flatten implemented

src/Flatten.cpp

@@ -0,0 +1,37 @@
+#include <iostream>
+
+#include "Layer.h"
+#include "kernels.h"
+
+namespace tkDNN {
+
+Flatten::Flatten(Network *net, dataDim_t input_dim) : 
+    Layer(net, input_dim) {
+
+    checkCuda( cudaMalloc(&dstData, input_dim.tot()*sizeof(value_type)) );
+
+    output_dim.n = 1;
+    output_dim.c = input_dim.tot();
+    output_dim.h = 1;
+    output_dim.w = 1;
+    output_dim.l = 1;
+
+}
+
+Flatten::~Flatten() {
+
+    checkCuda( cudaFree(dstData) );
+}
+
+value_type* Flatten::infer(dataDim_t &dim, value_type* srcData) {
+
+    //transpose per channel
+    matrixTranspose(net->cublasHandle, srcData, dstData, dim.c, dim.h*dim.w*dim.l);
+
+    //update data dimensions    
+    dim = output_dim;
+
+    return dstData;
+}
+
+}

src/utils.cpp

@@ -42,4 +42,15 @@ void resize(int size, value_type **data)
     if (*data != NULL)
         checkCuda( cudaFree(*data) );
     checkCuda( cudaMalloc(data, size*sizeof(value_type)) );
+}
+
+void matrixTranspose(cublasHandle_t handle, value_type* srcData, value_type* dstData, int rows, int cols) {
+
+    value_type *A = srcData, *clone = dstData;
+    int m = rows, n= cols;
+    checkCuda( cudaMemcpy(clone, A, m*n*sizeof(value_type), cudaMemcpyDeviceToDevice));
+   
+    float const alpha(1.0);
+    float const beta(0.0);
+    checkERROR( cublasSgeam( handle, CUBLAS_OP_T, CUBLAS_OP_N, m, n, &alpha, A, n, &beta, A, m, clone, m ));
 }