mirror of
https://github.com/huggingface/lerobot.git
synced 2026-08-08 01:19:46 +00:00
Maxime Ellerbach
75 lines
2.4 KiB
Python
75 lines
2.4 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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from __future__ import annotations
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import pytest
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import torch
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from lerobot.policies.vla_jepa.world_model import (
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ActionConditionedVideoPredictor,
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)
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_ACTION_EMBED_DIM = 8
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def _make_predictor(
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embed_dim: int = 8,
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action_embed_dim: int = _ACTION_EMBED_DIM,
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predictor_embed_dim: int = 24,
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num_action_tokens: int = 2,
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tokens_per_frame: int = 1,
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) -> ActionConditionedVideoPredictor:
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return ActionConditionedVideoPredictor(
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num_frames=1,
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img_size=(1, tokens_per_frame),
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patch_size=1,
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tubelet_size=1,
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embed_dim=embed_dim,
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action_embed_dim=action_embed_dim,
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predictor_embed_dim=predictor_embed_dim,
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depth=1,
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num_heads=2,
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mlp_ratio=2.0,
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num_action_tokens_per_step=num_action_tokens,
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)
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@pytest.mark.parametrize(
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"batch,num_steps,tokens_per_frame,embed_dim",
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[
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(1, 2, 1, 8),
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(2, 3, 4, 8),
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(4, 5, 2, 16),
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],
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)
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def test_predictor_output_shape(batch: int, num_steps: int, tokens_per_frame: int, embed_dim: int) -> None:
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predictor = _make_predictor(
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embed_dim=embed_dim, action_embed_dim=_ACTION_EMBED_DIM, tokens_per_frame=tokens_per_frame
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)
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frame_tokens = torch.randn(batch, num_steps * tokens_per_frame, embed_dim)
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action_tokens = torch.randn(batch, num_steps * 2, _ACTION_EMBED_DIM)
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out = predictor(frame_tokens, action_tokens)
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assert tuple(out.shape) == (batch, num_steps * tokens_per_frame, embed_dim)
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assert torch.isfinite(out).all()
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def test_predictor_step_mismatch_raises() -> None:
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predictor = _make_predictor(tokens_per_frame=4)
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frame_tokens = torch.randn(2, 3 * 4, 8) # 3 steps, 4 tokens each
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with pytest.raises(RuntimeError):
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predictor(frame_tokens, torch.randn(2, 2 * 2, 8)) # 2 steps → mismatch
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