42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import einops
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import torch
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from lerobot.common.transforms import apply_inverse_transform
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def preprocess_observation(observation, transform=None):
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# map to expected inputs for the policy
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obs = {}
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if isinstance(observation["pixels"], dict):
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imgs = {f"observation.images.{key}": img for key, img in observation["pixels"].items()}
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else:
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imgs = {"observation.image": observation["pixels"]}
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for imgkey, img in imgs.items():
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img = torch.from_numpy(img)
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# convert to (b c h w) torch format
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img = einops.rearrange(img, "b h w c -> b c h w")
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obs[imgkey] = img
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# TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing requirement for "agent_pos"
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obs["observation.state"] = torch.from_numpy(observation["agent_pos"]).float()
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# apply same transforms as in training
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if transform is not None:
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for key in obs:
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obs[key] = torch.stack([transform({key: item})[key] for item in obs[key]])
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return obs
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def postprocess_action(action, transform=None):
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action = action.to("cpu")
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# action is a batch (num_env,action_dim) instead of an item (action_dim),
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# we assume applying inverse transform on a batch works the same
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action = apply_inverse_transform({"action": action}, transform)["action"].numpy()
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assert (
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action.ndim == 2
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), "we assume dimensions are respectively the number of parallel envs, action dimensions"
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return action
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