cerebras.modelzoo.data.vision.classification.data.imagenet.ImageNet1KProcessorConfig#

class cerebras.modelzoo.data.vision.classification.data.imagenet.ImageNet1KProcessorConfig(*args, **kwargs)[source]#

Methods

`check_for_deprecated_fields`
`check_literal_discriminator_field`
`copy`
`get_orig_class`
`get_orig_class_args`
`model_copy`
`model_post_init`
`post_init`

Attributes

`batch_size`	Global batch size for the dataloader
`cutmix_alpha`	Alpha parameter for the cutmix transform.
`data_dir`	The path to the data
`discriminator`
`discriminator_value`
`drop_last`	Similar to the PyTorch drop_last setting except that samples that when set to True, samples that would have been dropped at the end of one epoch are yielded at the start of the next epoch so that there is no data loss.
`fp16_type`
`image_size`	The size of the images in the dataset
`mixed_precision`
`mixup_alpha`	Alpha parameter for the mixup transform.
`model_config`
`noaugment`	Indicates to skip augmentation as part of preprocessing.
`num_classes`	The number of classification classes in the dataset
`num_workers`	How many subprocesses to use for data loading
`persistent_workers`	Whether or not to keep workers persistent between epochs.
`prefetch_factor`	Number of batches loaded in advance by each worker
`ra_sampler_num_repeat`	Number of repeats for Repeated Augmentation sampler.
`sampler`	Type of data sampler to use
`shuffle`	Whether or not to shuffle the dataset.
`shuffle_seed`	The seed used for deterministic shuffling.
`split`	Dataset split.
`transforms`	List of transforms for preprocessing
`use_fake_data`
`use_worker_cache`
`data_processor`

cerebras.modelzoo.data.vision.classification.data.imagenet.ImageNet1KProcessor

cerebras.modelzoo.data.vision.classification.data.imagenet21k