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Pretrain_nlvr.py
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Pretrain_nlvr.py
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import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_pretrain_nlvr import ALBEF
from models.vit import interpolate_pos_embed
from models.tokenization_bert import BertTokenizer
import utils
from dataset import create_dataset, create_sampler, create_loader
from scheduler import create_scheduler
from optim import create_optimizer
def train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, scheduler, config):
# train
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
metric_logger.add_meter('loss', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
step_size = 100
warmup_iterations = warmup_steps*step_size
if args.distributed:
data_loader.sampler.set_epoch(epoch)
for i, (image, text) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
optimizer.zero_grad()
image = image.to(device,non_blocking=True)
text_input = tokenizer(text, padding='longest', truncation=True, max_length=25, return_tensors="pt").to(device)
loss = model(image, text_input)
loss.backward()
optimizer.step()
metric_logger.update(loss=loss.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
if epoch==0 and i%step_size==0 and i<=warmup_iterations:
scheduler.step(i//step_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger.global_avg())
return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
def main(args, config):
utils.init_distributed_mode(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
start_epoch = 0
max_epoch = config['schedular']['epochs']
warmup_steps = config['schedular']['warmup_epochs']
#### Dataset ####
print("Creating dataset")
datasets = [create_dataset('pretrain', config)]
if args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
samplers = create_sampler(datasets, [True], num_tasks, global_rank)
else:
samplers = [None]
data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0]
tokenizer = BertTokenizer.from_pretrained(args.text_encoder)
#### Model ####
print("Creating model")
model = ALBEF(config=config, text_encoder=args.text_encoder, tokenizer=tokenizer)
model = model.to(device)
arg_opt = utils.AttrDict(config['optimizer'])
optimizer = create_optimizer(arg_opt, model)
arg_sche = utils.AttrDict(config['schedular'])
lr_scheduler, _ = create_scheduler(arg_sche, optimizer)
if args.checkpoint:
checkpoint = torch.load(args.checkpoint, map_location='cpu')
state_dict = checkpoint['model']
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
for key in list(state_dict.keys()):
if 'bert' in key:
new_key = key.replace('bert.','')
if 'layer' in new_key:
keys = new_key.split('.')
layer_num = int(keys[3])
# replicate the multimodal encoder's blocks for two images
if layer_num>=6:
new_layer_num = (layer_num-6)*2+6
keys[3] = str(new_layer_num)
new_key_0 = '.'.join(keys)
state_dict[new_key_0] = state_dict[key]
keys[3] = str(new_layer_num+1)
new_key_1 = '.'.join(keys)
state_dict[new_key_1] = state_dict[key]
else:
state_dict[new_key] = state_dict[key]
else:
state_dict[new_key] = state_dict[key]
del state_dict[key]
msg = model.load_state_dict(state_dict,strict=False)
print('load checkpoint from %s'%args.checkpoint)
print(msg)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
print("Start training")
start_time = time.time()
for epoch in range(start_epoch, max_epoch):
if epoch>0:
lr_scheduler.step(epoch+warmup_steps)
train_stats = train(model, data_loader, optimizer, tokenizer, epoch, warmup_steps, device, lr_scheduler, config)
if utils.is_main_process():
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch,
}
save_obj = {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'config': config,
'epoch': epoch,
}
torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))
with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
f.write(json.dumps(log_stats) + "\n")
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', default='./configs/NLVR_pretrain.yaml')
parser.add_argument('--checkpoint', default='')
parser.add_argument('--output_dir', default='output/NLVR_pretrain')
parser.add_argument('--text_encoder', default='bert-base-uncased')
parser.add_argument('--device', default='cuda')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--distributed', default=True, type=bool)
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
main(args, config)