mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-20 02:59:50 +00:00
AdilZouitine
28fa7eae72
- Updated imports and references throughout the codebase to reflect the new naming convention. - Introduced a new processor file for PI0 to handle pre-processing and post-processing steps. - Adjusted tests to utilize the renamed classes, ensuring consistency and functionality. - Enhanced clarity and maintainability by removing outdated naming conventions.
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
#!/usr/bin/env python
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"""Test script to verify PI0OpenPI policy integration with LeRobot, only meant to be run locally!"""
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import os
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import pytest
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import torch
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# Skip this entire module in CI
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pytestmark = pytest.mark.skipif(
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os.environ.get("CI") == "true" or os.environ.get("GITHUB_ACTIONS") == "true",
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reason="This test requires local OpenPI installation and is not meant for CI",
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)
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from lerobot.policies.factory import make_policy_config # noqa: E402
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from lerobot.policies.pi0 import ( # noqa: E402
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PI0Config,
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PI0Policy,
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make_pi0_pre_post_processors, # noqa: E402
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)
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from lerobot.utils.random_utils import set_seed # noqa: E402
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from tests.utils import require_cuda # noqa: E402
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# Set seed
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@require_cuda
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def test_policy_instantiation():
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# Create config
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set_seed(42)
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config = PI0Config(max_action_dim=7, max_state_dim=14, dtype="float32")
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# Set up input_features and output_features in the config
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from lerobot.configs.types import FeatureType, PolicyFeature
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config.input_features = {
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"observation.state": PolicyFeature(
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type=FeatureType.STATE,
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shape=(14,),
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),
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"observation.images.base_0_rgb": PolicyFeature(
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type=FeatureType.VISUAL,
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shape=(3, 224, 224),
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),
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}
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config.output_features = {
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"action": PolicyFeature(
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type=FeatureType.ACTION,
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shape=(7,),
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),
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}
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# Create dummy dataset stats
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dataset_stats = {
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"observation.state": {
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"mean": torch.zeros(14),
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"std": torch.ones(14),
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},
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"action": {
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"mean": torch.zeros(7),
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"std": torch.ones(7),
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},
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"observation.images.base_0_rgb": {
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"mean": torch.zeros(3, 224, 224),
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"std": torch.ones(3, 224, 224),
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},
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}
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# Instantiate policy
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policy = PI0Policy(config)
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preprocessor, postprocessor = make_pi0_pre_post_processors(config=config, dataset_stats=dataset_stats)
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# Test forward pass with dummy data
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batch_size = 1
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device = config.device
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batch = {
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"observation.state": torch.randn(batch_size, 14, dtype=torch.float32, device=device),
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"action": torch.randn(batch_size, config.chunk_size, 7, dtype=torch.float32, device=device),
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"observation.images.base_0_rgb": torch.rand(
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batch_size, 3, 224, 224, dtype=torch.float32, device=device
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), # Use rand for [0,1] range
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"task": ["Pick up the object"] * batch_size,
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}
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batch = preprocessor(batch)
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try:
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loss, loss_dict = policy.forward(batch)
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print(f"Forward pass successful. Loss: {loss_dict['loss']:.4f}")
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except Exception as e:
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print(f"Forward pass failed: {e}")
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raise
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try:
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with torch.no_grad():
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action = policy.select_action(batch)
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action = postprocessor(action)
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print(f"Action: {action}")
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print(f"Action prediction successful. Action shape: {action.shape}")
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except Exception as e:
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print(f"Action prediction failed: {e}")
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raise
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@require_cuda
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def test_config_creation():
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"""Test policy config creation through factory."""
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try:
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config = make_policy_config(
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policy_type="pi0",
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max_action_dim=7,
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max_state_dim=14,
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)
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print("Config created successfully through factory")
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print(f" Config type: {type(config).__name__}")
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print(f" PaliGemma variant: {config.paligemma_variant}")
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print(f" Action expert variant: {config.action_expert_variant}")
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except Exception as e:
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print(f"Config creation failed: {e}")
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raise
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