traj_pred_demo.yml

results_root_dir: results/traj_pred
seed: 1

# model
model_name: traj_pred_vae
model_specs:
  nz: 128
  mlp_htype: relu

  context_encoder:
    in_mlp:
      hdim: [512, 256]
    temporal_net:
      num_layers: 2
      type: lstm
      hdim: 256
    out_mlp:
      hdim: [512, 256]

  data_encoder:
    input: init_heading_coord
    orient_type: axis_angle
    pooling: mean
    append_context: late  # early or late
    in_mlp:
      hdim: [512, 256]
    temporal_net:
      num_layers: 2
      type: lstm
      hdim: 256
    out_mlp:
      hdim: [512, 256]
    fusion_mlp:
      hdim: [512, 256]

  data_decoder:
    pooling: mean
    learn_prior: true
    out_mlp:
      hdim: [512, 256]
    prior_mlp:
      hdim: [512, 256]


# data
amass_dir: datasets/amass_processed/v1
max_epochs: 2000
save_n_epochs: 200
train_ntime_per_epoch: 2e6
val_ntime_per_epoch: 2e6
batch_size: 256
seq_len: 100
seq_sampling_method: length



# loss
lr: 1.e-4
gradient_clip_val: 5.0

loss_cfg:
  trans_mse:
    weight: 1.0
  orient_angle:
    weight: 1.0
  vae_z_kld:
    weight: 1.e-3
    min_clip: 0.0
  dheading:
    weight: 0.1