rename python package to pyGGNN.

CMakeLists.txt

@@ -200,7 +200,7 @@ if (nanobind_FOUND)
     message(STATUS "building nanobind module")
     nanobind_add_module(GGNN NB_STATIC NOMINSIZE "src/ggnn/python/nanobind.cu")
     target_link_libraries(GGNN PRIVATE GGNNBase GGNNConstruction GGNNQuery glog::glog)
-    nanobind_add_stub(GGNN_stub MODULE GGNN OUTPUT GGNN.pyi MARKER_FILE py.typed DEPENDS GGNN)
+    nanobind_add_stub(pyGGNN_stub MODULE GGNN OUTPUT pyGGNN.pyi MARKER_FILE py.typed DEPENDS GGNN)
 
     if (PY_BUILD_CMAKE_MODULE_NAME)
         message(STATUS "configuring python module installation")
@@ -217,7 +217,7 @@ if (nanobind_FOUND)
                 COMPONENT python_modules
                 DESTINATION ${PY_BUILD_CMAKE_MODULE_NAME})
         endif()
-        install(FILES ${CMAKE_BINARY_DIR}/py.typed ${CMAKE_BINARY_DIR}/GGNN.pyi
+        install(FILES ${CMAKE_BINARY_DIR}/py.typed ${CMAKE_BINARY_DIR}/pyGGNN.pyi
             EXCLUDE_FROM_ALL
             COMPONENT python_modules
             DESTINATION ${PY_BUILD_CMAKE_MODULE_NAME})

README.md

@@ -95,7 +95,7 @@ The GGNN python module can be used to perform GPU-accelerated approximate neares
 ```python
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 import torch
 
 # get detailed logs

docs/source/usage_python.rst

@@ -22,7 +22,7 @@ By default, the log level is set to ``0``:
 
   #! /usr/bin/python3
 
-  import ggnn
+  import pyggnn as ggnn
 
   #get detailed logs
   ggnn.set_log_level(4)
@@ -174,7 +174,7 @@ Otherwise, this works the same way as above.
 
   #! /usr/bin/python3
 
-  import ggnn
+  import pyggnn as ggnn
   import torch
 
   # create data
@@ -224,7 +224,7 @@ and passed to the ``Evaluator`` directly.
 
   #! /usr/bin/python3
 
-  import ggnn
+  import pyggnn as ggnn
 
   path_to_dataset = '/path/to/sift/'
 

examples/python/ggnn_pytorch.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 import torch
 
 # get detailed logs
@@ -36,5 +36,5 @@
 evaluator = ggnn.Evaluator(base, query, gt_indices, k_query=k_query)
 print(evaluator.evaluate_results(indices))
 
-# print the indices of the 10 NN of the first five queries and their squared euclidean distances 
+# print the indices of the 10 NN of the first five queries and their squared euclidean distances
 print('indices:', indices[:5], '\n squared dists:',  dists[:5], '\n')

examples/python/ggnn_pytorch_gpu_data.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 import torch
 
 # get detailed logs

examples/python/ggnn_pytorch_multi_gpu.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 import torch
 
 # create data

examples/python/sift1m_fvecs.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 
 path_to_dataset = '/graphics/scratch/datasets/ANN_datasets/SIFT1M/sift/'
 

examples/python/sift1m_hdf5.py

@@ -1,6 +1,6 @@
 #! /usr/bin/python3
 
-import ggnn
+import pyggnn as ggnn
 
 import h5py
 import numpy as np

pyproject.toml

@@ -1,5 +1,5 @@
 [project] # Project metadata
-name = "GGNN"
+name = "pyGGNN"
 readme = "README.md"
 requires-python = ">=3.8"
 license = { "file" = "LICENSE" }
@@ -32,7 +32,7 @@ dynamic = ["version", "description"]
 
 [project.urls]
 Homepage = "https://github.com/cgtuebingen/ggnn"
-# TODO: Documentation = "https://ggnn.readthedocs.io/"
+Documentation = "https://ggnn.readthedocs.io/"
 
 [build-system] # How pip and other frontends should build this project
 requires = ["py-build-cmake~=0.3.3", "nanobind", "typing_extensions"]