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

Summary

Classes:

BaseOptions

Reference

class BaseOptions

Bases: object

activation: torch.nn.modules.module.Module = Tanh()

Choose which activation to use.

checkpoints_dir: str = './checkpoints'

models are saved here

conv_layers_in_block: int = 2

specify number of convolution layers per resnet block

crop_size: int = 256

then crop to this size

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

dilation for individual conv layers in every resnet block

epoch: Union[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 = 5

weight for loss identity of domain A

lambda_idt_b: float = 5

weight for loss identity of domain B

lambda_cycle: float = 10

weight for cycle loss

load_size: int = 256

scale images to this size

mask: str = 'resources/mask.png'

Path to a mask overlaid over all images

n_layers_d: int = 3

number of layers in the discriminator network

name: str = 'dr_drift_256'

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 = -1

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