oxigdal-bench 0.1.4

//! ML inference benchmark scenarios.
//!
//! This module provides benchmark scenarios for ML operations including:
//! - ONNX model inference
//! - Batch processing performance
//! - Preprocessing overhead
//! - Postprocessing performance
//! - End-to-end inference pipeline

use crate::error::{BenchError, Result};
use crate::scenarios::BenchmarkScenario;
use std::path::PathBuf;

/// ML task types.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MlTask {
    /// Image classification.
    Classification,
    /// Object detection.
    Detection,
    /// Semantic segmentation.
    Segmentation,
    /// Instance segmentation.
    InstanceSegmentation,
}

/// ONNX inference benchmark scenario.
pub struct OnnxInferenceScenario {
    model_path: PathBuf,
    input_shape: Vec<usize>,
    batch_size: usize,
    task_type: MlTask,
    warmup_iterations: usize,
    benchmark_iterations: usize,
}

impl OnnxInferenceScenario {
    /// Creates a new ONNX inference benchmark scenario.
    pub fn new<P>(model_path: P, input_shape: Vec<usize>) -> Self
    where
        P: Into<PathBuf>,
    {
        Self {
            model_path: model_path.into(),
            input_shape,
            batch_size: 1,
            task_type: MlTask::Classification,
            warmup_iterations: 10,
            benchmark_iterations: 100,
        }
    }

    /// Sets the batch size.
    pub fn with_batch_size(mut self, batch_size: usize) -> Self {
        self.batch_size = batch_size;
        self
    }

    /// Sets the task type.
    pub fn with_task_type(mut self, task_type: MlTask) -> Self {
        self.task_type = task_type;
        self
    }

    /// Sets the warmup iterations.
    pub fn with_warmup_iterations(mut self, iterations: usize) -> Self {
        self.warmup_iterations = iterations;
        self
    }

    /// Sets the benchmark iterations.
    pub fn with_benchmark_iterations(mut self, iterations: usize) -> Self {
        self.benchmark_iterations = iterations;
        self
    }
}

impl BenchmarkScenario for OnnxInferenceScenario {
    fn name(&self) -> &str {
        "onnx_inference"
    }

    fn description(&self) -> &str {
        "Benchmark ONNX model inference performance"
    }

    fn setup(&mut self) -> Result<()> {
        if !self.model_path.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!("Model file does not exist: {}", self.model_path.display()),
            ));
        }

        if self.input_shape.is_empty() {
            return Err(BenchError::InvalidConfiguration(
                "Input shape cannot be empty".to_string(),
            ));
        }

        Ok(())
    }

    fn execute(&mut self) -> Result<()> {
        #[cfg(feature = "ml")]
        {
            // Placeholder for ONNX inference
            // use oxigdal_ml::inference::OnnxInferenceEngine;

            // let engine = OnnxInferenceEngine::new(&self.model_path)?;

            // Create dummy input data
            // let input_size: usize = self.input_shape.iter().product();
            // let input_data = vec![0.0f32; input_size * self.batch_size];

            // Warmup
            // for _ in 0..self.warmup_iterations {
            //     let _ = engine.infer(&input_data, &self.input_shape)?;
            // }

            // Benchmark
            // for _ in 0..self.benchmark_iterations {
            //     let _ = engine.infer(&input_data, &self.input_shape)?;
            // }
        }

        #[cfg(not(feature = "ml"))]
        {
            return Err(BenchError::missing_dependency("oxigdal-ml", "ml"));
        }

        Ok(())
    }

    fn teardown(&mut self) -> Result<()> {
        Ok(())
    }
}

/// Batch processing benchmark scenario.
pub struct BatchProcessingScenario {
    model_path: PathBuf,
    input_dir: PathBuf,
    batch_sizes: Vec<usize>,
    task_type: MlTask,
}

impl BatchProcessingScenario {
    /// Creates a new batch processing benchmark scenario.
    pub fn new<P1, P2>(model_path: P1, input_dir: P2) -> Self
    where
        P1: Into<PathBuf>,
        P2: Into<PathBuf>,
    {
        Self {
            model_path: model_path.into(),
            input_dir: input_dir.into(),
            batch_sizes: vec![1, 4, 8, 16, 32],
            task_type: MlTask::Classification,
        }
    }

    /// Sets the batch sizes to benchmark.
    pub fn with_batch_sizes(mut self, sizes: Vec<usize>) -> Self {
        self.batch_sizes = sizes;
        self
    }

    /// Sets the task type.
    pub fn with_task_type(mut self, task_type: MlTask) -> Self {
        self.task_type = task_type;
        self
    }
}

impl BenchmarkScenario for BatchProcessingScenario {
    fn name(&self) -> &str {
        "batch_processing"
    }

    fn description(&self) -> &str {
        "Benchmark batch processing performance with different batch sizes"
    }

    fn setup(&mut self) -> Result<()> {
        if !self.model_path.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!("Model file does not exist: {}", self.model_path.display()),
            ));
        }

        if !self.input_dir.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!(
                    "Input directory does not exist: {}",
                    self.input_dir.display()
                ),
            ));
        }

        Ok(())
    }

    fn execute(&mut self) -> Result<()> {
        #[cfg(feature = "ml")]
        {
            // Placeholder for batch processing benchmark
            // let engine = OnnxInferenceEngine::new(&self.model_path)?;

            // Load input images
            // let images = load_images_from_dir(&self.input_dir)?;

            // for batch_size in &self.batch_sizes {
            //     for chunk in images.chunks(*batch_size) {
            //         let batch = prepare_batch(chunk)?;
            //         let results = engine.infer_batch(&batch)?;
            //     }
            // }
        }

        #[cfg(not(feature = "ml"))]
        {
            return Err(BenchError::missing_dependency("oxigdal-ml", "ml"));
        }

        Ok(())
    }

    fn teardown(&mut self) -> Result<()> {
        Ok(())
    }
}

/// Preprocessing benchmark scenario.
pub struct PreprocessingScenario {
    input_dir: PathBuf,
    preprocessing_steps: Vec<PreprocessingStep>,
    image_count: usize,
}

/// Preprocessing steps.
#[derive(Debug, Clone, Copy)]
pub enum PreprocessingStep {
    /// Resize to target dimensions.
    Resize,
    /// Normalize pixel values.
    Normalize,
    /// Convert color space.
    ColorConversion,
    /// Apply data augmentation.
    Augmentation,
}

impl PreprocessingScenario {
    /// Creates a new preprocessing benchmark scenario.
    pub fn new<P>(input_dir: P) -> Self
    where
        P: Into<PathBuf>,
    {
        Self {
            input_dir: input_dir.into(),
            preprocessing_steps: vec![PreprocessingStep::Resize, PreprocessingStep::Normalize],
            image_count: 100,
        }
    }

    /// Sets the preprocessing steps.
    pub fn with_steps(mut self, steps: Vec<PreprocessingStep>) -> Self {
        self.preprocessing_steps = steps;
        self
    }

    /// Sets the number of images to process.
    pub fn with_image_count(mut self, count: usize) -> Self {
        self.image_count = count;
        self
    }
}

impl BenchmarkScenario for PreprocessingScenario {
    fn name(&self) -> &str {
        "preprocessing"
    }

    fn description(&self) -> &str {
        "Benchmark image preprocessing performance"
    }

    fn setup(&mut self) -> Result<()> {
        if !self.input_dir.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!(
                    "Input directory does not exist: {}",
                    self.input_dir.display()
                ),
            ));
        }

        Ok(())
    }

    fn execute(&mut self) -> Result<()> {
        #[cfg(feature = "ml")]
        {
            // Placeholder for preprocessing benchmark
            // let images = load_images_from_dir(&self.input_dir)?
            //     .into_iter()
            //     .take(self.image_count)
            //     .collect::<Vec<_>>();

            // for image in images {
            //     let mut processed = image;
            //     for step in &self.preprocessing_steps {
            //         processed = match step {
            //             PreprocessingStep::Resize => resize(processed, 224, 224)?,
            //             PreprocessingStep::Normalize => normalize(processed)?,
            //             PreprocessingStep::ColorConversion => rgb_to_bgr(processed)?,
            //             PreprocessingStep::Augmentation => augment(processed)?,
            //         };
            //     }
            // }
        }

        #[cfg(not(feature = "ml"))]
        {
            return Err(BenchError::missing_dependency("oxigdal-ml", "ml"));
        }

        Ok(())
    }

    fn teardown(&mut self) -> Result<()> {
        Ok(())
    }
}

/// Postprocessing benchmark scenario.
pub struct PostprocessingScenario {
    #[allow(dead_code)]
    task_type: MlTask,
    result_count: usize,
    nms_threshold: f32,
}

impl PostprocessingScenario {
    /// Creates a new postprocessing benchmark scenario.
    pub fn new(task_type: MlTask) -> Self {
        Self {
            task_type,
            result_count: 1000,
            nms_threshold: 0.5,
        }
    }

    /// Sets the number of results to process.
    pub fn with_result_count(mut self, count: usize) -> Self {
        self.result_count = count;
        self
    }

    /// Sets the NMS threshold for detection tasks.
    pub fn with_nms_threshold(mut self, threshold: f32) -> Self {
        self.nms_threshold = threshold;
        self
    }
}

impl BenchmarkScenario for PostprocessingScenario {
    fn name(&self) -> &str {
        "postprocessing"
    }

    fn description(&self) -> &str {
        "Benchmark postprocessing performance (NMS, etc.)"
    }

    fn setup(&mut self) -> Result<()> {
        Ok(())
    }

    fn execute(&mut self) -> Result<()> {
        #[cfg(feature = "ml")]
        {
            // Placeholder for postprocessing benchmark
            // Generate dummy predictions
            // let predictions = generate_dummy_predictions(self.task_type, self.result_count);

            // Apply postprocessing
            // match self.task_type {
            //     MlTask::Classification => {
            //         // Softmax + argmax
            //         let classes = apply_softmax_argmax(&predictions)?;
            //     }
            //     MlTask::Detection => {
            //         // NMS
            //         let filtered = apply_nms(&predictions, self.nms_threshold)?;
            //     }
            //     MlTask::Segmentation => {
            //         // Argmax per pixel
            //         let mask = apply_argmax_mask(&predictions)?;
            //     }
            //     MlTask::InstanceSegmentation => {
            //         // Instance mask extraction
            //         let instances = extract_instances(&predictions)?;
            //     }
            // }
        }

        #[cfg(not(feature = "ml"))]
        {
            return Err(BenchError::missing_dependency("oxigdal-ml", "ml"));
        }

        Ok(())
    }

    fn teardown(&mut self) -> Result<()> {
        Ok(())
    }
}

/// End-to-end inference pipeline benchmark.
pub struct EndToEndPipelineScenario {
    model_path: PathBuf,
    input_dir: PathBuf,
    #[allow(dead_code)]
    task_type: MlTask,
    batch_size: usize,
    pipeline_count: usize,
}

impl EndToEndPipelineScenario {
    /// Creates a new end-to-end pipeline benchmark scenario.
    pub fn new<P1, P2>(model_path: P1, input_dir: P2, task_type: MlTask) -> Self
    where
        P1: Into<PathBuf>,
        P2: Into<PathBuf>,
    {
        Self {
            model_path: model_path.into(),
            input_dir: input_dir.into(),
            task_type,
            batch_size: 4,
            pipeline_count: 50,
        }
    }

    /// Sets the batch size.
    pub fn with_batch_size(mut self, batch_size: usize) -> Self {
        self.batch_size = batch_size;
        self
    }

    /// Sets the number of pipeline iterations.
    pub fn with_pipeline_count(mut self, count: usize) -> Self {
        self.pipeline_count = count;
        self
    }
}

impl BenchmarkScenario for EndToEndPipelineScenario {
    fn name(&self) -> &str {
        "end_to_end_pipeline"
    }

    fn description(&self) -> &str {
        "Benchmark end-to-end inference pipeline (preprocessing + inference + postprocessing)"
    }

    fn setup(&mut self) -> Result<()> {
        if !self.model_path.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!("Model file does not exist: {}", self.model_path.display()),
            ));
        }

        if !self.input_dir.exists() {
            return Err(BenchError::scenario_failed(
                self.name(),
                format!(
                    "Input directory does not exist: {}",
                    self.input_dir.display()
                ),
            ));
        }

        Ok(())
    }

    fn execute(&mut self) -> Result<()> {
        #[cfg(feature = "ml")]
        {
            // Placeholder for end-to-end pipeline
            // let engine = OnnxInferenceEngine::new(&self.model_path)?;
            // let images = load_images_from_dir(&self.input_dir)?;

            // for _ in 0..self.pipeline_count {
            //     for chunk in images.chunks(self.batch_size) {
            //         // Preprocessing
            //         let preprocessed = preprocess_batch(chunk)?;

            //         // Inference
            //         let predictions = engine.infer_batch(&preprocessed)?;

            //         // Postprocessing
            //         let results = postprocess(&predictions, self.task_type)?;
            //     }
            // }
        }

        #[cfg(not(feature = "ml"))]
        {
            return Err(BenchError::missing_dependency("oxigdal-ml", "ml"));
        }

        Ok(())
    }

    fn teardown(&mut self) -> Result<()> {
        Ok(())
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_onnx_inference_scenario_creation() {
        let scenario = OnnxInferenceScenario::new("/tmp/model.onnx", vec![1, 3, 224, 224])
            .with_batch_size(8)
            .with_task_type(MlTask::Segmentation)
            .with_warmup_iterations(20);

        assert_eq!(scenario.name(), "onnx_inference");
        assert_eq!(scenario.batch_size, 8);
        assert_eq!(scenario.warmup_iterations, 20);
    }

    #[test]
    fn test_batch_processing_scenario_creation() {
        let scenario = BatchProcessingScenario::new("/tmp/model.onnx", "/tmp/images")
            .with_batch_sizes(vec![2, 4, 8])
            .with_task_type(MlTask::Detection);

        assert_eq!(scenario.name(), "batch_processing");
        assert_eq!(scenario.batch_sizes.len(), 3);
    }

    #[test]
    fn test_preprocessing_scenario_creation() {
        let scenario = PreprocessingScenario::new("/tmp/images")
            .with_steps(vec![
                PreprocessingStep::Resize,
                PreprocessingStep::Normalize,
                PreprocessingStep::ColorConversion,
            ])
            .with_image_count(50);

        assert_eq!(scenario.name(), "preprocessing");
        assert_eq!(scenario.preprocessing_steps.len(), 3);
        assert_eq!(scenario.image_count, 50);
    }

    #[test]
    fn test_postprocessing_scenario_creation() {
        let scenario = PostprocessingScenario::new(MlTask::Detection)
            .with_result_count(500)
            .with_nms_threshold(0.4);

        assert_eq!(scenario.name(), "postprocessing");
        assert_eq!(scenario.result_count, 500);
        assert_eq!(scenario.nms_threshold, 0.4);
    }

    #[test]
    fn test_end_to_end_pipeline_scenario_creation() {
        let scenario =
            EndToEndPipelineScenario::new("/tmp/model.onnx", "/tmp/images", MlTask::Classification)
                .with_batch_size(16)
                .with_pipeline_count(100);

        assert_eq!(scenario.name(), "end_to_end_pipeline");
        assert_eq!(scenario.batch_size, 16);
        assert_eq!(scenario.pipeline_count, 100);
    }
}