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Reproduce the results of the DDPPO paper

See original GitHub issue

❓ Questions and Help

Hi! First of all thank you for the amazing project you’re carrying out!

I’m trying to reproduce the results obtained in the ddppo paper. I just installed the latest versions of habitat-sim and habitat-api, downloaded the pre-trained models, downloaded the “Gibson dataset for Habitat” (Gibson_dataset_trainval) and the corresponding Gibson task dataset from here ( file). Then I slighly modified the habitat_baselines/config/pointnav/ddppo_pointnav.yaml config file, to use the correct sensor (RGB or Depth) and load the correct pretrained checkpoint. If I run the habitat_baselines/rl/ddppo/ with the eval flag the process freeze, I don’t know the exact reason, maybe the procedure expects a checkpoint including the config parameters, that it is not found. For this reason I launched the training process for few seconds, in order for the ckpt0 to be created, then I launched the eval process again (994 eval episodes). The model based on depth images returns the correct performances (SPL ~0.95) but unfortunately the one based on RGB images doesn’t, and it reports an SPL/SR of about 0.35/0.50 using the gibson-2plus-mp3d-train-val-test-se-resneXt50-rgb.pth checkpoint. Is there something I’m missing? Here there is the config file I’m using:

BASE_TASK_CONFIG_PATH: "configs/tasks/pointnav_gibson.yaml"
VIDEO_DIR: "video_dir"
EVAL_CKPT_PATH_DIR: "data/new_checkpoints"
CHECKPOINT_FOLDER: "data/new_checkpoints"


    name: "PointNavResNetPolicy"

    # ppo params
    clip_param: 0.2
    ppo_epoch: 2
    num_mini_batch: 2
    value_loss_coef: 0.5
    entropy_coef: 0.01
    lr: 2.5e-4
    eps: 1e-5
    max_grad_norm: 0.2
    num_steps: 128
    use_gae: True
    gamma: 0.99
    tau: 0.95
    use_linear_clip_decay: False
    use_linear_lr_decay: False
    reward_window_size: 50

    use_normalized_advantage: False

    hidden_size: 512

    sync_frac: 0.6
    # The PyTorch distributed backend to use
    distrib_backend: GLOO
    # Visual encoder backbone
    pretrained_weights: data/ddppo-models/gibson-2plus-mp3d-train-val-test-se-resneXt50-rgb.pth #data/ddppo-models/gibson-2plus-resnet50.pth
    # Initialize with pretrained weights
    pretrained: True
    # Initialize just the visual encoder backbone with pretrained weights
    pretrained_encoder: False
    # Whether or not the visual encoder backbone will be trained.
    train_encoder: False
    # Whether or not to reset the critic linear layer
    reset_critic: False

    # Model parameters
    backbone: se_resneXt50 #resnet50
    rnn_type: LSTM
    num_recurrent_layers: 2

Here the results for the Depth model:

Average episode reward: 7.6915
Average episode distance_to_goal: 0.0944
Average episode success: 0.9960
Average episode spl: 0.9514

and the results for the RGB model:

Average episode reward: 0.3978                                               
Average episode distance_to_goal: 3.5889                                     
Average episode success: 0.5000                                              
Average episode spl: 0.3489

Thank you!

Issue Analytics

  • State:closed
  • Created 3 years ago
  • Comments:7

github_iconTop GitHub Comments

erikwijmanscommented, Jan 27, 2021


rosanomcommented, Jan 27, 2021

Thank you @erikwijmans for your support! Just a quick question about the running mean and var task: can you give more details about what it is supposed to do? Does it compute mean and variance of input images all over the trainset, so that they are saved in the checkpoint file and loaded when I decide to resume the training? Thank you!

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