clip-symnets/main_coral_loss_mixup.py

import time
from clip import clip
import torch.nn as nn
import numpy as np
import torch.optim
from opts import opts  # The options for the project
# from trainer import validate  # For the validate (test) process
from models.DomainClassifierTarget import DClassifierForTarget
from models.DomainClassifierSource import DClassifierForSource
from utils.loss_utils import TargetDiscrimLoss, ConcatenatedCELoss
from utils.utils import prepare_directories, set_seed, get_dataset_loader, configure_clip_encoders, save_model, \
    set_adapter_weights, get_text_feature, AverageMeter, accuracy, calculate_zeroshot_weights
from Adapter import Weight_Adapter
import logging
import torch.nn.functional as F


def mixup_data(x, y, labels, alpha=1.0):
    '''对文本特征x和图像特征y执行Mixup操作，然后融合它们'''
    if alpha > 0:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1

    batch_size = x.size(0)
    index = torch.randperm(batch_size).to(x.device)
    mixed_x = lam * x + (1 - lam) * x[index, :]
    mixed_y = lam * y + (1 - lam) * y[index, :]
    # mixed_features=lam * x + (1 - lam) * y[index, :]
    # alpha = np.random.beta(1.0, 1.0)
    # pos_m = alpha * pos_1 + (1 - alpha) * pos_2[index, :]
    # 假设您想将混合的文本特征和图像特征进行某种形式的融合
    # 例如，通过简单的拼接（这里需要根据您的模型设计进行调整）
    # mixed_features = torch.cat((mixed_x, mixed_y), dim=1)

    mixed_labels = lam * labels + (1 - lam) * labels[index]

    return mixed_x,mixed_y#, mixed_labels, index


def coral_loss(source_features, target_features):
    """
    计算Deep CORAL损失。
    :param source_features: 源域特征，维度为[batch_size, feature_dim]
    :param target_features: 目标域特征，维度为[batch_size, feature_dim]
    :return: CORAL损失
    """
    d = source_features.data.shape[1]  # 特征维度
    source_mean = torch.mean(source_features, dim=0)
    target_mean = torch.mean(target_features, dim=0)
    source_cov = (source_features - source_mean).T @ (source_features - source_mean) / (source_features.shape[0] - 1)
    target_cov = (target_features - target_mean).T @ (target_features - target_mean) / (target_features.shape[0] - 1)
    coral_loss = torch.sum(torch.pow(source_cov - target_cov, 2))# / (4*d*d)
    return coral_loss

def train(classnames, templates, source_train_loader, source_train_loader_batch, model,
          adapter, optimizer,
          epoch, args, scheduler, criterion, CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses_classifier = AverageMeter()
    losses_G = AverageMeter()
    losses_T = AverageMeter()
    top1_source = AverageMeter()
    top1_target = AverageMeter()

    CLIP_Text.eval()
    CLIP_Image.eval()
    Text_Encoder.train()
    Image_Encoder.train()
    model.eval()
    logit_scale = model.logit_scale.exp()

    adapter.train()
    new_epoch_flag = False
    end = time.time()
    concatenatedCELoss = ConcatenatedCELoss(num_classes=len(classnames)).cuda()
    try:
        (image, label, _) = source_train_loader_batch.__next__()[1]
    except StopIteration:
        epoch = epoch + 1
        new_epoch_flag = True
        source_train_loader_batch = enumerate(source_train_loader)
        (image, label, _) = source_train_loader_batch.__next__()[1]
    target_target = label.cuda()

    # 自监督标签
    label_self_supervised = label.cuda()
    indices = torch.randperm(len(label))
    target_source = label[indices].cuda()

    # target_source = label.cuda()
    input_target = image.cuda()
    input_source = calculate_zeroshot_weights(classnames, target_source, templates, CLIP_Text, Text_Encoder)
    self_input_source = calculate_zeroshot_weights(classnames, label_self_supervised, templates, CLIP_Text,
                                                   Text_Encoder)
    data_time.update(time.time() - end)



    # clip图片编码器
    with torch.no_grad():
        input_target_temp = CLIP_Image(input_target)

    input_target_add = Image_Encoder(input_target_temp)


    mixed_x,mixed_y= mixup_data(self_input_source, input_target_add, label_self_supervised, alpha=1.0)

    # mix_pred = adapter(mixed_features)  # 确保模型可以处理这种形式的融合特征
    # mix_loss_1 = soft_label_criterion(mix_pred[:,:len(classnames)], mixed_labels)
    # mix_loss_2 = soft_label_criterion(mix_pred[:,len(classnames):], mixed_labels)
    target_target_temp = target_target + len(classnames)
    target_source_temp = target_source + len(classnames)
    target_target_temp = target_target_temp.cuda()


    # 计算CORAL损失
    # 总损失
    # 文本直接输入全连接层
    output_source = adapter(input_source) * logit_scale
    # 图片直接输入全连接层
    output_target = adapter(input_target_add) * logit_scale




    coral_loss_value = coral_loss(self_input_source, input_target_add)
    lambda_coral=50
    loss_1=lambda_coral*coral_loss_value

    # 自监督文本输入全连接层
    # self_output_source = adapter(self_input_source)
    # self_output_source = F.normalize(self_output_source[:,:len(classnames)])
    # self_output_source = F.normalize(self_input_source)
    #
    # # 自监督图像特征
    # # self_output_target = output_target / logit_scale
    # # self_output_target = F.normalize(self_output_target[:,len(classnames):])
    # self_output_target = F.normalize(input_target_add)

    mixed_x, mixed_y
    # # # 构造自监督标签0-255
    self_supervised_labels = torch.arange(mixed_x.shape[0], device="cuda:0", dtype=torch.long)
    logits_per_image = logit_scale * mixed_x @ mixed_y.T
    logits_per_text = logit_scale * mixed_y @ mixed_x.T
    loss_self_supervised_1 = (
                                     F.cross_entropy(logits_per_image, self_supervised_labels) +
                                     F.cross_entropy(logits_per_text, self_supervised_labels)
                             ) / 2
    # # 自监督文本输入全连接层
    # self_output_source = adapter(self_input_source)
    # self_output_source = F.normalize(self_output_source[:, :len(classnames)])
    # # self_output_source = F.normalize(self_input_source)
    #
    # # 自监督图像特征
    # self_output_target = output_target / logit_scale
    # self_output_target = F.normalize(self_output_target[:, len(classnames):])
    # # self_output_target = F.normalize(input_target_add)
    # # # 构造自监督标签0-255
    # self_supervised_labels = torch.arange(self_output_source.shape[0], device="cuda:0", dtype=torch.long)
    # logits_per_image = logit_scale * self_output_target @ self_output_source.T
    # logits_per_text = logit_scale * self_output_source @ self_output_target.T
    # loss_self_supervised_2 = (
    #                                  F.cross_entropy(logits_per_image, self_supervised_labels) +
    #                                  F.cross_entropy(logits_per_text, self_supervised_labels)
    #                          ) / 2
    #
    loss_self_supervised = loss_self_supervised_1 # + loss_self_supervised_2

    # 有监督分类的交叉熵损失
    loss_task_s_Cs = criterion(output_source[:, :len(classnames)], target_source)
    loss_task_s_Ct = criterion(output_target[:, len(classnames):], target_target)

    # 对于源域数据，它希望让分类器上半部分所占的概率尽可能大，对于目标域数据，它希望让分类器下半部分所占的概率尽可能大。
    loss_domain_st_Cst_part1 = criterion(output_source, target_source)
    loss_domain_st_Cst_part2 = criterion(output_target, target_target_temp)

    # 类级别混淆
    loss_category_st_G = 0.5 * criterion(output_target, target_target) + 0.5 * criterion(output_source,
                                                                                         target_source_temp)
    # 域级别混淆
    # loss_domain_st_G = 0.5 * criterion_classifier_target(output_source) + 0.5 * criterion_classifier_source(
    #     output_target)

    lam = 1  # 2 / (1 + math.exp(-1 * 10 * epoch / args.epochs)) - 1
    # if(epoch<30):
    #     self_lam= 5
    # else:
    self_lam = 1

    loss_confusion_target = concatenatedCELoss(output_target)
    loss_classifier = loss_task_s_Cs + loss_task_s_Ct + loss_domain_st_Cst_part1 + loss_domain_st_Cst_part2
    loss_G = loss_category_st_G + lam * loss_confusion_target
    loss_T = loss_G + loss_classifier+lambda_coral * coral_loss_value + self_lam * loss_self_supervised

    prec1_source, _ = accuracy(output_source.data[:, :len(classnames)], target_source, topk=(1, 5))
    prec1_target, _ = accuracy(output_target.data[:, len(classnames):], target_target, topk=(1, 5))

    losses_classifier.update(loss_classifier.item(), input_source.size(0))
    losses_G.update(loss_G.item(), input_source.size(0))
    losses_T.update(loss_T.item(), input_source.size(0))
    top1_source.update(prec1_source[0], input_source.size(0))
    top1_target.update(prec1_target[0], input_source.size(0))

    optimizer.zero_grad()
    loss_T.backward()
    optimizer.step()
    scheduler.step()

    batch_time.update(time.time() - end)
    if (epoch + 1) % args.print_freq == 0 or epoch == 0:
        print('Train: [{0}/{1}]\t'
              'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
              'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
              'Loss@C {loss_c.val:.4f} ({loss_c.avg:.4f})\t'
              'Loss@G {loss_g.val:.4f} ({loss_g.avg:.4f})\t'
              'Loss@T {loss_t.val:.4f} ({loss_t.avg:.4f})\t'
              'top1S {top1S.val:.3f} ({top1S.avg:.3f})\t'
              'top1T {top1T.val:.3f} ({top1T.avg:.3f})\t'.format(
            epoch, args.epochs, batch_time=batch_time,
            data_time=data_time, loss_c=losses_classifier, loss_g=losses_G, loss_t=losses_T, top1S=top1_source,
            top1T=top1_target))
    return source_train_loader_batch, epoch, new_epoch_flag


def validate(best_epoch,classnames, templates, val_loader, model, adapter, epoch, args, criterion, best_prec, CLIP_Text,
             Text_Encoder, CLIP_Image,
             Image_Encoder):
    batch_time = AverageMeter()
    losses_source = AverageMeter()
    losses_target = AverageMeter()
    top1_source = AverageMeter()
    top1_target = AverageMeter()

    CLIP_Text.eval()
    CLIP_Image.eval()
    Text_Encoder.eval()
    Image_Encoder.eval()

    model.eval()
    adapter.eval()
    end = time.time()
    logit_scale = model.logit_scale.exp()

    for i, (image, label, _) in enumerate(val_loader):
        image = image.cuda()
        label = label.cuda()

        input_source = calculate_zeroshot_weights(classnames, label, templates, CLIP_Text, Text_Encoder)
        input_target = image.cuda()
        target_target = label.cuda()
        target_source = label.cuda()

        # clip图片编码器
        with torch.no_grad():
            input_target_temp = CLIP_Image(input_target)
            input_target_add = Image_Encoder(input_target_temp)
            # output_source = adapter(input_source) * logit_scale
            output_target = adapter(input_target_add) * logit_scale
            output_source = output_target

        # 3
        loss_source = criterion(output_source[:, :len(classnames)], target_target)
        loss_target = criterion(output_target[:, len(classnames):], target_target)

        # measure accuracy and record loss
        prec1_source, _ = accuracy(output_source.data[:, :len(classnames)], target_target, topk=(1, 5))
        prec1_target, _ = accuracy(output_target.data[:, len(classnames):], target_target, topk=(1, 5))

        losses_source.update(loss_source.item(), image.size(0))
        losses_target.update(loss_target.item(), image.size(0))

        top1_source.update(prec1_source[0], image.size(0))
        top1_target.update(prec1_target[0], image.size(0))
        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Test: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'LS {lossS.val:.4f} ({lossS.avg:.4f})\t'
                  'LT {lossT.val:.4f} ({lossT.avg:.4f})\t'
                  'top1S {top1S.val:.3f} ({top1S.avg:.3f})\t'
                  'top1T {top1T.val:.3f} ({top1T.avg:.3f})'.format(
                epoch, i, len(val_loader), batch_time=batch_time, lossS=losses_source, lossT=losses_target,
                top1S=top1_source, top1T=top1_target))
    print(' * Top1@S {top1S.avg:.3f} Top1@T {top1T.avg:.3f}'
          .format(top1S=top1_source, top1T=top1_target))
    prec = max(top1_target.avg, top1_source.avg).item()
    if prec > best_prec:
        best_prec = max(top1_target.avg, top1_source.avg).item()
        best_epoch = epoch
    print('best_epoch', best_epoch, ' * Current_best_target@T:', best_prec)

    return prec,best_epoch


def main():
    args = opts()

    set_seed(2023)
    model, preprocess = clip.load(args.name)
    model = model.cuda()
    model.float(                                                )
    classnames, templates, loader, train_loader = get_dataset_loader(args, preprocess)
    CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder = configure_clip_encoders(args, model, 0, 1)

    prepare_directories(args, CLIP_Text, CLIP_Image)

    # 分类层
    weights = set_adapter_weights(model, classnames, templates)
    adapter = Weight_Adapter(args, classnames, weights).cuda()

    # 损失函数
    criterion = nn.CrossEntropyLoss().cuda()
    criterion_classifier_target = DClassifierForTarget(nClass=len(classnames)).cuda()
    criterion_classifier_source = DClassifierForSource(nClass=len(classnames)).cuda()

    # 为模型的每个部分定义学习率和权重衰减
    lr_adapter = 0.0001
    lr_image_encoder = 0.00001
    lr_text_encoder = 0.00001
    weight_decay = 0.00001
    # ADAM_BETAS 是用于控制移动平均衰减率的元组
    ADAM_BETAS = (0.9, 0.999)

    # 创建 AdamW 优化器实例
    optimizer = torch.optim.AdamW([
        {'params': adapter.parameters(), 'lr': lr_adapter, 'weight_decay': weight_decay, 'betas': ADAM_BETAS},
        {'params': Image_Encoder.parameters(), 'lr': lr_image_encoder, 'weight_decay': weight_decay,
         'betas': ADAM_BETAS},
        {'params': Text_Encoder.parameters(), 'lr': lr_text_encoder, 'weight_decay': weight_decay, 'betas': ADAM_BETAS}
    ], eps=1e-4)

    # 设置CosineAnnealingLR学习率调度器
    # T_max设置为epochs的数量，表示在每个epoch后更新学习率
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs * len(train_loader))
    source_train_loader_batch = enumerate(train_loader)

    current_epoch = 0
    best_prec = 0
    best_epoch=0
    while (current_epoch < args.epochs):
        source_train_loader_batch, current_epoch, new_epoch_flag = train(classnames, templates,
                                                                         train_loader,
                                                                         source_train_loader_batch,
                                                                         model,
                                                                         adapter,
                                                                         optimizer,
                                                                         current_epoch,
                                                                         args, scheduler, criterion, CLIP_Text,
                                                                         Text_Encoder, CLIP_Image, Image_Encoder)
        if new_epoch_flag:
            if (current_epoch + 1) % args.test_freq == 0 or current_epoch == 0:
                if current_epoch >= args.valepoch:
                    prec,best_epoch = validate(best_epoch,classnames, templates, loader, model, adapter, current_epoch, args, criterion,
                                    best_prec,
                                    CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder)
                    is_best = prec > best_prec
                    if prec > args.valacc:
                        if is_best:
                            save_model(current_epoch, Text_Encoder, Image_Encoder, adapter,args, prec)
                            best_prec = max(prec, best_prec)
                        # 更新日志
                        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
                        logging.info(
                                f"Current Time: {current_time},Epoch: {current_epoch}, Accuracy: {prec}, Best: {best_prec}")


if __name__ == '__main__':
    main()