exponential adaptive pooling for PyTorch

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Deep LearningadaPool
Overview

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling

supported versions Library GitHub license

Abstract

Pooling layers are essential building blocks of Convolutional Neural Networks (CNNs) that reduce computational overhead and increase the receptive fields of proceeding convolutional operations. They aim to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory efficient. It is a challenge to meet both requirements jointly. To this end, we propose an adaptive and exponentially weighted pooling method named adaPool. Our proposed method uses a parameterized fusion of two sets of pooling kernels that are based on the exponent of the Dice-Sørensen coefficient and the exponential maximum, respectively. A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, weights can be used to upsample a downsampled activation map. We term this method adaUnPool. We demonstrate how adaPool improves the preservation of detail through a range of tasks including image and video classification and object detection. We then evaluate adaUnPool on image and video frame super-resolution and frame interpolation tasks. For benchmarking, we introduce Inter4K, a novel high-quality, high frame-rate video dataset. Our combined experiments demonstrate that adaPool systematically achieves better results across tasks and backbone architectures, while introducing a minor additional computational and memory overhead.


[arXiv preprint -- coming soon]

Original
adaPool

Dependencies

All parts of the code assume that torch is of version 1.4 or higher. There might be instability issues on previous versions.

This work relies on the previous repo for exponential maximum pooling (alexandrosstergiou/SoftPool). Before opening an issue please do have a look at that repository as common problems in running or installation have been addressed.

! Disclaimer: This repository is heavily structurally influenced on Ziteng Gao's LIP repo https://github.com/sebgao/LIP

Installation

You can build the repo through the following commands:

$ git clone https://github.com/alexandrosstergiou/adaPool.git
$ cd adaPool-master/pytorch
$ make install
--- (optional) ---
$ make test

Usage

You can load any of the 1D, 2D or 3D variants after the installation with:

# Ensure that you import `torch` first!
import torch
import adapool_cuda

# For function calls
from adaPool import adapool1d, adapool2d, adapool3d, adaunpool
from adaPool import edscwpool1d, edscwpool2d, edscwpool3d
from adaPool import empool1d, empool2d, empool3d
from adaPool import idwpool1d, idwpool2d, idwpool3d

# For class calls
from adaPool import AdaPool1d, AdaPool2d, AdaPool3d
from adaPool import EDSCWPool1d, EDSCWPool2d, EDSCWPool3d
from adaPool import EMPool1d, EMPool2d, EMPool3d
from adaPool import IDWPool1d, IDWPool2d, IDWPool3d
  • (ada/edscw/em/idw)pool<x>d: Are functional interfaces for each of the respective pooling methods.
  • (Ada/Edscw/Em/Idw)Pool<x>d: Are the class version to create objects that can be referenced in the code.

Citation

@article{stergiou2021adapool,
  title={AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling},
  author={Stergiou, Alexandros and Poppe, Ronald},
  journal={arXiv preprint},
  year={2021}}

Licence

MIT

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Comments
  • Installation issue on Google Colab

    Installation issue on Google Colab

    Hi, Thanks for providing a Cuda optimized implementation. While building the lib I encountered an issue with "inf" at limits.cuh.

    CUDA/limits.cuh(119): error: identifier "inf" is undefined

CUDA/limits.cuh(120): error: identifier "inf" is undefined

CUDA/limits.cuh(128): error: identifier "inf" is undefined

CUDA/limits.cuh(129): error: identifier "inf" is undefined

4 errors detected in the compilation of "CUDA/adapool_cuda_kernel.cu".
error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1
Makefile:2: recipe for target 'install' failed
make: *** [install] Error 1

    The following notebook provides more details with environment informations: https://colab.research.google.com/drive/1T6Nxe2qbjKxXzo2IimFMYBn52qbthlZB?usp=sharing

    opened by okbalefthanded 2
  • Solution: Unresolved extern function '_Z3powdi'”

    Solution: Unresolved extern function '_Z3powdi'”

    cuda11. 0

    When I tried to build your project on win10, I encountered the following problems： “ptxas fatal : Unresolved extern function '_Z3powdi'”

    Reason: Wrong use of pow function in Cu code Solution: for example, pow (x, 2) can be changed to X * X

    opened by Culturenotes 1
  • Does AdaPool2d's beta require fixed image size?

    Does AdaPool2d's beta require fixed image size?

    I'm currently running AdaPool2d as a replacement of MaxPool2d in Resnet's stem similar on how you did it in SoftPool. However, I keep on getting an assertionError in line 1325 as shown below:

    assert isinstance(beta, tuple) or torch.is_tensor(beta), 'Agument `beta` can only be initialized with Tuple or Tensor type objects and should correspond to size (oH, oW)'

    Does this mean beta requires a fixed image size, e.g. (224,244)? Or is there a way to make it adaptive across varying image size (e.g. object detection)?

    opened by johnanthonyjose 1
  • The version of pytorch and how to deal with `nan_to_num` function in lower versions

    The version of pytorch and how to deal with `nan_to_num` function in lower versions

    Thank you for this amazing project. I saw it from SoftPool. After installing it, make test, but I got AttributeError: module 'torch' has no attribute 'nan_to_num', after I checked, this function used in idea.py was introduced in Pytorch 1.8.0, so the torch version in the README may need to be updated, or is there an easy way to be compatible with lower versions?

    opened by MaxChanger 1
Releases(v0.2)
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Alexandros Stergiou
Computer Vision and Machine Learning Researcher
Alexandros Stergiou
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