Add test and resource

Change-Id: I1a663b4cd31e06666d8baba0740c215ddc19f819
Signed-off-by: Kwanghoon Son <k.son@samsung.com>

diff --git a/efficientdet-lite1-uint8.tflite b/efficientdet-lite1-uint8.tflite
new file mode 100644 (file)
index 0000000..19195fd
Binary files /dev/null and b/efficientdet-lite1-uint8.tflite differ

diff --git a/test/src/inference_engine_profiler.cpp b/test/src/inference_engine_profiler.cpp
index c434cf5aba0537ceae730944394ea03f1f98fb70..b0ef26d91c90a697880912306a54e2e723579827 100644 (file)
--- a/test/src/inference_engine_profiler.cpp
+++ b/test/src/inference_engine_profiler.cpp
@@ -870,6 +870,67 @@ TEST_P(InferenceEngineDldtTest, Inference)
        models.clear();
 }
 
+TEST(TFLITE, Inference)
+{
+       printf("tc v2\n");
+       std::string backend_name = "tflite";
+       int target_devices = INFERENCE_TARGET_CPU;
+       std::string model_path = "/root/efficientdet-lite1-uint8.tflite";
+       unsigend int feature_input[384*384*3];
+
+       inference_engine_config config = { .backend_name = backend_name,
+                                                                          .backend_type = -1,
+                                                                          .target_devices = target_devices };
+
+       auto engine = std::make_unique<InferenceEngineCommon>();
+       ASSERT_TRUE(engine);
+
+       int ret = engine->BindBackend(&config);
+       ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
+
+       ret = engine->SetTargetDevices(target_devices);
+       EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
+
+       std::vector<std::string> models;
+       models.push_back(model_path);
+
+       ret = engine->Load(models, INFERENCE_MODEL_TFLITE);
+       ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
+
+       IETensorBuffer inputs, outputs;
+       ret = PrepareTensorBuffers(engine.get(), inputs, outputs);
+       ASSERT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
+
+       std::cout << inputs.size() << std::endl;
+
+       printf("input address = %p\n", inputs["predict_images:0:0"].buffer);
+       printf("output address = %p\n", outputs["StatefulPartitionedCall:0"].buffer);
+
+       for(int k=0; k<10; k++)
+       {
+               printf("iter %d\n", k);
+               for(int i=0;i < 467*67; ++i)
+               {
+                       feature_input[i] = (float)(rand()) / (float)(rand());
+               }
+               memcpy(inputs["serving_default_x:0"].buffer, feature_input, sizeof(float) * 467 *67);
+
+               ret = engine->Run(inputs, outputs);
+               EXPECT_EQ(ret, INFERENCE_ENGINE_ERROR_NONE);
+
+               auto *ori_buf = static_cast<float *>(outputs["StatefulPartitionedCall:0"].buffer);
+               printf("input address = %p\n", inputs["serving_default_x:0"].buffer);
+               printf("output address = %p\n", outputs["StatefulPartitionedCall:0"].buffer);
+
+               std::cout << *ori_buf << std::endl;
+       }
+
+       CleanupTensorBuffers(inputs, outputs);
+
+       engine->UnbindBackend();
+       models.clear();
+}
+
 INSTANTIATE_TEST_CASE_P(
                Opensource, InferenceEngineTfliteTest,
                testing::Values(