dmonitoring: improve yuvframe2tensor (#21996)

* use Y,U,V

* use a pre-calculated table

* add variable use_snpe_model

* .dlc

* remove if

selfdrive/modeld/models/dmonitoring.cc

@@ -13,17 +13,14 @@
 #define MODEL_HEIGHT 640
 #define FULL_W 852 // should get these numbers from camerad
 
-#if defined(QCOM) || defined(QCOM2)
-#define input_lambda(x) (x - 128.f) * 0.0078125f
-#else
-#define input_lambda(x) x // for non SNPE running platforms, assume keras model instead has lambda layer
-#endif
-
 void dmonitoring_init(DMonitoringModelState* s) {
-  const char *model_path = Hardware::PC() ? "../../models/dmonitoring_model.dlc" : "../../models/dmonitoring_model_q.dlc";
-  int runtime = USE_DSP_RUNTIME;
-  s->m = new DefaultRunModel(model_path, &s->output[0], OUTPUT_SIZE, runtime);
   s->is_rhd = Params().getBool("IsRHD");
+
+  const char *model_path = Hardware::PC() ? "../../models/dmonitoring_model.dlc" : "../../models/dmonitoring_model_q.dlc";
+  s->m = new DefaultRunModel(model_path, &s->output[0], OUTPUT_SIZE, USE_DSP_RUNTIME);
+  for (int x = 0; x < std::size(s->tensor); ++x) {
+    s->tensor[x] = (x - 128.f) * 0.0078125f;
+  }
 }
 
 template <class T>
@@ -112,17 +109,17 @@ DMonitoringResult dmonitoring_eval_frame(DMonitoringModelState* s, void* stream_
   for (int r = 0; r < MODEL_HEIGHT/2; r++) {
     for (int c = 0; c < MODEL_WIDTH/2; c++) {
       // Y_ul
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (0*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r)*resized_width + (2*c)]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (0*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_y[(2*r)*resized_width + 2*c]];
       // Y_dl
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (1*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r+1)*resized_width + (2*c)]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (1*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_y[(2*r+1)*resized_width + 2*c]];
       // Y_ur
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (2*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r)*resized_width + (2*c+1)]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (2*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_y[(2*r)*resized_width + 2*c+1]];
       // Y_dr
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (3*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(2*r+1)*resized_width + (2*c+1)]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (3*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_y[(2*r+1)*resized_width + 2*c+1]];
       // U
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (4*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(resized_width*resized_height) + r*resized_width/2 + c]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (4*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_u[r*resized_width/2 + c]];
       // V
-      net_input_buf[(r*MODEL_WIDTH/2) + c + (5*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = input_lambda(resized_buf[(resized_width*resized_height) + ((resized_width/2)*(resized_height/2)) + c + (r*resized_width/2)]);
+      net_input_buf[(r*MODEL_WIDTH/2) + c + (5*(MODEL_WIDTH/2)*(MODEL_HEIGHT/2))] = s->tensor[resized_v[r*resized_width/2 + c]];
     }
   }
 

selfdrive/modeld/models/dmonitoring.h

@@ -35,6 +35,7 @@ typedef struct DMonitoringModelState {
   std::vector<uint8_t> cropped_buf;
   std::vector<uint8_t> premirror_cropped_buf;
   std::vector<float> net_input_buf;
+  float tensor[UINT8_MAX + 1];
 } DMonitoringModelState;
 
 void dmonitoring_init(DMonitoringModelState* s);