mmr/tkDNN
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opencv viz

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This commit is contained in:
Francesco Gatti
2017-08-10 16:22:17 +02:00
parent b75fa637cb
commit 266330009c
31 changed files with 211 additions and 152 deletions
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src/utils.cpp
+23 -23
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@@ -14,7 +14,7 @@ void printCenteredTitle(const char *title, char fill, int dim) {
}
void readBinaryFile(const char* fname, int size, value_type** data_h, value_type** data_d, int seek)
void readBinaryFile(const char* fname, int size, dnnType** data_h, dnnType** data_d, int seek)
{
std::ifstream dataFile (fname, std::ios::in | std::ios::binary);
std::stringstream error_s;
@@ -25,11 +25,11 @@ void readBinaryFile(const char* fname, int size, value_type** data_h, value_type
}
if(seek != 0) {
dataFile.seekg(seek*sizeof(value_type), dataFile.cur);
dataFile.seekg(seek*sizeof(dnnType), dataFile.cur);
}
int size_b = size*sizeof(value_type);
*data_h = new value_type[size];
int size_b = size*sizeof(dnnType);
*data_h = new dnnType[size];
if (!dataFile.read ((char*) *data_h, size_b))
{
error_s << "Error reading file " << fname;
@@ -40,13 +40,13 @@ void readBinaryFile(const char* fname, int size, value_type** data_h, value_type
checkCuda( cudaMemcpy(*data_d, *data_h, size_b, cudaMemcpyHostToDevice) );
}
void printDeviceVector(int size, value_type* vec_d, bool device)
void printDeviceVector(int size, dnnType* vec_d, bool device)
{
value_type *vec;
dnnType *vec;
if(device) {
vec = new value_type[size];
vec = new dnnType[size];
cudaDeviceSynchronize();
cudaMemcpy(vec, vec_d, size*sizeof(value_type), cudaMemcpyDeviceToHost);
cudaMemcpy(vec, vec_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
} else {
vec = vec_d;
}
@@ -60,17 +60,17 @@ void printDeviceVector(int size, value_type* vec_d, bool device)
delete [] vec;
}
int checkResult(int size, value_type *data_d, value_type *correct_d, bool device) {
int checkResult(int size, dnnType *data_d, dnnType *correct_d, bool device) {
value_type *data_h, *correct_h;
dnnType *data_h, *correct_h;
const float eps = 0.0001f;
if(device) {
data_h = new value_type[size];
correct_h = new value_type[size];
data_h = new dnnType[size];
correct_h = new dnnType[size];
cudaDeviceSynchronize();
cudaMemcpy(data_h, data_d, size*sizeof(value_type), cudaMemcpyDeviceToHost);
cudaMemcpy(correct_h, correct_d, size*sizeof(value_type), cudaMemcpyDeviceToHost);
cudaMemcpy(data_h, data_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
cudaMemcpy(correct_h, correct_d, size*sizeof(dnnType), cudaMemcpyDeviceToHost);
} else {
data_h = data_d;
@@ -103,30 +103,30 @@ int checkResult(int size, value_type *data_d, value_type *correct_d, bool device
return diffs;
}
void resize(int size, value_type **data)
void resize(int size, dnnType **data)
{
if (*data != NULL)
checkCuda( cudaFree(*data) );
checkCuda( cudaMalloc(data, size*sizeof(value_type)) );
checkCuda( cudaMalloc(data, size*sizeof(dnnType)) );
}
void matrixTranspose(cublasHandle_t handle, value_type* srcData, value_type* dstData, int rows, int cols) {
void matrixTranspose(cublasHandle_t handle, dnnType* srcData, dnnType* dstData, int rows, int cols) {
value_type *A = srcData, *clone = dstData;
dnnType *A = srcData, *clone = dstData;
int m = rows, n= cols;
checkCuda( cudaMemcpy(clone, A, m*n*sizeof(value_type), cudaMemcpyDeviceToDevice));
checkCuda( cudaMemcpy(clone, A, m*n*sizeof(dnnType), 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 ));
}
void matrixMulAdd( cublasHandle_t handle, value_type* srcData, value_type* dstData,
value_type* add_vector, int dim, value_type mul) {
void matrixMulAdd( cublasHandle_t handle, dnnType* srcData, dnnType* dstData,
dnnType* add_vector, int dim, dnnType mul) {
checkCuda( cudaMemcpy(dstData, add_vector, dim*sizeof(value_type), cudaMemcpyDeviceToDevice));
checkCuda( cudaMemcpy(dstData, add_vector, dim*sizeof(dnnType), cudaMemcpyDeviceToDevice));
value_type alpha = mul;
dnnType alpha = mul;
checkERROR( cublasSaxpy(handle, dim, &alpha, srcData, 1, dstData, 1));
}