# TODO: NAMing conventions ? _g_conf = AttributeDict() """#### GENERAL CONFIGURATION PARAMETERS ####""" _g_conf.NUMBER_OF_LOADING_WORKERS = 12 _g_conf.SENSORS = {'rgb': (3, 88, 200)} _g_conf.MEASUREMENTS = {'targets': (31)} _g_conf.TARGETS = ['steer', 'throttle', 'brake'] _g_conf.INPUTS = ['speed_module'] _g_conf.STEERING_DIVISION = [0.05, 0.05, 0.1, 0.3, 0.3, 0.1, 0.05, 0.05] #_g_conf.STEERING_DIVISION = [0.01, 0.02, 0.07, 0.4, 0.4, 0.07, 0.02, 0.01] # Forcing curves alot _g_conf.LABELS_DIVISION = [[2]] _g_conf.BATCH_SIZE = 4 _g_conf.AUGMENTATION_SUITE = [ iag.ToGPU() ] #, iag.Add((0, 0)), iag.Dropout(0, 0), iag.Multiply((1, 1.04)), #iag.GaussianBlur(sigma=(0.0, 3.0)), # iag.ContrastNormalization((0.5, 1.5)) # ] #_g_conf.AUGMENTATION_SUITE_CPU = [ ia.Add((0, 0)), ia.Dropout(0, 0), # ia.GaussianBlur(sigma=(0.0, 3.0)), # ia.ContrastNormalization((0.5, 1.5)) # ] _g_conf.TRAIN_DATASET_NAME = '1HoursW1-3-6-8' # We only set the dataset in configuration for training _g_conf.LOG_SCALAR_WRITING_FREQUENCY = 2 # TODO NEEDS TO BE TESTED ON THE LOGGING FUNCTION ON CREATE LOG _g_conf.LOG_IMAGE_WRITING_FREQUENCY = 20 _g_conf.EXPERIMENT_BATCH_NAME = "eccv" _g_conf.EXPERIMENT_NAME = "default"
from logger.coil_logger import create_log, add_message import imgauggpu as iag # TODO: How do we KEEP A GOOD ITERATION COUNTER ?? # TODO: NAMing conventions ? _g_conf = AttributeDict() _g_conf.SENSORS = {'rgb': (3, 88, 200)} _g_conf.MEASUREMENTS = {'targets': (31)} _g_conf.STEERING_DIVISION = [0.05, 0.05, 0.1, 0.3, 0.3, 0.1, 0.05, 0.05] _g_conf.LABELS_DIVISION = [[0, 2, 5], [3], [4]] _g_conf.AUGMENTATION_SUITE = [iag.ToGPU(), iag.Add(0, 0)] _g_conf.DATASET_NAME = 'SmallTest' _g_conf.EXPERIMENT_BATCH_NAME = "eccv" _g_conf.EXPERIMENT_NAME = "default" # TODO: not necessarily the configuration need to know about this _g_conf.PROCESS_NAME = "None" _g_conf.NUMBER_ITERATIONS = 2000 * 120 _g_conf.SAVE_SCHEDULE = range(0, 2000, 200) _g_conf.NUMBER_FRAMES_FUSION = 1 _g_conf.NUMBER_IMAGES_SEQUENCE = 1 _g_conf.SEQUENCE_STRIDE = 1 _g_conf.TEST_SCHEDULE = range(0, 2000, 200) #self.param.MISC.DATASET_SIZE