Machine Learning

/**
 * Multi-dimensional tensor for numerical computations
 */
public class Tensor extends Pointer {
    /**
     * Create tensor filled with zeros
     * @param sizes Tensor dimensions
     * @return Zero-filled tensor
     */
    public static native Tensor zeros(long[] sizes);
    
    /**
     * Create tensor filled with ones
     * @param sizes Tensor dimensions
     * @return One-filled tensor
     */
    public static native Tensor ones(long[] sizes);
    
    /**
     * Create tensor from data
     * @param data Input data array
     * @param sizes Tensor dimensions
     * @return Tensor containing data
     */
    public static native Tensor from_blob(FloatPointer data, long[] sizes);
    
    /**
     * Add tensors element-wise
     * @param other Tensor to add
     * @return Result tensor
     */
    public native Tensor add(Tensor other);
    
    /**
     * Add scalar to tensor
     * @param scalar Scalar value to add
     * @return Result tensor
     */
    public native Tensor add(double scalar);
    
    /**
     * Matrix multiplication
     * @param mat2 Second matrix
     * @return Matrix product
     */
    public native Tensor mm(Tensor mat2);
    
    /**
     * Batch matrix multiplication
     * @param mat2 Second batch of matrices
     * @return Batch matrix product
     */
    public native Tensor bmm(Tensor mat2);
    
    /**
     * Get tensor size
     * @return Array of dimension sizes
     */
    public native long[] sizes();
    
    /**
     * Get number of dimensions
     * @return Number of dimensions
     */
    public native int dim();
    
    /**
     * Get data type
     * @return Tensor data type
     */
    public native ScalarType dtype();
    
    /**
     * Move tensor to device
     * @param device Target device
     * @return Tensor on specified device
     */
    public native Tensor to(Device device);
    
    /**
     * Reshape tensor
     * @param shape New shape
     * @return Reshaped tensor
     */
    public native Tensor reshape(long[] shape);
    
    /**
     * Get tensor as CPU float data
     * @return Float pointer to tensor data
     */
    public native FloatPointer data_ptr_float();
}

/**
 * Device specification for tensor placement
 */
public class Device extends Pointer {
    /**
     * CPU device
     */
    public static final int kCPU = 0;
    
    /**
     * CUDA device
     */
    public static final int kCUDA = 1;
    
    /**
     * Create device specification
     * @param type Device type (kCPU, kCUDA)
     * @param index Device index (for multi-GPU)
     */
    public Device(int type, int index);
    
    /**
     * Create CPU device
     * @return CPU device
     */
    public static native Device cpu();
    
    /**
     * Create CUDA device
     * @param index GPU index
     * @return CUDA device
     */
    public static native Device cuda(int index);
}

/**
 * Neural network module base class
 */
public class Module extends Pointer {
    /**
     * Forward pass
     * @param inputs Input tensors
     * @return Output tensors
     */
    public native TensorVector forward(TensorVector inputs);
    
    /**
     * Set training mode
     * @param mode Training mode flag
     */
    public native void train(boolean mode);
    
    /**
     * Set evaluation mode
     */
    public native void eval();
    
    /**
     * Get module parameters
     * @return Parameter tensors
     */
    public native TensorVector parameters();
    
    /**
     * Move module to device
     * @param device Target device
     */
    public native void to(Device device);
}

/**
 * TensorFlow Lite interpreter for model inference
 */
public class Interpreter extends Pointer {
    /**
     * Create interpreter from model buffer
     * @param modelBuffer Serialized model data
     */
    public Interpreter(ByteBuffer modelBuffer);
    
    /**
     * Create interpreter from byte array
     * @param modelData Serialized model data
     */
    public Interpreter(byte[] modelData);
    
    /**
     * Run inference
     * @param inputs Input tensors array
     * @param outputs Output tensors array
     */
    public native void run(Object[] inputs, Object[] outputs);
    
    /**
     * Run inference with single input/output
     * @param input Input tensor
     * @param output Output tensor
     */
    public native void run(Object input, Object output);
    
    /**
     * Resize input tensor
     * @param inputIndex Input tensor index
     * @param shape New input shape
     */
    public native void resizeInput(int inputIndex, int[] shape);
    
    /**
     * Allocate tensors for inference
     */
    public native void allocateTensors();
    
    /**
     * Get number of input tensors
     * @return Number of inputs
     */
    public native int getInputTensorCount();
    
    /**
     * Get number of output tensors
     * @return Number of outputs
     */
    public native int getOutputTensorCount();
    
    /**
     * Get input tensor info
     * @param inputIndex Input index
     * @return Tensor information
     */
    public native Tensor getInputTensor(int inputIndex);
    
    /**
     * Get output tensor info
     * @param outputIndex Output index
     * @return Tensor information
     */
    public native Tensor getOutputTensor(int outputIndex);
}

/**
 * TensorFlow Lite model representation
 */
public class Model extends Pointer {
    /**
     * Load model from file
     * @param modelPath Path to model file
     * @return Loaded model
     */
    public static native Model fromFile(String modelPath);
    
    /**
     * Load model from buffer
     * @param modelData Model data buffer
     * @return Loaded model
     */
    public static native Model fromBuffer(ByteBuffer modelData);
}

/**
 * ONNX model representation
 */
public class ModelProto extends Pointer {
    /**
     * Parse model from file
     * @param filename Path to ONNX model file
     * @return Parsed model
     */
    public static native ModelProto parseFromFile(String filename);
    
    /**
     * Parse model from bytes
     * @param data Serialized model data
     * @return Parsed model
     */
    public static native ModelProto parseFromBytes(BytePointer data);
    
    /**
     * Get model graph
     * @return Model computation graph
     */
    public native GraphProto graph();
    
    /**
     * Get model version
     * @return Model version
     */
    public native long ir_version();
    
    /**
     * Get model opset imports
     * @return Opset version information
     */
    public native OperatorSetIdProtoVector opset_import();
}

/**
 * ONNX computation graph
 */
public class GraphProto extends Pointer {
    /**
     * Get graph nodes
     * @return Graph computation nodes
     */
    public native NodeProtoVector node();
    
    /**
     * Get graph inputs
     * @return Input value info
     */
    public native ValueInfoProtoVector input();
    
    /**
     * Get graph outputs
     * @return Output value info
     */
    public native ValueInfoProtoVector output();
    
    /**
     * Get graph initializers
     * @return Constant tensor initializers
     */
    public native TensorProtoVector initializer();
}

/**
 * ONNX computation node
 */
public class NodeProto extends Pointer {
    /**
     * Get node operation type
     * @return Operation name
     */
    public native String op_type();
    
    /**
     * Get node inputs
     * @return Input tensor names
     */
    public native StringVector input();
    
    /**
     * Get node outputs
     * @return Output tensor names
     */
    public native StringVector output();
    
    /**
     * Get node attributes
     * @return Operation attributes
     */
    public native AttributeProtoVector attribute();
}

/**
 * ONNX Runtime environment
 */
public class OrtEnv extends Pointer {
    /**
     * Create environment with logging level
     * @param logLevel Logging level
     * @param logId Log identifier
     * @return Environment instance
     */
    public static native OrtEnv create(int logLevel, String logId);
    
    /**
     * Create default environment
     * @return Default environment
     */
    public static native OrtEnv create();
}

/**
 * ONNX Runtime inference session
 */
public class OrtSession extends Pointer {
    /**
     * Create session from model file
     * @param env Environment
     * @param modelPath Path to ONNX model
     * @param sessionOptions Session options
     * @return Inference session
     */
    public static native OrtSession create(OrtEnv env, String modelPath, 
        OrtSessionOptions sessionOptions);
    
    /**
     * Run inference
     * @param inputs Input tensors map
     * @return Output tensors
     */
    public native OrtValueVector run(OrtValueVector inputs);
    
    /**
     * Run inference with input/output names
     * @param inputNames Input tensor names
     * @param inputs Input tensors
     * @param outputNames Output tensor names
     * @return Output tensors
     */
    public native OrtValueVector run(StringVector inputNames, OrtValueVector inputs,
        StringVector outputNames);
    
    /**
     * Get input count
     * @return Number of model inputs
     */
    public native int getInputCount();
    
    /**
     * Get output count
     * @return Number of model outputs
     */  
    public native int getOutputCount();
    
    /**
     * Get input name
     * @param index Input index
     * @return Input name
     */
    public native String getInputName(int index);
    
    /**
     * Get output name
     * @param index Output index
     * @return Output name
     */
    public native String getOutputName(int index);
}

/**
 * ONNX Runtime tensor value
 */
public class OrtValue extends Pointer {
    /**
     * Create tensor from float array
     * @param data Float data
     * @param shape Tensor shape
     * @return Tensor value
     */
    public static native OrtValue createTensor(float[] data, long[] shape);
    
    /**
     * Create tensor from byte array
     * @param data Byte data
     * @param shape Tensor shape
     * @return Tensor value
     */
    public static native OrtValue createTensor(byte[] data, long[] shape);
    
    /**
     * Get tensor data as float array
     * @return Float data array
     */
    public native float[] getFloatArray();
    
    /**
     * Get tensor shape
     * @return Shape dimensions
     */
    public native long[] getShape();
    
    /**
     * Check if value is tensor
     * @return true if tensor, false otherwise
     */
    public native boolean isTensor();
}

/**
 * TensorRT inference builder
 */
public class IBuilder extends Pointer {
    /**
     * Create inference builder
     * @param logger Logger instance
     * @return Builder instance
     */
    public static native IBuilder createInferBuilder(ILogger logger);
    
    /**
     * Create network definition
     * @param flags Network creation flags
     * @return Network definition
     */
    public native INetworkDefinition createNetworkV2(int flags);
    
    /**
     * Create builder configuration
     * @return Builder configuration
     */
    public native IBuilderConfig createBuilderConfig();
    
    /**
     * Build serialized network
     * @param network Network definition
     * @param config Builder configuration
     * @return Serialized engine
     */
    public native IHostMemory buildSerializedNetwork(INetworkDefinition network,
        IBuilderConfig config);
}

/**
 * TensorRT network definition for building models
 */
public class INetworkDefinition extends Pointer {
    /**
     * Add input tensor
     * @param name Input name
     * @param type Data type
     * @param dims Tensor dimensions
     * @return Input tensor
     */
    public native ITensor addInput(String name, DataType type, Dims dims);
    
    /**
     * Add convolution layer
     * @param input Input tensor
     * @param nbOutputMaps Number of output feature maps
     * @param kernelSize Convolution kernel size
     * @param kernelWeights Kernel weights
     * @param biasWeights Bias weights
     * @return Convolution layer
     */
    public native IConvolutionLayer addConvolutionNd(ITensor input, int nbOutputMaps,
        DimsHW kernelSize, Weights kernelWeights, Weights biasWeights);
    
    /**
     * Add activation layer
     * @param input Input tensor
     * @param type Activation type
     * @return Activation layer
     */
    public native IActivationLayer addActivation(ITensor input, ActivationType type);
    
    /**
     * Add pooling layer
     * @param input Input tensor
     * @param type Pooling type
     * @param windowSize Pooling window size
     * @return Pooling layer
     */
    public native IPoolingLayer addPoolingNd(ITensor input, PoolingType type,
        DimsHW windowSize);
    
    /**
     * Mark tensor as network output
     * @param tensor Output tensor
     */
    public native void markOutput(ITensor tensor);
}

/**
 * TensorRT CUDA inference engine
 */
public class ICudaEngine extends Pointer {
    /**
     * Create execution context
     * @return Execution context
     */
    public native IExecutionContext createExecutionContext();
    
    /**
     * Get number of bindings (inputs + outputs)
     * @return Number of bindings
     */
    public native int getNbBindings();
    
    /**
     * Get binding name
     * @param index Binding index
     * @return Binding name
     */
    public native String getBindingName(int index);
    
    /**
     * Check if binding is input
     * @param index Binding index
     * @return true if input binding
     */
    public native boolean bindingIsInput(int index);
    
    /**
     * Get binding dimensions
     * @param index Binding index
     * @return Tensor dimensions
     */
    public native Dims getBindingDimensions(int index);
}

/**
 * TensorRT execution context for inference
 */
public class IExecutionContext extends Pointer {
    /**
     * Execute inference synchronously
     * @param bindings Array of device memory pointers for inputs/outputs
     * @return true if successful
     */
    public native boolean execute(PointerPointer bindings);
    
    /**
     * Execute inference asynchronously  
     * @param bindings Array of device memory pointers for inputs/outputs
     * @param stream CUDA stream
     * @return true if successful
     */
    public native boolean enqueue(PointerPointer bindings, Pointer stream);
    
    /**
     * Set binding dimensions for dynamic shapes
     * @param index Binding index
     * @param dimensions New dimensions
     * @return true if successful
     */
    public native boolean setBindingDimensions(int index, Dims dimensions);
}

import org.bytedeco.pytorch.*;
import org.bytedeco.pytorch.torch.*;
import static org.bytedeco.pytorch.global.torch.*;

public class TensorExample {
    static {
        Loader.load(torch.class);
    }
    
    public static void tensorOperations() {
        try (PointerScope scope = new PointerScope()) {
            // Create tensors
            Tensor a = zeros(new long[]{3, 4});
            Tensor b = ones(new long[]{3, 4});
            
            // Basic operations
            Tensor sum = a.add(b);
            Tensor scaled = sum.mul(2.0);
            
            // Matrix operations
            Tensor matrix1 = rand(new long[]{3, 4});
            Tensor matrix2 = rand(new long[]{4, 5});
            Tensor product = matrix1.mm(matrix2);
            
            System.out.println("Matrix product shape: " + 
                java.util.Arrays.toString(product.sizes()));
            
            // Move to GPU if available
            if (cuda_is_available()) {
                Device gpu = Device.cuda(0);
                Tensor gpuTensor = matrix1.to(gpu);
                System.out.println("Tensor moved to GPU");
            }
        }
    }
}

import org.bytedeco.tensorflowlite.*;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;

public class TFLiteInference {
    static {
        Loader.load(tensorflowlite.class);
    }
    
    public static void runInference(String modelPath, float[] inputData) {
        try (PointerScope scope = new PointerScope()) {
            // Load model
            Model model = Model.fromFile(modelPath);
            Interpreter interpreter = new Interpreter(model);
            
            // Prepare input
            int inputSize = inputData.length;
            ByteBuffer inputBuffer = ByteBuffer.allocateDirect(inputSize * 4);
            inputBuffer.order(ByteOrder.nativeOrder());
            for (float value : inputData) {
                inputBuffer.putFloat(value);
            }
            
            // Prepare output
            int outputSize = interpreter.getOutputTensor(0).numElements();
            ByteBuffer outputBuffer = ByteBuffer.allocateDirect(outputSize * 4);
            outputBuffer.order(ByteOrder.nativeOrder());
            
            // Run inference
            interpreter.run(inputBuffer, outputBuffer);
            
            // Process results
            outputBuffer.rewind();
            float[] results = new float[outputSize];
            outputBuffer.asFloatBuffer().get(results);
            
            System.out.println("Inference completed. Output size: " + outputSize);
        }
    }
}

import org.bytedeco.onnxruntime.*;
import static org.bytedeco.onnxruntime.global.onnxruntime.*;

public class ONNXInference {
    static {
        Loader.load(onnxruntime.class);
    }
    
    public static void runONNXModel(String modelPath, float[] inputData, long[] inputShape) {
        try (PointerScope scope = new PointerScope()) {
            // Create environment and session
            OrtEnv env = OrtEnv.create(ORT_LOGGING_LEVEL_WARNING, "ONNXInference");
            OrtSessionOptions sessionOptions = new OrtSessionOptions();
            OrtSession session = OrtSession.create(env, modelPath, sessionOptions);
            
            // Create input tensor
            OrtValue inputTensor = OrtValue.createTensor(inputData, inputShape);
            OrtValueVector inputs = new OrtValueVector(inputTensor);
            
            // Get input/output names
            String inputName = session.getInputName(0);
            String outputName = session.getOutputName(0);
            
            StringVector inputNames = new StringVector(inputName);
            StringVector outputNames = new StringVector(outputName);
            
            // Run inference
            OrtValueVector outputs = session.run(inputNames, inputs, outputNames);
            
            // Get results
            OrtValue outputTensor = outputs.get(0);
            float[] results = outputTensor.getFloatArray();
            long[] outputShape = outputTensor.getShape();
            
            System.out.println("ONNX inference completed");
            System.out.println("Output shape: " + java.util.Arrays.toString(outputShape));
            System.out.println("First 5 results: " + 
                java.util.Arrays.toString(java.util.Arrays.copyOf(results, 5)));
        }
    }
}

import org.bytedeco.tensorrt.*;
import org.bytedeco.cuda.cudart.*;
import static org.bytedeco.tensorrt.global.nvinfer.*;
import static org.bytedeco.cuda.global.cudart.*;

public class TensorRTInference {
    static {
        Loader.load(nvinfer.class);
        Loader.load(cudart.class);
    }
    
    public static void optimizeAndInfer() {
        try (PointerScope scope = new PointerScope()) {
            // Create logger and builder
            ILogger logger = new Logger();
            IBuilder builder = IBuilder.createInferBuilder(logger);
            
            // Create network
            INetworkDefinition network = builder.createNetworkV2(
                1 << NetworkDefinitionCreationFlag.kEXPLICIT_BATCH);
            
            // Define network architecture (simplified example)
            Dims inputDims = new Dims(4);
            inputDims.d(0, 1).d(1, 3).d(2, 224).d(3, 224);  // NCHW format
            
            ITensor input = network.addInput("input", DataType.kFLOAT, inputDims);
            
            // Add layers (this is a simplified example)
            // In practice, you would load weights and build complete network
            
            network.markOutput(input);  // Placeholder output
            
            // Build engine
            IBuilderConfig config = builder.createBuilderConfig();
            config.setMaxWorkspaceSize(1L << 30);  // 1GB workspace
            
            IHostMemory serializedEngine = builder.buildSerializedNetwork(network, config);
            
            // Create runtime and deserialize engine
            IRuntime runtime = IRuntime.createInferRuntime(logger);
            ICudaEngine engine = runtime.deserializeCudaEngine(
                serializedEngine.data(), serializedEngine.size());
            
            // Create execution context
            IExecutionContext context = engine.createExecutionContext();
            
            // Allocate GPU memory for inputs/outputs
            int inputSize = 1 * 3 * 224 * 224 * 4;  // batch_size * channels * height * width * sizeof(float)
            Pointer inputGPU = new Pointer();
            Pointer outputGPU = new Pointer();
            
            cudaMalloc(inputGPU, inputSize);
            cudaMalloc(outputGPU, inputSize);  // Assume same size for output
            
            // Setup bindings
            PointerPointer bindings = new PointerPointer(2);
            bindings.put(0, inputGPU);
            bindings.put(1, outputGPU);
            
            // Copy input data to GPU (simplified)
            // cudaMemcpy(inputGPU, hostInputData, inputSize, cudaMemcpyHostToDevice);
            
            // Execute inference
            boolean success = context.execute(bindings);
            
            if (success) {
                System.out.println("TensorRT inference completed successfully");
                
                // Copy results back to host
                // cudaMemcpy(hostOutputData, outputGPU, outputSize, cudaMemcpyDeviceToHost);
            }
            
            // Cleanup GPU memory
            cudaFree(inputGPU);
            cudaFree(outputGPU);
        }
    }
    
    static class Logger extends ILogger {
        @Override
        public void log(Severity severity, String msg) {
            System.out.println("[TensorRT " + severity + "] " + msg);
        }
    }
}