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main.py
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import argparse
import datetime
import json
import random
import time
from pathlib import Path
import logging
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import datasets
import util.misc as utils
from datasets import build_dataset
from engine import train_one_epoch, evaluate_hoi
from models import build_model
import os
from util.scheduler import CosineAnnealingLRWarmup, MultiStepLRWarmup
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def get_args_parser():
parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--lr_backbone', default=1e-5, type=float)
parser.add_argument('--lr_clip', default=1e-5, type=float)
parser.add_argument('--batch_size', default=2, type=int)
parser.add_argument('--weight_decay', default=1e-4, type=float)
parser.add_argument('--epochs', default=150, type=int)
parser.add_argument('--lr_drop', default=100, type=int)
parser.add_argument('--clip_max_norm', default=0.1, type=float,
help='gradient clipping max norm')
parser.add_argument('--eval_each', default=4, type=int)
parser.add_argument('--eval_each_lr_drop', default=2, type=int)
# Model parameters
parser.add_argument('--frozen_weights', type=str, default=None,
help="Path to the pretrained model. If set, only the mask head will be trained")
# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--dilation', action='store_true',
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
help="Type of positional embedding to use on top of the image features")
# * Transformer
parser.add_argument('--enc_layers', default=6, type=int,
help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=3, type=int,
help="Number of stage1 decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=2048, type=int,
help="Intermediate size of the feedforward layers in the transformer blocks")
parser.add_argument('--hidden_dim', default=256, type=int,
help="Size of the embeddings (dimension of the transformer)")
parser.add_argument('--dropout', default=0.1, type=float,
help="Dropout applied in the transformer")
parser.add_argument('--nheads', default=8, type=int,
help="Number of attention heads inside the transformer's attentions")
parser.add_argument('--num_queries', default=100, type=int,
help="Number of query slots")
parser.add_argument('--pre_norm', action='store_true')
# * Segmentation
parser.add_argument('--masks', action='store_true',
help="Train segmentation head if the flag is provided")
# HOI
parser.add_argument('--hoi', action='store_true',
help="Train for HOI if the flag is provided")
parser.add_argument('--num_obj_classes', type=int, default=80,
help="Number of object classes")
parser.add_argument('--num_verb_classes', type=int, default=117,
help="Number of verb classes")
parser.add_argument('--pretrained', type=str, default='',
help='Pretrained model path')
parser.add_argument('--subject_category_id', default=0, type=int)
parser.add_argument('--verb_loss_type', type=str, default='focal',
help='Loss type for the verb classification')
# Loss
parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
help="Disables auxiliary decoding losses (loss at each layer)")
parser.add_argument('--with_mimic', action='store_true',
help="Use clip feature mimic")
# * Matcher
parser.add_argument('--set_cost_class', default=1, type=float,
help="Class coefficient in the matching cost")
parser.add_argument('--set_cost_bbox', default=2.5, type=float,
help="L1 box coefficient in the matching cost")
parser.add_argument('--set_cost_giou', default=1, type=float,
help="giou box coefficient in the matching cost")
parser.add_argument('--set_cost_obj_class', default=1, type=float,
help="Object class coefficient in the matching cost")
parser.add_argument('--set_cost_verb_class', default=1, type=float,
help="Verb class coefficient in the matching cost")
parser.add_argument('--set_cost_hoi', default=1, type=float,
help="Hoi class coefficient")
# * Loss coefficients
parser.add_argument('--mask_loss_coef', default=1, type=float)
parser.add_argument('--dice_loss_coef', default=1, type=float)
parser.add_argument('--bbox_loss_coef', default=2.5, type=float)
parser.add_argument('--giou_loss_coef', default=1, type=float)
parser.add_argument('--obj_loss_coef', default=1, type=float)
parser.add_argument('--verb_loss_coef', default=2, type=float)
parser.add_argument('--hoi_loss_coef', default=2, type=float)
parser.add_argument('--mimic_loss_coef', default=20, type=float)
parser.add_argument('--alpha', default=0.5, type=float, help='focal loss alpha')
parser.add_argument('--eos_coef', default=0.1, type=float,
help="Relative classification weight of the no-object class")
# dataset parameters
parser.add_argument('--dataset_file', default='coco')
parser.add_argument('--coco_path', type=str)
parser.add_argument('--coco_panoptic_path', type=str)
parser.add_argument('--remove_difficult', action='store_true')
parser.add_argument('--hoi_path', type=str)
parser.add_argument('--output_dir', default='',
help='path where to save, empty for no saving')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true')
parser.add_argument('--num_workers', default=2, type=int)
# distributed training parameters
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')
# hoi eval parameters
parser.add_argument('--use_nms_filter', action='store_true', help='Use pair nms filter, default not use')
parser.add_argument('--thres_nms', default=0.7, type=float)
parser.add_argument('--nms_alpha', default=1, type=float)
parser.add_argument('--nms_beta', default=0.5, type=float)
parser.add_argument('--json_file', default='results.json', type=str)
# clip
parser.add_argument('--ft_clip_with_small_lr', action='store_true',
help='Use smaller learning rate to finetune clip weights')
parser.add_argument('--with_clip_label', action='store_true', help='Use clip to classify HOI')
parser.add_argument('--with_obj_clip_label', action='store_true', help='Use clip to classify object')
parser.add_argument('--clip_model', default='ViT-B/32',
help='clip pretrained model path')
parser.add_argument('--fix_clip', action='store_true', help='')
parser.add_argument('--clip_embed_dim', default=512, type=int)
# zero/few shot type
parser.add_argument('--zero_shot_type', default='default',
help='default, rare_first, non_rare_first, unseen_object, unseen_verb')
parser.add_argument('--del_unseen', action='store_true', help='')
# old parameter
parser.add_argument('--fix_backbone_mode', nargs='+', default=[], help='fix (part of) backbone')
# others
parser.add_argument('--use_ddp', default=1, type=int)
parser.add_argument('--with_random_shuffle', default=2, type=int, help='Time of random shuffle of annotation')
parser.add_argument('--gradient_accumulation_steps', default=1, type=int)
parser.add_argument('--opt_sched', default='multiStep', type=str, help='type of scheduler')
parser.add_argument('--no_clip_cls_init', action='store_true',
help='not init classifier weight with clip text encoder')
parser.add_argument('--enable_amp', action='store_true', help='')
parser.add_argument('--opt_level', default='O2', help='half precision optimization level', choices=('O1', 'O2'))
parser.add_argument('--fix_clip_label', action='store_true', help='')
parser.add_argument('--with_rec_loss', action='store_true', help='')
parser.add_argument('--rec_loss_coef', default=2, type=float)
parser.add_argument('--no_training', action='store_true', help='')
parser.add_argument('--dataset_root', default='GEN', help='')
parser.add_argument('--model_name', default='GEN', help='')
parser.add_argument('--eval_location', action='store_true', help='')
# DAB
parser.add_argument('--enable_cp', action='store_true',
help="use checkpoint to save memory")
parser.add_argument('--no_fix_clip_linear', action='store_true',
help="")
parser.add_argument('--analysis', action='store_true')
# tmp args
parser.add_argument('--alternative', default=1, type=int)
parser.add_argument('--eval_each_ap', action='store_true')
parser.add_argument('--topk_hoi', default=10, type=int)
parser.add_argument('--inter_dec_layers', default=3, type=int)
# verb setting
parser.add_argument('--verb_pth', default='', help='location for predefined verb feature', type=str)
parser.add_argument('--verb_weight', default=0.5, type=float)
# fractional training
parser.add_argument('--frac', default=-1., type=float)
# validation split
parser.add_argument('--validation_split', default=-1., type=int)
parser.add_argument('--lr_drop_gamma', default=0.1, type=float)
# zero shot enhancement
parser.add_argument('--training_free_enhancement_path', default='', type=str)
return parser
def main(args):
if args.use_ddp == 1:
utils.init_distributed_mode(args)
else:
args.distributed = False
# args.save_points = [int(i) for i in args.save_points]
print('setting up seeds')
setup_seed(233)
# sys.exit(0)
print("git:\n {}\n".format(utils.get_sha()))
if args.frozen_weights is not None:
assert args.masks, "Frozen training is meant for segmentation only"
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
model, criterion, postprocessors = build_model(args)
model.to(device)
print('****************')
# print(model)
print(args.model_name)
print('****************')
model_without_ddp = model
if args.distributed:
if args.enable_amp:
raise NotImplementedError
else:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
# model = convert_syncbn_model(model)
for name, p in model.named_parameters():
if 'eval_visual_projection' in name:
p.requires_grad = False
if args.fix_clip:
for name, p in model.named_parameters():
if 'obj_visual_projection' in name or 'visual_projection' in name or 'clip_model' in name:
p.requires_grad = False
if args.no_fix_clip_linear:
for name, p in model.named_parameters():
if 'obj_visual_projection' in name or 'visual_projection' in name:
p.requires_grad = True
if args.ft_clip_with_small_lr:
if args.with_obj_clip_label and args.with_clip_label:
param_dicts = [
{"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" not in n and 'visual_projection' not in n and 'obj_visual_projection' not in n
and 'clip_model' not in n and p.requires_grad and 'T5_model' not in n]},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" in n and p.requires_grad],
"lr": args.lr_backbone,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
(
'visual_projection' in n or 'obj_visual_projection' in n or 'clip_model' in n) and p.requires_grad],
"lr": args.lr_clip,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
(
'T5_model' in n or 'llm' in n) and p.requires_grad],
"lr": args.lr_llm,
},
]
elif args.with_clip_label:
param_dicts = [
{"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" not in n and 'visual_projection' not in n and 'clip_model' not in n and p.requires_grad]},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" in n and p.requires_grad],
"lr": args.lr_backbone,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
('visual_projection' in n or 'clip_model' in n) and p.requires_grad],
"lr": args.lr_clip,
},
]
elif args.with_obj_clip_label:
param_dicts = [
{"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" not in n and 'obj_visual_projection' not in n and 'clip_model' not in n and p.requires_grad]},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
"backbone" in n and p.requires_grad],
"lr": args.lr_backbone,
},
{
"params": [p for n, p in model_without_ddp.named_parameters() if
('obj_visual_projection' in n or 'clip_model' in n) and p.requires_grad],
"lr": args.lr_clip,
},
]
else:
raise
else:
param_dicts = [
{"params": [p for n, p in model_without_ddp.named_parameters() if "backbone" not in n and p.requires_grad]},
{
"params": [p for n, p in model_without_ddp.named_parameters() if "backbone" in n and p.requires_grad],
"lr": args.lr_backbone,
},
]
optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
weight_decay=args.weight_decay)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
if args.opt_sched == 'multiStep':
lr_scheduler = MultiStepLRWarmup(optimizer, [args.lr_drop], warmup_iter=0, warmup_ratio=0.01,
gamma=args.lr_drop_gamma)
elif args.opt_sched == 'cosine':
lr_scheduler = CosineAnnealingLRWarmup(optimizer, verbose=False,
warmup_iter=500,
warmup_ratio=0.01,
T_max=args.epochs - 1,
eta_min=0.01)
else:
raise KeyError('Unsupported scheduler type')
print('init dataloader')
# train dataloader initialization
dataset_train = build_dataset(image_set='train', args=args)
if args.distributed:
sampler_train = DistributedSampler(dataset_train)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
batch_sampler_train = torch.utils.data.BatchSampler(
sampler_train, args.batch_size, drop_last=True)
data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,
collate_fn=utils.collate_fn, num_workers=args.num_workers)
# test and val dataloader initialization
test_split = 'val'
dataset_val = build_dataset(image_set='val', args=args)
dataset_test = build_dataset(image_set=test_split, args=args)
if args.distributed:
sampler_val = DistributedSampler(dataset_val, shuffle=False)
sampler_test = DistributedSampler(dataset_test, shuffle=False)
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
sampler_test = torch.utils.data.SequentialSampler(dataset_test)
data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)
data_loader_test = DataLoader(dataset_test, args.batch_size, sampler=sampler_test,
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers)
print('dataloader finished')
if args.frozen_weights is not None:
checkpoint = torch.load(args.frozen_weights, map_location='cpu')
model_without_ddp.detr.load_state_dict(checkpoint['model'])
output_dir = Path(args.output_dir)
# init logging
_LOG_FMT = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
_DATE_FMT = '%m/%d/%Y %H:%M:%S'
logging.basicConfig(format=_LOG_FMT, datefmt=_DATE_FMT, level=logging.INFO)
LOGGER = logging.getLogger('__main__') # this is the global logger
fh = logging.FileHandler(os.path.join(output_dir, 'training_log.txt'))
formatter = logging.Formatter(_LOG_FMT, datefmt=_DATE_FMT)
fh.setFormatter(formatter)
LOGGER.addHandler(fh)
if args.resume and os.path.exists(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')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
# if args.enable_amp:
# amp.load_state_dict(checkpoint['amp'])
elif args.pretrained:
checkpoint = torch.load(args.pretrained, map_location='cpu')
if args.eval:
model_without_ddp.load_state_dict(checkpoint['model'], strict=True)
else:
model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
if args.eval:
if not os.path.exists(output_dir / "log.txt"):
with open(output_dir / "log.txt", 'w') as f:
f.write('')
with open(output_dir / "log.txt", 'r') as f:
previous_log = f.read()
if 'Test result:' not in previous_log:
print('Evaluating in test split!')
test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_test,
args.subject_category_id, device, args)
if args.output_dir and utils.is_main_process():
# add eval in log for my convenience
with (output_dir / "log.txt").open("a") as f:
f.write('Test result:' + json.dumps(test_stats) + "\n")
LOGGER.info('Epoch Test: [{}] '.format('eval') + json.dumps(test_stats))
if 'Val result:' not in previous_log:
print('Evaluating in val split!')
test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_val,
args.subject_category_id, device, args)
if args.output_dir and utils.is_main_process():
# add eval in log for my convenience
with (output_dir / "log.txt").open("a") as f:
f.write('Val result:' + json.dumps(test_stats) + "\n")
LOGGER.info('Epoch Val: [{}] '.format('eval') + json.dumps(test_stats))
return
best_performance = 0
if args.resume and os.path.exists(args.resume):
try:
with open(output_dir / "log.txt", 'r') as f:
previous_log = f.read().split('\n')
previous_log.remove('')
test_stats = json.loads(previous_log[-1])
if args.dataset_file == 'hico':
performance = test_stats['mAP']
elif args.dataset_file == 'vcoco':
performance = test_stats['mAP_all']
elif args.dataset_file == 'hoia':
performance = test_stats['mAP']
best_performance = performance
except:
best_performance = 0
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,
args.gradient_accumulation_steps, args.enable_amp, args.no_training, args)
lr_scheduler.step()
# if epoch == args.epochs - 1:
checkpoint_path = os.path.join(output_dir, 'checkpoint_last.pth')
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
# 'amp': amp.state_dict(),
'epoch': epoch,
'args': args,
} if args.enable_amp else {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
# 'amp': None,
'epoch': epoch,
'args': args,
}, checkpoint_path)
test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_val,
args.subject_category_id, device, args)
if args.dataset_file == 'hico':
performance = test_stats['mAP']
elif args.dataset_file == 'vcoco':
performance = test_stats['mAP_all']
elif args.dataset_file == 'hoia':
performance = test_stats['mAP']
if performance > best_performance:
checkpoint_path = os.path.join(output_dir, 'checkpoint_best.pth')
utils.save_on_master({
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
# 'amp': amp.state_dict(),
'epoch': epoch,
'args': args,
} if args.enable_amp else {
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
# 'amp': None,
'epoch': epoch,
'args': args,
}, checkpoint_path)
best_performance = performance
if epoch in args.save_points and utils.is_main_process():
checkpoint_path = os.path.join(output_dir, f'best_before_epoch_{epoch}.pth')
print('achieve save point')
if os.path.exists(os.path.join(output_dir, 'checkpoint_best.pth')):
os.system(f"cp {os.path.join(output_dir, 'checkpoint_best.pth')} {checkpoint_path}")
elif os.path.exists(os.path.join(output_dir, 'checkpoint_last.pth')):
os.system(f"cp {os.path.join(output_dir, 'checkpoint_last.pth')} {checkpoint_path}")
else:
raise ValueError
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_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")
LOGGER.info('Epoch: [{}] '.format(epoch) + json.dumps(log_stats))
# add eval in log for my convenience
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(test_stats) + "\n")
LOGGER.info('Epoch: [{}] '.format(epoch) + json.dumps(test_stats))
if epoch == args.epochs - 1 and os.path.exists(os.path.join(output_dir, 'checkpoint_best.pth')):
print('Loading best val checkpoint!')
checkpoint = torch.load(os.path.join(output_dir, 'checkpoint_best.pth'), map_location='cpu')
model_without_ddp.load_state_dict(checkpoint['model'])
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = -1
model.to(device)
print('Final evaluating in test split!')
test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_test,
args.subject_category_id, device, args)
if args.output_dir and utils.is_main_process():
# add eval in log for my convenience
with (output_dir / "log.txt").open("a") as f:
f.write('Test result:' + json.dumps(test_stats) + "\n")
LOGGER.info('Epoch Test: [{}] '.format(epoch) + json.dumps(test_stats))
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('GEN VLKT training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)