docs and cuda

docs/Makefile

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+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/conf.py

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+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = 'FaVoR'
+copyright = '2024, Vincenzo Polizzi'
+author = 'Vincenzo Polizzi'
+release = '1.0.0'
+
+import os
+import sys
+sys.path.insert(0, os.path.abspath('..'))
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = ['sphinx.ext.autodoc', 'sphinx.ext.coverage', 'sphinx.ext.napoleon']
+autodoc_mock_imports = []
+try:
+    import numpy
+except ImportError:
+    autodoc_mock_imports.append('numpy')
+
+
+templates_path = ['_templates']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = 'sphinx_rtd_theme'
+html_static_path = ['_static']

docs/datasets.rst

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+Datasets
+========
+
+Bla bla bla
+
+
+Download
+------------
+
+Bla bla bla
+
+
+Dataloader Base Class
+---------------------
+
+.. automodule:: lib.data_loaders.Dataloader
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :no-index:
+
+Dataloader for Cambridge Landmarks Dataset
+------------------------------------------
+
+The class :class:`lib.data_loaders.CambridgeLandmarksDataloader` is a subclass of :class:`lib.data_loaders.Dataloader` and is used to load the Cambridge Landmarks Dataset.
+
+Dataloader for 7Scenes Dataset
+-------------------------------
+
+The class :class:`lib.data_loaders.SevenScenesDataloader` is a subclass of :class:`lib.data_loaders.Dataloader` and is used to load the 7Scenes Dataset.

docs/home.rst

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+FaVoR Documentation
+===================
+
+.. figure:: media/eyecatcher.png
+    :alt: FaVoR pipeline
+    :figclass: align-center
+
+
+About
+-----
+FaVoR is a 3D representation of keypoints descriptors, which
+can be used for camera relocalization.
+If you use plan to use FaVoR for your research, please cite::
+
+        @misc{polizzi2024arXiv,
+           title={FaVoR: Features via Voxel Rendering for Camera Relocalization},
+           author={Vincenzo Polizzi and Marco Cannici and Davide Scaramuzza and Jonathan Kelly},
+           year={2024},
+           eprint={2409.07571},
+           archivePrefix={arXiv},
+           primaryClass={cs.CV},
+           url={https://arxiv.org/abs/2409.07571},
+        }

docs/index.rst

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+.. include::
+    home.rst
+
+Contents:
+----------
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   home
+   installation
+   datasets

docs/installation.rst

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+Installation
+============
+
+Follow the steps below to install and set up the environment for the project.
+
+Requirements
+------------
+
+To run the project, you need Python 3.x and the following dependencies:
+
+- `numpy`
+- `scipy`
+- `torch` (if using PyTorch)
+- `sphinx` (for generating documentation)
+
+You can install the dependencies by running:
+
+```bash
+pip install -r requirements.txt
+```

docs/make.bat

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+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

lib/cuda/__init__.py

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+try:
+    library_loaded = False
+
+
+    def load_library():
+        global library_loaded
+        if not library_loaded:
+            import torch
+            from pathlib import Path
+
+            file_path = Path(__file__)
+            so_files = list(file_path.parent.glob('*.so'))
+            if not so_files:
+                print('\033[1;41;37m No CUDA libraries found to load!\033[0m')
+                return
+
+            for f in so_files:
+                torch.ops.load_library(f)
+
+            library_loaded = True
+            print('\033[1;42;38m CUDA libraries loaded successfully!\033[0m')
+        else:
+            print('\033[1;43;38m CUDA libraries already loaded!\033[0m')
+
+
+    load_library()
+
+except ImportError as e:
+    print('\033[1;41;37m Failed to import torch! Make sure PyTorch is installed.\033[0m')
+    print(e)
+
+except OSError as e:
+    print('\033[1;41;37m Failed to load CUDA libraries! Build the CUDA libraries first.\033[0m')
+    print(e)
+
+except Exception as e:
+    print('\033[1;41;37m An unexpected error occurred!\033[0m')
+    print(e)

lib/cuda/adam_upd.cpp

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+#include <torch/extension.h>
+
+#include <vector>
+
+// CUDA forward declarations
+
+void adam_upd_cuda(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    int64_t step, double beta1, double beta2, double lr, double eps);
+
+void masked_adam_upd_cuda(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    int64_t step, double beta1, double beta2, double lr, double eps);
+
+void adam_upd_with_perlr_cuda(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    torch::Tensor perlr,
+    int64_t step, double beta1, double beta2, double lr, double eps);
+
+
+// C++ interface
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+
+void adam_upd(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    int64_t step, double beta1, double beta2, double lr, double eps) {
+  CHECK_INPUT(param);
+  CHECK_INPUT(grad);
+  CHECK_INPUT(exp_avg);
+  CHECK_INPUT(exp_avg_sq);
+  adam_upd_cuda(param, grad, exp_avg, exp_avg_sq,
+          step, beta1, beta2, lr, eps);
+}
+
+void masked_adam_upd(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    int64_t step, double beta1, double beta2, double lr, double eps) {
+  CHECK_INPUT(param);
+  CHECK_INPUT(grad);
+  CHECK_INPUT(exp_avg);
+  CHECK_INPUT(exp_avg_sq);
+  masked_adam_upd_cuda(param, grad, exp_avg, exp_avg_sq,
+          step, beta1, beta2, lr, eps);
+}
+
+void adam_upd_with_perlr(
+    torch::Tensor param,
+    torch::Tensor grad,
+    torch::Tensor exp_avg,
+    torch::Tensor exp_avg_sq,
+    torch::Tensor perlr,
+    int64_t step, double beta1, double beta2, double lr, double eps) {
+  CHECK_INPUT(param);
+  CHECK_INPUT(grad);
+  CHECK_INPUT(exp_avg);
+  CHECK_INPUT(exp_avg_sq);
+  adam_upd_with_perlr_cuda(param, grad, exp_avg, exp_avg_sq, perlr,
+          step, beta1, beta2, lr, eps);
+}
+
+TORCH_LIBRARY(adam_upd, m) {
+  m.def("adam_upd", &adam_upd); // "Adam update")
+  m.def("masked_adam_upd", &masked_adam_upd); // "Adam update ignoring zero grad"
+  m.def("adam_upd_with_perlr", &adam_upd_with_perlr); // "Adam update ignoring zero grad with per-voxel lr"
+}
+