JZTXT

sod

发布时间 2023-12-23 10:38:11作者: 太好了还有脑子可以用
import torch
import torch.nn.functional as F
from torchvision.transforms import RandomErasing

# 示例：生成显著性图的简单方法（使用随机擦除）
def generate_saliency_map(input):
    # 使用随机擦除生成显著性图
    transform = RandomErasing(p=1.0, scale=(0.02, 0.2), ratio=(0.3, 3.3))
    saliency_map = transform(input)
    return saliency_map

# 示例：执行 SaliencyMix 数据增强
def saliencymix_data(input, target, saliency_map):
    # 这里只是简单地使用 saliency_map 对输入图像进行擦除
    mixed_input = input * (1 - saliency_map)
    
    # 假设 mixup_data 函数已经实现，下面的实现只是示例
    input_var, target_a, target_b, lam = mixup_data(mixed_input, target)
    
    return input_var, target_a, target_b, lam

# 示例：修改损失函数以考虑显著性图
def saliencymix_criterion(cost_w, target_a, target_b, lam, saliency_map):
    # 这里只是简单地将损失函数与显著性图相关联
    # 实际上需要根据具体情况调整
    weighted_cost = cost_w * saliency_map
    l_f = lam * F.cross_entropy(weighted_cost, target_a) + (1 - lam) * F.cross_entropy(weighted_cost, target_b)
    
    return l_f

def train(train_loader, model, optimizer_a, epoch):
    losses = AverageMeter()
    top1 = AverageMeter()
    model.train()

    for i, (input, target) in enumerate(train_loader):
        target = torch.tensor(target, dtype=torch.long)
        target = target.cuda()

        # 生成显著性图
        saliency_map = generate_saliency_map(input)

        # SaliencyMix 数据增强
        input_var, target_a, target_b, lam = saliencymix_data(input, target, saliency_map)
        input_var, target_a, target_b = input_var.cuda(), target_a.cuda(), target_b.cuda()

        target_var = to_var(target, requires_grad=False)

        features, y_f = model(input_var)

        # 修改损失函数以考虑显著性图
        cost_w = F.cross_entropy(y_f, target_var, reduce=False)
        l_f = saliencymix_criterion(cost_w, target_a, target_b, lam, saliency_map)

        prec_train = accuracy(y_f.data, target_var.data, topk=(1,))[0]

        losses.update(l_f.item(), input.size(0))
        top1.update(prec_train.item(), input.size(0))

        optimizer_a.zero_grad()
        l_f.backward()
        optimizer_a.step()

        if i % args.print_freq == 0:
            print("--------------------------------Train------------------------------------")
            print('Epoch: [{0}]\t'
                  'Batch: [{1}/{2}]\t'
                  'Batch Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                epoch, i, len(train_loader),
                loss=losses, top1=top1))