Package: nnutils-pytorch-cuda
nnutils-pytorchPyTorch bindings of different neural network-related utilities implemented for
CPUs and GPUs (CUDA).So far, most of the utils are related to my need of working with images of
different sizes grouped into batches with padding.Included functionsAdaptive poolingAdaptive pooling layers included in several packages like Torch or PyTorch
assume that all images in the batch have the same size. This implementation
takes into account the size of each individual image within the batch
(before padding) to apply the adaptive pooling.Currently implemented: Average and maximum adaptive pooling.importtorchfromnnutils_pytorchimportadaptive_avgpool_2d,adaptive_maxpool_2d# Two random images, with three channels, 10 pixels height, 12 pixels widthx=torch.rand(2,3,10,12)# Matrix (N x 2) containing the height and width of each image.xs=torch.tensor([[10,6],[6,12],dtype=torch.int64)# Pool images to a fixed size, taking into account the original size of each# image before padding.## Output tensor has shape (2, 3, 3, 5)y1=adaptive_avgpool_2d(batch_input=x,output_sizes=(3,5),batch_sizes=xs)# Pool a single dimension of the images, taking into account the original# size of each image before padding. The None dimension is not pooled.## Output tensor has shape (2, 3, 5, 12)y2=adaptive_maxpool_2d(x,(5,None),xs)Important:The implementation assumes that the images are aligned to the
top-left corner.Masking images by sizeIf you are grouping images of different sizes into batches padded with zeros,
you may need to mask the output/input tensors after/before some layers.
This layer is very handy (and efficient) in these cases.importtorchfromnnutils_pytorchimportmask_image_from_size# Two random images, with three channels, 10 pixels height, 12 pixels widthx=torch.rand(2,3,10,12)# Matrix (N x 2) containing the height and width of each image.xs=torch.tensor([[10,6],[6,12],dtype=torch.int64)# Note: mask_image_from_size is differentiable w.r.t. xy=mask_image_from_size(x,xs,mask_value=0)# mask_value is optional.Important:The implementation assumes that the images are aligned to the
top-left corner.RequirementsPython: 3.6, 3.7 or 3.8 (tested with version 3.6, 3.7 and 3.8).PyTorch>= 1.6.0 (tested with version 1.6.0).C++14 compiler (tested with GCC 7.5.0).For GPU support:CUDA Toolkit.InstallationThe installation process should be pretty straightforward assuming that you
have correctly installed the required libraries and tools.The setup process compiles the package from source, and will compile with
CUDA support if this is available for PyTorch.From Pypi (recommended)pipinstallnnutils-pytorchYou may find the package already compiled for different Python, CUDA and CPU
configurations in:http://www.jpuigcerver.net/projects/nnutils-pytorch/whl/For instance, if you want to install the CPU-only version for Python 3.7:pipinstallhttp://www.jpuigcerver.net/projects/nnutils-pytorch/whl/cpu/nnutils_pytorch-1.6.0-cp37-cp37m-linux_x86_64.whlFrom GitHubgitclonehttps://github.com/jpuigcerver/nnutils.gitcdnnutils/pytorch
pythonsetup.pybuild
pythonsetup.pyinstallAVX512 related issuesSome compiling problems may arise when using CUDA and newer host compilers
with AVX512 instructions. Please, install GCC 7.5 or above and use it as the
host compiler for NVCC 10.2. You can simply set theCCandCXXenvironment
variables before the build/install commands:CC=gcc-4.9CXX=g++-4.9pipinstallnnutils-pytorchor (if you are using the GitHub source code):CC=gcc-4.9CXX=g++-4.9pythonsetup.pybuildTestingYou can test the library once installed usingunittest. In particular,
run the following commands:python-munittestnnutils_pytorch.adaptive_avgpool_2d_test
python-munittestnnutils_pytorch.adaptive_maxgpool_2d_test
python-munittestnnutils_pytorch.mask_image_from_size_testAll tests should pass (CUDA tests are only executed if supported).