快速上手/Get Started

• Train

• Evaluate

• Demo

使得用户能够在很短的时间内快速产出模型，掌握 UP 的使用方式。

Train

Step1: 修改dataset 路径

dataset:
  type: coco # dataset type
    kwargs:
      source: train
      meta_file: coco/annotations/instances_train2017.json
      image_reader:
        type: fs_opencv
        kwargs:
          image_dir: coco/train2017
          color_mode: BGR
      transformer: [*flip, *train_resize, *to_tensor, *normalize]

Step2: 训练

ROOT=../
T=`date +%m%d%H%M`
export ROOT=$ROOT
cfg=$2
export PYTHONPATH=$ROOT:$PYTHONPATH
python -m up train \
  --ng=$1 \
  --launch=pytorch \
  --config=$cfg \
  --display=10 \
  2>&1 | tee log.train.$T.$(basename $cfg)

# ./dist_train.sh <num_gpu> <config>
./dist_train.sh 2 configs/det/yolox/yolox_tiny.yaml

Step3: FP16 设置以及其他一些额外的设置

runtime:
  fp16: True # ddp 后端
  runner:
    type: base # 默认是base，也可以根据需求注册所需的runner，比如量化quant

Evaluate

评测脚本, 现在将train test 合成了一个指定，在命令行指定 -e 即可启动测试

ROOT=../
T=`date +%m%d%H%M`
export ROOT=$ROOT
cfg=$2
export PYTHONPATH=$ROOT:$PYTHONPATH

python -m up train \
  -e \
  --ng=$1
  --launch=pytorch \
  --config=$cfg \
  --display=10 \
  2>&1 | tee log.test.$T.$(basename $cfg)

# ./dist_test.sh <num_gpu> <config>
./dist_test.sh 2 configs/det/yolox/yolox_tiny.yaml

Demo

Step1: 修改cfg

runtime:
  inferencer:
    type: base
    kwargs:
      visualizer:
        type: plt
        kwargs:
          class_names: ['__background__', 'person'] # class names
          thresh: 0.5

Step2: inference

ROOT=../
T=`date +%m%d%H%M`
export ROOT=$ROOT
cfg=$2

python -m up inference \
  --ng=$1
  --launch=pytorch \
  --config=$cfg \
  2>&1 | tee log.inference.$T.$(basename $cfg)

# ./dist_inference.sh  <num_gpu> <config>
./dist_inference.sh 1 configs/det/yolox/yolox_tiny.yaml