Struct ModelInferenceConfig 

pub struct ModelInferenceConfig {
    pub model_path: Option<PathBuf>,
    pub model_name: Option<String>,
    pub batch_size: Option<usize>,
    pub enable_logging: Option<bool>,
    pub ort_session: Option<OrtSessionConfig>,
}

Configuration for model inference and ONNX Runtime session setup.

This struct provides configuration options for building ONNX inference engines, including runtime settings and model metadata.

Fields

model_path: Option<PathBuf>

The path to the model file (optional).

model_name: Option<String>

The name of the model (optional).

batch_size: Option<usize>

The batch size for processing (optional).

enable_logging: Option<bool>

Whether to enable logging (optional).

ort_session: Option<OrtSessionConfig>

ONNX Runtime session configuration for this model (optional).

Implementations

impl ModelInferenceConfig

pub fn new() -> ModelInferenceConfig

Creates a new ModelInferenceConfig with default values.

Returns

A new ModelInferenceConfig instance.

pub fn with_defaults( model_name: Option<String>, batch_size: Option<usize>, ) -> ModelInferenceConfig

Creates a new ModelInferenceConfig with default values for model name and batch size.

Arguments

• model_name - The name of the model (optional).
• batch_size - The batch size for processing (optional).

Returns

A new ModelInferenceConfig instance.

pub fn with_model_path(model_path: PathBuf) -> ModelInferenceConfig

Creates a new ModelInferenceConfig with a model path.

Arguments

• model_path - The path to the model file.

Returns

A new ModelInferenceConfig instance.

pub fn model_path(self, model_path: impl Into<PathBuf>) -> ModelInferenceConfig

Sets the model path for the configuration.

Arguments

• model_path - The path to the model file.

Returns

The updated ModelInferenceConfig instance.

pub fn model_name(self, model_name: impl Into<String>) -> ModelInferenceConfig

Sets the model name for the configuration.

Arguments

• model_name - The name of the model.

Returns

The updated ModelInferenceConfig instance.

pub fn batch_size(self, batch_size: usize) -> ModelInferenceConfig

Sets the batch size for the configuration.

Arguments

• batch_size - The batch size for processing.

Returns

The updated ModelInferenceConfig instance.

pub fn enable_logging(self, enable: bool) -> ModelInferenceConfig

Sets whether logging is enabled for the configuration.

Arguments

• enable - Whether to enable logging.

Returns

The updated ModelInferenceConfig instance.

pub fn get_enable_logging(&self) -> bool

Gets whether logging is enabled for the configuration.

Returns

True if logging is enabled, false otherwise.

pub fn ort_session(self, cfg: OrtSessionConfig) -> ModelInferenceConfig

Sets the ORT session configuration.

Arguments

• cfg - The ONNX Runtime session configuration.

Returns

The updated ModelInferenceConfig instance.

pub fn validate(&self) -> Result<(), ConfigError>

Validates the configuration.

Returns

A Result indicating success or a ConfigError if validation fails.

pub fn merge_with(self, other: &ModelInferenceConfig) -> ModelInferenceConfig

Merges this configuration with another configuration.

Values from the other configuration will override values in this configuration if they are present in the other configuration.

Arguments

• other - The other configuration to merge with.

Returns

The updated ModelInferenceConfig instance.

pub fn get_batch_size(&self) -> usize

Gets the effective batch size.

Returns

The batch size, or a default value if not set.

pub fn get_model_name(&self) -> String

Gets the model name.

Returns

The model name, or a default value if not set.

Trait Implementations

impl Clone for ModelInferenceConfig

fn clone(&self) -> ModelInferenceConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl ConfigValidator for ModelInferenceConfig

fn validate(&self) -> Result<(), ConfigError>

Validates the configuration.

This method checks that the batch size is valid and that the model path exists if it is specified.

Returns

A Result indicating success or a ConfigError if validation fails.

fn get_defaults() -> ModelInferenceConfig

Returns the default configuration.

Returns

The default ModelInferenceConfig instance.

fn validate_batch_size(&self, batch_size: usize) -> Result<(), ConfigError>

Validates a batch size.

fn validate_batch_size_with_limits( &self, batch_size: usize, max_batch_size: usize, ) -> Result<(), ConfigError>

Validates a batch size against limits.

fn validate_model_path(&self, path: &Path) -> Result<(), ConfigError>

Validates a model path.

fn validate_image_dimensions( &self, width: u32, height: u32, ) -> Result<(), ConfigError>

Validates image dimensions.

fn validate_confidence_threshold( &self, threshold: f32, ) -> Result<(), ConfigError>

Validates a confidence threshold.

fn validate_memory_limit(&self, limit_mb: usize) -> Result<(), ConfigError>

Validates a memory limit.

fn validate_thread_count(&self, thread_count: usize) -> Result<(), ConfigError>

Validates thread count.

fn validate_f32_range( &self, value: f32, min: f32, max: f32, field_name: &str, ) -> Result<(), ConfigError>

Validates a float value is within a specified range.

fn validate_positive_f32( &self, value: f32, field_name: &str, ) -> Result<(), ConfigError>

Validates a float value is positive.

fn validate_positive_usize( &self, value: usize, field_name: &str, ) -> Result<(), ConfigError>

Validates a usize value is positive.

impl Debug for ModelInferenceConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Default for ModelInferenceConfig

fn default() -> ModelInferenceConfig

This allows ModelInferenceConfig to be created with default values.

impl<'de> Deserialize<'de> for ModelInferenceConfig

fn deserialize<__D>( __deserializer: __D, ) -> Result<ModelInferenceConfig, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for ModelInferenceConfig

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.