ResNet

ResNet (Residual Networks) is a convolutional neural network architecture that introduced skip connections to solve the vanishing gradient problem in very deep networks. The model is widely used for image classification and feature extraction tasks. RBLN NPUs can accelerate ResNet model inference using Optimum RBLN.

Key Classes

• RBLNResNetForImageClassification: ResNet model implementation for image classification tasks on RBLN NPU
• RBLNResNetForImageClassificationConfig: Configuration class for ResNet image classification model

API Reference

Classes

RBLNResNetForImageClassification

Bases: RBLNModelForImageClassification

ResNet model for image classification tasks on RBLN NPU.

Functions

forward(pixel_values=None, return_dict=None, **kwargs)

Parameters:

Name	Type	Description	Default
pixel_values	Optional[FloatTensor]	Input pixel values.	None
return_dict	bool	Whether or not to return a ModelOutput instead of a plain tuple.	None
**kwargs	Dict[str, Any]	Additional arguments.	{}

Returns:

Type	Description
Union[ImageClassifierOutput, FloatTensor]	Union[ImageClassifierOutput, FloatTensor]: The output of the model.

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

RBLNResNetForImageClassificationConfig

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.