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If you are wondering where the data of this site comes from, please visit https://api.github.com/users/mrnabati/events. GitMemory does not store any data, but only uses NGINX to cache data for a period of time. The idea behind GitMemory is simply to give users a better reading experience.
Ramin Nabati mrnabati Knoxville, TN https://mrnabati.github.io/ Connected and Automated Vehicles Lead - University of Tennessee Knoxville

mrnabati/CenterFusion 177

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

mrnabati/RRPN 61

Code for 'RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles' (ICIP 2019)

aicip/RRPN 1

Radar Region Proposal Network for Object Detection in Autonomous Driving Vehicles

mrnabati/awesome-cavs 1

Awesome Connected and Automated Vehicles

aicip/CenterFusion 0

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

mrnabati/algorithms 0

Minimal examples of data structures and algorithms in Python

mrnabati/arxiv-latex-cleaner 0

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

mrnabati/cocoapi_plus 0

An enhanced COCO API for adding images with annotations or creating new COCO-style datasets.

mrnabati/mrnabati.github.io 0

My personal website:

mrnabati/myAwesomeSettings 0

My configs, snippets and dotfiles

startedmrnabati/CenterFusion

started time in 3 days

startedmrnabati/CenterFusion

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issue openedmrnabati/CenterFusion

Changing Batch Size for Training

When I try to change the --batch_size arin 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?

created time in 12 days

startedmrnabati/CenterFusion

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fork buyuncheng/CenterFusion

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

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startedmrnabati/RRPN

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/RRPN

started time in 20 days

startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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issue commentmrnabati/CenterFusion

Problem when I run convert_nuscenes.py

Hello, I have also encountered this problem. I downloaded metadata but it displayed "KeyError: 'bb867e2064014279863c71a29b1eb381' " when i run convert_nuscens.py . So is it OK to download the corresponding test set and data set, or i must download the same data set as you? Could you give me some advice? Thank you :)

jxchenlu-666

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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issue openedmrnabati/CenterFusion

Problem when I run convert_nuscenes.py

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!

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startedmrnabati/CenterFusion

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fork zoubing123/CenterFusion

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

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issue openedmrnabati/RRPN

RuntimeError: [enforce fail at conv_op_cudnn.cc:554] filter.dim32(1) == C / group_. 8 vs 256

when I run : bash 4_inference.sh The following problem arised, how to solve it ? INFO: Running inference... Found Detectron ops lib: /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/torch/lib/libcaffe2_detectron_ops_gpu.so [E init_intrinsics_check.cc:43] CPU feature avx is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. [E init_intrinsics_check.cc:43] CPU feature avx2 is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. [E init_intrinsics_check.cc:43] CPU feature fma is present on your machine, but the Caffe2 binary is not compiled with it. It means you may not get the full speed of your CPU. INFO coco.py: 49: loading COCO annotations into memory... INFO coco.py: 55: Done (t=0.19s) creating index... index created! INFO coco.py: 82: index created. WARNING cnn.py: 25: [====DEPRECATE WARNING====]: you are creating an object from CNNModelHelper class which will be deprecated soon. Please use ModelHelper object with brew module. For more information, please refer to caffe2.ai and python/brew.py, python/brew_test.py for more information. INFO net.py: 60: Loading weights from: /home/ZT/ZT_Project/RRPN-master/data/models/X_101_32x8d_FPN_1x_nucoco/train/nucoco_mini_train/generalized_rcnn/model_iter19999.pkl INFO net.py: 96: conv1_w loaded from weights file into gpu_0/conv1_w: (64, 3, 7, 7) INFO net.py: 96: res_conv1_bn_s loaded from weights file into gpu_0/res_conv1_bn_s: (64,) INFO net.py: 96: res_conv1_bn_b loaded from weights file into gpu_0/res_conv1_bn_b: (64,) INFO net.py: 96: res2_0_branch2a_w loaded from weights file into gpu_0/res2_0_branch2a_w: (256, 64, 1, 1) INFO net.py: 96: res2_0_branch2a_bn_s loaded from weights file into gpu_0/res2_0_branch2a_bn_s: (256,) INFO net.py: 96: res2_0_branch2a_bn_b loaded from weights file into gpu_0/res2_0_branch2a_bn_b: (256,) INFO net.py: 96: res2_0_branch2b_w loaded from weights file into gpu_0/res2_0_branch2b_w: (256, 8, 3, 3) INFO net.py: 96: res2_0_branch2b_bn_s loaded from weights file into gpu_0/res2_0_branch2b_bn_s: (256,) INFO net.py: 96: res2_0_branch2b_bn_b loaded 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net.py: 96: res2_1_branch2b_w loaded from weights file into gpu_0/res2_1_branch2b_w: (256, 8, 3, 3) INFO net.py: 96: res2_1_branch2b_bn_s loaded from weights file into gpu_0/res2_1_branch2b_bn_s: (256,) INFO net.py: 96: res2_1_branch2b_bn_b loaded from weights file into gpu_0/res2_1_branch2b_bn_b: (256,) INFO net.py: 96: res2_1_branch2c_w loaded from weights file into gpu_0/res2_1_branch2c_w: (256, 256, 1, 1) INFO net.py: 96: res2_1_branch2c_bn_s loaded from weights file into gpu_0/res2_1_branch2c_bn_s: (256,) INFO net.py: 96: res2_1_branch2c_bn_b loaded from weights file into gpu_0/res2_1_branch2c_bn_b: (256,) INFO net.py: 96: res2_2_branch2a_w loaded from weights file into gpu_0/res2_2_branch2a_w: (256, 256, 1, 1) INFO net.py: 96: res2_2_branch2a_bn_s loaded from weights file into gpu_0/res2_2_branch2a_bn_s: (256,) INFO net.py: 96: res2_2_branch2a_bn_b loaded from weights file into gpu_0/res2_2_branch2a_bn_b: (256,) INFO net.py: 96: res2_2_branch2b_w loaded from weights file into gpu_0/res2_2_branch2b_w: (256, 8, 3, 3) INFO net.py: 96: res2_2_branch2b_bn_s loaded from weights file into gpu_0/res2_2_branch2b_bn_s: (256,) INFO net.py: 96: res2_2_branch2b_bn_b loaded from weights file into gpu_0/res2_2_branch2b_bn_b: (256,) INFO net.py: 96: res2_2_branch2c_w loaded from weights file into gpu_0/res2_2_branch2c_w: (256, 256, 1, 1) INFO net.py: 96: res2_2_branch2c_bn_s loaded from weights file into gpu_0/res2_2_branch2c_bn_s: (256,) INFO net.py: 96: res2_2_branch2c_bn_b loaded from weights file into gpu_0/res2_2_branch2c_bn_b: (256,) INFO net.py: 96: res3_0_branch2a_w [+ momentum] loaded from weights file into gpu_0/res3_0_branch2a_w: (512, 256, 1, 1) INFO net.py: 96: res3_0_branch2a_bn_s loaded from weights file into gpu_0/res3_0_branch2a_bn_s: (512,) INFO net.py: 96: res3_0_branch2a_bn_b loaded from weights file into gpu_0/res3_0_branch2a_bn_b: (512,) INFO net.py: 96: res3_0_branch2b_w [+ momentum] loaded from weights file into gpu_0/res3_0_branch2b_w: (512, 16, 3, 3) INFO net.py: 96: res3_0_branch2b_bn_s loaded from weights file into gpu_0/res3_0_branch2b_bn_s: (512,) INFO net.py: 96: res3_0_branch2b_bn_b loaded from weights file into gpu_0/res3_0_branch2b_bn_b: (512,) INFO net.py: 96: res3_0_branch2c_w [+ momentum] loaded from weights file into gpu_0/res3_0_branch2c_w: (512, 512, 1, 1) INFO net.py: 96: res3_0_branch2c_bn_s loaded from weights file into gpu_0/res3_0_branch2c_bn_s: (512,) INFO net.py: 96: res3_0_branch2c_bn_b loaded from weights file into gpu_0/res3_0_branch2c_bn_b: (512,) INFO net.py: 96: res3_0_branch1_w [+ momentum] loaded from weights file into gpu_0/res3_0_branch1_w: (512, 256, 1, 1) INFO net.py: 96: res3_0_branch1_bn_s loaded from weights file into gpu_0/res3_0_branch1_bn_s: (512,) INFO net.py: 96: res3_0_branch1_bn_b loaded from weights file into gpu_0/res3_0_branch1_bn_b: (512,) INFO net.py: 96: res3_1_branch2a_w [+ momentum] loaded from weights file into gpu_0/res3_1_branch2a_w: (512, 512, 1, 1) INFO net.py: 96: 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into gpu_0/res4_22_branch2c_w: (1024, 1024, 1, 1) INFO net.py: 96: res4_22_branch2c_bn_s loaded from weights file into gpu_0/res4_22_branch2c_bn_s: (1024,) INFO net.py: 96: res4_22_branch2c_bn_b loaded from weights file into gpu_0/res4_22_branch2c_bn_b: (1024,) INFO net.py: 96: res5_0_branch2a_w [+ momentum] loaded from weights file into gpu_0/res5_0_branch2a_w: (2048, 1024, 1, 1) INFO net.py: 96: res5_0_branch2a_bn_s loaded from weights file into gpu_0/res5_0_branch2a_bn_s: (2048,) INFO net.py: 96: res5_0_branch2a_bn_b loaded from weights file into gpu_0/res5_0_branch2a_bn_b: (2048,) INFO net.py: 96: res5_0_branch2b_w [+ momentum] loaded from weights file into gpu_0/res5_0_branch2b_w: (2048, 64, 3, 3) INFO net.py: 96: res5_0_branch2b_bn_s loaded from weights file into gpu_0/res5_0_branch2b_bn_s: (2048,) INFO net.py: 96: res5_0_branch2b_bn_b loaded from weights file into gpu_0/res5_0_branch2b_bn_b: (2048,) INFO net.py: 96: res5_0_branch2c_w [+ momentum] loaded from weights file into gpu_0/res5_0_branch2c_w: (2048, 2048, 1, 1) INFO net.py: 96: res5_0_branch2c_bn_s loaded from weights file into gpu_0/res5_0_branch2c_bn_s: (2048,) INFO net.py: 96: res5_0_branch2c_bn_b loaded from weights file into gpu_0/res5_0_branch2c_bn_b: (2048,) INFO net.py: 96: res5_0_branch1_w [+ momentum] loaded from weights file into gpu_0/res5_0_branch1_w: (2048, 1024, 1, 1) INFO net.py: 96: res5_0_branch1_bn_s loaded from weights file into gpu_0/res5_0_branch1_bn_s: (2048,) INFO net.py: 96: res5_0_branch1_bn_b loaded from weights file into gpu_0/res5_0_branch1_bn_b: (2048,) INFO net.py: 96: res5_1_branch2a_w [+ momentum] loaded from weights file into gpu_0/res5_1_branch2a_w: (2048, 2048, 1, 1) INFO net.py: 96: res5_1_branch2a_bn_s loaded from weights file into gpu_0/res5_1_branch2a_bn_s: (2048,) INFO net.py: 96: res5_1_branch2a_bn_b loaded from weights file into gpu_0/res5_1_branch2a_bn_b: (2048,) INFO net.py: 96: res5_1_branch2b_w [+ momentum] loaded from weights file into gpu_0/res5_1_branch2b_w: (2048, 64, 3, 3) INFO net.py: 96: res5_1_branch2b_bn_s loaded from weights file into gpu_0/res5_1_branch2b_bn_s: (2048,) INFO net.py: 96: res5_1_branch2b_bn_b loaded from weights file into gpu_0/res5_1_branch2b_bn_b: (2048,) INFO net.py: 96: res5_1_branch2c_w [+ momentum] loaded from weights file into gpu_0/res5_1_branch2c_w: (2048, 2048, 1, 1) INFO net.py: 96: res5_1_branch2c_bn_s loaded from weights file into gpu_0/res5_1_branch2c_bn_s: (2048,) INFO net.py: 96: res5_1_branch2c_bn_b loaded from weights file into gpu_0/res5_1_branch2c_bn_b: (2048,) INFO net.py: 96: res5_2_branch2a_w [+ momentum] loaded from weights file into gpu_0/res5_2_branch2a_w: (2048, 2048, 1, 1) INFO net.py: 96: res5_2_branch2a_bn_s loaded from weights file into gpu_0/res5_2_branch2a_bn_s: (2048,) INFO net.py: 96: res5_2_branch2a_bn_b loaded from weights file into gpu_0/res5_2_branch2a_bn_b: (2048,) INFO net.py: 96: res5_2_branch2b_w [+ momentum] loaded from weights file into gpu_0/res5_2_branch2b_w: (2048, 64, 3, 3) INFO net.py: 96: res5_2_branch2b_bn_s loaded from weights file into gpu_0/res5_2_branch2b_bn_s: (2048,) INFO net.py: 96: res5_2_branch2b_bn_b loaded from weights file into gpu_0/res5_2_branch2b_bn_b: (2048,) INFO net.py: 96: res5_2_branch2c_w [+ momentum] loaded from weights file into gpu_0/res5_2_branch2c_w: (2048, 2048, 1, 1) INFO net.py: 96: res5_2_branch2c_bn_s loaded from weights file into gpu_0/res5_2_branch2c_bn_s: (2048,) INFO net.py: 96: res5_2_branch2c_bn_b loaded from weights file into gpu_0/res5_2_branch2c_bn_b: (2048,) INFO net.py: 96: fpn_inner_res5_2_sum_w [+ momentum] loaded from weights file into gpu_0/fpn_inner_res5_2_sum_w: (256, 2048, 1, 1) INFO net.py: 96: fpn_inner_res5_2_sum_b [+ momentum] loaded from weights file into gpu_0/fpn_inner_res5_2_sum_b: (256,) INFO net.py: 96: fpn_inner_res4_22_sum_lateral_w [+ momentum] loaded from weights file into gpu_0/fpn_inner_res4_22_sum_lateral_w: (256, 1024, 1, 1) INFO net.py: 96: fpn_inner_res4_22_sum_lateral_b [+ momentum] loaded from weights file into gpu_0/fpn_inner_res4_22_sum_lateral_b: (256,) INFO net.py: 96: fpn_inner_res3_3_sum_lateral_w [+ momentum] loaded from weights file into gpu_0/fpn_inner_res3_3_sum_lateral_w: (256, 512, 1, 1) INFO net.py: 96: fpn_inner_res3_3_sum_lateral_b [+ momentum] loaded from weights file into gpu_0/fpn_inner_res3_3_sum_lateral_b: (256,) INFO net.py: 96: fpn_inner_res2_2_sum_lateral_w [+ momentum] loaded from weights file into gpu_0/fpn_inner_res2_2_sum_lateral_w: (256, 256, 1, 1) INFO net.py: 96: fpn_inner_res2_2_sum_lateral_b [+ momentum] loaded from weights file into gpu_0/fpn_inner_res2_2_sum_lateral_b: (256,) INFO net.py: 96: fpn_res5_2_sum_w [+ momentum] loaded from weights file into gpu_0/fpn_res5_2_sum_w: (256, 256, 3, 3) INFO net.py: 96: fpn_res5_2_sum_b [+ momentum] loaded from weights file into gpu_0/fpn_res5_2_sum_b: (256,) INFO net.py: 96: fpn_res4_22_sum_w [+ momentum] loaded from weights file into gpu_0/fpn_res4_22_sum_w: (256, 256, 3, 3) INFO net.py: 96: fpn_res4_22_sum_b [+ momentum] loaded from weights file into gpu_0/fpn_res4_22_sum_b: (256,) INFO net.py: 96: fpn_res3_3_sum_w [+ momentum] loaded from weights file into gpu_0/fpn_res3_3_sum_w: (256, 256, 3, 3) INFO net.py: 96: fpn_res3_3_sum_b [+ momentum] loaded from weights file into gpu_0/fpn_res3_3_sum_b: (256,) INFO net.py: 96: fpn_res2_2_sum_w [+ momentum] loaded from weights file into gpu_0/fpn_res2_2_sum_w: (256, 256, 3, 3) INFO net.py: 96: fpn_res2_2_sum_b [+ momentum] loaded from weights file into gpu_0/fpn_res2_2_sum_b: (256,) INFO net.py: 96: fc6_w [+ momentum] loaded from weights file into gpu_0/fc6_w: (1024, 12544) INFO net.py: 96: fc6_b [+ momentum] loaded from weights file into gpu_0/fc6_b: (1024,) INFO net.py: 96: fc7_w [+ momentum] loaded from weights file into gpu_0/fc7_w: (1024, 1024) INFO net.py: 96: fc7_b [+ momentum] loaded from weights file into gpu_0/fc7_b: (1024,) INFO net.py: 96: cls_score_w [+ momentum] loaded from weights file into gpu_0/cls_score_w: (7, 1024) INFO net.py: 96: cls_score_b [+ momentum] loaded from weights file into gpu_0/cls_score_b: (7,) INFO net.py: 96: bbox_pred_w [+ momentum] loaded from weights file into gpu_0/bbox_pred_w: (28, 1024) INFO net.py: 96: bbox_pred_b [+ momentum] loaded from weights file into gpu_0/bbox_pred_b: (28,) INFO net.py: 133: pred_b preserved in workspace (unused) INFO net.py: 133: pred_w preserved in workspace (unused) [I net_dag_utils.cc:102] Operator graph pruning prior to chain compute took: 0.00015352 secs [I net_dag_utils.cc:102] Operator graph pruning prior to chain compute took: 0.000154624 secs [I net_async_base.h:207] Using specified CPU pool size: 4; device id: -1 [I net_async_base.h:212] Created new CPU pool, size: 4; device id: -1 [E net_async_base.cc:382] [enforce fail at conv_op_cudnn.cc:554] filter.dim32(1) == C / group_. 8 vs 256 Error from operator: input: "gpu_0/res2_0_branch2a" input: "gpu_0/res2_0_branch2b_w" output: "gpu_0/res2_0_branch2b" name: "" type: "Conv" arg { name: "kernel" i: 3 } arg { name: "order" s: "NCHW" } arg { name: "stride" i: 1 } arg { name: "pad" i: 1 } arg { name: "dilation" i: 1 } arg { name: "exhaustive_search" i: 0 } device_option { device_type: 1 device_id: 0 } engine: "CUDNN"frame #0: c10::ThrowEnforceNotMet(char const*, int, char const*, std::string const&, void const*) + 0x5b (0x7f52136813bb in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #1: <unknown function> + 0x44a5b4f (0x7f5217f78b4f in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #2: <unknown function> + 0x4491988 (0x7f5217f64988 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #3: <unknown function> + 0x4414805 (0x7f5217ee7805 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #4: caffe2::AsyncNetBase::run(int, int) + 0x10e (0x7f52163336de in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #5: <unknown function> + 0x28670e7 (0x7f521633a0e7 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #6: c10::ThreadPool::main_loop(unsigned long) + 0x293 (0x7f521367cc73 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #7: <unknown function> + 0x3989f (0x7f521368789f in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #8: <unknown function> + 0x76db (0x7f525cd986db in /lib/x86_64-linux-gnu/libpthread.so.0) frame #9: clone + 0x3f (0x7f525cac171f in /lib/x86_64-linux-gnu/libc.so.6) , op Conv [E net_async_base.cc:134] Rethrowing exception from the run of 'generalized_rcnn' WARNING workspace.py: 223: Original python traceback for operator 7 in network generalized_rcnn in exception above (most recent call last): WARNING workspace.py: 228: File "inference.py", line 197, in <module> WARNING workspace.py: 228: File "inference.py", line 143, in main WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/core/test_engine.py", line 327, in initialize_model_from_cfg WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/model_builder.py", line 124, in create WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/model_builder.py", line 89, in generalized_rcnn WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/model_builder.py", line 229, in build_generic_detection_model WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/optimizer.py", line 54, in build_data_parallel_model WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/model_builder.py", line 169, in _single_gpu_build_func WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/FPN.py", line 63, in add_fpn_ResNet101_conv5_body WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/FPN.py", line 104, in add_fpn_onto_conv_body WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/ResNet.py", line 48, in add_ResNet101_conv5_body WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/ResNet.py", line 103, in add_ResNet_convX_body WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/ResNet.py", line 85, in add_stage WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/ResNet.py", line 183, in add_residual_block WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/ResNet.py", line 316, in bottleneck_transformation WARNING workspace.py: 228: File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/modeling/detector.py", line 407, in ConvAffine WARNING workspace.py: 228: File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/cnn.py", line 97, in Conv WARNING workspace.py: 228: File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/brew.py", line 108, in scope_wrapper WARNING workspace.py: 228: File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/helpers/conv.py", line 186, in conv WARNING workspace.py: 228: File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/helpers/conv.py", line 139, in ConvBase Traceback (most recent call last): File "inference.py", line 197, in <module> main(args) File "inference.py", line 146, in main model_engine.im_detect_all(model, im, proposal_boxes) File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/core/test.py", line 66, in im_detect_all model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes=box_proposals File "/home/ZT/ZT_Project/RRPN-master/detectron/detectron/core/test.py", line 158, in im_detect_bbox workspace.RunNet(model.net.Proto().name) File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/workspace.py", line 255, in RunNet StringifyNetName(name), num_iter, allow_fail, File "/home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/workspace.py", line 216, in CallWithExceptionIntercept return func(*args, **kwargs) RuntimeError: [enforce fail at conv_op_cudnn.cc:554] filter.dim32(1) == C / group. 8 vs 256 Error from operator: input: "gpu_0/res2_0_branch2a" input: "gpu_0/res2_0_branch2b_w" output: "gpu_0/res2_0_branch2b" name: "" type: "Conv" arg { name: "kernel" i: 3 } arg { name: "order" s: "NCHW" } arg { name: "stride" i: 1 } arg { name: "pad" i: 1 } arg { name: "dilation" i: 1 } arg { name: "exhaustive_search" i: 0 } device_option { device_type: 1 device_id: 0 } engine: "CUDNN"frame #0: c10::ThrowEnforceNotMet(char const*, int, char const*, std::string const&, void const*) + 0x5b (0x7f52136813bb in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #1: <unknown function> + 0x44a5b4f (0x7f5217f78b4f in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #2: <unknown function> + 0x4491988 (0x7f5217f64988 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #3: <unknown function> + 0x4414805 (0x7f5217ee7805 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #4: caffe2::AsyncNetBase::run(int, int) + 0x10e (0x7f52163336de in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #5: <unknown function> + 0x28670e7 (0x7f521633a0e7 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libtorch.so) frame #6: c10::ThreadPool::main_loop(unsigned long) + 0x293 (0x7f521367cc73 in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #7: <unknown function> + 0x3989f (0x7f521368789f in /home/ZT/anaconda3/envs/pytorch-1.4.0/lib/python3.7/site-packages/caffe2/python/../../torch/lib/libc10.so) frame #8: <unknown function> + 0x76db (0x7f525cd986db in /lib/x86_64-linux-gnu/libpthread.so.0) frame #9: clone + 0x3f (0x7f525cac171f in /lib/x86_64-linux-gnu/libc.so.6)

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issue openedmrnabati/RRPN

how to make my own dataset using radar

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fork pha-nguyen/CenterFusion

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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startedmrnabati/CenterFusion

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