Using different YOLOX models about play_with_tensorrt HOT 4 CLOSED

diff --git a/pj_tensorrt_det_yolox/image_processor/detection_engine.cpp b/pj_tensorrt_det_yolox/image_processor/detection_engine.cpp
index 6f87969..08df8e3 100644
--- a/pj_tensorrt_det_yolox/image_processor/detection_engine.cpp
+++ b/pj_tensorrt_det_yolox/image_processor/detection_engine.cpp
@@ -41,12 +41,12 @@ limitations under the License.
 #define PRINT_E(...) COMMON_HELPER_PRINT_E(TAG, __VA_ARGS__)

 /* Model parameters */
-#define MODEL_NAME  "yolox_nano_480x640.onnx"
+#define MODEL_NAME  "yolox_m_736x1280.onnx"
 #define TENSORTYPE  TensorInfo::kTensorTypeFp32
 #define INPUT_NAME  "images"
-#define INPUT_DIMS  { 1, 3, 480, 640 }
+#define INPUT_DIMS  { 1, 3, 736, 1280 }
 #define IS_NCHW     true
-#define IS_RGB      true
+#define IS_RGB      false
 #define OUTPUT_NAME "output"

 static constexpr int32_t kGridScaleList[] = { 8, 16, 32 };
@@ -69,12 +69,12 @@ int32_t DetectionEngine::Initialize(const std::string& work_dir, const int32_t n
     InputTensorInfo input_tensor_info(INPUT_NAME, TENSORTYPE, IS_NCHW);
     input_tensor_info.tensor_dims = INPUT_DIMS;
     input_tensor_info.data_type = InputTensorInfo::kDataTypeImage;
-    input_tensor_info.normalize.mean[0] = 0.485f;
-    input_tensor_info.normalize.mean[1] = 0.456f;
-    input_tensor_info.normalize.mean[2] = 0.406f;
-    input_tensor_info.normalize.norm[0] = 0.229f;
-    input_tensor_info.normalize.norm[1] = 0.224f;
-    input_tensor_info.normalize.norm[2] = 0.225f;
+    input_tensor_info.normalize.mean[0] = 0.0f;
+    input_tensor_info.normalize.mean[1] = 0.0f;
+    input_tensor_info.normalize.mean[2] = 0.0f;
+    input_tensor_info.normalize.norm[0] = 1.0f / 255.0f;
+    input_tensor_info.normalize.norm[1] = 1.0f / 255.0f;
+    input_tensor_info.normalize.norm[2] = 1.0f / 255.0f;
     input_tensor_info_list_.push_back(input_tensor_info);