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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='',
help='ctdet | ddd | multi_pose '
'| tracking or combined with ,')
self.parser.add_argument('--dataset', default='coco',
help='see lib/dataset/dataset_facotry for ' +
'available datasets')
self.parser.add_argument('--test_dataset', default='',
help='coco | kitti | coco_hp | pascal')
self.parser.add_argument('--exp_id', default='default')
self.parser.add_argument('--test', action='store_true')
self.parser.add_argument('--debug', type=int, default=0,
help='level of visualization.'
'1: only show the final detection results'
'2: show the network output features'
'3: use matplot to display' # useful when lunching training with ipython notebook
'4: save all visualizations to disk')
self.parser.add_argument('--no_pause', action='store_true')
self.parser.add_argument('--demo', default='',
help='path to image/ image folders/ video. '
'or "webcam"')
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=4,
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
self.parser.add_argument('--not_set_cuda_env', action='store_true',
help='used when training in slurm clusters.')
# log
self.parser.add_argument('--print_iter', type=int, default=0,
help='disable progress bar and print to screen.')
self.parser.add_argument('--save_all', action='store_true',
help='save model to disk every 5 epochs.')
self.parser.add_argument('--vis_thresh', type=float, default=0.3,
help='visualization threshold.')
self.parser.add_argument('--debugger_theme', default='white',
choices=['white', 'black'])
self.parser.add_argument('--eval_val', action='store_true')
self.parser.add_argument('--save_imgs', default='', help='')
self.parser.add_argument('--save_img_suffix', default='', help='')
self.parser.add_argument('--skip_first', type=int, default=-1, help='')
self.parser.add_argument('--save_video', action='store_true')
self.parser.add_argument('--save_framerate', type=int, default=30)
self.parser.add_argument('--resize_video', action='store_true')
self.parser.add_argument('--video_h', type=int, default=512, help='')
self.parser.add_argument('--video_w', type=int, default=512, help='')
self.parser.add_argument('--transpose_video', action='store_true')
self.parser.add_argument('--show_track_color', action='store_true')
self.parser.add_argument('--not_show_bbox', action='store_true')
self.parser.add_argument('--not_show_number', action='store_true')
self.parser.add_argument('--not_show_txt', action='store_true')
self.parser.add_argument('--qualitative', action='store_true')
self.parser.add_argument('--tango_color', action='store_true')
self.parser.add_argument('--only_show_dots', action='store_true')
self.parser.add_argument('--show_trace', action='store_true')
# model
self.parser.add_argument('--arch', default='dla_34',
help='model architecture. Currently tested'
'res_18 | res_101 | resdcn_18 | resdcn_101 |'
'dlav0_34 | dla_34 | hourglass')
self.parser.add_argument('--dla_node', default='dcn')
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: '
'64 for resnets and 256 for dla.')
self.parser.add_argument('--num_head_conv', type=int, default=1)
self.parser.add_argument('--head_kernel', type=int, default=3, help='')
self.parser.add_argument('--down_ratio', type=int, default=4,
help='output stride. Currently only supports 4.')
self.parser.add_argument('--not_idaup', action='store_true')
self.parser.add_argument('--num_classes', type=int, default=-1)
self.parser.add_argument('--num_layers', type=int, default=101)
self.parser.add_argument('--backbone', default='dla34')
self.parser.add_argument('--neck', default='dlaup')
self.parser.add_argument('--msra_outchannel', type=int, default=256)
self.parser.add_argument('--efficient_level', type=int, default=0)
self.parser.add_argument('--prior_bias', type=float, default=-4.6) # -2.19
# 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.')
self.parser.add_argument('--dataset_version', default='')
# train
self.parser.add_argument('--optim', default='adam')
self.parser.add_argument('--lr', type=float, default=1.25e-4,
help='learning rate for batch size 32.')
self.parser.add_argument('--lr_step', type=str, default='60',
help='drop learning rate by 10.')
self.parser.add_argument('--save_point', type=str, default='90',
help='when to save the model to disk.')
self.parser.add_argument('--num_epochs', type=int, default=70,
help='total training epochs.')
self.parser.add_argument('--batch_size', type=int, default=32,
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=10000,
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('--ltrb', action='store_true',
help='')
self.parser.add_argument('--ltrb_weight', type=float, default=0.1,
help='')
self.parser.add_argument('--reset_hm', action='store_true')
self.parser.add_argument('--reuse_hm', action='store_true')
self.parser.add_argument('--use_kpt_center', action='store_true')
self.parser.add_argument('--add_05', action='store_true')
self.parser.add_argument('--dense_reg', type=int, default=1, help='')
# test
self.parser.add_argument('--flip_test', action='store_true',
help='flip data augmentation.')
self.parser.add_argument('--test_scales', type=str, default='1',
help='multi scale test augmentation.')
self.parser.add_argument('--nms', action='store_true',
help='run nms in testing.')
self.parser.add_argument('--K', type=int, default=100,
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_short', type=int, default=-1)
self.parser.add_argument('--keep_res', action='store_true',
help='keep the original resolution'
' during validation.')
self.parser.add_argument('--map_argoverse_id', action='store_true',
help='if trained on nuscenes and eval on kitti')
self.parser.add_argument('--out_thresh', type=float, default=-1,
help='')
self.parser.add_argument('--depth_scale', type=float, default=1,
help='')
self.parser.add_argument('--save_results', action='store_true')
self.parser.add_argument('--load_results', default='')
self.parser.add_argument('--use_loaded_results', action='store_true')
self.parser.add_argument('--ignore_loaded_cats', default='')
self.parser.add_argument('--model_output_list', action='store_true',
help='Used when convert to onnx')
self.parser.add_argument('--non_block_test', action='store_true')
self.parser.add_argument('--vis_gt_bev', default='', help='')
self.parser.add_argument('--kitti_split', default='3dop',
help='different validation split for kitti: '
'3dop | subcnn')
self.parser.add_argument('--test_focal_length', type=int, default=-1)
# dataset
self.parser.add_argument('--not_rand_crop', action='store_true',
help='not use the random crop data augmentation'
'from CornerNet.')
self.parser.add_argument('--not_max_crop', action='store_true',
help='used when the training dataset has'
'inbalanced aspect ratios.')
self.parser.add_argument('--shift', type=float, default=0,
help='when not using random crop, 0.1'
'apply shift augmentation.')
self.parser.add_argument('--scale', type=float, default=0,
help='when not using random crop, 0.4'
'apply scale augmentation.')
self.parser.add_argument('--aug_rot', type=float, default=0,
help='probability of applying '
'rotation augmentation.')
self.parser.add_argument('--rotate', type=float, default=0,
help='when not using random crop'
'apply rotation augmentation.')
self.parser.add_argument('--flip', type=float, default=0.5,
help='probability of applying flip augmentation.')
self.parser.add_argument('--no_color_aug', action='store_true',
help='not use the color augmenation '
'from CornerNet')
# Tracking
self.parser.add_argument('--tracking', action='store_true')
self.parser.add_argument('--pre_hm', action='store_true')
self.parser.add_argument('--same_aug_pre', action='store_true')
self.parser.add_argument('--zero_pre_hm', action='store_true')
self.parser.add_argument('--hm_disturb', type=float, default=0)
self.parser.add_argument('--lost_disturb', type=float, default=0)
self.parser.add_argument('--fp_disturb', type=float, default=0)
self.parser.add_argument('--pre_thresh', type=float, default=-1)
self.parser.add_argument('--track_thresh', type=float, default=0.3)
self.parser.add_argument('--match_thresh', type=float, default=0.8)
self.parser.add_argument('--track_buffer', type=int, default=30)
self.parser.add_argument('--new_thresh', type=float, default=0.3)
self.parser.add_argument('--max_frame_dist', type=int, default=3)
self.parser.add_argument('--ltrb_amodal', action='store_true')
self.parser.add_argument('--ltrb_amodal_weight', type=float, default=0.1)
self.parser.add_argument('--public_det', action='store_true')
self.parser.add_argument('--no_pre_img', action='store_true')
self.parser.add_argument('--zero_tracking', action='store_true')
self.parser.add_argument('--hungarian', action='store_true')
self.parser.add_argument('--max_age', type=int, default=-1)
# loss
self.parser.add_argument('--tracking_weight', type=float, default=1)
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('--hp_weight', type=float, default=1,
help='loss weight for human pose offset.')
self.parser.add_argument('--hm_hp_weight', type=float, default=1,
help='loss weight for human keypoint heatmap.')
self.parser.add_argument('--amodel_offset_weight', type=float, default=1,
help='Please forgive the typo.')
self.parser.add_argument('--dep_weight', type=float, default=1,
help='loss weight for depth.')
self.parser.add_argument('--dim_weight', type=float, default=1,
help='loss weight for 3d bounding box size.')
self.parser.add_argument('--rot_weight', type=float, default=1,
help='loss weight for orientation.')
self.parser.add_argument('--nuscenes_att', action='store_true')
self.parser.add_argument('--nuscenes_att_weight', type=float, default=1)
self.parser.add_argument('--velocity', action='store_true')
self.parser.add_argument('--velocity_weight', type=float, default=1)
# custom dataset
self.parser.add_argument('--custom_dataset_img_path', default='')
self.parser.add_argument('--custom_dataset_ann_path', default='')
self.parser.add_argument('--bird_view_world_size', type=int, default=64)
def parse(self, args=''):
if args == '':
opt = self.parser.parse_args()
else:
opt = self.parser.parse_args(args)
if opt.test_dataset == '':
opt.test_dataset = opt.dataset
opt.gpus_str = opt.gpus
opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')]
opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1]
opt.lr_step = [int(i) for i in opt.lr_step.split(',')]
opt.save_point = [int(i) for i in opt.save_point.split(',')]
opt.test_scales = [float(i) for i in opt.test_scales.split(',')]
opt.save_imgs = [i for i in opt.save_imgs.split(',')] \
if opt.save_imgs != '' else []
opt.ignore_loaded_cats = \
[int(i) for i in opt.ignore_loaded_cats.split(',')] \
if opt.ignore_loaded_cats != '' else []
opt.num_workers = max(opt.num_workers, 2 * len(opt.gpus))
opt.pre_img = False
if 'tracking' in opt.task:
print('Running tracking')
opt.tracking = True
# opt.out_thresh = max(opt.track_thresh, opt.out_thresh)
# opt.pre_thresh = max(opt.track_thresh, opt.pre_thresh)
# opt.new_thresh = max(opt.track_thresh, opt.new_thresh)
opt.pre_img = not opt.no_pre_img
print('Using tracking threshold for out threshold!', opt.track_thresh)
if 'ddd' in opt.task:
opt.show_track_color = True
opt.fix_res = not opt.keep_res
print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')
if opt.head_conv == -1: # init default head_conv
opt.head_conv = 256 if 'dla' in opt.arch else 64
opt.pad = 127 if 'hourglass' in opt.arch else 31
opt.num_stacks = 2 if opt.arch == 'hourglass' else 1
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)
if opt.debug > 0:
opt.num_workers = 0
opt.batch_size = 1
opt.gpus = [opt.gpus[0]]
opt.master_batch_size = -1
# log dirs
opt.root_dir = os.path.join(os.path.dirname(__file__), '..', '..')
opt.data_dir = os.path.join(opt.root_dir, 'data')
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')
if opt.resume and opt.load_model == '':
opt.load_model = os.path.join(opt.save_dir, 'model_last.pth')
return opt
def update_dataset_info_and_set_heads(self, opt, dataset):
opt.num_classes = dataset.num_categories \
if opt.num_classes < 0 else opt.num_classes
# input_h(w): opt.input_h overrides opt.input_res overrides dataset default
input_h, input_w = dataset.default_resolution
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)
opt.heads = {'hm': opt.num_classes, 'reg': 2, 'wh': 2}
if 'tracking' in opt.task:
opt.heads.update({'tracking': 2})
if 'ddd' in opt.task:
opt.heads.update({'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2})
if 'multi_pose' in opt.task:
opt.heads.update({
'hps': dataset.num_joints * 2, 'hm_hp': dataset.num_joints,
'hp_offset': 2})
if opt.ltrb:
opt.heads.update({'ltrb': 4})
if opt.ltrb_amodal:
opt.heads.update({'ltrb_amodal': 4})
if opt.nuscenes_att:
opt.heads.update({'nuscenes_att': 8})
if opt.velocity:
opt.heads.update({'velocity': 3})
weight_dict = {'hm': opt.hm_weight, 'wh': opt.wh_weight,
'reg': opt.off_weight, 'hps': opt.hp_weight,
'hm_hp': opt.hm_hp_weight, 'hp_offset': opt.off_weight,
'dep': opt.dep_weight, 'rot': opt.rot_weight,
'dim': opt.dim_weight,
'amodel_offset': opt.amodel_offset_weight,
'ltrb': opt.ltrb_weight,
'tracking': opt.tracking_weight,
'ltrb_amodal': opt.ltrb_amodal_weight,
'nuscenes_att': opt.nuscenes_att_weight,
'velocity': opt.velocity_weight}
opt.weights = {head: weight_dict[head] for head in opt.heads}
for head in opt.weights:
if opt.weights[head] == 0:
del opt.heads[head]
opt.head_conv = {head: [opt.head_conv \
for i in range(opt.num_head_conv if head != 'reg' else 1)] for head in opt.heads}
print('input h w:', opt.input_h, opt.input_w)
print('heads', opt.heads)
print('weights', opt.weights)
print('head conv', opt.head_conv)
return opt
def init(self, args=''):
# only used in demo
default_dataset_info = {
'ctdet': 'coco', 'multi_pose': 'coco_hp', 'ddd': 'nuscenes',
'tracking,ctdet': 'coco', 'tracking,multi_pose': 'coco_hp',
'tracking,ddd': 'nuscenes'
}
opt = self.parse()
from dataset.dataset_factory import dataset_factory
train_dataset = default_dataset_info[opt.task] \
if opt.task in default_dataset_info else 'coco'
dataset = dataset_factory[train_dataset]
opt = self.update_dataset_info_and_set_heads(opt, dataset)
return opt
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