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https://github.com/huggingface/lerobot.git
synced 2026-05-16 17:20:05 +00:00
refactor(tests): remove grpc import checks from test files for cleaner code
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@@ -15,9 +15,6 @@
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# limitations under the License.
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import pytest
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pytest.importorskip("grpc")
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import torch
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from torch import Tensor, nn
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@@ -240,7 +237,7 @@ def test_gaussian_actor_policy_forward(batch_size: int, state_dim: int, action_d
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@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
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def test_sac_training_through_algorithm(batch_size: int, state_dim: int, action_dim: int):
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def test_gaussian_actor_training_through_sac(batch_size: int, state_dim: int, action_dim: int):
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config = create_default_config(state_dim=state_dim, continuous_action_dim=action_dim)
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algorithm, policy = _make_algorithm(config)
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@@ -270,7 +267,7 @@ def test_sac_training_through_algorithm(batch_size: int, state_dim: int, action_
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@pytest.mark.parametrize("batch_size,state_dim,action_dim", [(2, 6, 6), (1, 10, 10)])
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def test_sac_training_with_visual_input(batch_size: int, state_dim: int, action_dim: int):
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def test_gaussian_actor_training_with_visual_input(batch_size: int, state_dim: int, action_dim: int):
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config = create_config_with_visual_input(state_dim=state_dim, continuous_action_dim=action_dim)
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algorithm, policy = _make_algorithm(config)
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@@ -331,7 +328,7 @@ def test_gaussian_actor_policy_with_pretrained_encoder(
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assert actor_loss.shape == ()
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def test_sac_training_with_shared_encoder():
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def test_gaussian_actor_training_with_shared_encoder():
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batch_size = 2
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action_dim = 10
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state_dim = 10
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@@ -358,7 +355,7 @@ def test_sac_training_with_shared_encoder():
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algorithm.optimizers["actor"].step()
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def test_sac_training_with_discrete_critic():
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def test_gaussian_actor_training_with_discrete_critic():
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batch_size = 2
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continuous_action_dim = 9
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full_action_dim = continuous_action_dim + 1
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@@ -475,7 +472,7 @@ def test_sac_algorithm_with_critics_number_of_heads(num_critics: int):
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def test_gaussian_actor_policy_save_and_load(tmp_path):
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"""Test that the policy can be saved and loaded from pretrained."""
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root = tmp_path / "test_sac_save_and_load"
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root = tmp_path / "test_gaussian_actor_save_and_load"
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state_dim = 10
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action_dim = 10
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@@ -506,7 +503,7 @@ def test_gaussian_actor_policy_save_and_load(tmp_path):
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def test_gaussian_actor_policy_save_and_load_with_discrete_critic(tmp_path):
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"""Discrete critic should be saved/loaded as part of the policy."""
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root = tmp_path / "test_sac_save_and_load_discrete"
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root = tmp_path / "test_gaussian_actor_save_and_load_discrete"
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state_dim = 10
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action_dim = 6
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@@ -13,10 +13,6 @@
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# limitations under the License.
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"""Tests for RL data mixing (DataMixer, OnlineOfflineMixer)."""
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import pytest
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pytest.importorskip("grpc")
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import torch
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from lerobot.rl.buffer import ReplayBuffer
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@@ -18,10 +18,6 @@ import threading
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import time
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from queue import Queue
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import pytest
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pytest.importorskip("grpc")
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from torch.multiprocessing import Queue as TorchMPQueue
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from lerobot.rl.queue import get_last_item_from_queue
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@@ -16,9 +16,6 @@
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"""Tests for the RL algorithm abstraction and SACAlgorithm implementation."""
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import pytest
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pytest.importorskip("grpc")
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import torch
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from lerobot.configs.types import FeatureType, PolicyFeature
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@@ -31,7 +28,7 @@ from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
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from lerobot.utils.random_utils import set_seed
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# ---------------------------------------------------------------------------
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# Helpers (reuse patterns from tests/policies/test_sac_policy.py)
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# Helpers (reuse patterns from tests/policies/test_gaussian_actor_policy.py)
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# ---------------------------------------------------------------------------
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@@ -14,10 +14,6 @@
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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 pytest
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pytest.importorskip("grpc")
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import torch
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from torch import Tensor
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@@ -22,8 +22,6 @@ from unittest.mock import patch
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import pytest
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pytest.importorskip("grpc")
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from lerobot.rl.process import ProcessSignalHandler
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@@ -19,7 +19,6 @@ from collections.abc import Callable
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import pytest
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pytest.importorskip("grpc")
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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import torch # noqa: E402
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