simulation.utils.machine_learning.cycle_gan.configs.train_options module

Reference

class TrainOptions[source]

Bases: simulation.utils.machine_learning.cycle_gan.configs.base_options.BaseOptions

dataset_a: List[str] = ['./../../../../data/real_images/maschinen_halle', './../../../../data/real_images/maschinen_halle_no_obstacles', './../../../../data/real_images/beg_2019']

Path to images of domain A (real images). Can be a list of folders.

dataset_b: List[str] = ['./../../../../data/simulated_images/random_roads']

Path to images of domain B (simulated images). Can be a list of folders

display_id: int = 1

Window id of the web display

display_port: int = 8097

Visdom port of the web display

is_train: bool = True

Enable or disable training mode

num_threads: int = 8

# threads for loading data

print_freq: int = 10

Frequency of showing training results on console

beta1: float = 0.5

Momentum term of adam

batch_size: int = 1

Input batch size

lr: float = 5e-06

Initial learning rate for adam

lr_decay_iters: int = 1

Multiply by a gamma every lr_decay_iters iterations

lr_policy: str = 'step'

Learning rate policy. [linear | step | plateau | cosine]

lr_step_factor: float = 0.1

Multiplication factor at every step in the step scheduler

n_epochs: int = 10

Number of epochs with the initial learning rate

n_epochs_decay: int = 0

Number of epochs to linearly decay learning rate to zero

no_flip: bool = False

Flip 50% of all training images vertically

continue_train: bool = False

Load checkpoints or start from scratch

class WassersteinCycleGANTrainOptions[source]

Bases: simulation.utils.machine_learning.cycle_gan.configs.train_options.TrainOptions

wgan_initial_n_critic: int = 1

Number of iterations of the critic before starting training loop

wgan_clip_upper: float = 0.001

Upper bound for weight clipping

wgan_clip_lower: float = -0.001

Lower bound for weight clipping

wgan_n_critic: int = 5

Number of iterations of the critic per generator iteration

is_wgan: bool = True

Decide whether to use wasserstein cycle gan or standard cycle gan

class CycleGANTrainOptions[source]

Bases: simulation.utils.machine_learning.cycle_gan.configs.train_options.TrainOptions