init

main_fusion.py

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+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):
+    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()
+
+    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(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):
+    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()
+    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(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()
+
+    # 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 = 0.00001
+    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': 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)
+        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)
+                    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()