vlm_dataset_utils — Model Optimizer 0.27.1 (original) (raw)
Utility functions for getting samples and forward loop function for different vlm datasets.
Functions
| get_vlm_dataset_dataloader | Get a dataloader with the dataset name and processor of the target model. |
|---|---|
| get_supported_vlm_datasets | Retrieves a list of vlm datasets supported. |
get_supported_vlm_datasets()
Retrieves a list of vlm datasets supported.
Returns:
A list of strings, where each string is the name of a supported dataset.
Return type:
_list_[_str_]
Example usage:
from modelopt.torch.utils import get_supported_vlm_datasets
print("Supported datasets:", get_supported_vlm_datasets())
get_vlm_dataset_dataloader(dataset_name='scienceqa', processor=None, batch_size=1, num_samples=512)
Get a dataloader with the dataset name and processor of the target model.
Parameters:
- dataset_name (str) – Name of the dataset to load.
- processor (MllamaImageProcessor) – Processor used for encoding images and text data.
- batch_size (int) – Batch size of the returned dataloader.
- num_samples (int) – Number of samples from the dataset.
Returns:
An instance of dataloader.
Return type:
DataLoader