From 9c7649f140fb437471f915423439ba7096da422b Mon Sep 17 00:00:00 2001
From: Alexander Soare <alexander.soare159@gmail.com>
Date: Fri, 23 Aug 2024 12:27:08 +0100
Subject: [PATCH] Make sure `init_hydra_config` does not require any keys
(#376)
---
lerobot/common/utils/utils.py | 9 ---------
lerobot/scripts/eval.py | 10 ++++++++++
lerobot/scripts/train.py | 10 ++++++++++
tests/test_utils.py | 9 +++++++++
4 files changed, 29 insertions(+), 9 deletions(-)
diff --git a/lerobot/common/utils/utils.py b/lerobot/common/utils/utils.py
index a7cb6374..1aa0bc2d 100644
--- a/lerobot/common/utils/utils.py
+++ b/lerobot/common/utils/utils.py
@@ -165,15 +165,6 @@ def init_hydra_config(config_path: str, overrides: list[str] | None = None) -> D
version_base="1.2",
)
cfg = hydra.compose(Path(config_path).stem, overrides)
- if cfg.eval.batch_size > cfg.eval.n_episodes:
- raise ValueError(
- "The eval batch size is greater than the number of eval episodes "
- f"({cfg.eval.batch_size} > {cfg.eval.n_episodes}). As a result, {cfg.eval.batch_size} "
- f"eval environments will be instantiated, but only {cfg.eval.n_episodes} will be used. "
- "This might significantly slow down evaluation. To fix this, you should update your command "
- f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={cfg.eval.batch_size}`), "
- f"or lower the batch size (e.g. `eval.batch_size={cfg.eval.n_episodes}`)."
- )
return cfg
diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py
index 980c373c..482af786 100644
--- a/lerobot/scripts/eval.py
+++ b/lerobot/scripts/eval.py
@@ -454,6 +454,16 @@ def main(
else:
hydra_cfg = init_hydra_config(hydra_cfg_path, config_overrides)
+ if hydra_cfg.eval.batch_size > hydra_cfg.eval.n_episodes:
+ raise ValueError(
+ "The eval batch size is greater than the number of eval episodes "
+ f"({hydra_cfg.eval.batch_size} > {hydra_cfg.eval.n_episodes}). As a result, {hydra_cfg.eval.batch_size} "
+ f"eval environments will be instantiated, but only {hydra_cfg.eval.n_episodes} will be used. "
+ "This might significantly slow down evaluation. To fix this, you should update your command "
+ f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={hydra_cfg.eval.batch_size}`), "
+ f"or lower the batch size (e.g. `eval.batch_size={hydra_cfg.eval.n_episodes}`)."
+ )
+
if out_dir is None:
out_dir = f"outputs/eval/{dt.now().strftime('%Y-%m-%d/%H-%M-%S')}_{hydra_cfg.env.name}_{hydra_cfg.policy.name}"
diff --git a/lerobot/scripts/train.py b/lerobot/scripts/train.py
index 2fa7ae80..45807503 100644
--- a/lerobot/scripts/train.py
+++ b/lerobot/scripts/train.py
@@ -288,6 +288,16 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
"you meant to resume training, please use `resume=true` in your command or yaml configuration."
)
+ if cfg.eval.batch_size > cfg.eval.n_episodes:
+ raise ValueError(
+ "The eval batch size is greater than the number of eval episodes "
+ f"({cfg.eval.batch_size} > {cfg.eval.n_episodes}). As a result, {cfg.eval.batch_size} "
+ f"eval environments will be instantiated, but only {cfg.eval.n_episodes} will be used. "
+ "This might significantly slow down evaluation. To fix this, you should update your command "
+ f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={cfg.eval.batch_size}`), "
+ f"or lower the batch size (e.g. `eval.batch_size={cfg.eval.n_episodes}`)."
+ )
+
# log metrics to terminal and wandb
logger = Logger(cfg, out_dir, wandb_job_name=job_name)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index d4a8e34a..e5ba2267 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,5 +1,6 @@
import random
from typing import Callable
+from uuid import uuid4
import numpy as np
import pytest
@@ -13,6 +14,7 @@ from lerobot.common.datasets.utils import (
)
from lerobot.common.utils.utils import (
get_global_random_state,
+ init_hydra_config,
seeded_context,
set_global_random_state,
set_global_seed,
@@ -83,3 +85,10 @@ def test_reset_episode_index():
correct_episode_index = [0, 0, 1, 2, 2, 2]
dataset = reset_episode_index(dataset)
assert dataset["episode_index"] == correct_episode_index
+
+
+def test_init_hydra_config_empty():
+ test_file = f"/tmp/test_init_hydra_config_empty_{uuid4().hex}.yaml"
+ with open(test_file, "w") as f:
+ f.write("\n")
+ init_hydra_config(test_file)