[Tizen/Api] basic implementation for single shot

Add base code for single shot model.
Update .spec and add simple testcases.

TODO: update name rules (function names and common util)

Signed-off-by: Jaeyun Jung <jy1210.jung@samsung.com>

Author: jaeyun-jung

packaging/nnstreamer.spec

@@ -184,6 +184,7 @@ meson --buildtype=plain --prefix=%{_prefix} --sysconfdir=%{_sysconfdir} --libdir
 ninja -C build %{?_smp_mflags}
 
 %if 0%{?unit_test}
+    export NNSTREAMER_BUILD_ROOT_PATH=$(pwd)
     pushd build
     export GST_PLUGIN_PATH=$(pwd)/gst/nnstreamer
     export NNSTREAMER_CONF=$(pwd)/nnstreamer-test.ini
@@ -316,6 +317,7 @@ popd
 
 %files -n capi-nnstreamer-devel
 %{_includedir}/nnstreamer/nnstreamer.h
+%{_includedir}/nnstreamer/nnstreamer-single.h
 %{_libdir}/pkgconfig/capi-nnstreamer.pc
 %{_libdir}/libcapi-nnstreamer.so
 %{_libdir}/libcapi-nnstreamer.a

tests/tizen_capi/unittest_tizen_capi.cpp

@@ -8,6 +8,7 @@
  */
 
 #include <nnstreamer.h>
+#include <nnstreamer-single.h>
 #include <gtest/gtest.h>
 #include <glib.h>
 #include <glib/gstdio.h> /* GStatBuf */
@@ -982,6 +983,228 @@ TEST (nnstreamer_capi_switch, failure_01)
   g_free (pipeline);
 }
 
+/**
+ * @brief Test NNStreamer single shot
+ */
+TEST (nnstreamer_capi_singleshot, invoke_01)
+{
+  ml_simpleshot_model_h model;
+  nns_tensors_info_s in_info, out_info;
+  nns_tensors_info_s in_res, out_res;
+  tensor_data *input, *output1, *output2;
+  int status;
+
+  const gchar *root_path = g_getenv ("NNSTREAMER_BUILD_ROOT_PATH");
+  gchar *test_model;
+
+  memset (&in_info, 0, sizeof (nns_tensors_info_s));
+  memset (&out_info, 0, sizeof (nns_tensors_info_s));
+  memset (&in_res, 0, sizeof (nns_tensors_info_s));
+  memset (&out_res, 0, sizeof (nns_tensors_info_s));
+
+  ASSERT_TRUE (root_path != NULL);
+  test_model = g_build_filename (root_path, "tests", "test_models", "models",
+      "mobilenet_v1_1.0_224_quant.tflite", NULL);
+
+  in_info.num_tensors = 1;
+  in_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  in_info.info[0].dimension[0] = 3;
+  in_info.info[0].dimension[1] = 224;
+  in_info.info[0].dimension[2] = 224;
+  in_info.info[0].dimension[3] = 1;
+
+  out_info.num_tensors = 1;
+  out_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  out_info.info[0].dimension[0] = 1001;
+  out_info.info[0].dimension[1] = 1;
+  out_info.info[0].dimension[2] = 1;
+  out_info.info[0].dimension[3] = 1;
+
+  status = ml_model_open (test_model, &model, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  /* input tensor in filter */
+  status = ml_model_get_input_type (model, &in_res);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  EXPECT_TRUE (in_info.num_tensors == in_res.num_tensors);
+  for (guint idx = 0; idx < in_res.num_tensors; idx++) {
+    EXPECT_TRUE (in_info.info[idx].type == in_res.info[idx].type);
+    EXPECT_TRUE (in_info.info[idx].dimension[0] == in_res.info[idx].dimension[0]);
+    EXPECT_TRUE (in_info.info[idx].dimension[1] == in_res.info[idx].dimension[1]);
+    EXPECT_TRUE (in_info.info[idx].dimension[2] == in_res.info[idx].dimension[2]);
+    EXPECT_TRUE (in_info.info[idx].dimension[3] == in_res.info[idx].dimension[3]);
+  }
+
+  /* output tensor in filter */
+  status = ml_model_get_output_type (model, &out_res);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  EXPECT_TRUE (out_info.num_tensors == out_res.num_tensors);
+  for (guint idx = 0; idx < out_res.num_tensors; idx++) {
+    EXPECT_TRUE (out_info.info[idx].type == out_res.info[idx].type);
+    EXPECT_TRUE (out_info.info[idx].dimension[0] == out_res.info[idx].dimension[0]);
+    EXPECT_TRUE (out_info.info[idx].dimension[1] == out_res.info[idx].dimension[1]);
+    EXPECT_TRUE (out_info.info[idx].dimension[2] == out_res.info[idx].dimension[2]);
+    EXPECT_TRUE (out_info.info[idx].dimension[3] == out_res.info[idx].dimension[3]);
+  }
+
+  /* generate dummy data */
+  input = ml_model_allocate_tensor_data (&in_info);
+  EXPECT_TRUE (input != NULL);
+
+  output1 = ml_model_inference (model, input, NULL);
+  EXPECT_TRUE (output1 != NULL);
+  ml_model_free_tensor_data (output1);
+
+  output2 = ml_model_allocate_tensor_data (&out_info);
+  EXPECT_TRUE (output2 != NULL);
+
+  output1 = ml_model_inference (model, input, output2);
+  EXPECT_TRUE (output1 != NULL);
+  EXPECT_TRUE (output1 == output2);
+  ml_model_free_tensor_data (output2);
+
+  ml_model_free_tensor_data (input);
+
+  status = ml_model_close (model);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  g_free (test_model);
+}
+
+/**
+ * @brief Test NNStreamer single shot
+ * @detail Start pipeline without tensor info
+ */
+TEST (nnstreamer_capi_singleshot, invoke_02)
+{
+  ml_simpleshot_model_h model;
+  nns_tensors_info_s in_info, out_info;
+  tensor_data *input, *output1, *output2;
+  int status;
+
+  const gchar *root_path = g_getenv ("NNSTREAMER_BUILD_ROOT_PATH");
+  gchar *test_model;
+
+  ASSERT_TRUE (root_path != NULL);
+  test_model = g_build_filename (root_path, "tests", "test_models", "models",
+      "mobilenet_v1_1.0_224_quant.tflite", NULL);
+
+  in_info.num_tensors = 1;
+  in_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  in_info.info[0].dimension[0] = 3;
+  in_info.info[0].dimension[1] = 224;
+  in_info.info[0].dimension[2] = 224;
+  in_info.info[0].dimension[3] = 1;
+
+  out_info.num_tensors = 1;
+  out_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  out_info.info[0].dimension[0] = 1001;
+  out_info.info[0].dimension[1] = 1;
+  out_info.info[0].dimension[2] = 1;
+  out_info.info[0].dimension[3] = 1;
+
+  status = ml_model_open (test_model, &model, NULL, NULL,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  /* generate dummy data */
+  input = ml_model_allocate_tensor_data (&in_info);
+  EXPECT_TRUE (input != NULL);
+
+  output1 = ml_model_inference (model, input, NULL);
+  EXPECT_TRUE (output1 != NULL);
+  ml_model_free_tensor_data (output1);
+
+  output2 = ml_model_allocate_tensor_data (&out_info);
+  EXPECT_TRUE (output2 != NULL);
+
+  output1 = ml_model_inference (model, input, output2);
+  EXPECT_TRUE (output1 != NULL);
+  EXPECT_TRUE (output1 == output2);
+  ml_model_free_tensor_data (output2);
+
+  ml_model_free_tensor_data (input);
+
+  status = ml_model_close (model);
+  EXPECT_EQ (status, NNS_ERROR_NONE);
+
+  g_free (test_model);
+}
+
+/**
+ * @brief Test NNStreamer single shot
+ * @detail Failure case with invalid param.
+ */
+TEST (nnstreamer_capi_singleshot, failure_01)
+{
+  ml_simpleshot_model_h model;
+  nns_tensors_info_s in_info, out_info;
+  int status;
+
+  const gchar *root_path = g_getenv ("NNSTREAMER_BUILD_ROOT_PATH");
+  gchar *test_model;
+
+  memset (&in_info, 0, sizeof (nns_tensors_info_s));
+  memset (&out_info, 0, sizeof (nns_tensors_info_s));
+
+  ASSERT_TRUE (root_path != NULL);
+  test_model = g_build_filename (root_path, "tests", "test_models", "models",
+      "mobilenet_v1_1.0_224_quant.tflite", NULL);
+
+  /* invalid file path */
+  status = ml_model_open ("wrong_file_name", &model, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  /* null file path */
+  status = ml_model_open (NULL, &model, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  /* invalid handle */
+  status = ml_model_open (test_model, NULL, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  /* invalid input tensor info */
+  status = ml_model_open (test_model, &model, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  in_info.num_tensors = 1;
+  in_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  in_info.info[0].dimension[0] = 3;
+  in_info.info[0].dimension[1] = 224;
+  in_info.info[0].dimension[2] = 224;
+  in_info.info[0].dimension[3] = 1;
+
+  /* invalid output tensor info */
+  status = ml_model_open (test_model, &model, &in_info, &out_info,
+      ML_NNFW_TENSORFLOW_LITE, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  out_info.num_tensors = 1;
+  out_info.info[0].type = NNS_TENSOR_TYPE_UINT8;
+  out_info.info[0].dimension[0] = 1001;
+  out_info.info[0].dimension[1] = 1;
+  out_info.info[0].dimension[2] = 1;
+  out_info.info[0].dimension[3] = 1;
+
+  /* unknown fw type */
+  status = ml_model_open (test_model, &model, &in_info, &out_info,
+      ML_NNFW_UNKNOWN, ML_NNFW_HW_DO_NOT_CARE);
+  EXPECT_EQ (status, NNS_ERROR_NOT_SUPPORTED);
+
+  /* invalid handle */
+  status = ml_model_close (model);
+  EXPECT_EQ (status, NNS_ERROR_INVALID_PARAMETER);
+
+  g_free (test_model);
+}
+
 /**
  * @brief Main gtest
  */

tizen-api/include/nnstreamer-single.h

@@ -90,13 +90,14 @@ typedef enum {
  * @param[in] model_path This is the path to the neural network model file.
  * @param[out] model This is the model opened. Users are required to close
  *                   the given model with ml_model_close().
- * @param[in] inputtype This is required if the given model has flexible input
+ * @param[in] input_type This is required if the given model has flexible input
  *                      dimension, where the input dimension MUST be given
  *                      before executing the model.
  *                      However, once it's given, the input dimension cannot
  *                      be changed for the given model handle.
- *                      Is is required by some custom filters of nnstreamer.
+ *                      It is required by some custom filters of nnstreamer.
  *                      You may set NULL if it's not required.
+ * @param[in] output_type This is required if the given model has flexible output dimension.
  * @param[in] nnfw The nerual network framework used to open the given
  *                 @model_path. Set ML_NNFW_UNKNOWN to let it auto-detect.
  * @param[in] hw Tell the corresponding @nnfw to use a specific hardware.
@@ -109,8 +110,8 @@ typedef enum {
  *         same dimension.
  */
 int ml_model_open (const char *model_path, ml_simpleshot_model_h *model,
-    const nns_tensors_info_s *inputtype, ml_model_nnfw nnfw,
-    ml_model_hw hw);
+    const nns_tensors_info_s *input_type, const nns_tensors_info_s *output_type,
+    ml_model_nnfw nnfw, ml_model_hw hw);
 
 /**
  * @brief Close the opened model handle.
@@ -154,12 +155,12 @@ tensor_data * ml_model_inference (ml_simpleshot_model_h model,
  *         types are available.
  * @since_tizen 5.5
  * @param[in] model The model to be investigated
- * @param[out] inputtype The type of input tensor.
+ * @param[out] input_type The type of input tensor.
  * @return @c 0 on success. otherwise a negative error value
  * @retval #NNS_ERROR_NONE Successful
  */
-int ml_model_get_inputtype (ml_simpleshot_model_h model,
-    nns_tensors_info_s *inputtype);
+int ml_model_get_input_type (ml_simpleshot_model_h model,
+    nns_tensors_info_s *input_type);
 
 /**
  * @brief Get type (tensor dimension, type, name and so on) of output
@@ -171,52 +172,52 @@ int ml_model_get_inputtype (ml_simpleshot_model_h model,
  *         types are available.
  * @since_tizen 5.5
  * @param[in] model The model to be investigated
- * @param[out] outputtype The type of output tensor.
+ * @param[out] output_type The type of output tensor.
  * @return @c 0 on success. otherwise a negative error value
  * @retval #NNS_ERROR_NONE Successful
  */
-int ml_model_get_outputtype (ml_simpleshot_model_h model,
-    nns_tensors_info_s *outputtype);
+int ml_model_get_output_type (ml_simpleshot_model_h model,
+    nns_tensors_info_s *output_type);
 
 /**
  * @brief Get the byte size of the given tensor type.
  * @since_tizen 5.5
- * @param[in] tensor_type The tensor type to be investigated.
+ * @param[in] info The tensor information to be investigated.
  * @return @c >= 0 on success with byte size. otherwise a negative error value
  */
-int ml_model_get_tensor_size (const nns_tensor_info_s *tensor_type);
+size_t ml_util_get_tensor_size (const nns_tensor_info_s *info);
 
 /**
  * @brief Get the byte size of the given tensors type.
  * @since_tizen 5.5
- * @param[in] tensors_type The tensors type to be investigated.
+ * @param[in] info The tensors information to be investigated.
  * @return @c >= 0 on success with byte size. otherwise a negative error value
  */
-int ml_model_get_tensors_size (const nns_tensors_info_s *tensors_type);
+size_t ml_util_get_tensors_size (const nns_tensors_info_s *info);
 
 /**
- * @brief Free the tensors type pointer
+ * @brief Free the tensors type pointer.
  * @since_tizen 5.5
  * @param[in] type the tensors type pointer to be freed.
  */
-void ml_model_free_tensorsinfo (nns_tensors_info_s *type);
+void ml_model_free_tensors_info (nns_tensors_info_s *type);
 
 /**
- * @brief Free the tensors data pointer
+ * @brief Free the tensors data pointer.
  * @since_tizen 5.5
  * @param[in] tensor the tensors data pointer to be freed.
  */
-void ml_model_free_tensordata (tensor_data *tensor);
+void ml_model_free_tensor_data (tensor_data *tensor);
 
 /**
  * @brief Allocate a tensor data frame with the given tensors type.
  * @since_tizen 5.5
- * @param[in] type the tensors type pointer for the allocation
+ * @param[in] info The tensors information for the allocation
  * @return @c Tensors data pointer allocated. Null if error.
  * @retval NULL there is an error. call get_last_result() to get specific
  *         error numbers.
  */
-tensor_data *ml_model_allocate_tensors (const nns_tensors_info_s *type);
+tensor_data *ml_model_allocate_tensor_data (const nns_tensors_info_s *info);
 
 /**
  * @brief Check the availability of the given execution environments.

tizen-api/include/nnstreamer.h

@@ -99,6 +99,7 @@ typedef enum _nns_tensor_type_e
   NNS_TENSOR_TYPE_FLOAT32,        /**< Float 32bit */
   NNS_TENSOR_TYPE_INT64,          /**< Integer 64bit */
   NNS_TENSOR_TYPE_UINT64,         /**< Unsigned integer 64bit */
+  ML_TENSOR_TYPE_UNKNOWN          /**< Unknown type */
 } nns_tensor_type_e;
 
 /**

tizen-api/meson.build

@@ -20,9 +20,11 @@ endif
 
 capi_main = []
 capi_main += join_paths(meson.current_source_dir(), 'src', 'tizen-api-pipeline.c')
+capi_main += join_paths(meson.current_source_dir(), 'src', 'nnstreamer-single.c')
 
 capi_devel_main = []
 capi_devel_main += join_paths(meson.current_source_dir(), 'include', 'nnstreamer.h')
+capi_devel_main += join_paths(meson.current_source_dir(), 'include', 'nnstreamer-single.h')
 
 inc = include_directories('include')
 nninc = include_directories('../gst')