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make_param.py
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make_param.py
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# 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 os, sys
from typing import Optional
from util.logger import setup_logger
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import torch.distributed as dist
import datasets
import util.misc as utils
from datasets import build_dataset
from engine import evaluate_hoi, train_one_epoch
from models import build_DABDETR, build_dab_deformable_detr
from util.utils import clean_state_dict
def get_args_parser():
parser = argparse.ArgumentParser('DAB-DETR', add_help=False)
# about lr
parser.add_argument('--lr', default=1e-4, type=float,
help='learning rate')
parser.add_argument('--lr_backbone', default=1e-5, type=float,
help='learning rate for backbone')
parser.add_argument('--batch_size', default=1, 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=120, type=int)
parser.add_argument('--save_checkpoint_interval', default=100, type=int)
parser.add_argument('--clip_max_norm', default=0.1, type=float,
help='gradient clipping max norm')
parser.add_argument('--nms_thresh', default=0.5, type=float)
parser.add_argument('--use_checkpoint', action='store_true')
parser.add_argument('--no_obj', action='store_true')
# Model parameters
# parser.add_argument('--modelname', '-m', type=str, required=True, choices=['dab_detr', 'dab_deformable_detr'])
parser.add_argument('--modelname', type=str, default='dab_deformable_detr')
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='swin_tiny', 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")
parser.add_argument('--pe_temperatureH', default=20, type=int,
help="Temperature for height positional encoding.")
parser.add_argument('--pe_temperatureW', default=20, type=int,
help="Temperature for width positional encoding.")
parser.add_argument('--batch_norm_type', default='FrozenBatchNorm2d', type=str,
choices=['SyncBatchNorm', 'FrozenBatchNorm2d', 'BatchNorm2d'], help="batch norm type for backbone")
# * Transformer
parser.add_argument('--return_interm_layers', action='store_true',
help="Train segmentation head if the flag is provided")
parser.add_argument('--backbone_freeze_keywords', nargs="+", type=str,
help='freeze some layers in backbone. for catdet5.')
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=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=0.0, 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('--pre_norm', action='store_true',
help="Using pre-norm in the Transformer blocks.")
parser.add_argument('--num_select', default=300, type=int,
help='the number of predictions selected for evaluation')
parser.add_argument('--transformer_activation', default='prelu', type=str)
parser.add_argument('--num_patterns', default=0, type=int,
help='number of pattern embeddings. See Anchor DETR for more details.')
parser.add_argument('--random_refpoints_xy', action='store_true',
help="Random init the x,y of anchor boxes and freeze them.")
# for DAB-Deformable-DETR
parser.add_argument('--two_stage', default=False, action='store_true',
help="Using two stage variant for DAB-Deofrmable-DETR")
parser.add_argument('--num_feature_levels', default=3, type=int,
help='number of feature levels')
parser.add_argument('--dec_n_points', default=4, type=int,
help="number of deformable attention sampling points in decoder layers")
parser.add_argument('--enc_n_points', default=4, type=int,
help="number of deformable attention sampling points in encoder layers")
parser.add_argument('--use_dab', type=str, default=False,
help='switch for anchor')
parser.add_argument('--with_box_refine', type=str, default=False,
help='switch for anchor')
# * Segmentation
parser.add_argument('--masks', action='store_true',
help="Train segmentation head if the flag is provided")
# HOI
parser.add_argument('--pretrained', type=str, default="param/swin_tiny_patch4_window7_224.pth",
help='Pretrained model path')
parser.add_argument('--num_verb_classes', type=int, default=29,
help="Number of verb classes")
parser.add_argument('--num_obj_classes', type=int, default=81,
help="Number of object classes")
parser.add_argument('--subject_category_id', default=0, type=int)
#dynamic anchor
parser.add_argument('--dynamic', type=str, default=False,
help='switch for anchor')
parser.add_argument('--offset_stride', type=list, default=[11, 7, 3],
help='offset_stride')
parser.add_argument('--ratios', type=list, default=[0.5, 1, 2],
help='anchor ratios')
parser.add_argument('--scales', type=list, default=[0.1, 0.2, 0.4],
help='anchor scales')
# 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('--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")
# * Loss coefficients
parser.add_argument('--cls_loss_coef', default=1, type=float,
help="loss coefficient for cls")
parser.add_argument('--mask_loss_coef', default=1, type=float,
help="loss coefficient for mask")
parser.add_argument('--dice_loss_coef', default=1, type=float,
help="loss coefficient for dice")
parser.add_argument('--eos_coef', default=0.1, type=float,
help="Relative classification weight of the no-object class")
parser.add_argument('--focal_alpha', type=float, default=0.25,
help="alpha for focal loss")
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=1, type=float)
parser.add_argument('--anchor_loss_coef', default=1, type=float)
parser.add_argument('--hoi_score_loss_coef', default=1, type=float)
# dataset parameters
parser.add_argument('--dataset_file', default='vcoco')
parser.add_argument('--coco_path', default='./data/coco', type=str)
parser.add_argument('--hake_path', default="/mnt/gluster/ssd/datasets/HAKE/", type=str)
parser.add_argument('--hake_split_train', default='our_train_4w', type=str)
parser.add_argument('--hake_split_test', default='our_train_4w', type=str)
parser.add_argument('--coco_panoptic_path', type=str)
parser.add_argument('--remove_difficult', action='store_true')
parser.add_argument('--hoi_path', default='/mnt/gluster/ssd/datasets/hico_20160224_det/hico_20160224_det/', type=str)
# parser.add_argument('--hoi_path', default='/mnt/gluster/ssd/datasets/v-coco/v-coco/', type=str)
parser.add_argument('--fix_size', action='store_true',
help="Using for debug only. It will fix the size of input images to the maximum.")
# Traing utils
parser.add_argument('--output_dir', default='logs/test', help='path where to save, empty for no saving')
parser.add_argument('--note', default='', help='add some notes to the experiment')
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('--pretrain_model_path', default = "", help='load from other checkpoint')
parser.add_argument('--finetune_ignore', type=str, nargs='+',
help="A list of keywords to ignore when loading pretrained models.")
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true', help="eval only. w/o Training.")
parser.add_argument('--eval_extra', action='store_true', help="eval only. w/o Training.")
parser.add_argument('--num_workers', default=2, type=int)
parser.add_argument('--debug', action='store_true',
help="For debug only. It will perform only a few steps during trainig and val.")
parser.add_argument('--find_unused_params', default=True)
parser.add_argument('--save_results', action='store_true',
help="For eval only. Save the outputs for all images.")
parser.add_argument('--save_log', action='store_true',
help="If save the training prints to the log file.")
parser.add_argument('--use_nms', default=True)
# distributed training parameters
parser.add_argument('--world_size', default=2, 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('--rank', default=0, type=int,
help='number of distributed processes')
parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel')
parser.add_argument('--amp', action='store_true',
help="Train with mixed precision")
return parser
def build_model_main(args):
if args.modelname.lower() == 'dab_detr':
model, criterion, postprocessors = build_DABDETR(args)
elif args.modelname.lower() == 'dab_deformable_detr':
model, criterion, postprocessors = build_dab_deformable_detr(args)
else:
raise NotImplementedError
return model, criterion, postprocessors
def main(args):
utils.init_distributed_mode(args)
# torch.autograd.set_detect_anomaly(True)
# setup logger
os.makedirs(args.output_dir, exist_ok=True)
os.environ['output_dir'] = args.output_dir
logger = setup_logger(output=os.path.join(args.output_dir, 'info.txt'), distributed_rank=args.rank, color=False, name="DAB-DETR")
logger.info("git:\n {}\n".format(utils.get_sha()))
logger.info("Command: "+' '.join(sys.argv))
if args.rank == 0:
save_json_path = os.path.join(args.output_dir, "config.json")
# print("args:", vars(args))
with open(save_json_path, 'w') as f:
json.dump(vars(args), f, indent=2)
logger.info("Full config saved to {}".format(save_json_path))
logger.info('world size: {}'.format(args.world_size))
logger.info('rank: {}'.format(args.rank))
logger.info('local_rank: {}'.format(args.local_rank))
logger.info("args: " + str(args) + '\n')
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)
# build model
model, criterion, postprocessors = build_model_main(args)
print('print loaded model')
wo_class_error = False
model.to(device)
Vcoco_param = {}
model_without_ddp = model
HICO_param = torch.load("/mnt/gluster/home/mashuailei/DAB-DETR-main/dahoi/logs/2KA_PLANBV1_MULTI_base/checkpoint0141.pth", map_location='cpu')['model']
for key, value in HICO_param.items():
if 'backbone' in key:
Vcoco_param[key] = value
DDETR_param = torch.load("/mnt/gluster/home/mashuailei/DAB-DETR-main/dahoi/param/r50_deformable_detr-checkpoint.pth", map_location='cpu')['model']
for key, value in DDETR_param.items():
if 'transformer' in key:
Vcoco_param[key] = value
Vcoco_param['transformer.level_embed'] = HICO_param['transformer.level_embed']
missing_keys, unexpected_keys = model_without_ddp.load_state_dict(Vcoco_param, strict=False)
if len(missing_keys) > 0:
print('Missing Keys: {}'.format(missing_keys))
if len(unexpected_keys) > 0:
print('Unexpected Keys: {}'.format(unexpected_keys))
a = 1
if __name__ == '__main__':
parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
main(args)