error when run tools/train.py #6

IAS-01 · 2021-11-23T07:11:46Z

[Warning] Your mxnet does not support ProposalTarget [Warning] Your mxnet does not support DecodeBBox [Warning] Your mxnet does not support BBoxNorm [Warning] Your mxnet does not support FocalLoss Traceback (most recent call last): File "tools/train.py", line 412, in <module> train_net(parse_args()) File "tools/train.py", line 407, in parse_args config = importlib.import_module(args.config.replace('.py', '').replace('/', '.')) File "/mmdetection3d/RangeDet/config/rangedet/rangedet_wo_aug_4_18e.py", line 22, in <module> from rangedet.symbol.head.builder import RangeRCNN as Detector File "/mmdetection3d/RangeDet/rangedet/symbol/head/builder.py", line 4, in <module> from operator_py import \ File "/mmdetection3d/RangeDet/operator_py/batch_rotated_iou.py", line 3, in <module> from mxnet.ndarray.contrib import RotatedIOU ImportError: cannot import name 'RotatedIOU' from 'mxnet.ndarray.contrib' (/usr/local/lib/python3.7/dist-packages/mxnet/ndarray/contrib.py)

the link to download MxNet is invalid, can you provide a new link?

this is provided by the SimpleDet, but it is invalid
https://1dv.aflat.top/mxnet_cu101-1.6.0b20191214-py2.py3-none-manylinux1_x86_64.whl

The text was updated successfully, but these errors were encountered:

Abyssaledge · 2021-11-23T07:27:23Z

Sorry for the inconvenience. This error is raised because our customized MXNet operators in operator_cxx/contrib is not built.
First you need to clone MXNet from its repo and build it from the source. This is because the release version of MXNet is 1.8.0, which does not support numpy-style API. If you are not familiar with the whole installation process, please be patient and wait for a couple of days. We will provide detailed instructions after finishing this period of CVPR. A little bit busy now.

IAS-01 · 2021-11-23T07:33:20Z

Sorry for the inconvenience. This error is raised because our customized MXNet operators in operator_cxx/contrib is not built. First you need to clone MXNet from its repo and build it from the source. This is because the release version of MXNet is 1.8.0, which does not support numpy-style API. If you are not familiar with the whole installation process, please be patient and wait for a couple of days. We will provide detailed instructions after finishing this period of CVPR. A little bit busy now.

Thank you very much, can you provide a compiled Mxnet whl package, if it is convenient

Abyssaledge · 2021-11-23T07:49:53Z

We have not tried to make the wheel file before. This might be a solution, but we need time to check. I suggest you follow our upcoming instructions if you are still stuck at this moment and not in a hurry.

Treemann · 2021-12-20T08:13:23Z

Hi @Abyssaledge ,
I prepare all the dependencies and try with mxnet-2.0 and error occurs.
from mxnet import metric ImportError: cannot import name 'metric'
Because in mxnet-2.0 metrics is under mxnet.gloun, I believe your code is developed under version<2.
Then I install mxnet-1.9.x and install rangedet by "pip install -v -e ." and there is no error in the log, but I notice that your self-defined operators seems not be compiled and installed. Is there anything else to do for the installation

[Warning] Your mxnet does not support ProposalTarget
[Warning] Your mxnet does not support DecodeBBox
[Warning] Your mxnet does not support BBoxNorm
[Warning] Your mxnet does not support FocalLoss
.......
    from mxnet.ndarray.contrib import RotatedIOU
ImportError: cannot import name 'RotatedIOU'

@IAS-01 Have you solve the problem ?

Abyssaledge · 2021-12-20T08:19:26Z

@Treemann Could you please provide the detailed information how you install MXNet and customized operators?

Treemann · 2021-12-20T08:28:43Z

@Abyssaledge , I follow the instruction of mxnet

# https://mxnet.apache.org/versions/1.8.0/get_started/build_from_source.html
git clone -b v1.9.x https://github.com/apache/incubator-mxnet.git --recursive
cd incubator-mxnet
cp config/linux_gpu.cmake config.cmake
mkdir build; cd build
cmake ..
cmake --build .
cd ..
python3 -m pip install -e ./python

Then following your instruction:
pip install -v -e .

Abyssaledge · 2021-12-20T08:34:40Z

@Treemann
Thanks for information and sorry for this inconvenience. The error about metric import is weird, because we develope RangeDet with MXNet 2.0 and we never meet this error. We will check this in couple of days and answer you as soon as we figure it out.

Treemann · 2021-12-20T08:40:14Z

@Abyssaledge FYI
For trying mxnet-2.0, I used mxnet-2.0.0a0 with pip installation since there is a released pip version, I didn't build mxnet-2.0 from source code.

Abyssaledge · 2021-12-26T09:12:04Z

@Treemann Hi, I found that the API of mxnet2.0 (in the master branch) has changed. You can pull the newest MXNet from the master branch and checkout to 1496c91871b9d81d6a18785bdc8a1c3450bedbca commit and build it from source, where we conduct our experiments and from mxnet import metric works fine.
As for the customized operators, you should modify the mx_home to your MXNet directory and simply run make in operator_cxx/contrib.
BTW, we have updated the train.py and test.py to import the customized operator.

Treemann · 2021-12-27T10:29:15Z

Thanks for your reply.
I'll try following your instruction and give some feedback.

Treemann · 2021-12-30T06:45:10Z

Hi @Abyssaledge, I followed your instruction to install all the dependencies (mxnet, openmpi, etc.), the aforementioned error disappeared, but some other error occurred.
Waiting for your update about the docs on environment setup.

[Warning] Your mxnet does not support ProposalTarget
[Warning] Your mxnet does not support DecodeBBox
[Warning] Your mxnet does not support BBoxNorm
[Warning] Your mxnet does not support FocalLoss
Please using new data mean and var
-------------------- meta_baseline_bias --------------------
*** An error occurred in MPI_Init_thread
*** on a NULL communicator
*** MPI_ERRORS_ARE_FATAL (processes in this communicator will now abort,
***    and potentially your MPI job)
......
  ompi_mpi_init: ompi_rte_init failed
  --> Returned "(null)" (-43) instead of "Success" (0)

Abyssaledge · 2021-12-30T06:51:41Z

Can you successfully run the code with single GPU training? @Treemann

Treemann · 2021-12-30T09:27:42Z

@Abyssaledge , I find the problem is related to horovod.
After I change the cuda and nccl version, horovod should be recompiled & reinstalled.
Now the training goes well. Thanks!

ruanhao5 · 2022-04-15T11:43:37Z

Hi, @Abyssaledge, I follow the instruction of mxnet:

git clone --recursive https://github.com/apache/incubator-mxnet mxnet
cd mxnet
git checkout 1496c91871b9d81d6a18785bdc8a1c3450bedbca
sudo apt-get update
sudo apt-get install -y build-essential git ninja-build ccache libopenblas-dev libopencv-dev cmake
cp config/linux_gpu.cmake config.cmake
mkdir build; cd build
cmake ..

After running cmake .., it reported an error as follow:

-- The C compiler identification is GNU 7.4.0                         
-- The CXX compiler identification is GNU 7.4.0                       
-- Detecting C compiler ABI info                                      
-- Detecting C compiler ABI info - done                               
-- Check for working C compiler: /usr/bin/cc - skipped                
-- Detecting C compile features                                       
-- Detecting C compile features - done                                
-- Detecting CXX compiler ABI info                                    
-- Detecting CXX compiler ABI info - done                             
-- Check for working CXX compiler: /usr/bin/c++ - skipped             
-- Detecting CXX compile features                                     
-- Detecting CXX compile features - done                              
-- CMAKE_CROSSCOMPILING FALSE                                         
-- CMAKE_HOST_SYSTEM_PROCESSOR x86_64                                 
-- CMAKE_SYSTEM_PROCESSOR x86_64                                      
-- CMAKE_SYSTEM_NAME Linux                                            
-- CMake version '3.22.3' using generator 'Unix Makefiles'            
-- Looking for a CUDA compiler                                        
-- Looking for a CUDA compiler - /data/cuda/cuda-11.2/cuda/bin/nvcc   
-- The CUDA compiler identification is NVIDIA 11.2.142                
-- Detecting CUDA compiler ABI info                                   
-- Detecting CUDA compiler ABI info - done                            
-- Check for working CUDA compiler: /data/cuda/cuda-11.2/cuda/bin/nvcc
 - skipped                                                            
-- Detecting CUDA compile features                                    
-- Detecting CUDA compile features - done                             
CMake Error at CMakeLists.txt:255 (add_subdirectory):                 
  The source directory                                                
                                                                      
    /home/ruanhao/mxnet/3rdparty/mkldnn                               
                                                                      
  does not contain a CMakeLists.txt file.

I find that mkldnn is a empty folder and its path is mxnet/3rdparty.
When branch is master, mxnet/3rdparty doesn't have 'mkldnn' folder. When checout 1496c91871b9d81d6a18785bdc8a1c3450bedbca, mxnet/3rdparty will create empty 'mkldnn' folder.

Could you please provide detailed installation instructions for mxnet? Thanks.

Abyssaledge · 2022-04-15T12:04:01Z

I did not occur this error if I recall correctly. I will try to reproduce this error in my free time. But I cannot guarantee the solution, it's indeed tricky to install MXNet in different environments . @ruanhao5

ruanhao5 · 2022-04-17T06:55:53Z

@Abyssaledge Thanks for your reply.
I had solved that error.
I think mkldnn is a submodule in mxnet. So I run the command:

git submodule sync
git submodule update --init --recursive

After that, there is no error.

ruanhao5 · 2022-04-18T11:44:04Z

@Abyssaledge Hi, I had installed all the dependencies (mxnet, openmpi, etc.).
As for the customized operators, I modified the mx_home to my MXNet directory and simply run make in operator_cxx/contrib.
Some other error occurred:

g++ -std=c++11 -shared -o contrib_cxx.so nms_3d.cc decode_3d_bbox.cc rotated_iou.cc nms_3d.o rotated_iou.o decode_3d_bbox.o -O2 -msse2 -msse3 -fPIC -I/home/ruanhao/range_view/mxnet/3rdparty/dmlc-core/include -I/home/ruanhao/range_view/mxnet/3rdparty/nnvm/include -I/home/ruanhao/range_view/mxnet/include -I/home/ruanhao/range_view/mxnet/3rdparty/mshadow -I/home/ruanhao/range_view/mxnet/3rdparty/dlpack/include -I/data/cuda/cuda-11.2/cuda/include -I/home/ruanhao/range_view/mxnet/src/operator -I/home/ruanhao/range_view/mxnet/src/common -I/data/cuda/cuda-11.2/cudnn/v8.1.0/include -D MSHADOW_USE_CBLAS -D MSHADOW_USE_CUDA -D MSHADOW_USE_CUDNN=1 -D MSHADOW_USE_CUSOLVER=1 -DMSHADOW_USE_F16C=0 -L/home/ruanhao/range_view/mxnet/lib -lmxnet
/usr/bin/ld: cannot find -lmxnet
collect2: error: ld returned 1 exit status
Makefile:30: recipe for target 'contrib_cxx.so' failed
make: *** [contrib_cxx.so] Error 1

I opened path /home/ruanhao/range_view/RangeDet/operator_cxx/contrib/ and checked Makefile file:

mx_home=/home/ruanhao/range_view/mxnet
dmlc_inc=${mx_home}/3rdparty/dmlc-core/include
nnvm_inc=${mx_home}/3rdparty/nnvm/include
mshadow_inc=${mx_home}/3rdparty/mshadow
dlpack_inc=${mx_home}/3rdparty/dlpack/include
mxnet_src_inc=${mx_home}/src/operator
mxnet_src_com=${mx_home}/src/common
mxnet_inc=${mx_home}/include
cuda_inc=/data/cuda/cuda-11.2/cuda/include
cudnn_inc=/data/cuda/cuda-11.2/cudnn/v8.1.0/include

mxnet_lib=${mx_home}

INC = -I${dmlc_inc} -I${nnvm_inc} -I${mxnet_inc} -I${mshadow_inc} -I${dlpack_inc} -I${cuda_inc} -I${mxnet_src_inc} -I${mxnet_src_com} -I${cudnn_inc}
DEFINE = -D MSHADOW_USE_CBLAS -D MSHADOW_USE_CUDA -D MSHADOW_USE_CUDNN=1 -D MSHADOW_USE_CUSOLVER=1 -DMSHADOW_USE_F16C=0
LIB = -L${mxnet_lib}/lib -lmxnet
# LIB = -L/home/ruanhao/range_view/mxnet/build -lmxnet

all: clean build cleancache

build: contrib_cxx.so

%.o: %.cu
	nvcc -std=c++11 -c -o $@ $? -O2 -x cu -Xcompiler -fPIC ${INC} ${DEFINE} 
contrib_cxx.so: $(wildcard *.cc) $(subst .cu,.o, $(wildcard *.cu))
	g++ -std=c++11 -shared -o $@ $? -O2 -msse2 -msse3 -fPIC ${INC} ${DEFINE} ${LIB}

clean:
	rm -f *.o *.so *.pyc

cleancache:
	rm -f *.o

I think the problem is the LIB path and then I opened the path ${mxnet_lib}. There was no lib folder.
But, path /home/ruanhao/range_view/mxnet/build had libmxnet.so and libmxnet.a files.
I guess LIB should be LIB = -L/home/ruanhao/range_view/mxnet/build -lmxnet. It didn't occur errors after running make in operator_cxx/contrib.

I run python tools/train.py --config "config/rangedet/rangedet_veh_wo_aug_all_36e.py" and error occurred:

Traceback (most recent call last):
  File "tools/train.py", line 2, in <module>
    _=ctypes.CDLL('./operator_cxx/contrib/contrib_cxx.so')
  File "/home/ruanhao/miniconda3/envs/rangedet/lib/python3.7/ctypes/__init__.py", line 364, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: libmxnet.so: cannot open shared object file: No such file or directory

Hence, customized operators did not install successfully.
Could you please provide some advice?

happynear · 2022-04-18T12:49:26Z

Now It seems that you already get './operator_cxx/contrib/contrib_cxx.so'. The problem is the program cannot find libmxnet.so. Maybe you can try to add the folder of libmxnet.so into PATH?

ruanhao5 · 2022-04-19T03:09:03Z

@happynear Hi, I added the folder of libmxnet.so into LD_LIBRARY_PATH:

export LD_LIBRARY_PATH="/home/ruanhao/range_view/mxnet/build:$LD_LIBRARY_PATH"

The error disappeared. Thanks for your reply.

ruanhao5 · 2022-04-20T05:44:14Z

@happynear @Abyssaledge Hi, I run python tools/train.py --config "config/rangedet/rangedet_veh_wo_aug_all_36e.py" and error occurred:

Click to expand error!

[Warning] Your mxnet does not support ProposalTarget
[Warning] Your mxnet does not support DecodeBBox
[Warning] Your mxnet does not support BBoxNorm
[Warning] Your mxnet does not support FocalLoss
Please using new data mean and var
-------------------- meta_baseline_bias --------------------
04-19 11:01:54 rank:0/1 gpu list:[0, 1, 2, 3, 4, 5, 6, 7]
cpu allowed list len:8, [36, 37, 38, 39, 40, 41, 42, 43]
04-19 11:01:54 rank: 0, pid:237, affinity to cpu [36, 37, 38, 39, 40, 41, 42, 43]
04-19 11:01:54 [0/1] local sync flag:True
04-19 11:01:54 input batch size:16
load all training data with 39447 records
loader rank:0, partition count:39447
{'input_data': (16, 8, 64, 2656), 'rpn_reg_target_s1': (16, 8, 64, 2656), 'rpn_reg_target_s2': (16, 8, 64, 1328), 'rpn_reg_target_s4': (16, 8, 64, 664), 'rpn_reg_weight_s1': (16, 8, 64, 2656), 'rpn_reg_weight_s2': (16, 8, 64, 1328), 'rpn_reg_weight_s4': (16, 8, 64, 664), 'range_image_mask_s1': (16, 1, 64, 2656), 'range_image_mask_s2': (16, 1, 64, 1328), 'range_image_mask_s4': (16, 1, 64, 664), 'reg_normalize_weight_s1': (16, 8, 64, 2656), 'reg_normalize_weight_s2': (16, 8, 64, 1328), 'reg_normalize_weight_s4': (16, 8, 64, 664), 'pc_vehicle_frame_s1': (16, 169984, 3), 'pc_vehicle_frame_s2': (16, 84992, 3), 'pc_vehicle_frame_s4': (16, 42496, 3), 'gt_bbox_veh_for_iou_pred': (16, 200, 8), 'coord_s1': (16, 3, 64, 2656)}
04-19 11:01:59 parameter shape
04-19 11:01:59 [('input_data', (2, 8, 64, 2656)),
 ('res1_unit1_conv1_weight', (64, 8, 3, 3)),
 ('res1_unit1_bn1_gamma', (64,)),
 ('res1_unit1_bn1_beta', (64,)),
 ('res1_unit1_bn1_moving_mean', (64,)),
 ('res1_unit1_bn1_moving_var', (64,)),
 ('res1_unit1_conv2_weight', (64, 64, 3, 3)),
 ('res1_unit1_bn2_gamma', (64,)),
 ('res1_unit1_bn2_beta', (64,)),
 ('res1_unit1_bn2_moving_mean', (64,)),
 ('res1_unit1_bn2_moving_var', (64,)),
 ('res1_unit1_sc_weight', (64, 8, 1, 1)),
 ('res1_unit1_sc_bn_gamma', (64,)),
 ('res1_unit1_sc_bn_beta', (64,)),
 ('res1_unit1_sc_bn_moving_mean', (64,)),
 ('res1_unit1_sc_bn_moving_var', (64,)),
 ('coord_s1', (2, 3, 64, 2656)),
 ('res1_unit2_2656_mlp0_weight', (32, 3, 1, 1)),
 ('res1_unit2_2656_mlp0_bias', (32,)),
 ('res1_unit2_2656_mlp1_weight', (64, 32, 1, 1)),
 ('res1_unit2_2656_mlp1_bias', (64,)),
 ('res1_unit2point_wise_mlp_bn1_gamma', (576,)),
 ('res1_unit2point_wise_mlp_bn1_beta', (576,)),
 ('res1_unit2point_wise_mlp_bn1_moving_mean', (576,)),
 ('res1_unit2point_wise_mlp_bn1_moving_var', (576,)),
 ('res1_unit2aggregation_conv1_weight', (64, 576, 1, 1)),
 ('res1_unit2aggregation_bn1_gamma', (64,)),
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 ('res3a_unit1_conv1_weight', (128, 128, 3, 3)),
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 ('res3a_unit1_bn2_moving_mean', (128,)),
 ('res3a_unit1_bn2_moving_var', (128,)),
 ('res3a_unit1_sc_weight', (128, 128, 1, 1)),
 ('res3a_unit1_sc_bn_gamma', (128,)),
 ('res3a_unit1_sc_bn_beta', (128,)),
 ('res3a_unit1_sc_bn_moving_mean', (128,)),
 ('res3a_unit1_sc_bn_moving_var', (128,)),
 ('res3a_unit2_conv1_weight', (128, 128, 3, 3)),
 ('res3a_unit2_bn1_gamma', (128,)),
 ('res3a_unit2_bn1_beta', (128,)),
 ('res3a_unit2_bn1_moving_mean', (128,)),
 ('res3a_unit2_bn1_moving_var', (128,)),
 ('res3a_unit2_conv2_weight', (128, 128, 3, 3)),
 ('res3a_unit2_bn2_gamma', (128,)),
 ('res3a_unit2_bn2_beta', (128,)),
 ('res3a_unit2_bn2_moving_mean', (128,)),
 ('res3a_unit2_bn2_moving_var', (128,)),
 ('res3a_unit3_conv1_weight', (128, 128, 3, 3)),
 ('res3a_unit3_bn1_gamma', (128,)),
 ('res3a_unit3_bn1_beta', (128,)),
 ('res3a_unit3_bn1_moving_mean', (128,)),
 ('res3a_unit3_bn1_moving_var', (128,)),
 ('res3a_unit3_conv2_weight', (128, 128, 3, 3)),
 ('res3a_unit3_bn2_gamma', (128,)),
 ('res3a_unit3_bn2_beta', (128,)),
 ('res3a_unit3_bn2_moving_mean', (128,)),
 ('res3a_unit3_bn2_moving_var', (128,)),
 ('res3a_unit4_conv1_weight', (128, 128, 3, 3)),
 ('res3a_unit4_bn1_gamma', (128,)),
 ('res3a_unit4_bn1_beta', (128,)),
 ('res3a_unit4_bn1_moving_mean', (128,)),
 ('res3a_unit4_bn1_moving_var', (128,)),
 ('res3a_unit4_conv2_weight', (128, 128, 3, 3)),
 ('res3a_unit4_bn2_gamma', (128,)),
 ('res3a_unit4_bn2_beta', (128,)),
 ('res3a_unit4_bn2_moving_mean', (128,)),
 ('res3a_unit4_bn2_moving_var', (128,)),
 ('res3a_unit5_conv1_weight', (128, 128, 3, 3)),
 ('res3a_unit5_bn1_gamma', (128,)),
 ('res3a_unit5_bn1_beta', (128,)),
 ('res3a_unit5_bn1_moving_mean', (128,)),
 ('res3a_unit5_bn1_moving_var', (128,)),
 ('res3a_unit5_conv2_weight', (128, 128, 3, 3)),
 ('res3a_unit5_bn2_gamma', (128,)),
 ('res3a_unit5_bn2_beta', (128,)),
 ('res3a_unit5_bn2_moving_mean', (128,)),
 ('res3a_unit5_bn2_moving_var', (128,)),
 ('res3_unit1_conv1_weight', (128, 128, 3, 3)),
 ('res3_unit1_bn1_gamma', (128,)),
 ('res3_unit1_bn1_beta', (128,)),
 ('res3_unit1_bn1_moving_mean', (128,)),
 ('res3_unit1_bn1_moving_var', (128,)),
 ('res3_unit1_conv2_weight', (128, 128, 3, 3)),
 ('res3_unit1_bn2_gamma', (128,)),
 ('res3_unit1_bn2_beta', (128,)),
 ('res3_unit1_bn2_moving_mean', (128,)),
 ('res3_unit1_bn2_moving_var', (128,)),
 ('res3_unit1_sc_weight', (128, 128, 1, 1)),
 ('res3_unit1_sc_bn_gamma', (128,)),
 ('res3_unit1_sc_bn_beta', (128,)),
 ('res3_unit1_sc_bn_moving_mean', (128,)),
 ('res3_unit1_sc_bn_moving_var', (128,)),
 ('res3_unit2_conv1_weight', (128, 128, 3, 3)),
 ('res3_unit2_bn1_gamma', (128,)),
 ('res3_unit2_bn1_beta', (128,)),
 ('res3_unit2_bn1_moving_mean', (128,)),
 ('res3_unit2_bn1_moving_var', (128,)),
 ('res3_unit2_conv2_weight', (128, 128, 3, 3)),
 ('res3_unit2_bn2_gamma', (128,)),
 ('res3_unit2_bn2_beta', (128,)),
 ('res3_unit2_bn2_moving_mean', (128,)),
 ('res3_unit2_bn2_moving_var', (128,)),
 ('res3_unit3_conv1_weight', (128, 128, 3, 3)),
 ('res3_unit3_bn1_gamma', (128,)),
 ('res3_unit3_bn1_beta', (128,)),
 ('res3_unit3_bn1_moving_mean', (128,)),
 ('res3_unit3_bn1_moving_var', (128,)),
 ('res3_unit3_conv2_weight', (128, 128, 3, 3)),
 ('res3_unit3_bn2_gamma', (128,)),
 ('res3_unit3_bn2_beta', (128,)),
 ('res3_unit3_bn2_moving_mean', (128,)),
 ('res3_unit3_bn2_moving_var', (128,)),
 ('res3_unit4_conv1_weight', (128, 128, 3, 3)),
 ('res3_unit4_bn1_gamma', (128,)),
 ('res3_unit4_bn1_beta', (128,)),
 ('res3_unit4_bn1_moving_mean', (128,)),
 ('res3_unit4_bn1_moving_var', (128,)),
 ('res3_unit4_conv2_weight', (128, 128, 3, 3)),
 ('res3_unit4_bn2_gamma', (128,)),
 ('res3_unit4_bn2_beta', (128,)),
 ('res3_unit4_bn2_moving_mean', (128,)),
 ('res3_unit4_bn2_moving_var', (128,)),
 ('res3_unit5_conv1_weight', (128, 128, 3, 3)),
 ('res3_unit5_bn1_gamma', (128,)),
 ('res3_unit5_bn1_beta', (128,)),
 ('res3_unit5_bn1_moving_mean', (128,)),
 ('res3_unit5_bn1_moving_var', (128,)),
 ('res3_unit5_conv2_weight', (128, 128, 3, 3)),
 ('res3_unit5_bn2_gamma', (128,)),
 ('res3_unit5_bn2_beta', (128,)),
 ('res3_unit5_bn2_moving_mean', (128,)),
 ('res3_unit5_bn2_moving_var', (128,)),
 ('agg2_deconv_weight', (128, 128, 3, 8)),
 ('agg2_deconv_bn_gamma', (128,)),
 ('agg2_deconv_bn_beta', (128,)),
 ('agg2_deconv_bn_moving_mean', (128,)),
 ('agg2_deconv_bn_moving_var', (128,)),
 ('agg2_res_unit1_conv1_weight', (128, 128, 3, 3)),
 ('agg2_res_unit1_bn1_gamma', (128,)),
 ('agg2_res_unit1_bn1_beta', (128,)),
 ('agg2_res_unit1_bn1_moving_mean', (128,)),
 ('agg2_res_unit1_bn1_moving_var', (128,)),
 ('agg2_res_unit1_conv2_weight', (128, 128, 3, 3)),
 ('agg2_res_unit1_bn2_gamma', (128,)),
 ('agg2_res_unit1_bn2_beta', (128,)),
 ('agg2_res_unit1_bn2_moving_mean', (128,)),
 ('agg2_res_unit1_bn2_moving_var', (128,)),
 ('agg2_res_unit1_sc_weight', (128, 128, 1, 1)),
 ('agg2_res_unit1_sc_bn_gamma', (128,)),
 ('agg2_res_unit1_sc_bn_beta', (128,)),
 ('agg2_res_unit1_sc_bn_moving_mean', (128,)),
 ('agg2_res_unit1_sc_bn_moving_var', (128,)),
 ('agg2_res_unit2_conv1_weight', (128, 128, 3, 3)),
 ('agg2_res_unit2_bn1_gamma', (128,)),
 ('agg2_res_unit2_bn1_beta', (128,)),
 ('agg2_res_unit2_bn1_moving_mean', (128,)),
 ('agg2_res_unit2_bn1_moving_var', (128,)),
 ('agg2_res_unit2_conv2_weight', (128, 128, 3, 3)),
 ('agg2_res_unit2_bn2_gamma', (128,)),
 ('agg2_res_unit2_bn2_beta', (128,)),
 ('agg2_res_unit2_bn2_moving_mean', (128,)),
 ('agg2_res_unit2_bn2_moving_var', (128,)),
 ('agg2a_deconv_weight', (128, 64, 3, 4)),
 ('agg2a_deconv_bn_gamma', (64,)),
 ('agg2a_deconv_bn_beta', (64,)),
 ('agg2a_deconv_bn_moving_mean', (64,)),
 ('agg2a_deconv_bn_moving_var', (64,)),
 ('agg2a_res_unit1_conv1_weight', (64, 64, 3, 3)),
 ('agg2a_res_unit1_bn1_gamma', (64,)),
 ('agg2a_res_unit1_bn1_beta', (64,)),
 ('agg2a_res_unit1_bn1_moving_mean', (64,)),
 ('agg2a_res_unit1_bn1_moving_var', (64,)),
 ('agg2a_res_unit1_conv2_weight', (64, 64, 3, 3)),
 ('agg2a_res_unit1_bn2_gamma', (64,)),
 ('agg2a_res_unit1_bn2_beta', (64,)),
 ('agg2a_res_unit1_bn2_moving_mean', (64,)),
 ('agg2a_res_unit1_bn2_moving_var', (64,)),
 ('agg2a_res_unit1_sc_weight', (64, 64, 1, 1)),
 ('agg2a_res_unit1_sc_bn_gamma', (64,)),
 ('agg2a_res_unit1_sc_bn_beta', (64,)),
 ('agg2a_res_unit1_sc_bn_moving_mean', (64,)),
 ('agg2a_res_unit1_sc_bn_moving_var', (64,)),
 ('agg3_deconv_weight', (64, 64, 3, 4)),
 ('agg3_deconv_bn_gamma', (64,)),
 ('agg3_deconv_bn_beta', (64,)),
 ('agg3_deconv_bn_moving_mean', (64,)),
 ('agg3_deconv_bn_moving_var', (64,)),
 ('agg3_res_unit1_conv1_weight', (64, 64, 3, 3)),
 ('agg3_res_unit1_bn1_gamma', (64,)),
 ('agg3_res_unit1_bn1_beta', (64,)),
 ('agg3_res_unit1_bn1_moving_mean', (64,)),
 ('agg3_res_unit1_bn1_moving_var', (64,)),
 ('agg3_res_unit1_conv2_weight', (64, 64, 3, 3)),
 ('agg3_res_unit1_bn2_gamma', (64,)),
 ('agg3_res_unit1_bn2_beta', (64,)),
 ('agg3_res_unit1_bn2_moving_mean', (64,)),
 ('agg3_res_unit1_bn2_moving_var', (64,)),
 ('agg3_res_unit1_sc_weight', (64, 64, 1, 1)),
 ('agg3_res_unit1_sc_bn_gamma', (64,)),
 ('agg3_res_unit1_sc_bn_beta', (64,)),
 ('agg3_res_unit1_sc_bn_moving_mean', (64,)),
 ('agg3_res_unit1_sc_bn_moving_var', (64,)),
 ('agg3_res_unit2_conv1_weight', (64, 64, 3, 3)),
 ('agg3_res_unit2_bn1_gamma', (64,)),
 ('agg3_res_unit2_bn1_beta', (64,)),
 ('agg3_res_unit2_bn1_moving_mean', (64,)),
 ('agg3_res_unit2_bn1_moving_var', (64,)),
 ('agg3_res_unit2_conv2_weight', (64, 64, 3, 3)),
 ('agg3_res_unit2_bn2_gamma', (64,)),
 ('agg3_res_unit2_bn2_beta', (64,)),
 ('agg3_res_unit2_bn2_moving_mean', (64,)),
 ('agg3_res_unit2_bn2_moving_var', (64,)),
 ('rpn_reg_conv_0_lvl_0_weight', (128, 72, 3, 3)),
 ('rpn_reg_conv_0_lvl_0_bn_gamma', (128,)),
 ('rpn_reg_conv_0_lvl_0_bn_beta', (128,)),
 ('rpn_reg_conv_0_lvl_0_bn_moving_mean', (128,)),
 ('rpn_reg_conv_0_lvl_0_bn_moving_var', (128,)),
 ('rpn_reg_conv_1_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_1_lvl_0_bn_gamma', (128,)),
 ('rpn_reg_conv_1_lvl_0_bn_beta', (128,)),
 ('rpn_reg_conv_1_lvl_0_bn_moving_mean', (128,)),
 ('rpn_reg_conv_1_lvl_0_bn_moving_var', (128,)),
 ('rpn_reg_conv_2_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_2_lvl_0_bn_gamma', (128,)),
 ('rpn_reg_conv_2_lvl_0_bn_beta', (128,)),
 ('rpn_reg_conv_2_lvl_0_bn_moving_mean', (128,)),
 ('rpn_reg_conv_2_lvl_0_bn_moving_var', (128,)),
 ('rpn_reg_conv_3_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_3_lvl_0_bn_gamma', (128,)),
 ('rpn_reg_conv_3_lvl_0_bn_beta', (128,)),
 ('rpn_reg_conv_3_lvl_0_bn_moving_mean', (128,)),
 ('rpn_reg_conv_3_lvl_0_bn_moving_var', (128,)),
 ('rpn_reg_delta_lvl_0_weight', (8, 128, 1, 1)),
 ('rpn_reg_delta_lvl_0_bias', (8,)),
 ('pc_vehicle_frame_s1', (2, 169984, 3)),
 ('decode3dbbox0_decoded_bbox', (2, 169984, 10)),
 ('gt_bbox_veh_for_iou_pred', (2, 200, 8)),
 ('batch_rotated_iou_veh_lvl_0_iou_map', (2, 169984)),
 ('rpn_cls_conv_0_lvl_0_weight', (128, 72, 3, 3)),
 ('rpn_cls_conv_0_lvl_0_bn_gamma', (128,)),
 ('rpn_cls_conv_0_lvl_0_bn_beta', (128,)),
 ('rpn_cls_conv_0_lvl_0_bn_moving_mean', (128,)),
 ('rpn_cls_conv_0_lvl_0_bn_moving_var', (128,)),
 ('rpn_cls_conv_1_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_1_lvl_0_bn_gamma', (128,)),
 ('rpn_cls_conv_1_lvl_0_bn_beta', (128,)),
 ('rpn_cls_conv_1_lvl_0_bn_moving_mean', (128,)),
 ('rpn_cls_conv_1_lvl_0_bn_moving_var', (128,)),
 ('rpn_cls_conv_2_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_2_lvl_0_bn_gamma', (128,)),
 ('rpn_cls_conv_2_lvl_0_bn_beta', (128,)),
 ('rpn_cls_conv_2_lvl_0_bn_moving_mean', (128,)),
 ('rpn_cls_conv_2_lvl_0_bn_moving_var', (128,)),
 ('rpn_cls_conv_3_lvl_0_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_3_lvl_0_bn_gamma', (128,)),
 ('rpn_cls_conv_3_lvl_0_bn_beta', (128,)),
 ('rpn_cls_conv_3_lvl_0_bn_moving_mean', (128,)),
 ('rpn_cls_conv_3_lvl_0_bn_moving_var', (128,)),
 ('rpn_cls_logit_lvl_0_weight', (1, 128, 1, 1)),
 ('rpn_cls_logit_lvl_0_bias', (1,)),
 ('range_image_mask_s1', (2, 1, 64, 2656)),
 ('rpn_reg_conv_0_lvl_1_weight', (128, 64, 3, 3)),
 ('rpn_reg_conv_0_lvl_1_bn_gamma', (128,)),
 ('rpn_reg_conv_0_lvl_1_bn_beta', (128,)),
 ('rpn_reg_conv_0_lvl_1_bn_moving_mean', (128,)),
 ('rpn_reg_conv_0_lvl_1_bn_moving_var', (128,)),
 ('rpn_reg_conv_1_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_1_lvl_1_bn_gamma', (128,)),
 ('rpn_reg_conv_1_lvl_1_bn_beta', (128,)),
 ('rpn_reg_conv_1_lvl_1_bn_moving_mean', (128,)),
 ('rpn_reg_conv_1_lvl_1_bn_moving_var', (128,)),
 ('rpn_reg_conv_2_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_2_lvl_1_bn_gamma', (128,)),
 ('rpn_reg_conv_2_lvl_1_bn_beta', (128,)),
 ('rpn_reg_conv_2_lvl_1_bn_moving_mean', (128,)),
 ('rpn_reg_conv_2_lvl_1_bn_moving_var', (128,)),
 ('rpn_reg_conv_3_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_3_lvl_1_bn_gamma', (128,)),
 ('rpn_reg_conv_3_lvl_1_bn_beta', (128,)),
 ('rpn_reg_conv_3_lvl_1_bn_moving_mean', (128,)),
 ('rpn_reg_conv_3_lvl_1_bn_moving_var', (128,)),
 ('rpn_reg_delta_lvl_1_weight', (8, 128, 1, 1)),
 ('rpn_reg_delta_lvl_1_bias', (8,)),
 ('pc_vehicle_frame_s2', (2, 84992, 3)),
 ('decode3dbbox1_decoded_bbox', (2, 84992, 10)),
 ('batch_rotated_iou_veh_lvl_1_iou_map', (2, 84992)),
 ('rpn_cls_conv_0_lvl_1_weight', (128, 64, 3, 3)),
 ('rpn_cls_conv_0_lvl_1_bn_gamma', (128,)),
 ('rpn_cls_conv_0_lvl_1_bn_beta', (128,)),
 ('rpn_cls_conv_0_lvl_1_bn_moving_mean', (128,)),
 ('rpn_cls_conv_0_lvl_1_bn_moving_var', (128,)),
 ('rpn_cls_conv_1_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_1_lvl_1_bn_gamma', (128,)),
 ('rpn_cls_conv_1_lvl_1_bn_beta', (128,)),
 ('rpn_cls_conv_1_lvl_1_bn_moving_mean', (128,)),
 ('rpn_cls_conv_1_lvl_1_bn_moving_var', (128,)),
 ('rpn_cls_conv_2_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_2_lvl_1_bn_gamma', (128,)),
 ('rpn_cls_conv_2_lvl_1_bn_beta', (128,)),
 ('rpn_cls_conv_2_lvl_1_bn_moving_mean', (128,)),
 ('rpn_cls_conv_2_lvl_1_bn_moving_var', (128,)),
 ('rpn_cls_conv_3_lvl_1_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_3_lvl_1_bn_gamma', (128,)),
 ('rpn_cls_conv_3_lvl_1_bn_beta', (128,)),
 ('rpn_cls_conv_3_lvl_1_bn_moving_mean', (128,)),
 ('rpn_cls_conv_3_lvl_1_bn_moving_var', (128,)),
 ('rpn_cls_logit_lvl_1_weight', (1, 128, 1, 1)),
 ('rpn_cls_logit_lvl_1_bias', (1,)),
 ('range_image_mask_s2', (2, 1, 64, 1328)),
 ('rpn_reg_conv_0_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_0_lvl_2_bn_gamma', (128,)),
 ('rpn_reg_conv_0_lvl_2_bn_beta', (128,)),
 ('rpn_reg_conv_0_lvl_2_bn_moving_mean', (128,)),
 ('rpn_reg_conv_0_lvl_2_bn_moving_var', (128,)),
 ('rpn_reg_conv_1_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_1_lvl_2_bn_gamma', (128,)),
 ('rpn_reg_conv_1_lvl_2_bn_beta', (128,)),
 ('rpn_reg_conv_1_lvl_2_bn_moving_mean', (128,)),
 ('rpn_reg_conv_1_lvl_2_bn_moving_var', (128,)),
 ('rpn_reg_conv_2_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_2_lvl_2_bn_gamma', (128,)),
 ('rpn_reg_conv_2_lvl_2_bn_beta', (128,)),
 ('rpn_reg_conv_2_lvl_2_bn_moving_mean', (128,)),
 ('rpn_reg_conv_2_lvl_2_bn_moving_var', (128,)),
 ('rpn_reg_conv_3_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_reg_conv_3_lvl_2_bn_gamma', (128,)),
 ('rpn_reg_conv_3_lvl_2_bn_beta', (128,)),
 ('rpn_reg_conv_3_lvl_2_bn_moving_mean', (128,)),
 ('rpn_reg_conv_3_lvl_2_bn_moving_var', (128,)),
 ('rpn_reg_delta_lvl_2_weight', (8, 128, 1, 1)),
 ('rpn_reg_delta_lvl_2_bias', (8,)),
 ('pc_vehicle_frame_s4', (2, 42496, 3)),
 ('decode3dbbox2_decoded_bbox', (2, 42496, 10)),
 ('batch_rotated_iou_veh_lvl_2_iou_map', (2, 42496)),
 ('rpn_cls_conv_0_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_0_lvl_2_bn_gamma', (128,)),
 ('rpn_cls_conv_0_lvl_2_bn_beta', (128,)),
 ('rpn_cls_conv_0_lvl_2_bn_moving_mean', (128,)),
 ('rpn_cls_conv_0_lvl_2_bn_moving_var', (128,)),
 ('rpn_cls_conv_1_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_1_lvl_2_bn_gamma', (128,)),
 ('rpn_cls_conv_1_lvl_2_bn_beta', (128,)),
 ('rpn_cls_conv_1_lvl_2_bn_moving_mean', (128,)),
 ('rpn_cls_conv_1_lvl_2_bn_moving_var', (128,)),
 ('rpn_cls_conv_2_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_2_lvl_2_bn_gamma', (128,)),
 ('rpn_cls_conv_2_lvl_2_bn_beta', (128,)),
 ('rpn_cls_conv_2_lvl_2_bn_moving_mean', (128,)),
 ('rpn_cls_conv_2_lvl_2_bn_moving_var', (128,)),
 ('rpn_cls_conv_3_lvl_2_weight', (128, 128, 3, 3)),
 ('rpn_cls_conv_3_lvl_2_bn_gamma', (128,)),
 ('rpn_cls_conv_3_lvl_2_bn_beta', (128,)),
 ('rpn_cls_conv_3_lvl_2_bn_moving_mean', (128,)),
 ('rpn_cls_conv_3_lvl_2_bn_moving_var', (128,)),
 ('rpn_cls_logit_lvl_2_weight', (1, 128, 1, 1)),
 ('rpn_cls_logit_lvl_2_bias', (1,)),
 ('range_image_mask_s4', (2, 1, 64, 664)),
 ('rpn_reg_target_s1', (2, 8, 64, 2656)),
 ('rpn_reg_weight_s1', (2, 8, 64, 2656)),
 ('reg_normalize_weight_s1', (2, 8, 64, 2656)),
 ('rpn_reg_target_s2', (2, 8, 64, 1328)),
 ('rpn_reg_weight_s2', (2, 8, 64, 1328)),
 ('reg_normalize_weight_s2', (2, 8, 64, 1328)),
 ('rpn_reg_target_s4', (2, 8, 64, 664)),
 ('rpn_reg_weight_s4', (2, 8, 64, 664)),
 ('reg_normalize_weight_s4', (2, 8, 64, 664))]
04-19 11:01:59 intermediate output shape
04-19 11:01:59 [('data_fp16_output', (2, 8, 64, 2656)),
 ('res1_unit1_conv1_output', (2, 64, 64, 2656)),
 ('res1_unit1_bn1_output', (2, 64, 64, 2656)),
 ('res1_unit1_relu1_output', (2, 64, 64, 2656)),
 ('res1_unit1_conv2_output', (2, 64, 64, 2656)),
 ('res1_unit1_bn2_output', (2, 64, 64, 2656)),
 ('res1_unit1_sc_output', (2, 64, 64, 2656)),
 ('res1_unit1_sc_bn_output', (2, 64, 64, 2656)),
 ('res1_unit1_plus_output', (2, 64, 64, 2656)),
 ('res1_unit1_relu_output', (2, 64, 64, 2656)),
 ('res1_unit2_data_sampler_output', (2, 576, 169984)),
 ('reshape3_output', (2, 64, 9, 64, 2656)),
 ('res1_unit2coord_data_fp16_output', (2, 3, 64, 2656)),
 ('res1_unit2_coord_sampler_output', (2, 27, 169984)),
 ('reshape0_output', (2, 3, 9, 64, 2656)),
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 ('_minus4_output', (2, 1, 64, 1328)),
 ('activation1_output', (2, 1, 64, 1328)),
 ('_minus5_output', (2, 1, 64, 1328)),
 ('_mul14_output', (2, 1, 64, 1328)),
 ('_plus2_output', (2, 1, 64, 1328)),
 ('_mulscalar12_output', (2, 1, 64, 1328)),
 ('_mulscalar13_output', (2, 1, 64, 1328)),
 ('_mulscalar14_output', (2, 1, 64, 1328)),
 ('sigmoid2_output', (2, 1, 64, 1328)),
 ('_minus6_output', (2, 1, 64, 1328)),
 ('abs1_output', (2, 1, 64, 1328)),
 ('_powerscalar1_output', (2, 1, 64, 1328)),
 ('_mul17_output', (2, 1, 64, 1328)),
 ('_zeros5_output', (1, 1)),
 ('broadcast_equal1_output', (2, 1, 64, 1328)),
 ('_mul18_output', (2, 1, 64, 1328)),
 ('_mul15_output', (2, 1, 64, 1328)),
 ('_zeros4_output', (1, 1)),
 ('broadcast_greater1_output', (2, 1, 64, 1328)),
 ('_mul16_output', (2, 1, 64, 1328)),
 ('_plus3_output', (2, 1, 64, 1328)),
 ('stop_gradient6_output', (2, 1, 64, 1328)),
 ('reshape9_output', (2, 1, 64, 1328)),
 ('broadcast_mul1_output', (2, 1, 64, 1328)),
 ('sum2_output', (1,)),
 ('_plusscalar2_output', (1,)),
 ('broadcast_div2_output', (2, 1, 64, 1328)),
 ('rpn_cls_loss_s2_output', (2, 1, 64, 1328)),
 ('rpn_reg_conv_0_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_0_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_0_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_1_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_1_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_1_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_2_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_2_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_2_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_3_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_3_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_reg_conv_3_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_reg_delta_lvl_2_output', (2, 8, 64, 664)),
 ('rpn_reg_delta_lvl_2_fp32_output', (2, 8, 64, 664)),
 ('bbox_delta_reshape_lvl_2_output', (2, 1, 8, 42496)),
 ('slice_axis5_output', (2, 1, 8, 42496)),
 ('squeeze5_output', (2, 8, 42496)),
 ('transpose2_output', (2, 42496, 8)),
 ('reshape7_output', (2, 1, 64, 664)),
 ('stop_gradient2_output', (2, 1, 64, 664)),
 ('concat2_output', (2, 1, 64, 664)),
 ('_mulscalar18_output', (2, 1, 64, 664)),
 ('rpn_cls_conv_0_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_0_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_0_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_1_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_1_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_1_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_2_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_2_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_2_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_3_lvl_2_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_3_lvl_2_bn_output', (2, 128, 64, 664)),
 ('rpn_cls_conv_3_lvl_2_relu_output', (2, 128, 64, 664)),
 ('rpn_cls_logit_lvl_2_output', (2, 1, 64, 664)),
 ('rpn_cls_logit_lvl_2_fp32_output', (2, 1, 64, 664)),
 ('sigmoid5_output', (2, 1, 64, 664)),
 ('clip2_output', (2, 1, 64, 664)),
 ('log2_output', (2, 1, 64, 664)),
 ('_mul23_output', (2, 1, 64, 664)),
 ('_rminusscalar2_output', (2, 1, 64, 664)),
 ('_mulscalar19_output', (2, 1, 64, 664)),
 ('_mulscalar16_output', (2, 1, 64, 664)),
 ('_zeros6_output', (1,)),
 ('broadcast_greater_equal2_output', (2, 1, 64, 664)),
 ('_mul21_output', (2, 1, 64, 664)),
 ('_mulscalar17_output', (2, 1, 64, 664)),
 ('_mul22_output', (2, 1, 64, 664)),
 ('_minus8_output', (2, 1, 64, 664)),
 ('activation2_output', (2, 1, 64, 664)),
 ('_minus9_output', (2, 1, 64, 664)),
 ('_mul24_output', (2, 1, 64, 664)),
 ('_plus4_output', (2, 1, 64, 664)),
 ('_mulscalar20_output', (2, 1, 64, 664)),
 ('_mulscalar21_output', (2, 1, 64, 664)),
 ('_mulscalar22_output', (2, 1, 64, 664)),
 ('sigmoid4_output', (2, 1, 64, 664)),
 ('_minus10_output', (2, 1, 64, 664)),
 ('abs2_output', (2, 1, 64, 664)),
 ('_powerscalar2_output', (2, 1, 64, 664)),
 ('_mul27_output', (2, 1, 64, 664)),
 ('_zeros8_output', (1, 1)),
 ('broadcast_equal2_output', (2, 1, 64, 664)),
 ('_mul28_output', (2, 1, 64, 664)),
 ('_mul25_output', (2, 1, 64, 664)),
 ('_zeros7_output', (1, 1)),
 ('broadcast_greater2_output', (2, 1, 64, 664)),
 ('_mul26_output', (2, 1, 64, 664)),
 ('_plus5_output', (2, 1, 64, 664)),
 ('stop_gradient9_output', (2, 1, 64, 664)),
 ('reshape10_output', (2, 1, 64, 664)),
 ('broadcast_mul2_output', (2, 1, 64, 664)),
 ('sum4_output', (1,)),
 ('_plusscalar4_output', (1,)),
 ('broadcast_div4_output', (2, 1, 64, 664)),
 ('rpn_cls_loss_s4_output', (2, 1, 64, 664)),
 ('_minus3_output', (2, 8, 64, 2656)),
 ('rpn_reg_smoothl1_output', (2, 8, 64, 2656)),
 ('stop_gradient4_output', (2, 8, 64, 2656)),
 ('_mul9_output', (2, 8, 64, 2656)),
 ('stop_gradient5_output', (2, 8, 64, 2656)),
 ('_mul10_output', (2, 8, 64, 2656)),
 ('sum1_output', (1,)),
 ('_plusscalar1_output', (1,)),
 ('broadcast_div1_output', (2, 8, 64, 2656)),
 ('_mulscalar7_output', (2, 8, 64, 2656)),
 ('rpn_reg_loss_s1_output', (2, 8, 64, 2656)),
 ('_minus7_output', (2, 8, 64, 1328)),
 ('rpn_reg_smoothl1_output', (2, 8, 64, 1328)),
 ('stop_gradient7_output', (2, 8, 64, 1328)),
 ('_mul19_output', (2, 8, 64, 1328)),
 ('stop_gradient8_output', (2, 8, 64, 1328)),
 ('_mul20_output', (2, 8, 64, 1328)),
 ('sum3_output', (1,)),
 ('_plusscalar3_output', (1,)),
 ('broadcast_div3_output', (2, 8, 64, 1328)),
 ('_mulscalar15_output', (2, 8, 64, 1328)),
 ('rpn_reg_loss_s2_output', (2, 8, 64, 1328)),
 ('_minus11_output', (2, 8, 64, 664)),
 ('rpn_reg_smoothl1_output', (2, 8, 64, 664)),
 ('stop_gradient10_output', (2, 8, 64, 664)),
 ('_mul29_output', (2, 8, 64, 664)),
 ('stop_gradient11_output', (2, 8, 64, 664)),
 ('_mul30_output', (2, 8, 64, 664)),
 ('sum5_output', (1,)),
 ('_plusscalar5_output', (1,)),
 ('broadcast_div5_output', (2, 8, 64, 664)),
 ('_mulscalar23_output', (2, 8, 64, 664)),
 ('rpn_reg_loss_s4_output', (2, 8, 64, 664))]
04-19 11:01:59 terminal output shape
04-19 11:01:59 [('rpn_cls_loss_s1_output', (2, 1, 64, 2656)),
 ('rpn_cls_loss_s2_output', (2, 1, 64, 1328)),
 ('rpn_cls_loss_s4_output', (2, 1, 64, 664)),
 ('rpn_reg_loss_s1_output', (2, 8, 64, 2656)),
 ('rpn_reg_loss_s2_output', (2, 8, 64, 1328)),
 ('rpn_reg_loss_s4_output', (2, 8, 64, 664))]
[11:01:59] /home/ruanhao/range_view/mxnet/src/executor/graph_executor.cc:1036: Disabling fusion due to altered topological order of inputs.
[1]    237 killed     python tools/train.py --config

As mentioned above, it has two major errors:

[Warning] Your mxnet does not support ProposalTarget
[Warning] Your mxnet does not support DecodeBBox
[Warning] Your mxnet does not support BBoxNorm
[Warning] Your mxnet does not support FocalLoss
Please using new data mean and var
[11:01:59] /home/ruanhao/range_view/mxnet/src/executor/graph_executor.cc:1036: Disabling fusion due to altered topological order of inputs.
[1] 237 killed python tools/train.py --config

Here are some of the results of my pip list:

rangedet                    1.0.0     /home/ruanhao/range_view/RangeDet
mxnet                       2.0.0     /home/ruanhao/range_view/mxnet/python
horovod                     0.19.0

cuda and nccl version are:

cuda-11.2
cudnn - 8.1.0
nccl - 2.8.4
openmpi - 4.0.7

Could you please provide some advice?

BAI-Yeqi · 2022-10-18T10:02:58Z

##About the Reproducibility

@happynear @ruanhao5 @Treemann @IAS-01

Thank you for the great discussion!

May I check with you if you are able to reproduce the reported results on Waymo or Kitti Dataset?

Abyssaledge added the good first issue Good for newcomers label Jan 11, 2022

Abyssaledge mentioned this issue Feb 21, 2022

Pretrain models and inference examples #15

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BAI-Yeqi mentioned this issue Oct 18, 2022

Installaiton problems #19

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error when run tools/train.py #6

error when run tools/train.py #6

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error when run tools/train.py #6

error when run tools/train.py #6

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