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diff --git a/doc/HOWTO.md b/doc/HOWTO.md index 85ce801..506bf0a 100644 --- a/doc/HOWTO.md +++ b/doc/HOWTO.md @@ -139,12 +139,18 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding # Train → Export → Build → Validate (default config) ./scripts/train_cnn_v2_full.sh +# Rapid debug (1 layer, 3×3, 5 epochs) +./scripts/train_cnn_v2_full.sh --num-layers 1 --kernel-sizes 3 --epochs 5 --output-weights test.bin + # Custom training parameters ./scripts/train_cnn_v2_full.sh --epochs 500 --batch-size 32 --checkpoint-every 100 # Custom architecture ./scripts/train_cnn_v2_full.sh --kernel-sizes 3,5,3 --num-layers 3 --mip-level 1 +# Custom output path +./scripts/train_cnn_v2_full.sh --output-weights workspaces/test/cnn_weights.bin + # Grayscale loss (compute loss on luminance instead of RGBA) ./scripts/train_cnn_v2_full.sh --grayscale-loss @@ -160,8 +166,11 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding **Defaults:** 200 epochs, 3×3 kernels, 8→4→4 channels, batch-size 16, patch-based (8×8, harris detector). - Live progress with single-line update +- Always saves final checkpoint (regardless of --checkpoint-every interval) +- When multiple kernel sizes provided (e.g., 3,5,3), num_layers derived from list length - Validates all input images on final epoch - Exports binary weights (storage buffer architecture) +- Streamlined output: single-line export summary, compact validation - All parameters configurable via command-line **Validation Only** (skip training): @@ -201,12 +210,19 @@ Enhanced CNN with parametric static features (7D input: RGBD + UV + sin encoding **Export Binary Weights:** ```bash +# Verbose output (shows all layer details) ./training/export_cnn_v2_weights.py checkpoints/checkpoint_epoch_100.pth \ --output-weights workspaces/main/cnn_v2_weights.bin + +# Quiet mode (single-line summary) +./training/export_cnn_v2_weights.py checkpoints/checkpoint_epoch_100.pth \ + --output-weights workspaces/main/cnn_v2_weights.bin \ + --quiet ``` Generates binary format: header + layer info + f16 weights (~3.2 KB for 3-layer model). Storage buffer architecture allows dynamic layer count. +Use `--quiet` for streamlined output in scripts (used automatically by train_cnn_v2_full.sh). **TODO:** 8-bit quantization for 2× size reduction (~1.6 KB). Requires quantization-aware training (QAT). @@ -268,6 +284,9 @@ See `doc/ASSET_SYSTEM.md` and `doc/WORKSPACE_SYSTEM.md`. # CNN v2 (recommended, fully functional) ./build/cnn_test input.png output.png --cnn-version 2 +# CNN v2 with runtime weight loading (loads layer config from .bin) +./build/cnn_test input.png output.png --weights checkpoints/checkpoint_epoch_100.pth.bin + # CNN v1 (produces incorrect output, debug only) ./build/cnn_test input.png output.png --cnn-version 1 @@ -282,6 +301,8 @@ See `doc/ASSET_SYSTEM.md` and `doc/WORKSPACE_SYSTEM.md`. - **CNN v2:** ✅ Fully functional, matches CNNv2Effect - **CNN v1:** ⚠️ Produces incorrect output, use CNNEffect in demo for validation +**Note:** `--weights` loads layer count and kernel sizes from the binary file, overriding `--layers` and forcing CNN v2. + See `doc/CNN_TEST_TOOL.md` for full documentation. --- |