mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-17 06:51:48 +00:00
pepijn223
b81909fc28
Make the LingBot-VA port runnable on both LIBERO and RoboTwin and clean up the package to LeRobot conventions. - Consolidate all vendored Wan2.2 model code (transformer, attention, VAE helpers, flow-matching scheduler, grid utils, flex-attention) into a single modeling_lingbot_va.py; remove the separate wan_*/schedulers modules. - Move the fixed action (un)normalization quantiles out of the config and into the post-processor (LIBERO 7-DoF + RoboTwin 16-d eef); remove the conversion script in favour of ready-to-use LeRobot-format checkpoints on the Hub. - Fixes found via on-sim validation: undo LIBERO's 180-degree image flip (image_hflip), encode obs as a multi-frame streaming-VAE clip, reset the streaming VAE cache between episodes, run the transformer in config.dtype, lazy-load frozen VAE/UMT5 by subfolder with the text encoder on CPU. - RoboTwin: add an end-effector-pose action mode to RoboTwinEnv (16-d per-arm xyz+quat+gripper deltas composed onto the initial eef pose, executed via CuRobo IK) and the robotwin_tshape latent layout (full-res head + half-res wrists via a second streaming VAE) with the upstream RoboTwin action quantiles + camera mapping. - Predicted-video saving works for both benchmarks; docs + tests updated. Co-authored-by: Cursor <cursoragent@cursor.com>
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 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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"""Unit tests for the vendored LingBot-VA helper code (scheduler + grid utilities)."""
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from __future__ import annotations
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import pytest
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import torch
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pytest.importorskip("diffusers") # the model code lives in modeling_lingbot_va, which imports diffusers
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from lerobot.policies.lingbot_va.modeling_lingbot_va import ( # noqa: E402
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FlowMatchScheduler,
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data_seq_to_patch,
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get_mesh_id,
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)
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def test_flow_match_scheduler_timesteps_monotone_decreasing() -> None:
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sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
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sch.set_timesteps(20)
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assert sch.timesteps.shape == (20,)
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diffs = sch.timesteps[1:] - sch.timesteps[:-1]
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assert torch.all(diffs <= 0) # decreasing
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def test_flow_match_scheduler_step_preserves_shape() -> None:
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sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
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sch.set_timesteps(20)
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sample = torch.zeros(1, 48, 4, 8, 16)
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out = sch.step(torch.ones_like(sample), sch.timesteps[0], sample)
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assert out.shape == sample.shape
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def test_flow_match_scheduler_add_noise() -> None:
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sch = FlowMatchScheduler(shift=5.0, sigma_min=0.0, extra_one_step=True)
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sch.set_timesteps(20)
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sample = torch.randn(1, 48, 4, 8, 16)
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noise = torch.randn_like(sample)
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noisy = sch.add_noise(sample, noise, sch.timesteps[:4], t_dim=2)
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assert noisy.shape == sample.shape
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def test_get_mesh_id_latent_shape() -> None:
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grid = get_mesh_id(4, 8, 16, 0, 1, 0)
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assert grid.shape == (4, 4 * 8 * 16) # (f, h, w, stream) x tokens
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def test_get_mesh_id_action_shape() -> None:
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grid = get_mesh_id(4, 4, 1, 1, 1, 0, action=True)
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assert grid.shape == (4, 4 * 4 * 1)
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# Action rows for h/w are sentinel -1.
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assert torch.all(grid[1] < 0)
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assert torch.all(grid[2] < 0)
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def test_data_seq_to_patch_roundtrip_shape() -> None:
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b, f, h, w, c = 1, 4, 8, 16, 48
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seq = torch.arange(b * f * h * w * c, dtype=torch.float32).reshape(b, f * h * w, c)
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out = data_seq_to_patch((1, 2, 2), seq, f, h, w, batch_size=b)
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assert out.shape == (b, c, f, h, w)
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