from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os import sys class opts(object): def __init__(self): self.parser = argparse.ArgumentParser() # basic experiment setting self.parser.add_argument('task', default='mot', help='mot') self.parser.add_argument('--dataset', default='jde', help='jde') self.parser.add_argument('--exp_id', default='default') self.parser.add_argument('--test', action='store_true') #self.parser.add_argument('--load_model', default='../models/ctdet_coco_dla_2x.pth', #help='path to pretrained model') self.parser.add_argument('--load_model', default='', help='path to pretrained model') self.parser.add_argument('--resume', action='store_true', help='resume an experiment. ' 'Reloaded the optimizer parameter and ' 'set load_model to model_last.pth ' 'in the exp dir if load_model is empty.') # system self.parser.add_argument('--gpus', default='0', help='-1 for CPU, use comma for multiple gpus') self.parser.add_argument('--num_workers', type=int, default=8, help='dataloader threads. 0 for single-thread.') self.parser.add_argument('--not_cuda_benchmark', action='store_true', help='disable when the input size is not fixed.') self.parser.add_argument('--seed', type=int, default=317, help='random seed') # from CornerNet # log self.parser.add_argument('--print_iter', type=int, default=0, help='disable progress bar and print to screen.') self.parser.add_argument('--hide_data_time', action='store_true', help='not display time during training.') self.parser.add_argument('--save_all', action='store_true', help='save model to disk every 5 epochs.') self.parser.add_argument('--metric', default='loss', help='main metric to save best model') self.parser.add_argument('--vis_thresh', type=float, default=0.5, help='visualization threshold.') self.parser.add_argument('--save_dir', type=str, default='/opt/ml/model', help='directory model saved') self.parser.add_argument('--checkpoint_format', type=str, default='/opt/ml/model/model-{epoch}.pth', help='checkpoint file format') # model self.parser.add_argument('--arch', default='dla_34', help='model architecture. Currently tested' 'resdcn_34 | resdcn_50 | resfpndcn_34 |' 'dla_34 | hrnet_18') self.parser.add_argument('--head_conv', type=int, default=-1, help='conv layer channels for output head' '0 for no conv layer' '-1 for default setting: ' '256 for resnets and 256 for dla.') self.parser.add_argument('--down_ratio', type=int, default=4, help='output stride. Currently only supports 4.') # input self.parser.add_argument('--input_res', type=int, default=-1, help='input height and width. -1 for default from ' 'dataset. Will be overriden by input_h | input_w') self.parser.add_argument('--input_h', type=int, default=-1, help='input height. -1 for default from dataset.') self.parser.add_argument('--input_w', type=int, default=-1, help='input width. -1 for default from dataset.') # train self.parser.add_argument('--lr', type=float, default=1e-4, help='learning rate for batch size 12.') self.parser.add_argument('--lr_step', type=str, default='20', help='drop learning rate by 10.') self.parser.add_argument('--num_epochs', type=int, default=30, help='total training epochs.') self.parser.add_argument('--batch_size', type=int, default=12, help='batch size') self.parser.add_argument('--master_batch_size', type=int, default=-1, help='batch size on the master gpu.') self.parser.add_argument('--num_iters', type=int, default=-1, help='default: #samples / batch_size.') self.parser.add_argument('--val_intervals', type=int, default=1, help='number of epochs to run validation.') self.parser.add_argument('--trainval', action='store_true', help='include validation in training and ' 'test on test set') # test self.parser.add_argument('--K', type=int, default=500, help='max number of output objects.') self.parser.add_argument('--not_prefetch_test', action='store_true', help='not use parallal data pre-processing.') self.parser.add_argument('--fix_res', action='store_true', help='fix testing resolution or keep ' 'the original resolution') self.parser.add_argument('--keep_res', action='store_true', help='keep the original resolution' ' during validation.') # tracking self.parser.add_argument('--test_mot16', default=False, help='test mot16') self.parser.add_argument('--val_mot15', default=False, help='val mot15') self.parser.add_argument('--test_mot15', default=False, help='test mot15') self.parser.add_argument('--val_mot16', default=False, help='val mot16 or mot15') self.parser.add_argument('--test_mot17', default=False, help='test mot17') self.parser.add_argument('--val_mot17', default=True, help='val mot17') self.parser.add_argument('--val_mot20', default=False, help='val mot20') self.parser.add_argument('--test_mot20', default=False, help='test mot20') self.parser.add_argument('--val_hie', default=False, help='val hie') self.parser.add_argument('--test_hie', default=False, help='test hie') self.parser.add_argument('--conf_thres', type=float, default=0.4, help='confidence thresh for tracking') self.parser.add_argument('--det_thres', type=float, default=0.3, help='confidence thresh for detection') self.parser.add_argument('--nms_thres', type=float, default=0.4, help='iou thresh for nms') self.parser.add_argument('--track_buffer', type=int, default=30, help='tracking buffer') self.parser.add_argument('--min-box-area', type=float, default=100, help='filter out tiny boxes') self.parser.add_argument('--input-video', type=str, default='../videos/MOT16-03.mp4', help='path to the input video') self.parser.add_argument('--output-format', type=str, default='video', help='video or text') self.parser.add_argument('--output-root', type=str, default='../demos', help='expected output root path') # mot self.parser.add_argument('--data_cfg', type=str, default='../src/lib/cfg/data.json', help='load data from cfg') self.parser.add_argument('--data_dir', type=str, default='/opt/ml/input') self.parser.add_argument('--data_val_dir', type=str, default='/opt/ml/input/data/val') # loss self.parser.add_argument('--mse_loss', action='store_true', help='use mse loss or focal loss to train ' 'keypoint heatmaps.') self.parser.add_argument('--reg_loss', default='l1', help='regression loss: sl1 | l1 | l2') self.parser.add_argument('--hm_weight', type=float, default=1, help='loss weight for keypoint heatmaps.') self.parser.add_argument('--off_weight', type=float, default=1, help='loss weight for keypoint local offsets.') self.parser.add_argument('--wh_weight', type=float, default=0.1, help='loss weight for bounding box size.') self.parser.add_argument('--id_loss', default='ce', help='reid loss: ce | focal') self.parser.add_argument('--id_weight', type=float, default=1, help='loss weight for id') self.parser.add_argument('--reid_dim', type=int, default=128, help='feature dim for reid') self.parser.add_argument('--ltrb', default=True, help='regress left, top, right, bottom of bbox') self.parser.add_argument('--multi_loss', default='uncertainty', help='multi_task loss: uncertainty | fix') self.parser.add_argument('--norm_wh', action='store_true', help='L1(\hat(y) / y, 1) or L1(\hat(y), y)') self.parser.add_argument('--dense_wh', action='store_true', help='apply weighted regression near center or ' 'just apply regression on center point.') self.parser.add_argument('--cat_spec_wh', action='store_true', help='category specific bounding box size.') self.parser.add_argument('--not_reg_offset', action='store_true', help='not regress local offset.') def parse(self, args=''): if args == '': opt = self.parser.parse_args() else: opt = self.parser.parse_args(args) opt.gpus_str = opt.gpus opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')] opt.lr_step = [int(i) for i in opt.lr_step.split(',')] opt.fix_res = not opt.keep_res print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.') opt.reg_offset = not opt.not_reg_offset if opt.head_conv == -1: # init default head_conv opt.head_conv = 256 if 'dla' in opt.arch else 256 opt.pad = 31 opt.num_stacks = 1 if opt.trainval: opt.val_intervals = 100000000 if opt.master_batch_size == -1: opt.master_batch_size = opt.batch_size // len(opt.gpus) rest_batch_size = (opt.batch_size - opt.master_batch_size) opt.chunk_sizes = [opt.master_batch_size] for i in range(len(opt.gpus) - 1): slave_chunk_size = rest_batch_size // (len(opt.gpus) - 1) if i < rest_batch_size % (len(opt.gpus) - 1): slave_chunk_size += 1 opt.chunk_sizes.append(slave_chunk_size) print('training chunk_sizes:', opt.chunk_sizes) opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..') opt.exp_dir = os.path.join(opt.root_dir, 'exp', opt.task) #opt.save_dir = os.path.join(opt.exp_dir, opt.exp_id) opt.debug_dir = os.path.join(opt.save_dir, 'debug') print('The output will be saved to ', opt.save_dir) if opt.resume and opt.load_model == '': model_path = opt.save_dir[:-4] if opt.save_dir.endswith('TEST') \ else opt.save_dir opt.load_model = os.path.join(model_path, 'model_last.pth') return opt def update_dataset_info_and_set_heads(self, opt, dataset, val_dataset=None): input_h, input_w = dataset.default_resolution opt.mean, opt.std = dataset.mean, dataset.std opt.num_classes = dataset.num_classes # input_h(w): opt.input_h overrides opt.input_res overrides dataset default input_h = opt.input_res if opt.input_res > 0 else input_h input_w = opt.input_res if opt.input_res > 0 else input_w opt.input_h = opt.input_h if opt.input_h > 0 else input_h opt.input_w = opt.input_w if opt.input_w > 0 else input_w opt.output_h = opt.input_h // opt.down_ratio opt.output_w = opt.input_w // opt.down_ratio opt.input_res = max(opt.input_h, opt.input_w) opt.output_res = max(opt.output_h, opt.output_w) if opt.task == 'mot': opt.heads = {'hm': opt.num_classes, 'wh': 2 if not opt.ltrb else 4, 'id': opt.reid_dim} if opt.reg_offset: opt.heads.update({'reg': 2}) opt.nID = max(dataset.nID, val_dataset.nID) if val_dataset else dataset.nID opt.img_size = (1088, 608) #opt.img_size = (864, 480) #opt.img_size = (576, 320) else: assert 0, 'task not defined!' print('heads', opt.heads) return opt def init(self, args=''): default_dataset_info = { 'mot': {'default_resolution': [608, 1088], 'num_classes': 1, 'mean': [0.408, 0.447, 0.470], 'std': [0.289, 0.274, 0.278], 'dataset': 'jde', 'nID': 14455}, } class Struct: def __init__(self, entries): for k, v in entries.items(): self.__setattr__(k, v) opt = self.parse(args) dataset = Struct(default_dataset_info[opt.task]) opt.dataset = dataset.dataset opt = self.update_dataset_info_and_set_heads(opt, dataset) return opt