mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-16 17:20:05 +00:00
Pepijn Kooijmans
245 lines
7.4 KiB
Python
245 lines
7.4 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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from lerobot.envs.lazy_vec_env import LazyVectorEnv
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from lerobot.scripts import lerobot_eval
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class _DummyTaskEnv:
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def __init__(self):
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self.close_calls = 0
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def close(self):
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self.close_calls += 1
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class _TrackedLazyEnv(LazyVectorEnv):
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def __init__(self, n_factory_fns: int = 1):
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super().__init__(lambda fns: None, [lambda: None for _ in range(n_factory_fns)])
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self.close_calls = 0
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def close(self):
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self.close_calls += 1
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super().close()
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def _fake_metrics():
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return {
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"sum_rewards": [1.0],
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"max_rewards": [1.0],
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"successes": [True],
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"video_paths": [],
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}
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def test_eval_policy_all_sequential_closes_envs(monkeypatch):
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def _fake_run_one(task_group, task_id, env, **kwargs): # noqa: ARG001
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return task_group, task_id, _fake_metrics()
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monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
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env_a = _DummyTaskEnv()
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env_b = _DummyTaskEnv()
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envs = {"suite": {0: env_a, 1: env_b}}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=1,
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max_parallel_tasks=1,
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)
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assert env_a.close_calls == 1
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assert env_b.close_calls == 1
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assert result["overall"]["n_episodes"] == 2
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def test_eval_policy_all_threaded_fallback_closes_envs(monkeypatch):
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def _fake_run_one(task_group, task_id, env, **kwargs): # noqa: ARG001
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return task_group, task_id, _fake_metrics()
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monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
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env_a = _DummyTaskEnv()
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env_b = _DummyTaskEnv()
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env_c = _DummyTaskEnv()
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envs = {"suite": {0: env_a, 1: env_b, 2: env_c}}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=1,
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max_parallel_tasks=2,
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)
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assert env_a.close_calls == 1
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assert env_b.close_calls == 1
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assert env_c.close_calls == 1
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assert result["overall"]["n_episodes"] == 3
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def test_eval_policy_all_uses_batched_lazy_mode(monkeypatch):
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def _run_one_should_not_be_called(*args, **kwargs):
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raise AssertionError("run_one should not run in batched lazy mode")
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chunk_sizes = []
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def _fake_eval_task_batch(chunk, **kwargs): # noqa: ARG001
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chunk_sizes.append(len(chunk))
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return [(tg, tid, _fake_metrics()) for tg, tid, _ in chunk]
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monkeypatch.setattr(lerobot_eval, "run_one", _run_one_should_not_be_called)
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monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _fake_eval_task_batch)
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envs = {
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"suite": {
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0: LazyVectorEnv(lambda fns: None, [lambda: None]),
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1: LazyVectorEnv(lambda fns: None, [lambda: None]),
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2: LazyVectorEnv(lambda fns: None, [lambda: None]),
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}
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}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=1,
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max_parallel_tasks=2,
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)
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assert chunk_sizes == [2, 1]
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assert result["overall"]["n_episodes"] == 3
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def test_eval_policy_all_disables_batched_lazy_when_n_episodes_not_one(monkeypatch):
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def _fake_run_one(task_group, task_id, env, **kwargs): # noqa: ARG001
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return task_group, task_id, _fake_metrics()
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def _batch_should_not_run(*args, **kwargs):
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raise AssertionError("_eval_task_batch should not run when n_episodes != 1")
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monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
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monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _batch_should_not_run)
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env_a = _TrackedLazyEnv()
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env_b = _TrackedLazyEnv()
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envs = {"suite": {0: env_a, 1: env_b}}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=2,
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max_parallel_tasks=2,
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)
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assert env_a.close_calls == 1
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assert env_b.close_calls == 1
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assert result["overall"]["n_episodes"] == 2
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def test_eval_policy_all_disables_batched_lazy_when_batch_size_above_one(monkeypatch):
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def _fake_run_one(task_group, task_id, env, **kwargs): # noqa: ARG001
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return task_group, task_id, _fake_metrics()
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def _batch_should_not_run(*args, **kwargs):
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raise AssertionError("_eval_task_batch should not run when eval.batch_size > 1")
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monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
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monkeypatch.setattr(lerobot_eval, "_eval_task_batch", _batch_should_not_run)
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env_a = _TrackedLazyEnv(n_factory_fns=2)
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env_b = _TrackedLazyEnv(n_factory_fns=2)
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envs = {"suite": {0: env_a, 1: env_b}}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=1,
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max_parallel_tasks=2,
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)
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assert env_a.close_calls == 1
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assert env_b.close_calls == 1
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assert result["overall"]["n_episodes"] == 2
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def test_eval_policy_all_applies_instance_sharding(monkeypatch):
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called = []
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def _fake_run_one(task_group, task_id, env, **kwargs): # noqa: ARG001
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called.append(task_id)
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return task_group, task_id, _fake_metrics()
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monkeypatch.setattr(lerobot_eval, "run_one", _fake_run_one)
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envs = {"suite": {0: _DummyTaskEnv(), 1: _DummyTaskEnv(), 2: _DummyTaskEnv(), 3: _DummyTaskEnv()}}
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result = lerobot_eval.eval_policy_all(
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envs=envs,
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policy=None,
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env_preprocessor=None,
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env_postprocessor=None,
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preprocessor=None,
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postprocessor=None,
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n_episodes=1,
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max_parallel_tasks=1,
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instance_count=2,
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instance_id=1,
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)
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assert called == [1, 3]
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assert result["overall"]["n_episodes"] == 2
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def test_aggregate_eval_from_per_task_merges_groups_and_overall():
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per_task = [
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{
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"task_group": "a",
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"task_id": 0,
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"metrics": {"sum_rewards": [1.0], "max_rewards": [2.0], "successes": [True], "video_paths": ["v0"]},
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},
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{
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"task_group": "b",
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"task_id": 1,
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"metrics": {"sum_rewards": [3.0], "max_rewards": [4.0], "successes": [False], "video_paths": []},
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},
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]
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merged = lerobot_eval._aggregate_eval_from_per_task(per_task, total_eval_s=10.0)
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assert merged["overall"]["n_episodes"] == 2
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assert merged["overall"]["avg_sum_reward"] == 2.0
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assert merged["overall"]["pc_success"] == 50.0
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assert merged["overall"]["eval_s"] == 10.0
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assert set(merged["per_group"]) == {"a", "b"}
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