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train_ytvos.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
from util.Logger import TreeEvaluation as Evaluation, TimeRecord, LogTime, Tee, Loss_record
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader
import util.misc as utils
import datasets.samplers as samplers
from ytvos_engine import train_one_epoch
from models import few_build_model
from tools.load_pretrained_weights import pre_trained_model_to_finetune
import opts
from datasets.ref_ytvos import build_yt_vos
def main(args):
args.masks = True
utils.init_distributed_mode(args)
print("git:\n {}\n".format(utils.get_sha()))
print(args)
print(f'\n Run on {args.dataset_file} dataset.')
print('\n')
device = torch.device(args.device)
args.distributed = False
seed = args.seed
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
model, criterion, postprocessor = few_build_model(args)
model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
def match_name_keywords(n, name_keywords):
out = False
for b in name_keywords:
if b in n:
out = True
break
return out
param_dicts = [
{
"params":
[p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, args.lr_backbone_names) and not match_name_keywords(n,
args.lr_text_encoder_names)
and not match_name_keywords(n, args.lr_linear_proj_names) and not match_name_keywords(n, args.lr_support_names) and p.requires_grad],
"lr": args.lr,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
match_name_keywords(n, args.lr_backbone_names) and p.requires_grad],
"lr": args.lr_backbone,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
match_name_keywords(n, args.lr_text_encoder_names) and p.requires_grad],
"lr": args.lr_text_encoder,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad],
"lr": args.lr * args.lr_linear_proj_mult,
}
]
optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, args.lr_drop)
print(args.dataset_file)
data_path = args.data_path
dataset_train = build_yt_vos('train', data_path, set_index=args.group, support_frames=5, query_frames=5, sample_per_class=args.sample_per_class)
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
data_loader_train = DataLoader(dataset_train, batch_size=1, num_workers=0, collate_fn=utils.collate_fn)
if args.dataset_file != "davis" and args.dataset_file != "jhmdb" and args.pretrained_weights is not None:
print("============================================>")
print("Load pretrained weights from {} ...".format(args.pretrained_weights))
checkpoint = torch.load(args.pretrained_weights, map_location="cpu")
checkpoint_dict = pre_trained_model_to_finetune(checkpoint, args)
model_without_ddp.load_state_dict(checkpoint_dict, strict=False)
print("============================================>")
output_dir = os.path.join(args.output_dir, 'group_%d' % args.group)
output_dir = Path(output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
args.resume, map_location='cpu', check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]
if len(missing_keys) > 0:
print('Missing Keys: {}'.format(missing_keys))
if len(unexpected_keys) > 0:
print('Unexpected Keys: {}'.format(unexpected_keys))
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
import copy
p_groups = copy.deepcopy(optimizer.param_groups)
optimizer.load_state_dict(checkpoint['optimizer'])
for pg, pg_old in zip(optimizer.param_groups, p_groups):
pg['lr'] = pg_old['lr']
pg['initial_lr'] = pg_old['initial_lr']
print(optimizer.param_groups)
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.override_resumed_lr_drop = True
if args.override_resumed_lr_drop:
print(
'Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.')
lr_scheduler.step_size = args.lr_drop
lr_scheduler.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
lr_scheduler.step(lr_scheduler.last_epoch)
args.start_epoch = checkpoint['epoch'] + 1
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
sampler_train.set_epoch(epoch)
train_stats = train_one_epoch(
model, criterion, data_loader_train, optimizer, device, epoch,
args.clip_max_norm)
lr_scheduler.step()
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
if (epoch + 1) % 1 == 0:
checkpoint_paths.append(output_dir / f'checkpoint{epoch:04}.pth')
for checkpoint_path in checkpoint_paths:
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}, checkpoint_path)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
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('RVOSNet training and evaluation script', parents=[opts.get_args_parser()])
args = parser.parse_args()
if args.output_dir:
output_dir = os.path.join(args.output_dir, 'group_%d' % args.group)
Path(output_dir).mkdir(parents=True, exist_ok=True)
main(args)