#include #include "tkdnn.h" const char *input_bin = "../tests/mnist/input.bin"; const char *c0_bin = "../tests/mnist/layers/c0.bin"; const char *c1_bin = "../tests/mnist/layers/c1.bin"; const char *d2_bin = "../tests/mnist/layers/d2.bin"; const char *d3_bin = "../tests/mnist/layers/d3.bin"; const char *output_bin = "../tests/mnist/output.bin"; int main() { // Network layout tkDNN::dataDim_t dim(1, 1, 28, 28, 1); tkDNN::Network net(dim); tkDNN::Conv2d l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin); tkDNN::Pooling l1(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX); tkDNN::Conv2d l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin); tkDNN::Pooling l3(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX); tkDNN::Dense l4(&net, 500, d2_bin); tkDNN::Activation l5(&net, tkDNN::ACTIVATION_LEAKY); tkDNN::Dense l6(&net, 10, d3_bin); tkDNN::Softmax l7(&net); tkDNN::NetworkRT netRT(&net); // Load input value_type *data; value_type *input_h; readBinaryFile(input_bin, dim.tot(), &input_h, &data); value_type *out_data, *out_data2; std::cout<<"CUDNN inference:\n"; { dim.print(); //print initial dimension TIMER_START out_data = net.infer(dim, data); TIMER_STOP dim.print(); } // Print result //std::cout<<"\n======= CUDNN RESULT =======\n"; //printDeviceVector(10, out_data); tkDNN::dataDim_t dim2(1, 1, 28, 28, 1); std::cout<<"TENSORRT inference:\n"; { dim2.print(); TIMER_START out_data2 = netRT.infer(dim2, data); TIMER_STOP dim2.print(); } // Print result //std::cout<<"\n======= TENRT RESULT =======\n"; //printDeviceVector(10, out_data); std::cout<<"\n======= CHECK RESULT =======\n"; checkResult(dim.tot(), out_data, out_data2); /* // Print real test std::cout<<"\n==== CHECK RESULT ====\n"; value_type *out; value_type *out_h; readBinaryFile(output_bin, dim.tot(), &out_h, &out); printDeviceVector(dim.tot(), out); */ return 0; }