361 lines
19 KiB
Python
361 lines
19 KiB
Python
from torch.nn.modules import loss
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from model.hash_model import DCMHT as DCMHT
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import os
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from tqdm import tqdm
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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import scipy.io as scio
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from .base import TrainBase
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from model.optimization import BertAdam
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from utils import get_args, calc_neighbor, cosine_similarity, euclidean_similarity
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from utils.calc_utils import calc_map_k_matrix as calc_map_k
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from dataset.dataloader import dataloader
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class Trainer(TrainBase):
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def __init__(self,
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rank=0):
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args = get_args()
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super(Trainer, self).__init__(args, rank)
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self.logger.info("dataset len: {}".format(len(self.train_loader.dataset)))
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self.run()
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def _init_model(self):
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self.logger.info("init model.")
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linear = False
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if self.args.hash_layer == "linear":
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linear = True
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self.logger.info("ViT+GPT!")
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HashModel = DCMHT
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self.model = HashModel(outputDim=self.args.output_dim, clipPath=self.args.clip_path,
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writer=self.writer, logger=self.logger, is_train=self.args.is_train, linear=linear).to(self.rank)
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if self.args.pretrained != "" and os.path.exists(self.args.pretrained):
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self.logger.info("load pretrained model.")
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self.model.load_state_dict(torch.load(self.args.pretrained, map_location=f"cuda:{self.rank}"))
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self.model.float()
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self.optimizer = BertAdam([
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{'params': self.model.clip.parameters(), 'lr': self.args.clip_lr},
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{'params': self.model.image_hash.parameters(), 'lr': self.args.lr},
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{'params': self.model.text_hash.parameters(), 'lr': self.args.lr}
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], lr=self.args.lr, warmup=self.args.warmup_proportion, schedule='warmup_cosine',
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b1=0.9, b2=0.98, e=1e-6, t_total=len(self.train_loader) * self.args.epochs,
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weight_decay=self.args.weight_decay, max_grad_norm=1.0)
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print(self.model)
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def _init_dataset(self):
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self.logger.info("init dataset.")
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self.logger.info(f"Using {self.args.dataset} dataset.")
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self.args.index_file = os.path.join("./dataset", self.args.dataset, self.args.index_file)
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self.args.caption_file = os.path.join("./dataset", self.args.dataset, self.args.caption_file)
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self.args.label_file = os.path.join("./dataset", self.args.dataset, self.args.label_file)
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train_data, query_data, retrieval_data = dataloader(captionFile=self.args.caption_file,
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indexFile=self.args.index_file,
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labelFile=self.args.label_file,
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maxWords=self.args.max_words,
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imageResolution=self.args.resolution,
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query_num=self.args.query_num,
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train_num=self.args.train_num,
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seed=self.args.seed)
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self.train_labels = train_data.get_all_label()
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self.query_labels = query_data.get_all_label()
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self.retrieval_labels = retrieval_data.get_all_label()
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self.args.retrieval_num = len(self.retrieval_labels)
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self.logger.info(f"query shape: {self.query_labels.shape}")
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self.logger.info(f"retrieval shape: {self.retrieval_labels.shape}")
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self.train_loader = DataLoader(
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dataset=train_data,
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batch_size=self.args.batch_size,
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num_workers=self.args.num_workers,
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pin_memory=True,
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shuffle=True
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)
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self.query_loader = DataLoader(
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dataset=query_data,
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batch_size=self.args.batch_size,
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num_workers=self.args.num_workers,
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pin_memory=True,
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shuffle=True
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)
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self.retrieval_loader = DataLoader(
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dataset=retrieval_data,
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batch_size=self.args.batch_size,
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num_workers=self.args.num_workers,
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pin_memory=True,
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shuffle=True
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)
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def train_epoch(self, epoch):
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self.change_state(mode="train")
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self.logger.info(">>>>>> epochs: %d/%d"%(epoch, self.args.epochs))
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all_loss = 0
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times = 0
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for image, text, label, index in self.train_loader:
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self.global_step += 1
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times += 1
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image.float()
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if self.args.dataset not in ["flickr25k", "coco", "nuswide"]:
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label = torch.ones([image.shape[0]], dtype=torch.int)
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label = label.diag()
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# print(text.dtype)
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# text.float()
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# label.float()
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image = image.to(self.rank, non_blocking=True)
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text = text.to(self.rank, non_blocking=True)
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# print("text shape:", text.shape)
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index = index.numpy()
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# print(text.shape)
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hash_img, hash_text = self.model(image, text)
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if self.args.hash_layer == "select":
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hash_img = torch.cat(hash_img, dim=-1) if isinstance(hash_img, list) else hash_img.view(hash_img.shape[0], -1)
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hash_text = torch.cat(hash_text, dim=-1)if isinstance(hash_text, list) else hash_text.view(hash_text.shape[0], -1)
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loss = self.compute_loss(hash_img, hash_text, label, epoch, times)
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all_loss += loss
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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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())))])}")
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def train(self):
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self.logger.info("Start train.")
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for epoch in range(self.args.epochs):
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self.train_epoch(epoch)
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self.valid(epoch)
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self.save_model(epoch)
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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}")
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def bayesian_loss(self, a: torch.Tensor, b: torch.Tensor, label_sim: torch.Tensor):
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s = torch.matmul(a, b.t())
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b_loss = -torch.mean(label_sim * s - torch.log(1 + torch.exp(s)))
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return b_loss
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def distribution_loss(self, a: torch.Tensor, b: torch.Tensor, label_sim: torch.Tensor):
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"""
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"""
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kl_divergence = torch.mean(a * torch.log(a / (b + 0.001)))
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print("mean", torch.mean(a - b))
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print("kl", kl_divergence)
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return kl_divergence
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def similarity_loss(self, a: torch.Tensor, b: torch.Tensor, label_sim: torch.Tensor, threshold=0.05):
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# $\vartheta$
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vartheta = self.args.vartheta
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if self.args.sim_threshold != 0:
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threshold = self.args.sim_threshold
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similarity = (1 - cosine_similarity(a, b)) if self.args.similarity_function == "cosine" else euclidean_similarity(a, b)
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positive_similarity = similarity * label_sim
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# 只要cosine为负值的全都算为计算正确了，因为优化到2确实很难。
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negative_similarity = similarity * (1 - label_sim)
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if self.args.similarity_function == "cosine":
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positive_similarity = positive_similarity.clip(threshold) - threshold
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negative_similarity = negative_similarity.clip(max=1.)
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negative_similarity = torch.tensor([1.]).expand_as(negative_similarity).to(self.rank) * (1 - label_sim) - negative_similarity
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elif self.args.similarity_function == "euclidean":
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# 有euclidean距离可知，当有一半长度的hash码不同时，其negative_similarity距离应该是长度（concat操作将outputdim翻倍），所以这里clip掉认为认定的值
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# 人为认定的最大值是一半长度的hash码不同。
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max_value = float(self.args.output_dim * 2 * vartheta) ** 0.5
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negative_similarity = negative_similarity.clip(max=max_value)
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negative_similarity = torch.tensor([max_value]).expand_as(negative_similarity).to(self.rank) * (1 - label_sim) - negative_similarity
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if self.args.loss_type == "l1":
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positive_loss = positive_similarity.mean()
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negative_loss = negative_similarity.mean()
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elif self.args.loss_type == "l2":
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positive_loss = torch.pow(positive_similarity, 2).mean()
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negative_loss = torch.pow(negative_similarity, 2).mean()
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else:
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raise ValueError("argument of loss_type is not support.")
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return similarity, positive_loss, negative_loss
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def make_hash_code(self, code: list) -> torch.Tensor:
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code = torch.stack(code)
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# print(code.shape)
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code = code.permute(1, 0, 2)
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hash_code = torch.argmax(code, dim=-1)
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hash_code[torch.where(hash_code == 0)] = -1
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hash_code = hash_code.float()
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return hash_code
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def get_code(self, data_loader, length: int):
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img_buffer = torch.empty(length, self.args.output_dim, dtype=torch.float).to(self.rank)
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text_buffer = torch.empty(length, self.args.output_dim, dtype=torch.float).to(self.rank)
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for image, text, label, index in tqdm(data_loader):
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image = image.to(self.rank, non_blocking=True)
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text = text.to(self.rank, non_blocking=True)
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index = index.numpy()
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image_hash = self.model.encode_image(image)
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image_hash = self.make_hash_code(image_hash)
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text_hash = self.model.encode_text(text)
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text_hash = self.make_hash_code(text_hash)
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# image_hash.to(self.rank)
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# text_hash.to(self.rank)
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img_buffer[index, :] = image_hash.data
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text_buffer[index, :] = text_hash.data
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return img_buffer, text_buffer# img_buffer.to(self.rank), text_buffer.to(self.rank)
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def our_loss(self, image, text, label, epoch, times):
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loss = 0
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label_sim = calc_neighbor(label, label)
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if image.is_cuda:
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label_sim = label_sim.to(image.device)
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intra_similarity, intra_positive_loss, intra_negative_loss = self.similarity_loss(image, text, label_sim)
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inter_similarity_i, inter_positive_loss_i, inter_negative_loss_i = self.similarity_loss(image, image, label_sim)
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inter_similarity_t, inter_positive_loss_t, inter_negative_loss_t = self.similarity_loss(text, text, label_sim)
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intra_similarity_loss = (intra_positive_loss + intra_negative_loss) if self.args.similarity_function == "euclidean" else (intra_positive_loss + intra_negative_loss)
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inter_similarity_loss = inter_positive_loss_t + inter_positive_loss_i + (inter_negative_loss_i + inter_negative_loss_t) if self.args.similarity_function == "euclidean" else inter_positive_loss_t + inter_positive_loss_i + inter_negative_loss_i + inter_negative_loss_t
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similarity_loss = inter_similarity_loss + intra_similarity_loss
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# if self.writer is not None:
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# self.writer.add_scalar("intra similarity max", intra_similarity.max(), self.global_step)
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# self.writer.add_scalar("intra similarity min", intra_similarity.min(), self.global_step)
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# self.writer.add_scalar("intra positive loss", intra_positive_loss.data, self.global_step)
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# self.writer.add_scalar("intra negative loss", intra_negative_loss.data, self.global_step)
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# self.writer.add_scalar("inter image similarity max", inter_similarity_i.max(), self.global_step)
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# self.writer.add_scalar("inter image similarity min", inter_similarity_i.min(), self.global_step)
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# self.writer.add_scalar("inter image positive loss", inter_positive_loss_i.data, self.global_step)
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# self.writer.add_scalar("inter image negative loss", inter_negative_loss_i.data, self.global_step)
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# self.writer.add_scalar("inter text similarity max", inter_similarity_t.max(), self.global_step)
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# self.writer.add_scalar("inter text similarity min", inter_similarity_t.min(), self.global_step)
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# self.writer.add_scalar("inter text positive loss", inter_positive_loss_t.data, self.global_step)
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# self.writer.add_scalar("inter text negative loss", inter_negative_loss_t.data, self.global_step)
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# self.writer.add_scalar("intra similarity loss", intra_similarity_loss.data, self.global_step)
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# self.writer.add_scalar("inter similarity loss", inter_similarity_loss.data, self.global_step)
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# self.writer.add_scalar("similarity loss", similarity_loss.data, self.global_step)
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if self.args.hash_layer != "select":
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quantization_loss = (self.hash_loss(image) + self.hash_loss(text)) / 2
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loss = similarity_loss + quantization_loss
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if self.global_step % self.args.display_step == 0:
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self.logger.info(f">>>>>> Display >>>>>> [{epoch}/{self.args.epochs}], [{times}/{len(self.train_loader)}]: all loss: {loss.data}, "\
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f"SIMILARITY LOSS, Intra, positive: {intra_positive_loss.data}, negitave: {intra_negative_loss.data}, sum: {intra_similarity_loss.data}, " \
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f"Inter, image positive: {inter_positive_loss_i.data}, image negitave: {inter_negative_loss_i.data}, "\
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f"text positive: {inter_positive_loss_t.data}, text negitave: {inter_negative_loss_t.data}, sum: {inter_similarity_loss.data}, "\
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f"QUATIZATION LOSS, {quantization_loss.data}, "\
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f"lr: {'-'.join([str('%.9f'%itm) for itm in sorted(list(set(self.optimizer.get_lr())))])}")
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else:
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loss = similarity_loss # + self.args.qua_gamma * (image_quantization_loss + text_quantization_loss)
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if self.global_step % self.args.display_step == 0:
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self.logger.info(f">>>>>> Display >>>>>> [{epoch}/{self.args.epochs}], [{times}/{len(self.train_loader)}]: all loss: {loss.data}, "\
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f"SIMILARITY LOSS, Intra, positive: {intra_positive_loss.data}, negitave: {intra_negative_loss.data}, sum: {intra_similarity_loss.data}, " \
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f"Inter, image positive: {inter_positive_loss_i.data}, image negitave: {inter_negative_loss_i.data}, "\
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f"text positive: {inter_positive_loss_t.data}, text negitave: {inter_negative_loss_t.data}, sum: {inter_similarity_loss.data}, "\
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# f"QUATIZATION LOSS, image: {image_quantization_loss.data}, text: {text_quantization_loss.data}, "\
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f"lr: {'-'.join([str('%.9f'%itm) for itm in sorted(list(set(self.optimizer.get_lr())))])}")
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return loss
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def compute_loss(self, image, text, label, epoch, times):
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loss = self.our_loss(image, text, label, epoch, times)
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return loss
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def test(self, mode_name="i2t"):
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if self.args.pretrained == "":
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raise RuntimeError("test step must load a model! please set the --pretrained argument.")
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self.change_state(mode="valid")
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save_dir = os.path.join(self.args.save_dir, "PR_cruve")
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os.makedirs(save_dir, exist_ok=True)
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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)
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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)
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mAPi2t = calc_map_k(query_img, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank)
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# print("map map")
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mAPt2i = calc_map_k(query_txt, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank)
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mAPi2i = calc_map_k(query_img, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank)
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mAPt2t = calc_map_k(query_txt, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank)
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self.max_mapt2i = max(self.max_mapt2i, mAPt2i)
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self.logger.info(f">>>>>> MAP(i->t): {mAPi2t}, MAP(t->i): {mAPt2i}, MAP(t->t): {mAPt2t}, MAP(i->i): {mAPi2i}")
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query_img = query_img.cpu().detach().numpy()
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query_txt = query_txt.cpu().detach().numpy()
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retrieval_img = retrieval_img.cpu().detach().numpy()
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retrieval_txt = retrieval_txt.cpu().detach().numpy()
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query_labels = self.query_labels.numpy()
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retrieval_labels = self.retrieval_labels.numpy()
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result_dict = {
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'q_img': query_img,
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'q_txt': query_txt,
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'r_img': retrieval_img,
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'r_txt': retrieval_txt,
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'q_l': query_labels,
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'r_l': retrieval_labels
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}
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scio.savemat(os.path.join(save_dir, str(self.args.output_dim) + "-ours-" + self.args.dataset + "-" + mode_name + ".mat"), result_dict)
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self.logger.info(">>>>>> save all data!")
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def valid(self, epoch):
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self.logger.info("Valid.")
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self.change_state(mode="valid")
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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)
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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)
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# print("get all code")
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mAPi2t = calc_map_k(query_img, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank)
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# print("map map")
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mAPt2i = calc_map_k(query_txt, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank)
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mAPi2i = calc_map_k(query_img, retrieval_img, self.query_labels, self.retrieval_labels, None, self.rank)
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mAPt2t = calc_map_k(query_txt, retrieval_txt, self.query_labels, self.retrieval_labels, None, self.rank)
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if self.max_mapi2t < mAPi2t:
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self.best_epoch_i = epoch
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self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t")
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self.max_mapi2t = max(self.max_mapi2t, mAPi2t)
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if self.max_mapt2i < mAPt2i:
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self.best_epoch_t = epoch
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self.save_mat(query_img, query_txt, retrieval_img, retrieval_txt, mode_name="t2i")
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self.max_mapt2i = max(self.max_mapt2i, mAPt2i)
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self.logger.info(f">>>>>> [{epoch}/{self.args.epochs}], MAP(i->t): {mAPi2t}, MAP(t->i): {mAPt2i}, MAP(t->t): {mAPt2t}, MAP(i->i): {mAPi2i}, \
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MAX MAP(i->t): {self.max_mapi2t}, MAX MAP(t->i): {self.max_mapt2i}")
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def save_mat(self, query_img, query_txt, retrieval_img, retrieval_txt, mode_name="i2t"):
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save_dir = os.path.join(self.args.save_dir, "PR_cruve")
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os.makedirs(save_dir, exist_ok=True)
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query_img = query_img.cpu().detach().numpy()
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query_txt = query_txt.cpu().detach().numpy()
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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!")
|
||
|
||
|