cleanup models, refactor keras_runner, fix model running

add lfs
cherry pick of "moving larte files to lfs"
use new model
Re-implement split loading
fix models
remove pathlib dep
final fix to submodules

remove panda temp

readd panda submodule

.gitmodules

@@ -1,9 +1,6 @@
-[submodule "panda"]
-	path = panda
-	url = https://github.com/boggyver/panda.git
 [submodule "opendbc"]
 	path = opendbc
-	url = https://github.com/boggyver/opendbc.git
+	url = git@github.com:BogGyver/opendbc.git
 [submodule "laika_repo"]
 	path = laika_repo
 	url = https://github.com/commaai/laika.git
@@ -12,4 +9,7 @@
 	url = https://github.com/commaai/openpilot-apks.git
 [submodule "cereal"]
 	path = cereal
-	url = https://github.com/boggyver/cereal.git
+	url = [email protected]:BogGyver/cereal.git
+[submodule "panda"]
+	path = panda
+	url = [email protected]:BogGyver/panda.git

selfdrive/controls/lib/lane_planner.py

@@ -102,7 +102,7 @@ def update_d_poly(self, v_ego, md, alca ):
     if self.shouldUseAlca and alca:
       self.r_poly,self.l_poly,self.r_prob,self.l_prob,self.lane_width, self.p_poly = self.ALCAMP.update(v_ego, md, np.array(self.r_poly), np.array(self.l_poly), self.r_prob, self.l_prob, self.lane_width, self.p_poly)
 
-    self.d_poly = calc_d_poly(self.l_poly, self.r_poly, self.p_poly, self.l_prob, self.r_prob, self.lane_width)
+    self.d_poly = calc_d_poly(self.l_poly, self.r_poly, self.p_poly, self.l_prob, self.r_prob, self.lane_width,v_ego)
 
   def update(self, v_ego, md, alca):
     self.parse_model(md)

selfdrive/modeld/models/driving.h

@@ -4,7 +4,7 @@
 // gate this here
 #define TEMPORAL
 #define DESIRE
-#define TRAFFIC_CONVENTION
+//#define TRAFFIC_CONVENTION
 
 #ifdef QCOM
 #include <eigen3/Eigen/Dense>

selfdrive/modeld/runners/keras_runner.py

@@ -1,4 +1,4 @@
-#!/usr/bin/env python3
+#!/usr/bin/env python
 # TODO: why are the keras models saved with python 2?
 from __future__ import print_function
 
@@ -8,9 +8,7 @@
 import tensorflow.keras as keras
 import numpy as np
 from tensorflow.keras.models import Model
-from tensorflow.keras.models import load_model
-from pathlib import Path
-
+from tensorflow.keras.models import model_from_json
 def read(sz):
   dd = []
   gt = 0
@@ -29,43 +27,35 @@ def run_loop(m,name):
   osize = m.outputs[0].shape[1]
   int 
   print("ready to run keras model %d -> %d" % (isize, osize), file=sys.stderr)
-  cnt = 0
   while 1:
-    if name == "supercombo":
-      cnt = (cnt + 1 ) % 100
-    if (cnt % 20 == 1):
-        print("Processing frame %d " % cnt, file=sys.stderr)
     # check parent process, if ppid is 1, then modeld is no longer running and the runner should exit.
     if os.getppid() == 1:
       print("exiting due to Parent PID", file=sys.stderr)  
-      #break
+      break
     idata = read(isize).reshape((1, isize))
     ret = m.predict_on_batch(idata)
-    if (cnt % 20 == 1):
-        print(ret, file=sys.stderr)
     write(ret)
 
 if __name__ == "__main__":
   print(tf.__version__, file=sys.stderr)
   # limit gram alloc
   gpus = tf.config.experimental.list_physical_devices('GPU')
-  model_path = Path(sys.argv[1]).parents[0]
-  name = Path(sys.argv[1]).stem
-  print("\n\nRunning [%s] with path [%s]\n\n" % (name,sys.argv[1]),file=sys.stderr)
-  if len(gpus) > 0:
-    if name == "supercombo":
-      tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048)])
-    else:
-      tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=300)])
-  m = load_model(sys.argv[1])
-  print(m, file=sys.stderr)
+  if os.path.splitext(os.path.basename(sys.argv[1]))[0]== "supercombo":
+    if len(gpus) > 0:
+      tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2548)])
+
+      with open(f"{os.path.splitext(sys.argv[1])}.model.keras", "r") as json_file:
+        m = model_from_json(json_file.read())
+      m.load_weights(f"{os.path.splitext(sys.argv[1])}.weights.keras")
+  else:
+    if len(gpus) > 0:
+      tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=256)])
+      m.load_model(sys.argv[1])
   bs = [int(np.product(ii.shape[1:])) for ii in m.inputs]
   ri = keras.layers.Input((sum(bs),))
-
   tii = []
   acc = 0
   for i, ii in enumerate(m.inputs):
-    print(ii, file=sys.stderr)
     ti = keras.layers.Lambda(lambda x: x[:,acc:acc+bs[i]], output_shape=(1, bs[i]))(ri)
     acc += bs[i]
     tr = keras.layers.Reshape(ii.shape[1:])(ti)