frequency_weighted_iou(preds, labels)
    num_samples = len(dataloader.dataset)
    avg_pixel_acc = total_pixel_acc / num_samples
    avg_mean_acc = total_mean_acc / num_samples
    avg_mean_iou = total_mean_iou / num_samples
    avg_freq_iou = total_freq_iou / num_samples
    print('Pixel accuracy: {:.4f}'.format(avg_pixel_acc))
    print('Mean accuracy: {:.4f}'.format(avg_mean_acc))
    print('Mean IoU: {:.4f}'.format(avg_mean_iou))
    print('Frequency weighted IoU: {:.4f}'.format(avg_freq_iou))

def pixel_accuracy(preds, labels):
    return np.mean(preds == labels)

def mean_accuracy(preds, labels):
    acc_per_class = np.zeros(11)
    for i in range(11):
        tp = np.sum((preds == i) & (labels == i))
        fp = np.sum((preds == i) & (labels != i))
        fn = np.sum((preds != i) & (labels == i))
        acc_per_class[i] = tp / (tp + fp + fn)
    return np.mean(acc_per_class)

def mean_iou(preds, labels):
    iou_per_class = np.zeros(11)
    for i in range(11):
        tp = np.sum((preds == i) & (labels == i))
        fp = np.sum((preds == i) & (labels != i))
        fn = np.sum((preds != i) & (labels == i))
        iou_per_class[i] = tp / (tp + fp + fn)
    return np.mean(iou_per_class)

def frequency_weighted_iou(preds, labels):
    freq_per_class = np.zeros(11)
    for i in range(11):
        freq_per_class[i] = np.sum(labels == i)
    freq_per_class = freq_per_class / np.sum(freq_per_class)
    iou_per_class = np.zeros(11)
    for i in range(11):
        tp = np.sum((preds == i) & (labels == i))
        fp = np.sum((preds == i) & (labels != i))
        fn = np.sum((preds != i) & (labels == i))
        iou_per_class[i] = tp / (tp + fp + fn)
    return np.sum(freq_per_class * iou_per_class)

在训练过程中，可以在每个epoch结束后调用evaluate函数，计算验证集上的评价指标：

for epoch in range(num_epochs):
    # 训练代码
    evaluate(model, val_dataloader, device)