From: Kwanghoon Son <k.son@samsung.com>
Date: Thu, 31 Oct 2024 05:21:12 +0000 (+0900)
Subject: Add test and resource
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=refs%2Fheads%2Fbug%2Fquantization;p=platform%2Fcore%2Fmultimedia%2Finference-engine-interface.git

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
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 c434cf5..b0ef26d 100644
--- 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(