AnyLoc on Torch Hub

This repository is for AnyLoc's release on Torch Hub.

Please see: anyloc.github.io or the main AnyLoc repository for the actual work.

Note: This is a work-in-progress (not ready for public release yet), mainly for internal use. Please don't use it if you're not in the AnyLoc team. Use force_reload = True while the API is unstable.

Please open issues about this work in AnyLoc/AnyLoc with label torch.hub

Table of contents

• AnyLoc on Torch Hub
  • Table of contents
  • Tutorial
    • Map-specific Vocabulary
  • TODO
  • References

Tutorial

Install the following

pip install einops      # Codebase uses this
pip install fast_pytorch_kmeans     # For VLAD codebase

Basic usage (pass in the domain, backbone, and set the device to cuda for using a GPU)

import torch
model = torch.hub.load("AnyLoc/DINO", "get_vlad_model", 
        domain="indoor", backbone="DINOv2", device="cuda")
# Images
img = torch.rand(1, 3, 224, 224)
# Result: VLAD descriptors of shape [1, 49152]
res = model(img)

It also supports batching

# Images
img = torch.rand(16, 3, 224, 224)
# Result: VLAD descriptors of shape [16, 49152]
res = model(img)

You can get more help from

# List of functions
print(torch.hub.list("AnyLoc/DINO"))
# Help about an individual function - like "get_vlad_model"
r = torch.hub.help("AnyLoc/DINO", "get_vlad_model")
print(r)

Map-specific Vocabulary

This is to use your own dataset for calculating the VLAD clusters

import einops as ein
# Load model
model = torch.hub.load("AnyLoc/DINO", "get_vlad_model", 
        domain=None, backbone="DINOv2", device="cuda")
# Extract features
imgs = torch.rand(16, 3, 224, 224)  # Database images
res = model.extract(imgs)
res_all = ein.rearrange(res, "B N D -> (B N) D")
# Fit VLAD (to get cluster centers)
model.fit(res_all)
# Get the descriptors
img = torch.rand(1, 3, 224, 224)    # Inference images
gd = model(img) # Global descriptors

TODO

• AnyLoc-VLAD-DINOv2
• AnyLoc-VLAD-DINO
• AnyLoc-VLAD-DINOv2-PCA
• AnyLoc-VLAD-DINO-PCA

References

• torch.hub
  • Download directory