mrnabati / centerfusion Goto Github PK

Hi，the work you did looks great in your paper, I want to know when do you share your code

In nuscenes we have train, val and test dataset. when i tried to run
bash test.sh after setting --val_split test.
i was getting this following error. so that i couldnot reproduce your results for test dataset.

Initializing nuScenes detection evaluation
Loaded results from /workspace/centerfusion_onlytest/src/lib/../../exp/ddd/centerfusion/results_nuscenes_det_test.json. Found detections for 6008 samples.
Loading annotations for test split from nuScenes version: v1.0-test
Traceback (most recent call last):
File "tools/nuscenes-devkit/python-sdk/nuscenes/eval/detection/evaluate.py", line 301, in
output_dir=output_dir_, verbose=verbose_)
File "tools/nuscenes-devkit/python-sdk/nuscenes/eval/detection/evaluate.py", line 82, in init
self.gt_boxes = load_gt(self.nusc, self.eval_set, DetectionBox, verbose=verbose)
File "/usr/local/lib/python3.7/dist-packages/nuscenes/eval/common/loaders.py", line 94, in load_gt
'Error: You are trying to evaluate on the test set but you do not have the annotations!'
AssertionError: Error: You are trying to evaluate on the test set but you do not have the annotations!

could you help me how to resolve this issue.

when i run test.py with mini_val,i can only get an evaluation,but how can i get a Visualization results ?

When I run bash train. sh, the error occurs as follows. How can I solve it?

train: [1][483/484]|Tot: 0:02:25 |ETA: 0:00:01 |tot 11.9668 |hm 1.2029 |wh 1.9665 |reg 0.2171 |dep 2.4322 |dep_sec 2.4956 |dim 0.2450 |rot 1.6286 |rot_sec 1.6110 |amodel_offset 1.1789 |nuscenes_att 0.1946 |velocity 0.5642 |Data 0.002s(0.005s) |Net 0.300s
ddd/centerfusionTraceback (most recent call last):
File "main.py", line 140, in
main(opt)
File "main.py", line 97, in main
log_dict_val, preds = trainer.val(epoch, val_loader)
File "/home/wz/wz-research/CenterFusion/src/lib/trainer.py", line 403, in val
return self.run_epoch('val', epoch, data_loader)
File "/home/wz/wz-research/CenterFusion/src/lib/trainer.py", line 178, in run_epoch
output, loss, loss_stats = model_with_loss(batch, phase)
File "/home/caslx/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/wz/wz-research/CenterFusion/src/lib/trainer.py", line 123, in forward
outputs = self.model(batch['image'], pc_hm=pc_hm, pc_dep=pc_dep, calib=calib)
File "/home/caslx/.local/lib/python3.8/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/home/wz/wz-research/CenterFusion/src/lib/model/networks/base_model.py", line 110, in forward
pc_hm = generate_pc_hm(z, pc_dep, calib, self.opt)
File "/home/wz/wz-research/CenterFusion/src/lib/utils/pointcloud.py", line 273, in generate_pc_hm
pc_dep_to_hm_torch(pc_hm[i], pc_dep_b, depth, bbox, dist_thresh, opt)
File "/home/wz/wz-research/CenterFusion/src/lib/utils/pointcloud.py", line 282, in pc_dep_to_hm_torch
bbox_int = torch.tensor([torch.floor(bbox[0]),
TypeError: only integer tensors of a single element can be converted to an index

The parameters in train. sh are shown like this:

export CUDA_DEVICE_ORDER=PCI_BUS_ID
export CUDA_VISIBLE_DEVICES=0,1


cd ../src
# train
python main.py \
    ddd \
    --exp_id centerfusion \
    --shuffle_train \
    --train_split mini_train \
    --val_split val \
    --val_intervals 1 \
    --run_dataset_eval \
    --nuscenes_att \
    --velocity \
    --batch_size 4 \
    --lr 2.5e-4 \
    --num_epochs 60 \
    --lr_step 50 \
    --save_point 20,40,50 \
    --gpus 0,1 \
    --not_rand_crop \
    --flip 0.5 \
    --shift 0.1 \
    --pointcloud \
    --radar_sweeps 6 \
    --pc_z_offset 0.0 \
    --pillar_dims 1.0,0.2,0.2 \
    --max_pc_dist 60.0 \
    #--load_model ../models/centerfusion_e60.pth \
    #--load_model ../models/centernet_baseline_e170.pth \
    # --freeze_backbone \
    # --resume \

cd .. 

Using tensorboardX

Bad key "text.kerning_factor" on line 4 in
/home/vincent/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template
or from the matplotlib source distribution
terminate called after throwing an instance of 'c10::Error'
what(): No CUDA GPUs are available
Exception raised from device_count_ensure_non_zero at /pytorch/c10/cuda/CUDAFunctions.cpp:111 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x42 (0x7faf745fa8b2 in /home/vincent/.local/lib/python3.6/site-packages/torch/lib/libc10.so)
frame #1: c10::cuda::device_count_ensure_non_zero() + 0xc5 (0x7faf748488c5 in /home/vincent/.local/lib/python3.6/site-packages/torch/lib/libc10_cuda.so)
frame #2: THCudaInit(THCState*) + 0x21 (0x7faf758169d1 in /home/vincent/.local/lib/python3.6/site-packages/torch/lib/libtorch_cuda.so)
frame #3: + 0xcd9644 (0x7faf75742644 in /home/vincent/.local/lib/python3.6/site-packages/torch/lib/libtorch_cuda.so)
frame #4: at::Context::lazyInitCUDA()::{lambda()#1}::operator()() const + 0x36 (0x7faf53a9a99c in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #5: void std::__invoke_impl<void, at::Context::lazyInitCUDA()::{lambda()#1}>(std::__invoke_other, at::Context::lazyInitCUDA()::{lambda()#1}&&) + 0x20 (0x7faf53a9b61f in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #6: std::__invoke_resultat::Context::lazyInitCUDA()::{lambda()#1}::type std::__invokeat::Context::lazyInitCUDA()::{lambda()#1}(std::__invoke_result&&, (at::Context::lazyInitCUDA()::{lambda()#1}&&)...) + 0x35 (0x7faf53a9b36e in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #7: std::call_onceat::Context::lazyInitCUDA()::{lambda()#1}(std::once_flag&, at::Context::lazyInitCUDA()::{lambda()#1}&&)::{lambda()#1}::operator()() const + 0x23 (0x7faf53a9ae5d in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #8: std::call_onceat::Context::lazyInitCUDA()::{lambda()#1}(std::once_flag&, at::Context::lazyInitCUDA()::{lambda()#1}&&)::{lambda()#2}::operator()() const + 0x27 (0x7faf53a9ae95 in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #9: std::call_onceat::Context::lazyInitCUDA()::{lambda()#1}(std::once_flag&, at::Context::lazyInitCUDA()::{lambda()#1}&&)::{lambda()#2}::_FUN() + 0xe (0x7faf53a9aea6 in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #10: + 0xf907 (0x7fafdfee8907 in /lib/x86_64-linux-gnu/libpthread.so.0)
frame #11: + 0x3c29a (0x7faf53a9629a in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #12: void std::call_onceat::Context::lazyInitCUDA()::{lambda()#1}(std::once_flag&, at::Context::lazyInitCUDA()::{lambda()#1}&&) + 0x82 (0x7faf53a9af3b in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #13: at::Context::lazyInitCUDA() + 0x36 (0x7faf53a9aa10 in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #14: + 0x40845 (0x7faf53a9a845 in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #15: + 0x40868 (0x7faf53a9a868 in /home/vincent/CenterFusion/src/lib/model/networks/DCNv2/_ext.cpython-36m-x86_64-linux-gnu.so)
frame #16: + 0x108f3 (0x7fafe04f98f3 in /lib64/ld-linux-x86-64.so.2)
frame #17: + 0x153bf (0x7fafe04fe3bf in /lib64/ld-linux-x86-64.so.2)
frame #18: _dl_catch_exception + 0x6f (0x7fafe025f1ef in /lib/x86_64-linux-gnu/libc.so.6)
frame #19: + 0x1498a (0x7fafe04fd98a in /lib64/ld-linux-x86-64.so.2)
frame #20: + 0xf96 (0x7fafdfcd5f96 in /lib/x86_64-linux-gnu/libdl.so.2)
frame #21: _dl_catch_exception + 0x6f (0x7fafe025f1ef in /lib/x86_64-linux-gnu/libc.so.6)
frame #22: _dl_catch_error + 0x2f (0x7fafe025f27f in /lib/x86_64-linux-gnu/libc.so.6)
frame #23: + 0x1745 (0x7fafdfcd6745 in /lib/x86_64-linux-gnu/libdl.so.2)
frame #24: dlopen + 0x71 (0x7fafdfcd6051 in /lib/x86_64-linux-gnu/libdl.so.2)

frame #27: python3() [0x5fb62d]
frame #30: python3() [0x507be4]
frame #31: python3() [0x509900]
frame #32: python3() [0x50a2fd]
frame #34: python3() [0x5095c8]
frame #35: python3() [0x50a2fd]
frame #37: python3() [0x5095c8]
frame #38: python3() [0x50a2fd]
frame #40: python3() [0x5095c8]
frame #41: python3() [0x50a2fd]
frame #43: python3() [0x5095c8]
frame #44: python3() [0x50a2fd]
frame #51: python3() [0x507be4]
frame #52: python3() [0x516069]
frame #55: python3() [0x507be4]
frame #56: python3() [0x509900]
frame #57: python3() [0x50a2fd]
frame #59: python3() [0x5095c8]
frame #60: python3() [0x50a2fd]
frame #62: python3() [0x5095c8]
frame #63: python3() [0x50a2fd]

experiments/test.sh: line 20: 8344 Aborted (core dumped) python3 test.py ddd --exp_id centerfusion --dataset nuscenes --val_split mini_val --run_dataset_eval --num_workers 4 --nuscenes_att --velocity --gpus 0 --pointcloud --radar_sweeps 3 --max_pc_dist 60.0 --pc_z_offset -0.0 --load_model ../models/centerfusion_e60.pth --flip_test

Sorry to bother you.
THX!

Hi,

As mentioned in convert_to_onnx.py script, I couldnt convert to onnx with dcn layers. Is there a pretrained model available with conv layers instead of dcn.?

Thank you so much for sharing your valuable research and code.
I am having a problem while training your model using the nuScenes dataset.

In lines 105-107 of the CenterFusion/src/main.py,
(https://github.com/mrnabati/CenterFusion/blob/master/src/main.py#L105-L107)

          # log dataset-specific evaluation metrics
          with open('{}/metrics_summary.json'.format(out_dir), 'r') as f:
            metrics = json.load(f)

it seems to load the 'metrics_summary.json' file.
But I can't find the part where this file is created even after looking through the whole code.

I would really appreciate it if you could tell me about this.

Hi, I am trying to understand more in detail how radar data is handled since I would like to add to the dataset my own radar+RGB data and add them to the training.

For this reason I found that the radar_pc is created starting from the nuscenes dataset here:

However, I could not understand why does it contain 18 arrays. At first I thought it might be related to the number of antennas (but this did not make too much sense since we are talking about the pointcloud and not the raw radar measurements). Then, I found this post which is quite useful:
https://forum.nuscenes.org/t/radar-data-details/40/2?

Where it explains that:
The antenna channels are described as “4TX/2x6RX = 24 channels = 2TX/6RX far - 2TX/6RX near

@mrnabati , could you help me understanding what does the 18 stand for?

I've been studying on your fantastic project, however, I noticed that this project does not support batch evaluation(i.e. batchsize >1), including both validation and testing, is there a good reason for that, or will the batch evaluation codes available in the near future?

Hello,Can centerfusion run with KITTI dataset?

Hello, I'm trying to convert nuscenes to coco format using the script, convert_nuScenes.py and I keep getting this error: FileNotFoundError: [Errno 2] No such file or directory: '../../data/nuscenes/samples/RADAR_FRONT_RIGHT/n008-2018-08-01-15-16-36-0400__RADAR_FRONT_RIGHT__1533151603512881.pcd' . I assume this is because I'm using only part 06 of the full dataset. Can you please tell me how to fix this error? Thank you, in advance.

Using tensorboardX

Bad key "text.kerning_factor" on line 4 in
/home/vincent/.local/lib/python3.6/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.
You probably need to get an updated matplotlibrc file from
http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template
or from the matplotlib source distribution
import DCN failed
Import DCN failed
import DCN failed
import DCN failed
Fix size testing.
training chunk_sizes: [32]
input h w: 448 800
heads {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}
Namespace(K=100, amodel_offset_weight=1, arch='dla_34', aug_rot=0, backbone='dla34', batch_size=32, chunk_sizes=[32], custom_dataset_ann_path='', custom_dataset_img_path='', custom_head_convs={'dep_sec': 3, 'rot_sec': 3, 'velocity': 3, 'nuscenes_att': 3}, data_dir='/home/vincent/CenterFusion/src/lib/../../data', dataset='nuscenes', dataset_version='', debug=0, debug_dir='/home/vincent/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug', debugger_theme='white', demo='', dense_reg=1, dep_res_weight=1, dep_weight=1, depth_scale=1, dim_weight=1, disable_frustum=False, dla_node='dcn', down_ratio=4, eval=False, eval_n_plots=0, eval_render_curves=False, exp_dir='/home/vincent/CenterFusion/src/lib/../../exp/ddd', exp_id='centerfusion', fix_res=True, fix_short=-1, flip=0.5, flip_test=True, fp_disturb=0, freeze_backbone=False, frustumExpansionRatio=0.0, gpus=[0], gpus_str='0', head_conv={'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}, head_kernel=3, heads={'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}, hm_dist_thresh={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 1, 8: 0, 9: 0}, hm_disturb=0, hm_hp_weight=1, hm_to_box_ratio=0.3, hm_transparency=0.7, hm_weight=1, hp_weight=1, hungarian=False, ignore_loaded_cats=[], img_format='jpg', input_h=448, input_res=800, input_w=800, iou_thresh=0, keep_res=False, kitti_split='3dop', layers_to_freeze=['base', 'dla_up', 'ida_up'], load_model='../models/centerfusion_e60.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, master_batch_size=32, max_age=-1, max_frame_dist=3, max_pc=1000, max_pc_dist=60.0, model_output_list=False, msra_outchannel=256, neck='dlaup', new_thresh=0.3, nms=False, no_color_aug=False, no_pause=False, no_pre_img=False, non_block_test=False, normalize_depth=True, not_cuda_benchmark=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_img_channels=3, num_iters=-1, num_resnet_layers=101, num_stacks=1, num_workers=4, nuscenes_att=True, nuscenes_att_weight=1, off_weight=1, optim='adam', out_thresh=-1, output_h=112, output_res=200, output_w=200, pad=31, 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'], pc_feat_channels={'pc_dep': 0, 'pc_vx': 1, 'pc_vz': 2}, pc_feat_lvl=['pc_dep', 'pc_vx', 'pc_vz'], pc_roi_method='pillars', pc_z_offset=-0.0, pillar_dims=[1.5, 0.2, 0.2], pointcloud=True, pre_hm=False, pre_img=False, pre_thresh=-1, print_iter=0, prior_bias=-4.6, public_det=False, qualitative=False, r_a=250, r_b=5, radar_sweeps=3, reg_loss='l1', reset_hm=False, resize_video=False, resume=False, reuse_hm=False, root_dir='/home/vincent/CenterFusion/src/lib/../..', rot_weight=1, rotate=0, run_dataset_eval=True, same_aug_pre=False, save_all=False, save_dir='/home/vincent/CenterFusion/src/lib/../../exp/ddd/centerfusion', save_framerate=30, save_img_suffix='', save_imgs=[], save_point=[90], save_results=False, save_video=False, scale=0, secondary_heads=['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec'], seed=317, shift=0, show_track_color=False, show_velocity=False, shuffle_train=False, sigmoid_dep_sec=True, skip_first=-1, sort_det_by_dist=False, tango_color=False, task='ddd', test_dataset='nuscenes', test_focal_length=-1, test_scales=[1.0], track_thresh=0.3, tracking=False, tracking_weight=1, train_split='train', trainval=False, transpose_video=False, use_loaded_results=False, val_intervals=10, val_split='mini_val', velocity=True, velocity_weight=1, video_h=512, video_w=512, vis_gt_bev='', vis_thresh=0.3, warm_start_weights=False, weights={'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}, wh_weight=0.1, zero_pre_hm=False, zero_tracking=False)
Dataset version
==> initializing mini_val data from /home/vincent/CenterFusion/src/lib/../../data/nuscenes/annotations_3sweeps/mini_val.json,
images from /home/vincent/CenterFusion/src/lib/../../data/nuscenes ...
loading annotations into memory...
Done (t=0.65s)
creating index...
index created!
Loaded mini_val 486 samples
Creating model...
Using node type: (<class 'model.networks.dla.DeformConv'>, <class 'model.networks.dla.DeformConv'>)
Warning: No ImageNet pretrain!!
Traceback (most recent call last):
File "test.py", line 215, in
prefetch_test(opt)
File "test.py", line 79, in prefetch_test
detector = Detector(opt)
File "/home/vincent/CenterFusion/src/lib/detector.py", line 34, in init
opt.arch, opt.heads, opt.head_conv, opt=opt)
File "/home/vincent/CenterFusion/src/lib/model/model.py", line 28, in create_model
model = model_class(num_layers, heads=head, head_convs=head_conv, opt=opt)
File "/home/vincent/CenterFusion/src/lib/model/networks/dla.py", line 611, in init
node_type=self.node_type)
File "/home/vincent/CenterFusion/src/lib/model/networks/dla.py", line 564, in init
node_type=node_type))
File "/home/vincent/CenterFusion/src/lib/model/networks/dla.py", line 526, in init
proj = node_type[0](c, o)
File "/home/vincent/CenterFusion/src/lib/model/networks/dla.py", line 513, in init
self.conv = DCN(chi, cho, kernel_size=(3,3), stride=1, padding=1, dilation=1, deformable_groups=1)
TypeError: 'NoneType' object is not callable

here is "test.sh", i didn't change anything.

export CUDA_VISIBLE_DEVICES=1
cd src

Perform detection and evaluation

python3 test.py ddd
--exp_id centerfusion
--dataset nuscenes
--val_split mini_val
--run_dataset_eval
--num_workers 4
--nuscenes_att
--velocity
--gpus 0
--pointcloud
--radar_sweeps 3
--max_pc_dist 60.0
--pc_z_offset -0.0
--load_model ../models/centerfusion_e60.pth
--flip_test
# --resume \

centerfusion_e60.pth is put in right path. nuscence dataset are covert to COCO. how can i fix this problem

hello,what's the reason for the jam when converting nuscenes data set to coco data set？

Excuse me! When I run bash train. sh, the error occurs as follows. How can I solve it?

Using tensorboardX
/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
DeprecationWarning)
Fix size testing.
training chunk_sizes: [16, 16]
input h w: 448 800
heads {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}
Namespace(K=100, amodel_offset_weight=1, arch='dla_34', aug_rot=0, backbone='dla34', batch_size=32, chunk_sizes=[16, 16], custom_dataset_ann_path='', custom_dataset_img_path='', custom_head_convs={'dep_sec': 3, 'rot_sec': 3, 'velocity': 3, 'nuscenes_att': 3}, data_dir='/content/drive/My Drive/CenterFusion/src/lib/../../data', dataset='nuscenes', dataset_version='', debug=0, debug_dir='/content/drive/My Drive/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug', debugger_theme='white', demo='', dense_reg=1, dep_res_weight=1, dep_weight=1, depth_scale=1, dim_weight=1, disable_frustum=False, dla_node='dcn', down_ratio=4, eval=False, eval_n_plots=0, eval_render_curves=False, exp_dir='/content/drive/My Drive/CenterFusion/src/lib/../../exp/ddd', exp_id='centerfusion', fix_res=True, fix_short=-1, flip=0.5, flip_test=False, fp_disturb=0, freeze_backbone=False, frustumExpansionRatio=0.0, gpus=[0, 1], gpus_str='0,1', head_conv={'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}, head_kernel=3, heads={'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}, hm_dist_thresh={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 1, 8: 0, 9: 0}, hm_disturb=0, hm_hp_weight=1, hm_to_box_ratio=0.3, hm_transparency=0.7, hm_weight=1, hp_weight=1, hungarian=False, ignore_loaded_cats=[], img_format='jpg', input_h=448, input_res=800, input_w=800, iou_thresh=0, keep_res=False, kitti_split='3dop', layers_to_freeze=['base', 'dla_up', 'ida_up'], load_model='../models/centerfusion_e60.pth', load_results='', lost_disturb=0, lr=0.00025, lr_step=[50], ltrb=False, ltrb_amodal=False, ltrb_amodal_weight=0.1, ltrb_weight=0.1, master_batch_size=16, max_age=-1, max_frame_dist=3, max_pc=1000, max_pc_dist=60.0, model_output_list=False, msra_outchannel=256, neck='dlaup', new_thresh=0.3, nms=False, no_color_aug=False, no_pause=False, no_pre_img=False, non_block_test=False, normalize_depth=True, not_cuda_benchmark=False, not_max_crop=False, not_prefetch_test=False, not_rand_crop=True, not_set_cuda_env=False, not_show_bbox=False, not_show_number=False, num_classes=10, num_epochs=60, num_head_conv=1, num_img_channels=3, num_iters=-1, num_resnet_layers=101, num_stacks=1, num_workers=4, nuscenes_att=True, nuscenes_att_weight=1, off_weight=1, optim='adam', out_thresh=-1, output_h=112, output_res=200, output_w=200, pad=31, 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'], pc_feat_channels={'pc_dep': 0, 'pc_vx': 1, 'pc_vz': 2}, pc_feat_lvl=['pc_dep', 'pc_vx', 'pc_vz'], pc_roi_method='pillars', pc_z_offset=0.0, pillar_dims=[1.5, 0.2, 0.2], pointcloud=True, pre_hm=False, pre_img=False, pre_thresh=-1, print_iter=0, prior_bias=-4.6, public_det=False, qualitative=False, r_a=250, r_b=5, radar_sweeps=3, reg_loss='l1', reset_hm=False, resize_video=False, resume=False, reuse_hm=False, root_dir='/content/drive/My Drive/CenterFusion/src/lib/../..', rot_weight=1, rotate=0, run_dataset_eval=True, same_aug_pre=False, save_all=False, save_dir='/content/drive/My Drive/CenterFusion/src/lib/../../exp/ddd/centerfusion', save_framerate=30, save_img_suffix='', save_imgs=[], save_point=[20, 40, 50], save_results=False, save_video=False, scale=0, secondary_heads=['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec'], seed=317, shift=0.1, show_track_color=False, show_velocity=False, shuffle_train=True, sigmoid_dep_sec=True, skip_first=-1, sort_det_by_dist=False, tango_color=False, task='ddd', test_dataset='nuscenes', test_focal_length=-1, test_scales=[1.0], track_thresh=0.3, tracking=False, tracking_weight=1, train_split='mini_train', trainval=False, transpose_video=False, use_loaded_results=False, val_intervals=1, val_split='mini_val', velocity=True, velocity_weight=1, video_h=512, video_w=512, vis_gt_bev='', vis_thresh=0.3, warm_start_weights=False, weights={'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}, wh_weight=0.1, zero_pre_hm=False, zero_tracking=False)
cp: target 'Drive/CenterFusion/src/lib/../../exp/ddd/centerfusion/logs_2021-02-02-14-41/' is not a directory
Creating model...
Using node type: (<class 'model.networks.dla.DeformConv'>, <class 'model.networks.dla.DeformConv'>)
Warning: No ImageNet pretrain!!
loaded ../models/centerfusion_e60.pth, epoch 60
Traceback (most recent call last):
File "main.py", line 140, in
main(opt)
File "main.py", line 52, in main
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
File "/content/drive/My Drive/CenterFusion/src/lib/trainer.py", line 141, in set_device
chunk_sizes=chunk_sizes).to(device)
File "/content/drive/My Drive/CenterFusion/src/lib/model/data_parallel.py", line 127, in DataParallel
return torch.nn.DataParallel(module, device_ids, output_device, dim)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 133, in init
_check_balance(self.device_ids)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 19, in _check_balance
dev_props = [torch.cuda.get_device_properties(i) for i in device_ids]
File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 19, in
dev_props = [torch.cuda.get_device_properties(i) for i in device_ids]
File "/usr/local/lib/python3.6/dist-packages/torch/cuda/init.py", line 318, in get_device_properties
raise AssertionError("Invalid device id")
AssertionError: Invalid device id

(my version:ubuntun18.04 CUDA=10.1 pytorch=1.2.0 torchvision=0.4.0 python3.6)

Thank you very much for your work. It's a great job. I have a dataset, but this data is similar to Kitti's format. How to apply it to centerfusion?

Hello, while trying to run test.py or main.py I have this error and I cannot solve it

13.3856 |hm 1.1735 |wh 1.8746 |reg 0.2269 |dep 1.9600 |dep_sec 4.7663 |dim 0.2071 |rot 1.6393 |rot_sec 1.7366 |amodel_offset 0.9284 |nuscenes_att 0.2896 |velocity 0.2704 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |#################### | val: [1][313/486]|Tot: 0:01:28 |ETA: 0:00:48 |tot 13.3699 |hm 1.1714 |wh 1.8753 |reg 0.2269 |dep 1.9559 |dep_sec 4.7584 |dim 0.2069 |rot 1.6384 |rot_sec 1.7362 |amodel_offset 0.9290 |nuscenes_att 0.2894 |velocity 0.2698 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |#################### | val: [1][314/486]|Tot: 0:01:28 |ETA: 0:00:48 |tot 13.3731 |hm 1.1693 |wh 1.8742 |reg 0.2269 |dep 1.9594 |dep_sec 4.7603 |dim 0.2067 |rot 1.6375 |rot_sec 1.7357 |amodel_offset 0.9306 |nuscenes_att 0.2899 |velocity 0.2695 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |#################### | val: [1][315/486]|Tot: 0:01:28 |ETA: 0:00:48 |tot 13.3608 |hm 1.1674 |wh 1.8732 |reg 0.2266 |dep 1.9559 |dep_sec 4.7569 |dim 0.2065 |rot 1.6366 |rot_sec 1.7353 |amodel_offset 0.9297 |nuscenes_att 0.2898 |velocity 0.2689 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |#################### | val: [1][316/486]|Tot: 0:01:29 |ETA: 0:00:48 |tot 13.3496 |hm 1.1657 |wh 1.8719 |reg 0.2266 |dep 1.9529 |dep_sec 4.7534 |dim 0.2062 |rot 1.6361 |rot_sec 1.7353 |amodel_offset 0.9281 |nuscenes_att 0.2896 |velocity 0.2683 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |#################### | val: [1][317/486]|Tot: 0:01:29 |ETA: 0:00:47 |tot 13.3385 |hm 1.1635 |wh 1.8709 |reg 0.2266 |dep 1.9483 |dep_sec 4.7530 |dim 0.2059 |rot 1.6356 |rot_sec 1.7354 |amodel_offset 0.9263 |nuscenes_att 0.2892 |velocity 0.2678 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][318/486]|Tot: 0:01:29 |ETA: 0:00:47 |tot 13.3271 |hm 1.1631 |wh 1.8706 |reg 0.2265 |dep 1.9447 |dep_sec 4.7488 |dim 0.2057 |rot 1.6350 |rot_sec 1.7353 |amodel_offset 0.9247 |nuscenes_att 0.2889 |velocity 0.2672 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][319/486]|Tot: 0:01:29 |ETA: 0:00:47 |tot 13.3195 |hm 1.1638 |wh 1.8752 |reg 0.2265 |dep 1.9426 |dep_sec 4.7417 |dim 0.2056 |rot 1.6351 |rot_sec 1.7356 |amodel_offset 0.9256 |nuscenes_att 0.2887 |velocity 0.2667 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][320/486]|Tot: 0:01:30 |ETA: 0:00:46 |tot 13.3111 |hm 1.1654 |wh 1.8820 |reg 0.2268 |dep 1.9410 |dep_sec 4.7339 |dim 0.2054 |rot 1.6352 |rot_sec 1.7352 |amodel_offset 0.9253 |nuscenes_att 0.2884 |velocity 0.2663 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][321/486]|Tot: 0:01:30 |ETA: 0:00:46 |tot 13.3024 |hm 1.1686 |wh 1.8858 |reg 0.2269 |dep 1.9391 |dep_sec 4.7260 |dim 0.2052 |rot 1.6344 |rot_sec 1.7343 |amodel_offset 0.9252 |nuscenes_att 0.2881 |velocity 0.2660 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][322/486]|Tot: 0:01:30 |ETA: 0:00:46 |tot 13.2984 |hm 1.1695 |wh 1.8864 |reg 0.2268 |dep 1.9393 |dep_sec 4.7247 |dim 0.2054 |rot 1.6339 |rot_sec 1.7340 |amodel_offset 0.9234 |nuscenes_att 0.2876 |velocity 0.2653 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][323/486]|Tot: 0:01:31 |ETA: 0:00:46 |tot 13.2932 |hm 1.1702 |wh 1.8860 |reg 0.2269 |dep 1.9391 |dep_sec 4.7234 |dim 0.2056 |rot 1.6330 |rot_sec 1.7334 |amodel_offset 0.9214 |nuscenes_att 0.2871 |velocity 0.2646 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][324/486]|Tot: 0:01:31 |ETA: 0:00:46 |tot 13.2953 |hm 1.1700 |wh 1.8895 |reg 0.2271 |dep 1.9414 |dep_sec 4.7248 |dim 0.2058 |rot 1.6328 |rot_sec 1.7332 |amodel_offset 0.9200 |nuscenes_att 0.2867 |velocity 0.2645 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][325/486]|Tot: 0:01:31 |ETA: 0:00:46 |tot 13.2986 |hm 1.1699 |wh 1.8910 |reg 0.2271 |dep 1.9414 |dep_sec 4.7239 |dim 0.2059 |rot 1.6361 |rot_sec 1.7351 |amodel_offset 0.9198 |nuscenes_att 0.2863 |velocity 0.2641 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][326/486]|Tot: 0:01:31 |ETA: 0:00:45 |tot 13.2924 |hm 1.1699 |wh 1.8939 |reg 0.2272 |dep 1.9373 |dep_sec 4.7208 |dim 0.2061 |rot 1.6352 |rot_sec 1.7343 |amodel_offset 0.9212 |nuscenes_att 0.2863 |velocity 0.2647 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][327/486]|Tot: 0:01:32 |ETA: 0:00:45 |tot 13.2903 |hm 1.1694 |wh 1.8959 |reg 0.2272 |dep 1.9343 |dep_sec 4.7198 |dim 0.2061 |rot 1.6357 |rot_sec 1.7345 |amodel_offset 0.9227 |nuscenes_att 0.2862 |velocity 0.2648 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][328/486]|Tot: 0:01:32 |ETA: 0:00:45 |tot 13.3079 |hm 1.1691 |wh 1.8977 |reg 0.2273 |dep 1.9392 |dep_sec 4.7318 |dim 0.2060 |rot 1.6364 |rot_sec 1.7348 |amodel_offset 0.9228 |nuscenes_att 0.2859 |velocity 0.2647 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][329/486]|Tot: 0:01:32 |ETA: 0:00:44 |tot 13.3193 |hm 1.1695 |wh 1.8997 |reg 0.2273 |dep 1.9413 |dep_sec 4.7429 |dim 0.2061 |rot 1.6355 |rot_sec 1.7341 |amodel_offset 0.9229 |nuscenes_att 0.2854 |velocity 0.2643 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][330/486]|Tot: 0:01:33 |ETA: 0:00:45 |tot 13.3287 |hm 1.1688 |wh 1.8982 |reg 0.2274 |dep 1.9427 |dep_sec 4.7556 |dim 0.2061 |rot 1.6348 |rot_sec 1.7336 |amodel_offset 0.9213 |nuscenes_att 0.2848 |velocity 0.2637 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][331/486]|Tot: 0:01:33 |ETA: 0:00:44 |tot 13.3401 |hm 1.1681 |wh 1.8985 |reg 0.2275 |dep 1.9466 |dep_sec 4.7680 |dim 0.2061 |rot 1.6337 |rot_sec 1.7329 |amodel_offset 0.9200 |nuscenes_att 0.2842 |velocity 0.2631 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][332/486]|Tot: 0:01:33 |ETA: 0:00:44 |tot 13.3404 |hm 1.1674 |wh 1.8981 |reg 0.2275 |dep 1.9450 |dep_sec 4.7742 |dim 0.2062 |rot 1.6330 |rot_sec 1.7325 |amodel_offset 0.9188 |nuscenes_att 0.2836 |velocity 0.2625 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |##################### | val: [1][333/486]|Tot: 0:01:33 |ETA: 0:00:44 |tot 13.3426 |hm 1.1664 |wh 1.8972 |reg 0.2274 |dep 1.9442 |dep_sec 4.7811 |dim 0.2062 |rot 1.6326 |rot_sec 1.7326 |amodel_offset 0.9175 |nuscenes_att 0.2830 |velocity 0.2619 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][334/486]|Tot: 0:01:34 |ETA: 0:00:43 |tot 13.3573 |hm 1.1658 |wh 1.8981 |reg 0.2276 |dep 1.9496 |dep_sec 4.7916 |dim 0.2061 |rot 1.6323 |rot_sec 1.7328 |amodel_offset 0.9178 |nuscenes_att 0.2826 |velocity 0.2614 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][335/486]|Tot: 0:01:34 |ETA: 0:00:43 |tot 13.3765 |hm 1.1656 |wh 1.8974 |reg 0.2276 |dep 1.9563 |dep_sec 4.8024 |dim 0.2061 |rot 1.6326 |rot_sec 1.7335 |amodel_offset 0.9196 |nuscenes_att 0.2821 |velocity 0.2608 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][336/486]|Tot: 0:01:34 |ETA: 0:00:43 |tot 13.3781 |hm 1.1654 |wh 1.8968 |reg 0.2276 |dep 1.9557 |dep_sec 4.8050 |dim 0.2061 |rot 1.6328 |rot_sec 1.7341 |amodel_offset 0.9198 |nuscenes_att 0.2818 |velocity 0.2603 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][337/486]|Tot: 0:01:35 |ETA: 0:00:43 |tot 13.3909 |hm 1.1649 |wh 1.8959 |reg 0.2277 |dep 1.9597 |dep_sec 4.8124 |dim 0.2061 |rot 1.6338 |rot_sec 1.7353 |amodel_offset 0.9202 |nuscenes_att 0.2813 |velocity 0.2598 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][338/486]|Tot: 0:01:35 |ETA: 0:00:42 |tot 13.3884 |hm 1.1654 |wh 1.8952 |reg 0.2277 |dep 1.9589 |dep_sec 4.8118 |dim 0.2060 |rot 1.6336 |rot_sec 1.7361 |amodel_offset 0.9191 |nuscenes_att 0.2809 |velocity 0.2593 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][339/486]|Tot: 0:01:35 |ETA: 0:00:42 |tot 13.3862 |hm 1.1659 |wh 1.8947 |reg 0.2278 |dep 1.9569 |dep_sec 4.8115 |dim 0.2061 |rot 1.6334 |rot_sec 1.7364 |amodel_offset 0.9190 |nuscenes_att 0.2806 |velocity 0.2591 |Data 0.013s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][340/486]|Tot: 0:01:35 |ETA: 0:00:42 |tot 13.3957 |hm 1.1672 |wh 1.8937 |reg 0.2279 |dep 1.9583 |dep_sec 4.8174 |dim 0.2061 |rot 1.6337 |rot_sec 1.7366 |amodel_offset 0.9195 |nuscenes_att 0.2806 |velocity 0.2591 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][341/486]|Tot: 0:01:36 |ETA: 0:00:41 |tot 13.4132 |hm 1.1679 |wh 1.8975 |reg 0.2279 |dep 1.9663 |dep_sec 4.8246 |dim 0.2062 |rot 1.6342 |rot_sec 1.7369 |amodel_offset 0.9190 |nuscenes_att 0.2812 |velocity 0.2592 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][342/486]|Tot: 0:01:36 |ETA: 0:00:40 |tot 13.4250 |hm 1.1679 |wh 1.9038 |reg 0.2280 |dep 1.9671 |dep_sec 4.8254 |dim 0.2063 |rot 1.6335 |rot_sec 1.7363 |amodel_offset 0.9300 |nuscenes_att 0.2812 |velocity 0.2590 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][343/486]|Tot: 0:01:36 |ETA: 0:00:40 |tot 13.4420 |hm 1.1694 |wh 1.9063 |reg 0.2281 |dep 1.9742 |dep_sec 4.8280 |dim 0.2063 |rot 1.6333 |rot_sec 1.7360 |amodel_offset 0.9360 |nuscenes_att 0.2814 |velocity 0.2587 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][344/486]|Tot: 0:01:36 |ETA: 0:00:40 |tot 13.4336 |hm 1.1692 |wh 1.9069 |reg 0.2283 |dep 1.9722 |dep_sec 4.8217 |dim 0.2063 |rot 1.6328 |rot_sec 1.7353 |amodel_offset 0.9376 |nuscenes_att 0.2814 |velocity 0.2583 |Data 0.013s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][345/486]|Tot: 0:01:37 |ETA: 0:00:39 |tot 13.4431 |hm 1.1695 |wh 1.9101 |reg 0.2284 |dep 1.9763 |dep_sec 4.8263 |dim 0.2064 |rot 1.6325 |rot_sec 1.7352 |amodel_offset 0.9381 |nuscenes_att 0.2815 |velocity 0.2579 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][346/486]|Tot: 0:01:37 |ETA: 0:00:38 |tot 13.4368 |hm 1.1696 |wh 1.9110 |reg 0.2283 |dep 1.9752 |dep_sec 4.8213 |dim 0.2065 |rot 1.6317 |rot_sec 1.7345 |amodel_offset 0.9400 |nuscenes_att 0.2813 |velocity 0.2573 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][347/486]|Tot: 0:01:37 |ETA: 0:00:38 |tot 13.4295 |hm 1.1696 |wh 1.9125 |reg 0.2284 |dep 1.9718 |dep_sec 4.8173 |dim 0.2065 |rot 1.6318 |rot_sec 1.7345 |amodel_offset 0.9404 |nuscenes_att 0.2811 |velocity 0.2569 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |###################### | val: [1][348/486]|Tot: 0:01:38 |ETA: 0:00:38 |tot 13.4224 |hm 1.1701 |wh 1.9135 |reg 0.2284 |dep 1.9699 |dep_sec 4.8093 |dim 0.2065 |rot 1.6317 |rot_sec 1.7344 |amodel_offset 0.9425 |nuscenes_att 0.2812 |velocity 0.2568 |Data 0.016s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][349/486]|Tot: 0:01:38 |ETA: 0:00:38 |tot 13.4133 |hm 1.1710 |wh 1.9164 |reg 0.2282 |dep 1.9678 |dep_sec 4.8016 |dim 0.2066 |rot 1.6314 |rot_sec 1.7339 |amodel_offset 0.9428 |nuscenes_att 0.2814 |velocity 0.2569 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][350/486]|Tot: 0:01:38 |ETA: 0:00:38 |tot 13.4032 |hm 1.1713 |wh 1.9182 |reg 0.2282 |dep 1.9640 |dep_sec 4.7949 |dim 0.2068 |rot 1.6308 |rot_sec 1.7335 |amodel_offset 0.9430 |nuscenes_att 0.2817 |velocity 0.2571 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][351/486]|Tot: 0:01:38 |ETA: 0:00:38 |tot 13.3975 |hm 1.1712 |wh 1.9185 |reg 0.2283 |dep 1.9607 |dep_sec 4.7922 |dim 0.2067 |rot 1.6312 |rot_sec 1.7339 |amodel_offset 0.9428 |nuscenes_att 0.2817 |velocity 0.2569 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][352/486]|Tot: 0:01:39 |ETA: 0:00:37 |tot 13.3942 |hm 1.1711 |wh 1.9184 |reg 0.2283 |dep 1.9582 |dep_sec 4.7939 |dim 0.2066 |rot 1.6309 |rot_sec 1.7335 |amodel_offset 0.9421 |nuscenes_att 0.2815 |velocity 0.2563 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][353/486]|Tot: 0:01:39 |ETA: 0:00:37 |tot 13.3871 |hm 1.1705 |wh 1.9175 |reg 0.2284 |dep 1.9567 |dep_sec 4.7915 |dim 0.2066 |rot 1.6301 |rot_sec 1.7329 |amodel_offset 0.9417 |nuscenes_att 0.2812 |velocity 0.2558 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][354/486]|Tot: 0:01:39 |ETA: 0:00:37 |tot 13.3822 |hm 1.1700 |wh 1.9166 |reg 0.2284 |dep 1.9551 |dep_sec 4.7910 |dim 0.2066 |rot 1.6295 |rot_sec 1.7324 |amodel_offset 0.9413 |nuscenes_att 0.2811 |velocity 0.2553 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][355/486]|Tot: 0:01:39 |ETA: 0:00:37 |tot 13.3767 |hm 1.1697 |wh 1.9147 |reg 0.2283 |dep 1.9531 |dep_sec 4.7900 |dim 0.2065 |rot 1.6289 |rot_sec 1.7320 |amodel_offset 0.9410 |nuscenes_att 0.2809 |velocity 0.2548 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][356/486]|Tot: 0:01:40 |ETA: 0:00:37 |tot 13.3680 |hm 1.1694 |wh 1.9147 |reg 0.2284 |dep 1.9533 |dep_sec 4.7836 |dim 0.2064 |rot 1.6282 |rot_sec 1.7313 |amodel_offset 0.9409 |nuscenes_att 0.2808 |velocity 0.2542 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][357/486]|Tot: 0:01:40 |ETA: 0:00:36 |tot 13.3603 |hm 1.1692 |wh 1.9149 |reg 0.2285 |dep 1.9531 |dep_sec 4.7797 |dim 0.2061 |rot 1.6274 |rot_sec 1.7306 |amodel_offset 0.9394 |nuscenes_att 0.2810 |velocity 0.2538 |Data 0.011s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][358/486]|Tot: 0:01:40 |ETA: 0:00:36 |tot 13.3543 |hm 1.1692 |wh 1.9152 |reg 0.2284 |dep 1.9509 |dep_sec 4.7783 |dim 0.2059 |rot 1.6266 |rot_sec 1.7299 |amodel_offset 0.9389 |nuscenes_att 0.2812 |velocity 0.2535 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][359/486]|Tot: 0:01:41 |ETA: 0:00:36 |tot 13.3531 |hm 1.1687 |wh 1.9174 |reg 0.2283 |dep 1.9527 |dep_sec 4.7763 |dim 0.2057 |rot 1.6261 |rot_sec 1.7293 |amodel_offset 0.9391 |nuscenes_att 0.2817 |velocity 0.2533 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][360/486]|Tot: 0:01:41 |ETA: 0:00:36 |tot 13.3436 |hm 1.1671 |wh 1.9155 |reg 0.2283 |dep 1.9490 |dep_sec 4.7746 |dim 0.2055 |rot 1.6254 |rot_sec 1.7286 |amodel_offset 0.9381 |nuscenes_att 0.2820 |velocity 0.2533 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][361/486]|Tot: 0:01:41 |ETA: 0:00:35 |tot 13.3440 |hm 1.1652 |wh 1.9142 |reg 0.2284 |dep 1.9493 |dep_sec 4.7793 |dim 0.2054 |rot 1.6250 |rot_sec 1.7281 |amodel_offset 0.9365 |nuscenes_att 0.2817 |velocity 0.2537 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][362/486]|Tot: 0:01:42 |ETA: 0:00:37 |tot 13.3468 |hm 1.1648 |wh 1.9143 |reg 0.2283 |dep 1.9520 |dep_sec 4.7787 |dim 0.2054 |rot 1.6248 |rot_sec 1.7278 |amodel_offset 0.9379 |nuscenes_att 0.2814 |velocity 0.2543 |Data 0.010s(0.013s) |Net 0.281ddd/centerfusion |####################### | val: [1][363/486]|Tot: 0:01:42 |ETA: 0:00:36 |tot 13.3437 |hm 1.1658 |wh 1.9148 |reg 0.2283 |dep 1.9542 |dep_sec 4.7709 |dim 0.2056 |rot 1.6260 |rot_sec 1.7283 |amodel_offset 0.9366 |nuscenes_att 0.2810 |velocity 0.2555 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][364/486]|Tot: 0:01:42 |ETA: 0:00:36 |tot 13.3348 |hm 1.1641 |wh 1.9125 |reg 0.2282 |dep 1.9559 |dep_sec 4.7630 |dim 0.2057 |rot 1.6263 |rot_sec 1.7280 |amodel_offset 0.9346 |nuscenes_att 0.2806 |velocity 0.2571 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][365/486]|Tot: 0:01:42 |ETA: 0:00:36 |tot 13.3275 |hm 1.1641 |wh 1.9109 |reg 0.2280 |dep 1.9575 |dep_sec 4.7532 |dim 0.2058 |rot 1.6271 |rot_sec 1.7282 |amodel_offset 0.9337 |nuscenes_att 0.2802 |velocity 0.2587 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][366/486]|Tot: 0:01:43 |ETA: 0:00:35 |tot 13.3137 |hm 1.1623 |wh 1.9096 |reg 0.2279 |dep 1.9548 |dep_sec 4.7443 |dim 0.2057 |rot 1.6267 |rot_sec 1.7276 |amodel_offset 0.9328 |nuscenes_att 0.2798 |velocity 0.2608 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][367/486]|Tot: 0:01:43 |ETA: 0:00:35 |tot 13.3019 |hm 1.1616 |wh 1.9080 |reg 0.2279 |dep 1.9520 |dep_sec 4.7357 |dim 0.2056 |rot 1.6272 |rot_sec 1.7275 |amodel_offset 0.9310 |nuscenes_att 0.2795 |velocity 0.2629 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][368/486]|Tot: 0:01:43 |ETA: 0:00:34 |tot 13.2988 |hm 1.1604 |wh 1.9070 |reg 0.2276 |dep 1.9549 |dep_sec 4.7292 |dim 0.2054 |rot 1.6292 |rot_sec 1.7282 |amodel_offset 0.9292 |nuscenes_att 0.2791 |velocity 0.2649 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][369/486]|Tot: 0:01:43 |ETA: 0:00:34 |tot 13.2990 |hm 1.1609 |wh 1.9049 |reg 0.2275 |dep 1.9615 |dep_sec 4.7200 |dim 0.2059 |rot 1.6312 |rot_sec 1.7290 |amodel_offset 0.9276 |nuscenes_att 0.2791 |velocity 0.2658 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][370/486]|Tot: 0:01:44 |ETA: 0:00:31 |tot 13.2976 |hm 1.1626 |wh 1.9043 |reg 0.2273 |dep 1.9661 |dep_sec 4.7108 |dim 0.2058 |rot 1.6331 |rot_sec 1.7302 |amodel_offset 0.9255 |nuscenes_att 0.2790 |velocity 0.2666 |Data 0.021s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][371/486]|Tot: 0:01:44 |ETA: 0:00:31 |tot 13.3229 |hm 1.1642 |wh 1.9167 |reg 0.2275 |dep 1.9741 |dep_sec 4.7110 |dim 0.2065 |rot 1.6350 |rot_sec 1.7310 |amodel_offset 0.9357 |nuscenes_att 0.2792 |velocity 0.2670 |Data 0.020s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][372/486]|Tot: 0:01:44 |ETA: 0:00:31 |tot 13.3216 |hm 1.1648 |wh 1.9183 |reg 0.2279 |dep 1.9737 |dep_sec 4.7069 |dim 0.2066 |rot 1.6365 |rot_sec 1.7322 |amodel_offset 0.9345 |nuscenes_att 0.2795 |velocity 0.2673 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][373/486]|Tot: 0:01:44 |ETA: 0:00:30 |tot 13.3213 |hm 1.1660 |wh 1.9179 |reg 0.2281 |dep 1.9730 |dep_sec 4.7057 |dim 0.2066 |rot 1.6371 |rot_sec 1.7324 |amodel_offset 0.9332 |nuscenes_att 0.2798 |velocity 0.2676 |Data 0.020s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][374/486]|Tot: 0:01:45 |ETA: 0:00:31 |tot 13.3267 |hm 1.1684 |wh 1.9167 |reg 0.2283 |dep 1.9733 |dep_sec 4.7074 |dim 0.2067 |rot 1.6383 |rot_sec 1.7329 |amodel_offset 0.9318 |nuscenes_att 0.2804 |velocity 0.2675 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][375/486]|Tot: 0:01:45 |ETA: 0:00:31 |tot 13.3307 |hm 1.1694 |wh 1.9154 |reg 0.2283 |dep 1.9706 |dep_sec 4.7102 |dim 0.2069 |rot 1.6397 |rot_sec 1.7340 |amodel_offset 0.9316 |nuscenes_att 0.2810 |velocity 0.2675 |Data 0.020s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][376/486]|Tot: 0:01:45 |ETA: 0:00:30 |tot 13.3266 |hm 1.1693 |wh 1.9147 |reg 0.2282 |dep 1.9687 |dep_sec 4.7091 |dim 0.2068 |rot 1.6394 |rot_sec 1.7334 |amodel_offset 0.9308 |nuscenes_att 0.2820 |velocity 0.2674 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][377/486]|Tot: 0:01:46 |ETA: 0:00:30 |tot 13.3260 |hm 1.1697 |wh 1.9164 |reg 0.2283 |dep 1.9701 |dep_sec 4.7064 |dim 0.2068 |rot 1.6392 |rot_sec 1.7334 |amodel_offset 0.9302 |nuscenes_att 0.2830 |velocity 0.2672 |Data 0.020s(0.013s) |Net 0.281ddd/centerfusion |######################## | val: [1][378/486]|Tot: 0:01:46 |ETA: 0:00:30 |tot 13.3174 |hm 1.1685 |wh 1.9150 |reg 0.2283 |dep 1.9685 |dep_sec 4.7020 |dim 0.2065 |rot 1.6391 |rot_sec 1.7334 |amodel_offset 0.9284 |nuscenes_att 0.2838 |velocity 0.2672 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################### | val: [1][379/486]|Tot: 0:01:46 |ETA: 0:00:29 |tot 13.3095 |hm 1.1683 |wh 1.9127 |reg 0.2283 |dep 1.9656 |dep_sec 4.6960 |dim 0.2063 |rot 1.6401 |rot_sec 1.7342 |amodel_offset 0.9279 |nuscenes_att 0.2845 |velocity 0.2670 |Data 0.020s(0.013s) |Net 0.281ddd/centerfusion |######################### | val: [1][380/486]|Tot: 0:01:46 |ETA: 0:00:29 |tot 13.3033 |hm 1.1668 |wh 1.9125 |reg 0.2284 |dep 1.9634 |dep_sec 4.6926 |dim 0.2060 |rot 1.6413 |rot_sec 1.7349 |amodel_offset 0.9264 |nuscenes_att 0.2854 |velocity 0.2668 |Data 0.022s(0.013s) |Net 0.281ddd/centerfusion |######################### | val: [1][381/486]|Tot: 0:01:47 |ETA: 0:00:29 |tot 13.2979 |hm 1.1664 |wh 1.9133 |reg 0.2285 |dep 1.9623 |dep_sec 4.6887 |dim 0.2058 |rot 1.6419 |rot_sec 1.7354 |amodel_offset 0.9248 |nuscenes_att 0.2864 |velocity 0.2665 |Data 0.022s(0.013s) |Net 0.280ddd/centerfusion |######################### | val: [1][382/486]|Tot: 0:01:47 |ETA: 0:00:29 |tot 13.2907 |hm 1.1662 |wh 1.9126 |reg 0.2285 |dep 1.9603 |dep_sec 4.6835 |dim 0.2056 |rot 1.6422 |rot_sec 1.7360 |amodel_offset 0.9235 |nuscenes_att 0.2874 |velocity 0.2661 |Data 0.022s(0.013s) |Net 0.280ddd/centerfusion |######################### | val: [1][383/486]|Tot: 0:01:47 |ETA: 0:00:28 |tot 13.2880 |hm 1.1661 |wh 1.9130 |reg 0.2286 |dep 1.9603 |dep_sec 4.6790 |dim 0.2054 |rot 1.6429 |rot_sec 1.7369 |amodel_offset 0.9233 |nuscenes_att 0.2883 |velocity 0.2658 |Data 0.022s(0.013s) |Net 0.280ddd/centerfusion |######################### | val: [1][384/486]|Tot: 0:01:47 |ETA: 0:00:28 |tot 13.2840 |hm 1.1659 |wh 1.9125 |reg 0.2286 |dep 1.9588 |dep_sec 4.6746 |dim 0.2053 |rot 1.6438 |rot_sec 1.7377 |amodel_offset 0.9237 |nuscenes_att 0.2891 |velocity 0.2654 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][385/486]|Tot: 0:01:48 |ETA: 0:00:28 |tot 13.2818 |hm 1.1653 |wh 1.9124 |reg 0.2286 |dep 1.9600 |dep_sec 4.6704 |dim 0.2053 |rot 1.6446 |rot_sec 1.7388 |amodel_offset 0.9230 |nuscenes_att 0.2896 |velocity 0.2650 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][386/486]|Tot: 0:01:48 |ETA: 0:00:28 |tot 13.2826 |hm 1.1653 |wh 1.9129 |reg 0.2286 |dep 1.9634 |dep_sec 4.6658 |dim 0.2054 |rot 1.6459 |rot_sec 1.7399 |amodel_offset 0.9226 |nuscenes_att 0.2899 |velocity 0.2646 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][387/486]|Tot: 0:01:48 |ETA: 0:00:27 |tot 13.2801 |hm 1.1648 |wh 1.9118 |reg 0.2287 |dep 1.9650 |dep_sec 4.6612 |dim 0.2055 |rot 1.6468 |rot_sec 1.7409 |amodel_offset 0.9219 |nuscenes_att 0.2900 |velocity 0.2641 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][388/486]|Tot: 0:01:49 |ETA: 0:00:27 |tot 13.2757 |hm 1.1642 |wh 1.9115 |reg 0.2286 |dep 1.9667 |dep_sec 4.6562 |dim 0.2054 |rot 1.6475 |rot_sec 1.7416 |amodel_offset 0.9207 |nuscenes_att 0.2901 |velocity 0.2635 |Data 0.021s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][389/486]|Tot: 0:01:49 |ETA: 0:00:27 |tot 13.2710 |hm 1.1641 |wh 1.9115 |reg 0.2287 |dep 1.9683 |dep_sec 4.6507 |dim 0.2056 |rot 1.6476 |rot_sec 1.7419 |amodel_offset 0.9199 |nuscenes_att 0.2901 |velocity 0.2630 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][390/486]|Tot: 0:01:49 |ETA: 0:00:27 |tot 13.2685 |hm 1.1636 |wh 1.9107 |reg 0.2287 |dep 1.9704 |dep_sec 4.6473 |dim 0.2057 |rot 1.6479 |rot_sec 1.7423 |amodel_offset 0.9191 |nuscenes_att 0.2900 |velocity 0.2624 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][391/486]|Tot: 0:01:49 |ETA: 0:00:26 |tot 13.2659 |hm 1.1637 |wh 1.9109 |reg 0.2287 |dep 1.9708 |dep_sec 4.6456 |dim 0.2059 |rot 1.6478 |rot_sec 1.7423 |amodel_offset 0.9181 |nuscenes_att 0.2900 |velocity 0.2619 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][392/486]|Tot: 0:01:50 |ETA: 0:00:26 |tot 13.2658 |hm 1.1637 |wh 1.9108 |reg 0.2287 |dep 1.9706 |dep_sec 4.6461 |dim 0.2065 |rot 1.6479 |rot_sec 1.7426 |amodel_offset 0.9173 |nuscenes_att 0.2900 |velocity 0.2614 |Data 0.020s(0.014s) |Net 0.280ddd/centerfusion |######################### | val: [1][393/486]|Tot: 0:01:50 |ETA: 0:00:26 |tot 13.2673 |hm 1.1639 |wh 1.9105 |reg 0.2288 |dep 1.9696 |dep_sec 4.6483 |dim 0.2070 |rot 1.6481 |rot_sec 1.7429 |amodel_offset 0.9168 |nuscenes_att 0.2901 |velocity 0.2609 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][394/486]|Tot: 0:01:50 |ETA: 0:00:26 |tot 13.2703 |hm 1.1645 |wh 1.9111 |reg 0.2286 |dep 1.9698 |dep_sec 4.6501 |dim 0.2076 |rot 1.6484 |rot_sec 1.7433 |amodel_offset 0.9164 |nuscenes_att 0.2901 |velocity 0.2604 |Data 0.020s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][395/486]|Tot: 0:01:50 |ETA: 0:00:25 |tot 13.2733 |hm 1.1647 |wh 1.9106 |reg 0.2287 |dep 1.9686 |dep_sec 4.6546 |dim 0.2082 |rot 1.6485 |rot_sec 1.7437 |amodel_offset 0.9154 |nuscenes_att 0.2899 |velocity 0.2599 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][396/486]|Tot: 0:01:51 |ETA: 0:00:25 |tot 13.2756 |hm 1.1653 |wh 1.9104 |reg 0.2286 |dep 1.9685 |dep_sec 4.6583 |dim 0.2087 |rot 1.6482 |rot_sec 1.7437 |amodel_offset 0.9141 |nuscenes_att 0.2898 |velocity 0.2594 |Data 0.021s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][397/486]|Tot: 0:01:51 |ETA: 0:00:25 |tot 13.2769 |hm 1.1657 |wh 1.9103 |reg 0.2286 |dep 1.9676 |dep_sec 4.6622 |dim 0.2093 |rot 1.6478 |rot_sec 1.7436 |amodel_offset 0.9126 |nuscenes_att 0.2895 |velocity 0.2589 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][398/486]|Tot: 0:01:51 |ETA: 0:00:25 |tot 13.2800 |hm 1.1661 |wh 1.9093 |reg 0.2286 |dep 1.9663 |dep_sec 4.6680 |dim 0.2098 |rot 1.6475 |rot_sec 1.7435 |amodel_offset 0.9114 |nuscenes_att 0.2893 |velocity 0.2584 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][399/486]|Tot: 0:01:52 |ETA: 0:00:24 |tot 13.2860 |hm 1.1670 |wh 1.9085 |reg 0.2286 |dep 1.9665 |dep_sec 4.6736 |dim 0.2103 |rot 1.6474 |rot_sec 1.7434 |amodel_offset 0.9112 |nuscenes_att 0.2891 |velocity 0.2580 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][400/486]|Tot: 0:01:52 |ETA: 0:00:24 |tot 13.2952 |hm 1.1679 |wh 1.9082 |reg 0.2285 |dep 1.9700 |dep_sec 4.6797 |dim 0.2106 |rot 1.6475 |rot_sec 1.7434 |amodel_offset 0.9101 |nuscenes_att 0.2890 |velocity 0.2576 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][401/486]|Tot: 0:01:52 |ETA: 0:00:24 |tot 13.3021 |hm 1.1685 |wh 1.9078 |reg 0.2284 |dep 1.9731 |dep_sec 4.6849 |dim 0.2110 |rot 1.6472 |rot_sec 1.7432 |amodel_offset 0.9088 |nuscenes_att 0.2890 |velocity 0.2572 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][402/486]|Tot: 0:01:52 |ETA: 0:00:24 |tot 13.3052 |hm 1.1682 |wh 1.9069 |reg 0.2284 |dep 1.9750 |dep_sec 4.6889 |dim 0.2112 |rot 1.6467 |rot_sec 1.7429 |amodel_offset 0.9072 |nuscenes_att 0.2890 |velocity 0.2569 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][403/486]|Tot: 0:01:53 |ETA: 0:00:23 |tot 13.3084 |hm 1.1678 |wh 1.9053 |reg 0.2284 |dep 1.9755 |dep_sec 4.6933 |dim 0.2115 |rot 1.6465 |rot_sec 1.7428 |amodel_offset 0.9065 |nuscenes_att 0.2890 |velocity 0.2565 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][404/486]|Tot: 0:01:53 |ETA: 0:00:23 |tot 13.3051 |hm 1.1698 |wh 1.9126 |reg 0.2282 |dep 1.9761 |dep_sec 4.6855 |dim 0.2113 |rot 1.6476 |rot_sec 1.7433 |amodel_offset 0.9059 |nuscenes_att 0.2889 |velocity 0.2572 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][405/486]|Tot: 0:01:53 |ETA: 0:00:23 |tot 13.2955 |hm 1.1716 |wh 1.9127 |reg 0.2281 |dep 1.9735 |dep_sec 4.6793 |dim 0.2110 |rot 1.6473 |rot_sec 1.7429 |amodel_offset 0.9043 |nuscenes_att 0.2891 |velocity 0.2571 |Data 0.018s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][406/486]|Tot: 0:01:54 |ETA: 0:00:23 |tot 13.3110 |hm 1.1748 |wh 1.9157 |reg 0.2285 |dep 1.9767 |dep_sec 4.6794 |dim 0.2120 |rot 1.6486 |rot_sec 1.7443 |amodel_offset 0.9086 |nuscenes_att 0.2894 |velocity 0.2570 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][407/486]|Tot: 0:01:54 |ETA: 0:00:22 |tot 13.3091 |hm 1.1756 |wh 1.9139 |reg 0.2286 |dep 1.9749 |dep_sec 4.6801 |dim 0.2118 |rot 1.6484 |rot_sec 1.7443 |amodel_offset 0.9071 |nuscenes_att 0.2904 |velocity 0.2565 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][408/486]|Tot: 0:01:54 |ETA: 0:00:22 |tot 13.3138 |hm 1.1763 |wh 1.9138 |reg 0.2283 |dep 1.9774 |dep_sec 4.6840 |dim 0.2117 |rot 1.6481 |rot_sec 1.7441 |amodel_offset 0.9052 |nuscenes_att 0.2914 |velocity 0.2559 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################## | val: [1][409/486]|Tot: 0:01:54 |ETA: 0:00:22 |tot 13.3282 |hm 1.1784 |wh 1.9137 |reg 0.2284 |dep 1.9840 |dep_sec 4.6912 |dim 0.2116 |rot 1.6480 |rot_sec 1.7440 |amodel_offset 0.9035 |nuscenes_att 0.2923 |velocity 0.2554 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][410/486]|Tot: 0:01:55 |ETA: 0:00:21 |tot 13.3529 |hm 1.1811 |wh 1.9183 |reg 0.2286 |dep 1.9927 |dep_sec 4.6972 |dim 0.2127 |rot 1.6483 |rot_sec 1.7440 |amodel_offset 0.9080 |nuscenes_att 0.2932 |velocity 0.2552 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][411/486]|Tot: 0:01:55 |ETA: 0:00:21 |tot 13.3601 |hm 1.1804 |wh 1.9204 |reg 0.2284 |dep 1.9987 |dep_sec 4.7011 |dim 0.2128 |rot 1.6479 |rot_sec 1.7433 |amodel_offset 0.9060 |nuscenes_att 0.2942 |velocity 0.2552 |Data 0.022s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][412/486]|Tot: 0:01:55 |ETA: 0:00:21 |tot 13.3652 |hm 1.1790 |wh 1.9232 |reg 0.2284 |dep 2.0032 |dep_sec 4.7045 |dim 0.2128 |rot 1.6474 |rot_sec 1.7428 |amodel_offset 0.9045 |nuscenes_att 0.2952 |velocity 0.2551 |Data 0.019s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][413/486]|Tot: 0:01:55 |ETA: 0:00:20 |tot 13.3586 |hm 1.1789 |wh 1.9234 |reg 0.2285 |dep 2.0021 |dep_sec 4.7007 |dim 0.2125 |rot 1.6472 |rot_sec 1.7425 |amodel_offset 0.9029 |nuscenes_att 0.2956 |velocity 0.2555 |Data 0.016s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][414/486]|Tot: 0:01:56 |ETA: 0:00:20 |tot 13.3476 |hm 1.1790 |wh 1.9269 |reg 0.2287 |dep 1.9984 |dep_sec 4.6935 |dim 0.2122 |rot 1.6468 |rot_sec 1.7422 |amodel_offset 0.9020 |nuscenes_att 0.2969 |velocity 0.2553 |Data 0.011s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][415/486]|Tot: 0:01:56 |ETA: 0:00:19 |tot 13.3357 |hm 1.1791 |wh 1.9315 |reg 0.2287 |dep 1.9952 |dep_sec 4.6853 |dim 0.2119 |rot 1.6467 |rot_sec 1.7421 |amodel_offset 0.9004 |nuscenes_att 0.2980 |velocity 0.2551 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][416/486]|Tot: 0:01:56 |ETA: 0:00:19 |tot 13.3239 |hm 1.1788 |wh 1.9341 |reg 0.2287 |dep 1.9919 |dep_sec 4.6777 |dim 0.2116 |rot 1.6465 |rot_sec 1.7422 |amodel_offset 0.8993 |nuscenes_att 0.2988 |velocity 0.2549 |Data 0.012s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][417/486]|Tot: 0:01:56 |ETA: 0:00:19 |tot 13.3151 |hm 1.1793 |wh 1.9371 |reg 0.2287 |dep 1.9887 |dep_sec 4.6692 |dim 0.2114 |rot 1.6469 |rot_sec 1.7427 |amodel_offset 0.8999 |nuscenes_att 0.3001 |velocity 0.2545 |Data 0.013s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][418/486]|Tot: 0:01:57 |ETA: 0:00:18 |tot 13.3054 |hm 1.1800 |wh 1.9438 |reg 0.2288 |dep 1.9851 |dep_sec 4.6607 |dim 0.2111 |rot 1.6476 |rot_sec 1.7435 |amodel_offset 0.8991 |nuscenes_att 0.3007 |velocity 0.2543 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][419/486]|Tot: 0:01:57 |ETA: 0:00:18 |tot 13.3035 |hm 1.1827 |wh 1.9494 |reg 0.2287 |dep 1.9830 |dep_sec 4.6524 |dim 0.2115 |rot 1.6501 |rot_sec 1.7449 |amodel_offset 0.8998 |nuscenes_att 0.3012 |velocity 0.2541 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][420/486]|Tot: 0:01:57 |ETA: 0:00:18 |tot 13.3028 |hm 1.1850 |wh 1.9549 |reg 0.2288 |dep 1.9800 |dep_sec 4.6447 |dim 0.2120 |rot 1.6507 |rot_sec 1.7453 |amodel_offset 0.9049 |nuscenes_att 0.3020 |velocity 0.2538 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][421/486]|Tot: 0:01:57 |ETA: 0:00:17 |tot 13.2982 |hm 1.1867 |wh 1.9642 |reg 0.2290 |dep 1.9777 |dep_sec 4.6396 |dim 0.2120 |rot 1.6512 |rot_sec 1.7456 |amodel_offset 0.9038 |nuscenes_att 0.3029 |velocity 0.2533 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][422/486]|Tot: 0:01:58 |ETA: 0:00:17 |tot 13.2902 |hm 1.1879 |wh 1.9655 |reg 0.2291 |dep 1.9747 |dep_sec 4.6317 |dim 0.2120 |rot 1.6510 |rot_sec 1.7454 |amodel_offset 0.9054 |nuscenes_att 0.3035 |velocity 0.2529 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][423/486]|Tot: 0:01:58 |ETA: 0:00:17 |tot 13.2783 |hm 1.1876 |wh 1.9718 |reg 0.2293 |dep 1.9716 |dep_sec 4.6226 |dim 0.2120 |rot 1.6504 |rot_sec 1.7448 |amodel_offset 0.9069 |nuscenes_att 0.3035 |velocity 0.2524 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |########################### | val: [1][424/486]|Tot: 0:01:58 |ETA: 0:00:17 |tot 13.2680 |hm 1.1873 |wh 1.9758 |reg 0.2290 |dep 1.9677 |dep_sec 4.6130 |dim 0.2125 |rot 1.6499 |rot_sec 1.7445 |amodel_offset 0.9114 |nuscenes_att 0.3034 |velocity 0.2519 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |############################ | val: [1][425/486]|Tot: 0:01:59 |ETA: 0:00:17 |tot 13.2521 |hm 1.1856 |wh 1.9771 |reg 0.2289 |dep 1.9636 |dep_sec 4.6046 |dim 0.2124 |rot 1.6494 |rot_sec 1.7444 |amodel_offset 0.9111 |nuscenes_att 0.3030 |velocity 0.2514 |Data 0.010s(0.014s) |Net 0.280ddd/centerfusion |############################ | val: [1][426/486]|Tot: 0:01:59 |ETA: 0:00:17 |tot 13.2422 |hm 1.1847 |wh 1.9793 |reg 0.2289 |dep 1.9599 |dep_sec 4.5965 |dim 0.2125 |rot 1.6504 |rot_sec 1.7452 |amodel_offset 0.9125 |nuscenes_att 0.3027 |velocity 0.2509 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][427/486]|Tot: 0:01:59 |ETA: 0:00:16 |tot 13.2366 |hm 1.1856 |wh 1.9800 |reg 0.2290 |dep 1.9576 |dep_sec 4.5897 |dim 0.2126 |rot 1.6499 |rot_sec 1.7449 |amodel_offset 0.9163 |nuscenes_att 0.3026 |velocity 0.2505 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][428/486]|Tot: 0:01:59 |ETA: 0:00:16 |tot 13.2254 |hm 1.1855 |wh 1.9841 |reg 0.2290 |dep 1.9549 |dep_sec 4.5803 |dim 0.2125 |rot 1.6493 |rot_sec 1.7444 |amodel_offset 0.9186 |nuscenes_att 0.3025 |velocity 0.2501 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][429/486]|Tot: 0:02:00 |ETA: 0:00:16 |tot 13.2211 |hm 1.1874 |wh 1.9860 |reg 0.2290 |dep 1.9516 |dep_sec 4.5723 |dim 0.2124 |rot 1.6491 |rot_sec 1.7444 |amodel_offset 0.9242 |nuscenes_att 0.3024 |velocity 0.2496 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][430/486]|Tot: 0:02:00 |ETA: 0:00:16 |tot 13.2090 |hm 1.1862 |wh 1.9889 |reg 0.2291 |dep 1.9481 |dep_sec 4.5631 |dim 0.2122 |rot 1.6485 |rot_sec 1.7439 |amodel_offset 0.9273 |nuscenes_att 0.3024 |velocity 0.2492 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][431/486]|Tot: 0:02:00 |ETA: 0:00:15 |tot 13.1935 |hm 1.1842 |wh 1.9966 |reg 0.2290 |dep 1.9445 |dep_sec 4.5547 |dim 0.2122 |rot 1.6479 |rot_sec 1.7434 |amodel_offset 0.9272 |nuscenes_att 0.3021 |velocity 0.2488 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][432/486]|Tot: 0:02:00 |ETA: 0:00:15 |tot 13.1914 |hm 1.1826 |wh 2.0076 |reg 0.2291 |dep 1.9439 |dep_sec 4.5496 |dim 0.2123 |rot 1.6472 |rot_sec 1.7430 |amodel_offset 0.9323 |nuscenes_att 0.3022 |velocity 0.2483 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][433/486]|Tot: 0:02:01 |ETA: 0:00:15 |tot 13.1847 |hm 1.1844 |wh 2.0120 |reg 0.2292 |dep 1.9413 |dep_sec 4.5399 |dim 0.2124 |rot 1.6479 |rot_sec 1.7435 |amodel_offset 0.9351 |nuscenes_att 0.3019 |velocity 0.2479 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][434/486]|Tot: 0:02:01 |ETA: 0:00:14 |tot 13.1885 |hm 1.1862 |wh 2.0135 |reg 0.2291 |dep 1.9395 |dep_sec 4.5311 |dim 0.2124 |rot 1.6475 |rot_sec 1.7428 |amodel_offset 0.9488 |nuscenes_att 0.3022 |velocity 0.2476 |Data 0.021s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][435/486]|Tot: 0:02:01 |ETA: 0:00:14 |tot 13.2022 |hm 1.2003 |wh 2.0149 |reg 0.2288 |dep 1.9366 |dep_sec 4.5237 |dim 0.2127 |rot 1.6469 |rot_sec 1.7422 |amodel_offset 0.9602 |nuscenes_att 0.3022 |velocity 0.2471 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][436/486]|Tot: 0:02:02 |ETA: 0:00:14 |tot 13.1914 |hm 1.2030 |wh 2.0224 |reg 0.2287 |dep 1.9326 |dep_sec 4.5151 |dim 0.2127 |rot 1.6462 |rot_sec 1.7419 |amodel_offset 0.9600 |nuscenes_att 0.3021 |velocity 0.2468 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][437/486]|Tot: 0:02:02 |ETA: 0:00:14 |tot 13.1807 |hm 1.2029 |wh 2.0245 |reg 0.2291 |dep 1.9288 |dep_sec 4.5068 |dim 0.2127 |rot 1.6456 |rot_sec 1.7413 |amodel_offset 0.9629 |nuscenes_att 0.3017 |velocity 0.2464 |Data 0.022s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][438/486]|Tot: 0:02:02 |ETA: 0:00:13 |tot 13.1761 |hm 1.2030 |wh 2.0300 |reg 0.2291 |dep 1.9256 |dep_sec 4.5000 |dim 0.2127 |rot 1.6454 |rot_sec 1.7409 |amodel_offset 0.9690 |nuscenes_att 0.3012 |velocity 0.2461 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################ | val: [1][439/486]|Tot: 0:02:02 |ETA: 0:00:13 |tot 13.1795 |hm 1.2069 |wh 2.0298 |reg 0.2293 |dep 1.9242 |dep_sec 4.4953 |dim 0.2128 |rot 1.6449 |rot_sec 1.7403 |amodel_offset 0.9761 |nuscenes_att 0.3008 |velocity 0.2460 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][440/486]|Tot: 0:02:03 |ETA: 0:00:13 |tot 13.1738 |hm 1.2111 |wh 2.0343 |reg 0.2302 |dep 1.9210 |dep_sec 4.4900 |dim 0.2127 |rot 1.6446 |rot_sec 1.7402 |amodel_offset 0.9744 |nuscenes_att 0.3004 |velocity 0.2459 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][441/486]|Tot: 0:02:03 |ETA: 0:00:13 |tot 13.1876 |hm 1.2155 |wh 2.0355 |reg 0.2301 |dep 1.9222 |dep_sec 4.4928 |dim 0.2130 |rot 1.6460 |rot_sec 1.7418 |amodel_offset 0.9768 |nuscenes_att 0.3000 |velocity 0.2457 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][442/486]|Tot: 0:02:03 |ETA: 0:00:12 |tot 13.2407 |hm 1.2191 |wh 2.0332 |reg 0.2300 |dep 1.9453 |dep_sec 4.5186 |dim 0.2138 |rot 1.6468 |rot_sec 1.7427 |amodel_offset 0.9756 |nuscenes_att 0.3001 |velocity 0.2453 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][443/486]|Tot: 0:02:03 |ETA: 0:00:12 |tot 13.2683 |hm 1.2216 |wh 2.0336 |reg 0.2300 |dep 1.9527 |dep_sec 4.5338 |dim 0.2151 |rot 1.6469 |rot_sec 1.7430 |amodel_offset 0.9768 |nuscenes_att 0.3001 |velocity 0.2449 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][444/486]|Tot: 0:02:04 |ETA: 0:00:12 |tot 13.2892 |hm 1.2226 |wh 2.0320 |reg 0.2302 |dep 1.9585 |dep_sec 4.5499 |dim 0.2155 |rot 1.6464 |rot_sec 1.7429 |amodel_offset 0.9758 |nuscenes_att 0.2999 |velocity 0.2446 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][445/486]|Tot: 0:02:04 |ETA: 0:00:12 |tot 13.3017 |hm 1.2240 |wh 2.0331 |reg 0.2299 |dep 1.9644 |dep_sec 4.5563 |dim 0.2153 |rot 1.6464 |rot_sec 1.7431 |amodel_offset 0.9739 |nuscenes_att 0.3008 |velocity 0.2441 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][446/486]|Tot: 0:02:04 |ETA: 0:00:11 |tot 13.3098 |hm 1.2262 |wh 2.0333 |reg 0.2297 |dep 1.9677 |dep_sec 4.5601 |dim 0.2151 |rot 1.6468 |rot_sec 1.7433 |amodel_offset 0.9721 |nuscenes_att 0.3014 |velocity 0.2440 |Data 0.022s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][447/486]|Tot: 0:02:04 |ETA: 0:00:11 |tot 13.3170 |hm 1.2242 |wh 2.0334 |reg 0.2298 |dep 1.9740 |dep_sec 4.5644 |dim 0.2153 |rot 1.6464 |rot_sec 1.7427 |amodel_offset 0.9707 |nuscenes_att 0.3019 |velocity 0.2442 |Data 0.022s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][448/486]|Tot: 0:02:05 |ETA: 0:00:11 |tot 13.3155 |hm 1.2239 |wh 2.0345 |reg 0.2299 |dep 1.9742 |dep_sec 4.5643 |dim 0.2154 |rot 1.6461 |rot_sec 1.7422 |amodel_offset 0.9694 |nuscenes_att 0.3024 |velocity 0.2444 |Data 0.020s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][449/486]|Tot: 0:02:05 |ETA: 0:00:10 |tot 13.3110 |hm 1.2246 |wh 2.0338 |reg 0.2301 |dep 1.9725 |dep_sec 4.5602 |dim 0.2151 |rot 1.6466 |rot_sec 1.7423 |amodel_offset 0.9690 |nuscenes_att 0.3022 |velocity 0.2450 |Data 0.016s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][450/486]|Tot: 0:02:05 |ETA: 0:00:10 |tot 13.3138 |hm 1.2243 |wh 2.0347 |reg 0.2302 |dep 1.9759 |dep_sec 4.5604 |dim 0.2149 |rot 1.6465 |rot_sec 1.7419 |amodel_offset 0.9680 |nuscenes_att 0.3030 |velocity 0.2452 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][451/486]|Tot: 0:02:06 |ETA: 0:00:10 |tot 13.3155 |hm 1.2234 |wh 2.0344 |reg 0.2302 |dep 1.9795 |dep_sec 4.5605 |dim 0.2147 |rot 1.6464 |rot_sec 1.7415 |amodel_offset 0.9669 |nuscenes_att 0.3041 |velocity 0.2451 |Data 0.020s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][452/486]|Tot: 0:02:06 |ETA: 0:00:10 |tot 13.3102 |hm 1.2225 |wh 2.0338 |reg 0.2303 |dep 1.9794 |dep_sec 4.5578 |dim 0.2144 |rot 1.6462 |rot_sec 1.7410 |amodel_offset 0.9654 |nuscenes_att 0.3052 |velocity 0.2448 |Data 0.017s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][453/486]|Tot: 0:02:06 |ETA: 0:00:09 |tot 13.3041 |hm 1.2213 |wh 2.0330 |reg 0.2303 |dep 1.9796 |dep_sec 4.5546 |dim 0.2141 |rot 1.6460 |rot_sec 1.7406 |amodel_offset 0.9638 |nuscenes_att 0.3061 |velocity 0.2445 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################# | val: [1][454/486]|Tot: 0:02:06 |ETA: 0:00:09 |tot 13.2994 |hm 1.2215 |wh 2.0337 |reg 0.2303 |dep 1.9781 |dep_sec 4.5497 |dim 0.2138 |rot 1.6470 |rot_sec 1.7411 |amodel_offset 0.9634 |nuscenes_att 0.3070 |velocity 0.2442 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][455/486]|Tot: 0:02:07 |ETA: 0:00:09 |tot 13.2917 |hm 1.2216 |wh 2.0342 |reg 0.2303 |dep 1.9757 |dep_sec 4.5436 |dim 0.2136 |rot 1.6475 |rot_sec 1.7415 |amodel_offset 0.9627 |nuscenes_att 0.3079 |velocity 0.2438 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][456/486]|Tot: 0:02:07 |ETA: 0:00:08 |tot 13.2855 |hm 1.2232 |wh 2.0405 |reg 0.2303 |dep 1.9739 |dep_sec 4.5369 |dim 0.2135 |rot 1.6478 |rot_sec 1.7420 |amodel_offset 0.9618 |nuscenes_att 0.3086 |velocity 0.2435 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][457/486]|Tot: 0:02:07 |ETA: 0:00:08 |tot 13.2827 |hm 1.2245 |wh 2.0471 |reg 0.2304 |dep 1.9731 |dep_sec 4.5301 |dim 0.2136 |rot 1.6486 |rot_sec 1.7425 |amodel_offset 0.9630 |nuscenes_att 0.3090 |velocity 0.2432 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][458/486]|Tot: 0:02:07 |ETA: 0:00:08 |tot 13.2777 |hm 1.2264 |wh 2.0531 |reg 0.2304 |dep 1.9703 |dep_sec 4.5234 |dim 0.2135 |rot 1.6500 |rot_sec 1.7434 |amodel_offset 0.9625 |nuscenes_att 0.3094 |velocity 0.2430 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][459/486]|Tot: 0:02:08 |ETA: 0:00:08 |tot 13.2726 |hm 1.2298 |wh 2.0574 |reg 0.2301 |dep 1.9664 |dep_sec 4.5175 |dim 0.2134 |rot 1.6509 |rot_sec 1.7438 |amodel_offset 0.9623 |nuscenes_att 0.3101 |velocity 0.2427 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][460/486]|Tot: 0:02:08 |ETA: 0:00:07 |tot 13.2665 |hm 1.2323 |wh 2.0584 |reg 0.2301 |dep 1.9641 |dep_sec 4.5098 |dim 0.2132 |rot 1.6512 |rot_sec 1.7441 |amodel_offset 0.9626 |nuscenes_att 0.3109 |velocity 0.2424 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][461/486]|Tot: 0:02:08 |ETA: 0:00:07 |tot 13.2539 |hm 1.2322 |wh 2.0572 |reg 0.2301 |dep 1.9617 |dep_sec 4.5018 |dim 0.2131 |rot 1.6506 |rot_sec 1.7435 |amodel_offset 0.9619 |nuscenes_att 0.3112 |velocity 0.2421 |Data 0.022s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][462/486]|Tot: 0:02:09 |ETA: 0:00:07 |tot 13.2426 |hm 1.2315 |wh 2.0594 |reg 0.2301 |dep 1.9596 |dep_sec 4.4930 |dim 0.2131 |rot 1.6501 |rot_sec 1.7429 |amodel_offset 0.9633 |nuscenes_att 0.3112 |velocity 0.2418 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][463/486]|Tot: 0:02:09 |ETA: 0:00:07 |tot 13.2299 |hm 1.2324 |wh 2.0631 |reg 0.2302 |dep 1.9557 |dep_sec 4.4844 |dim 0.2131 |rot 1.6494 |rot_sec 1.7422 |amodel_offset 0.9636 |nuscenes_att 0.3110 |velocity 0.2415 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][464/486]|Tot: 0:02:09 |ETA: 0:00:07 |tot 13.2228 |hm 1.2330 |wh 2.0650 |reg 0.2300 |dep 1.9531 |dep_sec 4.4776 |dim 0.2130 |rot 1.6489 |rot_sec 1.7416 |amodel_offset 0.9661 |nuscenes_att 0.3116 |velocity 0.2414 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][465/486]|Tot: 0:02:09 |ETA: 0:00:06 |tot 13.2154 |hm 1.2326 |wh 2.0680 |reg 0.2302 |dep 1.9510 |dep_sec 4.4698 |dim 0.2130 |rot 1.6498 |rot_sec 1.7419 |amodel_offset 0.9680 |nuscenes_att 0.3113 |velocity 0.2410 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][466/486]|Tot: 0:02:10 |ETA: 0:00:06 |tot 13.2117 |hm 1.2323 |wh 2.0698 |reg 0.2304 |dep 1.9493 |dep_sec 4.4663 |dim 0.2130 |rot 1.6510 |rot_sec 1.7433 |amodel_offset 0.9672 |nuscenes_att 0.3111 |velocity 0.2406 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][467/486]|Tot: 0:02:10 |ETA: 0:00:06 |tot 13.2040 |hm 1.2326 |wh 2.0683 |reg 0.2306 |dep 1.9468 |dep_sec 4.4611 |dim 0.2129 |rot 1.6513 |rot_sec 1.7441 |amodel_offset 0.9667 |nuscenes_att 0.3109 |velocity 0.2403 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][468/486]|Tot: 0:02:10 |ETA: 0:00:06 |tot 13.1963 |hm 1.2329 |wh 2.0695 |reg 0.2306 |dep 1.9452 |dep_sec 4.4558 |dim 0.2129 |rot 1.6508 |rot_sec 1.7442 |amodel_offset 0.9664 |nuscenes_att 0.3107 |velocity 0.2399 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################## | val: [1][469/486]|Tot: 0:02:11 |ETA: 0:00:05 |tot 13.2079 |hm 1.2342 |wh 2.0743 |reg 0.2308 |dep 1.9490 |dep_sec 4.4528 |dim 0.2130 |rot 1.6518 |rot_sec 1.7451 |amodel_offset 0.9738 |nuscenes_att 0.3104 |velocity 0.2396 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][470/486]|Tot: 0:02:11 |ETA: 0:00:05 |tot 13.1991 |hm 1.2329 |wh 2.0757 |reg 0.2312 |dep 1.9471 |dep_sec 4.4446 |dim 0.2140 |rot 1.6515 |rot_sec 1.7450 |amodel_offset 0.9757 |nuscenes_att 0.3102 |velocity 0.2393 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][471/486]|Tot: 0:02:11 |ETA: 0:00:05 |tot 13.1924 |hm 1.2323 |wh 2.0766 |reg 0.2311 |dep 1.9449 |dep_sec 4.4386 |dim 0.2141 |rot 1.6511 |rot_sec 1.7443 |amodel_offset 0.9792 |nuscenes_att 0.3101 |velocity 0.2390 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][472/486]|Tot: 0:02:11 |ETA: 0:00:04 |tot 13.1813 |hm 1.2307 |wh 2.0776 |reg 0.2309 |dep 1.9425 |dep_sec 4.4331 |dim 0.2141 |rot 1.6507 |rot_sec 1.7436 |amodel_offset 0.9795 |nuscenes_att 0.3097 |velocity 0.2386 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][473/486]|Tot: 0:02:12 |ETA: 0:00:04 |tot 13.1745 |hm 1.2292 |wh 2.0776 |reg 0.2307 |dep 1.9413 |dep_sec 4.4284 |dim 0.2140 |rot 1.6502 |rot_sec 1.7430 |amodel_offset 0.9825 |nuscenes_att 0.3093 |velocity 0.2383 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][474/486]|Tot: 0:02:12 |ETA: 0:00:04 |tot 13.1629 |hm 1.2285 |wh 2.0749 |reg 0.2310 |dep 1.9394 |dep_sec 4.4225 |dim 0.2137 |rot 1.6496 |rot_sec 1.7424 |amodel_offset 0.9814 |nuscenes_att 0.3089 |velocity 0.2380 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][475/486]|Tot: 0:02:12 |ETA: 0:00:04 |tot 13.1692 |hm 1.2363 |wh 2.0770 |reg 0.2309 |dep 1.9400 |dep_sec 4.4197 |dim 0.2138 |rot 1.6493 |rot_sec 1.7420 |amodel_offset 0.9829 |nuscenes_att 0.3088 |velocity 0.2377 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][476/486]|Tot: 0:02:13 |ETA: 0:00:03 |tot 13.1668 |hm 1.2344 |wh 2.0752 |reg 0.2307 |dep 1.9377 |dep_sec 4.4241 |dim 0.2135 |rot 1.6488 |rot_sec 1.7414 |amodel_offset 0.9825 |nuscenes_att 0.3088 |velocity 0.2374 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][477/486]|Tot: 0:02:13 |ETA: 0:00:03 |tot 13.1677 |hm 1.2343 |wh 2.0733 |reg 0.2307 |dep 1.9381 |dep_sec 4.4284 |dim 0.2133 |rot 1.6482 |rot_sec 1.7409 |amodel_offset 0.9805 |nuscenes_att 0.3088 |velocity 0.2372 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][478/486]|Tot: 0:02:13 |ETA: 0:00:03 |tot 13.1777 |hm 1.2324 |wh 2.0726 |reg 0.2311 |dep 1.9453 |dep_sec 4.4360 |dim 0.2130 |rot 1.6476 |rot_sec 1.7405 |amodel_offset 0.9789 |nuscenes_att 0.3086 |velocity 0.2369 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][479/486]|Tot: 0:02:13 |ETA: 0:00:02 |tot 13.1955 |hm 1.2325 |wh 2.0713 |reg 0.2312 |dep 1.9566 |dep_sec 4.4451 |dim 0.2128 |rot 1.6471 |rot_sec 1.7401 |amodel_offset 0.9778 |nuscenes_att 0.3086 |velocity 0.2366 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][480/486]|Tot: 0:02:14 |ETA: 0:00:02 |tot 13.1940 |hm 1.2355 |wh 2.0721 |reg 0.2313 |dep 1.9550 |dep_sec 4.4383 |dim 0.2126 |rot 1.6465 |rot_sec 1.7394 |amodel_offset 0.9821 |nuscenes_att 0.3097 |velocity 0.2363 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][481/486]|Tot: 0:02:14 |ETA: 0:00:02 |tot 13.1725 |hm 1.2330 |wh 2.0678 |reg 0.2308 |dep 1.9509 |dep_sec 4.4291 |dim 0.2122 |rot 1.6460 |rot_sec 1.7387 |amodel_offset 0.9801 |nuscenes_att 0.3090 |velocity 0.2358 |Data 0.016s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][482/486]|Tot: 0:02:14 |ETA: 0:00:02 |tot 13.1511 |hm 1.2307 |wh 2.0635 |reg 0.2304 |dep 1.9469 |dep_sec 4.4199 |dim 0.2117 |rot 1.6455 |rot_sec 1.7380 |amodel_offset 0.9781 |nuscenes_att 0.3084 |velocity 0.2353 |Data 0.010s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][483/486]|Tot: 0:02:14 |ETA: 0:00:01 |tot 13.1297 |hm 1.2282 |wh 2.0592 |reg 0.2299 |dep 1.9429 |dep_sec 4.4108 |dim 0.2113 |rot 1.6449 |rot_sec 1.7373 |amodel_offset 0.9760 |nuscenes_att 0.3077 |velocity 0.2348 |Data 0.011s(0.014s) |Net 0.279ddd/centerfusion |############################### | val: [1][484/486]|Tot: 0:02:15 |ETA: 0:00:01 |tot 13.1084 |hm 1.2257 |wh 2.0550 |reg 0.2294 |dep 1.9388 |dep_sec 4.4017 |dim 0.2109 |rot 1.6444 |rot_sec 1.7365 |amodel_offset 0.9740 |nuscenes_att 0.3071 |velocity 0.2344 |Data 0.014s(0.014s) |Net 0.278ddd/centerfusion |################################| val: [1][485/486]|Tot: 0:02:15 |ETA: 0:00:01 |tot 13.0872 |hm 1.2232 |wh 2.0507 |reg 0.2289 |dep 1.9349 |dep_sec 4.3926 |dim 0.2104 |rot 1.6439 |rot_sec 1.7358 |amodel_offset 0.9720 |nuscenes_att 0.3065 |velocity 0.2339 |Data 0.010s(0.014s) |Net 0.278s Converting nuscenes format... python: can't open file 'tools/nuscenes-devkit/python-sdk/nuscenes/eval/detection/evaluate.py': [Errno 2] No such file or directory Traceback (most recent call last): File "../src/main.py", line 140, in <module> main(opt) File "../src/main.py", line 106, in main with open('{}results_nuscenes_det_mini_val.json'.format(out_dir), 'r') as f: FileNotFoundError: [Errno 2] No such file or directory: '/home/CenterFusion/src/lib/../../exp/ddd/centerfusion/nuscenes_eval_det_output_mini_val/results_nuscenes_det_mini_val.json'

Unable to open the evaluate.py file, while the training and evaluation is done on MINI-VAL AND MINI-TRAIN.

help is requested please

When I try to change the --batch_size in the file train.sh to something higher than 16: that is like 32 or 64, I get an insufficient memory error. Which forces me to keep the batch size to 16. I use 2 Nvidia RTX 2080 Ti GPUs which have 11GB memory. (Total 22GB) I really need to increase the batch size as I experience fluctuations in training accuracy.
Can you please suggest me a way to increase the batch size without buying GPUs with more memory. And also is there a relationship between --num_workers and --batch_size parameters?

Hi,

I'm trying to run train.sh and then test.sh with only mini dataset of the nuScenes but for some reason when I run train.sh it reports back that train.json file in annotation_3sweeps is missing, even though the mini_train and mini_val json files are there.
Has anyone already solved this issue?
Where in the code do I remove the request for train.json file, or where that file is located on nuScenes since I don't have it?
I'm trying to use only the mini dataset so that I don't have to wait too much time or allocate too much memory trying to perform experiments with Centerfusion.

i want to see some visible results just like /figures/qualitative_results.jpg and pillars.png by using Pretrained Models.

which py document should i use ?

Hi authors,

I went into this error when I run bash experiments/test.sh

Using tensorboardX
/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/sklearn/utils/linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
  DeprecationWarning)
Fix size testing.
training chunk_sizes: [32]
input h w: 448 800
heads {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}
Namespace(K=100, amodel_offset_weight=1, arch='dla_34', aug_rot=0, backbone='dla34', batch_size=32, chunk_sizes=[32], custom_dataset_ann_path='', custom_dataset_img_path='', custom_head_convs={'dep_sec': 3, 'rot_sec': 3, 'velocity': 3, 'nuscenes_att': 3}, data_dir='/home/ubuntu/CenterFusion/src/lib/../../data', dataset='nuscenes', dataset_version='', debug=0, debug_dir='/home/ubuntu/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug', debugger_theme='white', demo='', dense_reg=1, dep_res_weight=1, dep_weight=1, depth_scale=1, dim_weight=1, disable_frustum=False, dla_node='dcn', down_ratio=4, eval=False, eval_n_plots=0, eval_render_curves=False, exp_dir='/home/ubuntu/CenterFusion/src/lib/../../exp/ddd', exp_id='centerfusion', fix_res=True, fix_short=-1, flip=0.5, flip_test=True, fp_disturb=0, freeze_backbone=False, frustumExpansionRatio=0.0, gpus=[0], gpus_str='1', head_conv={'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}, head_kernel=3, heads={'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}, hm_dist_thresh={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 1, 8: 0, 9: 0}, hm_disturb=0, hm_hp_weight=1, hm_to_box_ratio=0.3, hm_transparency=0.7, hm_weight=1, hp_weight=1, hungarian=False, ignore_loaded_cats=[], img_format='jpg', input_h=448, input_res=800, input_w=800, iou_thresh=0, keep_res=False, kitti_split='3dop', layers_to_freeze=['base', 'dla_up', 'ida_up'], load_model='../models/centerfusion_e60.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, master_batch_size=32, max_age=-1, max_frame_dist=3, max_pc=1000, max_pc_dist=60.0, model_output_list=False, msra_outchannel=256, neck='dlaup', new_thresh=0.3, nms=False, no_color_aug=False, no_pause=False, no_pre_img=False, non_block_test=False, normalize_depth=True, not_cuda_benchmark=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_img_channels=3, num_iters=-1, num_resnet_layers=101, num_stacks=1, num_workers=2, nuscenes_att=True, nuscenes_att_weight=1, off_weight=1, optim='adam', out_thresh=-1, output_h=112, output_res=200, output_w=200, pad=31, 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'], pc_feat_channels={'pc_dep': 0, 'pc_vx': 1, 'pc_vz': 2}, pc_feat_lvl=['pc_dep', 'pc_vx', 'pc_vz'], pc_roi_method='pillars', pc_z_offset=-0.0, pillar_dims=[1.5, 0.2, 0.2], pointcloud=True, pre_hm=False, pre_img=False, pre_thresh=-1, print_iter=0, prior_bias=-4.6, public_det=False, qualitative=False, r_a=250, r_b=5, radar_sweeps=3, reg_loss='l1', reset_hm=False, resize_video=False, resume=False, reuse_hm=False, root_dir='/home/ubuntu/CenterFusion/src/lib/../..', rot_weight=1, rotate=0, run_dataset_eval=True, same_aug_pre=False, save_all=False, save_dir='/home/ubuntu/CenterFusion/src/lib/../../exp/ddd/centerfusion', save_framerate=30, save_img_suffix='', save_imgs=[], save_point=[90], save_results=False, save_video=False, scale=0, secondary_heads=['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec'], seed=317, shift=0, show_track_color=False, show_velocity=False, shuffle_train=False, sigmoid_dep_sec=True, skip_first=-1, sort_det_by_dist=False, tango_color=False, task='ddd', test_dataset='nuscenes', test_focal_length=-1, test_scales=[1.0], track_thresh=0.3, tracking=False, tracking_weight=1, train_split='train', trainval=False, transpose_video=False, use_loaded_results=False, val_intervals=10, val_split='mini_val', velocity=True, velocity_weight=1, video_h=512, video_w=512, vis_gt_bev='', vis_thresh=0.3, warm_start_weights=False, weights={'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}, wh_weight=0.1, zero_pre_hm=False, zero_tracking=False)
Dataset version 
==> initializing mini_val data from /home/ubuntu/CenterFusion/src/lib/../../data/nuscenes/annotations_3sweeps/mini_val.json, 
 images from /home/ubuntu/CenterFusion/src/lib/../../data/nuscenes ...
loading annotations into memory...
Done (t=0.66s)
creating index...
index created!
Loaded mini_val 486 samples
Creating model...
Using node type: (<class 'model.networks.dla.DeformConv'>, <class 'model.networks.dla.DeformConv'>)
Warning: No ImageNet pretrain!!
loaded ../models/centerfusion_e60.pth, epoch 60
Traceback (most recent call last):
  File "test.py", line 215, in <module>
    prefetch_test(opt)
  File "test.py", line 79, in prefetch_test
    detector = Detector(opt)
  File "/home/ubuntu/CenterFusion/src/lib/detector.py", line 36, in __init__
    self.model = self.model.to(opt.device)
  File "/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/nn/modules/module.py", line 386, in to
    return self._apply(convert)
  File "/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/nn/modules/module.py", line 193, in _apply
    module._apply(fn)
  File "/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/nn/modules/module.py", line 193, in _apply
    module._apply(fn)
  File "/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/nn/modules/module.py", line 199, in _apply
    param.data = fn(param.data)
  File "/home/ubuntu/anaconda3/envs/centerpoint/lib/python3.6/site-packages/torch/nn/modules/module.py", line 384, in convert
    return t.to(device, dtype if t.is_floating_point() else None, non_blocking)
RuntimeError: CUDA error: unknown error

If this error is not easily solvable. Can I ask for the prediction files (.json) on val and mini_val sets, like provided in CenterPoint model??

Thank you so much for your help!

this project support windows 10?

Can you provide the visualization code? Thank you!

When I run bash training.sh, there are a warning and a error. How can I solve it?

718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
Traceback (most recent call last):
File "main.py", line 140, in
main(opt)
File "main.py", line 84, in main
log_dict_train, _ = trainer.train(epoch, train_loader)
File "/home/PyProject/CenterFusion-master/src/lib/trainer.py", line 406, in train
return self.run_epoch('train', epoch, data_loader)
File "/home/PyProject/CenterFusion-master/src/lib/trainer.py", line 178, in run_epoch
output, loss, loss_stats = model_with_loss(batch, phase)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/PyProject/CenterFusion-master/src/lib/trainer.py", line 123, in forward
outputs = self.model(batch['image'], pc_hm=pc_hm, pc_dep=pc_dep, calib=calib)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/PyProject/CenterFusion-master/src/lib/model/networks/base_model.py", line 118, in forward
z[head] = self.getattr(head)(sec_feats)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/container.py", line 139, in forward
input = module(input)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 443, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/.local/lib/python3.7/site-packages/torch/nn/modules/conv.py", line 440, in _conv_forward
self.padding, self.dilation, self.groups)
RuntimeError: [enforce fail at CPUAllocator.cpp:71] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 734003200 bytes. Error code 12 (Cannot allocate memory)

I train without GPU and my version: ubuntun18.04 torch=1.9.0 torchvision=0.10.0 python3.7.

Thanks.

when Build the deformable convolution library
error: command 'g++' failed with exit status 1

CUDA out of memory
My GPU memory is 11G
and whatever the batch_size is
also CUDA out of memory
help me

Hi, Thank you for your works. Actually, I am interested in this works, but when I tried to start training your code using Docker, I met a problem RuntimeError: CUDA error: out of memory as shown here:

I am using Multi GPU GeForce GTX 1080 as following:

Here, how I run your code:

python3 main.py
ddd
--exp_id centerfusion
--shuffle_train
--train_split mini_train
--val_split mini_val
--val_intervals 1
--run_dataset_eval
--nuscenes_att
--velocity
--batch_size 4
--lr 2.5e-4
--num_epochs 60
--lr_step 50
--save_point 20,40,50
--gpus 0,2,3
--not_rand_crop
--flip 0.5
--shift 0.1
--pointcloud
--radar_sweeps 6
--pc_z_offset 0.0
--pillar_dims 1.0,0.2,0.2
--max_pc_dist 60.0
--num_workers 0
--load_model ../models/centerfusion_e60.pth \

Please give any suggestion regarding this issue. Thank you very much.

I am trying to train and evaluate centerfusion on the subset of the data, more specifically, using exclusively v1.0-trainval01_blobs.tgz.

I understood that I needed to modify the convert_nuScenes.py script to make my train/val scenes match up with the scenes present in trainval01_blobs.

Training was no issue, but evaluation is where I get an issue.

Traceback (most recent call last): File "tools/nuscenes-devkit/python-sdk/nuscenes/eval/detection/evaluate.py", line 305, in <module> output_dir=output_dir_, verbose=verbose_) File "tools/nuscenes-devkit/python-sdk/nuscenes/eval/detection/evaluate.py", line 89, in __init__ "Samples in split doesn't match samples in predictions." AssertionError: Samples in split doesn't match samples in predictions.
Any idea what could be causing issue?

Hi，look forward to it.

Hi! friend，I need your help. When I try test.sh , I change the model from centerfusion modle to nuscens_3Ddetection_e140 model,like the picture:

But the result like this

,the error is too big,Is this a normal result?OR, Is there any parameters that I need to change or add

In the README of this repository in the section "Pretrained-models" there are written some results.

@mrnabati , I was wondering if you could be more precise about in which conditions they are achieved. What are the parameters used in the test.sh file?

I am using the following test.sh file:

export CUDA_VISIBLE_DEVICES=0
cd src

## Perform detection and evaluation
python test.py ddd \
    --exp_id centerfusion \
    --dataset nuscenes \
    --val_split mini_val \
    --run_dataset_eval \
    --num_workers 8 \
    --nuscenes_att \
    --velocity \
    --gpus 0 \
    --pointcloud \
    --radar_sweeps 6 \
    --max_pc_dist 60.0 \
    --pc_z_offset -0.0 \
    --load_model ../models/centerfusion_e60.pth \
    --flip_test \

And I got the following results:

Converting nuscenes format...
======
Loading NuScenes tables for version v1.0-mini...
23 category,
8 attribute,
4 visibility,
911 instance,
12 sensor,
120 calibrated_sensor,
31206 ego_pose,
8 log,
10 scene,
404 sample,
31206 sample_data,
18538 sample_annotation,
4 map,
Done loading in 0.316 seconds.
======
Reverse indexing ...
Done reverse indexing in 0.1 seconds.
======
Initializing nuScenes detection evaluation
Loaded results from /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/results_nuscenes_det_mini_val.json. Found detections for 81 samples.
Loading annotations for mini_val split from nuScenes version: v1.0-mini
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [00:00<00:00, 360.56it/s]
Loaded ground truth annotations for 81 samples.
Filtering predictions
=> Original number of boxes: 40500
=> After distance based filtering: 32179
=> After LIDAR and RADAR points based filtering: 32179
=> After bike rack filtering: 32105
Filtering ground truth annotations
=> Original number of boxes: 4441
=> After distance based filtering: 3785
=> After LIDAR and RADAR points based filtering: 3393
=> After bike rack filtering: 3393
Accumulating metric data...
Calculating metrics...
Saving metrics to: /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/nuscenes_eval_det_output_mini_val/
mAP: 0.3129
mATE: 0.6590
mASE: 0.4630
mAOE: 0.6008
mAVE: 0.7622
mAAE: 0.3084
NDS: 0.3771
Eval time: 5.4s

Per-class results:
Object Class	AP	ATE	ASE	AOE	AVE	AAE
car	0.542	0.418	0.159	0.120	0.152	0.073
truck	0.442	0.542	0.168	0.133	0.141	0.122
bus	0.549	0.484	0.081	0.079	1.400	0.090
trailer	0.000	1.000	1.000	1.000	1.000	1.000
construction_vehicle	0.000	1.000	1.000	1.000	1.000	1.000
pedestrian	0.468	0.561	0.262	0.491	0.512	0.118
motorcycle	0.318	0.689	0.342	1.167	0.061	0.016
bicycle	0.187	0.448	0.299	0.417	1.832	0.048
traffic_cone	0.624	0.447	0.319	nan	nan	nan
barrier	0.000	1.000	1.000	1.000	nan	nan
[log - test.py] End __main__

which are different from what you wrote in the README

If you are interested to the full trace you can find it below

(centerfusion) fabrizioschiano@astroteo-HP-ZBook-Create-G7-Notebook-PC:~/repositories/CenterFusion$ bash experiments/test.sh 
Using tensorboardX
[log - coco.py]in class COCO
[log - test.py] Parsing parameters
[log - opts.py] started parsing ...
[log - opts.py] Fix size testing.
[log - opts.py] len(opt.gpus):  1
[log - opts.py] opt.master_batch_size:  32
[log - opts.py] opt.batch_size:  32
[log - opts.py] training chunk_sizes: [32]
[log - opts.py] The categories and their IDs are:  {'car': 0, 'truck': 1, 'bus': 2, 'trailer': 3, 'construction_vehicle': 4, 'pedestrian': 5, 'motorcycle': 6, 'bicycle': 7, 'traffic_cone': 8, 'barrier': 9}
[log - opts.py] ... finished parsing
[log - test.py] not opt.not_prefetch_test
[log - opts.py] input h w: 448 800
[log - opts.py] heads {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}
[log - opts.py] weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}
[log - opts.py] head conv {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}
[log - test.py] Printing all the options
Namespace(K=100, amodel_offset_weight=1, arch='dla_34', aug_rot=0, backbone='dla34', batch_size=32, chunk_sizes=[32], custom_dataset_ann_path='', custom_dataset_img_path='', custom_head_convs={'dep_sec': 3, 'rot_sec': 3, 'velocity': 3, 'nuscenes_att': 3}, data_dir='/home/fabrizioschiano/repositories/CenterFusion/src/lib/../../data', dataset='nuscenes', dataset_version='', debug=0, debug_dir='/home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug', debugger_theme='white', demo='', dense_reg=1, dep_res_weight=1, dep_weight=1, depth_scale=1, dim_weight=1, disable_frustum=False, dla_node='dcn', down_ratio=4, eval=False, eval_n_plots=0, eval_render_curves=False, exp_dir='/home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd', exp_id='centerfusion', fix_res=True, fix_short=-1, flip=0.5, flip_test=True, fp_disturb=0, freeze_backbone=False, frustumExpansionRatio=0.0, gpus=[0], gpus_str='0', head_conv={'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}, head_kernel=3, heads={'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}, hm_dist_thresh={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 1, 8: 0, 9: 0}, hm_disturb=0, hm_hp_weight=1, hm_to_box_ratio=0.3, hm_transparency=0.7, hm_weight=1, hp_weight=1, hungarian=False, ignore_loaded_cats=[], img_format='jpg', input_h=448, input_res=800, input_w=800, iou_thresh=0, keep_res=False, kitti_split='3dop', layers_to_freeze=['base', 'dla_up', 'ida_up'], load_model='../models/centerfusion_e60.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, master_batch_size=32, max_age=-1, max_frame_dist=3, max_pc=1000, max_pc_dist=60.0, model_output_list=False, msra_outchannel=256, neck='dlaup', new_thresh=0.3, nms=False, no_color_aug=False, no_pause=False, no_pre_img=False, non_block_test=False, normalize_depth=True, not_cuda_benchmark=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_img_channels=3, num_iters=-1, num_resnet_layers=101, num_stacks=1, num_workers=8, nuscenes_att=True, nuscenes_att_weight=1, off_weight=1, optim='adam', out_thresh=-1, output_h=112, output_res=200, output_w=200, pad=31, 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'], pc_feat_channels={'pc_dep': 0, 'pc_vx': 1, 'pc_vz': 2}, pc_feat_lvl=['pc_dep', 'pc_vx', 'pc_vz'], pc_roi_method='pillars', pc_z_offset=-0.0, pillar_dims=[1.5, 0.2, 0.2], pointcloud=True, pre_hm=False, pre_img=False, pre_thresh=-1, print_iter=0, prior_bias=-4.6, public_det=False, qualitative=False, r_a=250, r_b=5, radar_sweeps=6, reg_loss='l1', reset_hm=False, resize_video=False, resume=False, reuse_hm=False, root_dir='/home/fabrizioschiano/repositories/CenterFusion/src/lib/../..', rot_weight=1, rotate=0, run_dataset_eval=True, same_aug_pre=False, save_all=False, save_dir='/home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion', save_framerate=30, save_img_suffix='', save_imgs=[], save_point=[90], save_results=False, save_video=False, scale=0, secondary_heads=['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec'], seed=317, shift=0, show_track_color=False, show_velocity=False, shuffle_train=False, sigmoid_dep_sec=True, skip_first=-1, sort_det_by_dist=False, tango_color=False, task='ddd', test_dataset='nuscenes', test_focal_length=-1, test_scales=[1.0], track_thresh=0.3, tracking=False, tracking_weight=1, train_split='train', trainval=False, transpose_video=False, use_loaded_results=False, val_intervals=10, val_split='mini_val', velocity=True, velocity_weight=1, video_h=512, video_w=512, vis_gt_bev='', vis_thresh=0.3, warm_start_weights=False, weights={'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}, wh_weight=0.1, zero_pre_hm=False, zero_tracking=False)


opt.save_dir: /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion
opt.debug_dir: /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/debug
[log - nuscenes.py] Dataset version 
[log - generic_dataset.py] in __init__()
[log - generic_dataset.py] ==> initializing mini_val data from /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../data/nuscenes/annotations_6sweeps/mini_val.json, 
images from /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../data/nuscenes ...
loading annotations into memory...
Done (t=0.95s)
creating index...
index created!
[log - generic_dataset.py] constructed COCO object
[log - generic_dataset.py] got ImgIds from COCO object
[log - generic_dataset.py] finished __init__
[log - nuscenes.py] Loaded mini_val 486 samples
[log - test.py] instancing Detector
[log - detector.py] Creating model...
[log - model.py] in create_model()
[log - model.py] arch: dla
[log - dla.py] Using node type: (<class 'model.networks.dla.DeformConv'>, <class 'model.networks.dla.DeformConv'>)
Warning: No ImageNet pretrain!!
[log - detector.py] Loading model...
[log - model.py] in load_model()
loaded ../models/centerfusion_e60.pth, epoch 60
[log - detector.py] Evaluating model...
[log - detector.py] getting dataset
[log - detector.py] opt.dataset: nuscenes
[log - detector.py] instancing Tracker
[log - detector.py] finished __init__()
[log - test.py] dataset.img_dir: /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../data/nuscenes
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     | [21/486]|Tot: 0:00:25 |ETA: 0:08:42 |tot 1.003s (1.170s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [22/486]|Tot: 0:00:27 |ETA: 0:08:30 |tot 1.089s (1.166s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [23/486]|Tot: 0:00:28 |ETA: 0:08:23 |tot 1.228s (1.169s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [24/486]|Tot: 0:00:29 |ETA: 0:08:20 |tot 1.307s (1.175s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [25/486]|Tot: 0:00:30 |ETA: 0:08:17 |tot 1.376s (1.182s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [26/486]|Tot: 0:00:32 |ETA: 0:08:15 |tot 1.242s (1.184s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [27/486]|Tot: 0:00:33 |ETA: 0:08:20 |tot 1.122s (1.182s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [28/486]|Tot: 0:00:34 |ETA: 0:08:31 |tot 1.394s (1.190s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#                               | [29/486]|Tot: 0:00:35 |ETA: 0:08:54 |tot 0.988s (1.183s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [30/486]|Tot: 0:00:36 |ETA: 0:08:53 |tot 0.909s (1.174s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [31/486]|Tot: 0:00:37 |ETA: 0:08:51 |tot 1.073s (1.171s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [32/486]|Tot: 0:00:38 |ETA: 0:08:53 |tot 1.053s (1.167s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [33/486]|Tot: 0:00:39 |ETA: 0:08:50 |tot 0.952s (1.161s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |##                              | [34/486]|Tot: 0:00:40 |ETA: 0:08:49 |tot 0.841s (1.152s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [35/486]|Tot: 0:00:41 |ETA: 0:08:15 |tot 1.164s (1.152s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [36/486]|Tot: 0:00:43 |ETA: 0:08:04 |tot 1.325s (1.157s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [37/486]|Tot: 0:00:44 |ETA: 0:08:07 |tot 1.511s (1.166s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [38/486]|Tot: 0:00:46 |ETA: 0:08:23 |tot 1.474s (1.174s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [39/486]|Tot: 0:00:47 |ETA: 0:08:25 |tot 1.128s (1.173s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [40/486]|Tot: 0:00:47 |ETA: 0:08:30 |tot 0.790s (1.164s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [41/486]|Tot: 0:00:48 |ETA: 0:08:29 |tot 0.812s (1.155s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [42/486]|Tot: 0:00:49 |ETA: 0:08:11 |tot 0.805s (1.147s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [43/486]|Tot: 0:00:50 |ETA: 0:08:10 |tot 0.879s (1.141s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |##                              | [44/486]|Tot: 0:00:51 |ETA: 0:07:55 |tot 0.862s (1.135s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [45/486]|Tot: 0:00:52 |ETA: 0:07:54 |tot 0.861s (1.129s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [46/486]|Tot: 0:00:53 |ETA: 0:07:40 |tot 0.918s (1.124s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [47/486]|Tot: 0:00:53 |ETA: 0:07:39 |tot 0.799s (1.118s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [48/486]|Tot: 0:00:54 |ETA: 0:06:49 |tot 0.976s (1.115s) |load 0.001s (0.001s) |pre 0.002s (0centerfusion |###                             | [49/486]|Tot: 0:00:56 |ETA: 0:06:48 |tot 1.180s (1.116s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [50/486]|Tot: 0:00:57 |ETA: 0:06:28 |tot 1.225s (1.118s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [51/486]|Tot: 0:00:58 |ETA: 0:06:46 |tot 1.304s (1.122s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [52/486]|Tot: 0:00:59 |ETA: 0:07:06 |tot 1.202s (1.123s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [53/486]|Tot: 0:01:01 |ETA: 0:07:23 |tot 1.344s (1.127s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [54/486]|Tot: 0:01:02 |ETA: 0:07:42 |tot 1.312s (1.131s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [55/486]|Tot: 0:01:03 |ETA: 0:08:00 |tot 1.203s (1.132s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [56/486]|Tot: 0:01:04 |ETA: 0:08:14 |tot 1.211s (1.133s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [57/486]|Tot: 0:01:06 |ETA: 0:08:25 |tot 1.190s (1.134s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |###                             | [58/486]|Tot: 0:01:07 |ETA: 0:08:41 |tot 1.004s (1.132s) |load 0.001s (0.001s) |pre 0.002s (0centerfusion |###                             | [59/486]|Tot: 0:01:07 |ETA: 0:08:41 |tot 0.893s (1.128s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |####                            | [60/486]|Tot: 0:01:08 |ETA: 0:08:39 |tot 0.833s (1.123s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [61/486]|Tot: 0:01:09 |ETA: 0:08:09 |tot 1.050s (1.122s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [62/486]|Tot: 0:01:10 |ETA: 0:07:57 |tot 0.827s (1.117s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [63/486]|Tot: 0:01:11 |ETA: 0:07:56 |tot 0.854s (1.113s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |####                            | [64/486]|Tot: 0:01:12 |ETA: 0:07:19 |tot 0.835s (1.109s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [65/486]|Tot: 0:01:13 |ETA: 0:07:18 |tot 0.899s (1.106s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [66/486]|Tot: 0:01:14 |ETA: 0:06:44 |tot 0.880s (1.102s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [67/486]|Tot: 0:01:15 |ETA: 0:06:43 |tot 1.382s (1.107s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [68/486]|Tot: 0:01:16 |ETA: 0:06:36 |tot 1.306s (1.109s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [69/486]|Tot: 0:01:18 |ETA: 0:06:48 |tot 1.389s (1.113s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [70/486]|Tot: 0:01:19 |ETA: 0:07:07 |tot 1.408s (1.118s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [71/486]|Tot: 0:01:20 |ETA: 0:07:30 |tot 1.315s (1.120s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [72/486]|Tot: 0:01:22 |ETA: 0:07:40 |tot 1.401s (1.124s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |####                            | [73/486]|Tot: 0:01:23 |ETA: 0:08:03 |tot 1.321s (1.127s) |load 0.001s (0.001s) |pre 0.002s (0centerfusion |####                            | [74/486]|Tot: 0:01:25 |ETA: 0:08:21 |tot 1.433s (1.131s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [75/486]|Tot: 0:01:26 |ETA: 0:08:44 |tot 1.363s (1.134s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [76/486]|Tot: 0:01:27 |ETA: 0:09:02 |tot 1.379s (1.137s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [77/486]|Tot: 0:01:29 |ETA: 0:09:21 |tot 1.376s (1.140s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [78/486]|Tot: 0:01:30 |ETA: 0:09:19 |tot 1.250s (1.142s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |#####                           | [79/486]|Tot: 0:01:31 |ETA: 0:09:16 |tot 1.422s (1.145s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [80/486]|Tot: 0:01:32 |ETA: 0:09:16 |tot 1.068s (1.144s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [81/486]|Tot: 0:01:34 |ETA: 0:09:00 |tot 1.326s (1.146s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [82/486]|Tot: 0:01:35 |ETA: 0:09:00 |tot 1.251s (1.148s) |load 0.001s (0.001s) |pre 0.002s (0centerfusion |#####                           | [83/486]|Tot: 0:01:36 |ETA: 0:08:52 |tot 1.296s (1.149s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [84/486]|Tot: 0:01:38 |ETA: 0:08:50 |tot 1.304s (1.151s) |load 0.000s (0.001s) |pre 0.002s (0centerfusion |#####                           | [85/486]|Tot: 0:01:39 |ETA: 0:08:43 |tot 1.309s (1.153s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [86/486]|Tot: 0:01:40 |ETA: 0:08:40 |tot 1.245s (1.154s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [87/486]|Tot: 0:01:41 |ETA: 0:08:33 |tot 1.160s (1.154s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [88/486]|Tot: 0:01:42 |ETA: 0:08:23 |tot 1.180s (1.154s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [89/486]|Tot: 0:01:44 |ETA: 0:08:19 |tot 1.206s (1.155s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |#####                           | [90/486]|Tot: 0:01:45 |ETA: 0:08:09 |tot 1.171s (1.155s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [91/486]|Tot: 0:01:46 |ETA: 0:08:12 |tot 1.299s (1.157s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [92/486]|Tot: 0:01:47 |ETA: 0:08:10 |tot 1.152s (1.157s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [93/486]|Tot: 0:01:49 |ETA: 0:08:05 |tot 1.237s (1.158s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [94/486]|Tot: 0:01:50 |ETA: 0:08:01 |tot 1.111s (1.157s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |######                          | [95/486]|Tot: 0:01:51 |ETA: 0:07:53 |tot 1.054s (1.156s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [96/486]|Tot: 0:01:52 |ETA: 0:07:41 |tot 0.794s (1.152s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [97/486]|Tot: 0:01:52 |ETA: 0:07:40 |tot 0.760s (1.148s) |load 0.000s (0.001s) |pre 0.001s (0centerfusion |######                          | [98/486]|Tot: 0:01:53 |ETA: 0:07:06 |tot 0.767s (1.144s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [99/486]|Tot: 0:01:54 |ETA: 0:07:05 |tot 0.773s (1.141s) |load 0.001s (0.001s) |pre 0.001s (0centerfusion |######                          | [100/486]|Tot: 0:01:55 |ETA: 0:06:31 |tot 0.753s (1.137s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######                          | [101/486]|Tot: 0:01:55 |ETA: 0:06:30 |tot 0.765s (1.133s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######                          | [102/486]|Tot: 0:01:56 |ETA: 0:05:53 |tot 0.830s (1.130s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######                          | [103/486]|Tot: 0:01:57 |ETA: 0:05:52 |tot 0.789s (1.127s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######                          | [104/486]|Tot: 0:01:58 |ETA: 0:05:21 |tot 0.810s (1.124s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######                          | [105/486]|Tot: 0:01:59 |ETA: 0:05:20 |tot 0.806s (1.121s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [106/486]|Tot: 0:01:59 |ETA: 0:04:59 |tot 0.793s (1.118s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [107/486]|Tot: 0:02:00 |ETA: 0:04:58 |tot 0.776s (1.115s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [108/486]|Tot: 0:02:01 |ETA: 0:04:58 |tot 0.807s (1.112s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [109/486]|Tot: 0:02:02 |ETA: 0:04:57 |tot 0.772s (1.109s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [110/486]|Tot: 0:02:02 |ETA: 0:04:58 |tot 0.784s (1.106s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [111/486]|Tot: 0:02:03 |ETA: 0:04:57 |tot 0.815s (1.103s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [112/486]|Tot: 0:02:04 |ETA: 0:04:59 |tot 0.968s (1.102s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [113/486]|Tot: 0:02:05 |ETA: 0:04:58 |tot 0.854s (1.100s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |#######                         | [114/486]|Tot: 0:02:06 |ETA: 0:05:05 |tot 0.799s (1.097s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [115/486]|Tot: 0:02:07 |ETA: 0:05:04 |tot 0.813s (1.095s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [116/486]|Tot: 0:02:08 |ETA: 0:05:03 |tot 0.807s (1.092s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [117/486]|Tot: 0:02:08 |ETA: 0:05:02 |tot 0.781s (1.090s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [118/486]|Tot: 0:02:09 |ETA: 0:05:02 |tot 0.781s (1.087s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [119/486]|Tot: 0:02:10 |ETA: 0:05:02 |tot 0.809s (1.085s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######                         | [120/486]|Tot: 0:02:11 |ETA: 0:05:01 |tot 0.777s (1.082s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [121/486]|Tot: 0:02:11 |ETA: 0:05:00 |tot 0.772s (1.080s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [122/486]|Tot: 0:02:12 |ETA: 0:04:58 |tot 0.792s (1.077s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [123/486]|Tot: 0:02:13 |ETA: 0:04:57 |tot 0.774s (1.075s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [124/486]|Tot: 0:02:14 |ETA: 0:04:47 |tot 0.779s (1.073s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [125/486]|Tot: 0:02:15 |ETA: 0:04:46 |tot 0.767s (1.070s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |########                        | [126/486]|Tot: 0:02:15 |ETA: 0:04:43 |tot 0.775s (1.068s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [127/486]|Tot: 0:02:16 |ETA: 0:04:42 |tot 0.781s (1.066s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [128/486]|Tot: 0:02:17 |ETA: 0:04:40 |tot 0.786s (1.063s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [129/486]|Tot: 0:02:18 |ETA: 0:04:39 |tot 0.797s (1.061s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [130/486]|Tot: 0:02:19 |ETA: 0:04:38 |tot 0.815s (1.059s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [131/486]|Tot: 0:02:19 |ETA: 0:04:37 |tot 0.794s (1.057s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [132/486]|Tot: 0:02:20 |ETA: 0:04:39 |tot 0.830s (1.056s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [133/486]|Tot: 0:02:21 |ETA: 0:04:38 |tot 0.786s (1.054s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########                        | [134/486]|Tot: 0:02:22 |ETA: 0:04:39 |tot 0.788s (1.052s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |########                        | [135/486]|Tot: 0:02:23 |ETA: 0:04:38 |tot 0.844s (1.050s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [136/486]|Tot: 0:02:23 |ETA: 0:04:40 |tot 0.839s (1.049s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [137/486]|Tot: 0:02:24 |ETA: 0:04:40 |tot 0.845s (1.047s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [138/486]|Tot: 0:02:25 |ETA: 0:04:43 |tot 0.915s (1.046s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#########                       | [139/486]|Tot: 0:02:26 |ETA: 0:04:42 |tot 0.954s (1.046s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [140/486]|Tot: 0:02:27 |ETA: 0:04:52 |tot 0.868s (1.044s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [141/486]|Tot: 0:02:28 |ETA: 0:04:51 |tot 0.864s (1.043s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [142/486]|Tot: 0:02:29 |ETA: 0:04:54 |tot 0.831s (1.042s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [143/486]|Tot: 0:02:30 |ETA: 0:04:53 |tot 0.853s (1.040s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [144/486]|Tot: 0:02:30 |ETA: 0:04:55 |tot 0.848s (1.039s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [145/486]|Tot: 0:02:31 |ETA: 0:04:54 |tot 0.832s (1.038s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [146/486]|Tot: 0:02:32 |ETA: 0:04:55 |tot 0.877s (1.036s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [147/486]|Tot: 0:02:33 |ETA: 0:04:54 |tot 0.839s (1.035s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [148/486]|Tot: 0:02:34 |ETA: 0:04:54 |tot 0.838s (1.034s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#########                       | [149/486]|Tot: 0:02:35 |ETA: 0:04:53 |tot 0.884s (1.033s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########                       | [150/486]|Tot: 0:02:36 |ETA: 0:04:47 |tot 0.834s (1.032s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [151/486]|Tot: 0:02:36 |ETA: 0:04:46 |tot 0.866s (1.030s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [152/486]|Tot: 0:02:37 |ETA: 0:04:45 |tot 0.858s (1.029s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [153/486]|Tot: 0:02:38 |ETA: 0:04:44 |tot 0.819s (1.028s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [154/486]|Tot: 0:02:39 |ETA: 0:04:43 |tot 0.866s (1.027s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [155/486]|Tot: 0:02:40 |ETA: 0:04:42 |tot 0.828s (1.026s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |##########                      | [156/486]|Tot: 0:02:41 |ETA: 0:04:41 |tot 0.873s (1.025s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [157/486]|Tot: 0:02:41 |ETA: 0:04:41 |tot 0.858s (1.024s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [158/486]|Tot: 0:02:42 |ETA: 0:04:40 |tot 0.841s (1.022s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [159/486]|Tot: 0:02:43 |ETA: 0:04:39 |tot 0.876s (1.022s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [160/486]|Tot: 0:02:44 |ETA: 0:04:38 |tot 0.786s (1.020s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [161/486]|Tot: 0:02:45 |ETA: 0:04:37 |tot 0.807s (1.019s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [162/486]|Tot: 0:02:46 |ETA: 0:04:33 |tot 0.769s (1.017s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [163/486]|Tot: 0:02:46 |ETA: 0:04:32 |tot 0.778s (1.016s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [164/486]|Tot: 0:02:47 |ETA: 0:04:27 |tot 0.810s (1.015s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########                      | [165/486]|Tot: 0:02:48 |ETA: 0:04:26 |tot 0.805s (1.013s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |##########                      | [166/486]|Tot: 0:02:49 |ETA: 0:04:23 |tot 0.799s (1.012s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [167/486]|Tot: 0:02:50 |ETA: 0:04:22 |tot 0.798s (1.011s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [168/486]|Tot: 0:02:50 |ETA: 0:04:17 |tot 0.794s (1.009s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [169/486]|Tot: 0:02:51 |ETA: 0:04:16 |tot 0.791s (1.008s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [170/486]|Tot: 0:02:52 |ETA: 0:04:11 |tot 0.816s (1.007s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [171/486]|Tot: 0:02:53 |ETA: 0:04:11 |tot 0.932s (1.007s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [172/486]|Tot: 0:02:54 |ETA: 0:04:15 |tot 0.971s (1.006s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [173/486]|Tot: 0:02:55 |ETA: 0:04:14 |tot 0.829s (1.005s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [174/486]|Tot: 0:02:56 |ETA: 0:04:21 |tot 0.885s (1.005s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [175/486]|Tot: 0:02:56 |ETA: 0:04:20 |tot 0.819s (1.004s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [176/486]|Tot: 0:02:57 |ETA: 0:04:22 |tot 0.833s (1.003s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [177/486]|Tot: 0:02:58 |ETA: 0:04:21 |tot 0.844s (1.002s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [178/486]|Tot: 0:02:59 |ETA: 0:04:23 |tot 0.805s (1.001s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [179/486]|Tot: 0:03:00 |ETA: 0:04:22 |tot 0.803s (1.000s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |###########                     | [180/486]|Tot: 0:03:01 |ETA: 0:04:22 |tot 0.825s (0.999s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########                     | [181/486]|Tot: 0:03:01 |ETA: 0:04:21 |tot 0.813s (0.998s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [182/486]|Tot: 0:03:02 |ETA: 0:04:17 |tot 0.801s (0.996s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [183/486]|Tot: 0:03:03 |ETA: 0:04:16 |tot 0.931s (0.996s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [184/486]|Tot: 0:03:04 |ETA: 0:04:13 |tot 0.975s (0.996s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [185/486]|Tot: 0:03:05 |ETA: 0:04:12 |tot 0.854s (0.995s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [186/486]|Tot: 0:03:06 |ETA: 0:04:15 |tot 0.948s (0.995s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [187/486]|Tot: 0:03:07 |ETA: 0:04:14 |tot 0.944s (0.995s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [188/486]|Tot: 0:03:08 |ETA: 0:04:20 |tot 0.839s (0.994s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [189/486]|Tot: 0:03:08 |ETA: 0:04:19 |tot 0.883s (0.993s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [190/486]|Tot: 0:03:09 |ETA: 0:04:21 |tot 0.852s (0.993s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [191/486]|Tot: 0:03:10 |ETA: 0:04:21 |tot 0.843s (0.992s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [192/486]|Tot: 0:03:11 |ETA: 0:04:21 |tot 0.911s (0.991s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [193/486]|Tot: 0:03:12 |ETA: 0:04:21 |tot 0.870s (0.991s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [194/486]|Tot: 0:03:13 |ETA: 0:04:21 |tot 0.826s (0.990s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [195/486]|Tot: 0:03:14 |ETA: 0:04:20 |tot 0.857s (0.989s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############                    | [196/486]|Tot: 0:03:15 |ETA: 0:04:15 |tot 0.871s (0.989s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [197/486]|Tot: 0:03:15 |ETA: 0:04:14 |tot 0.822s (0.988s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [198/486]|Tot: 0:03:16 |ETA: 0:04:08 |tot 0.874s (0.987s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [199/486]|Tot: 0:03:17 |ETA: 0:04:07 |tot 0.803s (0.986s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [200/486]|Tot: 0:03:18 |ETA: 0:04:04 |tot 0.796s (0.985s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [201/486]|Tot: 0:03:19 |ETA: 0:04:04 |tot 0.849s (0.985s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [202/486]|Tot: 0:03:20 |ETA: 0:04:01 |tot 0.855s (0.984s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [203/486]|Tot: 0:03:20 |ETA: 0:04:01 |tot 0.874s (0.983s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#############                   | [204/486]|Tot: 0:03:21 |ETA: 0:03:58 |tot 0.850s (0.983s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [205/486]|Tot: 0:03:22 |ETA: 0:03:57 |tot 0.870s (0.982s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [206/486]|Tot: 0:03:23 |ETA: 0:03:58 |tot 0.929s (0.982s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [207/486]|Tot: 0:03:24 |ETA: 0:03:57 |tot 0.894s (0.982s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [208/486]|Tot: 0:03:25 |ETA: 0:04:00 |tot 0.798s (0.981s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [209/486]|Tot: 0:03:26 |ETA: 0:03:59 |tot 0.829s (0.980s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [210/486]|Tot: 0:03:26 |ETA: 0:03:56 |tot 0.820s (0.979s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############                   | [211/486]|Tot: 0:03:27 |ETA: 0:03:56 |tot 0.815s (0.978s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [212/486]|Tot: 0:03:28 |ETA: 0:03:54 |tot 0.820s (0.978s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [213/486]|Tot: 0:03:29 |ETA: 0:03:54 |tot 0.802s (0.977s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [214/486]|Tot: 0:03:30 |ETA: 0:03:50 |tot 0.824s (0.976s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [215/486]|Tot: 0:03:30 |ETA: 0:03:49 |tot 0.799s (0.975s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [216/486]|Tot: 0:03:31 |ETA: 0:03:45 |tot 0.796s (0.975s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [217/486]|Tot: 0:03:32 |ETA: 0:03:45 |tot 0.820s (0.974s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [218/486]|Tot: 0:03:33 |ETA: 0:03:38 |tot 0.807s (0.973s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [219/486]|Tot: 0:03:34 |ETA: 0:03:37 |tot 0.810s (0.972s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [220/486]|Tot: 0:03:34 |ETA: 0:03:36 |tot 0.804s (0.972s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [221/486]|Tot: 0:03:35 |ETA: 0:03:36 |tot 0.804s (0.971s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [222/486]|Tot: 0:03:36 |ETA: 0:03:34 |tot 0.856s (0.970s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [223/486]|Tot: 0:03:37 |ETA: 0:03:33 |tot 0.823s (0.970s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [224/486]|Tot: 0:03:38 |ETA: 0:03:34 |tot 0.811s (0.969s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [225/486]|Tot: 0:03:39 |ETA: 0:03:33 |tot 0.873s (0.969s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############                  | [226/486]|Tot: 0:03:40 |ETA: 0:03:34 |tot 0.878s (0.968s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [227/486]|Tot: 0:03:40 |ETA: 0:03:33 |tot 0.825s (0.968s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [228/486]|Tot: 0:03:41 |ETA: 0:03:35 |tot 0.861s (0.967s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [229/486]|Tot: 0:03:42 |ETA: 0:03:34 |tot 0.849s (0.967s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [230/486]|Tot: 0:03:43 |ETA: 0:03:35 |tot 0.859s (0.966s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [231/486]|Tot: 0:03:44 |ETA: 0:03:34 |tot 0.797s (0.965s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [232/486]|Tot: 0:03:45 |ETA: 0:03:35 |tot 0.823s (0.965s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [233/486]|Tot: 0:03:45 |ETA: 0:03:34 |tot 0.872s (0.964s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [234/486]|Tot: 0:03:46 |ETA: 0:03:33 |tot 0.837s (0.964s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [235/486]|Tot: 0:03:47 |ETA: 0:03:33 |tot 0.861s (0.963s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [236/486]|Tot: 0:03:48 |ETA: 0:03:32 |tot 0.842s (0.963s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [237/486]|Tot: 0:03:49 |ETA: 0:03:31 |tot 0.860s (0.962s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [238/486]|Tot: 0:03:50 |ETA: 0:03:30 |tot 0.898s (0.962s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [239/486]|Tot: 0:03:51 |ETA: 0:03:30 |tot 0.849s (0.962s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [240/486]|Tot: 0:03:51 |ETA: 0:03:30 |tot 0.799s (0.961s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###############                 | [241/486]|Tot: 0:03:52 |ETA: 0:03:29 |tot 0.816s (0.960s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [242/486]|Tot: 0:03:53 |ETA: 0:03:27 |tot 0.810s (0.960s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [243/486]|Tot: 0:03:54 |ETA: 0:03:26 |tot 0.884s (0.959s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [244/486]|Tot: 0:03:55 |ETA: 0:03:25 |tot 0.883s (0.959s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [245/486]|Tot: 0:03:56 |ETA: 0:03:24 |tot 0.857s (0.959s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [246/486]|Tot: 0:03:56 |ETA: 0:03:25 |tot 0.869s (0.958s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [247/486]|Tot: 0:03:57 |ETA: 0:03:24 |tot 0.840s (0.958s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [248/486]|Tot: 0:03:58 |ETA: 0:03:23 |tot 0.880s (0.958s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [249/486]|Tot: 0:03:59 |ETA: 0:03:22 |tot 0.842s (0.957s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [250/486]|Tot: 0:04:00 |ETA: 0:03:21 |tot 0.875s (0.957s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [251/486]|Tot: 0:04:01 |ETA: 0:03:20 |tot 0.857s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [252/486]|Tot: 0:04:02 |ETA: 0:03:22 |tot 1.031s (0.957s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [253/486]|Tot: 0:04:03 |ETA: 0:03:26 |tot 0.912s (0.957s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [254/486]|Tot: 0:04:04 |ETA: 0:03:25 |tot 0.986s (0.957s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [255/486]|Tot: 0:04:05 |ETA: 0:03:27 |tot 0.883s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [256/486]|Tot: 0:04:06 |ETA: 0:03:26 |tot 0.943s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################                | [257/486]|Tot: 0:04:06 |ETA: 0:03:28 |tot 0.904s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [258/486]|Tot: 0:04:07 |ETA: 0:03:27 |tot 0.890s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [259/486]|Tot: 0:04:08 |ETA: 0:03:28 |tot 0.941s (0.956s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [260/486]|Tot: 0:04:09 |ETA: 0:03:27 |tot 0.868s (0.955s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [261/486]|Tot: 0:04:10 |ETA: 0:03:28 |tot 0.891s (0.955s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [262/486]|Tot: 0:04:11 |ETA: 0:03:27 |tot 0.910s (0.955s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [263/486]|Tot: 0:04:12 |ETA: 0:03:24 |tot 0.888s (0.955s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [264/486]|Tot: 0:04:13 |ETA: 0:03:23 |tot 0.927s (0.955s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#################               | [265/486]|Tot: 0:04:14 |ETA: 0:03:20 |tot 0.908s (0.954s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [266/486]|Tot: 0:04:15 |ETA: 0:03:20 |tot 0.851s (0.954s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [267/486]|Tot: 0:04:15 |ETA: 0:03:17 |tot 0.883s (0.954s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [268/486]|Tot: 0:04:16 |ETA: 0:03:16 |tot 0.863s (0.953s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [269/486]|Tot: 0:04:17 |ETA: 0:03:14 |tot 0.892s (0.953s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [270/486]|Tot: 0:04:18 |ETA: 0:03:13 |tot 0.876s (0.953s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [271/486]|Tot: 0:04:19 |ETA: 0:03:12 |tot 0.898s (0.953s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#################               | [272/486]|Tot: 0:04:20 |ETA: 0:03:11 |tot 0.897s (0.953s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [273/486]|Tot: 0:04:21 |ETA: 0:03:10 |tot 0.903s (0.952s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [274/486]|Tot: 0:04:22 |ETA: 0:03:09 |tot 0.848s (0.952s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [275/486]|Tot: 0:04:23 |ETA: 0:03:07 |tot 0.966s (0.952s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [276/486]|Tot: 0:04:23 |ETA: 0:03:06 |tot 0.846s (0.952s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [277/486]|Tot: 0:04:24 |ETA: 0:03:06 |tot 0.899s (0.951s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [278/486]|Tot: 0:04:25 |ETA: 0:03:05 |tot 0.902s (0.951s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [279/486]|Tot: 0:04:26 |ETA: 0:03:05 |tot 0.863s (0.951s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [280/486]|Tot: 0:04:27 |ETA: 0:03:04 |tot 0.807s (0.950s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [281/486]|Tot: 0:04:28 |ETA: 0:03:02 |tot 0.874s (0.950s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |##################              | [282/486]|Tot: 0:04:29 |ETA: 0:03:01 |tot 0.866s (0.950s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [283/486]|Tot: 0:04:29 |ETA: 0:02:59 |tot 0.866s (0.950s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [284/486]|Tot: 0:04:30 |ETA: 0:02:58 |tot 0.898s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [285/486]|Tot: 0:04:31 |ETA: 0:02:57 |tot 0.908s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [286/486]|Tot: 0:04:32 |ETA: 0:02:56 |tot 0.949s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##################              | [287/486]|Tot: 0:04:33 |ETA: 0:02:56 |tot 0.855s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [288/486]|Tot: 0:04:34 |ETA: 0:02:55 |tot 0.903s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [289/486]|Tot: 0:04:35 |ETA: 0:02:54 |tot 0.866s (0.949s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [290/486]|Tot: 0:04:36 |ETA: 0:02:53 |tot 0.916s (0.948s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [291/486]|Tot: 0:04:37 |ETA: 0:02:54 |tot 0.864s (0.948s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |###################             | [292/486]|Tot: 0:04:38 |ETA: 0:02:53 |tot 0.876s (0.948s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [293/486]|Tot: 0:04:38 |ETA: 0:02:52 |tot 0.893s (0.948s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [294/486]|Tot: 0:04:39 |ETA: 0:02:51 |tot 0.852s (0.947s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [295/486]|Tot: 0:04:40 |ETA: 0:02:50 |tot 0.900s (0.947s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [296/486]|Tot: 0:04:41 |ETA: 0:02:49 |tot 0.869s (0.947s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |###################             | [297/486]|Tot: 0:04:42 |ETA: 0:02:47 |tot 0.865s (0.947s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [298/486]|Tot: 0:04:43 |ETA: 0:02:46 |tot 0.871s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [299/486]|Tot: 0:04:44 |ETA: 0:02:45 |tot 0.892s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [300/486]|Tot: 0:04:45 |ETA: 0:02:44 |tot 0.865s (0.946s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |###################             | [301/486]|Tot: 0:04:46 |ETA: 0:02:42 |tot 1.036s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###################             | [302/486]|Tot: 0:04:47 |ETA: 0:02:45 |tot 1.005s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [303/486]|Tot: 0:04:47 |ETA: 0:02:46 |tot 0.811s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [304/486]|Tot: 0:04:48 |ETA: 0:02:45 |tot 0.842s (0.946s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [305/486]|Tot: 0:04:49 |ETA: 0:02:43 |tot 0.836s (0.945s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [306/486]|Tot: 0:04:50 |ETA: 0:02:42 |tot 0.888s (0.945s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [307/486]|Tot: 0:04:51 |ETA: 0:02:40 |tot 0.844s (0.945s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [308/486]|Tot: 0:04:52 |ETA: 0:02:39 |tot 0.785s (0.944s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [309/486]|Tot: 0:04:52 |ETA: 0:02:36 |tot 0.813s (0.944s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [310/486]|Tot: 0:04:53 |ETA: 0:02:36 |tot 0.848s (0.944s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [311/486]|Tot: 0:04:54 |ETA: 0:02:33 |tot 0.807s (0.943s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |####################            | [312/486]|Tot: 0:04:55 |ETA: 0:02:32 |tot 0.831s (0.943s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [313/486]|Tot: 0:04:56 |ETA: 0:02:24 |tot 0.820s (0.942s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [314/486]|Tot: 0:04:57 |ETA: 0:02:23 |tot 0.823s (0.942s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [315/486]|Tot: 0:04:57 |ETA: 0:02:22 |tot 0.797s (0.942s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [316/486]|Tot: 0:04:58 |ETA: 0:02:22 |tot 0.802s (0.941s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |####################            | [317/486]|Tot: 0:04:59 |ETA: 0:02:19 |tot 0.831s (0.941s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [318/486]|Tot: 0:05:00 |ETA: 0:02:18 |tot 0.835s (0.940s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [319/486]|Tot: 0:05:01 |ETA: 0:02:18 |tot 0.760s (0.940s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [320/486]|Tot: 0:05:01 |ETA: 0:02:17 |tot 0.792s (0.939s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [321/486]|Tot: 0:05:02 |ETA: 0:02:14 |tot 0.846s (0.939s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [322/486]|Tot: 0:05:03 |ETA: 0:02:13 |tot 0.832s (0.939s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [323/486]|Tot: 0:05:04 |ETA: 0:02:13 |tot 0.826s (0.938s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [324/486]|Tot: 0:05:05 |ETA: 0:02:12 |tot 0.844s (0.938s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [325/486]|Tot: 0:05:06 |ETA: 0:02:12 |tot 0.824s (0.938s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#####################           | [326/486]|Tot: 0:05:06 |ETA: 0:02:11 |tot 0.931s (0.938s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [327/486]|Tot: 0:05:07 |ETA: 0:02:13 |tot 0.834s (0.937s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [328/486]|Tot: 0:05:08 |ETA: 0:02:12 |tot 0.844s (0.937s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [329/486]|Tot: 0:05:09 |ETA: 0:02:11 |tot 0.836s (0.937s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [330/486]|Tot: 0:05:10 |ETA: 0:02:11 |tot 0.840s (0.937s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [331/486]|Tot: 0:05:11 |ETA: 0:02:12 |tot 0.820s (0.936s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [332/486]|Tot: 0:05:11 |ETA: 0:02:11 |tot 0.809s (0.936s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#####################           | [333/486]|Tot: 0:05:12 |ETA: 0:02:09 |tot 0.927s (0.936s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [334/486]|Tot: 0:05:13 |ETA: 0:02:08 |tot 0.816s (0.935s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [335/486]|Tot: 0:05:14 |ETA: 0:02:09 |tot 0.848s (0.935s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [336/486]|Tot: 0:05:15 |ETA: 0:02:08 |tot 0.796s (0.935s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [337/486]|Tot: 0:05:16 |ETA: 0:02:05 |tot 0.774s (0.934s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [338/486]|Tot: 0:05:16 |ETA: 0:02:04 |tot 0.760s (0.934s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [339/486]|Tot: 0:05:17 |ETA: 0:02:01 |tot 0.804s (0.933s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [340/486]|Tot: 0:05:18 |ETA: 0:02:01 |tot 0.775s (0.933s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [341/486]|Tot: 0:05:19 |ETA: 0:01:58 |tot 0.813s (0.933s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [342/486]|Tot: 0:05:20 |ETA: 0:01:58 |tot 0.788s (0.932s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [343/486]|Tot: 0:05:20 |ETA: 0:01:56 |tot 0.774s (0.932s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [344/486]|Tot: 0:05:21 |ETA: 0:01:56 |tot 0.804s (0.931s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [345/486]|Tot: 0:05:22 |ETA: 0:01:52 |tot 0.801s (0.931s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [346/486]|Tot: 0:05:23 |ETA: 0:01:52 |tot 0.834s (0.931s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [347/486]|Tot: 0:05:24 |ETA: 0:01:51 |tot 0.799s (0.930s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |######################          | [348/486]|Tot: 0:05:24 |ETA: 0:01:50 |tot 0.836s (0.930s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [349/486]|Tot: 0:05:25 |ETA: 0:01:51 |tot 0.796s (0.930s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [350/486]|Tot: 0:05:26 |ETA: 0:01:50 |tot 0.781s (0.929s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [351/486]|Tot: 0:05:27 |ETA: 0:01:49 |tot 0.857s (0.929s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [352/486]|Tot: 0:05:28 |ETA: 0:01:48 |tot 0.792s (0.929s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#######################         | [353/486]|Tot: 0:05:28 |ETA: 0:01:48 |tot 0.801s (0.928s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [354/486]|Tot: 0:05:29 |ETA: 0:01:47 |tot 0.801s (0.928s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [355/486]|Tot: 0:05:30 |ETA: 0:01:47 |tot 1.001s (0.928s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [356/486]|Tot: 0:05:31 |ETA: 0:01:48 |tot 0.840s (0.928s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [357/486]|Tot: 0:05:32 |ETA: 0:01:48 |tot 0.790s (0.927s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [358/486]|Tot: 0:05:33 |ETA: 0:01:47 |tot 0.999s (0.928s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [359/486]|Tot: 0:05:34 |ETA: 0:01:46 |tot 0.788s (0.927s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [360/486]|Tot: 0:05:34 |ETA: 0:01:47 |tot 0.802s (0.927s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [361/486]|Tot: 0:05:35 |ETA: 0:01:46 |tot 0.794s (0.927s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [362/486]|Tot: 0:05:36 |ETA: 0:01:45 |tot 0.777s (0.926s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#######################         | [363/486]|Tot: 0:05:37 |ETA: 0:01:44 |tot 0.849s (0.926s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [364/486]|Tot: 0:05:38 |ETA: 0:01:44 |tot 0.850s (0.926s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [365/486]|Tot: 0:05:38 |ETA: 0:01:43 |tot 0.789s (0.925s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |########################        | [366/486]|Tot: 0:05:39 |ETA: 0:01:40 |tot 0.791s (0.925s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [367/486]|Tot: 0:05:40 |ETA: 0:01:39 |tot 0.788s (0.925s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [368/486]|Tot: 0:05:41 |ETA: 0:01:38 |tot 0.799s (0.924s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [369/486]|Tot: 0:05:42 |ETA: 0:01:37 |tot 0.804s (0.924s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [370/486]|Tot: 0:05:43 |ETA: 0:01:34 |tot 0.819s (0.924s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [371/486]|Tot: 0:05:43 |ETA: 0:01:33 |tot 0.794s (0.923s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [372/486]|Tot: 0:05:44 |ETA: 0:01:32 |tot 0.859s (0.923s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [373/486]|Tot: 0:05:45 |ETA: 0:01:32 |tot 0.800s (0.923s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [374/486]|Tot: 0:05:46 |ETA: 0:01:31 |tot 0.806s (0.923s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [375/486]|Tot: 0:05:47 |ETA: 0:01:30 |tot 0.833s (0.922s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [376/486]|Tot: 0:05:47 |ETA: 0:01:30 |tot 0.809s (0.922s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [377/486]|Tot: 0:05:48 |ETA: 0:01:29 |tot 0.836s (0.922s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |########################        | [378/486]|Tot: 0:05:49 |ETA: 0:01:29 |tot 0.831s (0.922s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [379/486]|Tot: 0:05:50 |ETA: 0:01:28 |tot 0.810s (0.921s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [380/486]|Tot: 0:05:51 |ETA: 0:01:27 |tot 0.817s (0.921s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [381/486]|Tot: 0:05:52 |ETA: 0:01:27 |tot 0.843s (0.921s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [382/486]|Tot: 0:05:52 |ETA: 0:01:26 |tot 0.847s (0.921s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [383/486]|Tot: 0:05:53 |ETA: 0:01:25 |tot 0.853s (0.920s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [384/486]|Tot: 0:05:54 |ETA: 0:01:25 |tot 0.796s (0.920s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [385/486]|Tot: 0:05:55 |ETA: 0:01:24 |tot 0.800s (0.920s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |#########################       | [386/486]|Tot: 0:05:56 |ETA: 0:01:23 |tot 0.841s (0.920s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |#########################       | [387/486]|Tot: 0:05:56 |ETA: 0:01:22 |tot 0.815s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [388/486]|Tot: 0:05:57 |ETA: 0:01:21 |tot 0.892s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [389/486]|Tot: 0:05:58 |ETA: 0:01:21 |tot 0.867s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [390/486]|Tot: 0:05:59 |ETA: 0:01:21 |tot 0.845s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [391/486]|Tot: 0:06:00 |ETA: 0:01:20 |tot 0.877s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [392/486]|Tot: 0:06:01 |ETA: 0:01:20 |tot 0.869s (0.919s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#########################       | [393/486]|Tot: 0:06:02 |ETA: 0:01:19 |tot 0.859s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [394/486]|Tot: 0:06:03 |ETA: 0:01:18 |tot 0.865s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [395/486]|Tot: 0:06:03 |ETA: 0:01:18 |tot 0.862s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [396/486]|Tot: 0:06:04 |ETA: 0:01:18 |tot 0.853s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [397/486]|Tot: 0:06:05 |ETA: 0:01:17 |tot 0.904s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [398/486]|Tot: 0:06:06 |ETA: 0:01:17 |tot 0.830s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [399/486]|Tot: 0:06:07 |ETA: 0:01:16 |tot 0.844s (0.918s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [400/486]|Tot: 0:06:08 |ETA: 0:01:15 |tot 0.866s (0.917s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [401/486]|Tot: 0:06:09 |ETA: 0:01:14 |tot 0.871s (0.917s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [402/486]|Tot: 0:06:09 |ETA: 0:01:13 |tot 0.886s (0.917s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [403/486]|Tot: 0:06:10 |ETA: 0:01:12 |tot 0.827s (0.917s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [404/486]|Tot: 0:06:11 |ETA: 0:01:11 |tot 0.769s (0.917s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [405/486]|Tot: 0:06:12 |ETA: 0:01:10 |tot 0.834s (0.916s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [406/486]|Tot: 0:06:13 |ETA: 0:01:08 |tot 0.829s (0.916s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [407/486]|Tot: 0:06:14 |ETA: 0:01:08 |tot 0.828s (0.916s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [408/486]|Tot: 0:06:14 |ETA: 0:01:06 |tot 0.788s (0.916s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##########################      | [409/486]|Tot: 0:06:15 |ETA: 0:01:05 |tot 0.809s (0.915s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [410/486]|Tot: 0:06:16 |ETA: 0:01:04 |tot 0.802s (0.915s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [411/486]|Tot: 0:06:17 |ETA: 0:01:03 |tot 0.805s (0.915s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |###########################     | [412/486]|Tot: 0:06:18 |ETA: 0:01:01 |tot 0.805s (0.915s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [413/486]|Tot: 0:06:18 |ETA: 0:01:00 |tot 0.818s (0.914s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [414/486]|Tot: 0:06:19 |ETA: 0:00:59 |tot 0.772s (0.914s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [415/486]|Tot: 0:06:20 |ETA: 0:00:58 |tot 0.796s (0.914s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [416/486]|Tot: 0:06:21 |ETA: 0:00:57 |tot 0.770s (0.913s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [417/486]|Tot: 0:06:22 |ETA: 0:00:56 |tot 0.767s (0.913s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [418/486]|Tot: 0:06:22 |ETA: 0:00:54 |tot 0.798s (0.913s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [419/486]|Tot: 0:06:23 |ETA: 0:00:54 |tot 0.773s (0.912s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [420/486]|Tot: 0:06:24 |ETA: 0:00:53 |tot 0.827s (0.912s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [421/486]|Tot: 0:06:25 |ETA: 0:00:52 |tot 0.812s (0.912s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [422/486]|Tot: 0:06:25 |ETA: 0:00:51 |tot 0.772s (0.912s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [423/486]|Tot: 0:06:26 |ETA: 0:00:51 |tot 0.823s (0.912s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |###########################     | [424/486]|Tot: 0:06:27 |ETA: 0:00:50 |tot 0.836s (0.911s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [425/486]|Tot: 0:06:28 |ETA: 0:00:49 |tot 0.834s (0.911s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [426/486]|Tot: 0:06:29 |ETA: 0:00:49 |tot 0.812s (0.911s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [427/486]|Tot: 0:06:30 |ETA: 0:00:48 |tot 0.837s (0.911s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [428/486]|Tot: 0:06:30 |ETA: 0:00:48 |tot 0.845s (0.911s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [429/486]|Tot: 0:06:31 |ETA: 0:00:47 |tot 0.761s (0.910s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [430/486]|Tot: 0:06:32 |ETA: 0:00:46 |tot 0.837s (0.910s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [431/486]|Tot: 0:06:33 |ETA: 0:00:45 |tot 0.827s (0.910s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [432/486]|Tot: 0:06:34 |ETA: 0:00:45 |tot 0.808s (0.910s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [433/486]|Tot: 0:06:34 |ETA: 0:00:44 |tot 0.762s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [434/486]|Tot: 0:06:35 |ETA: 0:00:43 |tot 0.831s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [435/486]|Tot: 0:06:36 |ETA: 0:00:42 |tot 0.848s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [436/486]|Tot: 0:06:37 |ETA: 0:00:41 |tot 0.799s (0.909s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |############################    | [437/486]|Tot: 0:06:38 |ETA: 0:00:41 |tot 0.827s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [438/486]|Tot: 0:06:39 |ETA: 0:00:40 |tot 0.853s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################    | [439/486]|Tot: 0:06:40 |ETA: 0:00:39 |tot 0.869s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [440/486]|Tot: 0:06:40 |ETA: 0:00:39 |tot 0.828s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [441/486]|Tot: 0:06:41 |ETA: 0:00:38 |tot 0.828s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [442/486]|Tot: 0:06:42 |ETA: 0:00:37 |tot 0.820s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [443/486]|Tot: 0:06:43 |ETA: 0:00:36 |tot 0.891s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [444/486]|Tot: 0:06:44 |ETA: 0:00:36 |tot 0.832s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [445/486]|Tot: 0:06:44 |ETA: 0:00:35 |tot 0.741s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [446/486]|Tot: 0:06:45 |ETA: 0:00:34 |tot 0.807s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [447/486]|Tot: 0:06:46 |ETA: 0:00:33 |tot 0.833s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [448/486]|Tot: 0:06:47 |ETA: 0:00:32 |tot 0.797s (0.907s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |#############################   | [449/486]|Tot: 0:06:48 |ETA: 0:00:31 |tot 0.763s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [450/486]|Tot: 0:06:49 |ETA: 0:00:30 |tot 0.917s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [451/486]|Tot: 0:06:49 |ETA: 0:00:29 |tot 0.839s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [452/486]|Tot: 0:06:50 |ETA: 0:00:29 |tot 0.820s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [453/486]|Tot: 0:06:51 |ETA: 0:00:28 |tot 1.235s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |#############################   | [454/486]|Tot: 0:06:53 |ETA: 0:00:28 |tot 1.090s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [455/486]|Tot: 0:06:54 |ETA: 0:00:28 |tot 1.164s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [456/486]|Tot: 0:06:55 |ETA: 0:00:28 |tot 1.158s (0.908s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |##############################  | [457/486]|Tot: 0:06:56 |ETA: 0:00:28 |tot 1.075s (0.909s) |load 0.001s (0.001s) |pre 0.002s (centerfusion |##############################  | [458/486]|Tot: 0:06:57 |ETA: 0:00:28 |tot 0.988s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [459/486]|Tot: 0:06:58 |ETA: 0:00:27 |tot 0.922s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [460/486]|Tot: 0:06:59 |ETA: 0:00:27 |tot 0.822s (0.909s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [461/486]|Tot: 0:07:00 |ETA: 0:00:26 |tot 0.831s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [462/486]|Tot: 0:07:00 |ETA: 0:00:25 |tot 0.840s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [463/486]|Tot: 0:07:01 |ETA: 0:00:24 |tot 0.903s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [464/486]|Tot: 0:07:02 |ETA: 0:00:22 |tot 0.890s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [465/486]|Tot: 0:07:03 |ETA: 0:00:21 |tot 0.979s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [466/486]|Tot: 0:07:04 |ETA: 0:00:19 |tot 0.966s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [467/486]|Tot: 0:07:05 |ETA: 0:00:18 |tot 0.816s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [468/486]|Tot: 0:07:06 |ETA: 0:00:17 |tot 0.796s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |##############################  | [469/486]|Tot: 0:07:06 |ETA: 0:00:16 |tot 0.766s (0.908s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [470/486]|Tot: 0:07:07 |ETA: 0:00:14 |tot 0.790s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [471/486]|Tot: 0:07:08 |ETA: 0:00:13 |tot 0.795s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [472/486]|Tot: 0:07:09 |ETA: 0:00:12 |tot 0.772s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [473/486]|Tot: 0:07:10 |ETA: 0:00:12 |tot 0.769s (0.907s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [474/486]|Tot: 0:07:10 |ETA: 0:00:11 |tot 0.810s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [475/486]|Tot: 0:07:11 |ETA: 0:00:10 |tot 0.806s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [476/486]|Tot: 0:07:12 |ETA: 0:00:09 |tot 0.728s (0.906s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |############################### | [477/486]|Tot: 0:07:13 |ETA: 0:00:08 |tot 0.744s (0.906s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [478/486]|Tot: 0:07:13 |ETA: 0:00:07 |tot 0.721s (0.905s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |############################### | [479/486]|Tot: 0:07:14 |ETA: 0:00:06 |tot 0.712s (0.905s) |load 0.000s (0.001s) |pre 0.001s (centerfusion |############################### | [480/486]|Tot: 0:07:15 |ETA: 0:00:05 |tot 0.699s (0.904s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [481/486]|Tot: 0:07:16 |ETA: 0:00:04 |tot 0.716s (0.904s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [482/486]|Tot: 0:07:16 |ETA: 0:00:03 |tot 0.694s (0.903s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [483/486]|Tot: 0:07:17 |ETA: 0:00:03 |tot 0.710s (0.903s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |############################### | [484/486]|Tot: 0:07:18 |ETA: 0:00:02 |tot 0.674s (0.903s) |load 0.001s (0.001s) |pre 0.001s (centerfusion |################################| [485/486]|Tot: 0:07:18 |ETA: 0:00:01 |tot 0.693s (0.902s) |load 0.001s (0.001s) |pre 0.001s (0.001s) |net 0.679s (0.887s) |dec 0.001s (0.002s) |post 0.010s (0.011s) |merge 0.000s (0.000s) |track 0.000s (0.000s) 
Converting nuscenes format...
======
Loading NuScenes tables for version v1.0-mini...
23 category,
8 attribute,
4 visibility,
911 instance,
12 sensor,
120 calibrated_sensor,
31206 ego_pose,
8 log,
10 scene,
404 sample,
31206 sample_data,
18538 sample_annotation,
4 map,
Done loading in 0.316 seconds.
======
Reverse indexing ...
Done reverse indexing in 0.1 seconds.
======
Initializing nuScenes detection evaluation
Loaded results from /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/results_nuscenes_det_mini_val.json. Found detections for 81 samples.
Loading annotations for mini_val split from nuScenes version: v1.0-mini
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 81/81 [00:00<00:00, 360.56it/s]
Loaded ground truth annotations for 81 samples.
Filtering predictions
=> Original number of boxes: 40500
=> After distance based filtering: 32179
=> After LIDAR and RADAR points based filtering: 32179
=> After bike rack filtering: 32105
Filtering ground truth annotations
=> Original number of boxes: 4441
=> After distance based filtering: 3785
=> After LIDAR and RADAR points based filtering: 3393
=> After bike rack filtering: 3393
Accumulating metric data...
Calculating metrics...
Saving metrics to: /home/fabrizioschiano/repositories/CenterFusion/src/lib/../../exp/ddd/centerfusion/nuscenes_eval_det_output_mini_val/
mAP: 0.3129
mATE: 0.6590
mASE: 0.4630
mAOE: 0.6008
mAVE: 0.7622
mAAE: 0.3084
NDS: 0.3771
Eval time: 5.4s

Per-class results:
Object Class	AP	ATE	ASE	AOE	AVE	AAE
car	0.542	0.418	0.159	0.120	0.152	0.073
truck	0.442	0.542	0.168	0.133	0.141	0.122
bus	0.549	0.484	0.081	0.079	1.400	0.090
trailer	0.000	1.000	1.000	1.000	1.000	1.000
construction_vehicle	0.000	1.000	1.000	1.000	1.000	1.000
pedestrian	0.468	0.561	0.262	0.491	0.512	0.118
motorcycle	0.318	0.689	0.342	1.167	0.061	0.016
bicycle	0.187	0.448	0.299	0.417	1.832	0.048
traffic_cone	0.624	0.447	0.319	nan	nan	nan
barrier	0.000	1.000	1.000	1.000	nan	nan
[log - test.py] End __main__

i want to run demo.py for video detection by using centerfusion_e60.pth.
what should i do in terminal.

Hi, I train the model from scratch(use pretrained dla34 in imagenet)，but the training loss is about 12 after 50 epochs, the loss is too huge. In your code, you freeze the dla34 backbone when training, is it the reason why I can not get a lower loss?
another question is the inference time of the model. When I test your model in Tesla V 100 GPU， the inference time of one sample is about 0.37s, it is slower than original centerNet model. centerNet is real-time model, but your model is too slow to be real-time
hope for your answer, thanks

Hello @mrnabati , I understand you are very busy but if you could respond to this issue, I would be very grateful. My work is heavily dependent on your research. I have included the --debug 4 argument when running test.py to visualise the predicted boxes against the ground truth as well as visualise the radar pointcloud. However, only the predictions appear. Please tell me what I should do to visualize the point cloud and the ground truth in the bird's eye view similar to qualitative_results.jpg.

Hi @mrnabati , maybe this is a naive question but I was wondering why you did not implement the logging in Tensorboard for the testing phase and it is just implemented for the training phase. Is it due to a lack of time or it does not make sense in your opinion?

For people interested, I am talking about such lines (for the training part) which I would like to "have" also for the testing part of the pipeline.

Thanks!

I am trying to install the packages required in the requirements.txt file but I get the following error:

pip install -r requirements.txt
ERROR: Could not find a version that satisfies the requirement scikit-learn==0.21.0 (from versions: 0.9, 0.10, 0.11, 0.12, 0.12.1, 0.13, 0.13.1, 0.14, 0.14.1, 0.15.0b1, 0.15.0b2, 0.15.0, 0.15.1, 0.15.2, 0.16b1, 0.16.0, 0.16.1, 0.17b1, 0.17, 0.17.1, 0.18, 0.18.1, 0.18.2, 0.19b2, 0.19.0, 0.19.1, 0.19.2, 0.20rc1, 0.20.0, 0.20.1, 0.20.2, 0.20.3, 0.20.4, 0.21rc2, 0.21.1, 0.21.2, 0.21.3, 0.22rc2.post1, 0.22rc3, 0.22, 0.22.1, 0.22.2, 0.22.2.post1, 0.23.0rc1, 0.23.0, 0.23.1, 0.23.2, 0.24.dev0, 0.24.0rc1, 0.24.0, 0.24.1, 0.24.2, 1.0rc1, 1.0rc2, 1.0)
ERROR: No matching distribution found for scikit-learn==0.21.0

@mrnabati , is there a specific reason for using this version of scikit-learn?

thanks!

Hello, I got an error when running bash experiments/test.sh, the specific information is as follows:

Using tensorboardX
/home/guopu/anaconda3/envs/Pytorch1.2/lib/python3.7/site-packages/sklearn/utils/linear_assignment_.py:21: DeprecationWarning: The linear_assignment_ module is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.
DeprecationWarning)
Fix size testing.
training chunk_sizes: [32]
input h w: 448 800
heads {'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}
weights {'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}
head conv {'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}
Namespace(K=100, amodel_offset_weight=1, arch='dla_34', aug_rot=0, backbone='dla34', batch_size=32, chunk_sizes=[32], custom_dataset_ann_path='', custom_dataset_img_path='', custom_head_convs={'dep_sec': 3, 'rot_sec': 3, 'velocity': 3, 'nuscenes_att': 3}, data_dir='/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/../../data', dataset='nuscenes', dataset_version='', debug=0, debug_dir='/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/../../exp/ddd/centerfusion/debug', debugger_theme='white', demo='', dense_reg=1, dep_res_weight=1, dep_weight=1, depth_scale=1, dim_weight=1, disable_frustum=False, dla_node='dcn', down_ratio=4, eval=False, eval_n_plots=0, eval_render_curves=False, exp_dir='/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/../../exp/ddd', exp_id='centerfusion', fix_res=True, fix_short=-1, flip=0.5, flip_test=True, fp_disturb=0, freeze_backbone=False, frustumExpansionRatio=0.0, gpus=[0], gpus_str='0', head_conv={'hm': [256], 'reg': [256], 'wh': [256], 'dep': [256], 'rot': [256], 'dim': [256], 'amodel_offset': [256], 'dep_sec': [256, 256, 256], 'rot_sec': [256, 256, 256], 'nuscenes_att': [256, 256, 256], 'velocity': [256, 256, 256]}, head_kernel=3, heads={'hm': 10, 'reg': 2, 'wh': 2, 'dep': 1, 'rot': 8, 'dim': 3, 'amodel_offset': 2, 'dep_sec': 1, 'rot_sec': 8, 'nuscenes_att': 8, 'velocity': 3}, hm_dist_thresh={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 1, 6: 1, 7: 1, 8: 0, 9: 0}, hm_disturb=0, hm_hp_weight=1, hm_to_box_ratio=0.3, hm_transparency=0.7, hm_weight=1, hp_weight=1, hungarian=False, ignore_loaded_cats=[], img_format='jpg', input_h=448, input_res=800, input_w=800, iou_thresh=0, keep_res=False, kitti_split='3dop', layers_to_freeze=['base', 'dla_up', 'ida_up'], load_model='../models/centerfusion_e60.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, master_batch_size=32, max_age=-1, max_frame_dist=3, max_pc=1000, max_pc_dist=60.0, model_output_list=False, msra_outchannel=256, neck='dlaup', new_thresh=0.3, nms=False, no_color_aug=False, no_pause=False, no_pre_img=False, non_block_test=False, normalize_depth=True, not_cuda_benchmark=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_img_channels=3, num_iters=-1, num_resnet_layers=101, num_stacks=1, num_workers=4, nuscenes_att=True, nuscenes_att_weight=1, off_weight=1, optim='adam', out_thresh=-1, output_h=112, output_res=200, output_w=200, pad=31, 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'], pc_feat_channels={'pc_dep': 0, 'pc_vx': 1, 'pc_vz': 2}, pc_feat_lvl=['pc_dep', 'pc_vx', 'pc_vz'], pc_roi_method='pillars', pc_z_offset=-0.0, pillar_dims=[1.5, 0.2, 0.2], pointcloud=True, pre_hm=False, pre_img=False, pre_thresh=-1, print_iter=0, prior_bias=-4.6, public_det=False, qualitative=False, r_a=250, r_b=5, radar_sweeps=3, reg_loss='l1', reset_hm=False, resize_video=False, resume=False, reuse_hm=False, root_dir='/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/../..', rot_weight=1, rotate=0, run_dataset_eval=True, same_aug_pre=False, save_all=False, save_dir='/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/../../exp/ddd/centerfusion', save_framerate=30, save_img_suffix='', save_imgs=[], save_point=[90], save_results=False, save_video=False, scale=0, secondary_heads=['velocity', 'nuscenes_att', 'dep_sec', 'rot_sec'], seed=317, shift=0, show_track_color=False, show_velocity=False, shuffle_train=False, sigmoid_dep_sec=True, skip_first=-1, sort_det_by_dist=False, tango_color=False, task='ddd', test_dataset='nuscenes', test_focal_length=-1, test_scales=[1.0], track_thresh=0.3, tracking=False, tracking_weight=1, train_split='train', trainval=False, transpose_video=False, use_loaded_results=False, val_intervals=10, val_split='mini_val', velocity=True, velocity_weight=1, video_h=512, video_w=512, vis_gt_bev='', vis_thresh=0.3, warm_start_weights=False, weights={'hm': 1, 'reg': 1, 'wh': 0.1, 'dep': 1, 'rot': 1, 'dim': 1, 'amodel_offset': 1, 'dep_sec': 1, 'rot_sec': 1, 'nuscenes_att': 1, 'velocity': 1}, wh_weight=0.1, zero_pre_hm=False, zero_tracking=False)
fatal: 不是一个 git 仓库（或者任何父目录）：.git
Traceback (most recent call last):
File "test.py", line 215, in
prefetch_test(opt)
File "test.py", line 73, in prefetch_test
Logger(opt)
File "/home/guopu/CenterFusion-master-pu/CenterFusion-master-puV1/src/lib/logger.py", line 34, in init
subprocess.check_output(["git", "describe", "--always"])))
File "/home/guopu/anaconda3/envs/Pytorch1.2/lib/python3.7/subprocess.py", line 411, in check_output
**kwargs).stdout
File "/home/guopu/anaconda3/envs/Pytorch1.2/lib/python3.7/subprocess.py", line 512, in run
output=stdout, stderr=stderr)
subprocess.CalledProcessError: Command '['git', 'describe', '--always']' returned non-zero exit status 128.

Excuse me, i want to use the project to train our own dataset, which cotains picture and radar data. How can i do?

Hi, Could you please point me on how to run demo.py with both image and Radar data. I was able to run the demo.py with images and video files but I was wondering how to incorporate radar data as well.

On an additional note could you please help me on how to use kitti dataset with Image and Lidar data instead of Radar and how to convert Lidar to COCO format.

Thanks for your great work!I successfully installed the environment and trained the model according to the Readme.md.And I want to visualize the detection results of the test. Could you provide support or could you provide a way that I can visualize the results?Thank you!

I am trying to visualize the intermediate results of testing. As @mrnabati pointed out in this issue I tried to enable debug by putting --debug 4 \ in the test.sh file. However, I get the following error:

Traceback (most recent call last):
  File "test.py", line 224, in <module>
    prefetch_test(opt)
  File "test.py", line 131, in prefetch_test
    ret = detector.run(pre_processed_images)
  File "/home/fabrizioschiano/repositories/CenterFusion/src/lib/detector.py", line 159, in run
    self.show_results(self.debugger, image, results)
  File "/home/fabrizioschiano/repositories/CenterFusion/src/lib/detector.py", line 435, in show_results
    debugger.add_bird_view(
  File "/home/fabrizioschiano/repositories/CenterFusion/src/lib/utils/debugger.py", line 424, in add_bird_view
    cv2.line(bird_view, (rect[e[0]][0], rect[e[0]][1]),
cv2.error: OpenCV(4.5.3) :-1: error: (-5:Bad argument) in function 'line'
> Overload resolution failed:
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type
>  - Can't parse 'pt1'. Sequence item with index 0 has a wrong type

my version of opencv is the following

python -c "import cv2; print(cv2.__version__)"
4.5.3

Did anyone experience this issue?

Thanks!

I would like to understand what does ddd stands for in this repository?

It is a task of the train.sh or test.sh files and I was wondering what it stands for.

Hi, I downloaded nuscenes dataset and when I run the convert_nuscenes.py, it told me a file doesn't exist in the dataset (from what I remember this file is a frame from one of the RADAR folders and its timestamp is'1531883530444336' from sample_data json file from v1.0-trainval), then I checked the dataset and this file is indeed not there. I have downloaded many different datasets from nuscenes website but neither of them have this file. Thus I found which json file (from sample data json file) includes the request of this file and then deleted that specific cell in the json file. Then when I run convert_nuscenes.py again, it gave me killed.
I am not sure what happened. Can someone give me some suggestions?
Thank you!

When I trained with nuscene mini (5.4gb) according to the steps in readme, an error occurs:

python convert_nuScenes.py

FileNotFoundError: [Errno 2] No such file or directory: '../../data/nuscenes/samples/RADAR_FRONT_RIGHT/n015-2018-07-18-11-07-57+0800__RADAR_FRONT_RIGHT__1531883530444336.pcd'

I later tried to add meta-trainval to the dataset, but it's useless.
Now,I am not sure what happened. Can someone give me some suggestions?
Thank you!

Excuse me, I trained for 100 epochs according to your configuration, with nuscenes-mini dataset and centerfusion_e60 as pretrained model，but it didn’t work.

The parameter of training:
python main.py
ddd
--exp_id centerfusion
--shuffle_train
--train_split train
--val_split mini_val
--val_intervals 1
--run_dataset_eval
--nuscenes_att
--velocity
--batch_size 16
--lr 2.5e-4
--num_epochs 100
--lr_step 50
--save_point 20,40,60,80,90
--gpus 0,1
--not_rand_crop
--flip 0.5
--shift 0.1
--pointcloud
--radar_sweeps 6
--pc_z_offset 0.0
--pillar_dims 1.0,0.2,0.2
--max_pc_dist 60.0
--load_model ../models/centerfusion_e60.pth

The output logs when training:
mAP: 0.2157
mATE: 0.8099
mASE: 0.5006
mAOE: 0.6867
mAVE: 0.8113
mAAE: 0.3678
NDS: 0.2902
Eval time: 9.6s

Per-class results:
Object Class AP ATE ASE AOE AVE AAE
car 0.421 0.573 0.182 0.158 0.231 0.142
truck 0.227 0.658 0.197 0.151 0.309 0.494
bus 0.368 0.812 0.089 0.383 2.193 0.012
trailer 0.000 1.000 1.000 1.000 1.000 1.000
construction_vehicle 0.000 1.000 1.000 1.000 1.000 1.000
pedestrian 0.402 0.664 0.278 0.620 0.545 0.162
motorcycle 0.195 0.830 0.456 1.174 0.129 0.133
bicycle 0.003 0.963 0.377 0.695 1.083 0.000
traffic_cone 0.540 0.599 0.428 nan nan nan
barrier 0.000 1.000 1.000 1.000 nan nan

The data on the test set after 100 epochs:
mAP: 0.2322
mATE: 0.7755
mASE: 0.4998
mAOE: 0.6901
mAVE: 0.8836
mAAE: 0.3804
NDS: 0.2932
Eval time: 9.9s

Per-class results:
Object Class AP ATE ASE AOE AVE AAE
car 0.442 0.536 0.181 0.145 0.247 0.140
truck 0.262 0.531 0.203 0.152 0.358 0.574
bus 0.432 0.786 0.078 0.291 2.033 0.009
trailer 0.000 1.000 1.000 1.000 1.000 1.000
construction_vehicle 0.000 1.000 1.000 1.000 1.000 1.000
pedestrian 0.426 0.640 0.281 0.647 0.555 0.138
motorcycle 0.198 0.907 0.454 1.368 0.153 0.179
bicycle 0.010 0.817 0.369 0.609 1.723 0.003
traffic_cone 0.553 0.538 0.433 nan nan nan
barrier 0.000 1.000 1.000 1.000 nan nan

the mAP looks the same in each epoch with no fluctuations, may I ask if it is normal ？

Hello, I'm trying to build the deformable convolution library and I keep getting this error:

I've checked issue #14 but the solution wasn't given. Some help with this would be very much appreciated.

The package version error occurred when I executed the command 'pip install -r requirements.txt' from line 1.
So I modified the line.
'scikit-learn==0.21.0' -> 'scikit-learn==0.21rc2'
And it worked.
Please reconsider it. Thanks.