CSRMM Not Implemented yet

[leochli]

Hi @wenwei202 ,
After finishing the three steps of Resnet20 baseline, SSL and finetuning, I am having trouble evaluating the performance of finetuned models.
I changed the conv mode to LOWERED_CSRMM but got an error as "Not Implemented yet". Besides, I have also tried the python script cifar10_classifier.py but it wouldn't work neither.
I am wondering how could I evaluate the inference performance of sparsified models?

FYI, here's the error info:
CSRMM:
I0727 12:35:50.174033 1970 base_conv_layer.cpp:17] layer conv1 has sparsity of 0.0578704
I0727 12:35:50.174360 1970 base_conv_layer.cpp:29] ConvolutionParameter_ConvMode_LOWERED_CSRMM
F0727 12:35:50.174373 1970 math_functions.cpp:411] Not Implemented Yet
*** Check failure stack trace: ***
@ 0x7fb2ad0255cd google::LogMessage::Fail()
@ 0x7fb2ad027433 google::LogMessage::SendToLog()
@ 0x7fb2ad02515b google::LogMessage::Flush()
@ 0x7fb2ad027e1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7fb2ad6ca9d0 caffe::caffe_cpu_sparse_dense2csr<>()
@ 0x7fb2ad7fe3c9 caffe::BaseConvolutionLayer<>::WeightAlign()
@ 0x7fb2ad69308b caffe::Net<>::CopyTrainedLayersFrom()
@ 0x7fb2ad69d5f5 caffe::Net<>::CopyTrainedLayersFromBinaryProto()
@ 0x7fb2ad69d68e caffe::Net<>::CopyTrainedLayersFrom()
@ 0x40c222 time()
@ 0x407520 main
@ 0x7fb2abf95830 __libc_start_main
@ 0x407d49 _start
@ (nil) (unknown)

CCNMM:
I0727 11:48:19.621824 24561 base_conv_layer.cpp:17] layer res_grp1_1_conv1 has sparsity of 1
I0727 11:48:19.622687 24561 base_conv_layer.cpp:61] ConvolutionParameter_ConvMode_LOWERED_CCNMM
I0727 11:48:19.622701 24561 base_conv_layer.cpp:80] concatenating weight matrix
I0727 11:48:19.622706 24561 base_conv_layer.cpp:88] res_grp1_1_conv1 left_cols=0 left_rows=0
I0727 11:48:19.622711 24561 base_conv_layer.cpp:91] squeezing weight matrix
I0727 11:48:19.622715 24561 base_conv_layer.cpp:102] res_grp1_1_conv1 squeezing to 0x0
F0727 11:48:19.622720 24561 blob.cpp:131] Check failed: data_
*** Check failure stack trace: ***
@ 0x7f18331595cd google::LogMessage::Fail()
@ 0x7f183315b433 google::LogMessage::SendToLog()
@ 0x7f183315915b google::LogMessage::Flush()
@ 0x7f183315be1e google::LogMessageFatal::~LogMessageFatal()
@ 0x7f18337ea41b caffe::Blob<>::mutable_cpu_data()
@ 0x7f18339330e5 caffe::BaseConvolutionLayer<>::WeightAlign()
@ 0x7f18337c709b caffe::Net<>::CopyTrainedLayersFrom()
@ 0x7f18337d1605 caffe::Net<>::CopyTrainedLayersFromBinaryProto()
@ 0x7f18337d169e caffe::Net<>::CopyTrainedLayersFrom()
@ 0x40c222 time()
@ 0x407520 main
@ 0x7f18320c9830 __libc_start_main
@ 0x407d49 _start
@ (nil) (unknown)

Best Regards,
Leo

Your system configuration

Operating system: Ubuntu 16.04
Compiler:
CUDA version (if applicable): 9.0
CUDNN version (if applicable): 5
BLAS: MLK
Python or MATLAB version (for pycaffe and matcaffe respectively):

[wenwei202]

Please enable MKL in your makefile.config

[leochli]

Hi @wenwei202 ,
I have enabled MKL for BLAS before this error. What else do you think could be wrong?
Bests

[leochli]

Hi @wenwei202 ,
As I cannot test the performance of sparsified convolutions, it would be very nice of you to share the sparse scheme timing
Something similar to:

I0907 14:37:47.134873 26836 base_conv_layer.cpp:651] conv2 group 0: 320 us (Dense Scheme Timing)

There seems no performance results of sparse scheme in your code documentation.
Thanks a lot!