Depth Anything V2

Depth Anything V2는 합성 데이터와 대규모 티처-스튜던트 프레임워크를 사용하여 더 정교하고 안정적인 예측을 달성하는 깊이 추정 모델입니다. RBLN NPU는 Optimum RBLN을 사용하여 Depth Anything V2 모델의 추론을 가속화할 수 있습니다.

주요 클래스

• RBLNDepthAnythingForDepthEstimation: RBLN NPU에서 깊이 추정을 위한 Depth Anything V2를 실행하는 주요 모델 클래스
• RBLNDepthAnythingForDepthEstimationConfig: Depth Anything V2 깊이 추정 모델을 위한 구성 클래스

API 참조

Classes

RBLNDepthAnythingForDepthEstimation

Bases: RBLNModelForDepthEstimation

RBLN optimized DepthAnythingForDepthEstimation model for depth estimation tasks.

This class provides hardware-accelerated inference for Depth Anything V2 models on RBLN devices, providing the most capable monocular depth estimation (MDE) model.

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., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	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., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.	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:

1. Compiles the PyTorch model into an optimized RBLN graph
2. Configures the model for the specified NPU device
3. Creates the necessary runtime objects if requested
4. 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

Classes

RBLNDepthAnythingForDepthEstimationConfig

Bases: RBLNModelForDepthEstimationConfig

Configuration class for DepthAnythingForDepthEstimation.

This configuration class stores the configuration parameters specific to RBLN-optimized Depth Anything V2 models for depth estimation tasks.

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.