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Each workspace now has a weights/ directory to store binary weight files
from CNN training (e.g., cnn_v2_weights.bin).
Changes:
- Created workspaces/{main,test}/weights/
- Moved cnn_v2_weights.bin → workspaces/main/weights/
- Updated assets.txt reference
- Updated training scripts and export tool paths
handoff(Claude): Workspace weights/ directories added
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Moved main.cc, stub_main.cc, and test_demo.cc from src/ to src/app/
for better organization. Updated cmake/DemoExecutables.cmake paths.
handoff(Claude): App files reorganized into src/app/ directory
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Updated:
- HOWTO.md: Complete pipeline, storage buffer, --validate mode
- TODO.md: Mark CNN v2 complete, add QAT TODO
- PROJECT_CONTEXT.md: Update Effects status
- CNN_V2.md: Mark complete, add storage buffer notes
- train_cnn_v2_full.sh: Add --help message
All documentation now reflects:
- Storage buffer architecture
- Binary weight format
- Live training progress
- Validation-only mode
- 8-bit quantization TODO
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Usage:
./train_cnn_v2_full.sh --validate [checkpoint.pth]
Skips training and weight export, uses existing weights.
Validates all input images with latest (or specified) checkpoint.
Example:
./train_cnn_v2_full.sh --validate checkpoints/checkpoint_epoch_50.pth
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1. Loss printed at every epoch with \r (no scrolling)
2. Validation only on final epoch (not all checkpoints)
3. Process all input images (not just img_000.png)
Training output now shows live progress with single line update.
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- Add binary weight format (header + layer info + packed f16)
- New export_cnn_v2_weights.py for binary weight export
- Single cnn_v2_compute.wgsl shader with storage buffer
- Load weights in CNNv2Effect::load_weights()
- Create layer compute pipeline with 5 bindings
- Fast training config: 100 epochs, 3×3 kernels, 8→4→4 channels
Next: Complete bind group creation and multi-layer compute execution
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Salient point detection on original images with patch extraction.
Changes:
- Added PatchDataset class (harris/fast/shi-tomasi/gradient detectors)
- Detects salient points on ORIGINAL images (no resize)
- Extracts 32×32 patches around salient points
- Default: 64 patches/image, harris detector
- Batch size: 16 (512 patches per batch)
Training modes:
1. Patch-based (default): --patch-size 32 --patches-per-image 64 --detector harris
2. Full-image (option): --full-image --image-size 256
Benefits:
- Focuses training on interesting regions
- Handles variable image sizes naturally
- Matches CNN v1 workflow
- Better convergence with limited data (8 images → 512 patches)
Script updated:
- train_cnn_v2_full.sh: Patch-based by default
- Configuration exposed for easy switching
Example:
./scripts/train_cnn_v2_full.sh # Patch-based
# Edit script: uncomment FULL_IMAGE for resize mode
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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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>
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