- Changed paths in documentation and configuration files to reflect the new naming convention for the NetVLAD model, transitioning from `models/netvlad/netvlad.pt` to `models/net_vlad/net_vlad.pt`.
- Updated the `.gitignore` to include additional file types and directories related to input data and locally-generated evidence frames.
- Removed the old NetVLAD checkpoint file as part of the transition to the new naming scheme.
These changes ensure consistency across the project and improve the management of generated files.
AZ-965 ships the NetVLAD .pt checkpoint that clears the AZ-839
empty-c10_provisioning.backbones SKIP gate. Pipeline-integration
scaffold — encoder is real, NetVLAD tail is honestly labelled as
untrained.
Composition:
* Encoder (26 keys, encoder.0..encoder.28): torchvision
vgg16(weights=IMAGENET1K_V1) features [:-2], BSD-3-Clause.
Real ImageNet-pretrained VGG16 conv stack.
* NetVLAD pool + PCA tail (5 keys: pool.conv.{weight,bias},
pool.centroids, pca.{weight,bias}): random-init via
torch.manual_seed(0). NOT trained for visual place recognition.
Total: 149,002,112 params (568.4 MiB fp32, sha256=745c6f29...).
Round-trip verified locally: torch.load(weights_only=True) +
load_state_dict(strict=True) succeed; forward(1,3,480,480) emits
{'vlad_descriptor': (1, 4096) fp32} — matches NetVladStrategy
contract per net_vlad.py:247-251.
Two material discoveries documented in the AZ-965 spec:
1. The NetVLAD-VGG16 architecture already lives in repo at
src/gps_denied_onboard/components/c2_vpr/_net_vlad_architecture.py
— we instantiate it and save a state_dict, NOT externally source.
2. The PyTorch FP16 runtime expects a .pt state_dict (NOT .onnx).
BackboneConfig.onnx_path is a misnomer for NetVLAD: per AZ-321
design + c2_vpr description.md §1, NetVLAD runs on PyTorch FP16
(NOT TRT). compile_engine is a no-op sha256+path wrap;
deserialize_engine does torch.load(weights_only=True) +
load_state_dict(strict=True).
User skipped Option A/B/C/D/E question — judgment call = Option B
(IMAGENET1K_V1 + random tail) per "use judgment, don't block":
* Option A (Nanne translation) was 5-8 SP, above the 5 SP budget.
* Option B is 3 SP, fits the budget, honestly labelled.
* Option C (pure random) was borderline-dishonest per Real Results.
Files:
* scripts/mk_netvlad_checkpoint.py — deterministic generator.
* models/netvlad/netvlad.pt — 568 MiB, via git-lfs (.gitattributes
extended for models/**/*.pt, *.onnx, *.engine).
* configs/operator_replay.yaml — c2_vpr + c10_provisioning blocks
populated; the field literally named onnx_path actually points
at the .pt for NetVLAD per the runtime semantics noted above.
* docker-compose.test.jetson.yml — ./models:/opt/models:ro bind
mount added to e2e-runner.
* _docs/03_ip_attribution/netvlad.md — provenance, licence, how-to-
reproduce, honest scope statement ("NOT a real-retrieval
checkpoint; ESKF divergence under garbage retrievals is the
expected next gate").
* _docs/02_tasks/todo/AZ-965_netvlad_onnx_backbone_provisioning.md
— rewritten to reflect the .pt-not-.onnx + Option B discoveries.
Tier-2 verification follows in a separate commit after the harness
run confirms the empty-backbones SKIP gate clears.
Out of scope (filed as follow-ups):
* Real-retrieval NetVLAD weights (Nanne Pittsburgh-30k translation
or internal team checkpoint) — separate ticket.
* AZ-840 orchestrator PASSing end-to-end (depends on retrieval
quality + ESKF stability).
* AZ-963 60s smoke ESKF divergence (independent chain).
Co-authored-by: Cursor <cursoragent@cursor.com>
Oleksandr Bezdieniezhnykh