mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-11 14:49:43 +00:00
Jade Choghari
6e86a69dcd
* more changes * working changes * more changes * more fixes * fix style * more * clean * put axis-1 * more fixes * more styling fixes: * iterate on review: * more changes * add env processor * style * more changes * add docs * fix imports * fix test, add to train * Update src/lerobot/envs/factory.py Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Signed-off-by: Jade Choghari <chogharijade@gmail.com> * iterate on review --------- Signed-off-by: Jade Choghari <chogharijade@gmail.com> Co-authored-by: jade.choghari@huggingface.co <“chogharijade@gmail.com”> Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co>
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import torch
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from lerobot.envs.utils import preprocess_observation
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from lerobot.processor.env_processor import LiberoProcessorStep
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from lerobot.processor.pipeline import PolicyProcessorPipeline
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seed = 42
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np.random.seed(seed)
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B = 5
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obs1 = {
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"pixels": {
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"image": (np.random.rand(B, 256, 256, 3) * 255).astype(np.uint8),
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"image2": (np.random.rand(B, 256, 256, 3) * 255).astype(np.uint8),
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},
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"robot_state": {
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"eef": {
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"pos": np.random.randn(B, 3),
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"quat": np.random.randn(B, 4),
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"mat": np.random.randn(B, 3, 3),
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},
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"gripper": {
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"qpos": np.random.randn(B, 2),
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"qvel": np.random.randn(B, 2),
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},
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"joints": {
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"pos": np.random.randn(B, 7),
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"vel": np.random.randn(B, 7),
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},
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},
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}
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observation = preprocess_observation(obs1)
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libero_preprocessor = PolicyProcessorPipeline(
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steps=[
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LiberoProcessorStep(),
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]
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)
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processed_obs = libero_preprocessor(observation)
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assert "observation.state" in processed_obs
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state = processed_obs["observation.state"]
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assert isinstance(state, torch.Tensor)
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assert state.dtype == torch.float32
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assert state.shape[0] == B
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assert state.shape[1] == 8
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assert "observation.images.image" in processed_obs
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assert "observation.images.image2" in processed_obs
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assert isinstance(processed_obs["observation.images.image"], torch.Tensor)
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assert isinstance(processed_obs["observation.images.image2"], torch.Tensor)
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assert processed_obs["observation.images.image"].shape == (B, 3, 256, 256)
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assert processed_obs["observation.images.image2"].shape == (B, 3, 256, 256)
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