NVDLA accuracy drop and results are different from caffe.

[sean90175]

Following #45 #80 and using appropriate preprocessing method, the result on virtual platform is still different from caffe.
I compiled my caffemodel using the following command:
./nvdla_compiler --prototxt cifar10_quick.prototxt --caffemodel cifar10_quick_iter_5000.caffemodel --cprecision fp16
I ran virtual platform using the following command:
./nvdla_runtime --loadable fast-math.nvdla --image 0_10.jpg --rawdump --normalize 1.0 --mean 125.3,123.0,113.9
Caffe model acheived ~75% accuracy while nvdla on vp only acheived ~40% accuracy on cifar10 test set.
Has anyone faced similar problem or have I used incorrect preprocessing in vp?

Thank you so much!

[annshen0023]

similar problem occured when I tested resnet50 on FPGA(xilinx zcu102) with nv_small and int8.

I have tested resnet50 caffemodel of KaimingHe version on FPGA successfully.Everything turned out Okay.
Then I trained my own resnet50 caffemodel and tested it with caffe classification,the accuracy was acceptable,
but when I transformed the caffemodel to loadable with nvdla_compiler, and tested it with nvdla_runtime on FPGA the result is completely wrong.

During both tests I generated calibration json file with tensorRT in same process,so the problem may not lie in here.

Any suggestions will be highly appreciated.

[sean90175]

Problem solved as I give correct infomat (ncxhwx) in nvdla_compiler option.