From 61104d5b9e1774c11f0dba3b6d6018dabc2bce8f Mon Sep 17 00:00:00 2001
From: skal <pascal.massimino@gmail.com>
Date: Tue, 10 Feb 2026 16:44:39 +0100
Subject: feat: CNN RGBD→grayscale with 7-channel augmented input
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Upgrade CNN architecture to process RGBD input, output grayscale, with
7-channel layer inputs (RGBD + UV coords + grayscale).

Architecture changes:
- Inner layers: Conv2d(7→4) output RGBD
- Final layer: Conv2d(7→1) output grayscale
- All inputs normalized to [-1,1] for tanh activation
- Removed CoordConv2d in favor of unified 7-channel input

Training (train_cnn.py):
- SimpleCNN: 7→4 (inner), 7→1 (final) architecture
- Forward: Normalize RGBD/coords/gray to [-1,1]
- Weight export: array<array<f32, 8>, 36> (inner), array<f32, 8>, 9> (final)
- Dataset: Load RGBA (RGBD) input

Shaders (cnn_conv3x3.wgsl):
- Added cnn_conv3x3_7to4: 7-channel input → RGBD output
- Added cnn_conv3x3_7to1: 7-channel input → grayscale output
- Both normalize inputs and use flattened weight arrays

Documentation:
- CNN_EFFECT.md: Updated architecture, training, weight format
- CNN_RGBD_GRAYSCALE_SUMMARY.md: Implementation summary
- HOWTO.md: Added training command example

Next: Train with RGBD input data

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
---
 doc/CNN_EFFECT.md | 75 ++++++++++++++++++++++++++++++++++---------------------
 1 file changed, 47 insertions(+), 28 deletions(-)

(limited to 'doc/CNN_EFFECT.md')

diff --git a/doc/CNN_EFFECT.md b/doc/CNN_EFFECT.md
index ae0f38a..b7d157f 100644
--- a/doc/CNN_EFFECT.md
+++ b/doc/CNN_EFFECT.md
@@ -21,27 +21,44 @@ Trainable convolutional neural network layers for artistic stylization (painterl
 
 ## Architecture
 
-### Coordinate-Aware Layer 0
+### RGBD → Grayscale Pipeline
 
-Layer 0 accepts normalized (x,y) patch center coordinates alongside RGBA samples:
+**Input:** RGBD (RGB + inverse depth D=1/z)
+**Output:** Grayscale (1 channel)
+**Layer Input:** 7 channels = [RGBD, UV coords, grayscale] all normalized to [-1,1]
+
+**Architecture:**
+- **Inner layers (0..N-2):** Conv2d(7→4) - output RGBD
+- **Final layer (N-1):** Conv2d(7→1) - output grayscale
 
 ```wgsl
-fn cnn_conv3x3_with_coord(
+// Inner layers: 7→4 (RGBD output)
+fn cnn_conv3x3_7to4(
   tex: texture_2d<f32>,
   samp: sampler,
-  uv: vec2<f32>,                          # Center position [0,1]
+  uv: vec2<f32>,
   resolution: vec2<f32>,
-  rgba_weights: array<mat4x4<f32>, 9>,    # 9 samples × 4×4 matrix
-  coord_weights: mat2x4<f32>,             # 2 coords → 4 outputs
-  bias: vec4<f32>
+  original: vec4<f32>,                     # Original RGBD [0,1]
+  weights: array<array<f32, 8>, 36>       # 9 pos × 4 out × (7 weights + bias)
 ) -> vec4<f32>
-```
 
-**Input structure:** 9 RGBA samples (36 values) + 1 xy coordinate (2 values) = 38 inputs → 4 outputs
+// Final layer: 7→1 (grayscale output)
+fn cnn_conv3x3_7to1(
+  tex: texture_2d<f32>,
+  samp: sampler,
+  uv: vec2<f32>,
+  resolution: vec2<f32>,
+  original: vec4<f32>,
+  weights: array<array<f32, 8>, 9>        # 9 pos × (7 weights + bias)
+) -> f32
+```
 
-**Size impact:** +32B coord weights, kernel-agnostic
+**Input normalization (all to [-1,1]):**
+- RGBD: `(rgbd - 0.5) * 2`
+- UV coords: `(uv - 0.5) * 2`
+- Grayscale: `(0.2126*R + 0.7152*G + 0.0722*B - 0.5) * 2`
 
-**Use cases:** Position-dependent stylization (vignettes, corner darkening, radial gradients)
+**Activation:** tanh for inner layers, none for final layer
 
 ### Multi-Layer Architecture
 
@@ -80,18 +97,15 @@ workspaces/main/shaders/cnn/
 ### 1. Prepare Training Data
 
 Collect input/target image pairs:
-- **Input:** Raw 3D render
-- **Target:** Artistic style (hand-painted, filtered, stylized)
+- **Input:** RGBA (RGB + depth as alpha channel, D=1/z)
+- **Target:** Grayscale stylized output
 
 ```bash
-training/input/img_000.png   # Raw render
-training/output/img_000.png  # Stylized target
+training/input/img_000.png   # RGBA render (RGB + depth)
+training/output/img_000.png  # Grayscale target
 ```
 
-Use `image_style_processor.py` to generate targets:
-```bash
-python3 training/image_style_processor.py input/ output/ pencil_sketch
-```
+**Note:** Input images must be RGBA where alpha = inverse depth (1/z)
 
 ### 2. Train Network
 
@@ -245,20 +259,25 @@ Expands to:
 
 **Weight Storage:**
 
-**Layer 0 (coordinate-aware):**
+**Inner layers (7→4 RGBD output):**
 ```wgsl
-const rgba_weights_layer0: array<mat4x4<f32>, 9> = array(...);
-const coord_weights_layer0 = mat2x4<f32>(
-  0.1, -0.2, 0.0, 0.0,  # x-coord weights
-  -0.1, 0.0, 0.2, 0.0   # y-coord weights
+// Structure: array<array<f32, 8>, 36>
+// 9 positions × 4 output channels, each with 7 weights + bias
+const weights_layer0: array<array<f32, 8>, 36> = array(
+  array<f32, 8>(w0_r, w0_g, w0_b, w0_d, w0_u, w0_v, w0_gray, bias0),  // pos0_ch0
+  array<f32, 8>(w1_r, w1_g, w1_b, w1_d, w1_u, w1_v, w1_gray, bias1),  // pos0_ch1
+  // ... 34 more entries
 );
-const bias_layer0 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
 ```
 
-**Layers 1+ (standard):**
+**Final layer (7→1 grayscale output):**
 ```wgsl
-const weights_layer1: array<mat4x4<f32>, 9> = array(...);
-const bias_layer1 = vec4<f32>(0.0, 0.0, 0.0, 0.0);
+// Structure: array<array<f32, 8>, 9>
+// 9 positions, each with 7 weights + bias
+const weights_layerN: array<array<f32, 8>, 9> = array(
+  array<f32, 8>(w0_r, w0_g, w0_b, w0_d, w0_u, w0_v, w0_gray, bias0),  // pos0
+  // ... 8 more entries
+);
 ```
 
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