config.py

import os
AUGMENT = True

#CAMERA
CAMERA_TYPE = "PICAM"   # (PICAM|WEBCAM|CVCAM|CSIC|V4L|MOCK)
IMAGE_W = 160
IMAGE_H = 120
IMAGE_DEPTH = 3         # default RGB=3, make 1 for mono
CAMERA_FRAMERATE = 20
# For CSIC camera - If the camera is mounted in a rotated position, changing the below parameter will correct the output frame orientation
#CSIC_CAM_GSTREAMER_FLIP_PARM = 0 # (0 => none , 4 => Flip horizontally, 6 => Flip vertically)

#TRAINING
#The DEFAULT_MODEL_TYPE will choose which model will be created at training time. This chooses
#between different neural network designs. You can override this setting by passing the command
#line parameter --type to the python manage.py train and drive commands.
DEFAULT_MODEL_TYPE = 'categorical'   #(linear|categorical|rnn|imu|behavior|3d|localizer|latent)
BATCH_SIZE = 128                #how many records to use when doing one pass of gradient decent. Use a smaller number if your gpu is running out of memory.
TRAIN_TEST_SPLIT = 0.8          #what percent of records to use for training. the remaining used for validation.
MAX_EPOCHS = 100                #how many times to visit all records of your data
SHOW_PLOT = True                #would you like to see a pop up display of final loss?
VERBOSE_TRAIN = True             #would you like to see a progress bar with text during training?
USE_EARLY_STOP = True           #would you like to stop the training if we see it's not improving fit?
EARLY_STOP_PATIENCE = 5         #how many epochs to wait before no improvement
MIN_DELTA = .0005               #early stop will want this much loss change before calling it improved.
PRINT_MODEL_SUMMARY = True      #print layers and weights to stdout
OPTIMIZER = None                #adam, sgd, rmsprop, etc.. None accepts default
LEARNING_RATE = 0.001           #only used when OPTIMIZER specified
LEARNING_RATE_DECAY = 0.0       #only used when OPTIMIZER specified
SEND_BEST_MODEL_TO_PI = False   #change to true to automatically send best model during training
CACHE_IMAGES = True             #keep images in memory. will speed succesive epochs, but crater if not enough mem.

PRUNE_CNN = False               #This will remove weights from your model. The primary goal is to increase performance.
PRUNE_PERCENT_TARGET = 75       # The desired percentage of pruning.
PRUNE_PERCENT_PER_ITERATION = 20 # Percenge of pruning that is perform per iteration.
PRUNE_VAL_LOSS_DEGRADATION_LIMIT = 0.2 # The max amout of validation loss that is permitted during pruning.
PRUNE_EVAL_PERCENT_OF_DATASET = .05  # percent of dataset used to perform evaluation of model.

# Region of interst cropping
# only supported in Categorical and Linear models.
# If these crops values are too large, they will cause the stride values to become negative and the model with not be valid.
ROI_CROP_TOP = 0                    #the number of rows of pixels to ignore on the top of the image
ROI_CROP_BOTTOM = 0                 #the number of rows of pixels to ignore on the bottom of the image


#Model transfer options
#When copying weights during a model transfer operation, should we freeze a certain number of layers
#to the incoming weights and not allow them to change during training?
FREEZE_LAYERS = False               #default False will allow all layers to be modified by training
NUM_LAST_LAYERS_TO_TRAIN = 7        #when freezing layers, how many layers from the last should be allowed to train?


#For the categorical model, this limits the upper bound of the learned throttle
#it's very IMPORTANT that this value is matched from the training PC config.py and the robot.py
#and ideally wouldn't change once set.
MODEL_CATEGORICAL_MAX_THROTTLE_RANGE = 0.5

#RNN or 3D
SEQUENCE_LENGTH = 3             #some models use a number of images over time. This controls how many.

#IMU
HAVE_IMU = False                #when true, this add a Mpu6050 part and records the data. Can be used with a


#BEHAVIORS
#When training the Behavioral Neural Network model, make a list of the behaviors,
#Set the TRAIN_BEHAVIORS = True, and use the BEHAVIOR_LED_COLORS to give each behavior a color
TRAIN_BEHAVIORS = False
BEHAVIOR_LIST = ['Left_Lane', "Right_Lane"]
BEHAVIOR_LED_COLORS =[ (0, 10, 0), (10, 0, 0) ] #RGB tuples 0-100 per chanel

#Localizer
#The localizer is a neural network that can learn to predice it's location on the track.
#This is an experimental feature that needs more developement. But it can currently be used
#to predict the segement of the course, where the course is divided into NUM_LOCATIONS segments.
TRAIN_LOCALIZER = False
NUM_LOCATIONS = 10
BUTTON_PRESS_NEW_TUB = False #when enabled, makes it easier to divide our data into one tub per track length if we make a new tub on each X button press.


#When racing, to give the ai a boost, configure these values.
AI_LAUNCH_DURATION = 0.0            # the ai will output throttle for this many seconds
AI_LAUNCH_THROTTLE = 0.0            # the ai will output this throttle value
AI_LAUNCH_ENABLE_BUTTON = 'R2'      # this keypress will enable this boost. It must be enabled before each use to prevent accidental trigger.
AI_LAUNCH_KEEP_ENABLED = False      # when False ( default) you will need to hit the AI_LAUNCH_ENABLE_BUTTON for each use. This is safest. When this True, is active on each trip into "local" ai mode.

#Scale the output of the throttle of the ai pilot for all model types.
AI_THROTTLE_MULT = 1.0              # this multiplier will scale every throttle value for all output from NN models

#Path following
PATH_FILENAME = "path.pkl"   #the path will be saved to this filename
PATH_SCALE = 5.0                    # the path display will be scaled by this factor in the web page
PATH_OFFSET = (0, 0)                # 255, 255 is the center of the map. This offset controls where the origin is displayed.
PATH_MIN_DIST = 0.3                 # after travelling this distance (m), save a path point
PID_P = -10.0                       # proportional mult for PID path follower
PID_I = 0.000                       # integral mult for PID path follower
PID_D = -0.2                        # differential mult for PID path follower
PID_THROTTLE = 0.2                  # constant throttle value during path following
SAVE_PATH_BTN = "cross"             # joystick button to save path
RESET_ORIGIN_BTN = "triangle"       # joystick button to press to move car back to origin