cerebras.modelzoo.data.vision.diffusion.config.DiffusionLatentImageNet1KProcessorConfig#

class cerebras.modelzoo.data.vision.diffusion.config.DiffusionLatentImageNet1KProcessorConfig(batch_size: int = <object object at 0x7f0436677b60>, shuffle: bool = True, shuffle_seed: int = 0, num_workers: int = 0, prefetch_factor: Optional[int] = None, persistent_workers: Optional[bool] = None, data_dir: Union[str, List[str]] = <object object at 0x7f0436677b60>, use_worker_cache: bool = False, mixed_precision: Optional[bool] = None, num_classes: int = <object object at 0x7f0436677b60>, noaugment: bool = False, fp16_type: Optional[bool] = None, transforms: Optional[List[dict]] = None, vae_scaling_factor: Optional[float] = None, label_dropout_rate: Optional[float] = None, latent_size: Optional[List[int]] = None, latent_channels: Optional[int] = None, num_diffusion_steps: Optional[int] = None, schedule_name: Optional[str] = None, drop_last: bool = True)[source]#
batch_size: int = <object object>#

Batch size to be used

data_dir: Union[str, List[str]] = <object object>#
drop_last: bool = True#
fp16_type: Optional[bool] = None#
label_dropout_rate: Optional[float] = None#
latent_channels: Optional[int] = None#
latent_size: Optional[List[int]] = None#
mixed_precision: Optional[bool] = None#
noaugment: bool = False#
num_classes: int = <object object>#
num_diffusion_steps: Optional[int] = None#
num_workers: int = 0#

The number of PyTorch processes used in the dataloader

persistent_workers: Optional[bool] = None#

Whether or not to keep workers persistent between epochs

prefetch_factor: Optional[int] = None#

The number of batches to prefetch in the dataloader

schedule_name: Optional[str] = None#
shuffle: bool = True#

Whether or not to shuffle the dataset

shuffle_seed: int = 0#

Seed used for deterministic shuffling

transforms: Optional[List[dict]] = None#
use_worker_cache: bool = False#
vae_scaling_factor: Optional[float] = None#