tiny yolo not working

CMakeLists.txt

@@ -28,4 +28,7 @@ add_executable(test_mnistRT tests/mnist/test_mnistRT.cpp)
 target_link_libraries(test_mnistRT tkDNN)
 
 add_executable(test_yolo tests/yolo/yolo.cpp)
-target_link_libraries(test_yolo tkDNN)
+target_link_libraries(test_yolo tkDNN)
+
+add_executable(test_yolo_tiny tests/yolo-tiny/yolo-tiny.cpp)
+target_link_libraries(test_yolo_tiny tkDNN)

include/Layer.h

@@ -190,6 +190,7 @@ class Pooling : public Layer {
 public:
     int winH, winW;
     int strideH, strideW;
+    int paddingH, paddingW;
 
     Pooling(Network *net, int winH, int winW, 
             int strideH, int strideW, tkdnnPoolingMode_t pool_mode); 

src/Network.cpp

@@ -32,9 +32,10 @@ Network::~Network() {
 value_type* Network::infer(dataDim_t &dim, value_type* data) {
 
     //do infer for every layer
-    for(int i=0; i<num_layers; i++)
+    for(int i=0; i<num_layers; i++) {
         data = layers[i]->infer(dim, data);
-
+        //dim.print();
+    }
     checkCuda(cudaDeviceSynchronize());
     return data;
 }

src/NetworkRT.cpp

@@ -45,7 +45,11 @@ NetworkRT::NetworkRT(Network *net) {
     }
     if(input == NULL)
         FatalError("conversion failed");
-    output_dim = net->layers[net->num_layers-1]->output_dim;
+    output_dim = dim;
+    Dims oDim = input->getDimensions();
+    output_dim.c = oDim.d[0];
+    output_dim.h = oDim.d[1];
+    output_dim.w = oDim.d[2];
 
     //build tensorRT
     input->setName("out");

src/Pooling.cpp

@@ -5,20 +5,18 @@
 
 namespace tkDNN {
 
-Pooling::Pooling(   Network *net, int winH, int winW, 
-                    int strideH, int strideW, tkdnnPoolingMode_t pool_mode) : 
+Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
+                  tkdnnPoolingMode_t pool_mode) : 
     Layer(net) {
 
-
-    if(winH != strideH || winW != strideW)
-        FatalError("stride pooling not yet implemented");
-
     this->winH = winH;
     this->winW = winW;
     this->strideH = strideH;
     this->strideW = strideW;
     this->pool_mode = pool_mode;
-    
+    this->paddingH = 0;
+    this->paddingW = 0;
+
     checkCUDNN( cudnnCreatePoolingDescriptor(&poolingDesc) );
 
     int n = input_dim.n;
@@ -46,12 +44,13 @@ Pooling::Pooling(   Network *net, int winH, int winW,
                 net->tensorFormat, net->dataType, n, c, h, w) );
 
     //get out dim
-    h = h / winH; w = w / winW;
-    
+    checkCUDNN( cudnnGetPooling2dForwardOutputDim(poolingDesc, srcTensorDesc, &n, &c, &h, &w)); 
+    //h = (h + winH*this->paddingH)/strideH;
+    //w = (w + winW*this->paddingW)/strideW;
+
     checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc,
                 net->tensorFormat, net->dataType, n, c, h, w) );
        
-
     output_dim.n = n;
     output_dim.c = c;
     output_dim.h = h;

tests/yolo-tiny/yolo-tiny.cpp

@@ -0,0 +1,90 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin  = "../tests/yolo-tiny/layers/input.bin";
+const char *c0_bin     = "../tests/yolo-tiny/layers/c0.bin";
+const char *c2_bin     = "../tests/yolo-tiny/layers/c2.bin";
+const char *c4_bin     = "../tests/yolo-tiny/layers/c4.bin";
+const char *c5_bin     = "../tests/yolo-tiny/layers/c5.bin";
+const char *c6_bin     = "../tests/yolo-tiny/layers/c6.bin";
+const char *c8_bin     = "../tests/yolo-tiny/layers/c8.bin";
+const char *c10_bin    = "../tests/yolo-tiny/layers/c10.bin";
+const char *c12_bin    = "../tests/yolo-tiny/layers/c12.bin";
+const char *c13_bin    = "../tests/yolo-tiny/layers/c13.bin";
+const char *c14_bin    = "../tests/yolo-tiny/layers/c14.bin";
+const char *output_bin = "../tests/yolo-tiny/layers/outputLEL.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
+    tkDNN::Network net(dim);
+
+    tkDNN::Conv2d     c0 (&net, 16, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c2 (&net, 32, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c4 (&net, 64, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p5 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p7(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c8(&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tkDNN::Activation a8(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p9(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
+    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p11(&net, 2, 2, 1, 1, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c12(&net, 1024, 3, 3, 1, 1, 1, 1, c12_bin, true);
+    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c13(&net, 1024, 3, 3, 1, 1, 1, 1, c13_bin, true);
+    tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c14(&net, 125, 1, 1, 1, 1, 0, 0, c14_bin, false);
+    tkDNN::Region     g15(&net, 20, 4, 5, 0.6f);
+
+    // Load input
+    value_type *data;
+    value_type *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+
+    //convert network to tensorRT
+    tkDNN::NetworkRT netRT(&net);
+
+    value_type *out_data, *out_data2; // cudnn output, tensorRT output
+
+    tkDNN::dataDim_t dim1 = dim; //input dim
+    std::cout<<"\n==== CUDNN inference =======\n"; {
+        dim1.print();
+        TIMER_START
+        out_data = net.infer(dim1, data);    
+        TIMER_STOP
+        dim1.print();   
+    }
+ 
+    tkDNN::dataDim_t dim2 = dim;
+    std::cout<<"\n==== TENSORRT inference ====\n"; {
+        dim2.print();
+        TIMER_START
+        out_data2 = netRT.infer(dim2, data);
+        TIMER_STOP
+        dim2.print();
+    }
+
+    std::cout<<"\n======= CHECK RESULT =======\n";
+    value_type *out, *out_h;
+    int out_dim = net.getOutputDim().tot();
+    readBinaryFile(output_bin, out_dim, &out_h, &out);
+    std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
+    std::cout<<"TRT   vs correct"; checkResult(out_dim, out_data2, out);
+    std::cout<<"CUDNN vs TRT    "; checkResult(out_dim, out_data, out_data2);
+    return 0;
+}