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TRAINING.md

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Training

We experimented on the ActEv dataset by following the Next-prediction evaluation protocol. First download the processed annotations from the Next-prediction repo.

$ wget https://precognition.team/next/data/072019_prepare_data/072019_next_my_prepare_nopersoncnn.tgz
$ tar -zxvf 072019_next_my_prepare_nopersoncnn.tgz

Then download pretrained models by running the script bash scripts/download_single_models.sh.

Step 1: Preprocess

Preprocess the data for training and testing. The following is for ActEv experiments.

$ python code/preprocess.py prepared_data/traj_2.5fps/ actev_preprocess \
--obs_len 8 --pred_len 12 --add_kp --kp_path prepared_data/anno_kp/ \
--add_scene --scene_feat_path prepared_data/actev_all_video_frames_scene_seg_every30_36x64/ \
--scene_map_path prepared_data/anno_scene/ --scene_id2name prepared_data/scene36_64_id2name_top10.json \
--scene_h 36 --scene_w 64 --video_h 1080 --video_w 1920 --add_grid \
--add_person_box --person_box_path prepared_data/anno_person_box/ \
--add_other_box --other_box_path prepared_data/anno_other_box/ \
--add_activity --activity_path prepared_data/anno_activity/ \
--person_boxkey2id_p prepared_data/person_boxkey2id.p --add_all_reg

Step 2: Train the model

You can train your model by running:

$ python code/train.py actev_preprocess multiverse-models new_train/ \
--wd 0.001 --runId 0 --obs_len 8 --pred_len 12 --emb_size 32 \
--enc_hidden_size 256 --dec_hidden_size 256 --activation_func tanh \
--keep_prob 1.0 --num_epochs 80 --batch_size 20 --init_lr 0.3 --use_gnn \
--use_scene --learning_rate_decay 0.95 --num_epoch_per_decay 2.0 \
--grid_loss_weight 1.0 --grid_reg_loss_weight 0.2 --save_period 2000 \
--scene_h 36 --scene_w 64 --scene_conv_kernel 3 --scene_conv_dim 64 \
--scene_grid_strides 2,4 --use_grids 1,1 --val_grid_num 0 --train_w_onehot --gpuid 0

By default this will train the multiverse model, periodically saving model files to multiverse-models/new_train/00/save at the current working directory. The script will also periodically evaluate the model on the validation set and save the latest best model to multiverse-models/new_train/00/best. On a TITAN X GPU it takes about 48 hours to finish training. Detailed commands of the training script:

Training options

  • --batch_size: How many trajectories to use in each minibatch during training.
  • --num_epochs: Number of training epochs.
  • --init_lr: Initial Learning rate.
  • --optimizer: Optimizer to use. Default is AdaDelta.
  • --grid_loss_weight: Weight for grid classification loss.
  • --grid_reg_loss_weight: Weight for grid regress loss.

Basic model options

  • --emb_size: Embedding size.
  • --enc_hidden_size: Encoder hidden size.
  • --dec_hidden_size: Decoder hidden size.
  • --activation_func: Activation function. You could choose from relu/lrelu/tanh.

Step 4: Test the model - Single Future Trajectory Prediction

You can use following command to test the newly trained model:

$ python code/test.py actev_preprocess multiverse-models new_train/ \
--wd 0.001 --runId 0 --obs_len 8 --pred_len 12 --emb_size 32 \
--enc_hidden_size 256 --dec_hidden_size 256 --activation_func tanh \
--keep_prob 1.0 --num_epochs 80 --batch_size 20 --init_lr 0.3 --use_gnn \
--use_scene --learning_rate_decay 0.95 --num_epoch_per_decay 2.0 \
--grid_loss_weight 1.0 --grid_reg_loss_weight 0.2 --save_period 2000 \
--scene_h 36 --scene_w 64 --scene_conv_kernel 3 --scene_conv_dim 64 \
--scene_grid_strides 2,4 --use_grids 1,0 --val_grid_num 0 --gpuid 0 --load_best

The best model on the validation set will be used.

Step 5: Multi-future Trajectory Prediction

Please follow this for multi-future trajectory prediction inferencing.