onnx-tensor-math

onnx-tensor-math Skill

Context

Slice 7 requires extracting a PDF page as an image and converting it into a normalized [1, 3, H, W] float32 tensor for ONNX inference (RT-DETR). Image processing in .NET can be a massive performance bottleneck if done naively (e.g., GetPixel()). This skill defines the strict, high-performance workflow for creating these tensors.

Trigger

Use this skill when implementing the apply_layout_inference pipeline stage, specifically when building the image extraction and tensor normalization logic for OnnxLayoutProvider.

The RT-DETR Preprocessing Specification

According to the upstream preprocessor_config.json for docling-layout-heron-onnx:

• Target Size: H = 640, W = 640
• Format: RGB (Channels First: [Batch, Channel, Height, Width])
• Rescale Factor: 1/255.0 (Scale pixel values from [0, 255] to [0.0, 1.0])
• Mean (RGB): [0.485, 0.456, 0.406]
• Std (RGB): [0.229, 0.224, 0.225]
• Padding: None (do_pad = false)
• Formula: tensor_val = ((pixel_val / 255.0) - mean) / std

Workflow

1. Choose the Image Library

Do NOT use System.Drawing.Common (it is Windows-only and slow). DO use SkiaSharp. It is cross-platform, fast, and provides direct memory access.

2. The Vectorized Extraction Pattern

To convert a SKBitmap to a DenseTensor<float> efficiently, you must use unsafe pointers to bypass bounds checking and object allocation overhead.

// Example high-performance pattern
using SkiaSharp;
using Microsoft.ML.OnnxRuntime.Tensors;

public static DenseTensor<float> CreateNormalizedTensor(SKBitmap bitmap)
{
    // 1. Resize to target (640x640)
    using var resized = bitmap.Resize(new SKImageInfo(640, 640), SKFilterQuality.Medium);
    
    // Ensure format is exactly 8888 (RGBA or BGRA)
    // ...

    var tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
    
    // Pre-calculate constants
    const float scale = 1f / 255f;
    float meanR = 0.485f, meanG = 0.456f, meanB = 0.406f;
    float stdR = 0.229f, stdG = 0.224f, stdB = 0.225f;

    int width = 640;
    int height = 640;
    int channelStride = width * height;

    unsafe
    {
        // 2. Get direct pointer to pixel memory
        byte* srcPtr = (byte*)resized.GetPixels().ToPointer();
        
        // 3. Get direct pointer to tensor memory (using Span/MemoryMarshal or indexing)
        // ... (Implementation specific)
        
        for (int y = 0; y < height; y++)
        {
            for (int x = 0; x < width; x++)
            {
                // Calculate source index (assuming 4 bytes per pixel: R, G, B, A)
                int srcIdx = (y * width + x) * 4;
                
                // Extract R, G, B (Handling BGRA vs RGBA depending on Skia platform defaults!)
                byte r = srcPtr[srcIdx]; // (Or srcIdx+2 if BGRA)
                byte g = srcPtr[srcIdx + 1];
                byte b = srcPtr[srcIdx + 2]; // (Or srcIdx if BGRA)

                // Normalize and assign to contiguous channel planes
                int destIdxR = 0 * channelStride + (y * width + x);
                int destIdxG = 1 * channelStride + (y * width + x);
                int destIdxB = 2 * channelStride + (y * width + x);

                // Assuming tensor is backed by a 1D array we can index into
                // tensorBuffer[destIdxR] = ((r * scale) - meanR) / stdR;
            }
        }
    }

    return tensor;
}

3. Critical Edge Cases to Validate

1. Color Type (BGRA vs RGBA): SkiaSharp defaults to SKColorType.Bgra8888 on Windows but Rgba8888 on Android/Linux. Always explicitly check bitmap.ColorType or convert it to a known type before blindly indexing [srcIdx + 2].
2. Memory Leaks: Always Dispose() or use using statements for SKBitmap, SKImage, and SKData.
3. Bounding Box Descaling: The ONNX model will output bounding boxes relative to the [640, 640] space. You must multiply these coordinates by (OriginalWidth / 640.0) and (OriginalHeight / 640.0) to map them back to the PDF's native coordinate system before passing them to the LayoutPostprocessor.