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

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pre-commit-ci[bot]
2025-03-04 13:38:47 +00:00
committed by Michel Aractingi
parent bb69cb3c8c
commit 85fe8a3f4e
79 changed files with 2800 additions and 794 deletions
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45
tests/policies/hilserl/classifier/check_hiserl_reward_classifier.py
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@@ -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