feat(cnn_v3): Phase 5 complete — parity validation passing (36/36 tests)

- Add test_cnn_v3_parity.cc: zero_weights + random_weights tests
- Add gen_test_vectors.py: PyTorch reference implementation for enc0/enc1/bn/dec1/dec0
- Add test_vectors.h: generated C header with enc0, dec1, output expected values
- Fix declare_nodes(): intermediate textures at fractional resolutions (W/2, W/4)
  using new NodeRegistry::default_width()/default_height() getters
- Add layer-by-layer readback (enc0, dec1) for regression coverage
- Final parity: enc0 max_err=1.95e-3, dec1 max_err=1.95e-3, out max_err=4.88e-4

handoff(Claude): CNN v3 parity done. Next: train_cnn_v3.py (FiLM MLP training).

1 files changed, 4 insertions, 0 deletions

diff --git a/cnn_v3/src/cnn_v3_effect.h b/cnn_v3/src/cnn_v3_effect.h
index c358990..36e2797 100644
--- a/cnn_v3/src/cnn_v3_effect.h
+++ b/cnn_v3/src/cnn_v3_effect.h
@@ -89,6 +89,10 @@ class CNNv3Effect : public Effect {
   // Update FiLM conditioning; call before render() each frame.
   void set_film_params(const CNNv3FiLMParams& fp);
 
+  // Upload packed-f16 weights (kWeightsBufBytes bytes of u32 pairs).
+  // Used for testing and inference from trained .bin files.
+  void upload_weights(WGPUQueue queue, const void* data, uint32_t size_bytes);
+
  private:
   // Intermediate node names (prefixed from output[0])
   std::string node_enc0_;