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- import argparse
- import time
- import datetime
- import random
- from pathlib import Path
- import json
-
- 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
-
- 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('--batch_size', default=2, type=int)
- parser.add_argument('--weight_decay', default=1e-4, type=float)
- parser.add_argument('--epochs', default=90, type=int)
- parser.add_argument('--lr_drop', default=60, type=int)
- parser.add_argument('--clip_max_norm', default=0.1, type=float,
- help='gradient clipping max norm')
-
- # 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_hopd', default=3, type=int,
- help="Number of hopd decoding layers in the transformer")
- parser.add_argument('--dec_layers_interaction', default=3, type=int,
- help="Number of interaction 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('--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('--use_matching', action='store_true',
- help="Use obj/sub matching 2class loss in first decoder, default not use")
-
- # * 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_matching', default=1, type=float,
- help="Sub and obj box matching coefficient in the matching cost")
-
- # * 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('--alpha', default=0.5, type=float, help='focal loss alpha')
- parser.add_argument('--matching_loss_coef', default=1, type=float)
- 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')
-
- # decoupling training parameters
- parser.add_argument('--freeze_mode', default=0, type=int)
- parser.add_argument('--obj_reweight', action='store_true')
- parser.add_argument('--verb_reweight', action='store_true')
- parser.add_argument('--use_static_weights', action='store_true',
- help='use static weights or dynamic weights, default use dynamic')
- parser.add_argument('--queue_size', default=4704*1.0, type=float,
- help='Maxsize of queue for obj and verb reweighting, default 1 epoch')
- parser.add_argument('--p_obj', default=0.7, type=float,
- help='Reweighting parameter for obj')
- parser.add_argument('--p_verb', default=0.7, type=float,
- help='Reweighting parameter for verb')
-
- # hoi eval parameters
- parser.add_argument('--use_nms_filter', default=False, type=bool)
- parser.add_argument('--thres_nms', default=0.7, type=float)
- parser.add_argument('--nms_alpha', default=1.0, type=float)
- parser.add_argument('--nms_beta', default=0.5, type=float)
- parser.add_argument('--json_file', default='results.json', type=str)
-
- parser.add_argument('--GT', default=False, type=bool)
- parser.add_argument('--Guass', default=False, type=bool)
- parser.add_argument('--gt_lr_cross', action='store_true')
- parser.add_argument('--model_name', type=str)
- parser.add_argument('--hard_stop', action='store_true')
- parser.add_argument('--img_ratio', default=1, type=float)
- parser.add_argument('--pose', default=False, type=bool)
- parser.add_argument('--weight_pose', default=0.01, type=float)
- return parser
-
-
- def main(args):
- utils.init_distributed_mode(args)
- 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)
-
- seed = args.seed + utils.get_rank()
- torch.manual_seed(seed)
- np.random.seed(seed)
- random.seed(seed)
-
- model, criterion, postprocessors = build_model(args)
- model.to(device)
-
- 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
- n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
-
- if args.freeze_mode == 1:
- for name, p in model.named_parameters():
- if 'decoder' not in name and 'verb_class_embed' not in name and 'obj_class_embed' not in name \
- and 'sub_bbox_embed' not in name and 'obj_bbox_embed' not in name:
- p.requires_grad = False
- if args.use_matching and 'matching_embed' in name:
- p.requires_grad = True
-
- 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)
- lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)
-
- dataset_train = build_dataset(image_set='train', args=args)
- dataset_val = build_dataset(image_set='val', args=args)
-
- if args.distributed:
- sampler_train = DistributedSampler(dataset_train)
- sampler_val = DistributedSampler(dataset_val, shuffle=False)
- else:
- sampler_train = torch.utils.data.RandomSampler(dataset_train)
- sampler_val = torch.utils.data.SequentialSampler(dataset_val)
-
- 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)
- 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)
-
- 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)
- 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')
- 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
- elif args.pretrained:
- checkpoint = torch.load(args.pretrained, map_location='cpu')
- if args.eval:
- model_without_ddp.load_state_dict(checkpoint['model'])
- else:
- model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
-
- if args.eval:
- test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_val, args.subject_category_id, device, args)
- return
-
- print("Start training")
- start_time = time.time()
- best_performance = 0
- 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, 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(),
- 'epoch': epoch,
- 'args': args,
- }, checkpoint_path)
-
- if args.freeze_mode == 0 and epoch < args.lr_drop and epoch % 5 != 0: ## eval every 5 epoch before lr_drop
- continue
- '''
- elif args.freeze_mode == 0 and epoch >= args.lr_drop and epoch % 2 == 0: ## eval every 2 epoch after lr_drop
- continue
- '''
- test_stats = evaluate_hoi(args.dataset_file, model, postprocessors, data_loader_val, args.subject_category_id, device, args)
- coco_evaluator = None
- if args.dataset_file == 'hico':
- performance = test_stats['mAP']
- elif args.dataset_file == 'vcoco':
- performance = test_stats['mAP_all']
-
- 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(),
- 'epoch': epoch,
- 'args': args,
- }, checkpoint_path)
-
- best_performance = performance
-
- checkpoint_path = os.path.join(output_dir, f'checkpoint_{epoch}.pth')
- 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()},
- **{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")
-
- # for evaluation logs
- if coco_evaluator is not None:
- (output_dir / 'eval').mkdir(exist_ok=True)
- if "bbox" in coco_evaluator.coco_eval:
- filenames = ['latest.pth']
- if epoch % 50 == 0:
- filenames.append(f'{epoch:03}.pth')
- for name in filenames:
- torch.save(coco_evaluator.coco_eval["bbox"].eval,
- output_dir / "eval" / name)
-
- 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('DETR 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)
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