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更新 train/hash_train.py

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liwenyun 2024-06-08 16:03:17 +08:00
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commit 5cce92d540
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train/hash_train.py
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@ -9,9 +9,7 @@ import scipy.io as scio
import numpy as np
from .base import TrainBase
from torch.optim import Adam
import torch.nn.functional as F
# from model.optimization import BertAdam
from model.optimization import BertAdam
# from model.GAN import Discriminator, Generator, LabelEncoder, GANLoss
from utils import get_args, calc_neighbor, cosine_similarity, euclidean_similarity,find_indices
from utils.calc_utils import calc_map_k_matrix as calc_map_k
@ -19,6 +17,8 @@ from dataset.dataloader import dataloader
import open_clip
# from transformers import BertModel
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def clamp(delta, clean_imgs):
clamp_imgs = (delta.data + clean_imgs.data).clamp(0, 1)
@ -26,8 +26,6 @@ def clamp(delta, clean_imgs):
return clamp_delta
class Trainer(TrainBase):
def __init__(self,
@ -35,63 +33,18 @@ class Trainer(TrainBase):
args = get_args()
super(Trainer, self).__init__(args, rank)
self.logger.info("dataset len: {}".format(len(self.train_loader.dataset)))
text_mean_representation, text_var_representation=self.generate_mapping()
self.text_mean=text_mean_representation
self.text_var=text_var_representation
text_representation, text_representation=self.generate_mapping()
self.image_representation=text_representation
self.text_representation=text_representation
self.device=rank
# self.run()
def _init_model(self):
self.logger.info("init model.")
# self.generator=Generator()
# linear = False
# if self.args.hash_layer == "linear":
# linear = True
# self.bert=BertModel.from_pretrained("bert-base-cased", output_hidden_states=True).to(self.rank)
# self.bert.eval()
# self.logger.info("ViT+GPT!")
# HashModel = DCMHT
# if self.args.victim_model == 'JDSH':
# from model.JDSH import TxtNet, ImgNet
# # self.img_model = HashModel(outputDim=self.args.output_dim, clipPath=self.args.clip_path,
# # writer=self.writer, logger=self.logger, is_train=self.args.is_train, linear=linear).to(self.rank)
# self.img_model=ImgNet(code_len=self.args.output_dim).to(self.rank)
# self.txt_model=TxtNet(code_len=self.args.output_dim, txt_feat_len=self.args.txt_dim).to(self.rank)
# path=os.path.join(self.args.checkpoints,self.args.victim_model+'/'+str(self.args.output_dim)+'_'+self.args.dataset+'latest.pth')
# checkpoint=torch.load(path)
# self.img_model.load_state_dict(torch.load(checkpoint['ImgNet'], map_location=f"cuda:{self.rank}"))
# self.txt_model.load_state_dict(torch.load(checkpoint['TxtNet'], map_location=f"cuda:{self.rank}"))
# self.img_model.eval()
# self.txt_model.eval()
# elif self.args.victim_model == 'DJSRH':
# self.victim_model = HashModel(outputDim=self.args.output_dim, clipPath=self.args.clip_path,
# writer=self.writer, logger=self.logger, is_train=self.args.is_train, linear=linear).to(self.rank)
# self.victim_model.load_state_dict(torch.load(self.args.pretrained, map_location=f"cuda:{self.rank}"))
# elif self.args.victim_model == 'SSAH':
# self.victim_model = HashModel(outputDim=self.args.output_dim, clipPath=self.args.clip_path,
# writer=self.writer, logger=self.logger, is_train=self.args.is_train, linear=linear).to(self.rank)
# self.victim_model.load_state_dict(torch.load(self.args.pretrained, map_location=f"cuda:{self.rank}"))
# elif self.args.victim_model == 'DCHUC':
# self.victim_model = HashModel(outputDim=self.args.output_dim, clipPath=self.args.clip_path,
# writer=self.writer, logger=self.logger, is_train=self.args.is_train, linear=linear).to(self.rank)
# self.victim_model.load_state_dict(torch.load(self.args.pretrained, map_location=f"cuda:{self.rank}"))
# if self.args.pretrained != "" and os.path.exists(self.args.pretrained):
# self.logger.info("load pretrained model.")
# self.model.load_state_dict(torch.load(self.args.pretrained, map_location=f"cuda:{self.rank}"))
model_clip, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', device=self.device)
model_clip, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16', device=device)
self.model= model_clip
self.model.eval()
self.model.float()
self.optimizer =Adam(self.model.visual.parameters,lr=self.args.lr ,betas=[0.9,0.98] )
# self.optimizer = BertAdam([
# {'params': self.model.clip.parameters(), 'lr': self.args.clip_lr},
# {'params': self.model.image_hash.parameters(), 'lr': self.args.lr},
# {'params': self.model.text_hash.parameters(), 'lr': self.args.lr}
# ], lr=self.args.lr, warmup=self.args.warmup_proportion, schedule='warmup_cosine',
# b1=0.9, b2=0.98, e=1e-6, t_total=len(self.train_loader) * self.args.epochs,
# weight_decay=self.args.weight_decay, max_grad_norm=1.0)
# print(self.model)
def _init_dataset(self):
self.logger.info("init dataset.")
@ -110,7 +63,7 @@ class Trainer(TrainBase):
self.train_labels = train_data.get_all_label()
self.query_labels = query_data.get_all_label()
self.retrieval_labels = retrieval_data.get_all_label()
# self.args.retrieval_num = len(self.retrieval_labels)
self.args.retrieval_num = len(self.retrieval_labels)
self.logger.info(f"query shape: {self.query_labels.shape}")
self.logger.info(f"retrieval shape: {self.retrieval_labels.shape}")
self.train_loader = DataLoader(
@ -134,46 +87,42 @@ class Trainer(TrainBase):
pin_memory=True,
shuffle=True
)
def generate_mapping(self):
text_train=[]
label_train=[]
# image_train=[]
# self.change_state(mode="valid")
for image, text, label, index in self.train_loader:
# image=image.to(self.device, non_blocking=True)
text=text.to(self.device, non_blocking=True)
text=text.to(device, non_blocking=True)
# print(self.model.vocab_size)
temp_text=self.model.encode_text(text)
# temp_image=self.model.encode_image(image)
# image_train.append(temp_image.cpu().detach().numpy())
text_train.append(temp_text.cpu().detach().numpy())
label_train.append(label.detach().numpy())
text_train=np.concatenate(text_train, axis=0)
# image_train=np.concatenate(image_train, axis=0)
label_train=np.concatenate(label_train, axis=0)
label_unipue=np.unique(label_train,axis=0)
# image_centroids =np.stack([image_train[find_indices(label_train,label_unipue[i])].mean(axis=0) for i in range(len(label_unipue))], axis=0)
text_centroids =np.stack([text_train[find_indices(label_train,label_unipue[i])].mean(axis=0) for i in range(len(label_unipue))], axis=0)
text_var=np.stack([text_train[find_indices(label_train,label_unipue[i])].var(axis=0) for i in range(len(label_unipue))], axis=0)
text_mean_representation = {}
text_representation = {}
text_var_representation = {}
for i, centroid in enumerate(label_unipue):
text_mean_representation[centroid.tobytes()] = text_centroids[i]
text_representation[centroid.tobytes()] = text_centroids[i]
text_var_representation[centroid.tobytes()]= text_var[i]
return text_mean_representation, text_var_representation
return text_representation, text_var_representation
def target_adv(self, image, positive, positive_mean,positive_var, negative, negative_mean, negative_var,
def target_adv(self, image, positive, negative,
epsilon=0.03125, alpha=3/255, num_iter=100):
delta = torch.zeros_like(image,requires_grad=True)
# clean_output = self.model.encode_image(image)
one=torch.zeros_like(positive)
alienation_loss = nn.TripletMarginLoss(margin=1.0, p=2, eps=1e-7)
for i in range(num_iter):
self.model.zero_grad()
anchor=self.model.encode_image(image+delta)
loss1=alienation_loss(anchor, positive, negative)
loss=loss1 + self.args.beta * self.distribution_loss(anchor,positive_mean,positive_var,negative_mean, negative_var)
loss=alienation_loss(anchor, positive, negative)
loss.backward(retain_graph=True)
delta.data = delta - alpha * delta.grad.detach().sign()
@ -182,18 +131,14 @@ class Trainer(TrainBase):
return delta.detach()
def train_epoch(self):
def train_epoch(self, epoch):
self.change_state(mode="valid")
# self.logger.info(">>>>>> epochs: %d/%d"%(epoch, self.args.epochs))
self.logger.info(">>>>>> epochs: %d/%d"%(epoch, self.args.epochs))
all_loss = 0
times = 0
save_dir = os.path.join(self.args.save_dir, "adv_PR_cruve")
os.makedirs(save_dir, exist_ok=True)
# query_img, query_txt = self.get_code(self.query_loader, self.args.query_num) if self.args.hash_layer == "select" else super().get_code(self.query_loader, self.args.query_num)
retrieval_img, retrieval_txt = self.get_code(self.retrieval_loader, self.args.retrieval_num) if self.args.hash_layer == "select" else super().get_code(self.retrieval_loader, self.args.retrieval_num)
adv_images=[]
adv_labels=[]
# target_texts=[]
texts=[]
for image, text, label, index in self.train_loader:
self.global_step += 1
times += 1
@ -204,63 +149,30 @@ class Trainer(TrainBase):
image = image.to(self.rank, non_blocking=True)
text = text.to(self.rank, non_blocking=True)
index = index.numpy()
# image_anchor=self.image_representation(label.detach().cpu().numpy())
negetive_mean=self.text_mean(label.detach().cpu().numpy())
negetive_var=self.text_var(label.detach().cpu().numpy())
# negetive_code=np.concatenate([image_anchor,text_anchor],axis=0).mean(axis=0)
negetive_code=self.model.encode_text(text)
image_anchor=self.image_representation(label.detach().cpu().numpy())
text_anchor=self.text_representation(label.detach().cpu().numpy())
negetive_code=np.concatenate([image_anchor,text_anchor],axis=0).mean(axis=0)
target_label=label.flip(dims=[0])
# target_image_anchor=self.image_representation(target_label.detach().cpu().numpy())
positive_mean=self.text_mean(target_label.detach().cpu().numpy())
positive_var=self.text_var(target_label.detach().cpu().numpy())
# positive_code=np.concatenate([target_image_anchor,target_text_anchor],axis=0).mean(axis=0)
positive_code=self.model.encode_text(text.flip(dims=[0]))
# print("text shape:", text.shape)
# index = index.numpy()
# print(text.shape)
delta=self.target_adv(image,positive_code,torch.from_numpy(positive_mean).to(self.rank, non_blocking=True), torch.from_numpy(positive_var).to(self.rank, non_blocking=True),
negetive_code, torch.from_numpy(negetive_mean).to(self.rank, non_blocking=True), torch.from_numpy(negetive_var).to(self.rank, non_blocking=True))
target_image_anchor=self.image_representation(target_label.detach().cpu().numpy())
target_text_anchor=self.text_representation(target_label.detach().cpu().numpy())
positive_code=np.concatenate([target_image_anchor,target_text_anchor],axis=0).mean(axis=0)
delta=self.target_adv(image,torch.from_numpy(positive_code).to(self.rank, non_blocking=True),
torch.from_numpy(negetive_code).to(self.rank, non_blocking=True))
adv_image=delta+image
adv_images.append(self.model.encode_image(adv_image))
adv_images.append(adv_image)
adv_labels.append(target_label)
# target_texts.append(self.model.encode_text(text))
adv_image=torch.cat(adv_image).to(self.device)
adv_labels=torch.cat(adv_labels).to(self.device)
mAPi2t = calc_map_k(adv_images, retrieval_txt, adv_labels, self.retrieval_labels, None, self.rank)
mAPt2t = calc_map_k(adv_images, retrieval_img, adv_labels, self.retrieval_labels, None, self.rank)
self.logger.info(f">>>>>> t-MAP(i->t): {mAPi2t}, t-MAP(t->t): {mAPt2t}")
adv_images = adv_images.cpu().detach().numpy()
# query_txt = query_txt.cpu().detach().numpy()
retrieval_img = retrieval_img.cpu().detach().numpy()
retrieval_txt = retrieval_txt.cpu().detach().numpy()
adv_labels = adv_labels.numpy()
retrieval_labels = self.retrieval_labels.numpy()
result_dict = {
'adv_img': adv_images,
# 'q_txt': query_txt,
'r_img': retrieval_img,
'r_txt': retrieval_txt,
'adv_l': adv_labels,
'r_l': retrieval_labels
}
scio.savemat(os.path.join(save_dir, str(self.args.output_dim) + "-ours-" + self.args.dataset + ".mat"), result_dict)
self.logger.info(">>>>>> save all data!")
# self.logger.info(f">>>>>> [{epoch}/{self.args.epochs}] loss: {all_loss.data / (len(self.train_loader))}, lr: {'-'.join([str('%.9f'%itm) for itm in sorted(list(set(self.optimizer.get_lr())))])}")
# return adv_images, texts, adv_labels
texts.append(text)
return adv_images, texts, adv_labels
def train(self):
self.logger.info("Start train.")
self.valid()
self.train_epoch()
# self.valid()
# for epoch in range(self.args.epochs):
# self.train_epoch()
# self.valid(epoch)
# self.save_model(epoch)
for epoch in range(self.args.epochs):
self.train_epoch(epoch)
self.valid(epoch)
self.save_model(epoch)
self.logger.info(f">>>>>>> FINISHED >>>>>> Best epoch, I-T: {self.best_epoch_i}, mAP: {self.max_mapi2t}, T-I: {self.best_epoch_t}, mAP: {self.max_mapt2i}")
@ -271,15 +183,13 @@ class Trainer(TrainBase):
return b_loss
def distribution_loss(self, x: torch.Tensor, positive_mean,positive_var, negative_mean, negative_var):
def distribution_loss(self, a: torch.Tensor, b: torch.Tensor, label_sim: torch.Tensor):
"""
"""
norm_fun= lambda mean, var, x: 50- torch.mean(torch.exp(-(x-mean) **2 /(2*var)) /(2* torch.pi * var))
positive_distribution=norm_fun(positive_mean,positive_var,x)
negative_distribution=norm_fun(negative_mean,negative_var,x)
# alienation_loss=nn.MarginRankingLoss()
return F.margin_ranking_loss(positive_distribution,negative_distribution,1)
kl_divergence = torch.mean(a * torch.log(a / (b + 0.001)))
print("mean", torch.mean(a - b))
print("kl", kl_divergence)
return kl_divergence
def similarity_loss(self, a: torch.Tensor, b: torch.Tensor, label_sim: torch.Tensor, threshold=0.05):
@ -336,9 +246,9 @@ class Trainer(TrainBase):
image = image.to(self.rank, non_blocking=True)
text = text.to(self.rank, non_blocking=True)
index = index.numpy()
image_hash=self.model.encode_image(image)
# text_feat=self.bert(text)[0]
text_hash=self.model.encode_text(text)
image_hash=self.img_model(image)
text_feat=self.bert(text)[0]
text_hash=self.txt_model(text_feat)
img_buffer[index, :] = image_hash.data
text_buffer[index, :] = text_hash.data
@ -408,7 +318,7 @@ class Trainer(TrainBase):
def valid(self, epoch):
self.logger.info("Valid.")
# self.change_state(mode="valid")
self.change_state(mode="valid")
query_img, query_txt = self.get_code(self.query_loader, self.args.query_num) if self.args.hash_layer == "select" else super().get_code(self.query_loader, self.args.query_num)
retrieval_img, retrieval_txt = self.get_code(self.retrieval_loader, self.args.retrieval_num) if self.args.hash_layer == "select" else super().get_code(self.retrieval_loader, self.args.retrieval_num)
# print("get all code")