Revert "Filet o Fish model" (#34780)

Revert "Filet o Fish model (#34775)"

This reverts commit 31076b818d.

selfdrive/modeld/constants.py

@@ -14,14 +14,8 @@ class ModelConstants:
 
   # model inputs constants
   MODEL_FREQ = 20
-  HISTORY_FREQ = 5
-  HISTORY_LEN_SECONDS = 5
-  TEMPORAL_SKIP = MODEL_FREQ // HISTORY_FREQ
-  FULL_HISTORY_BUFFER_LEN = MODEL_FREQ * HISTORY_LEN_SECONDS
-  INPUT_HISTORY_BUFFER_LEN = HISTORY_FREQ * HISTORY_LEN_SECONDS
-
   FEATURE_LEN = 512
-
+  FULL_HISTORY_BUFFER_LEN = 100
   DESIRE_LEN = 8
   TRAFFIC_CONVENTION_LEN = 2
   LAT_PLANNER_STATE_LEN = 4

selfdrive/modeld/modeld.py

@@ -56,24 +56,16 @@ class ModelState:
   prev_desire: np.ndarray  # for tracking the rising edge of the pulse
 
   def __init__(self, context: CLContext):
-    self.frames = {
-      'input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP),
-      'big_input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP)
-    }
+    self.frames = {'input_imgs': DrivingModelFrame(context), 'big_input_imgs': DrivingModelFrame(context)}
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
 
-    self.full_features_buffer = np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32)
-    self.full_desire = np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.DESIRE_LEN), dtype=np.float32)
-    self.full_prev_desired_curv = np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32)
-    self.temporal_idxs = slice(-1-(ModelConstants.TEMPORAL_SKIP*(ModelConstants.INPUT_HISTORY_BUFFER_LEN-1)), None, ModelConstants.TEMPORAL_SKIP)
-
     # policy inputs
     self.numpy_inputs = {
-      'desire': np.zeros((1, ModelConstants.INPUT_HISTORY_BUFFER_LEN, ModelConstants.DESIRE_LEN), dtype=np.float32),
+      'desire': np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.DESIRE_LEN), dtype=np.float32),
       'traffic_convention': np.zeros((1, ModelConstants.TRAFFIC_CONVENTION_LEN), dtype=np.float32),
       'lateral_control_params': np.zeros((1, ModelConstants.LATERAL_CONTROL_PARAMS_LEN), dtype=np.float32),
-      'prev_desired_curv': np.zeros((1, ModelConstants.INPUT_HISTORY_BUFFER_LEN, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32),
-      'features_buffer': np.zeros((1, ModelConstants.INPUT_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32),
+      'prev_desired_curv': np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32),
+      'features_buffer': np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN,  ModelConstants.FEATURE_LEN), dtype=np.float32),
     }
 
     with open(VISION_METADATA_PATH, 'rb') as f:
@@ -112,9 +104,8 @@ class ModelState:
     new_desire = np.where(inputs['desire'] - self.prev_desire > .99, inputs['desire'], 0)
     self.prev_desire[:] = inputs['desire']
 
-    self.full_desire[0,:-1] = self.full_desire[0,1:]
-    self.full_desire[0,-1] = new_desire
-    self.numpy_inputs['desire'][:] = self.full_desire.reshape((1,ModelConstants.INPUT_HISTORY_BUFFER_LEN,ModelConstants.TEMPORAL_SKIP,-1)).max(axis=2)
+    self.numpy_inputs['desire'][0,:-1] = self.numpy_inputs['desire'][0,1:]
+    self.numpy_inputs['desire'][0,-1] = new_desire
 
     self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
     self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
@@ -137,17 +128,15 @@ class ModelState:
     self.vision_output = self.vision_run(**self.vision_inputs).numpy().flatten()
     vision_outputs_dict = self.parser.parse_vision_outputs(self.slice_outputs(self.vision_output, self.vision_output_slices))
 
-    self.full_features_buffer[0,:-1] = self.full_features_buffer[0,1:]
-    self.full_features_buffer[0,-1] = vision_outputs_dict['hidden_state'][0, :]
-    self.numpy_inputs['features_buffer'][:] = self.full_features_buffer[0, self.temporal_idxs]
+    self.numpy_inputs['features_buffer'][0,:-1] = self.numpy_inputs['features_buffer'][0,1:]
+    self.numpy_inputs['features_buffer'][0,-1] = vision_outputs_dict['hidden_state'][0, :]
 
     self.policy_output = self.policy_run(**self.policy_inputs).numpy().flatten()
     policy_outputs_dict = self.parser.parse_policy_outputs(self.slice_outputs(self.policy_output, self.policy_output_slices))
 
     # TODO model only uses last value now
-    self.full_prev_desired_curv[0,:-1] = self.full_prev_desired_curv[0,1:]
-    self.full_prev_desired_curv[0,-1,:] = policy_outputs_dict['desired_curvature'][0, :]
-    self.numpy_inputs['prev_desired_curv'][:] = self.full_prev_desired_curv[0, self.temporal_idxs]
+    self.numpy_inputs['prev_desired_curv'][0,:-1] = self.numpy_inputs['prev_desired_curv'][0,1:]
+    self.numpy_inputs['prev_desired_curv'][0,-1,:] = policy_outputs_dict['desired_curvature'][0, :]
 
     combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
     if SEND_RAW_PRED:

selfdrive/modeld/models/commonmodel.cc

@@ -5,12 +5,11 @@
 
 #include "common/clutil.h"
 
-DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context, int _temporal_skip) : ModelFrame(device_id, context) {
+DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context) : ModelFrame(device_id, context) {
   input_frames = std::make_unique<uint8_t[]>(buf_size);
-  temporal_skip = _temporal_skip;
   input_frames_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, buf_size, NULL, &err));
-  img_buffer_20hz_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, (temporal_skip+1)*frame_size_bytes, NULL, &err));
-  region.origin = temporal_skip * frame_size_bytes;
+  img_buffer_20hz_cl = CL_CHECK_ERR(clCreateBuffer(context, CL_MEM_READ_WRITE, 2*frame_size_bytes, NULL, &err));
+  region.origin = 1 * frame_size_bytes;
   region.size = frame_size_bytes;
   last_img_cl = CL_CHECK_ERR(clCreateSubBuffer(img_buffer_20hz_cl, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, &region, &err));
 
@@ -21,7 +20,7 @@ DrivingModelFrame::DrivingModelFrame(cl_device_id device_id, cl_context context,
 cl_mem* DrivingModelFrame::prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection) {
   run_transform(yuv_cl, MODEL_WIDTH, MODEL_HEIGHT, frame_width, frame_height, frame_stride, frame_uv_offset, projection);
 
-  for (int i = 0; i < temporal_skip; i++) {
+  for (int i = 0; i < 1; i++) {
     CL_CHECK(clEnqueueCopyBuffer(q, img_buffer_20hz_cl, img_buffer_20hz_cl, (i+1)*frame_size_bytes, i*frame_size_bytes, frame_size_bytes, 0, nullptr, nullptr));
   }
   loadyuv_queue(&loadyuv, q, y_cl, u_cl, v_cl, last_img_cl);

selfdrive/modeld/models/commonmodel.h

@@ -64,21 +64,20 @@ protected:
 
 class DrivingModelFrame : public ModelFrame {
 public:
-  DrivingModelFrame(cl_device_id device_id, cl_context context, int _temporal_skip);
+  DrivingModelFrame(cl_device_id device_id, cl_context context);
   ~DrivingModelFrame();
   cl_mem* prepare(cl_mem yuv_cl, int frame_width, int frame_height, int frame_stride, int frame_uv_offset, const mat3& projection);
 
   const int MODEL_WIDTH = 512;
   const int MODEL_HEIGHT = 256;
   const int MODEL_FRAME_SIZE = MODEL_WIDTH * MODEL_HEIGHT * 3 / 2;
-  const int buf_size = MODEL_FRAME_SIZE * 2; // 2 frames are temporal_skip frames apart
+  const int buf_size = MODEL_FRAME_SIZE * 2;
   const size_t frame_size_bytes = MODEL_FRAME_SIZE * sizeof(uint8_t);
 
 private:
   LoadYUVState loadyuv;
   cl_mem img_buffer_20hz_cl, last_img_cl, input_frames_cl;
   cl_buffer_region region;
-  int temporal_skip;
 };
 
 class MonitoringModelFrame : public ModelFrame {

selfdrive/modeld/models/commonmodel.pxd

@@ -20,7 +20,7 @@ cdef extern from "selfdrive/modeld/models/commonmodel.h":
 
   cppclass DrivingModelFrame:
     int buf_size
-    DrivingModelFrame(cl_device_id, cl_context, int)
+    DrivingModelFrame(cl_device_id, cl_context)
 
   cppclass MonitoringModelFrame:
     int buf_size

selfdrive/modeld/models/commonmodel_pyx.pyx

@@ -59,8 +59,8 @@ cdef class ModelFrame:
 cdef class DrivingModelFrame(ModelFrame):
   cdef cppDrivingModelFrame * _frame
 
-  def __cinit__(self, CLContext context, int temporal_skip):
-    self._frame = new cppDrivingModelFrame(context.device_id, context.context, temporal_skip)
+  def __cinit__(self, CLContext context):
+    self._frame = new cppDrivingModelFrame(context.device_id, context.context)
     self.frame = <cppModelFrame*>(self._frame)
     self.buf_size = self._frame.buf_size
 

selfdrive/modeld/models/driving_policy.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5fd38c3b4d465d14a8ed128f8b1b265bff1589d42f398faa678b93f25b3385dc
-size 15966721
+oid sha256:5cae3285c876804e649b14adadcfb8be79a9bd5a1b928113e37f1f08e25e9688
+size 16581121

selfdrive/modeld/models/driving_vision.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:897f80d0388250f99bba69b6a8434560cc0fd83157cbeb0bc134c67fe4e64624
+oid sha256:29bbf79f9dfd7048c0013bb81e86d9b2979275b95ea1ed8a86d1a86a88695240
 size 34882971