[pre-commit.ci] auto fixes from pre-commit.com hooks

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tests/policies/hilserl/classifier/check_hiserl_reward_classifier.py

@@ -25,7 +25,10 @@ from torchmetrics import AUROC, Accuracy, F1Score, Precision, Recall
 from torchvision.datasets import CIFAR10
 from torchvision.transforms import ToTensor
 
-from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier, ClassifierConfig
+from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+    Classifier,
+    ClassifierConfig,
+)
 
 BATCH_SIZE = 1000
 LR = 0.1
@@ -43,7 +46,9 @@ def train_evaluate_multiclass_classifier():
     logging.info(
         f"Start multiclass classifier train eval with {DEVICE} device, batch size {BATCH_SIZE}, learning rate {LR}"
     )
-    multiclass_config = ClassifierConfig(model_name="microsoft/resnet-18", device=DEVICE, num_classes=10)
+    multiclass_config = ClassifierConfig(
+        model_name="microsoft/resnet-18", device=DEVICE, num_classes=10
+    )
     multiclass_classifier = Classifier(multiclass_config)
 
     trainset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
@@ -114,10 +119,18 @@ def train_evaluate_multiclass_classifier():
     test_probs = torch.stack(test_probs)
 
     accuracy = Accuracy(task="multiclass", num_classes=multiclass_num_classes)
-    precision = Precision(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
-    recall = Recall(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
-    f1 = F1Score(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
-    auroc = AUROC(task="multiclass", num_classes=multiclass_num_classes, average="weighted")
+    precision = Precision(
+        task="multiclass", average="weighted", num_classes=multiclass_num_classes
+    )
+    recall = Recall(
+        task="multiclass", average="weighted", num_classes=multiclass_num_classes
+    )
+    f1 = F1Score(
+        task="multiclass", average="weighted", num_classes=multiclass_num_classes
+    )
+    auroc = AUROC(
+        task="multiclass", num_classes=multiclass_num_classes, average="weighted"
+    )
 
     # Calculate metrics
     acc = accuracy(test_predictions, test_labels)
@@ -146,18 +159,28 @@ def train_evaluate_binary_classifier():
             new_label = float(1.0) if label == target_class else float(0.0)
             new_targets.append(new_label)
 
-        dataset.targets = new_targets  # Replace the original labels with the binary ones
+        dataset.targets = (
+            new_targets  # Replace the original labels with the binary ones
+        )
         return dataset
 
-    binary_train_dataset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
-    binary_test_dataset = CIFAR10(root="data", train=False, download=True, transform=ToTensor())
+    binary_train_dataset = CIFAR10(
+        root="data", train=True, download=True, transform=ToTensor()
+    )
+    binary_test_dataset = CIFAR10(
+        root="data", train=False, download=True, transform=ToTensor()
+    )
 
     # Apply one-vs-rest labeling
     binary_train_dataset = one_vs_rest(binary_train_dataset, target_binary_class)
     binary_test_dataset = one_vs_rest(binary_test_dataset, target_binary_class)
 
-    binary_trainloader = DataLoader(binary_train_dataset, batch_size=BATCH_SIZE, shuffle=True)
-    binary_testloader = DataLoader(binary_test_dataset, batch_size=BATCH_SIZE, shuffle=False)
+    binary_trainloader = DataLoader(
+        binary_train_dataset, batch_size=BATCH_SIZE, shuffle=True
+    )
+    binary_testloader = DataLoader(
+        binary_test_dataset, batch_size=BATCH_SIZE, shuffle=False
+    )
 
     binary_epoch = 1
 

tests/policies/hilserl/classifier/test_modelling_classifier.py

@@ -9,7 +9,9 @@ from tests.utils import require_package
 
 def test_classifier_output():
     output = ClassifierOutput(
-        logits=torch.tensor([1, 2, 3]), probabilities=torch.tensor([0.1, 0.2, 0.3]), hidden_states=None
+        logits=torch.tensor([1, 2, 3]),
+        probabilities=torch.tensor([0.1, 0.2, 0.3]),
+        hidden_states=None,
     )
 
     assert (
@@ -20,7 +22,9 @@ def test_classifier_output():
 
 @require_package("transformers")
 def test_binary_classifier_with_default_params():
-    from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+        Classifier,
+    )
 
     config = ClassifierConfig()
     classifier = Classifier(config)
@@ -41,7 +45,9 @@ def test_binary_classifier_with_default_params():
 
 @require_package("transformers")
 def test_multiclass_classifier():
-    from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+        Classifier,
+    )
 
     num_classes = 5
     config = ClassifierConfig(num_classes=num_classes)
@@ -63,7 +69,9 @@ def test_multiclass_classifier():
 
 @require_package("transformers")
 def test_default_device():
-    from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+        Classifier,
+    )
 
     config = ClassifierConfig()
     assert config.device == "cpu"
@@ -75,7 +83,9 @@ def test_default_device():
 
 @require_package("transformers")
 def test_explicit_device_setup():
-    from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
+    from lerobot.common.policies.hilserl.classifier.modeling_classifier import (
+        Classifier,
+    )
 
     config = ClassifierConfig(device="meta")
     assert config.device == "meta"

tests/policies/test_policies.py

@@ -172,7 +172,9 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     # Test updating the policy (and test that it does not mutate the batch)
     batch_ = deepcopy(batch)
     policy.forward(batch)
-    assert set(batch) == set(batch_), "Batch keys are not the same after a forward pass."
+    assert set(batch) == set(
+        batch_
+    ), "Batch keys are not the same after a forward pass."
     assert all(
         torch.equal(batch[k], batch_[k]) if isinstance(batch[k], torch.Tensor) else batch[k] == batch_[k]
         for k in batch
@@ -186,7 +188,9 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     observation = preprocess_observation(observation)
 
     # send observation to device/gpu
-    observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation}
+    observation = {
+        key: observation[key].to(DEVICE, non_blocking=True) for key in observation
+    }
 
     # get the next action for the environment (also check that the observation batch is not modified)
     observation_ = deepcopy(observation)
@@ -452,7 +456,9 @@ def test_act_temporal_ensembler():
     batch_size = batch_seq.shape[0]
     # Exponential weighting (normalized). Unsqueeze once to match the position of the `episode_length`
     # dimension of `batch_seq`.
-    weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)).unsqueeze(-1)
+    weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)).unsqueeze(
+        -1
+    )
 
     # Simulate stepping through a rollout and computing a batch of actions with model on each step.
     for i in range(episode_length):
@@ -475,7 +481,8 @@ def test_act_temporal_ensembler():
         episode_step_indices = torch.arange(i + 1)[-len(chunk_indices) :]
         seq_slice = batch_seq[:, episode_step_indices, chunk_indices]
         offline_avg = (
-            einops.reduce(seq_slice * weights[: i + 1], "b s 1 -> b 1", "sum") / weights[: i + 1].sum()
+            einops.reduce(seq_slice * weights[: i + 1], "b s 1 -> b 1", "sum")
+            / weights[: i + 1].sum()
         )
         # Sanity check. The average should be between the extrema.
         assert torch.all(einops.reduce(seq_slice, "b s 1 -> b 1", "min") <= offline_avg)