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main.py
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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 os
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
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
from models import build_model
def get_args_parser():
parser = argparse.ArgumentParser('AnchorDETR Detector', add_help=False)
parser.add_argument('--lr', default=1e-4, type=float)
parser.add_argument('--lr_backbone_names', default=["backbone"], type=str, nargs='+')
parser.add_argument('--lr_backbone', default=1e-5, type=float)
parser.add_argument('--lr_linear_proj_names', default=[], type=str, nargs='+')
parser.add_argument('--lr_linear_proj_mult', default=0.1, type=float)
parser.add_argument('--batch_size', default=1, 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('--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('--sgd', 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")
# * Backbone
parser.add_argument('--backbone', default='resnet50', type=str,
help="Name of the convolutional backbone to use")
parser.add_argument('--dilation', default=True,
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
parser.add_argument('--num_feature_levels', default=1, 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., 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_query_position', default=300, type=int,
help="Number of query positions")
parser.add_argument('--num_query_pattern', default=3, type=int,
help="Number of query patterns")
parser.add_argument('--spatial_prior', default='learned', choices=['learned', 'grid'],
type=str,help="Number of query patterns")
parser.add_argument('--attention_type',
# default='nn.MultiheadAttention',
default="RCDA",
choices=['RCDA', 'nn.MultiheadAttention'],
type=str,help="Type of attention module")
# * 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('--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('--coco_path', default='/data/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='/data/detr-workdir/r50-dc5',
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('--auto_resume', default=False, action='store_true', help='whether to resume from last 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)
parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')
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"
if isinstance(args.dilation,str):
if args.dilation.lower() == 'false':
args.dilation = False
elif args.dilation.lower() == 'true':
args.dilation = True
else:
raise ValueError("The dilation should be True or False")
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)
data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,
collate_fn=utils.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=utils.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
for n, p in model_without_ddp.named_parameters():
print(n)
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])
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'])
output_dir = Path(args.output_dir)
if args.auto_resume:
if not args.resume:
args.resume = os.path.join(args.output_dir, 'checkpoint.pth')
if not os.path.isfile(args.resume):
args.resume=''
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']
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
# check the resumed model
if not args.eval:
test_stats, coco_evaluator = evaluate(
model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir
)
if args.eval:
test_stats, coco_evaluator = evaluate(model, criterion, postprocessors,
data_loader_val, base_ds, device, args.output_dir, save_json=True)
if args.output_dir:
utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
if utils.is_main_process():
with open(os.path.join(args.output_dir, 'results.json'), 'w') as f:
json.dump(coco_evaluator.results['bbox'], f)
return
print("Start training")
print(args.output_dir)
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) % args.lr_drop == 0 or (epoch + 1) % 100 == 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)
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}
print(args.output_dir)
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('AnchorDETR 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)