setup_help.txt

# Guid to the net_params.json
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    # Trajectory encoder config
   "traj_input_size": 4, # trajectory encoder input size (size of each bounding box=[xc,yc,w,h])
    "traj_hidden_size": 4, # feature dimention in the hidden state for the LSTM encoder
    "traj_num_layer": 1, # number of LSTM stacks
    "traj_emb_encod": false, # whether to use FC layer after the LSTM stacks 
    "traj_out_size": 256, # size of the output FC layer after the LSTM stacks 

    # Motion encoder config
    "mot_map_size": 10, # feature map input size (each map will be 10*10*2 after the roi-pooling)
    "mot_filter1_size": 16, # number of filters in the first ConvLSTM cell
    "mot_filter2_size": 8, # number of filters in the second ConvLSTM cell
    "mot_out_size": 512, # size of the output FC layer (if used before concatination)
    "mot_num_layer": 2, # number of recurrent stacks
    "mot_use_MLP": false, # whether to use FC layer after the LSTM stacks 

    # Contextual encoder config
    "con_image_size": 224, # image size
    "con_filter1_size": 32, # number of filters in the first ConvLSTM cell
    "con_filter2_size": 16, # number of filters in the second ConvLSTM cell
    "con_out_size": 1024, # size of the output FC layer (if used before concatination)
    "con_num_layer": 2, # number of recurrent stacks
    "con_use_MLP": false, # whether to use FC layer after the LSTM stacks 

    "encoder_mlp": 1024, # size of the concatination FC layer 

    "decoder_input_dim": 512, # size of the decoder input (must be half of the "encoder_mlp") 
    "decoder_hidden_dim": 1024, # size of the hidden state in the LSTM decoder 
    "decoder_mlp_size": 256, # size of the FC layer before concatinating the odometery with each new input

    "prediction_length": 30, # length of the prediction
    "roi_size": 10, # desired size after roi-pooling (must be equal to the "mot_map_size") 

    "exp_num": 5, # experiment number (just for log saving)
    "cuda": true, # whether to use GPU or not
    "learning_rate": 1e-3, # learning rate
    "batch_size": 2, # batch size
    "save_summary_steps": 100, # save train summary every n steps
    "num_steps": 2500 # number of epochs in train job