simulation.utils.machine_learning.cycle_gan.configs package

Submodules

simulation.utils.machine_learning.cycle_gan.configs.base_options module

Classes:

BaseOptions()

class BaseOptions

Bases: object

Attributes:

activation(*input, **kwargs)	Choose which activation to use.
checkpoints_dir	Models are saved here.
conv_layers_in_block	Specify number of convolution layers per resnet block.
crop_size	Then crop to this size.
dilations	Dilation for individual conv layers in every resnet block.
epoch	Which epoch to load?
init_gain	Scaling factor for normal, xavier and orthogonal.
init_type	Network initialization [normal | xavier | kaiming | orthogonal]
input_nc	3 for RGB and 1 for grayscale
lambda_idt_a	Weight for loss identity of domain A.
lambda_idt_b	Weight for loss identity of domain B.
lambda_cycle	Weight for cycle loss.
load_size	Scale images to this size.
mask	Path to a mask overlaid over all images.
n_layers_d	Number of layers in the discriminator network.
name	Name of the experiment.
ndf	# of discriminator filters in the first conv layer
netd	Specify discriminator architecture.
netg	Specify generator architecture [resnet_<ANY_INTEGER>blocks | unet_256 | unet_128]
ngf	# of gen filters in the last conv layer
no_dropout	No dropout for the generator.
norm	Instance normalization or batch normalization [instance | batch | none]
output_nc	3 for RGB and 1 for grayscale.
preprocess	Scaling and cropping of images at load time.
verbose	If specified, print more debugging information.
cycle_noise_stddev	Standard deviation of noise added to the cycle input.
pool_size	The size of image buffer that stores previously generated images.
max_dataset_size	Maximum amount of images to load; -1 means infinity.
is_wgan	Decide whether to use wasserstein cycle gan or standard cycle gan.
l1_or_l2_loss	"l1" or "l2"; Decide whether to use l1 or l2 as cycle and identity loss functions.
use_sigmoid	Use sigmoid activation at end of discriminator.

Methods:

to_dict()

activation(*input, **kwargs): Module = Tanh()

Choose which activation to use.

checkpoints_dir: str = './checkpoints'

Models are saved here.

conv_layers_in_block: int = 3

Specify number of convolution layers per resnet block.

crop_size: int = 512

Then crop to this size.

dilations: List[int] = [1, 2, 4]

Dilation for individual conv layers in every resnet block.

epoch: int | str = 'latest'

Which epoch to load?

set to latest to use latest cached model

init_gain: float = 0.02

Scaling factor for normal, xavier and orthogonal.

init_type: str = 'normal'

Network initialization [normal | xavier | kaiming | orthogonal]

input_nc: int = 1

3 for RGB and 1 for grayscale

Type:

# of input image channels

lambda_idt_a: float = 0.5

Weight for loss identity of domain A.

lambda_idt_b: float = 0.5

Weight for loss identity of domain B.

lambda_cycle: float = 10

Weight for cycle loss.

load_size: int = 512

Scale images to this size.

mask: str = 'resources/mask.png'

Path to a mask overlaid over all images.

n_layers_d: int = 4

Number of layers in the discriminator network.

name: str = 'dr_drift'

Name of the experiment.

It decides where to store samples and models

ndf: int = 32

# of discriminator filters in the first conv layer

netd: str = 'basic'

Specify discriminator architecture.

[basic | n_layers | no_patch]. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator.

netg: str = 'resnet_9blocks'

Specify generator architecture [resnet_<ANY_INTEGER>blocks | unet_256 | unet_128]

ngf: int = 32

# of gen filters in the last conv layer

no_dropout: bool = True

No dropout for the generator.

norm: str = 'instance'

Instance normalization or batch normalization [instance | batch | none]

output_nc: int = 1

3 for RGB and 1 for grayscale.

Type:

Of output image channels

preprocess: set = {'crop', 'resize'}

Scaling and cropping of images at load time.

[resize | crop | scale_width]

verbose: bool = False

If specified, print more debugging information.

cycle_noise_stddev: float = 0

Standard deviation of noise added to the cycle input.

Mean is 0.

pool_size: int = 75

The size of image buffer that stores previously generated images.

max_dataset_size: int = 15000

Maximum amount of images to load; -1 means infinity.

is_wgan: bool = False

Decide whether to use wasserstein cycle gan or standard cycle gan.

l1_or_l2_loss: str = 'l1'

“l1” or “l2”; Decide whether to use l1 or l2 as cycle and identity loss functions.

use_sigmoid: bool = True

Use sigmoid activation at end of discriminator.

classmethod to_dict() → dict

simulation.utils.machine_learning.cycle_gan.configs.test_options module

Classes:

TestOptions()
WassersteinCycleGANTestOptions()
CycleGANTestOptions()

class TestOptions

Bases: BaseOptions

Attributes:

dataset_a	Path to images of domain A (real images).
dataset_b	Path to images of domain B (simulated images).
results_dir	Saves results here.
aspect_ratio	Aspect ratio of result images.
is_train	Enable or disable training mode.

dataset_a: List[str] = ['./../../../../data/real_images/maschinen_halle_parking']

Path to images of domain A (real images).

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

Path to images of domain B (simulated images).

results_dir: str = './results/'

Saves results here.

aspect_ratio: float = 1

Aspect ratio of result images.

is_train: bool = False

Enable or disable training mode.

class WassersteinCycleGANTestOptions

Bases: TestOptions

class CycleGANTestOptions

Bases: TestOptions

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

Classes:

TrainOptions()
WassersteinCycleGANTrainOptions()
CycleGANTrainOptions()

class TrainOptions

Bases: BaseOptions

Attributes:

dataset_a	Path to images of domain A (real images).
dataset_b	Path to images of domain B (simulated images).
display_id	Window id of the web display.
display_port	Visdom port of the web display.
is_train	Enable or disable training mode.
num_threads	# threads for loading data
save_freq	Frequency of saving the current models.
print_freq	Frequency of showing training results on console.
beta1	Momentum term of adam.
batch_size	Input batch size.
lr	Initial learning rate for adam.
lr_decay_iters	Multiply by a gamma every lr_decay_iters iterations.
lr_policy	Learning rate policy.
lr_step_factor	Multiplication factor at every step in the step scheduler.
n_epochs	Number of epochs with the initial learning rate.
n_epochs_decay	Number of epochs to linearly decay learning rate to zero.
no_flip	Flip 50% of all training images vertically.
continue_train	Load checkpoints or start from scratch.

dataset_a: List[str] = ['./../../../../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

save_freq: int = 100

Frequency of saving the current models.

print_freq: int = 5

Frequency of showing training results on console.

beta1: float = 0.5

Momentum term of adam.

batch_size: int = 3

Input batch size.

lr: float = 0.0005

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 = 0

Number of epochs with the initial learning rate.

n_epochs_decay: int = 10

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

Bases: TrainOptions

Attributes:

wgan_initial_n_critic	Number of iterations of the critic before starting training loop.
wgan_clip_upper	Upper bound for weight clipping.
wgan_clip_lower	Lower bound for weight clipping.
wgan_n_critic	Number of iterations of the critic per generator iteration.
is_wgan	Decide whether to use wasserstein cycle gan or standard cycle gan.

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

Bases: TrainOptions

Module contents