refactor(cnn): isolate CNN v2 to cnn_v2/ subdirectory

Move all CNN v2 files to dedicated cnn_v2/ directory to prepare for CNN v3 development. Zero functional changes.

Structure:
- cnn_v2/src/ - C++ effect implementation
- cnn_v2/shaders/ - WGSL shaders (6 files)
- cnn_v2/weights/ - Binary weights (3 files)
- cnn_v2/training/ - Python training scripts (4 files)
- cnn_v2/scripts/ - Shell scripts (train_cnn_v2_full.sh)
- cnn_v2/tools/ - Validation tools (HTML)
- cnn_v2/docs/ - Documentation (4 markdown files)

Changes:
- Update CMake source list to cnn_v2/src/cnn_v2_effect.cc
- Update assets.txt with relative paths to cnn_v2/
- Update includes to ../../cnn_v2/src/cnn_v2_effect.h
- Add PROJECT_ROOT resolution to Python/shell scripts
- Update doc references in HOWTO.md, TODO.md
- Add cnn_v2/README.md

Verification: 34/34 tests passing, demo runs correctly.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

1 files changed, 0 insertions, 251 deletions

diff --git a/tools/cnn_v2_test/README.md b/tools/cnn_v2_test/README.md
deleted file mode 100644
index d41a00f..0000000
--- a/tools/cnn_v2_test/README.md
+++ /dev/null
@@ -1,251 +0,0 @@
-# CNN v2 Testing Tool
-
-WebGPU-based browser tool for testing trained CNN v2 weights.
-
----
-
-## Features
-
-- Drag-drop PNG images and `.bin` weights (or click to browse)
-- Real-time CNN inference with WebGPU compute shaders
-- View modes: CNN output, original input, difference (×10)
-- Adjustable blend amount and depth
-- Data-driven pipeline (supports variable layer count)
-- GPU timing display
-- **Left Panel:** Weights info + kernel visualization (1px/weight, all layers)
-- **Right Panel:** Layer activation viewer with 4-channel split + 4× zoom
-
----
-
-## Requirements
-
-- Browser with WebGPU support:
-  - Chrome/Edge 113+ (enable `chrome://flags/#enable-unsafe-webgpu` if needed)
-  - Safari 18+ (macOS Ventura+)
-- Trained CNN v2 weights in binary format (`.bin`)
-- Test images (PNG format)
-
----
-
-## Usage
-
-### 1. Open Tool
-
-```bash
-open tools/cnn_v2_test/index.html
-```
-
-Or use a local server to avoid CORS:
-```bash
-python3 -m http.server 8000
-# Open http://localhost:8000/tools/cnn_v2_test/
-```
-
-### 2. Load Data
-
-1. **Drop `.bin` weights** into left sidebar zone (or click to browse)
-2. **Drop PNG image** anywhere in center canvas area
-3. CNN runs automatically when both loaded
-
-### 3. Layout
-
-**Left Sidebar:**
-- Weights drop zone (click or drag-drop `.bin` files)
-- Weights info panel (layer specs, ranges, file size)
-- Weights visualization (click Layer 0/1/2 buttons)
-  - 1 pixel per weight, all input channels horizontally
-  - Output channels (Out 0-3) stacked vertically
-
-**Center Canvas:**
-- Main output view (CNN result, original, or diff)
-- Keyboard: `SPACE` = original, `D` = diff (×10)
-
-**Right Sidebar:**
-- Layer selection buttons (Static 0-3/4-7, Layer 0/1/2)
-- 4 small activation views (Ch0/1/2/3) in a row
-- Large zoom view below (4× magnification, follows mouse)
-
-**Header Controls:**
-- **Blend:** Mix between original (0.0) and CNN output (1.0)
-- **Depth:** Uniform depth value for all pixels (0.0–1.0)
-- **View:** Current display mode
-
-**Footer:**
-- Status: GPU timing (ms), image dimensions, view mode
-- Console: Timestamped event log (file loads, errors)
-
----
-
-## Preparing Test Data
-
-### Export Weights
-
-```bash
-# From trained checkpoint
-./training/export_cnn_v2_weights.py \
-  checkpoints/checkpoint_epoch_100.pth \
-  --output-weights tools/cnn_v2_test/test_weights.bin
-```
-
-Binary format: 16-byte header + 20 bytes per layer + f16 weights (~3.2 KB for 3-layer model)
-
-### Test Images
-
-Use training images or any PNG:
-```bash
-# Copy test image
-cp training/input/test.png tools/cnn_v2_test/
-```
-
-**Note:** Grayscale images automatically converted to RGB.
-
----
-
-## Validation
-
-### Visual Comparison
-
-Compare browser output with C++ tool:
-
-```bash
-# Generate C++ output
-./build/cnn_test training/input/test.png /tmp/cpp_output.png
-
-# Load same image in browser tool
-# Visually compare outputs
-```
-
-### GPU Timing
-
-Expected performance:
-- 512×512: ~1-2 ms (integrated GPU)
-- 1024×1024: ~3-5 ms
-- 1920×1080: ~5-8 ms
-
-Slower than expected? Check:
-- WebGPU enabled in browser
-- Dedicated GPU selected (if available)
-- No background tabs consuming GPU
-
----
-
-## Troubleshooting
-
-### "WebGPU not supported"
-
-- Update browser to latest version
-- Enable WebGPU flag: `chrome://flags/#enable-unsafe-webgpu`
-- Try Safari 18+ (native WebGPU on macOS)
-
-### "Invalid .bin file"
-
-- Check magic number: `hexdump -C weights.bin | head`
-- Should start with: `43 4e 4e 32` ('CNN2')
-- Re-export weights: `./training/export_cnn_v2_weights.py`
-
-### Black output / incorrect colors
-
-- Check blend slider (set to 1.0 for full CNN output)
-- Verify training converged (loss < 0.01)
-- Compare with C++ tool output
-
-### Shader compilation errors
-
-Open browser console (F12) for detailed errors. Common issues:
-- Image too large (>4096×4096 not tested)
-- Unsupported texture format (rare on modern GPUs)
-
----
-
-## Architecture
-
-**Pipeline:**
-1. **Static Features Pass** - Generate 8D features (RGBD, UV, sin, bias)
-2. **CNN Layer Passes** - Compute N layers with ping-pong textures
-3. **Display Pass** - Unpack and render with view mode
-
-**Textures:**
-- Input: RGBA8 (original image)
-- Depth: R32F (uniform depth)
-- Static features: RGBA32Uint (8×f16 packed)
-- Layer buffers: RGBA32Uint (ping-pong)
-
-**Data-Driven Execution:**
-- Layer count read from binary header
-- Per-layer params (kernel size, channels, offsets) from binary
-- Single CNN shader dispatched N times
-
----
-
-## Implemented Features
-
-**✓ Weights Metadata Panel:**
-- Layer descriptions (kernel size, channels, weight count)
-- Weight statistics (min/max per layer)
-- File size and layer count
-
-**✓ Weights Visualization:**
-- Per-layer kernel heatmaps (1px/weight)
-- All input channels displayed horizontally
-- Output channels stacked vertically
-- Normalized grayscale display
-
-**✓ Layer Activation Viewer:**
-- Static features (8D split into 0-3 and 4-7 views)
-- All CNN layer outputs (Layer 0/1/2...)
-- 4-channel split view (grayscale per channel)
-- Mouse-driven 4× zoom view
-
-## TODO
-
-**Future Enhancements:**
-- Weight distribution histograms per layer
-- Activation statistics (min/max/mean overlay)
-- Side-by-side diff mode (browser vs C++ output)
-- Export rendered layers as PNG
-
----
-
-## Extensions (v2+)
-
-Planned enhancements:
-
-**Variable Feature Count:**
-- Binary v2: Add `num_features` to header
-- Shader: Dynamic feature array or multiple textures
-
-**Multi-Scale Input (Mip Levels):**
-- Uncomment mip bindings in static shader
-- No binary format change needed
-
-**8-bit Quantized Weights:**
-- Binary version bump (format field already present)
-- Add quantization codepath in `get_weight()` function
-- 2× size reduction (~1.6 KB)
-
-**Pre-defined Test Images:**
-- Dropdown menu with training/input/*.png
-- Requires local file server
-
----
-
-## Size
-
-- HTML structure: ~2 KB
-- CSS styling: ~2 KB
-- JavaScript logic: ~10 KB (includes zoom + weights viz)
-- Static shader: ~1 KB
-- CNN shader: ~3 KB
-- Display shader: ~1 KB
-- Layer viz shader: ~2 KB
-- Zoom shader: ~1 KB
-- **Total: ~22 KB** (single file, no dependencies)
-
----
-
-## See Also
-
-- `doc/CNN_V2.md` - Architecture and design
-- `doc/HOWTO.md` - Training workflows
-- `training/export_cnn_v2_weights.py` - Binary format
-- `src/effects/cnn_v2_effect.cc` - C++ reference implementation