daynauth
commented
3 years ago Go to line 322 where this block of code is in mobilenetv2ssd.cpp
// classification header 0
tk::dnn::Layer *header_0[1] = {&relu_14_1};
tk::dnn::Route rout_ch_0(&net, header_0, 1);
tk::dnn::Conv2d ch_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, classification_header0[0], true, false, 576, true);
tk::dnn::Activation ch_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_0_conv2(&net, 126, 1, 1, 1, 1, 0, 0, classification_header0[1], false);
tk::dnn::Layer *conf0[1] = {&ch_0_conv2};Change it to
// classification header 0
tk::dnn::Layer *header_0[1] = {&relu_14_1};
tk::dnn::Route rout_ch_0(&net, header_0, 1);
tk::dnn::Conv2d ch_0_conv1(&net, 576, 3, 3, 1, 1, 1, 1, classification_header0[0], true, false, 576, true);
tk::dnn::Activation ch_relu_0_1(&net, CUDNN_ACTIVATION_CLIPPED_RELU, 6);
tk::dnn::Conv2d ch_0_conv2(&net, 6 * classes, 1, 1, 1, 1, 0, 0, classification_header0[1], false); //change 126 to 6 x classes
tk::dnn::Layer *conf0[1] = {&ch_0_conv2};Do that for the next 4 blocks
SokPhanith
My model have 4 classes and I follow training on this source : https://github.com/mive93/pytorch-ssd. I test a simple on jetson nano it work around 12fps and output 2 folder layers and debug and then copy to build folder I go to change in tests/mobilent/mobilenetv2ssd/mobilenetv2ssd.cpp change classes = 5 and then go to path src/MobilenetDetection.cpp add more class like : else if(classes == 5){ const char *classesnames[] = { "a","b","c","d"}; classesNames = std::vector(classesnames, std::end(classesnames));}
and build again.
when run: ./test_mobilenetv2ssd
Error like this :
New NETWORK (tkDNN v0.5, CUDNN v8)
Reading weights: I=3 O=32 KERNEL=3x3x1
Reading weights: I=1 O=32 KERNEL=3x3x1
Reading weights: I=32 O=16 KERNEL=1x1x1
Reading weights: I=16 O=96 KERNEL=1x1x1
Reading weights: I=1 O=96 KERNEL=3x3x1
Reading weights: I=96 O=24 KERNEL=1x1x1
Reading weights: I=24 O=144 KERNEL=1x1x1
Reading weights: I=1 O=144 KERNEL=3x3x1
Reading weights: I=144 O=24 KERNEL=1x1x1
Reading weights: I=24 O=144 KERNEL=1x1x1
Reading weights: I=1 O=144 KERNEL=3x3x1
Reading weights: I=144 O=32 KERNEL=1x1x1
Reading weights: I=32 O=192 KERNEL=1x1x1
Reading weights: I=1 O=192 KERNEL=3x3x1
Reading weights: I=192 O=32 KERNEL=1x1x1
Reading weights: I=32 O=192 KERNEL=1x1x1
Reading weights: I=1 O=192 KERNEL=3x3x1
Reading weights: I=192 O=32 KERNEL=1x1x1
Reading weights: I=32 O=192 KERNEL=1x1x1
Reading weights: I=1 O=192 KERNEL=3x3x1
Reading weights: I=192 O=64 KERNEL=1x1x1
Reading weights: I=64 O=384 KERNEL=1x1x1
Reading weights: I=1 O=384 KERNEL=3x3x1
Reading weights: I=384 O=64 KERNEL=1x1x1
Reading weights: I=64 O=384 KERNEL=1x1x1
Reading weights: I=1 O=384 KERNEL=3x3x1
Reading weights: I=384 O=64 KERNEL=1x1x1
Reading weights: I=64 O=384 KERNEL=1x1x1
Reading weights: I=1 O=384 KERNEL=3x3x1
Reading weights: I=384 O=64 KERNEL=1x1x1
Reading weights: I=64 O=384 KERNEL=1x1x1
Reading weights: I=1 O=384 KERNEL=3x3x1
Reading weights: I=384 O=96 KERNEL=1x1x1
Reading weights: I=96 O=576 KERNEL=1x1x1
Reading weights: I=1 O=576 KERNEL=3x3x1
Reading weights: I=576 O=96 KERNEL=1x1x1
Reading weights: I=96 O=576 KERNEL=1x1x1
Reading weights: I=1 O=576 KERNEL=3x3x1
Reading weights: I=576 O=96 KERNEL=1x1x1
Reading weights: I=96 O=576 KERNEL=1x1x1
Reading weights: I=1 O=576 KERNEL=3x3x1
Reading weights: I=576 O=160 KERNEL=1x1x1
Reading weights: I=160 O=960 KERNEL=1x1x1
Reading weights: I=1 O=960 KERNEL=3x3x1
Reading weights: I=960 O=160 KERNEL=1x1x1
Reading weights: I=160 O=960 KERNEL=1x1x1
Reading weights: I=1 O=960 KERNEL=3x3x1
Reading weights: I=960 O=160 KERNEL=1x1x1
Reading weights: I=160 O=960 KERNEL=1x1x1
Reading weights: I=1 O=960 KERNEL=3x3x1
Reading weights: I=960 O=320 KERNEL=1x1x1
Reading weights: I=320 O=1280 KERNEL=1x1x1
Reading weights: I=1280 O=256 KERNEL=1x1x1
Reading weights: I=1 O=256 KERNEL=3x3x1
Reading weights: I=256 O=512 KERNEL=1x1x1
Reading weights: I=512 O=128 KERNEL=1x1x1
Reading weights: I=1 O=128 KERNEL=3x3x1
Reading weights: I=128 O=256 KERNEL=1x1x1
Reading weights: I=256 O=128 KERNEL=1x1x1
Reading weights: I=1 O=128 KERNEL=3x3x1
Reading weights: I=128 O=256 KERNEL=1x1x1
Reading weights: I=256 O=64 KERNEL=1x1x1
Reading weights: I=1 O=64 KERNEL=3x3x1
Reading weights: I=64 O=64 KERNEL=1x1x1
Reading weights: I=1 O=576 KERNEL=3x3x1
Reading weights: I=576 O=126 KERNEL=1x1x1
Error reading file mobilenetv2ssd/layers/classification_headers-0-3.bin with n of float: 72576 seek: 0 size: 290304
/home/phanith/tkDNN/src/utils.cpp:58 Aborting...