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pretrain.py
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pretrain.py
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'''
* RAM++ & RAM & Tag2Text pretrain
* Written by Xinyu Huang
'''
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
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from torch.utils.data import DataLoader
from ram.models import ram_plus, ram, tag2text
import utils
from utils import warmup_lr_schedule, step_lr_schedule
from ram.data import create_dataset, create_sampler, create_loader
import clip
def build_text_embed(model_clip, caption):
run_on_gpu = torch.cuda.is_available()
with torch.no_grad():
texts = clip.tokenize(caption,truncate = True) # tokenize
if run_on_gpu:
texts = texts.cuda()
model_clip = model_clip.cuda()
text_embeddings = model_clip.encode_text(texts)
text_embeddings /= text_embeddings.norm(dim=-1, keepdim=True)
# text_embedding = text_embeddings.mean(dim=0)
# text_embedding /= text_embedding.norm()
return text_embeddings
def train_ram_plus(model, data_loader, optimizer, epoch, device, config, model_clip):
# 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_tag', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_dis', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_alignment', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
data_loader.sampler.set_epoch(epoch)
for i, (image, caption, image_tag, parse_tag) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if epoch==0:
warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
optimizer.zero_grad()
batch_text_embed = build_text_embed(model_clip,caption)
image = image.to(device,non_blocking=True)
with torch.no_grad():
clip_image_feature = model_clip.encode_image(image)
loss_tag, loss_dis, loss_alignment = model(image, caption, image_tag, clip_image_feature, batch_text_embed)
loss = loss_tag + loss_dis + loss_alignment
loss.backward()
optimizer.step()
metric_logger.update(loss_tag=loss_tag.item())
metric_logger.update(loss_dis=loss_dis.item())
metric_logger.update(loss_alignment=loss_alignment.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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 train_ram(model, data_loader, optimizer, epoch, device, config, model_clip):
# 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_t2t', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_tag', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_dis', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
data_loader.sampler.set_epoch(epoch)
for i, (image, caption, image_tag, parse_tag) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if epoch==0:
warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
optimizer.zero_grad()
image = image.to(device,non_blocking=True)
with torch.no_grad():
clip_image_feature = model_clip.encode_image(image)
loss_t2t, loss_tag, loss_dis = model(image, caption, image_tag, parse_tag, clip_image_feature)
loss = loss_t2t + loss_tag/(loss_tag/loss_t2t).detach() + loss_dis
loss.backward()
optimizer.step()
metric_logger.update(loss_t2t=loss_t2t.item())
metric_logger.update(loss_tag=loss_tag.item())
metric_logger.update(loss_dis=loss_dis.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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 train_tag2text(model, data_loader, optimizer, epoch, device, 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_t2t', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_tag', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
data_loader.sampler.set_epoch(epoch)
for i, (image, caption, _, parse_tag) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
if epoch==0:
warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
optimizer.zero_grad()
image = image.to(device,non_blocking=True)
loss_t2t, loss_tag = model(image, caption, parse_tag)
loss = loss_t2t + loss_tag/(loss_tag/loss_t2t).detach()
loss.backward()
optimizer.step()
metric_logger.update(loss_t2t=loss_t2t.item())
metric_logger.update(loss_tag=loss_tag.item())
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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
#### Dataset ####
print("Creating dataset")
datasets = [create_dataset('pretrain', config, min_scale=0.2)]
print('number of training samples: %d'%len(datasets[0]))
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
samplers = create_sampler(datasets, [True], num_tasks, global_rank)
data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0]
#### Model ####
if args.model_type == 'ram_plus':
print("Creating pretrained CLIP model")
model_clip, _ = clip.load("ViT-B/16", device=device)
print("Creating RAM model")
model = ram_plus(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'],
vit_ckpt_layer=config['vit_ckpt_layer'], stage = 'train_from_scratch')
elif args.model_type == 'ram':
print("Creating pretrained CLIP model")
model_clip, _ = clip.load("ViT-B/16", device=device)
print("Creating RAM model")
model = ram(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'],
vit_ckpt_layer=config['vit_ckpt_layer'], stage = 'train_from_scratch')
elif args.model_type == 'tag2text':
print("Creating Tag2Text model")
model = tag2text(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'],
vit_ckpt_layer=config['vit_ckpt_layer'], stage = 'train_from_scratch', tag_list='ram/data/ram_tag_list.txt')
model = model.to(device)
### Frozen CLIP model ###
model_clip = model_clip.to(device)
for _, param in model_clip.named_parameters():
param.requires_grad = False
### Frozen label embedding for open-set recogniztion ###
model.label_embed.requires_grad = False
optimizer = torch.optim.AdamW(filter(lambda x: x.requires_grad, model.parameters()), lr=config['init_lr'], weight_decay=config['weight_decay'])
start_epoch = 0
if args.checkpoint:
checkpoint = torch.load(args.checkpoint, map_location='cpu')
state_dict = checkpoint['model']
model.load_state_dict(state_dict)
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']+1
print('resume 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, config['max_epoch']):
step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate'])
if args.model_type == 'ram_plus':
train_stats = train_ram_plus(model, data_loader, optimizer, epoch, device, config, model_clip)
elif args.model_type == 'ram':
train_stats = train_ram(model, data_loader, optimizer, epoch, device, config, model_clip)
elif args.model_type == 'tag2text':
train_stats = train_tag2text(model, data_loader, optimizer, epoch, device, 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(),
'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("--model-type",type=str,choices=("ram_plus", "ram", "tag2text"),required=True)
parser.add_argument('--output-dir', default='output/Pretrain')
parser.add_argument('--checkpoint', default='')
parser.add_argument('--evaluate', action='store_true')
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)