clip-symnets/main_fusion_adapter.py

import time
from clip import clip
import torch.nn as nn
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,set_adapter_weights_single
from Adapter import Weight_Adapter,Classifier,Res_Adapter
import logging
import torch.nn.functional as F




def train(classnames, templates, source_train_loader, source_train_loader_batch, model,
          classifier, optimizer,
          epoch, args, scheduler, criterion, CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder,adapter):
    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()
    adapter.train()
    model.eval()
    logit_scale = model.logit_scale.exp()

    classifier.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_image = Image_Encoder(input_target_temp)

    #特征1
    fusion_feature=self_input_source+input_image



    total_feature=torch.cat((fusion_feature,input_image,input_source),dim=0)
    total_label=torch.cat((label_self_supervised,label_self_supervised,target_source),dim=0)



    # 文本直接输入全连接层
    output_source = classifier(adapter(total_feature)) * logit_scale
    # 图片直接输入全连接层


    # 有监督分类的交叉熵损失
    loss = criterion(output_source, total_label)


    loss_T = loss

    prec1_source, _ = accuracy(output_source.data, total_label, topk=(1, 5))




    losses_T.update(loss_T.item(), input_source.size(0))
    top1_source.update(prec1_source[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, classifier, epoch, args, criterion, best_prec, CLIP_Text,
             Text_Encoder, CLIP_Image,
             Image_Encoder,adapter):
    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()
    adapter.eval()
    model.eval()
    classifier.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 = classifier(adapter(input_target_add)) * logit_scale
            output_source = output_target

        # 3
        loss_source = criterion(output_source, target_target)


        # measure accuracy and record loss
        prec1_source, _ = accuracy(output_source.data, target_target, topk=(1, 5))

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


        top1_source.update(prec1_source[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_single(model, classnames, templates)
    classifier = Classifier(args, classnames, weights).cuda()
    adapter=Res_Adapter(1024).cuda()
    # 损失函数
    criterion = nn.CrossEntropyLoss().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 = 0.0001
    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': classifier.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,
                                                                         classifier,
                                                                         optimizer,
                                                                         current_epoch,
                                                                         args, scheduler, criterion, CLIP_Text,
                                                                         Text_Encoder, CLIP_Image, Image_Encoder,adapter)
        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, classifier, current_epoch, args, criterion,
                                    best_prec,
                                    CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder,adapter)
                    is_best = prec > best_prec
                    if prec > args.valacc:
                        if is_best:
                            save_model(current_epoch, Text_Encoder, Image_Encoder, classifier,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()