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main.py
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# ------------------------------------------------------------------------
# Copyright (c) 2021 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
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 datasets
from util.motdet_eval import motdet_evaluate, detmotdet_evaluate
from util.tool import load_model
import util.misc as utils
import datasets.samplers as samplers
from datasets import build_dataset, get_coco_api_from_dataset
from engine import evaluate, train_one_epoch, train_one_epoch_mot
from models import build_model
def get_args_parser():
parser = argparse.ArgumentParser('Deformable DETR Detector', add_help=False)
parser.add_argument('--lr', default=2e-4, type=float)
parser.add_argument('--lr_backbone_names', default=["backbone.0"], type=str, nargs='+')
parser.add_argument('--lr_backbone', default=2e-5, type=float)
parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets',], type=str, nargs='+')
parser.add_argument('--lr_linear_proj_mult', default=0.1, 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=50, type=int)
parser.add_argument('--lr_drop', default=40, type=int)
parser.add_argument('--save_period', default=50, type=int)
parser.add_argument('--lr_drop_epochs', default=None, type=int, nargs='+')
parser.add_argument('--clip_max_norm', default=0.1, type=float,
help='gradient clipping max norm')
parser.add_argument('--meta_arch', default='deformable_detr', type=str)
parser.add_argument('--sgd', action='store_true')
# Variants of Deformable DETR
parser.add_argument('--with_box_refine', default=False, action='store_true')
parser.add_argument('--two_stage', default=False, action='store_true')
parser.add_argument('--accurate_ratio', default=False, action='store_true')
# 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")
parser.add_argument('--num_anchors', default=1, type=int)
# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--enable_fpn', action='store_true')
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")
parser.add_argument('--position_embedding_scale', default=2 * np.pi, type=float,
help="position / size * scale")
parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')
# * Transformer
parser.add_argument('--enc_layers', default=6, type=int,
help="Number of encoding layers in the transformer")
parser.add_argument('--dec_layers', default=6, type=int,
help="Number of decoding layers in the transformer")
parser.add_argument('--dim_feedforward', default=1024, 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=300, type=int,
help="Number of query slots")
parser.add_argument('--dec_n_points', default=4, type=int)
parser.add_argument('--enc_n_points', default=4, type=int)
parser.add_argument('--decoder_cross_self', default=False, action='store_true')
parser.add_argument('--sigmoid_attn', default=False, action='store_true')
parser.add_argument('--crop', action='store_true')
parser.add_argument('--cj', action='store_true')
parser.add_argument('--extra_track_attn', action='store_true')
parser.add_argument('--loss_normalizer', action='store_true')
parser.add_argument('--max_size', default=1333, type=int)
parser.add_argument('--val_width', default=800, type=int)
parser.add_argument('--filter_ignore', action='store_true')
# * Segmentation
parser.add_argument('--masks', action='store_true',
help="Train segmentation head if the flag is provided")
# Loss
parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
help="Disables auxiliary decoding losses (loss at each layer)")
# * Matcher
parser.add_argument('--mix_match', action='store_true',)
parser.add_argument('--set_cost_class', default=2, type=float,
help="Class coefficient in the matching cost")
parser.add_argument('--set_cost_bbox', default=5, type=float,
help="L1 box coefficient in the matching cost")
parser.add_argument('--set_cost_giou', default=2, type=float,
help="giou box 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('--cls_loss_coef', default=2, type=float)
parser.add_argument('--bbox_loss_coef', default=5, type=float)
parser.add_argument('--giou_loss_coef', default=2, type=float)
parser.add_argument('--focal_alpha', default=0.25, type=float)
# dataset parameters
parser.add_argument('--dataset_file', default='coco')
parser.add_argument('--gt_file_train', type=str)
parser.add_argument('--gt_file_val', type=str)
parser.add_argument('--coco_path', default='/data/workspace/detectron2/datasets/coco/', type=str)
parser.add_argument('--coco_panoptic_path', type=str)
parser.add_argument('--remove_difficult', action='store_true')
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('--vis', action='store_true')
parser.add_argument('--num_workers', default=2, type=int)
parser.add_argument('--pretrained', default=None, help='resume from checkpoint')
parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')
# end-to-end mot settings.
parser.add_argument('--mot_path', default='/data/Dataset/mot', type=str)
parser.add_argument('--input_video', default='figs/demo.mp4', type=str)
parser.add_argument('--data_txt_path_train',
default='./datasets/data_path/detmot17.train', type=str,
help="path to dataset txt split")
parser.add_argument('--data_txt_path_val',
default='./datasets/data_path/detmot17.train', type=str,
help="path to dataset txt split")
parser.add_argument('--img_path', default='data/valid/JPEGImages/')
parser.add_argument('--query_interaction_layer', default='QIM', type=str,
help="")
parser.add_argument('--sample_mode', type=str, default='fixed_interval')
parser.add_argument('--sample_interval', type=int, default=1)
parser.add_argument('--random_drop', type=float, default=0)
parser.add_argument('--fp_ratio', type=float, default=0)
parser.add_argument('--merger_dropout', type=float, default=0.1)
parser.add_argument('--update_query_pos', action='store_true')
parser.add_argument('--sampler_steps', type=int, nargs='*')
parser.add_argument('--sampler_lengths', type=int, nargs='*')
parser.add_argument('--exp_name', default='submit', type=str)
parser.add_argument('--memory_bank_score_thresh', type=float, default=0.)
parser.add_argument('--memory_bank_len', type=int, default=4)
parser.add_argument('--memory_bank_type', type=str, default=None)
parser.add_argument('--memory_bank_with_self_attn', action='store_true', default=False)
parser.add_argument('--use_checkpoint', action='store_true', default=False)
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)
# fix the seed for reproducibility
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
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
dataset_train = build_dataset(image_set='train', args=args)
dataset_val = build_dataset(image_set='val', args=args)
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
sampler_val = samplers.NodeDistributedSampler(dataset_val, shuffle=False)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
sampler_val = samplers.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)
if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:
collate_fn = utils.mot_collate_fn
else:
collate_fn = utils.collate_fn
data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,
collate_fn=collate_fn, num_workers=args.num_workers,
pin_memory=True)
data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,
drop_last=False, collate_fn=collate_fn, num_workers=args.num_workers,
pin_memory=True)
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_linear_proj_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_linear_proj_names) and p.requires_grad],
"lr": args.lr * args.lr_linear_proj_mult,
}
]
if args.sgd:
optimizer = torch.optim.SGD(param_dicts, lr=args.lr, momentum=0.9,
weight_decay=args.weight_decay)
else:
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)
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
model_without_ddp = model.module
if args.dataset_file == "coco_panoptic":
# We also evaluate AP during panoptic training, on original coco DS
coco_val = datasets.coco.build("val", args)
base_ds = get_coco_api_from_dataset(coco_val)
else:
base_ds = get_coco_api_from_dataset(dataset_val)
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'])
if args.pretrained is not None:
model_without_ddp = load_model(model_without_ddp, args.pretrained)
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')
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'])
# todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance).
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
if args.eval:
test_stats, coco_evaluator = evaluate(model, criterion, postprocessors,
data_loader_val, base_ds, device, args.output_dir)
if args.output_dir:
utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
return
print("Start training")
start_time = time.time()
train_func = train_one_epoch
if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:
train_func = train_one_epoch_mot
dataset_train.set_epoch(args.start_epoch)
dataset_val.set_epoch(args.start_epoch)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
sampler_train.set_epoch(epoch)
train_stats = train_func(
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']
# extra checkpoint before LR drop and every 5 epochs
if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % args.save_period == 0 or (((args.epochs >= 100 and (epoch + 1) > 100) or args.epochs < 100) and (epoch + 1) % 5 == 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)
if args.dataset_file not in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:
test_stats, coco_evaluator = evaluate(
model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir
)
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)
if args.dataset_file in ['e2e_mot', 'e2e_dance', 'mot', 'ori_mot', 'e2e_static_mot', 'e2e_joint']:
dataset_train.step_epoch()
dataset_val.step_epoch()
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('Deformable 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)