ViT¶
ViT (Vision Transformer)는 이미지 분류 작업을 위해 설계된 Transformer 기반 모델입니다. 이미지를 패치 시퀀스로 취급하고 NLP에서 사용되는 것과 유사한 Transformer 아키텍처를 사용하여 처리합니다. RBLN NPU는 Optimum RBLN을 사용하여 ViT 모델 추론을 가속화할 수 있습니다.
주요 클래스¶
RBLNViTForImageClassificationConfig
: RBLN ViT 모델의 설정 클래스입니다.RBLNViTForImageClassification
: 이미지 분류 작업을 위한 RBLN ViT 모델입니다.
API 참조¶
Classes¶
RBLNViTForImageClassificationConfig
¶
Bases: RBLNModelForImageClassificationConfig
Functions¶
__init__(image_size=None, batch_size=None, **kwargs)
¶
Base configuration class for image models running on RBLN NPUs.
This class extends the RBLNModelConfig to include parameters specific to image models, such as image dimensions and batch sizes for inference.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
image_size
|
Optional[Union[int, Tuple[int, int]]]
|
The size of input images. Can be an integer for square images or a tuple (height, width). |
None
|
batch_size
|
Optional[int]
|
The batch size for inference. Defaults to 1. |
None
|
**kwargs
|
Dict[str, Any]
|
Additional arguments passed to the parent RBLNModelConfig. |
{}
|
Raises:
Type | Description |
---|---|
ValueError
|
If batch_size is not a positive integer. |
Classes¶
RBLNViTForImageClassification
¶
Bases: RBLNModelForImageClassification
Functions¶
from_pretrained(model_id, export=False, rbln_config=None, **kwargs)
classmethod
¶
The from_pretrained()
function is utilized in its standard form as in the HuggingFace transformers library.
User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model_id
|
Union[str, Path]
|
The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler. |
required |
export
|
bool
|
A boolean flag to indicate whether the model should be compiled. |
False
|
rbln_config
|
Optional[Union[Dict, RBLNModelConfig]]
|
Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., |
None
|
kwargs
|
Dict[str, Any]
|
Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library. |
{}
|
Returns:
Type | Description |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
from_model(model, *, rbln_config=None, **kwargs)
classmethod
¶
Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model
|
PreTrainedModel
|
The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class. |
required |
rbln_config
|
Optional[Union[Dict, RBLNModelConfig]]
|
Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., |
None
|
kwargs
|
Dict[str, Any]
|
Additional keyword arguments. Arguments with the prefix 'rbln_' are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library. |
{}
|
The method performs the following steps:
- Compiles the PyTorch model into an optimized RBLN graph
- Configures the model for the specified NPU device
- Creates the necessary runtime objects if requested
- Saves the compiled model and configurations
Returns:
Type | Description |
---|---|
Self
|
A RBLN model instance ready for inference on RBLN NPU devices. |
save_pretrained(save_directory)
¶
Saves a model and its configuration file to a directory, so that it can be re-loaded using the
[from_pretrained
] class method.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
save_directory
|
Union[str, PathLike]
|
The directory to save the model and its configuration files. Will be created if it doesn't exist. |
required |