508 lines
21 KiB
Python
508 lines
21 KiB
Python
import random
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import time
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import numpy as np
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import torch
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import os
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import math
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import clip
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import ipdb
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import torch.nn.functional as F
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import torch.nn as nn
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from utils.loss_utils import TargetDiscrimLoss, ConcatenatedCELoss
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def zeroshot_classifier(classname, templates, CLIP_Text):
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with torch.no_grad():
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classname = classname.replace('_', ' ')
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str_prompts = [template.format(classname) for template in templates]
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prompts = torch.cat([clip.tokenize(p) for p in str_prompts]).cuda()
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features, eot_indices = CLIP_Text(prompts)
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return features, eot_indices
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def warm_train(classnames, templates,source_train_loader, source_train_loader_batch, model,
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adapter, criterion_classifier_source, criterion_classifier_target, optimizer,
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epoch, args, scheduler, criterion, CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder):
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random.seed(1)
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batch_time = AverageMeter()
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data_time = AverageMeter()
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losses_classifier = AverageMeter()
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losses_G = AverageMeter()
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losses_T = AverageMeter()
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top1_source = AverageMeter()
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top1_target = AverageMeter()
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CLIP_Text.eval()
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CLIP_Image.eval()
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Text_Encoder.eval()
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Image_Encoder.eval()
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logit_scale = 4.60517
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logit_scale = math.exp(logit_scale)
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model.eval()
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adapter.train()
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new_epoch_flag = False
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end = time.time()
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concatenatedCELoss = ConcatenatedCELoss(num_classes=len(classnames)).cuda()
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try:
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(image, label,_) = source_train_loader_batch.__next__()[1]
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except StopIteration:
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epoch = epoch + 1
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new_epoch_flag = True
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source_train_loader_batch = enumerate(source_train_loader)
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(image, label,_) = source_train_loader_batch.__next__()[1]
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target_target = label.cuda()
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#自监督标签
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label_self_supervised = label.cuda()
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indices = torch.randperm(len(label))
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target_source = label[indices].cuda()
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# target_source = label.cuda()
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input_target = image.cuda()
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zeroshot_weights = []
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for i in range(len(target_source)):
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features, eot_indices = zeroshot_classifier(classnames[target_source[i]], templates, CLIP_Text)
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class_embeddings = Text_Encoder(features, eot_indices)
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class_embeddings = class_embeddings / class_embeddings.norm(dim=-1, keepdim=True)
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class_embedding = class_embeddings.mean(dim=0)
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class_embedding = class_embedding / class_embedding.norm()
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class_embedding = class_embedding / class_embedding.norm(dim=-1, keepdim=True)
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zeroshot_weights.append(class_embedding)
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zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
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input_source = zeroshot_weights.T
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data_time.update(time.time() - end)
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target_target_temp = target_target + len(classnames)
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target_source_temp = target_source + len(classnames)
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target_target_temp = target_target_temp.cuda()
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# clip图片编码器
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with torch.no_grad():
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input_target_temp = CLIP_Image(input_target)
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input_target_add = Image_Encoder(input_target_temp)
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# 文本直接输入全连接层
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output_source = adapter(input_source) * logit_scale
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# 输入编码图片
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output_target = adapter(input_target_add) * logit_scale
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self_zeroshot_weights = []
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for i in range(len(label_self_supervised)):
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features, eot_indices = zeroshot_classifier(classnames[label_self_supervised[i]], templates, CLIP_Text)
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class_embeddings = Text_Encoder(features, eot_indices)
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class_embeddings = class_embeddings / class_embeddings.norm(dim=-1, keepdim=True)
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class_embedding = class_embeddings.mean(dim=0)
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class_embedding = class_embedding / class_embedding.norm()
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self_zeroshot_weights.append(class_embedding)
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self_zeroshot_weights = torch.stack(self_zeroshot_weights, dim=1).cuda()
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self_input_source = self_zeroshot_weights.T
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# 自监督文本输入全连接层
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self_output_source = adapter(self_input_source)
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self_output_source = F.normalize(self_output_source)
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# 自监督图像特征
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self_output_target = output_target / logit_scale
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self_output_target = F.normalize(self_output_target)
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# # 构造自监督标签0-255
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self_supervised_labels = torch.arange(self_output_source.shape[0], device="cuda:0", dtype=torch.long)
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logits_per_image = logit_scale * self_output_target @ self_output_source.T
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logits_per_text = logit_scale * self_output_source @ self_output_target.T
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loss_self_supervised = (
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F.cross_entropy(logits_per_image, self_supervised_labels) +
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F.cross_entropy(logits_per_text, self_supervised_labels)
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) / 2
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# 有监督分类的交叉熵损失
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loss_task_s_Cs = criterion(output_source[:, :len(classnames)], target_source)
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loss_task_s_Ct = criterion(output_target[:, len(classnames):], target_target)
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# 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。
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loss_domain_st_Cst_part1 = criterion(output_source,target_source)
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loss_domain_st_Cst_part2 = criterion(output_target, target_target_temp)
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# 类级别混淆
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loss_category_st_G = 0.5 * criterion(output_target, target_target) + 0.5 * criterion(output_source,
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target_source_temp)
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# 域级别混淆
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# loss_domain_st_G = 0.5 * criterion_classifier_target(output_source) + 0.5 * criterion_classifier_source(
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# output_target)
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lam = 2 / (1 + math.exp(-1 * 10 * epoch / args.epochs)) - 1
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if(epoch<30):
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self_lam=3
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else:
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self_lam=1/5
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loss_confusion_target = concatenatedCELoss(output_target)
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loss_classifier = loss_task_s_Cs + loss_task_s_Ct + loss_domain_st_Cst_part1 + loss_domain_st_Cst_part2
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loss_G = loss_category_st_G+lam*loss_confusion_target
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loss_T = loss_G + loss_classifier+self_lam*loss_self_supervised
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prec1_source, _ = accuracy(output_source.data[:, :len(classnames)], target_source, topk=(1, 5))
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prec1_target, _ = accuracy(output_target.data[:, len(classnames):], target_target, topk=(1, 5))
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losses_classifier.update(loss_classifier.item(), input_source.size(0))
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losses_G.update(loss_G.item(), input_source.size(0))
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losses_T.update(loss_T.item(), input_source.size(0))
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top1_source.update(prec1_source[0], input_source.size(0))
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top1_target.update(prec1_target[0], input_source.size(0))
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optimizer.zero_grad()
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# loss_classifier.backward(retain_graph=True)
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# optimizer.step()
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#
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# optimizer.zero_grad()
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# loss_G.backward()
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loss_T.backward()
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optimizer.step()
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scheduler.step()
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batch_time.update(time.time() - end)
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if (epoch + 1) % args.print_freq == 0 or epoch == 0:
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print('Train: [{0}/{1}]\t'
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'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
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'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
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'Loss@C {loss_c.val:.4f} ({loss_c.avg:.4f})\t'
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'Loss@G {loss_g.val:.4f} ({loss_g.avg:.4f})\t'
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'Loss@T {loss_t.val:.4f} ({loss_t.avg:.4f})\t'
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'top1S {top1S.val:.3f} ({top1S.avg:.3f})\t'
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'top1T {top1T.val:.3f} ({top1T.avg:.3f})\t'.format(
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epoch, args.epochs, batch_time=batch_time,
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data_time=data_time, loss_c=losses_classifier, loss_g=losses_G, loss_t=losses_T, top1S=top1_source,
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top1T=top1_target))
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if new_epoch_flag:
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log = open(os.path.join(args.log, 'log.txt'), 'a')
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log.write("\n")
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log.write("Train:epoch: %d, loss@min: %4f, loss@max: %4f, Top1S acc: %3f, Top1T acc: %3f" % (
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epoch, losses_classifier.avg, losses_G.avg, top1_source.avg, top1_target.avg))
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log.close()
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return source_train_loader_batch, epoch, new_epoch_flag
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def train(classnames, templates,source_train_loader, source_train_loader_batch, model,
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adapter, criterion_classifier_source, criterion_classifier_target, optimizer,
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epoch, args, scheduler, criterion, CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder):
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random.seed(1)
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batch_time = AverageMeter()
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data_time = AverageMeter()
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losses_classifier = AverageMeter()
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losses_G = AverageMeter()
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losses_T = AverageMeter()
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top1_source = AverageMeter()
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top1_target = AverageMeter()
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CLIP_Text.eval()
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CLIP_Image.eval()
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Text_Encoder.eval()
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Image_Encoder.train()
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logit_scale = 4.60517
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logit_scale = math.exp(logit_scale)
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model.eval()
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adapter.train()
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new_epoch_flag = False
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end = time.time()
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concatenatedCELoss = ConcatenatedCELoss(num_classes=len(classnames)).cuda()
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try:
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(image, label,_) = source_train_loader_batch.__next__()[1]
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except StopIteration:
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epoch = epoch + 1
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new_epoch_flag = True
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source_train_loader_batch = enumerate(source_train_loader)
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(image, label,_) = source_train_loader_batch.__next__()[1]
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target_target = label.cuda()
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#自监督标签
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label_self_supervised = label.cuda()
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indices = torch.randperm(len(label))
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target_source = label[indices].cuda()
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# target_source = label.cuda()
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input_target = image.cuda()
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zeroshot_weights = []
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for i in range(len(target_source)):
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features, eot_indices = zeroshot_classifier(classnames[target_source[i]], templates, CLIP_Text)
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class_embeddings = Text_Encoder(features, eot_indices)
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class_embeddings = class_embeddings / class_embeddings.norm(dim=-1, keepdim=True)
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class_embedding = class_embeddings.mean(dim=0)
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class_embedding = class_embedding / class_embedding.norm()
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class_embedding = class_embedding / class_embedding.norm(dim=-1, keepdim=True)
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zeroshot_weights.append(class_embedding)
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zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
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input_source = zeroshot_weights.T
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data_time.update(time.time() - end)
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target_target_temp = target_target + len(classnames)
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target_source_temp = target_source + len(classnames)
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target_target_temp = target_target_temp.cuda()
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# clip图片编码器
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with torch.no_grad():
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input_target_temp = CLIP_Image(input_target)
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input_target_add = Image_Encoder(input_target_temp)
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# 文本直接输入全连接层
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output_source = adapter(input_source) * logit_scale
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# 输入编码图片
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output_target = adapter(input_target_add) * logit_scale
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self_zeroshot_weights = []
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for i in range(len(label_self_supervised)):
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features, eot_indices = zeroshot_classifier(classnames[label_self_supervised[i]], templates, CLIP_Text)
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class_embeddings = Text_Encoder(features, eot_indices)
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class_embeddings = class_embeddings / class_embeddings.norm(dim=-1, keepdim=True)
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class_embedding = class_embeddings.mean(dim=0)
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class_embedding = class_embedding / class_embedding.norm()
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self_zeroshot_weights.append(class_embedding)
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self_zeroshot_weights = torch.stack(self_zeroshot_weights, dim=1).cuda()
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self_input_source = self_zeroshot_weights.T
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# 自监督文本输入全连接层
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# self_output_source = adapter(self_input_source)
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# self_output_source = F.normalize(self_output_source[:,:len(classnames)])
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self_output_source = F.normalize(self_input_source)
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# 自监督图像特征
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# self_output_target = output_target / logit_scale
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# self_output_target = F.normalize(self_output_target[:,len(classnames):])
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self_output_target = F.normalize(input_target_add)
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# # 构造自监督标签0-255
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self_supervised_labels = torch.arange(self_output_source.shape[0], device="cuda:0", dtype=torch.long)
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logits_per_image = logit_scale * self_output_target @ self_output_source.T
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logits_per_text = logit_scale * self_output_source @ self_output_target.T
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loss_self_supervised_1 = (
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F.cross_entropy(logits_per_image, self_supervised_labels) +
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F.cross_entropy(logits_per_text, self_supervised_labels)
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) / 2
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# 自监督文本输入全连接层
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self_output_source = adapter(self_input_source)
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self_output_source = F.normalize(self_output_source[:,:len(classnames)])
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# self_output_source = F.normalize(self_input_source)
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# 自监督图像特征
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self_output_target = output_target / logit_scale
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self_output_target = F.normalize(self_output_target[:,len(classnames):])
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# self_output_target = F.normalize(input_target_add)
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# # 构造自监督标签0-255
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self_supervised_labels = torch.arange(self_output_source.shape[0], device="cuda:0", dtype=torch.long)
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logits_per_image = logit_scale * self_output_target @ self_output_source.T
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logits_per_text = logit_scale * self_output_source @ self_output_target.T
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loss_self_supervised_2 = (
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F.cross_entropy(logits_per_image, self_supervised_labels) +
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F.cross_entropy(logits_per_text, self_supervised_labels)
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) / 2
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loss_self_supervised=loss_self_supervised_2+loss_self_supervised_1
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# 有监督分类的交叉熵损失
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loss_task_s_Cs = criterion(output_source[:, :len(classnames)], target_source)
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loss_task_s_Ct = criterion(output_target[:, len(classnames):], target_target)
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# 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。
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loss_domain_st_Cst_part1 = criterion(output_source,target_source)
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loss_domain_st_Cst_part2 = criterion(output_target, target_target_temp)
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# 类级别混淆
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loss_category_st_G = 0.5 * criterion(output_target, target_target) + 0.5 * criterion(output_source,
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target_source_temp)
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# 域级别混淆
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# loss_domain_st_G = 0.5 * criterion_classifier_target(output_source) + 0.5 * criterion_classifier_source(
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# output_target)
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lam = 2 / (1 + math.exp(-1 * 10 * epoch / args.epochs)) - 1
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# if(epoch<30):
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# self_lam= 5
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# else:
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self_lam=0.6
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loss_confusion_target = concatenatedCELoss(output_target)
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loss_classifier = loss_task_s_Cs + loss_task_s_Ct + loss_domain_st_Cst_part1 + loss_domain_st_Cst_part2
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loss_G = loss_category_st_G+lam*loss_confusion_target
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loss_T = loss_G + loss_classifier+self_lam*loss_self_supervised
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prec1_source, _ = accuracy(output_source.data[:, :len(classnames)], target_source, topk=(1, 5))
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prec1_target, _ = accuracy(output_target.data[:, len(classnames):], target_target, topk=(1, 5))
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losses_classifier.update(loss_classifier.item(), input_source.size(0))
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losses_G.update(loss_G.item(), input_source.size(0))
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losses_T.update(loss_T.item(), input_source.size(0))
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top1_source.update(prec1_source[0], input_source.size(0))
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top1_target.update(prec1_target[0], input_source.size(0))
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optimizer.zero_grad()
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# loss_classifier.backward(retain_graph=True)
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# optimizer.step()
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#
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# optimizer.zero_grad()
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# loss_G.backward()
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loss_T.backward()
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optimizer.step()
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scheduler.step()
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batch_time.update(time.time() - end)
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if (epoch + 1) % args.print_freq == 0 or epoch == 0:
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print('Train: [{0}/{1}]\t'
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'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
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'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
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'Loss@C {loss_c.val:.4f} ({loss_c.avg:.4f})\t'
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'Loss@G {loss_g.val:.4f} ({loss_g.avg:.4f})\t'
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'Loss@T {loss_t.val:.4f} ({loss_t.avg:.4f})\t'
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'top1S {top1S.val:.3f} ({top1S.avg:.3f})\t'
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'top1T {top1T.val:.3f} ({top1T.avg:.3f})\t'.format(
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epoch, args.epochs, batch_time=batch_time,
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data_time=data_time, loss_c=losses_classifier, loss_g=losses_G, loss_t=losses_T, top1S=top1_source,
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top1T=top1_target))
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if new_epoch_flag:
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log = open(os.path.join(args.log, 'log.txt'), 'a')
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log.write("\n")
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log.write("Train:epoch: %d, loss@min: %4f, loss@max: %4f, Top1S acc: %3f, Top1T acc: %3f" % (
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epoch, losses_classifier.avg, losses_G.avg, top1_source.avg, top1_target.avg))
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log.close()
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return source_train_loader_batch, epoch, new_epoch_flag
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best_target_acc = 0
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best_epoch=0
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def validate(classnames, templates,val_loader, model, adapter, epoch, args, zero_shots, criterion, CLIP_Text, Text_Encoder, CLIP_Image,
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Image_Encoder):
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global best_target_acc
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global best_epoch
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batch_time = AverageMeter()
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losses_source = AverageMeter()
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losses_target = AverageMeter()
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top1_source = AverageMeter()
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top1_target = AverageMeter()
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zero_acc_I_acc = AverageMeter()
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clip_acc_aver = AverageMeter()
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Compu4_acc = AverageMeter()
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# switch to evaluate mode
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CLIP_Text.eval()
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CLIP_Image.eval()
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Text_Encoder.eval()
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Image_Encoder.eval()
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model.eval()
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adapter.eval()
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end = time.time()
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logit_scale = 4.60517
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logit_scale = math.exp(logit_scale)
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for i, (image, label,_) in enumerate(val_loader):
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image = image.cuda()
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label = label.cuda()
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zeroshot_weights = []
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for j in range(len(label)):
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features, eot_indices = zeroshot_classifier(classnames[label[j]], templates, CLIP_Text)
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with torch.no_grad():
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class_embeddings = Text_Encoder(features, eot_indices)
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class_embeddings = class_embeddings / class_embeddings.norm(dim=-1, keepdim=True)
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class_embedding = class_embeddings.mean(dim=0)
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class_embedding = class_embedding / class_embedding.norm()
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class_embedding = class_embedding / class_embedding.norm(dim=-1, keepdim=True)
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zeroshot_weights.append(class_embedding)
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zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda()
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input_source = zeroshot_weights
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input_source = input_source.T
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input_target = image.cuda()
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target_target = label.cuda()
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target_source = label.cuda()
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|
|
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# clip图片编码器
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with torch.no_grad():
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input_target_temp = CLIP_Image(input_target)
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input_target_add = Image_Encoder(input_target_temp)
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#output_source = adapter(input_source) * logit_scale
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output_target = adapter(input_target_add) * logit_scale
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output_source=output_target
|
|
|
|
# 3
|
|
loss_source = criterion(output_source[:, :len(classnames)], target_target)
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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))
|
|
if max(top1_target.avg,top1_source.avg) > best_target_acc:
|
|
best_target_acc = max(top1_target.avg,top1_source.avg)
|
|
best_epoch=epoch
|
|
print('best_epoch', best_epoch,' * Current_best_target@T:', best_target_acc.item())
|
|
|
|
log = open(os.path.join(args.log, 'log.txt'), 'a')
|
|
log.write("\n")
|
|
log.write(" Test:epoch: %d, LS: %4f, LT: %4f, Top1S: %3f, Top1T: %3f" % \
|
|
(epoch, losses_source.avg, losses_target.avg, top1_source.avg, top1_target.avg))
|
|
log.close()
|
|
return best_target_acc.item()
|
|
|
|
|
|
class AverageMeter(object):
|
|
"""Computes and stores the average and current value"""
|
|
|
|
def __init__(self):
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.val = 0
|
|
self.avg = 0
|
|
self.sum = 0
|
|
self.count = 0
|
|
|
|
def update(self, val, n=1):
|
|
self.val = val
|
|
self.sum += val * n
|
|
self.count += n
|
|
self.avg = self.sum / self.count
|
|
|
|
|
|
def accuracy(output, target, topk=(1,)):
|
|
"""Computes the precision@k for the specified values of k"""
|
|
maxk = max(topk)
|
|
batch_size = target.size(0)
|
|
_, pred = output.topk(maxk, 1, True, True)
|
|
pred = pred.t()
|
|
_2, pred2 = output.topk(1, 1, True, True)
|
|
a = target.view(1, -1)
|
|
correct = pred.eq(target.view(1, -1).expand_as(pred))
|
|
# print(correct)
|
|
res = []
|
|
for k in topk:
|
|
correct_k = correct[:k].contiguous().view(-1).float().sum(0, keepdim=True)
|
|
res.append(correct_k.mul_(100.0 / batch_size))
|
|
return res
|