when i run "bash experiments/test.sh" TypeError: 'NoneType' object is not callable

[VanniZhou]

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

[mrnabati]

Looks like DCN was either not built or not found. Try installing DCN again by removing the DCNv2 folder and cloning it again:

cd $CF_ROOT/src/lib/model/networks
rm -rf DCNv2
git clone https://github.com/CharlesShang/DCNv2/
cd DCNv2
./make.sh

and make sure installation finishes with no errors.

[VanniZhou]

https://github.com/lbin/DCNv2/tree/pytorch_1.6
my pytorch is 1.6, this can helps.
THX