File size: 3,589 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | """This script demonstrates how to train ACT Policy on a real-world dataset."""
from pathlib import Path
import torch
from lerobot.configs.types import FeatureType
from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
from lerobot.datasets.feature_utils import dataset_to_policy_features
from lerobot.datasets.lerobot_dataset import LeRobotDataset
from lerobot.policies.act.configuration_act import ACTConfig
from lerobot.policies.act.modeling_act import ACTPolicy
from lerobot.policies.factory import make_pre_post_processors
def make_delta_timestamps(delta_indices: list[int] | None, fps: int) -> list[float]:
if delta_indices is None:
return [0]
return [i / fps for i in delta_indices]
def main():
output_directory = Path("outputs/robot_learning_tutorial/act")
output_directory.mkdir(parents=True, exist_ok=True)
# Select your device
device = torch.device("mps") # or "cuda" or "cpu"
dataset_id = "lerobot/svla_so101_pickplace"
# This specifies the inputs the model will be expecting and the outputs it will produce
dataset_metadata = LeRobotDatasetMetadata(dataset_id)
features = dataset_to_policy_features(dataset_metadata.features)
output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
input_features = {key: ft for key, ft in features.items() if key not in output_features}
cfg = ACTConfig(input_features=input_features, output_features=output_features)
policy = ACTPolicy(cfg)
preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)
policy.train()
policy.to(device)
# To perform action chunking, ACT expects a given number of actions as targets
delta_timestamps = {
"action": make_delta_timestamps(cfg.action_delta_indices, dataset_metadata.fps),
}
# add image features if they are present
delta_timestamps |= {
k: make_delta_timestamps(cfg.observation_delta_indices, dataset_metadata.fps)
for k in cfg.image_features
}
# Instantiate the dataset
dataset = LeRobotDataset(dataset_id, delta_timestamps=delta_timestamps)
# Create the optimizer and dataloader for offline training
optimizer = cfg.get_optimizer_preset().build(policy.parameters())
batch_size = 32
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
pin_memory=device.type != "cpu",
drop_last=True,
)
# Number of training steps and logging frequency
training_steps = 1
log_freq = 1
# Run training loop
step = 0
done = False
while not done:
for batch in dataloader:
batch = preprocessor(batch)
loss, _ = policy.forward(batch)
loss.backward()
optimizer.step()
optimizer.zero_grad()
if step % log_freq == 0:
print(f"step: {step} loss: {loss.item():.3f}")
step += 1
if step >= training_steps:
done = True
break
# Save the policy checkpoint, alongside the pre/post processors
policy.save_pretrained(output_directory)
preprocessor.save_pretrained(output_directory)
postprocessor.save_pretrained(output_directory)
# Save all assets to the Hub
policy.push_to_hub("<user>/robot_learning_tutorial_act")
preprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
postprocessor.push_to_hub("<user>/robot_learning_tutorial_act")
if __name__ == "__main__":
main()
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