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='',
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')
self.parser.add_argument('--batch_size_val', type=int, default=12,
help='batch size for validation')
self.parser.add_argument('--num_workers_val', type=int, default=2,
help='dataloader threads. 0 for single-thread. For Validation')
# 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