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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 numpy as np
- import torch
- from torch.utils.data import DataLoader, DistributedSampler
-
- import datasets
- import util.misc as utils
- 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('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=300, type=int)
- parser.add_argument('--lr_drop', default=200, 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', 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=0.1, 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")
-
- # 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=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('--bbox_loss_coef', default=5, type=float)
- parser.add_argument('--giou_loss_coef', default=2, 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('--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='detr-r50-e632da11.pth', 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')
- 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
- if args.distributed:
- model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
- model_without_ddp = model.module
- n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
- print('number of params:', n_parameters)
-
- 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.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.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')
-
- if not args.eval:
- ckpt = {}
- for key, value in checkpoint['model'].items():
- if 'class_embed' not in key:
- ckpt[key] = value
- model_without_ddp.load_state_dict(ckpt, strict=False)
- else:
- 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.eval:
- test_stats, coco_evaluator = evaluate(model, criterion, postprocessors,
- data_loader_val, base_ds, device, args.output_dir)
- print('-'* 100)
- 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()
- 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']
- # extra checkpoint before LR drop and every 100 epochs
- 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}
-
- 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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