481 lines
21 KiB
Python
481 lines
21 KiB
Python
import os
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import time
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from clip import clip
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import torch.nn as nn
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import numpy as np
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import torch.optim
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from opts import opts # The options for the project
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# from trainer import validate # For the validate (test) process
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from models.DomainClassifierTarget import DClassifierForTarget
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from models.DomainClassifierSource import DClassifierForSource
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from utils.loss_utils import TargetDiscrimLoss, ConcatenatedCELoss
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from utils.utils import prepare_directories, set_seed, get_dataset_loader, configure_clip_encoders, save_model, \
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set_adapter_weights, get_text_feature, AverageMeter, accuracy, calculate_zeroshot_weights, gpt_clip_classifier,calculate_zeroshot_weights_GPT
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from Adapter import Weight_Adapter
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import logging
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import torch.nn.functional as F
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import yaml
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import json
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import glob
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class CustomCrossAttention(nn.Module):
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def __init__(self, feature_dim):
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super(CustomCrossAttention, self).__init__()
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self.query_projection = nn.Linear(feature_dim, feature_dim)
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self.key_projection = nn.Linear(feature_dim, feature_dim)
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self.value_projection = nn.Linear(feature_dim, feature_dim)
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self.softmax = nn.Softmax(dim=-1)
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def forward(self, text_features, image_features):
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# 假设 text_features 的 batch_size < image_features 的 batch_size
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text_batch_size = text_features.size(0)
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image_batch_size = image_features.size(0)
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# 重复 text_features 以匹配 image_features 的 batch_size
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if text_batch_size < image_batch_size:
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repeat_times = image_batch_size // text_batch_size
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text_features = text_features.repeat(repeat_times, 1)
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query = self.query_projection(text_features)
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key = self.key_projection(image_features)
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value = self.value_projection(image_features)
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# 计算注意力分数
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attention_scores = torch.matmul(query, key.transpose(-2, -1))
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attention_scores = self.softmax(attention_scores)
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# 应用注意力分数到 value 上
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attended_features = torch.matmul(attention_scores, value)
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return attended_features
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# def coral_loss(source_features, target_features):
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# """
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# 计算Deep CORAL损失。
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# :param source_features: 源域特征，维度为[batch_size, feature_dim]
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# :param target_features: 目标域特征，维度为[batch_size, feature_dim]
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# :return: CORAL损失
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# """
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# d = source_features.data.shape[1] # 特征维度
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# source_mean = torch.mean(source_features, dim=0)
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# target_mean = torch.mean(target_features, dim=0)
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# source_cov = (source_features - source_mean).T @ (source_features - source_mean) / (source_features.shape[0] - 1)
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# target_cov = (target_features - target_mean).T @ (target_features - target_mean) / (target_features.shape[0] - 1)
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# coral_loss = torch.sum(torch.pow(source_cov - target_cov, 2)) # / (4*d*d)
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# return coral_loss
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def coral_loss(source_features, target_features):
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"""
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计算Deep CORAL损失。
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:param source_features: 源域特征，维度为[batch_size, feature_dim]
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:param target_features: 目标域特征，维度为[batch_size, feature_dim]
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:return: CORAL损失
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"""
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# 特征维度
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d = source_features.data.shape[1]
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# 计算均值
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source_mean = torch.mean(source_features, dim=0)
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target_mean = torch.mean(target_features, dim=0)
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# 计算均值差异
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# mean_diff = torch.pow(source_mean - target_mean, 2).sum().sqrt().mean()
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mean_diff = torch.pow(source_mean - target_mean, 2).mean()
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# 计算协方差矩阵
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source_cov = (source_features - source_mean).T @ (source_features - source_mean) / (source_features.shape[0] - 1)
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target_cov = (target_features - target_mean).T @ (target_features - target_mean) / (target_features.shape[0] - 1)
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# 计算协方差矩阵差异的平均值
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# cov_diff = torch.pow(source_cov - target_cov, 2).sum().sqrt().mean()
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cov_diff = torch.pow(source_cov - target_cov, 2).mean()
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# 返回均值差异和协方差矩阵差异的和
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total_coral_loss = mean_diff + cov_diff
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return total_coral_loss
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def shuffle_data(weights, labels):
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# 生成索引
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indices = torch.randperm(len(weights))
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# 使用索引来打乱数据和标签
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shuffled_weights = weights[indices]
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shuffled_labels = labels[indices]
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return shuffled_weights, shuffled_labels
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def compute_kernel(x, y):
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"""
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计算高斯核矩阵
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"""
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x_size = x.size(0)
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y_size = y.size(0)
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dim = x.size(1)
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tiled_x = x.view(x_size, 1, dim).repeat(1, y_size, 1)
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tiled_y = y.view(1, y_size, dim).repeat(x_size, 1, 1)
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kernel_matrix = torch.exp(-torch.mean((tiled_x - tiled_y) ** 2, dim=2) / float(dim))
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return kernel_matrix
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def mmd_loss(source_features, target_features):
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"""
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计算源域和目标域特征之间的最大均值差异（MMD）损失
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"""
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source_kernel = compute_kernel(source_features, source_features)
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target_kernel = compute_kernel(target_features, target_features)
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cross_kernel = compute_kernel(source_features, target_features)
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mmd = source_kernel.mean() + target_kernel.mean() - 2 * cross_kernel.mean()
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return mmd
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def train(classnames, templates, source_train_loader, source_train_loader_batch, model,
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adapter, optimizer,
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epoch, args, scheduler, criterion, CLIP_Text, Text_Encoder, CLIP_Image, Image_Encoder, gpt_weight, gpt_label,gpt3_prompt):
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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_CR = 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.train()
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Image_Encoder.train()
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model.eval()
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logit_scale = model.logit_scale.exp()
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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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# input_source = calculate_zeroshot_weights(classnames, target_source, templates, CLIP_Text, Text_Encoder)
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input_source = calculate_zeroshot_weights_GPT(classnames, target_source, templates, CLIP_Text, Text_Encoder,gpt3_prompt)
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gpt_weight, gpt_label=shuffle_data(gpt_weight, gpt_label)
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# input_source = torch.cat((
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# input_source, gpt_weight
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# ), dim=0)
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# target_source = torch.cat((
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# target_source, gpt_label
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# ), dim=0)
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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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# 计算CORAL损失
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# 总损失
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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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# 图片直接输入全连接层
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output_target = adapter(input_target_add) * logit_scale
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# self_input_source = calculate_zeroshot_weights(classnames, label_self_supervised, templates, CLIP_Text,
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# Text_Encoder)
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self_input_source = calculate_zeroshot_weights_GPT(classnames, label_self_supervised, templates, CLIP_Text,
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Text_Encoder,gpt3_prompt)
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# input_source = calculate_zeroshot_weights_GPT(classnames, target_source, templates, CLIP_Text, Text_Encoder,gpt3_prompt)
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# 计算MMD损失
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# mmd_loss_val = mmd_loss(self_input_source, input_target_add)
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# lambda_mmd=1000
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# mmd_loss_val =lambda_mmd*mmd_loss_val
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# 总损失
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coral_loss_value =2* coral_loss(self_input_source, input_target_add)
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lambda_coral = 1
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loss_1 = lambda_coral * coral_loss_value
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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_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 = 1 # 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
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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 + lambda_coral * coral_loss_value#+ mmd_loss_val
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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_CR.update(loss_G.item(), input_source.size(0))
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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_CR.update(coral_loss_value.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_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@CR {loss_cr.val:.4f} ({loss_cr.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_cr=losses_CR,loss_t=losses_T, top1S=top1_source,
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top1T=top1_target))
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return source_train_loader_batch, epoch, new_epoch_flag
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def validate(best_epoch, classnames, templates, val_loader, model, adapter, epoch, args, criterion, best_prec,
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CLIP_Text,
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Text_Encoder, CLIP_Image,
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Image_Encoder):
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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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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 = model.logit_scale.exp()
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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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input_source = calculate_zeroshot_weights(classnames, label, templates, CLIP_Text, Text_Encoder)
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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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# 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
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# 3
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loss_source = criterion(output_source[:, :len(classnames)], target_target)
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loss_target = criterion(output_target[:, len(classnames):], target_target)
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# measure accuracy and record loss
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prec1_source, _ = accuracy(output_source.data[:, :len(classnames)], target_target, 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_source.update(loss_source.item(), image.size(0))
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losses_target.update(loss_target.item(), image.size(0))
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top1_source.update(prec1_source[0], image.size(0))
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top1_target.update(prec1_target[0], image.size(0))
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# measure elapsed time
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batch_time.update(time.time() - end)
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end = time.time()
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if i % args.print_freq == 0:
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print('Test: [{0}][{1}/{2}]\t'
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'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
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'LS {lossS.val:.4f} ({lossS.avg:.4f})\t'
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'LT {lossT.val:.4f} ({lossT.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})'.format(
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epoch, i, len(val_loader), batch_time=batch_time, lossS=losses_source, lossT=losses_target,
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top1S=top1_source, top1T=top1_target))
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print(' * Top1@S {top1S.avg:.3f} Top1@T {top1T.avg:.3f}'
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.format(top1S=top1_source, top1T=top1_target))
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prec = max(top1_target.avg, top1_source.avg).item()
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if prec > best_prec:
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best_prec = max(top1_target.avg, top1_source.avg).item()
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best_epoch = epoch
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print('best_epoch', best_epoch, ' * Current_best_target@T:', best_prec)
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return prec, best_epoch
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def main():
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args = opts()
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set_seed(2023)
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model, preprocess = clip.load(args.name)
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model = model.cuda()
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model.float()
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classnames, templates, loader, train_loader,va_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)
|
||
# cfg = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
|
||
# 获取'gpt_file'文件夹下所有的.yaml文件
|
||
|
||
json_files = glob.glob('gpt_file/caltech_prompt.json')
|
||
|
||
for file_path in json_files:
|
||
# 打开并读取每个YAML文件
|
||
with open(file_path, 'r') as f:
|
||
gpt3_prompt = json.load(f)
|
||
gpt_weight = gpt_clip_classifier(classnames, gpt3_prompt, CLIP_Text, Text_Encoder)
|
||
gpt_label = torch.arange(len(classnames), device="cuda:0", dtype=torch.long)
|
||
gpt_weight, gpt_label
|
||
# 分类层
|
||
weights = set_adapter_weights(model, classnames, templates)
|
||
init_weight = torch.cat([gpt_weight.T ,gpt_weight.T], dim=1).T # adapter = Weight_Adapter(args, classnames, weights).cuda()
|
||
adapter = Weight_Adapter(args, classnames, init_weight).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
|
||
#caltech
|
||
lr_adapter = 0.0001
|
||
lr_image_encoder = 0.0000001
|
||
lr_text_encoder = 0.000001
|
||
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,
|
||
gpt_weight, gpt_label,gpt3_prompt)
|
||
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()
|