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train_bert.py
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train_bert.py
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# -----------------------------------------------------------
# Dual Semantic Relations Attention Network (DSRAN) implementation based on
# "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"
# "Learning Dual Semantic Relations with Graph Attention for Image-Text Matching"
# Keyu Wen, Xiaodong Gu, and Qingrong Cheng
# IEEE Transactions on Circuits and Systems for Video Technology, 2020
# Writen by Keyu Wen, 2020
# ------------------------------------------------------------
import pickle
import os
import time
import shutil
import torch
import data_bert as data
from model_bert import VSE
from evaluation_bert import i2t, t2i, AverageMeter, LogCollector, encode_data, simrank
import numpy as np
import logging
import tensorboard_logger as tb_logger
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', default='data',
help='path to datasets')
parser.add_argument('--data_name', default='coco',
help='{coco,f30k}')
parser.add_argument('--margin', default=0.2, type=float,
help='Rank loss margin.')
parser.add_argument('--num_epochs', default=12, type=int,
help='Number of training epochs.')
parser.add_argument('--batch_size', default=128, type=int,
help='Size of a training mini-batch.')
parser.add_argument('--embed_size', default=1024, type=int,
help='Dimensionality of the joint embedding.')
parser.add_argument('--crop_size', default=224, type=int,
help='Size of an image crop as the CNN input.')
parser.add_argument('--learning_rate', default=2e-5, type=float,
help='Initial learning rate.')
parser.add_argument('--lr_update', default=6, type=int,
help='Number of epochs to update the learning rate.')
parser.add_argument('--workers', default=10, type=int,
help='Number of data loader workers.')
parser.add_argument('--log_step', default=100, type=int,
help='Number of steps to print and record the log.')
parser.add_argument('--logger_name', default='runs/grg',
help='Path to save the model and Tensorboard log.')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--img_dim', default=2048, type=int,
help='Dimensionality of the image embedding.')
parser.add_argument('--ft_res', action='store_true',
help='Fine-tune the image encoder.')
parser.add_argument('--bert_path', default='uncased_L-12_H-768_A-12/',
help='path of pre-trained BERT.')
parser.add_argument('--ft_bert', action='store_true',
help='Fine-tune the text encoder.')
parser.add_argument('--bert_size', default=768, type=int,
help='Dimensionality of the text embedding')
parser.add_argument('--warmup', default=-1, type=float)
parser.add_argument('--K', default=2, type=int,help='num of JSR.')
parser.add_argument('--feature_path', default='data/joint-pretrain/flickr30k/region_feat_gvd_wo_bgd/trainval/',
type=str, help='path to the pre-computed image features')
parser.add_argument('--region_bbox_file',
default='data/joint-pretrain/flickr30k/region_feat_gvd_wo_bgd/flickr30k_detection_vg_thresh0.2_feat_gvd_checkpoint_trainvaltest.h5',
type=str, help='path to the region_bbox_file(.h5)')
opt = parser.parse_args()
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
tb_logger.configure(opt.logger_name, flush_secs=5)
train_loader, val_loader = data.get_loaders(opt.data_name, opt.batch_size, opt.workers, opt)
opt.l_train = len(train_loader)
print(opt)
model = VSE(opt)
best_rsum = 0
if opt.resume:
if os.path.isfile(opt.resume):
print("=> loading checkpoint '{}'".format(opt.resume))
checkpoint = torch.load(opt.resume)
start_epoch = checkpoint['epoch']
best_rsum = checkpoint['best_rsum']
model.load_state_dict(checkpoint['model'])
model.Eiters = checkpoint['Eiters']
print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
.format(opt.resume, start_epoch, best_rsum))
validate(opt, val_loader, model)[-1]
else:
print("=> no checkpoint found at '{}'".format(opt.resume))
for epoch in range(opt.num_epochs):
adjust_learning_rate(opt, model.optimizer, epoch)
train(opt, train_loader, model, epoch, val_loader)
rsum = validate(opt, val_loader, model)[-1]
is_best = rsum > best_rsum
best_rsum = max(rsum, best_rsum)
save_checkpoint({
'epoch': epoch + 1,
'model': model.state_dict(),
'best_rsum': best_rsum,
'opt': opt,
'Eiters': model.Eiters,
}, is_best, epoch, prefix=opt.logger_name + '/')
def train(opt, train_loader, model, epoch, val_loader):
batch_time = AverageMeter()
data_time = AverageMeter()
train_logger = LogCollector()
model.train_start()
end = time.time()
for i, train_data in enumerate(train_loader):
data_time.update(time.time() - end)
model.logger = train_logger
model.train_emb(*train_data)
batch_time.update(time.time() - end)
end = time.time()
if model.Eiters % opt.log_step == 0:
logging.info(
'Epoch: [{0}][{1}/{2}]\t'
'{e_log}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, e_log=str(model.logger)))
tb_logger.log_value('epoch', epoch, step=model.Eiters)
tb_logger.log_value('step', i, step=model.Eiters)
tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters)
tb_logger.log_value('data_time', data_time.val, step=model.Eiters)
model.logger.tb_log(tb_logger, step=model.Eiters)
def validate(opt, val_loader, model):
_, _, sims = encode_data(
model, val_loader, opt.log_step, logging.info)
rs = simrank(sims)
del sims
return rs
def save_checkpoint(state, is_best, epoch, filename='checkpoint.pth.tar', prefix=''):
torch.save(state, prefix + filename)
if is_best:
shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')
def adjust_learning_rate(opt, optimizer, epoch):
lr = opt.learning_rate * (0.1 ** (epoch // opt.lr_update))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if __name__ == '__main__':
main()