batch size > 1

tests/test_rtinference/rtinference.cpp

@@ -2,31 +2,42 @@
 #include "tkdnn.h"
 #include <stdlib.h>     /* srand, rand */
 
+
 int main(int argc, char *argv[]) {
 
     if(argc < 2 || !fileExist(argv[1]))
         FatalError("unable to read serialRT file");
 
+    int BATCH_SIZE = 1;
+    if(argc >2)
+        BATCH_SIZE = atoi(argv[2]);
+
     //always same test 
     srand (0); 
 
     //convert network to tensorRT
     tk::dnn::NetworkRT netRT(NULL, argv[1]);
+    
+    tk::dnn::dataDim_t idim = netRT.input_dim;
+    tk::dnn::dataDim_t odim = netRT.output_dim;
+    idim.n = BATCH_SIZE;
+    odim.n = BATCH_SIZE;
+    dnnType *input = new float[idim.tot()];
+    dnnType *output = new float[odim.tot()];
+    dnnType *input_d;
+    checkCuda( cudaMalloc(&input_d, idim.tot()*sizeof(dnnType)));
 
-    dnnType *input = new float[netRT.input_dim.tot()];
-    dnnType *output = new float[netRT.input_dim.tot()];
-
+    std::cout<<"Testing with batchsize: "<<BATCH_SIZE<<"\n";
     printCenteredTitle(" TENSORRT inference ", '=', 30); 
-    for(int i=0; i<100; i++) {
-        for(int j=0; j<netRT.input_dim.tot(); j++)
+    for(int i=0; i<10; i++) {
+        for(int j=0; j<idim.tot(); j++) {
             input[j] = ((float) rand() / (RAND_MAX));
+        }
+        checkCuda(cudaMemcpy(input_d, input, idim.tot()*sizeof(dnnType), cudaMemcpyHostToDevice));
+
+        tk::dnn::dataDim_t dim = idim;
         TIMER_START
-        checkCuda(  cudaMemcpyAsync(netRT.buffersRT[netRT.buf_input_idx], input, 
-                    netRT.input_dim.tot()*sizeof(float), cudaMemcpyHostToDevice, netRT.stream));
-        netRT.enqueue();
-        checkCuda(  cudaMemcpyAsync(output, netRT.buffersRT[netRT.buf_output_idx], 
-                                    netRT.output_dim.tot()*sizeof(float), cudaMemcpyDeviceToHost, netRT.stream));
-        cudaStreamSynchronize(netRT.stream);
+        netRT.infer(dim, input_d);
         TIMER_STOP
     }