Add Yolov3 (COCO80) and Yolov3-tiny (COCO80), TensorRT for tiny not working

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>

src/NetworkRT.cpp

@@ -209,7 +209,9 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Dense *l) {
 
 
 ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
-    //std::cout<<"convert conv2D\n";
+    std::cout<<"convert conv2D\n";
+    printf("%d %d %d %d %d\n", l->kernelH, l->kernelW, l->inputs, l->outputs, l->batchnorm);
+
 
     void *data_b, *bias_b, *power_b, *mean_b, *variance_b, *scales_b;
     if(dtRT == DataType::kHALF) {
@@ -274,7 +276,8 @@ ILayer* NetworkRT::convert_layer(ITensor *input, Conv2d *l) {
 }
 
 ILayer* NetworkRT::convert_layer(ITensor *input, Pooling *l) {
-    //std::cout<<"convert Pooling\n";
+    std::cout<<"convert Pooling\n";
+    // printf("%d %d\n", l->winW, l->winH);
 
     PoolingType ptype;
     if(l->pool_mode == tkdnnPoolingMode_t::POOLING_MAX) ptype = PoolingType::kMAX;

src/Pooling.cpp

@@ -26,6 +26,9 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
     int w = input_dim.w;
     int l = input_dim.l;
 
+    printf("before: %d %d\n", h, w);
+    
+
     poolOn3d = false;
     
     if(l > 1) { 
@@ -38,6 +41,8 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
         n = l;
     }
 
+    
+
     checkCUDNN( cudnnSetPooling2dDescriptor(poolingDesc, cudnnPoolingMode_t(pool_mode),
                 CUDNN_NOT_PROPAGATE_NAN, winH, winW, paddingH, paddingW, strideH, strideW) );
 
@@ -45,12 +50,23 @@ Pooling::Pooling( Network *net, int winH, int winW, int strideH, int strideW,
                 net->tensorFormat, net->dataType, n, c, h, w) );
 
     //get out dim
-    checkCUDNN( cudnnGetPooling2dForwardOutputDim(poolingDesc, srcTensorDesc, &n, &c, &h, &w)); 
-    //h = (h + winH*this->paddingH)/strideH;
-    //w = (w + winW*this->paddingW)/strideW;
+    // checkCUDNN( cudnnGetPooling2dForwardOutputDim(poolingDesc, srcTensorDesc, &n, &c, &h, &w)); 
+
+    //compute w and h as in darknet
+
+    int padH = paddingH == 0? winH -1 : paddingH;
+    int padW = paddingW == 0? winW -1 : paddingW;
+
+    h = (h + padH - winH)/strideH +1;
+    w =  (w + padW - winW)/strideW +1;
+
+    // h = (h + winH*this->paddingH)/strideH;
+    // w = (w + winW*this->paddingW)/strideW;
 
     checkCUDNN( cudnnSetTensor4dDescriptor(dstTensorDesc,
                 net->tensorFormat, net->dataType, n, c, h, w) );
+
+    printf("after: %d %d\n", h, w);
        
     output_dim.n = n;
     output_dim.c = c;

src/Yolo.cpp

@@ -15,14 +15,16 @@ Yolo::Yolo(Network *net, int classes, int num, std::string fname_weights) :
     Layer(net) {
     
     this->classes = classes;
-    this->num = num;
+    this->num = 3;
 
     // load anchors
     if(fname_weights != "") {
         int seek = 0;
-        readBinaryFile(fname_weights, num, &mask_h, &mask_d, seek);
-        seek += num;
+        readBinaryFile(fname_weights, 3, &mask_h, &mask_d, seek);
+        seek += 3;
         readBinaryFile(fname_weights, 3*num*2, &bias_h, &bias_d, seek);
+        for(int i=0; i<3*num*2; i++)
+            printf("%f\n", bias_h[i]);
     }
 
     // init default classes name

src/utils.cpp

@@ -38,7 +38,9 @@ void readBinaryFile(std::string fname, int size, dnnType** data_h, dnnType** dat
             dataFile.seekg(seek * sizeof(dnnType), dataFile.cur);
         }
 
+        // printf("data_h %d size_b %d\n", *data_h,size_b);
         if (!dataFile.read((char *) *data_h, size_b)) {
+            
             error_s << "Error reading file " << fname;
             FatalError(error_s.str());
         }