Get Started

Train
Evaluate
Demo

Guiding users to produce models in a short time and master the using of UP.

Train

Step1: change the path of dataset.

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: training

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: the setting of FP16 and others.

runtime:
  fp16: True # ddp backend
  runner:
    type: base # Default is base, or register the runner according to the requirement such as quant.

Evaluate

The evaluation script merges tesing into training where tesing can be started by assigned -e in the training order.

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 1 configs/det/yolox/yolox_tiny.yaml

Demo

Step1: revise the config.

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