mmr/tkDNN
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Refactoring and modularization

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Signed-off-by: Micaela Verucchi <micaela.verucchi@unimore.it>
This commit is contained in:
Micaela Verucchi
2019-10-04 11:12:01 +02:00
parent bb7d382d96
commit 35787cc771
25 changed files with 1708 additions and 1560 deletions
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include/NetworkRT.h
+33 -28
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@@ -7,17 +7,22 @@
#include "Layer.h"
#include "NvInfer.h"
namespace tk { namespace dnn {
template<typename T> void writeBUF(char*& buffer, const T& val)
namespace tk
{
*reinterpret_cast<T*>(buffer) = val;
namespace dnn
{
template <typename T>
void writeBUF(char *&buffer, const T &val)
{
*reinterpret_cast<T *>(buffer) = val;
buffer += sizeof(T);
}
template<typename T> T readBUF(const char*& buffer)
template <typename T>
T readBUF(const char *&buffer)
{
T val = *reinterpret_cast<const T*>(buffer);
T val = *reinterpret_cast<const T *>(buffer);
buffer += sizeof(T);
return val;
}
@@ -38,24 +43,23 @@ public:
YoloRT *yolos[16];
int n_yolos;
virtual IPlugin* createPlugin(const char* layerName, const void* serialData, size_t serialLength);
virtual IPlugin *createPlugin(const char *layerName, const void *serialData, size_t serialLength);
};
class NetworkRT {
class NetworkRT
{
public:
nvinfer1::DataType dtRT;
nvinfer1::IBuilder *builderRT;
nvinfer1::IRuntime *runtimeRT;
nvinfer1::INetworkDefinition *networkRT;
nvinfer1::INetworkDefinition *networkRT;
nvinfer1::ICudaEngine *engineRT;
nvinfer1::IExecutionContext *contextRT;
const static int MAX_BUFFERS_RT = 10;
void* buffersRT[MAX_BUFFERS_RT];
void *buffersRT[MAX_BUFFERS_RT];
int buf_input_idx, buf_output_idx;
dataDim_t input_dim, output_dim;
@@ -70,25 +74,26 @@ public:
/**
Do inferece
*/
dnnType* infer(dataDim_t &dim, dnnType* data);
void enqueue();
dnnType *infer(dataDim_t &dim, dnnType *data);
void enqueue();
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Layer *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Conv2d *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Activation *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Dense *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Pooling *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Softmax *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Route *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Reorg *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Region *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Shortcut *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Yolo *l);
nvinfer1::ILayer* convert_layer(nvinfer1::ITensor *input, Upsample *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Layer *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Conv2d *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Activation *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Dense *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Pooling *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Softmax *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Route *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Reorg *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Region *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Shortcut *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Yolo *l);
nvinfer1::ILayer *convert_layer(nvinfer1::ITensor *input, Upsample *l);
bool serialize(const char *filename);
bool deserialize(const char *filename);
};
}}
} // namespace dnn
} // namespace tk
#endif //NETWORKRT_H