/pytorch-pooling

Test different pooling method used in CNN for Computer Vision Task

Primary LanguagePython

Pooling

This is a collection of different pooling methods used in image classification segmentation detection

Features

  • Multi-GPU support
  • Easy and Useful Training log file
  • Easy to test different pooling method on classification task

Requirements

  • python3.6
  • pytorch1.6.0 + cuda10.1
  • tensorboard 2.3.0

Installation

  • clone 
    git clone https://github.com/rentainhe/pytorch-pooling.git
  • make data directory for cifar100 
    $ cd pytorch-pooling
$ mkdir data

Usage

1. enter directory

$ cd pytorch-pooling

2. dataset

  • Only support cifar100 now (Will support Imagenet Later)
  • Using cifar100 dataset from torchvision since it's more convinient

3. run tensorboard

Install tensorboard

$ pip install tensorboard
Run tensorboard
$ tensorboard --logdir runs --port 6006 --host localhost

4. training

Our base backbone is vgg16 with batch_normalization

$ python3 train.py --run train --name test --pooling max

  • --run={'train','test','visual'} to set the mode to be executed

  • --name=str to set the name of this training

  • --pooling=str, e.g, --pooling='max' to set the pooling method in vgg16 to be max_pool2d

  • --gpu=str, e.g, --gpu='1' to set the specified GPU for training

The supported pooling args are

max pooling
average pooling
mixed pooling
Lp pooling
lip pooling
soft pooling

5. Add a new pooling method

You should add a new pooling method pool.py in "/Pooling/pooling_method" and update the __init__.py file

6. Addition

  • lip pooling: the backbone in original paper is ResNet, But I use VggNet in this repo, so there might be something wrong with the accuracy

Results

The result I can get from this repo, I train every model with the same hyperparam and I don't use any tricks in this repo.

dataset backbone pooling acc epoch(lr = 0.1) epoch(lr = 0.02) epoch(lr = 0.004) epoch(lr = 0.0008) total epoch
cifar100 vgg16_bn max 70.89% 60 60 40 40 200
cifar100 vgg16_bn avg 70.56% 60 60 40 40 200
cifar100 vgg16_bn mixed 71.19% 60 60 40 40 200
cifar100 vgg16_bn Lp(p=2) 70.65% 60 60 40 40 200
cifar100 vgg16_bn Lp(p=3) 70.67% 60 60 40 40 200
cifar100 vgg16_bn lip 71.23% 60 60 40 40 200
cifar100 vgg16_bn softpool 71.39% 60 60 40 40 200

Implementated Pooling