idun/SegmenterConfig.py at master · putertubby/idun · GitHub

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import numpy as np
from easydict import EasyDict as edict

def idun_config():

  cfg = edict()

  cfg.training_data_dir = "./dataset/training"
  cfg.validation_data_dir = "./dataset/validation"
  cfg.testing_data_dir = "./dataset/testing"
  cfg.saved_models = "./saved_models"
  cfg.model_name = "idunmodel.h5"

  # classes
  #  Unknown: 0
  #  Car: 1
  #  Tram: 2
  #  Cyclist: 3
  #  Van: 4
  #  Pedestrian: 5
  #  Misc: 6
  #  Truck: 7
  cfg.CLASSES = [
      'Unknown',
      'Car',
      'Tram',
      'Cyclist',
      'Van',
      'Pedestrian',
      'Misc',
      'Truck']

  cfg.NUM_CLASS = len(cfg.CLASSES) # number of classes

  cfg.GPU = 0                      # GPU ID, set to -1 to use CPU
  cfg.DROPOUT_PROB = 0.5           # Probability to keep a node in dropout
  cfg.FRAME_WIDTH = 512            # frame width
  cfg.FRAME_HEIGHT = 64            # frame height
  cfg.FRAME_CHANNEL = 4            # frame channel
  cfg.NUM_EPOCHS = 25              # epoch number
  cfg.BATCH_SIZE = 32              # batch size
  cfg.LEARNING_RATE = 0.001        # learning rate
  cfg.LR_DECAY_FACTOR = 0.1        # multiply the learning rate by this factor
  cfg.PRINT_EVERY = 20             # print in every 50 epochs
  cfg.SAVE_EVERY = 1               # save after each epoch
  cfg.DEBUG_MODE = True            # print log to console in debug mode
  cfg.DATA_AUGMENTATION = True     # Whether to do data augmentation

  return cfg