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train_mydecoder_pixelvit_txtimg_3_bert.py
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from option.options import options, config
from data.dataloader import get_dataloader
import torch
import random
from model.model import TextImgPersonReidNet
from loss.Id_loss import Id_Loss
from loss.RankingLoss import RankingLoss
from torch import optim
import logging
import os
from test_during_train import test , test_part
from torch.autograd import Variable
from model.DETR_model import TextImgPersonReidNet_mydecoder_pixelVit_transTXT_3_bert
import torch.nn as nn
seed_num = 233
torch.manual_seed(seed_num)
random.seed(seed_num)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def save_checkpoint(state, opt):
filename = os.path.join(opt.save_path, 'model/best.pth.tar')
torch.save(state, filename)
def load_checkpoint(opt):
filename = os.path.join(opt.save_path, 'model/best.pth.tar')
state = torch.load(filename)
return state
def calculate_similarity(image_embedding, text_embedding):
image_embedding_norm = image_embedding / image_embedding.norm(dim=1, keepdim=True)
text_embedding_norm = text_embedding / text_embedding.norm(dim=1, keepdim=True)
similarity = torch.mm(image_embedding_norm, text_embedding_norm.t())
return similarity
def calculate_similarity_part(numpart,image_embedding, text_embedding):
image_embedding = torch.cat([image_embedding[i] for i in range(numpart)],dim=1)
text_embedding = torch.cat([text_embedding[i] for i in range(numpart)], dim=1)
image_embedding_norm = image_embedding / image_embedding.norm(dim=1, keepdim=True)
text_embedding_norm = text_embedding / text_embedding.norm(dim=1, keepdim=True)
similarity = torch.mm(image_embedding_norm, text_embedding_norm.t())
return similarity
def calculate_part_id(id_loss_fun,num_query,image_embedding,text_embedding):
id_loss_ = []
pred_i2t_ = []
pred_t2i_ = []
for i in range(num_query):
id_loss, pred_i2t_local, pred_t2i_local = id_loss_fun[i](image_embedding[i], text_embedding[i], label)
id_loss_.append(id_loss)
pred_i2t_.append(pred_i2t_local)
pred_t2i_.append(pred_t2i_local)
id_loss_ = torch.stack(id_loss_)
id_loss = torch.mean(id_loss_)
pred_i2t_ = torch.stack(pred_i2t_)
pred_i2t_local = torch.mean(pred_i2t_)
pred_t2i_ = torch.stack(pred_t2i_)
pred_t2i_local = torch.mean(pred_t2i_)
return id_loss , pred_i2t_local, pred_t2i_local
if __name__ == '__main__':
opt = options().opt
opt.GPU_id = '0'
opt.device = torch.device('cuda:{}'.format(opt.GPU_id))
opt.data_augment = False
opt.lr = 0.001
opt.margin = 0.3
opt.feature_length = 512
opt.dataset = 'CUHK-PEDES'
if opt.dataset == 'MSMT-PEDES':
opt.pkl_root = '/home/zhiyin/tran_ACMMM/processed_data_singledata_ICFG/'
opt.class_num = 3102
opt.vocab_size = 2500
opt.dataroot = '/home/zhiyin/ICFG_PEDES/ICFG_PEDES'
# opt.class_num = 2802
# opt.vocab_size = 2300
elif opt.dataset == 'CUHK-PEDES':
opt.pkl_root = '/home/zhiyin/tran_ACMMM/processed_data_singledata_CUHK/' # same_id_new_
opt.class_num = 11000
opt.vocab_size = 5000
opt.dataroot = '/home/zhiyin/CUHK-PEDES'
opt.d_model = 1024
opt.nhead = 4
opt.dim_feedforward = 2048
opt.normalize_before = False
opt.num_encoder_layers = 3
opt.num_decoder_layers = 3
opt.num_query = 6
opt.detr_lr = 0.0001
opt.txt_detr_lr = 0.0001
opt.txt_lstm_lr = 0.001
opt.res_y = False
opt.noself = False
opt.post_norm = False
opt.n_heads = 4
opt.n_layers = 2
opt.share_query = True
opt.ViT_layer = 8
opt.wordtype = 'bert'
model_name = 'model_get'
# model_name = 'test'
opt.save_path = './checkpoints/dual_modal/{}/'.format(opt.dataset) + model_name
opt.epoch = 60
opt.epoch_decay = [20, 40, 50]
opt.batch_size = 64
opt.start_epoch = 0
opt.trained = False
config(opt)
opt.epoch_decay = [i - opt.start_epoch for i in opt.epoch_decay]
train_dataloader = get_dataloader(opt)
opt.mode = 'test'
test_img_dataloader, test_txt_dataloader = get_dataloader(opt)
opt.mode = 'train'
id_loss_fun = nn.ModuleList()
for _ in range(opt.num_query):
id_loss_fun.append(Id_Loss(opt).to(opt.device))
ranking_loss_fun = RankingLoss(opt)
network = TextImgPersonReidNet_mydecoder_pixelVit_transTXT_3_bert(opt).to(opt.device)
logging.info("Model_size: {:.5f}M".format(sum(p.numel() for p in network.parameters()) / 1000000.0))
ignored_params = (list(map(id, network.ImageExtract.parameters()))
+ list(map(id, network.TextExtract.parameters()))
+ list(map(id, network.conv_1X1_2.parameters()))
# + list(map(id, network.conv_1X1.parameters()))
# + list(map(id, network.TXTEncoder.parameters()))
# + list(map(id, network.TXTDecoder.parameters()))
)
DETR_params = filter(lambda p: id(p) not in ignored_params, network.parameters())
DETR_params = list(DETR_params)
param_groups = [{'params': DETR_params, 'lr': opt.detr_lr},
# {'params': network.TXTEncoder.parameters(), 'lr': opt.txt_detr_lr},
# {'params': network.TXTDecoder.parameters(), 'lr': opt.txt_detr_lr},
{'params': network.ImageExtract.parameters(), 'lr': opt.lr * 0.1},
{'params': network.TextExtract.parameters(), 'lr': opt.lr},
{'params': network.conv_1X1_2.parameters(), 'lr': opt.lr},
# {'params': network.conv_1X1.parameters(), 'lr': opt.lr},
{'params': id_loss_fun.parameters(), 'lr': opt.lr}
]
optimizer = optim.Adam(param_groups, betas=(opt.adam_alpha, opt.adam_beta))
test_best = 0
test_history = 0
if opt.trained:
state = load_checkpoint(opt)
network.load_state_dict(state['network'])
test_best = state['test_best']
test_history = test_best
id_loss_fun.load_state_dict(state['W'])
print('load the {} epoch param successfully'.format(state['epoch']))
"""
network.eval()
test_best = test(opt, 0, 0, network,
test_img_dataloader, test_txt_dataloader, test_best)
network.train()
exit(0)
"""
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, opt.epoch_decay)
for epoch in range(opt.start_epoch, opt.epoch):
id_loss_sum = 0
ranking_loss_sum = 0
pred_i2t_local_sum = 0
pred_t2i_local_sum = 0
scheduler.step()
for param in optimizer.param_groups:
logging.info('lr:{}'.format(param['lr']))
for times, [image, label, caption_code, caption_length, caption_mask] in enumerate(train_dataloader):
# network.eval()
# test_best = test_part(opt, epoch + 1, times + 1, network,
# test_img_dataloader, test_txt_dataloader, test_best)
# network.train()
image = Variable(image.to(opt.device))
label = Variable(label.to(opt.device))
caption_code = Variable(caption_code.to(opt.device).long())
caption_mask = Variable(caption_mask.to(opt.device))
image_embedding,image_embedding_dict, text_embedding ,text_embedding_dict= network(image, caption_code, caption_mask)
id_loss , pred_i2t_local, pred_t2i_local = calculate_part_id(id_loss_fun,opt.num_query ,image_embedding, text_embedding)
id_loss_dict, pred_i2t_local_dict, pred_t2i_local_dict = calculate_part_id(id_loss_fun,opt.num_query, image_embedding_dict, text_embedding_dict)
similarity = calculate_similarity_part(opt.num_query,image_embedding, text_embedding)
ranking_loss = ranking_loss_fun(similarity, label)
similarity_dict = calculate_similarity_part(opt.num_query, image_embedding_dict, text_embedding_dict)
ranking_loss_dict = ranking_loss_fun(similarity_dict, label)
similarity_dict_text = calculate_similarity_part(opt.num_query, text_embedding, text_embedding_dict)
ranking_loss_dict_text = ranking_loss_fun(similarity_dict_text, label)
similarity_dict_image = calculate_similarity_part(opt.num_query, image_embedding, image_embedding_dict)
ranking_loss_dict_image = ranking_loss_fun(similarity_dict_image, label)
optimizer.zero_grad()
loss = (id_loss + ranking_loss + id_loss_dict + ranking_loss_dict + ranking_loss_dict_text + ranking_loss_dict_image)
loss.backward()
# network.eval()
# test_best = test_part(opt, epoch + 1, times + 1, network,
# test_img_dataloader, test_txt_dataloader, test_best)
# network.train()
optimizer.step()
# network.eval()
# test_best = test_part(opt, epoch + 1, times + 1, network,
# test_img_dataloader, test_txt_dataloader, test_best)
# network.train()
if (times + 1) % 50 == 0:
logging.info("Epoch: %d/%d Setp: %d, ranking_loss: %.2f, id_loss: %.2f, ranking_loss_dict: %.2f, id_loss_dict: %.2f,ranking_loss_dict_text: %.2f, ranking_loss_dict_image: %.2f,"
"pred_i2t_local: %.3f pred_t2i_local %.3f"
% (epoch+1, opt.epoch, times+1, ranking_loss, id_loss, ranking_loss_dict,id_loss_dict,ranking_loss_dict_text,ranking_loss_dict_image,pred_i2t_local, pred_t2i_local))
ranking_loss_sum += ranking_loss
id_loss_sum += id_loss
pred_i2t_local_sum += pred_i2t_local
pred_t2i_local_sum += pred_t2i_local
ranking_loss_avg = ranking_loss_sum / (times + 1)
id_loss_avg = id_loss_sum / (times + 1)
pred_i2t_local_avg = pred_i2t_local_sum / (times + 1)
pred_t2i_local_avg = pred_t2i_local_sum / (times + 1)
logging.info("Epoch: %d/%d , ranking_loss: %.2f, id_loss: %.2f,"
" pred_i2t_local: %.3f, pred_t2i_local %.3f "
% (epoch+1, opt.epoch, ranking_loss_avg, id_loss_avg, pred_i2t_local_avg, pred_t2i_local_avg))
print(model_name)
network.eval()
test_best = test_part(opt, epoch + 1, times + 1, network,
test_img_dataloader, test_txt_dataloader, test_best)
network.train()
if test_best > test_history:
state = {
'test_best': test_best,
'network': network.cpu().state_dict(),
'optimizer': optimizer.state_dict(),
'W': id_loss_fun.cpu().state_dict(),
'epoch': epoch + 1}
save_checkpoint(state, opt)
network.to(opt.device)
id_loss_fun.to(opt.device)
test_history = test_best
logging.info('Training Done')