# 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
