43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import einops
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import torch
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def preprocess_observation(observation):
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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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# sanity check that images are channel last
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_, h, w, c = img.shape
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assert c < h and c < w, f"expect channel first images, but instead {img.shape}"
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# sanity check that images are uint8
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assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"
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# convert to channel first of type float32 in range [0,1]
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img = einops.rearrange(img, "b h w c -> b c h w").contiguous()
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img = img.type(torch.float32)
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img /= 255
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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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return obs
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def postprocess_action(action):
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action = action.to("cpu").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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