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简体中文 | English

MoViNet

Contents

Introduction

Movinet is a mobile video network developed by Google research. It uses causal convolution operator with stream buffer and temporal ensembles to improve accuracy. It is a lightweight and efficient video model that can be used for online reasoning video stream.

Data

Please refer to Kinetics400 data download and preparation doc k400-data

Train

  • Train MoViNet on kinetics-400 scripts:
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_movinet main.py --validate -c configs/recognition/movinet/movinet_k400_frame.yaml

Test

  • For uniform sampling, test accuracy can be found in training-logs by search key word best, such as:
Already save the best model (top1 acc)0.6489
  • Test scripts:
python3.7 main.py --test -c configs/recognition/movinet/movinet_k400_frame.yaml -w output/MoViNet/MoViNet_best.pdparams

Accuracy on Kinetics400:

Config Sampling method num_seg target_size Top-1 checkpoints
A0 Uniform 50 172 66.62 MoViNetA0_k400.pdparams

Inference

export inference model

To get model architecture file MoViNetA0.pdmodel and parameters file MoViNetA0.pdiparams, use:

python3.7 tools/export_model.py -c configs/recognition/movinet/movinet_k400_frame.yaml \
                                -p data/MoViNetA0_k400.pdparams \
                                -o inference/MoViNetA0

infer

python3.7 tools/predict.py --input_file data/example.avi \
                           --config configs/recognition/movinet/movinet_k400_frame.yaml \
                           --model_file inference/MoViNetA0/MoViNet.pdmodel \
                           --params_file inference/MoViNetA0/MoViNet.pdiparams \
                           --use_gpu=True \
                           --use_tensorrt=False

example of logs:

Current video file: data/example.avi
        top-1 class: 5
        top-1 score: 0.7667049765586853

Reference