In-Place Activated BatchNorm for Memory-Optimized Training of DNNs
In-Place Activated BatchNorm (InPlace-ABN) is a novel approach to reduce the memory required for training deep networks. It allows for up to 50% memory savings in modern architectures such as ResNet, ResNeXt and Wider ResNet by redefining BN + non linear activation as a single in-place operation, while smartly dropping or recomputing intermediate buffers as needed.
This repository contains a PyTorch implementation of the InPlace-ABN layer, as well as some training scripts to reproduce the ImageNet classification results reported in our paper.
We have now also released the inference code for semantic segmentation, together with the Mapillary Vistas trained model leading to #1 position on the Mapillary Vistas Semantic Segmentation leaderboard. More information can be found at the bottom of this page.
If you use In-Place Activated BatchNorm in your research, please cite:
@inproceedings{rotabulo2017place,
title={In-Place Activated BatchNorm for Memory-Optimized Training of DNNs},
author={Rota Bul\`o, Samuel and Porzi, Lorenzo and Kontschieder, Peter},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2018}
}
When processing a BN-Activation-Convolution sequence in the forward pass, most deep learning frameworks need to store
two big buffers, i.e. the input x of BN and the input z of Conv.
This is necessary because the standard implementations of the backward passes of BN and Conv depend on their inputs to
calculate the gradients.
Using Inplace-ABN to replace the BN-Activation sequence, we can safely discard x, thus saving up to 50% GPU memory at
training time.
To achieve this, we rewrite the backward pass of BN in terms of its output y, which is in turn reconstructed from z
by inverting the activation function.
The parametrization for the scaling factor of BN changed compared to standard BN, in order to ensure an invertible transformation. Specifically, the scaling factor becomes
.
To install PyTorch, please refer to https://github.com/pytorch/pytorch#installation.
NOTE: our code requires PyTorch v1.1 or later.
To install the package containing the iABN layers:
pip install git+https://github.com/mapillary/[email protected]Note that some parts of InPlace-ABN have native C++/CUDA implementations, meaning that the command above will need to compile them.
Alternatively, to download and install the latest version of our library, also obtaining a copy of the Imagenet / Vistas scripts:
git clone https://github.com/mapillary/inplace_abn.git
cd inplace_abn
python setup.py install
cd scripts
pip install -r requirements.txtThe last of the commands above will install some additional libraries required by the Imagenet / Vistas scripts.
Here you can find the results from our arXiv paper (top-1 / top-5 scores) with corresponding, trained models and md5 checksums, respectively. The model files provided below are made available under the license attached to ImageNet.
Our script uses torchvision.datasets.ImageFolder for loading ImageNet data, which expects folders organized as follows:
root/train/[class_id1]/xxx.{jpg,png,jpeg}
root/train/[class_id1]/xxy.{jpg,png,jpeg}
root/train/[class_id2]/xxz.{jpg,png,jpeg}
...
root/val/[class_id1]/asdas.{jpg,png,jpeg}
root/val/[class_id1]/123456.{jpg,png,jpeg}
root/val/[class_id2]/__32_.{jpg,png,jpeg}
...
Images can have any name, as long as the extension is that of a recognized image format. Class ids are also free-form, but they are expected to match between train and validation data. Note that the training data in the standard ImageNet distribution is already given in the required format, while validation images need to be split into class sub-folders as described above.
The main training script is scripts/train_imagenet.py: this supports training on ImageNet, or any other dataset
formatted as described above, while keeping a log of relevant metrics in Tensorboard format and periodically saving
snapshots.
Most training parameters can be specified as a json-formatted configuration file (look
here for a complete list of configurable parameters).
All parameters not explicitly specified in the configuration file are set to their defaults, also available in
scripts/imagenet/config.py.
Our arXiv results can be reproduced by running scripts/train_imagenet.py with the configuration files in
scripts/experiments.
As an example, the command to train ResNeXt101 with InPlace-ABN, Leaky ReLU and batch_size = 512 is:
cd scripts
python -m torch.distributed.launch --nproc_per_node <n. GPUs per node> train_imagenet.py --log-dir /path/to/tensorboard/logs experiments/resnext101_ipabn_lr_512.json /path/to/imagenet/rootValidation is run by scripts/train_imagenet.py at the end of every training epoch.
To validate a trained model, you can use the scripts/test_imagenet.py script, which allows for 10-crops validation and
transferring weights across compatible networks (e.g. from ResNeXt101 with ReLU to ResNeXt101 with Leaky
ReLU).
This script accepts the same configuration files as scripts/train_imagenet.py, but note that the scale_val and
crop_val parameters are ignored in favour of the --scale and --crop command-line arguments.
As an example, to validate the ResNeXt101 trained above using 10-crops of size 224 from images scaled to 256
pixels, you can run:
cd scripts
python -m torch.distributed.launch --nproc_per_node <n. GPUs per node> test_imagenet.py --crop 224 --scale 256 --ten_crops experiments/resnext101_ipabn_lr_512.json /path/to/checkpoint /path/to/imagenet/rootWe have successfully used InPlace-ABN with a DeepLab3 segmentation head that was trained on top of the WideResNet38 model above. Due to InPlace-ABN, we can significantly increase the amount of input data to this model, which eventually allowed us to obtain #1 positions on Cityscapes, Mapillary Vistas, AutoNUE, Kitti and ScanNet segmentation leaderboards. The training settings mostly follow the description in our paper.
We release our WideResNet38 + DeepLab3 segmentation model trained on the Mapillary Vistas research set. This is the model used to reach #1 position on the MVD semantic segmentation leaderboard. The segmentation model file provided below is made available under a CC BY-NC-SA 4.0 license.
| Network | mIOU | Trained model (+md5) |
|---|---|---|
| WideResNet38 + DeepLab3 | 53.42 | 913f78486a34aa1577a7cd295e8a33bb |
To use this, please download the .pth.tar model file linked above and run the test_vistas.py script as follows:
cd scripts
python test_vistas.py /path/to/model.pth.tar /path/to/input/folder /path/to/output/folderThe script will process all .png, .jpg and .jpeg images from the input folder and write the predictions in the
output folder as .png images.
For additional options, e.g. test time augmentation, please consult the script's help message.
The results on the test data written above were obtained by employing only scale 1.0 + flipping.
Update 05 Jul. 2019: version 1.0.1
- Restored support for float16 inputs with float32
weightandbias.
Update 04 Jul. 2019: version 1.0.0
- Complete rewrite of the CUDA code following the most recent native BN implementation from Pytorch
- Improved synchronized BN implementation, correctly handling different per-GPU batch sizes and Pytorch distributed groups
- The iABN layers are now packaged in an installable python library to simplify use in other projects
- The Imagenet / Vistas scripts are still available in the
scriptsfolder
Update 08 Jan. 2019:
- Enabled multiprocessing and inplace ABN synchronization over multiple processes (previously using threads). It now requires to use DistributedDataParallel instead of DataParallel
- Added compatibility with fp16 (currently allows fp16 input but requires the module to stay in fp32 mode)
- Requires now PyTorch 1.0
Update Feb. 2019:
- Added ResNet34v1, ResNet50v1 and ResNet101v1 ImageNet-1k pre-trained models
We have modified the imagenet training code and BN synchronization in order to work with multiple processes. We have also added compatibility of our Inplace ABN module with fp16.