Skip to content
/ Snipper Public

This is the re-implementation of paper "Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet"

35 stars 4 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

JimmyZou/Snipper

BranchesTags

Repository files navigation

Snipper

This is the re-implementation of paper "Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet"

Dataset preprocess

Dependencies

  • compile cuda version of deformable attention module according to Deformable-DETR 
    cd ./models/ops
sh ./make.sh
# unit test (should see all checking is True)
python test.py
  • Python 3.6
  • PyTorch 1.7.0
  • scipy 1.5.2

Inference

Pre-trained models can be downloaded from Google Drive or OneDrive.

trained models
|-- model/12-06_13-31-59/checkpoint.pth  # T=1, encoder_layer=6, decoder_layer=6
|-- model/12-06_20-17-34/checkpoint.pth  # T=4, encoder_layer=6, decoder_layer=6
|-- model/12-06_20-18-30/checkpoint.pth  # T=4+2, encoder_layer=6, decoder_layer=6
|
|-- model/12-05_06-37-50/checkpoint.pth  # T=1, encoder_layer=2, decoder_layer=4
|-- model/12-05_06-39-49/checkpoint.pth  # T=4, encoder_layer=2, decoder_layer=4
|-- model/12-05_06-39-03/checkpoint.pth  # T=4+2, encoder_layer=2, decoder_layer=4

The model model/12-06_20-17-34/checkpoint.pth # T=4, encoder_layer=6, decoder_layer=6 is used to generate the three example demos. For new sequence inference, set the following arguments data_dir to the target folder.

python inference.py \
    # the path to trained model
    --resume               'model/12-06_20-17-34/checkpoint.pth' \  
    # path to the test sequence
    --data_dir             'demos/seq1' \  
    # path to save predicitions
    --output_dir           'demos' \  
    # number of observed frames
    --num_frames           4 \ 
    # number of forecasting frames
    --num_future_frames    0 \  
    # select snippet every 5 frames (30fps --> 6fps of a snippet)
    --seq_gap              5 \  
    # frame filename to see its heatmaps
    --vis_heatmap_frame_name '000005.jpg'

Please remember to remove the comments # ... before run the command.

Train

Settings to train on multiple datasets (4 observed frames pose tracking only).

python -u -m torch.distributed.launch --nproc_per_node=8 main.py \
    --output_dir           "$LOG_OUTDIR" \
    --dataset_file         'hybrid' \
    --posetrack_dir        '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/posetrack2018'\
    --use_posetrack        1 \
    --coco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/coco' \
    --use_coco             1 \
    --muco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/muco' \
    --use_muco             1 \
    --jta_dir              '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/jta_dataset' \
    --use_jta              1 \
    --panoptic_dir         '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/panoptic' \
    --use_panoptic         0 \
    --protocol             1 \
    --pretrained_dir       '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/pretrained_models' \
    --resume               '' \
    --input_height         600 \
    --input_width          800 \
    --seq_max_gap          4 \
    --seq_min_gap          4 \
    --num_frames           4 \
    --num_future_frames    0 \
    --max_depth            15 \
    --batch_size           2 \
    --num_queries          60 \
    --num_kpts             15 \
    --set_cost_is_human    1 \ 
    --set_cost_root        1 \
    --set_cost_root_depth  1 \
    --set_cost_root_vis    1 \
    --set_cost_joint       1 \
    --set_cost_joint_depth 1 \
    --set_cost_joint_vis   1 \
    --is_human_loss_coef   1 \ 
    --root_loss_coef       5 \
    --root_depth_loss_coef 5 \
    --root_vis_loss_coef   1 \
    --joint_loss_coef      5 \
    --joint_depth_loss_coef 5 \
    --joint_vis_loss_coef  1 \
    --joint_disp_loss_coef 1 \
    --joint_disp_depth_loss_coef 1 \
    --heatmap_loss_coef    0.001 \
    --cont_loss_coef       0.1 \
    --eos_coef             0.25 \
    --epochs               40 \
    --lr_drop              30 \
    --lr                   0.0001 \
    --lr_backbone          0.00001 \
    --dropout              0.1 \
    --num_feature_levels   3 \
    --hidden_dim           384 \
    --nheads               8 \
    --enc_layers           6 \
    --dec_layers           6 \
    --dec_n_points         4 \
    --enc_n_points         4 \
    --use_pytorch_deform   0 \

Settings to train on JTA dataset (4 observed frames pose tracking + 2 future frames motion prediction)

python -u -m torch.distributed.launch --nproc_per_node=8 main.py \
    --output_dir           "$LOG_OUTDIR" \
    --dataset_file         'hybrid' \
    --posetrack_dir        '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/posetrack2018'\
    --use_posetrack        0 \
    --coco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/coco' \
    --use_coco             0 \
    --muco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/muco' \
    --use_muco             0 \
    --jta_dir              '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/jta_dataset' \
    --use_jta              1 \
    --panoptic_dir         '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/panoptic' \
    --use_panoptic         0 \
    --protocol             1 \
    --pretrained_dir       '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/pretrained_models' \
    --resume               '' \
    --input_height         540 \
    --input_width          960 \
    --seq_max_gap          4 \
    --seq_min_gap          4 \
    --num_frames           4 \
    --num_future_frames    2 \
    --max_depth            60 \
    --batch_size           2 \
    --num_queries          60 \
    --num_kpts             15 \
    --set_cost_is_human    1 \
    --set_cost_root        5 \
    --set_cost_root_depth  5 \
    --set_cost_root_vis    0.1 \
    --set_cost_joint       1 \
    --set_cost_joint_depth 1 \
    --set_cost_joint_vis   0.1 \
    --is_human_loss_coef   1 \
    --root_loss_coef       5 \
    --root_depth_loss_coef 5 \
    --root_vis_loss_coef   0.1 \
    --joint_loss_coef      5 \
    --joint_depth_loss_coef 5 \
    --joint_vis_loss_coef  0.1 \
    --joint_disp_loss_coef 1 \
    --joint_disp_depth_loss_coef 1 \
    --heatmap_loss_coef    0.001 \
    --cont_loss_coef       0.1 \
    --eos_coef             0.25 \
    --epochs               100 \
    --lr_drop              80 \
    --lr                   0.0001 \
    --lr_backbone          0.00001 \
    --dropout              0.1 \
    --num_feature_levels   3 \
    --hidden_dim           384 \
    --nheads               8 \
    --enc_layers           6 \
    --dec_layers           6 \
    --dec_n_points         4 \
    --enc_n_points         4 \
    --use_pytorch_deform   0 \

Settings to train on CMU-Panoptic dataset (4 observed frames pose tracking + 2 future frames motion prediction)

python -u -m torch.distributed.launch --nproc_per_node=8 main.py \
    --output_dir           "$LOG_OUTDIR" \
    --dataset_file         'hybrid' \
    --posetrack_dir        '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/posetrack2018'\
    --use_posetrack        0 \
    --coco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/coco' \
    --use_coco             0 \
    --muco_dir             '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/muco' \
    --use_muco             0 \
    --jta_dir              '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/jta_dataset' \
    --use_jta              0 \
    --panoptic_dir         '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/panoptic' \
    --use_panoptic         1 \
    --protocol             1 \
    --pretrained_dir       '/mnt/graphics_ssd/home/minhpvo/Internship/2021/shihaozou/pretrained_models' \
    --resume               '' \
    --input_height         540 \
    --input_width          960 \
    --seq_max_gap          10 \
    --seq_min_gap          10 \
    --num_frames           4 \
    --num_future_frames    2 \
    --max_depth            5 \
    --batch_size           2 \
    --num_queries          20 \
    --num_kpts             15 \
    --set_cost_is_human    1 \
    --set_cost_root        5 \
    --set_cost_root_depth  5 \
    --set_cost_root_vis    0.1 \
    --set_cost_joint       1 \
    --set_cost_joint_depth 1 \
    --set_cost_joint_vis   0.1 \
    --is_human_loss_coef   1 \
    --root_loss_coef       5 \
    --root_depth_loss_coef 5 \
    --root_vis_loss_coef   0.1 \
    --joint_loss_coef      5 \
    --joint_depth_loss_coef 5 \
    --joint_vis_loss_coef  0.1 \
    --joint_disp_loss_coef 1 \
    --joint_disp_depth_loss_coef 1 \
    --heatmap_loss_coef    0.001 \
    --cont_loss_coef       0.1 \
    --eos_coef             0.25 \
    --epochs               10 \
    --lr_drop              8 \
    --lr                   0.0001 \
    --lr_backbone          0.00001 \
    --dropout              0.1 \
    --num_feature_levels   3 \
    --hidden_dim           384 \
    --nheads               8 \
    --enc_layers           6 \
    --dec_layers           6 \
    --dec_n_points         4 \
    --enc_n_points         4 \
    --use_pytorch_deform   0 \

Demos

About

This is the re-implementation of paper "Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet"

Resources

Readme
Activity

Stars

35 stars

Watchers

4 watching

Forks

4 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages