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#