main.py

# ------------------------------------------------------------------------
# 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 torch.multiprocessing as mp
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
from util.tool import load_model
from util.dist import configure_nccl, init_process_group, synchronize
from util.log import setup_logger, setup_writer
import functools

print = functools.partial(print, flush=True)

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=10, 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='solq', 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')

    # VecInst
    parser.add_argument('--with_vector', default=False, action='store_true')
    parser.add_argument('--n_keep', default=256, type=int,
                        help="Number of coeffs to be remained")
    parser.add_argument('--gt_mask_len', default=128, type=int,
                        help="Size of target mask")
    parser.add_argument('--vector_loss_coef', default=0.7, type=float)
    parser.add_argument('--vector_hidden_dim', default=256, type=int,
                        help="Size of the vector embeddings (dimension of the transformer)")
    parser.add_argument('--no_vector_loss_norm', default=False, action='store_true')
    parser.add_argument('--activation', default='relu', type=str, help="Activation function to use")
    parser.add_argument('--checkpoint', default=False, action='store_true')
    parser.add_argument('--vector_start_stage', default=0, type=int)
    parser.add_argument('--num_machines', default=1, type=int)
    parser.add_argument('--loss_type', default='l1', type=str)
    parser.add_argument('--dcn', 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('--pretrained', default=None, help='resume from checkpoint')

    # * 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")
    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=384, 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)

    # * 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('--alg', default='instformer', 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('--test', 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')
    
    # distributed
    parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
    parser.add_argument('--dist-url', default=None, type=str, help='url used to set up distributed training')
    parser.add_argument('--rank', default=None, type=int, help='node rank for distributed training')
    parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
    parser.add_argument('--num-machines', default=None, type=int)
    return parser


def main(gpu, ngpus_per_node, args):
    if args.num_machines == 1:
        utils.init_distributed_mode(args)
    else:
        configure_nccl()
        # ------------ set environment variables for distributed training ------------------------------------- #
        if args.rank is None:
            args.rank = int(os.getenv('RLAUNCH_REPLICA', '0'))

        os.environ['LOCAL_RANK'] = '0'
        os.environ['LOCAL_SIZE'] = '1'
        args.distributed = True

        args.gpu = gpu
        if ngpus_per_node > 1:
            args.rank = args.rank * ngpus_per_node + gpu

            # initialize process group
            init_process_group(args)
        synchronize()

    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)

    if args.num_machines == 1:
        device = torch.device(args.device)
    else:
        device = torch.device(args.gpu)
        args.device = args.gpu # fix multi-machines launch problems

    # 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)
    if args.num_machines == 1:
        model.to(device)
    else:
        torch.cuda.set_device(args.gpu)
        model = model.cuda()

    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)
    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)


    # lr_backbone_names = ["backbone.0", "backbone.neck", "input_proj", "transformer.encoder"]
    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:
        print('Training with distributed.')
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        synchronize()
        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
        # 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)
        if args.output_dir:
            utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
            # here dump results in json format
            image_set = 'test-dev' if args.test else 'val' 
            print('Dump results to {}.'.format(os.path.join(args.output_dir, f'detections_{image_set}2017_{args.alg}_results.json')))
            with open(os.path.join(args.output_dir, f'detections_{image_set}2017_{args.alg}_results.json'), 'w', encoding='utf-8') as f:
                json.dump(coco_evaluator.eval_results, f, cls=utils.AdaptiveEncoder, indent=4)
        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 and utils.is_main_process():
            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:
                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('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)

    # multi-processing
    if args.num_machines is None:
        args.num_machines = int(os.getenv('RLAUNCH_REPLICA_TOTAL', '1'))

    print('Total number of using machines: {}'.format(args.num_machines))
    ngpus_per_node = torch.cuda.device_count()
    if args.num_machines > 1 and ngpus_per_node > 1:
        args.world_size = ngpus_per_node * args.num_machines
        mp.spawn(main, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        args.world_size = 1
        main(0, ngpus_per_node, args)