Added onnx support for C samples (#2747)

* ngraph python sample

This sample demonstrates how to execute an inference using ngraph::Function to create a network
- added sample
- added readme
- added lenet weights

* Added onnx support for C samples

* Revert "ngraph python sample"

This reverts commit 8033292dc367017f6325ea4f96614fdcb797e9dd.

* Added onnx support for C samples

Fixed codestyle mistake

* Removed optional code

Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>

diff --git a/inference-engine/ie_bridges/c/samples/hello_classification/README.md b/inference-engine/ie_bridges/c/samples/hello_classification/README.md
index 671e26e..845a19e 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/hello_classification/README.md
+++ b/inference-engine/ie_bridges/c/samples/hello_classification/README.md
@@ -17,6 +17,8 @@ To properly demonstrate this API, it is required to run several networks in pipe
 To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
 
 You can do inference of an image using a trained AlexNet network on a GPU using the following command:
 

diff --git a/inference-engine/ie_bridges/c/samples/hello_classification/main.c b/inference-engine/ie_bridges/c/samples/hello_classification/main.c
index d47aa24..d961bce 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/hello_classification/main.c
+++ b/inference-engine/ie_bridges/c/samples/hello_classification/main.c
@@ -92,7 +92,7 @@ int main(int argc, char **argv) {
         goto err;
     // -----------------------------------------------------------------------------------------------------
 
-    // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
+    // 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
     status = ie_core_read_network(core, input_model, NULL, &network);
     if (status != OK)
         goto err;

diff --git a/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md b/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md
index 9293506..eeadef1 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md
+++ b/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md
@@ -40,6 +40,8 @@ or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the
 > Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
 
 You can perform inference on an NV12 image using a trained AlexNet network on CPU with the following command:
 ```sh

diff --git a/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/main.c b/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/main.c
index ad1690a..529c8ac 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/main.c
+++ b/inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/main.c
@@ -152,7 +152,7 @@ int main(int argc, char **argv) {
         goto err;
     // -----------------------------------------------------------------------------------------------------
 
-    // --------------------------- 2. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
+    // 2. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
     status = ie_core_read_network(core, input_model, NULL, &network);
     if (status != OK)
         goto err;

diff --git a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md
index 882131d..31ba028 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md
+++ b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md
@@ -40,6 +40,8 @@ Running the application with the empty list of options yields the usage message
 To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
+>
+> The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
 
 For example, to do inference on a CPU with the OpenVINO™ toolkit person detection SSD models, run one of the following commands:
 

diff --git a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c
index c67c21d..14ffa77 100644 (file)
--- a/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c
+++ b/inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/main.c
@@ -344,15 +344,10 @@ int main(int argc, char **argv) {
     }
     // -----------------------------------------------------------------------------------------------------
 
-    // --------------------------- 4. Read IR Generated by ModelOptimizer (.xml and .bin files) ------------
-    input_weight = (char *)calloc(strlen(input_model) + 1, sizeof(char));
-    memcpy(input_weight, input_model, strlen(input_model) - 4);
-    memcpy(input_weight + strlen(input_model) - 4, ".bin", strlen(".bin") + 1);
-    printf("%sLoading network files:\n", info);
+    // 4. Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
+    printf("%sLoading network:\n", info);
     printf("\t%s\n", input_model);
-    printf("\t%s\n", input_weight);
-
-    status = ie_core_read_network(core, input_model, input_weight, &network);
+    status = ie_core_read_network(core, input_model, NULL, &network);
     if (status != OK)
         goto err;
     // -----------------------------------------------------------------------------------------------------