Merge pull request #189 from CVCUDA/feat/milesp/release_cvcuda_0110

feat: adding release v0.11.0 of cvcuda

Author: milesp-nvidia

3rdparty/pybind11

@@ -17,13 +17,13 @@ cmake_minimum_required(VERSION 3.20.1)
 
 # We need to check this variable before starting a CUDA project - otherwise it will appear
 # as set, with the default value pointing to the oldest supported architecture (52 as of CUDA 11.8)
-if(DEFINED CMAKE_CUDA_ARCHITECTURES)
+if(CMAKE_CUDA_ARCHITECTURES)
     set(USE_CMAKE_CUDA_ARCHITECTURES TRUE)
 endif()
 
 project(cvcuda
         LANGUAGES C CXX
-        VERSION 0.10.0
+        VERSION 0.11.0
         DESCRIPTION "CUDA-accelerated Computer Vision algorithms"
 )
 
@@ -49,6 +49,7 @@ endif()
 option(BUILD_TESTS "Enable testsuite" OFF)
 option(BUILD_PYTHON "Build python bindings" OFF)
 option(BUILD_BENCH "Build benchmark" OFF)
+option(BUILD_DOCS "Build documentation" OFF)
 option(ENABLE_SANITIZER "Enabled sanitized build" OFF)
 
 # Configure build tree ======================

CMakeLists.txt

@@ -18,7 +18,7 @@
 
 [![License](https://img.shields.io/badge/License-Apache_2.0-yellogreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
-![Version](https://img.shields.io/badge/Version-v0.10.1--beta-blue)
+![Version](https://img.shields.io/badge/Version-v0.11.0--beta-blue)
 
 ![Platform](https://img.shields.io/badge/Platform-linux--64_%7C_win--64_wsl2%7C_aarch64-gray)
 
@@ -60,10 +60,28 @@ To get a local copy up and running follow these steps.
 - [CV-CUDA Samples] require driver r535 or later to run and are only officially supported with CUDA 12.
 - Only one CUDA version (CUDA 11.x or CUDA 12.x) of CV-CUDA packages (Debian packages, tarballs, Python Wheels) can be installed at a time. Please uninstall all packages from a given CUDA version before installing packages from a different version.
 - Documentation built on Ubuntu 20.04 needs an up-to-date version of sphinx (`pip install --upgrade sphinx`) as well as explicitly parsing the system's default python version ` ./ci/build_docs path/to/build -DPYTHON_VERSIONS="<py_ver>"`.
-- Python bindings installed via Debian packages and Python tests fail with Numpy 2.0. We recommend using an older version of Numpy (e.g. 1.26) until we have implemented a fix.
 - The Resize and RandomResizedCrop operators incorrectly interpolate pixel values near the boundary of an image or tensor when using cubic interpolation. This will be fixed in an upcoming release.
-- Cache/resource management introduced in v0.10 add micro-second-level overhead to Python operator calls. Based on the performance analysis of our Python samples, we expect the production- and pipeline-level impact to be negligible. CUDA kernel and C++ call performance is not affected. We aim to investigate and reduce this overhead further in a future release.​
-- Sporadic Pybind11-deallocation crashes have been reported in long-lasting multi-threaded Python pipelines with externally allocated memory (eg wrapped Pytorch buffers). We are evaluating an upgrade of Pybind11 (currently using 2.10) as a potential fix in an upcoming release.
+- The CvtColor operator incorrectly computes the data location of the second chromaticity channel for conversions that involve YUV(420) semi-planar formats. This issue persists through the current release and we intend to address this bug in CV-CUDA v0.12. We do not recommend using these formats.​
+  - Known affected formats:​
+    - NVCV_COLOR_YUV2RGB_I420​
+    - NVCV_COLOR_RGB2YUV_I420​
+    - NVCV_COLOR_YUV2BGR_I420​
+    - NVCV_COLOR_BGR2YUV_I420​
+    - NVCV_COLOR_YUV2RGBA_I420​
+    - NVCV_COLOR_RGBA2YUV_I420​
+    - NVCV_COLOR_YUV2BGRA_I420​
+    - NVCV_COLOR_BGRA2YUV_I420​
+    - NVCV_COLOR_RGB2YUV_I420​
+    - NVCV_COLOR_YUV2RGB_YV12​
+    - NVCV_COLOR_RGB2YUV_YV12​
+    - NVCV_COLOR_YUV2BGR_YV12​
+    - NVCV_COLOR_BGR2YUV_YV12​
+    - NVCV_COLOR_YUV2RGBA_YV12​
+    - NVCV_COLOR_RGBA2YUV_YV12​
+    - NVCV_COLOR_YUV2BGRA_YV12​
+    - NVCV_COLOR_BGRA2YUV_YV12​
+    - NVCV_COLOR_RGB2YUV_YV12​
+    - NVCV_COLOR_YUV2GRAY_420​
 
 ### Installation
 
@@ -209,7 +227,6 @@ For instructions on how to build samples from source and run them, see the [Samp
 Install the dependencies required for running the tests:
 - python3, python3-pip: to run python bindings tests
 - torch: dependencies needed by python bindings tests
-- numpy: known limitation: Python tests fail with numpy 2.0. We recommend using an older version (eg 1.26) until we have implemented a fix.
 
 On Ubuntu >= 20.04, install the following packages using `apt` and `pip`:
 ```shell

README.md

@@ -23,6 +23,7 @@
 
 class OpAdaptiveThreshold(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         self.maxval = 255.0
         self.adaptive_method = cvcuda.AdaptiveThresholdType.GAUSSIAN_C
         self.threshold_type = cvcuda.ThresholdType.BINARY

bench/python/all_ops/op_adaptivethreshold.py

@@ -23,6 +23,7 @@
 
 class OpAsImageFromNVCVImage(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         # dummy run that does not use cache
         img = nvcv.Image((128, 128), nvcv.Format.RGBA8)
 

bench/python/all_ops/op_as_image.py

@@ -23,6 +23,7 @@
 
 class OpAsImagesFromNVCVImage(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         # dummy run that does not use cache
         nvcv.ImageBatchVarShape(100)
         img = nvcv.Image((128, 128), nvcv.Format.RGBA8)

bench/python/all_ops/op_as_images.py

@@ -23,6 +23,7 @@
 
 class OpAverageBlur(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         self.kernel_size = (3, 3)
         self.kernel_anchor = (-1, -1)
 

bench/python/all_ops/op_averageblur.py

@@ -26,6 +26,7 @@
 
 class OpBlurBox(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         self.kernel_size = 5
 
         data = read_image(os.path.join(self.assets_dir, "brooklyn.jpg"))

bench/python/all_ops/op_blurbox.py

@@ -26,6 +26,7 @@
 
 class OpBoundingBox(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         self.border_color = (0, 255, 0, 255)
         self.fill_color = (0, 0, 255, 0)
         self.thickness = 5

bench/python/all_ops/op_boundingbox.py

@@ -24,6 +24,7 @@
 
 class OpBrightnessContrast(AbstractOpBase):
     def setup(self, input):
+        super().setup(input)
         brightness = torch.tensor([1.2]).cuda(self.device_id)
         self.brightness = cvcuda.as_tensor(brightness, "N")