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Pretrain.py
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Pretrain.py
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'''
* Copyright (c) 2021, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
'''
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 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
from torchvision.utils import save_image
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_mlm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_itm', 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)
if epoch>0:
alpha = config['alpha']
else:
alpha = config['alpha']*min(1,i/len(data_loader))
loss_mlm, loss_ita, loss_itm = model(image, text_input, alpha = alpha)
loss = loss_mlm + loss_ita + loss_itm
loss.backward()
optimizer.step()
metric_logger.update(loss_mlm=loss_mlm.item())
metric_logger.update(loss_ita=loss_ita.item())
metric_logger.update(loss_itm=loss_itm.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)
samplers = create_sampler(datasets, [False], 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, init_deit=False)
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']
if args.resume:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
start_epoch = checkpoint['epoch']+1
else:
pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder)
m_pos_embed_reshaped = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'],model.visual_encoder_m)
state_dict['visual_encoder.pos_embed'] = pos_embed_reshaped
state_dict['visual_encoder_m.pos_embed'] = m_pos_embed_reshaped
model.load_state_dict(state_dict)
print('load checkpoint from %s'%args.checkpoint)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
print("Start training")
start_time = time.time()
for epoch in range(start_epoch, max_epoch):
if config['selective_sampling']:
#at every epoch, shuffle data with custom sampling function for medical data
print(f"Shuffling training data for epoch {epoch}")
data_loader.dataset.shuffle(bs=config['batch_size'],
rare_grp_ratio=config['rare_grp_ratio'],
batch_shuffle=config['batch_shuffle']
)
# elif (config['selective_sampling'] and data_loader.shuffle) or (not config['selective_sampling'] and not data_loader.shuffle):
# print("Incorrect combination of parameters: selective_sampling and shuffle cannot be\
# true/false at the same time, will lead to erroneous behavior.")
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/Pretrain.yaml')
parser.add_argument('--checkpoint', default='')
parser.add_argument('--resume', default=False, type=bool)
parser.add_argument('--output_dir', default='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)