文件内容:CenterFusion/src/lib/opts.py
文件作用:train.sh 脚本中参数的处理
self.parser.add_argument('--not_set_cuda_env', action='store_true',
help='used when training in slurm clusters.')
下面的 opts.py 文件中的具体内容:
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='nuscenes',
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('--eval', action='store_true',
help='only evaluate the val split and quit 只评估val split和quit')
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',
help='do not pause after debugging visualizations')
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,-1表示CPU,逗号表示多个gpu')
self.parser.add_argument('--num_workers', type=int, default=4,
help='dataloader threads. 0 for single-thread. dataloader线程。0为单线程')
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.在slurm训练时使用')
# 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. 每5个epoch将模型保存到磁盘')
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('--run_dataset_eval', action='store_true',
help='use dataset specific evaluation function in eval')
self.parser.add_argument('--save_imgs', default='',
help='list of images to save in debug. empty to save all 要在调试中保存的图像列表。为空保存所有')
self.parser.add_argument('--save_img_suffix', default='', help='')
self.parser.add_argument('--skip_first', type=int, default=-1,
help='skip first n images in demo mode')
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('--qualitative', action='store_true')
self.parser.add_argument('--tango_color', 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,
help='number of conv layers before each output head')
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_resnet_layers', type=int, default=101)
self.parser.add_argument('--backbone', default='dla34',
help='backbone for the generic detection network')
self.parser.add_argument('--neck', default='dlaup',
help='neck for the generic detection network')
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. 学习速度除以10')
self.parser.add_argument('--save_point', type=str, default='90',
help='when to save the model to disk. 何时将模型保存到磁盘')
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=10,
help='number of epochs to run validation. 运行验证的纪元数')
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='')
self.parser.add_argument('--shuffle_train', action='store_true',
help='shuffle training dataloader')
# 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. 在验证期间保持原始分辨率')
# 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='path to gt bev images')
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('--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.边框尺寸的损失重量')
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('--dep_res_weight', type=float, default=1,
help='loss weight for depth residual.')
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='')
# point clouds and nuScenes dataset
self.parser.add_argument('--pointcloud', action='store_true')
self.parser.add_argument('--train_split', default='train',
choices=['train','mini_train', 'train_detect', 'train_track', 'mini_train_2', 'trainval'])
self.parser.add_argument('--val_split', default='val',
choices=['val','mini_val','test'])
self.parser.add_argument('--max_pc', type=int, default=1000,
help='maximum number of points in the point cloud')
self.parser.add_argument('--r_a', type=float, default=250,
help='alpha parameter for hm size calculation')
self.parser.add_argument('--r_b', type=float, default=5,
help='beta parameter for hm size calculation')
self.parser.add_argument('--img_format', default='jpg',
help='debug image format')
self.parser.add_argument('--max_pc_dist', type=float, default=100.0,
help='remove points beyond max_pc_dist meters')
self.parser.add_argument('--freeze_backbone', action='store_true',
help='freeze the backbone network and only train heads 冻结骨干网络,仅限train头部')
self.parser.add_argument('--radar_sweeps', type=int, default=1,
help='number of radar sweeps in point cloud')
self.parser.add_argument('--warm_start_weights', action='store_true',
help='try to reuse weights even if dimensions dont match')
self.parser.add_argument('--pc_z_offset', type=float, default=0,
help='raise all Radar points in z direction')
self.parser.add_argument('--eval_n_plots', type=int, default=0,
help='number of sample plots drawn in eval')
self.parser.add_argument('--eval_render_curves', action='store_true',
help='render and save evaluation curves')
self.parser.add_argument('--hm_transparency', type=float, default=0.7,
help='heatmap visualization transparency')
self.parser.add_argument('--iou_thresh', type=float, default=0,
help='IOU threshold for filtering overlapping detections')
self.parser.add_argument('--pillar_dims', type=str, default='2,0.5,0.5',
help='Radar pillar dimensions (h,w,l)')
self.parser.add_argument('--show_velocity', action='store_true')
def parse(self, args=''):
if args == '':
opt = self.parser.parse_args()
else:
opt = self.parser.parse_args(args)
'''
把 parser 中设置的所有 "add_argument" 给返回到 args 子类实例当中
'''
if opt.test_dataset == '':
opt.test_dataset = opt.dataset
'''
设置数据集为 nuscenes
test_dataset 默认值为 '' ,dataset 默认值为 nuscenes
'''
opt.gpus_str = opt.gpus
'''
为 opt 添加一个新的参数 gpus_str,用来临时保存 gpus 的值
gpus 默认值为 0,是一个字符串,在 train.sh 中的值为 0,1
'''
opt.gpus = [int(gpu) for gpu in opt.gpus.split(',')]
'''
split() 函数:拆分字符串,通过指定分隔符对字符串进行切片,并返回分割后的字符串列表(list)
这里是将字符串 '0,1' 整数化成整数型数组 [0, 1]
'''
opt.gpus = [i for i in range(len(opt.gpus))] if opt.gpus[0] >=0 else [-1]
'''
重新设置 GPU 索引号,其结果依然没变
'''
opt.lr_step = [int(i) for i in opt.lr_step.split(',')]
'''
lr_step 默认值为 60,在 train.sh 中的值为 50
这里也是将字符串处理成整数型数组
最后 lr_step 值为 [50]
'''
opt.save_point = [int(i) for i in opt.save_point.split(',')]
'''
save_point 默认值为 50,在 train.sh 中的值为 20,40,50
这里也是将字符串处理成整数型数组
save_point 参数意义:何时将模型保存到磁盘
'''
opt.test_scales = [float(i) for i in opt.test_scales.split(',')]
'''
test_scales 默认值为 1
这里是将 test_scales 处理成浮点型
test_scales 参数意义:多尺度测试增强
'''
opt.save_imgs = [i for i in opt.save_imgs.split(',')] \
if opt.save_imgs != '' else []
'''
save_imgs 默认值为 ''
save_imgs 参数意义:要在调试中保存的图像列表。为空保存所有
'''
opt.ignore_loaded_cats = \
[int(i) for i in opt.ignore_loaded_cats.split(',')] \
if opt.ignore_loaded_cats != '' else []
'''
ignore_loaded_cats 默认值为 ''
'''
opt.num_workers = max(opt.num_workers, 2 * len(opt.gpus))
'''
num_workers 默认值为 4
最后 num_workers 的值为 4
num_workers 参数意义:dataloader 线程,0 为单线程
'''
opt.pre_img = False
'''
为 opt 添加了一个新的参数 pre_img,它的值为 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
'''
tast 默认值为 '',但在 train.sh 中赋值为 ddd,则 opt.task = 'ddd'
所以该 if 语句没有执行
'''
opt.fix_res = not opt.keep_res
print('Fix size testing.' if opt.fix_res else 'Keep resolution testing.')
'''
keep_res 由于 train.sh 没有添加该参数,所以值为 False
最后新添加的参数 fix_res 的值为 true,则打印 'Fix size testing.'
keep_res 参数意义:在验证期间保持原始分辨率
'''
if opt.head_conv == -1:
opt.head_conv = 256 if 'dla' in opt.arch else 64
'''
head_conv 默认值为 -1
arch 默认值为 dla_34
最后 head_conv 的值为 256
head_conv 参数意义:输出头的转换层通道
0 表示没有 conv 层
-1 默认设置:
resnets 是 64
dla 是 256
'''
opt.pad = 127 if 'hourglass' in opt.arch else 31
'''
新添加参数 pad 的值为 31
'''
opt.num_stacks = 2 if opt.arch == 'hourglass' else 1
'''
新添加参数 num_stacks 的值为 1
'''
if opt.master_batch_size == -1:
opt.master_batch_size = opt.batch_size // len(opt.gpus)
'''
batch_size 默认值为 32,在 train.sh 中的值也为 32
master_batch_size 默认值为 -1
最后 master_batch_size 的值为 16
master_batch_size 参数意义:主图形处理器上的批处理大小
'''
rest_batch_size = (opt.batch_size - opt.master_batch_size)
'''
rest_batch_size 的值为 32 - 16 = 16
'''
opt.chunk_sizes = [opt.master_batch_size]
'''
添加新参数 chunk_sizes 的值为 [16]
'''
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)
'''
根据 GPU 的数量设置训练块的大小
一块 GPU 的训练块大小为 32
两块 GPU 其中每个训练块大小为 16
'''
if opt.debug > 0:
opt.num_workers = 0
opt.batch_size = 1
opt.gpus = [opt.gpus[0]]
opt.master_batch_size = -1
'''
debug 默认值为 0
该 if 语句没有执行
debug 参数含义:可视化的水平
1:只显示最终检测结果
2:显示网络输出特征
3:使用 matplot 显示
4:将所有可视化内容保存到磁盘
'''
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')
'''
添加路径参数,并设置 log 路径
root_dir = ~/CenterFusion/src/lib/../..
data_dir = ~/CenterFusion/src/lib/../../data
exp_dir = ~/CenterFusion/src/lib/../../exp/ddd
save_dir = ~/CenterFusion/src/lib/../../exp/ddd/centerfusion
debug_dir = ~/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug
'''
if opt.resume and opt.load_model == '':
opt.load_model = os.path.join(opt.save_dir, 'model_last.pth')
'''
resume 在 train.sh 没有添加该参数,所以为 False
load_model 默认值为 '' ,在 train.sh 中设置了该参数的值
所以该 if 语句没有执行
参数含义:
resume:重新加载优化器参数,并在 load_model 为空时将 load_model 设置为 model_last.pth
load_model:预训练模型的路径
'''
opt.pc_atts = ['x', 'y', 'z', 'dyn_prop', 'id', 'rcs', 'vx', 'vy',
'vx_comp', 'vy_comp', 'is_quality_valid',
'ambig_state', 'x_rms', 'y_rms', 'invalid_state',
'pdh0', 'vx_rms', 'vy_rms']
'''
添加新参数 pc_atts,并设置雷达点云相关属性
'''
pc_attr_ind = {x:i for i,x in enumerate(opt.pc_atts)}
'''
enumerate():函数用于将一个可遍历的数据对象(如列表、元组或字符串)组合为一个索引序列,同时列出数据和数据下标,一般用在 for 循环当中
这里是对 opt.pc_atts 中的属性递增赋值
结果 = {'x': 0, 'y': 1, 'z': 2, 'dyn_prop': 3, 'id': 4, 'rcs': 5,......}
'''
opt.pillar_dims = [float(i) for i in opt.pillar_dims.split(',')]
'''
pillar_dims 默认值为 2.0,0.5,0.5 在 train.sh 中的值为 1.0,0.2,0.2
这里是将字符串处理成浮点型数组
pillar_dims 参数含义:雷达柱尺寸(h、w、l)
'''
opt.num_img_channels = 3
opt.hm_dist_thresh = None
opt.sigmoid_dep_sec = False
opt.hm_to_box_ratio = 0.3
opt.secondary_heads = []
opt.custom_head_convs = {}
opt.normalize_depth = False
opt.disable_frustum = False
opt.layers_to_freeze = [
'base',
'dla_up',
'ida_up',
# 'hm'
# 'reg'
# 'wh'
# 'dep'
# 'rot'
# 'dim'
# 'amodel_offset'
# 'dep_sec'
# 'nuscenes_att'
# 'velocity'
]
'''
添加一些新参数,并设值
'''
if opt.pointcloud:
'''
在 train.sh 中添加了该参数,所以 pointcloud = True
该 if 语句中添加一些新参数并设值
'''
extra_pc_feats = []
opt.pc_roi_method = "pillars"
opt.pillar_dims = [1.5,0.2,0.2]
opt.pc_feat_lvl = [
'pc_dep',
'pc_vx',
'pc_vz',
]
opt.frustumExpansionRatio = 0.0
opt.disable_frustum = False
opt.sort_det_by_dist = False
opt.sigmoid_dep_sec = True
opt.normalize_depth = True
opt.secondary_heads = ['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec']
opt.hm_dist_thresh = {
'car': 0,
'truck': 0,
'bus': 0,
'trailer': 0,
'construction_vehicle': 0,
'pedestrian': 1,
'motorcycle': 1,
'bicycle': 1,
'traffic_cone': 0,
'barrier': 0
}
opt.custom_head_convs = {
'dep_sec': 3,
'rot_sec': 3,
'velocity': 3,
'nuscenes_att': 3,
}
opt.pc_feat_channels = {feat: i for i,feat in enumerate(opt.pc_feat_lvl)}
CATS = ['car', 'truck', 'bus', 'trailer', 'construction_vehicle',
'pedestrian', 'motorcycle', 'bicycle', 'traffic_cone', 'barrier']
CAT_IDS = {v: i for i, v in enumerate(CATS)}
'''
设置目标物体类别及 id
'''
if opt.hm_dist_thresh is not None:
temp = {}
for (k,v) in opt.hm_dist_thresh.items():
temp[CAT_IDS[k]] = v
opt.hm_dist_thresh = temp
'''
items():将一个字典以列表的形式返回
'''
return opt
def update_dataset_info_and_set_heads(self, opt, dataset):
'''
opt 为 opts 类,被定义在本文件中
dataset 为 nuScenes 类,被定义在 CenterFusion-master/src/lib/dataset/datasets/nuscenes.py 中
'''
opt.num_classes = dataset.num_categories \
if opt.num_classes < 0 else opt.num_classes
'''
dataset.num_categories 默认值为 10 ,num_classes 值为 10
这里是为了获取目标物体种类数
参数含义:
num_categories :在 nuScenes 中目标物体种类数
num_classes :目标物体类别数
'''
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
'''
默认值 default_rerolution = [448,800],opt.input_res = opt.input_h = opt.input_w = -1
获取 nuScenes 中的图片像素值
结果:
opt.input_h = 448
opt.input_w = 800
'''
opt.output_h = opt.input_h // opt.down_ratio
opt.output_w = opt.input_w // opt.down_ratio
'''
down_ratio 默认值为 4
结果
opt.output_h = 112
opt.output_w = 200
'''
opt.input_res = max(opt.input_h, opt.input_w)
opt.output_res = max(opt.output_h, opt.output_w)
'''
结果
opt.input_res = 800
opt.output_res = 200
'''
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 opt.pointcloud:
opt.heads.update({'dep_sec': 1})
opt.heads.update({'rot_sec': 8})
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})
'''
设置 opt.heads 中的内容
'''
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, 'dep_res': opt.dep_res_weight,
'rot': opt.rot_weight, 'dep_sec': opt.dep_weight,
'dim': opt.dim_weight, 'rot_sec': opt.rot_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}
'''
设置 weight_dict 中的内容
'''
opt.weights = {head: weight_dict[head] for head in opt.heads}
'''
根据 opt.heads 和 weight_dict 设置 opt.weights 中的内容
'''
for head in opt.weights:
if opt.weights[head] == 0:
del opt.heads[head]
'''
遍历 weights 并删除值为 0 的属性
'''
temp_head_conv = opt.head_conv
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}
'''
设置 opt.head_conv 中的内容
'''
if opt.pointcloud:
temp = {k: [temp_head_conv for i in range(v)] for k,v in opt.custom_head_convs.items()}
opt.head_conv.update(temp)
'''
更新自定义头部变换
'''
#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