* @return 0 if OK. non-zero if error.
*/
int
-TFCore::init (const GstTensorFilterProperties * prop)
+TFCore::init (const GstTensorFilterProperties * prop,
+ const gboolean tf_mem_optmz)
{
gst_tensors_info_copy (&inputTensorMeta, &prop->input_meta);
gst_tensors_info_copy (&outputTensorMeta, &prop->output_meta);
+ mem_optmz = tf_mem_optmz;
+
return loadModel ();
}
}
/**
+ * @brief return the data type of the tensor for Tensorflow
+ * @param tType : the defined type of NNStreamer
+ * @return the enum of defined tensorflow::DataType
+ */
+TF_DataType
+TFCore::getTensorTypeToTF_Capi (tensor_type tType)
+{
+ switch (tType) {
+ case _NNS_INT32:
+ return TF_INT32;
+ case _NNS_UINT32:
+ return TF_UINT32;
+ case _NNS_INT16:
+ return TF_INT16;
+ case _NNS_UINT16:
+ return TF_UINT16;
+ case _NNS_INT8:
+ return TF_INT8;
+ case _NNS_UINT8:
+ return TF_UINT8;
+ case _NNS_INT64:
+ return TF_INT64;
+ case _NNS_UINT64:
+ return TF_UINT64;
+ case _NNS_FLOAT32:
+ return TF_FLOAT;
+ case _NNS_FLOAT64:
+ return TF_DOUBLE;
+ default:
+ /** @todo Support other types */
+ break;
+ }
+ /* Since there is no INVALID, TF_RESOURCE is used to detect invalid datatype temporally */
+ return TF_RESOURCE;
+}
+
+/**
* @brief check the inserted information about input tensor with model
* @return 0 if OK. non-zero if error.
* -1 if the number of input tensors is not matched.
}
/**
+ * @brief ring cache structure
+ */
+class TFBuffer : public TensorBuffer {
+ public:
+ void* data_;
+ size_t len_;
+
+ void* data () const override { return data_; }
+ size_t size () const override { return len_; }
+ TensorBuffer* root_buffer () override { return this; }
+ void FillAllocationDescription (AllocationDescription* proto) const override {
+ int64 rb = size ();
+ proto->set_requested_bytes (rb);
+ proto->set_allocator_name (cpu_allocator ()->Name ());
+ }
+
+ // Prevents input forwarding from mutating this buffer.
+ bool OwnsMemory () const override { return false; }
+};
+
+/**
* @brief run the model with the input.
* @param[in] input : The array of input tensors
* @param[out] output : The array of output tensors
std::vector <std::pair <string, Tensor>> input_feeds;
std::vector <Tensor> outputs;
+ Tensor in;
for (int i = 0; i < inputTensorMeta.num_tensors; ++i) {
- Tensor in (input_tensor_info[i].type, input_tensor_info[i].shape);
-
- /* copy data */
- if (input_tensor_info[i].type == DT_STRING) {
- in.scalar<string>()() = string ((char *) input[i].data, input[i].size);
- } else {
- std::copy_n ((char *) input[i].data, input[i].size,
- const_cast<char *>(in.tensor_data().data()));
+
+ if (mem_optmz) {
+ TFBuffer *buf = new TFBuffer;
+ buf->len_ = input[i].size;
+ buf->data_ = input[i].data;
+
+ TF_DataType dataType = getTensorTypeToTF_Capi (input[i].type);
+
+ if (dataType == TF_RESOURCE){
+ g_critical ("This data type is not valid: %d", input[i].type);
+ buf->Unref();
+ return -1;
+ }
+
+ in = TensorCApi::MakeTensor (
+ dataType,
+ input_tensor_info[i].shape,
+ buf
+ );
+ if (!in.IsAligned ()) {
+ g_critical ("the input tensor %s is not aligned", inputTensorMeta.info[i].name);
+ buf->Unref();
+ return -2;
+ }
+ }
+ else {
+ in = Tensor (input_tensor_info[i].type, input_tensor_info[i].shape);
+
+ /* copy data */
+ if (input_tensor_info[i].type == DT_STRING) {
+ in.scalar<string>()() = string ((char *) input[i].data, input[i].size);
+ } else {
+ std::copy_n ((char *) input[i].data, input[i].size,
+ const_cast<char *>(in.tensor_data().data()));
+ }
}
input_feeds.push_back ({inputTensorMeta.info[i].name, in});
Status run_status =
session->Run (input_feeds, output_tensor_names, {}, &outputs);
+ if (mem_optmz) {
+ TensorBuffer *buf = TensorCApi::Buffer (in);
+ buf->Unref();
+ }
+
if (!run_status.ok()) {
- GST_ERROR ("Failed to run model: %s\n", run_status.ToString().c_str());
+ g_critical ("Failed to run model: %s\n", run_status.ToString().c_str());
return -1;
}
/* validate output tensor once */
if (!configured && validateOutputTensor (outputs)) {
- GST_ERROR ("Output Tensor Information is not valid");
+ g_critical ("Output Tensor Information is not valid");
return -2;
}
* @return 0 if OK. non-zero if error.
*/
int
-tf_core_init (void * tf, const GstTensorFilterProperties * prop)
+tf_core_init (void * tf, const GstTensorFilterProperties * prop,
+ const gboolean tf_mem_optmz)
{
TFCore *c = (TFCore *) tf;
- return c->init (prop);
+ return c->init (prop, tf_mem_optmz);
}
/**
#define TENSOR_FILTER_TENSORFLOW_CORE_H
#include <tensor_typedef.h>
-
-#ifdef __cplusplus
#include <glib.h>
#include <gst/gst.h>
#include <setjmp.h>
#include <stdio.h>
#include <string.h>
+
+#ifdef __cplusplus
#include <iostream>
#include <fstream>
#include <algorithm>
#include <vector>
+#include <tensorflow/c/c_api.h>
+#include <tensorflow/c/c_api_internal.h>
#include <tensorflow/core/public/session.h>
using namespace tensorflow;
TFCore (const char * _model_path);
~TFCore ();
- int init(const GstTensorFilterProperties * prop);
+ int init (const GstTensorFilterProperties * prop, const gboolean tf_mem_optmz);
int loadModel ();
const char* getModelPath();
int getInputTensorDim (GstTensorsInfo * info);
private:
const char *model_path;
+ gboolean mem_optmz;
GstTensorsInfo inputTensorMeta; /**< The tensor info of input tensors */
GstTensorsInfo outputTensorMeta; /**< The tensor info of output tensors */
Session *session;
tensor_type getTensorTypeFromTF (DataType tfType);
+ TF_DataType getTensorTypeToTF_Capi (tensor_type tType);
int validateInputTensor (const GraphDef &graph_def);
int validateOutputTensor (const std::vector <Tensor> &outputs);
};
void *tf_core_new (const char *_model_path);
void tf_core_delete (void * tf);
- int tf_core_init (void * tf, const GstTensorFilterProperties * prop);
+ int tf_core_init (void * tf, const GstTensorFilterProperties * prop,
+ const gboolean tf_mem_optmz);
const char *tf_core_getModelPath (void * tf);
int tf_core_getInputDim (void * tf, GstTensorsInfo * info);
int tf_core_getOutputDim (void * tf, GstTensorsInfo * info);
projects / platform / upstream / nnstreamer.git / commitdiff