AlphaPose

This implements training of AlphaPose on the COCO dataset, mainly modified from AlphaPose

AlphaPose Detail

As of the current date, Ascend-Pytorch is still inefficient for contiguous operations. Therefore, AlphaPose is re-implemented using semantics such as custom OP. For details, see alphapose/models/fastpose.py .

Requirements

• install Ascend-Pytorch

• install apex

• install related lib

  #ubuntu 
  apt-get install libyaml-dev
  #centOS 
  yum install libyaml-devel
  

• install alphapose

  python setup.py build develop
  

Before Training

1. Prepare COCO datasets

   |-- coco
       `-- |-- annotations
       |   |-- person_keypoints_train2017.json
       |   `-- person_keypoints_val2017.json
       |-- train2017
       |   |-- 000000000009.jpg
       |   |-- 000000000025.jpg
       |   |-- 000000000030.jpg
       |   |-- ... 
       `-- val2017
       |-- 000000000139.jpg
       |-- 000000000285.jpg
       |-- 000000000632.jpg
       |-- ... 
   

2. Modify datasets path config ROOT:'/home/dataset/coco2017' in /configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml

   DATASET:
     TRAIN:
       TYPE: 'Mscoco'
       ROOT: '/home/dataset/coco2017/'
       IMG_PREFIX: 'train2017'
       ANN: 'annotations/person_keypoints_train2017.json'
       AUG:
         FLIP: true
         ROT_FACTOR: 40
         SCALE_FACTOR: 0.3
         NUM_JOINTS_HALF_BODY: 8
         PROB_HALF_BODY: -1
     VAL:
       TYPE: 'Mscoco'
       ROOT: '/home/dataset/coco2017/'
       IMG_PREFIX: 'val2017'
       ANN: 'annotations/person_keypoints_val2017.json'
     TEST:
       TYPE: 'Mscoco_det'
       ROOT: '/home/dataset/coco2017/'
       IMG_PREFIX: 'val2017'
       DET_FILE: './exp/json/test_det_yolo.json'
       ANN: 'annotations/person_keypoints_val2017.json'
     DEMO:
       TYPE: 'Mscoco_Infer'
       ROOT: '/home/dataset/coco2017/'
       IMG_PREFIX: 'val2017'
       ANN: 'annotations/person_keypoints_val2017.json'
   ``
   
   

Training & Inference

1p training

bash test/train_full_1p.sh # training full epoches bash test/train_performance_1p.sh # training one epoch to see performance

8p training

bash test/train_full_8p.sh # training full epoches bash test/train_performance_8p.sh # training one epoch to see performance

eval default 8p

bash test/train_eval_8p.sh

Online inference demo

python scripts/demo.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint exp/exp_test-256x192_res50_lr1e-3_1x.yaml/model_199.pth

To ONNX

python scripts/pthtar2onnx.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml

AlphaPose training result

	gt mAP	Epochs	AMP_Type
1p-GPU	-	200	O2
1p-NPU	-	200	O2
8p-GPU	72.24	200	O2
8P-NPU	71.61	200	O2

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md

公网地址说明

代码涉及公网地址参考 public_address_statement.md