demo.git - Vide-coded 64k demo system

Age	Commit message (Collapse)	Author
20 hours	Fix: CNN v2 training - handle variable image sizes	skal
	Training script now resizes all images to fixed size before batching. Issue: RuntimeError when batching variable-sized images - Images had different dimensions (376x626 vs 344x361) - PyTorch DataLoader requires uniform tensor sizes for batching Solution: - Add --image-size parameter (default: 256) - Resize all images to target_size using LANCZOS interpolation - Preserves aspect ratio independent training Changes: - train_cnn_v2.py: ImagePairDataset now resizes to fixed dimensions - train_cnn_v2_full.sh: Added IMAGE_SIZE=256 configuration Tested: 8 image pairs, variable sizes → uniform 256×256 batches Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
20 hours	CNN v2: parametric static features - Phases 1-4	skal
	Infrastructure for enhanced CNN post-processing with 7D feature input. Phase 1: Shaders - Static features compute (RGBD + UV + sin10_x + bias → 8×f16) - Layer template (convolution skeleton, packing/unpacking) - 3 mip level support for multi-scale features Phase 2: C++ Effect - CNNv2Effect class (multi-pass architecture) - Texture management (static features, layer buffers) - Build integration (CMakeLists, assets, tests) Phase 3: Training Pipeline - train_cnn_v2.py: PyTorch model with static feature concatenation - export_cnn_v2_shader.py: f32→f16 quantization, WGSL generation - Configurable architecture (kernels, channels) Phase 4: Validation - validate_cnn_v2.sh: End-to-end pipeline - Checkpoint → shaders → build → test images Tests: 36/36 passing Next: Complete render pipeline implementation (bind groups, multi-pass) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>