From 73c901a18cce59457c65490324f5858659434fac Mon Sep 17 00:00:00 2001 From: leewlving Date: Tue, 18 Jun 2024 22:40:19 +0800 Subject: [PATCH] new update 2 modality --- train/hash_train.py | 81 ++++----------------------------------------- train/text_train.py | 55 +++++++----------------------- 2 files changed, 19 insertions(+), 117 deletions(-) diff --git a/train/hash_train.py b/train/hash_train.py index a42f011..7bab447 100644 --- a/train/hash_train.py +++ b/train/hash_train.py @@ -112,21 +112,19 @@ class Trainer(TrainBase): text_var_representation[str(centroid.astype(int))]= text_var[i] return text_representation, text_var_representation - def target_adv(self, image, negetive_code,negetive_mean,negative_var, positive_code,positive_mean,positive_var, - beta=10 ,epsilon=0.03125, alpha=3/255, num_iter=1500, temperature=0.05): + def target_adv(self, image, negetive_code,negetive_mean,negative_var, positive_code,positive_mean,positive_var + ,epsilon=0.03125, alpha=3/255, num_iter=1500): delta = torch.zeros_like(image,requires_grad=True) - # 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=F.triplet_margin_with_distance_loss(anchor, positive_code,negetive_code, distance_function=nn.CosineSimilarity()) negative_dist=(anchor-negetive_mean)**2 / negative_var positive_dist=(anchor-positive_mean)**2 /positive_var - negatives=torch.exp(negative_dist / temperature) - positives= torch.exp(positive_dist / temperature) - loss= torch.log(positives/(positives+negatives)).mean() + beta* loss1 + negatives=torch.exp(negative_dist / self.args.temperature) + positives= torch.exp(positive_dist / self.args.temperature) + loss= torch.log(positives/(positives+negatives)).mean() + self.args.beta* loss1 loss.backward(retain_graph=True) delta.data = delta - alpha * delta.grad.detach().sign() delta.data =clamp(delta, image).clamp(-epsilon, epsilon) @@ -192,7 +190,6 @@ class Trainer(TrainBase): 'r_txt': retrieval_txt, 'adv_l': adv_labels, 'r_l': retrieval_labels - # 'q_l':query_labels # 'pr': pr, # 'pr_t': pr_t } @@ -204,28 +201,9 @@ class Trainer(TrainBase): - def train(self): - self.logger.info("Start train.") - - 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}") - - def make_hash_code(self, code: list) -> torch.Tensor: - code = torch.stack(code) - # print(code.shape) - code = code.permute(1, 0, 2) - hash_code = torch.argmax(code, dim=-1) - hash_code[torch.where(hash_code == 0)] = -1 - hash_code = hash_code.float() - - return hash_code def get_code(self, data_loader, length: int): @@ -247,9 +225,7 @@ class Trainer(TrainBase): - def valid_attack(self,adv_images, texts, adv_labels): - save_dir = os.path.join(self.args.save_dir, "adv_PR_cruve") - os.makedirs(save_dir, exist_ok=True) + @@ -287,49 +263,6 @@ class Trainer(TrainBase): self.logger.info(">>>>>> save all data!") - # def valid(self, epoch): - # self.logger.info("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") - # mAPi2t = calc_map_k(query_img, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank) - # # print("map map") - # mAPt2i = calc_map_k(query_txt, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank) - # mAPi2i = calc_map_k(query_img, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank) - # mAPt2t = calc_map_k(query_txt, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank) - # if self.max_mapi2t < mAPi2t: - # self.best_epoch_i = epoch - # self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t") - # self.max_mapi2t = max(self.max_mapi2t, mAPi2t) - # if self.max_mapt2i < mAPt2i: - # self.best_epoch_t = epoch - # self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="t2i") - # self.max_mapt2i = max(self.max_mapt2i, mAPt2i) - # self.logger.info(f">>>>>> [{epoch}/{self.args.epochs}], MAP(i->t): {mAPi2t}, MAP(t->i): {mAPt2i}, MAP(t->t): {mAPt2t}, MAP(i->i): {mAPi2i}, \ - # MAX MAP(i->t): {self.max_mapi2t}, MAX MAP(t->i): {self.max_mapt2i}") - - def save_mat(self, query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t"): - - save_dir = os.path.join(self.args.save_dir, "PR_cruve") - os.makedirs(save_dir, exist_ok=True) - - query_img = query_img.cpu().detach().numpy() - query_txt = query_txt.cpu().detach().numpy() - retrieval_img = retrieval_img.cpu().detach().numpy() - retrieval_txt = retrieval_txt.cpu().detach().numpy() - query_labels = self.query_labels.numpy() - retrieval_labels = self.retrieval_labels.numpy() - - result_dict = { - 'q_img': query_img, - 'q_txt': query_txt, - 'r_img': retrieval_img, - 'r_txt': retrieval_txt, - 'q_l': query_labels, - 'r_l': retrieval_labels - } - scio.savemat(os.path.join(save_dir, str(self.args.output_dim) + "-ours-" + self.args.dataset + "-" + mode_name + ".mat"), result_dict) - self.logger.info(f">>>>>> save best {mode_name} data!") + diff --git a/train/text_train.py b/train/text_train.py index 0ee3ff2..3a4c133 100644 --- a/train/text_train.py +++ b/train/text_train.py @@ -244,6 +244,15 @@ class Trainer(TrainBase): return import_scores.sum(dim=-1) + def adv_loss(self,anchor, negetive_code,negetive_mean,negative_var, positive_code,positive_mean,positive_var): + loss1=F.triplet_margin_with_distance_loss(anchor, positive_code,negetive_code, distance_function=nn.CosineSimilarity(),reduction='none') + negative_dist=(anchor-negetive_mean)**2 / negative_var + positive_dist=(anchor-positive_mean)**2 /positive_var + negatives=torch.exp(negative_dist / self.args.temperature) + positives= torch.exp(positive_dist / self.args.temperature) + loss= torch.log(positives/(positives+negatives)) + self.args.beta* loss1 + return loss + def target_adv(self, text_tokens, negetive_code,negetive_mean,negative_var, positive_code,positive_mean,positive_var, beta=10 ,epsilon=0.03125, alpha=3/255, num_iter=1500, temperature=0.05): @@ -296,8 +305,7 @@ class Trainer(TrainBase): replace_text_input = self.clip_tokenizer(replace_texts).to(device) replace_embeds = self.model.encode_text(replace_text_input) - criterion = torch.nn.KLDivLoss(reduction='none') - loss = criterion(replace_embeds.log_softmax(dim=-1), clean_embeds[i].softmax(dim=-1).repeat(len(replace_embeds), 1)) + loss = self.adv_loss(replace_embeds, negetive_code,negetive_mean,negative_var,positive_code,positive_mean,positive_var) loss = loss.sum(dim=-1) candidate_idx = loss.argmax() final_words[top_index[0]] = available_substitutes[candidate_idx] @@ -375,28 +383,11 @@ class Trainer(TrainBase): - def train(self): - self.logger.info("Start train.") - - 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}") + - def make_hash_code(self, code: list) -> torch.Tensor: - - code = torch.stack(code) - # print(code.shape) - code = code.permute(1, 0, 2) - hash_code = torch.argmax(code, dim=-1) - hash_code[torch.where(hash_code == 0)] = -1 - hash_code = hash_code.float() - - return hash_code + def get_code(self, data_loader, length: int): @@ -456,28 +447,6 @@ class Trainer(TrainBase): self.logger.info(">>>>>> save all data!") - # def valid(self, epoch): - # self.logger.info("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") - # mAPi2t = calc_map_k(query_img, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank) - # # print("map map") - # mAPt2i = calc_map_k(query_txt, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank) - # mAPi2i = calc_map_k(query_img, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank) - # mAPt2t = calc_map_k(query_txt, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank) - # if self.max_mapi2t < mAPi2t: - # self.best_epoch_i = epoch - # self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t") - # self.max_mapi2t = max(self.max_mapi2t, mAPi2t) - # if self.max_mapt2i < mAPt2i: - # self.best_epoch_t = epoch - # self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="t2i") - # self.max_mapt2i = max(self.max_mapt2i, mAPt2i) - # self.logger.info(f">>>>>> [{epoch}/{self.args.epochs}], MAP(i->t): {mAPi2t}, MAP(t->i): {mAPt2i}, MAP(t->t): {mAPt2t}, MAP(i->i): {mAPi2i}, \ - # MAX MAP(i->t): {self.max_mapi2t}, MAX MAP(t->i): {self.max_mapt2i}") - def save_mat(self, query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t"): save_dir = os.path.join(self.args.save_dir, "PR_cruve")