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Start

Please refer to installation documents to prepare the enviroment, and follow the steps mentioned in the data preparation documents to construct dataset, we will take you through the basic functions supported by PaddleVideo, all of it takes the ucf101 dataset with frame format as example.

PaddleVideo only support linux operation system and GPU running time environment now.

Default detination folder of PaddleVideo files. running the example config as example.

PaddleVideo
    ├── paddlevideo
    ├── ... #other source codes
    ├── output #ouput destination
    |    ├── example
    |    |   ├── example_best.pdparams #path_to_weights
    |    |   └── ...  
    |    └── ...  
    ├── log  #log file destination.
    |    ├── worker.0
    |    ├── worker.1
    |    └── ...  
    └── inference #inference files destination.
         ├── .pdiparams file
         ├── .pdimodel file
         └── .pdiparmas.info file

1. Train and Test

Start running multi-cards training scripts or test scripts by paddle.distributed.launch, or run the run.sh directly.

sh run.sh

We put all the start commands in advanced in the run.sh, please uncomment the selected one to run.

1.1 Train

Switch --validate on to validating while training.

export CUDA_VISIBLE_DEVICES=0,1,2,3

python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    main.py \
        --validate \
        -c ./configs/example.yaml

Indicating -c to set configuration, and one can flexible add -o in the script to update it.

python -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    main.py \
        -c ./configs/example.yaml \
        --validate \
        -o DATASET.batch_size=16

Indicating -o DATASET.batch_size=16 can update batch size to 16, please refer to configuration for more information.

After starting training, log files will generated, and its format is shown as below, it will output to both the screen and files. Default destination of log is under the .log/ folder, and stored in the files named like worker.0, worker.1 ...

[train phase] current time, current epoch/ total epoch, batch id, metrics, elapse time, ips, etc.:

[12/28 17:31:26] epoch:[ 1/80 ] train step:0   loss: 0.04656 lr: 0.000100 top1: 1.00000 top5: 1.00000 elapse: 0.326 reader: 0.001s ips: 98.22489 instance/sec.

[eval phase] current time, current epoch/ total epoch, batch id, metrics, elapse time, ips, etc.:

[12/28 17:31:32] epoch:[ 80/80 ] val step:0    loss: 0.20538 top1: 0.88281 top5: 0.99219 elapse: 1.589 reader: 0.000s ips: 20.14003 instance/sec.

[epoch end] current time, metrics, elapse time, ips, etc.

[12/28 17:31:38] END epoch:80  val loss_avg: 0.52208 top1_avg: 0.84398 top5_avg: 0.97393 elapse_avg: 0.234 reader_avg: 0.000 elapse_sum: 7.021s ips: 136.73686 instance/sec.

[the best Acc]

[12/28 17:28:42] Already save the best model (top1 acc)0.8494

1.2 Resume

Indicate -o resume_epoch to resume, It will training from resume_epoch epoch, PaddleVideo will auto load optimizers parameters and checkpoints from ./output folder, as it is the default output destination.

export CUDA_VISIBLE_DEVICES=0,1,2,3

python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    main.py \
        -c ./configs/example.yaml \
        --validate \
        -o resume_epoch=5

1.3 Finetune

Indicate --weights to load pretrained parameters, PaddleVideo will auto treat it as a finetune mission.

export CUDA_VISIBLE_DEVICES=0,1,2,3

python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    main.py \
        -c ./configs/example.yaml \
        --validate \
        --weights=./outputs/example/path_to_weights

Note: PaddleVideo will NOT load shape unmatched parameters.

1.4 Test

Switch --test on to start test mode, and indicate --weights to load pretrained model.

export CUDA_VISIBLE_DEVICES=0,1,2,3

python3 -m paddle.distributed.launch \
    --gpus="0,1,2,3" \
    main.py \
        -c ./configs/example.yaml \
        --test \
        --weights=./output/example/path_to_weights

2. Infer

First, export model. Indicate -c to set configuration, -p to load pretrained model, -o to set inference files destination.

python tools/export_model.py \
    -c ./configs/example.yaml \
    -p ./output/example/path_to_weights \
    -o ./inference

It will generate model_name.pdmodel , model_name.pdiparams and model_name.pdiparames.info. Second, start PaddleInference engine to infer a video.

python tools/predict.py \
    --video_file "data/example.avi" \
    --model_file "./inference/example.pdmodel" \
    --params_file "./inference/example.pdiparams" \
    --use_gpu=True \
    --use_tensorrt=False

  • Note: For models that combine N and T during calculation (such as TSN, TSM), when use_tensorrt=True, you need to specify the batch_size argument as batch_size*num_seg.

    python tools/predict.py \
    --video_file "data/example.avi" \
    --model_file "./inference/example.pdmodel" \
    --params_file "./inference/example.pdiparams" \
    --batch_size 8 \
    --use_gpu=True \
    --use_tensorrt=True

Attributes:

  • video_file: video file path.
  • model_file: pdmodel file path.
  • params_file: pdiparams file path.
  • use_tensorrt: use tensorrt to acclerate or not, default: False.
  • use_gpu: use gpu to infer or not, default: True.

benchmark results are shown in th benchmark.