File size: 2,187 Bytes
a8eb6e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import torch
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.policies.factory import make_policy, make_pre_post_processors
from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
def main():
# Device to use for training
device = "mps" # or "cuda", or "cpu"
# Load the dataset used for training
repo_id = "lerobot/example_hil_serl_dataset"
dataset = LeRobotDataset(repo_id)
# Configure the policy to extract features from the image frames
camera_keys = dataset.meta.camera_keys
config = RewardClassifierConfig(
num_cameras=len(camera_keys),
device=device,
# backbone model to extract features from the image frames
model_name="microsoft/resnet-18",
)
# Make policy, preprocessor, and optimizer
policy = make_policy(config, ds_meta=dataset.meta)
optimizer = config.get_optimizer_preset().build(policy.parameters())
preprocessor, _ = make_pre_post_processors(policy_cfg=config, dataset_stats=dataset.meta.stats)
classifier_id = "<user>/reward_classifier_hil_serl_example"
# Instantiate a dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)
# Training loop
num_epochs = 5
for epoch in range(num_epochs):
total_loss = 0
total_accuracy = 0
for batch in dataloader:
# Preprocess the batch and move it to the correct device.
batch = preprocessor(batch)
# Forward pass
loss, output_dict = policy.forward(batch)
# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
total_accuracy += output_dict["accuracy"]
avg_loss = total_loss / len(dataloader)
avg_accuracy = total_accuracy / len(dataloader)
print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_loss:.4f}, Accuracy: {avg_accuracy:.2f}%")
print("Training finished!")
# You can now save the trained policy.
policy.push_to_hub(classifier_id)
if __name__ == "__main__":
main()
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