Fetch the repository succeeded.
This action will force synchronization from PaddlePaddle/PaddleVideo, which will overwrite any changes that you have made since you forked the repository, and can not be recovered!!!
Synchronous operation will process in the background and will refresh the page when finishing processing. Please be patient.
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
#export FLAGS_conv_workspace_size_limit=800 #MB
#export FLAGS_cudnn_exhaustive_search=1
#export FLAGS_cudnn_batchnorm_spatial_persistent=1
start_time=$(date +%s)
# run pp-tsm training
#python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_pptsm main.py --validate -c configs/recognition/pptsm/pptsm_k400_frames_uniform.yaml
# run pp-tsm_v2 distillation training
python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_pptsm_v2 main.py --validate -c configs/recognition/pptsm/v2/pptsm_lcnet_k400_16frames_uniform_dml_distillation.yaml
# run ava training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3" --log_dir=logdir.ava_part main.py --validate -w paddle.init_param.pdparams -c configs/detection/ava/ava_part.yaml
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=logdir.ava_all.1203 main.py --validate -w paddle.init_param.pdparams -c configs/detection/ava/ava_all.yaml
# run adds training
# python3.7 main.py --validate -c configs/estimation/adds/adds.yaml --seed 20
# run tsm training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_tsm main.py --validate -c configs/recognition/tsm/tsm_k400_frames.yaml
# run tsm amp training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_tsm main.py --amp --validate -c configs/recognition/tsm/tsm_k400_frames.yaml
# run tsm amp training, nhwc
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_tsm main.py --amp --validate -c configs/recognition/tsm/tsm_k400_frames_nhwc.yaml
# run tsn training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_tsn main.py --validate -c configs/recognition/tsn/tsn_k400_frames.yaml
# run video-swin-transformer training
# python3.7 -u -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_videoswin main.py --amp --validate -c configs/recognition/videoswin/videoswin_k400_videos.yaml
# run slowfast training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_slowfast main.py --validate -c configs/recognition/slowfast/slowfast.yaml
# run slowfast multi-grid training
# python3.7 -B -m paddle.distributed.launch --selected_gpus="0,1,2,3,4,5,6,7" --log_dir=log-slowfast main.py --validate --multigrid -c configs/recognition/slowfast/slowfast_multigrid.yaml
# run bmn training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3" --log_dir=log_bmn main.py --validate -c configs/localization/bmn.yaml
# run attention_lstm training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_attetion_lstm main.py --validate -c configs/recognition/attention_lstm/attention_lstm_youtube-8m.yaml
# run pp-tsn training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_pptsn main.py --validate -c configs/recognition/pptsn/pptsn_k400_frames.yaml
# run timesformer training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_timesformer main.py --validate -c configs/recognition/timesformer/timesformer_k400_videos.yaml
# run pp-timesformer training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_pptimesformer main.py --validate -c configs/recognition/pptimesformer/pptimesformer_k400_videos.yaml
# run st-gcn training
# python3.7 main.py -c configs/recognition/stgcn/stgcn_fsd.yaml
# run agcn training
# python3.7 main.py -c configs/recognition/agcn/agcn_fsd.yaml
# run actbert training
# python3.7 main.py --validate -c configs/multimodal/actbert/actbert.yaml
# run tsn dali training
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3" --log_dir=log_tsn main.py --train_dali -c configs/recognition/tsn/tsn_dali.yaml
# test.sh
# just use `example` as example, please replace to real name.
# python3.7 -B -m paddle.distributed.launch --gpus="0,1,2,3,4,5,6,7" --log_dir=log_test main.py --test -c configs/example.yaml -w "output/example/example_best.pdparams"
# NOTE: run bmn test, only support single card, bs=1
# python3.7 main.py --test -c configs/localization/bmn.yaml -w output/BMN/BMN_epoch_00010.pdparams -o DATASET.batch_size=1
# export_models script
# just use `example` as example, please replace to real name.
# python3.7 tools/export_model.py -c configs/example.yaml -p output/example/example_best.pdparams -o ./inference
# predict script
# just use `example` as example, please replace to real name.
# python3.7 tools/predict.py -v example.avi --model_file "./inference/example.pdmodel" --params_file "./inference/example.pdiparams" --enable_benchmark=False --model="example" --num_seg=8
end_time=$(date +%s)
cost_time=$[ $end_time-$start_time ]
echo "Time to train is $(($cost_time/60))min $(($cost_time%60))s"
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。