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synced 2026-05-23 04:30:10 +00:00
test(rl): skip ci tests for resnet10
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@@ -33,7 +33,7 @@ class RewardClassifierConfig(PreTrainedConfig):
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latent_dim: int = 256
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latent_dim: int = 256
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image_embedding_pooling_dim: int = 8
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image_embedding_pooling_dim: int = 8
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dropout_rate: float = 0.1
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dropout_rate: float = 0.1
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model_name: str = "helper2424/resnet10"
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model_name: str = "helper2424/resnet10" # TODO: This needs to be updated. The model on the Hub doesn't call self.post_init() in its __init__, which is required by transformers v5 to set all_tied_weights_keys. The from_pretrained call fails when it tries to access this attribute during _finalize_model_loading.
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device: str = "cpu"
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device: str = "cpu"
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model_type: str = "cnn" # "transformer" or "cnn"
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model_type: str = "cnn" # "transformer" or "cnn"
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num_cameras: int = 2
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num_cameras: int = 2
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@@ -14,6 +14,7 @@
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# See the License for the specific language governing permissions and
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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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# limitations under the License.
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import pytest
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import torch
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import torch
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from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
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from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
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@@ -37,6 +38,9 @@ def test_classifier_output():
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@require_package("transformers")
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@require_package("transformers")
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@pytest.mark.skip(
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reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
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)
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def test_binary_classifier_with_default_params():
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def test_binary_classifier_with_default_params():
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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@@ -78,6 +82,9 @@ def test_binary_classifier_with_default_params():
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@require_package("transformers")
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@require_package("transformers")
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@pytest.mark.skip(
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reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
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)
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def test_multiclass_classifier():
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def test_multiclass_classifier():
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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@@ -117,6 +124,9 @@ def test_multiclass_classifier():
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@require_package("transformers")
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@require_package("transformers")
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@pytest.mark.skip(
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reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
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)
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def test_default_device():
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def test_default_device():
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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@@ -129,6 +139,9 @@ def test_default_device():
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@require_package("transformers")
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@require_package("transformers")
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@pytest.mark.skip(
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reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
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)
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def test_explicit_device_setup():
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def test_explicit_device_setup():
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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from lerobot.policies.sac.reward_model.modeling_classifier import Classifier
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@@ -305,6 +305,9 @@ def test_sac_policy_with_visual_input(batch_size: int, state_dim: int, action_di
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[(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
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[(1, 6, 6, "helper2424/resnet10"), (1, 6, 6, "facebook/convnext-base-224")],
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)
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)
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@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
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@pytest.mark.skipif(not TRANSFORMERS_AVAILABLE, reason="Transformers are not installed")
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@pytest.mark.skip(
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reason="helper2424/resnet10 needs to be updated to work with the latest version of transformers"
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)
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def test_sac_policy_with_pretrained_encoder(
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def test_sac_policy_with_pretrained_encoder(
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batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
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batch_size: int, state_dim: int, action_dim: int, vision_encoder_name: str
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):
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):
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