deeplabv3p_gluon

DeepLab v3+ in MXNet Gluon

Note

• train_multi_gpu.py: multi-gpu training on Pascal VOC dataset, with validation.
• train.py: single-gpu training on Pascal VOC dataset, with validation.
• evaluate.py: single-gpu evaluation on Pascal VOC validation.
• extract_weights.py: convert the weights from official model release.
• mylib: lib-style clean code.
• workspace: the notebooks where I did experiments, with messy staffs (ignore them).
• GPU version only, but it should be modified easily into a CPU version.
• My running environments, not tested with other environments:
  • Python==3.6
  • MXNet>=1.2.0 (MXNet==1.3.0 for multi-gpu SyncBatchNorm)
  • gluoncv==0.3.0
  • TensorFlow==1.4.0, Keras==2.1.5 (for converting the weights)
• Download the dataset

git clone https://github.com/dmlc/gluon-cv
cd gluon-cv/scripts/datasets
python pascal_voc.py

Models

My porting on Pascal VOC validation:

Model	EvalOS (w/ or w/o inference tricks)	mIoU (%)
xception_coco_voc_trainaug (TF release)	16 (w/o) 8 (w/)	82.20 83.58
xception_coco_voc_trainaug (MXNet porting)	16 (w/o) 8 (w/o)	79.19 81.82
xception_coco_voc_trainaug (MXNet finetune TrainOS=16)	16 (w/o) 8 (w/o)	82.75 82.56
xception_coco_voc_trainaug (MXNet finetune TrainOS=8)	16 (w/o) 8 (w/o)	82.02 83.14
xception_voc_trainaug ImageNet pretrained only, without MSCOCO pretrained	16 (w/o) 8 (w/o)	77.06 76.44

AWS Runtime & Cost

Measured with fixing batch stats (use_global_stats=True), just for reference.

Instance	GPUs	Pricing	Train OS	Speed	Train on train_aug	Eval on val	Time per epoch	Cost per epoch
p2.8xlarge	K80x8	7.20$/h	16 8	1.5s/b16 3.4s/b16	17.0min 37.5min	3.5min 10min (BUGS: gpus do not use sufficiently during eval)	20.5min 47.5min	$2.5 $5.7
p3.8xlarge	V100x4	12.24$/h	16 8	0.5s/b16 3.0s/b12	5.5min 44.5min	0.7min 1.3min	6.2min 45.8min	$1.3 $9.3

Memo

• transfer all the weights
• add OS=8
• test iou on PASCAL val
• add training scripts
• add multi-gpu training scripts
• train more and open source the best models
• VOCAug dataset pull request
• Model pull request
• Finish pull request to gluoncv

Acknowledge

This repository is a part of MXNet summer code hosted by AWS, TuSimple and Jiangmen. Specifically, I would like to thank Hang Zhang (@AWS) and Hengchen Dai (@TuSimple) for kind suggestions on tuning and implementation. Plus, I would like to thank AWS for providing generous credits for tuning the computationally intensive models.