lhiceu
commented
2 years ago Details about opt.txt :
==> commit hash: b'0443c36\n'
==> torch version: 1.3.1
==> cudnn version: 7603
==> Cmd:
['test.py', 'tracking,ddd', '--exp_id', 'nuScenes_3Dtracking', '--dataset', 'nuscenes', '--pre_hm', '--track_thresh', '0.1', '--gpus', '0', '--inference', '--load_model', '../models/nuscenes.pth', '--clip_len', '2', '--trades']
==> Opt:
K: 100
add_05: False
amodel_offset_weight: 1
arch: dla_34
aug_rot: 0
backbone: dla34
batch_size: 32
box_nms: -1
chunk_sizes: [32]
clip_len: 2
custom_dataset_ann_path:
custom_dataset_img_path:
data_dir: /media/he/Disk/TraDeS/src/lib/../../data
dataset: nuscenes
dataset_version:
debug: 0
debug_dir: /media/he/Disk/TraDeS/src/lib/../../exp/tracking,ddd/nuScenes_3Dtracking/debug
debugger_theme: white
deform_kernel_size: 3
demo:
dense_reg: 1
dep_weight: 1
depth_scale: 1
dim_weight: 1
dla_node: dcn
down_ratio: 4
efficient_level: 0
embedding: False
eval_val: False
exp_dir: /media/he/Disk/TraDeS/src/lib/../../exp/tracking,ddd
exp_id: nuScenes_3Dtracking
fix_res: True
fix_short: -1
flip: 0.5
flip_test: False
fp_disturb: 0
gpus: [0]
gpus_str: 0
head_conv: {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256]}
head_kernel: 3
heads: {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2}
hm_disturb: 0
hm_hp_weight: 1
hm_weight: 1
hp_weight: 1
hungarian: False
ignore_loaded_cats: []
inference: True
input_h: 448
input_res: 800
input_w: 800
keep_res: False
kitti_split: 3dop
load_model: ../models/nuscenes.pth
load_results:
lost_disturb: 0
lr: 0.000125
lr_step: [60]
ltrb: False
ltrb_amodal: False
ltrb_amodal_weight: 0.1
ltrb_weight: 0.1
map_argoverse_id: False
master_batch_size: 32
max_age: -1
max_frame_dist: 3
model_output_list: False
msra_outchannel: 256
nID: -1
neck: dlaup
new_thresh: 0.1
nms: False
no_color_aug: False
no_pause: False
no_pre_img: False
no_repeat: True
non_block_test: False
not_cuda_benchmark: False
not_idaup: False
not_max_crop: False
not_prefetch_test: False
not_rand_crop: False
not_set_cuda_env: False
not_show_bbox: False
not_show_number: False
num_classes: 10
num_epochs: 70
num_head_conv: 1
num_iters: -1
num_layers: 101
num_stacks: 1
num_workers: 4
nuscenes_att: False
nuscenes_att_weight: 1
off_weight: 1
optim: adam
out_thresh: 0.1
output_h: 112
output_res: 200
output_w: 200
overlap_thresh: -1
pad: 31
pre_hm: True
pre_img: True
pre_thresh: 0.1
print_iter: 0
prior_bias: -4.6
public_det: False
qualitative: False
reg_loss: l1
reset_hm: False
resize_video: False
resume: False
reuse_hm: False
root_dir: /media/he/Disk/TraDeS/src/lib/../..
rot_weight: 1
rotate: 0
same_aug_pre: False
save_all: False
save_dir: /media/he/Disk/TraDeS/src/lib/../../exp/tracking,ddd/nuScenes_3Dtracking
save_framerate: 30
save_img_suffix:
save_imgs: []
save_point: [90]
save_results: False
save_video: False
scale: 0
seed: 317
seg: False
seg_feat_channel: 8
shift: 0
show_track_color: True
skip_first: -1
tango_color: False
task: tracking,ddd
test: False
test_dataset: nuscenes
test_focal_length: -1
test_scales: [1.0]
track_thresh: 0.1
tracking: True
tracking_weight: 1
trades: True
trainval: False
transpose_video: False
use_kpt_center: False
use_loaded_results: False
val_intervals: 10000
velocity: False
velocity_weight: 1
video_h: 512
video_w: 512
vis_gt_bev:
vis_thresh: 0.3
weights: {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'cost_volume': 1.0}
wh_weight: 0.1
window_size: 7
zero_pre_hm: False
zero_tracking: False
Hello @JialianW I test the performance of the model you provided (nuscenes.pth)on the nuscenes validation set following the readme. But I got low performance which is different from the results in the paper.
I also tested the results from CenterTrack and got good performance. So I ruled out the issues of data preparation and nuScenes dataset API. What anything else should I do to get the same performance as you provided? Thank you.