spatial-memory-checkpoints
DD-PPO PointNav checkpoints (Habitat, GPS-PointGoal task), full training trajectory from initialisation to convergence.
| folder | # checkpoints | frames per checkpoint |
|---|---|---|
blind/ |
35 (0..34) |
10.06 M |
coarse/ |
50 (0..49) |
5.0 M |
foveated/ |
50 (0..49) |
5.0 M |
foveated_logpolar/ |
50 (0..49) |
5.0 M |
uniform/ |
50 (0..49) |
5.0 M |
frames per ckpt differs across folders, so to align at the same training
step, convert ckpt index to absolute frame count (blind/ckpt.20.pth ≈
coarse/ckpt.40.pth ≈ 200 M frames).
Load a checkpoint
import torch
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(
repo_id="alunxu/spatial-memory-checkpoints",
filename="foveated/ckpt.49.pth",
)
ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False)
state_dict = ckpt["state_dict"]
config = ckpt["config"]
Each .pth is a habitat-baselines checkpoint with keys state_dict,
config, and extra_state.
Rebuild the policy and run rollouts
from habitat_baselines.common.baseline_registry import baseline_registry
# Build env from ckpt's config (env_config = config.habitat).
policy_cls = baseline_registry.get_policy(
config.habitat_baselines.rl.policy.name)
policy = policy_cls.from_config(
config=config,
observation_space=env.observation_space,
action_space=env.action_space,
)
policy.load_state_dict(state_dict)
policy.eval()
# policy.act(...) returns (action, recurrent_hidden_states) where
# recurrent_hidden_states has shape (num_envs, num_layers, hidden_dim).
# Pass it back at the next step to keep the recurrent state.