Single Shot MultiBox Detector training in PyTorch

This example shows how DALI can be used in detection networks, specifically Single Shot Multibox Detector originally published by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg as SSD: Single Shot MultiBox Detector.

Code is based on MLPerf example and has been modified to use DALI.

To run training on 8 GPUs using half-precission with COCO 2017 dataset under /coco use following command:

python -m torch.distributed.launch --nproc_per_node=8 ./main.py --warmup 300 --bs 64 --fp16 --data /coco/

Requirements

• This example was tested with python3.5.2 and it should work with later versions. It will not work with python2.7 and earlier.

• Download COCO 2017 dataset. You can also use:

  dir=$(pwd)
  mkdir /coco; cd /coco
  curl -O http://images.cocodataset.org/zips/train2017.zip; unzip train2017.zip
  curl -O http://images.cocodataset.org/zips/val2017.zip; unzip val2017.zip
  curl -O http://images.cocodataset.org/annotations/annotations_trainval2017.zip; unzip annotations_trainval2017.zip
  cd $dir
  

• Install packages listed below into your python interpreter:

  numpy torch torchvision mlperf_compliance matplotlib Cython pycocotools

Usage

usage: main.py [-h] --data DATA [--epochs EPOCHS] [--batch-size BATCH_SIZE]
              [--eval-batch-size EVAL_BATCH_SIZE] [--seed SEED]
              [--evaluation [EVALUATION [EVALUATION ...]]]
              [--multistep [MULTISTEP [MULTISTEP ...]]]
              [--learning-rate LEARNING_RATE] [--momentum MOMENTUM]
              [--weight-decay WEIGHT_DECAY] [--warmup WARMUP]
              [--num-workers NUM_WORKERS] [--fp16] [--local_rank LOCAL_RANK]
              [--data_pipeline {dali,no_dali}]

All arguments with descriptions you can find in table below:

Argument	Description
-h, –help	show this help message and exit
–data DATA, -d DATA	path to test and training data files
–epochs EPOCHS, -e EPOCHS	number of epochs for training
–batch-size BATCH_SIZE, -b BATCH_SIZE	number of examples for each iteration
–seed SEED, -s SEED	manually set random seed for torch
–evaluation [EVALUATION [EVALUATION …]]	epochs at which to evaluate
–multistep [MULTISTEP [MULTISTEP …]]	epochs at which to decay learning rate
–learning-rate LEARNING_RATE	learning rate
–momentum MOMENTUM	momentum argument for SGD optimizer
–weight-decay WEIGHT_DECAY	weight decay value
–warmup WARMUP	number of warmup iterations
–num-workers NUM_WORKERS	number of worker threads
–fp16	use half precission
–local_rank LOCAL_RANK	local rank of current process
–data_pipeline {dali,no_dali}	data pipeline to use for training