mirror of
https://github.com/huggingface/lerobot.git
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add testing
This commit is contained in:
Jade Choghari
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test script to verify XVLA policy integration with LeRobot vs the original implementation, only meant to be run locally!"""
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import gc
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import os
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import random
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from copy import deepcopy
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from typing import Any
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import numpy as np
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import pytest
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import torch
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# Skip if transformers is not available
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pytest.importorskip("transformers")
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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 XVLA model access and is not meant for CI",
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)
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from transformers import AutoModel, AutoProcessor # noqa: E402
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from lerobot.configs.policies import PreTrainedConfig # noqa: E402
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from lerobot.envs.factory import make_env_config # noqa: E402
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from lerobot.policies.factory import make_policy, make_pre_post_processors # noqa: E402
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from lerobot.processor import PolicyAction, PolicyProcessorPipeline # noqa: E402
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from lerobot.utils.constants import OBS_IMAGES, OBS_STATE # noqa: E402
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# Constants
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DUMMY_ACTION_DIM = 7 # Standard robot arm action dimension
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DUMMY_STATE_DIM = 20 # Proprioceptive state dimension
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IMAGE_HEIGHT = 224
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IMAGE_WIDTH = 224
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NUM_VIEWS = 2 # Number of camera views
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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MODEL_PATH_LEROBOT = "lerobot/xvla-base"
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MODEL_PATH_ORIGINAL = "2toINF/X-VLA-Pt"
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LIBERO_DOMAIN_ID = 0 # Domain ID for examples purposes
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def cleanup_memory():
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"""Clean up GPU/MPS memory to prevent OOM errors between tests."""
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print("\nCleaning up memory...")
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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if torch.backends.mps.is_available():
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torch.mps.empty_cache()
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print("Memory cleanup complete.")
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def set_seed_all(seed: int):
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"""Set random seed for all RNG sources to ensure reproducibility."""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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# Set deterministic behavior
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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torch.use_deterministic_algorithms(True, warn_only=True)
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def instantiate_lerobot_xvla(
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from_pretrained: bool = False,
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model_path: str = MODEL_PATH_LEROBOT,
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) -> tuple[
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Any, # Policy
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PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
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PolicyProcessorPipeline[PolicyAction, PolicyAction],
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]:
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"""Instantiate LeRobot XVLA policy with preprocessor and postprocessor."""
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if from_pretrained:
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cfg = PreTrainedConfig.from_pretrained(model_path)
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cfg.pretrained_path = model_path
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else:
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# For non-pretrained, we'd need to create a config from scratch
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raise NotImplementedError("Non-pretrained XVLA instantiation not implemented yet")
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cfg.device = DEVICE
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env_cfg = make_env_config("libero", task="libero_spatial")
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policy = make_policy(
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cfg=cfg,
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env_cfg=env_cfg,
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)
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policy.to(DEVICE)
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policy.eval()
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preprocessor_overrides = {
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"device_processor": {"device": str(cfg.device)},
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}
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=cfg,
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pretrained_path=cfg.pretrained_path,
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preprocessor_overrides=preprocessor_overrides,
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)
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return policy, preprocessor, postprocessor
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def instantiate_original_xvla(
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from_pretrained: bool = False,
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model_path: str = MODEL_PATH_ORIGINAL,
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):
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"""Instantiate original XVLA policy from the original implementation."""
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if from_pretrained:
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processor = AutoProcessor.from_pretrained(
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model_path,
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num_views=NUM_VIEWS,
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trust_remote_code=True
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)
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model = AutoModel.from_pretrained(
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model_path,
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trust_remote_code=True
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)
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else:
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raise NotImplementedError("Non-pretrained XVLA instantiation not implemented yet")
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model.to(DEVICE)
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model.eval()
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return model, processor
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def create_dummy_data(device=DEVICE):
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"""Create dummy data for testing both implementations."""
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batch_size = 2
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prompt = "Pick up the red block and place it in the bin"
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# Create random RGB images in [0, 255] uint8 range (as PIL images would be)
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# Then convert to [0, 1] float32 range for LeRobot
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def fake_rgb(H, W):
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arr = np.random.randint(0, 255, (H, W, 3), dtype=np.uint8)
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t = torch.from_numpy(arr).permute(2, 0, 1) # CHW
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t = t.float() / 255.0 # Normalize to [0, 1]
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return t
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batch = {
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f"{OBS_IMAGES}.image": torch.stack([fake_rgb(IMAGE_HEIGHT, IMAGE_WIDTH) for _ in range(batch_size)]).to(device),
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f"{OBS_IMAGES}.image2": torch.stack([fake_rgb(IMAGE_HEIGHT, IMAGE_WIDTH) for _ in range(batch_size)]).to(device),
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OBS_STATE: torch.randn(batch_size, DUMMY_STATE_DIM, dtype=torch.float32, device=device),
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"task": [prompt for _ in range(batch_size)],
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}
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return batch
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def prepare_original_inputs(batch, processor, device=DEVICE):
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"""Prepare inputs for the original XVLA model."""
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batch_size = batch[OBS_STATE].shape[0]
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# Convert images from [0, 1] to [0, 255] uint8 for processor
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image1 = (batch[f"{OBS_IMAGES}.image"] * 255).byte()
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image2 = (batch[f"{OBS_IMAGES}.image2"] * 255).byte()
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# Get task instruction (use first one if batch)
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task_instruction = batch["task"][0] if isinstance(batch["task"], list) else batch["task"]
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# Process images and text through original processor
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# The processor expects a list of images per sample
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processed_inputs = processor(
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[image1[0], image2[0]], # Process first sample only for now
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task_instruction
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)
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# Move to correct device and dtype
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dtype = torch.float32
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inputs = {k: v.to(device=device, dtype=dtype) if v.is_floating_point() else v.to(device=device)
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for k, v in processed_inputs.items()}
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# Add proprio and domain_id
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inputs.update({
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"proprio": batch[OBS_STATE][:1].to(device), # First sample only
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"domain_id": torch.tensor([LIBERO_DOMAIN_ID], dtype=torch.long, device=device),
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})
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return inputs
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def test_xvla_preprocessor_alignment():
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"""Test that LeRobot and Original XVLA preprocessors produce similar outputs."""
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print("\n" + "=" * 80)
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print("Test: XVLA Preprocessor Alignment")
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print("=" * 80)
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set_seed_all(42)
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print("\n[LeRobot] Instantiating policy and preprocessor...")
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lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_xvla(
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from_pretrained=True
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)
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print("\n[Original] Instantiating model and processor...")
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original_model, original_processor = instantiate_original_xvla(from_pretrained=True)
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print("\nCreating dummy data...")
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batch = create_dummy_data()
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print("\n[LeRobot] Preprocessing...")
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lerobot_observation = lerobot_preprocessor(deepcopy(batch))
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lerobot_inputs = lerobot_policy._build_model_inputs(lerobot_observation)
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print("\n[Original] Preprocessing...")
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original_inputs = prepare_original_inputs(batch, original_processor)
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print("\nComparing preprocessor outputs:")
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print("-" * 80)
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# Compare common keys
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common_keys = set(lerobot_inputs.keys()) & set(original_inputs.keys())
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print(f"Common keys: {common_keys}")
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for key in common_keys:
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lerobot_tensor = lerobot_inputs[key]
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original_tensor = original_inputs[key]
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print(f"\n🔎 Key: {key}")
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print(f" LeRobot shape: {lerobot_tensor.shape}")
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print(f" Original shape: {original_tensor.shape}")
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# Handle batch size difference (we only process first sample for original)
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if lerobot_tensor.shape[0] > original_tensor.shape[0]:
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lerobot_tensor = lerobot_tensor[:1]
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if lerobot_tensor.shape == original_tensor.shape:
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if torch.allclose(lerobot_tensor, original_tensor, atol=1e-5, rtol=1e-5):
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print(" ✔️ Tensors are equal (allclose with atol=1e-5)")
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else:
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diff = torch.abs(lerobot_tensor - original_tensor)
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print(" ⚠️ Tensors differ")
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print(f" Max diff: {diff.max().item():.6e}")
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print(f" Mean diff: {diff.mean().item():.6e}")
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print(f" Std diff: {diff.std().item():.6e}")
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else:
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print(" ⚠️ Shapes don't match after alignment")
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cleanup_memory()
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def test_xvla_original_vs_lerobot_pretrained():
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"""Test XVLA original implementation vs LeRobot implementation with pretrained weights."""
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print("\n" + "=" * 80)
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print("Test: XVLA Original vs LeRobot with Pretrained Weights (Inference)")
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print("=" * 80)
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set_seed_all(42)
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print("\n[LeRobot] Instantiating policy...")
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lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_xvla(
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from_pretrained=True
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)
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print("\n[Original] Instantiating model...")
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original_model, original_processor = instantiate_original_xvla(from_pretrained=True)
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print("\nCreating dummy data...")
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batch = create_dummy_data()
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print("\n[LeRobot] Running inference...")
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lerobot_observation = lerobot_preprocessor(deepcopy(batch))
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lerobot_inputs = lerobot_policy._build_model_inputs(lerobot_observation)
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# Reset seed for inference
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torch.manual_seed(42)
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with torch.no_grad():
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lerobot_actions = lerobot_policy.model.generate_actions(**lerobot_inputs, steps=10)
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lerobot_actions = lerobot_actions.squeeze(0).float().cpu()
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print(f"LeRobot actions shape: {lerobot_actions.shape}")
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print(f"LeRobot actions mean: {lerobot_actions.mean().item():.6f}")
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print(f"LeRobot actions std: {lerobot_actions.std().item():.6f}")
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print("\n[Original] Running inference...")
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original_inputs = prepare_original_inputs(batch, original_processor)
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# Reset seed for inference
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torch.manual_seed(42)
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with torch.no_grad():
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original_actions = original_model.generate_actions(**original_inputs, steps=10)
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original_actions = original_actions.squeeze(0).float().cpu()
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print(f"Original actions shape: {original_actions.shape}")
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print(f"Original actions mean: {original_actions.mean().item():.6f}")
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print(f"Original actions std: {original_actions.std().item():.6f}")
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print("\nAction Comparison:")
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print("-" * 80)
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# Compare actions
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if lerobot_actions.shape == original_actions.shape:
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diff = torch.abs(lerobot_actions - original_actions)
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max_diff = diff.max().item()
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mean_diff = diff.mean().item()
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print(f"Max absolute difference: {max_diff:.6e}")
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print(f"Mean absolute difference: {mean_diff:.6e}")
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print(f"Relative difference: {(mean_diff / (torch.abs(original_actions).mean().item() + 1e-8) * 100):.2f}%")
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# Check with different tolerances
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tolerances = [1e-5, 1e-4, 1e-3, 1e-2]
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for tol in tolerances:
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is_close = torch.allclose(lerobot_actions, original_actions, atol=tol)
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status = "✔️" if is_close else "❌"
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print(f"{status} Actions close (atol={tol}): {is_close}")
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# Assert with reasonable tolerance
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tolerance = 1e-3
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assert torch.allclose(lerobot_actions, original_actions, atol=tolerance), (
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f"Actions differ by more than tolerance ({tolerance}): max diff = {max_diff:.6e}"
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)
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print(f"\n✅ Success: Actions match within tolerance ({tolerance})!")
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else:
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print(f"⚠️ Shape mismatch: LeRobot {lerobot_actions.shape} vs Original {original_actions.shape}")
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cleanup_memory()
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def test_xvla_inference_reproducibility():
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"""Test that XVLA inference is reproducible with the same seed."""
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print("\n" + "=" * 80)
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print("Test: XVLA Inference Reproducibility")
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print("=" * 80)
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print("\n[LeRobot] Instantiating policy...")
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lerobot_policy, lerobot_preprocessor, lerobot_postprocessor = instantiate_lerobot_xvla(
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from_pretrained=True
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)
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print("\nCreating dummy data...")
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batch = create_dummy_data()
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# First inference
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print("\n[Run 1] Running inference...")
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set_seed_all(42)
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lerobot_observation = lerobot_preprocessor(deepcopy(batch))
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lerobot_inputs = lerobot_policy._build_model_inputs(lerobot_observation)
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with torch.no_grad():
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actions_1 = lerobot_policy.model.generate_actions(**lerobot_inputs, steps=10)
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actions_1 = actions_1.squeeze(0).float().cpu()
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# Second inference with same seed
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print("\n[Run 2] Running inference with same seed...")
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set_seed_all(42)
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lerobot_observation = lerobot_preprocessor(deepcopy(batch))
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lerobot_inputs = lerobot_policy._build_model_inputs(lerobot_observation)
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with torch.no_grad():
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actions_2 = lerobot_policy.model.generate_actions(**lerobot_inputs, steps=10)
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actions_2 = actions_2.squeeze(0).float().cpu()
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print("\nComparing two runs:")
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print("-" * 80)
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if torch.allclose(actions_1, actions_2, atol=1e-8):
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print("✔️ Inference is perfectly reproducible!")
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else:
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diff = torch.abs(actions_1 - actions_2)
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print(f"⚠️ Small differences detected:")
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print(f" Max diff: {diff.max().item():.6e}")
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print(f" Mean diff: {diff.mean().item():.6e}")
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assert torch.allclose(actions_1, actions_2, atol=1e-6), "Inference should be reproducible!"
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cleanup_memory()
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if __name__ == "__main__":
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print("\n" + "=" * 80)
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print("XVLA Original vs LeRobot Comparison Test Suite")
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print("=" * 80)
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try:
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test_xvla_preprocessor_alignment()
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test_xvla_original_vs_lerobot_pretrained()
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test_xvla_inference_reproducibility()
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print("\n" + "=" * 80)
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print("✅ All tests passed!")
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print("=" * 80)
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except Exception as e:
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print("\n" + "=" * 80)
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print(f"❌ Test failed with error: {e}")
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print("=" * 80)
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raise
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@@ -0,0 +1,190 @@
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import random
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import numpy as np
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import torch
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from xvla.models.modeling_xvla import XVLA
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# from lerobot.policies.xvla.configuration_xvla import XVLAConfig
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from lerobot.configs.policies import PreTrainedConfig
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from lerobot.envs.factory import make_env_config
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from lerobot.policies.factory import make_policy, make_pre_post_processors
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from lerobot.utils.constants import OBS_IMAGES, OBS_STATE
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torch.manual_seed(42)
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random.seed(42)
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np.random.seed(42)
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observation_height: int = 224
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observation_width: int = 224 # todo: jadechoghari, image size is different for the two models
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# create an observation dict
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OBS = {
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f"{OBS_IMAGES}.image": torch.randn(1, 3, observation_height, observation_width),
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f"{OBS_IMAGES}.image2": torch.randn(1, 3, observation_height, observation_width),
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OBS_STATE: torch.randn(1, 20), # ONLY if OBS_STATE is already a string
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"task": "put the object in the box",
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}
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IMAGENET_MEAN = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
|
||||
IMAGENET_STD = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
|
||||
|
||||
|
||||
def fake_rgb(H, W):
|
||||
arr = np.random.randint(0, 255, (H, W, 3), dtype=np.uint8)
|
||||
t = torch.from_numpy(arr).permute(2, 0, 1) # CHW
|
||||
t = t.unsqueeze(0).float()
|
||||
# normalize pixel to imagenet
|
||||
return t
|
||||
|
||||
|
||||
OBS[f"{OBS_IMAGES}.image"] = fake_rgb(observation_height, observation_width)
|
||||
OBS[f"{OBS_IMAGES}.image2"] = fake_rgb(observation_height, observation_width)
|
||||
|
||||
cfg = PreTrainedConfig.from_pretrained("/raid/jade/models/xvla-libero-og_migrated")
|
||||
cfg.pretrained_path = "/raid/jade/models/xvla-libero-og_migrated"
|
||||
env_cfg = make_env_config("libero", task="libero_spatial")
|
||||
policy = make_policy(
|
||||
cfg=cfg,
|
||||
env_cfg=env_cfg,
|
||||
)
|
||||
|
||||
policy.eval()
|
||||
|
||||
preprocessor_overrides = {
|
||||
"device_processor": {"device": str(cfg.device)},
|
||||
}
|
||||
|
||||
preprocessor, postprocessor = make_pre_post_processors(
|
||||
policy_cfg=cfg,
|
||||
pretrained_path=cfg.pretrained_path,
|
||||
preprocessor_overrides=preprocessor_overrides,
|
||||
)
|
||||
|
||||
observation = preprocessor(OBS)
|
||||
inputs = policy._build_model_inputs(observation)
|
||||
|
||||
|
||||
#### now the og model ###########################################################
|
||||
from xvla.models.processing_xvla import XVLAProcessor
|
||||
|
||||
processor = XVLAProcessor.from_pretrained("/raid/jade/models/xvla-libero", num_views=2)
|
||||
inputs_1 = processor([OBS[f"{OBS_IMAGES}.image"], OBS[f"{OBS_IMAGES}.image2"]], OBS["task"])
|
||||
domain_id = torch.tensor([3], dtype=torch.long)
|
||||
inputs.update(
|
||||
{
|
||||
"proprio": OBS[OBS_STATE].to("cuda"),
|
||||
"domain_id": domain_id.to("cuda"),
|
||||
}
|
||||
)
|
||||
|
||||
# check the preprocessor
|
||||
for k in inputs.keys() & inputs_1.keys(): # intersection of keys
|
||||
a = inputs[k]
|
||||
b = inputs_1[k].to("cuda")
|
||||
|
||||
print(f"\n🔎 Key: {k}")
|
||||
|
||||
# Check shape
|
||||
print(" shape:", a.shape, b.shape)
|
||||
|
||||
# Check if close
|
||||
if torch.allclose(a, b, atol=1e-5, rtol=1e-5):
|
||||
print(" ✔️ tensors are equal (allclose)")
|
||||
else:
|
||||
diff = torch.abs(a - b)
|
||||
print(" ❌ tensors differ")
|
||||
print(" max diff:", diff.max().item())
|
||||
print(" mean diff:", diff.mean().item())
|
||||
|
||||
|
||||
model = XVLA.from_pretrained("/raid/jade/models/xvla-libero")
|
||||
model.eval()
|
||||
model.to("cuda")
|
||||
|
||||
action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
|
||||
action_1 = policy.model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
|
||||
|
||||
# np all close
|
||||
print(np.allclose(action, action_1, atol=1e-2, rtol=1e-2))
|
||||
print("max diff:", np.max(np.abs(action - action_1)))
|
||||
print("mean diff:", np.mean(np.abs(action - action_1)))
|
||||
|
||||
|
||||
import random
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from PIL import Image
|
||||
from xvla.models.configuration_xvla import XVLAConfig
|
||||
from xvla.models.modeling_xvla import XVLA
|
||||
from xvla.models.processor_xvla import XVLAProcessor
|
||||
|
||||
from lerobot.configs.policies import PreTrainedConfig
|
||||
from lerobot.envs.factory import make_env_config
|
||||
from lerobot.policies.factory import make_policy
|
||||
|
||||
cfg = XVLAConfig.from_pretrained("/raid/jade/models/xvla-libero")
|
||||
model = XVLA.from_pretrained("/raid/jade/models/xvla-libero")
|
||||
model.eval()
|
||||
model.to("cuda")
|
||||
processor = XVLAProcessor.from_pretrained("/raid/jade/models/xvla-libero")
|
||||
# /raid/jade/models/xvla-libero
|
||||
# seet seed
|
||||
torch.manual_seed(42)
|
||||
random.seed(42)
|
||||
np.random.seed(42)
|
||||
|
||||
|
||||
def make_random_pil_images(num_images=3, H=480, W=640):
|
||||
images = []
|
||||
for _ in range(num_images):
|
||||
# Random RGB image
|
||||
arr = np.random.randint(0, 256, (H, W, 3), dtype=np.uint8)
|
||||
img = Image.fromarray(arr)
|
||||
images.append(img)
|
||||
return images
|
||||
|
||||
|
||||
# Example:
|
||||
images = make_random_pil_images()
|
||||
language_instruction = "This is a random image"
|
||||
# Multimodal preprocessing by processor
|
||||
inputs = processor(images, language_instruction)
|
||||
if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
|
||||
raise ValueError("Processor did not return the expected keys.")
|
||||
|
||||
proprio = torch.randn(1, 20)
|
||||
domain_id = torch.tensor([0], dtype=torch.long)
|
||||
|
||||
# Align to model's device/dtype
|
||||
device = model.device
|
||||
dtype = next(model.parameters()).dtype
|
||||
|
||||
|
||||
def to_model(t: torch.Tensor) -> torch.Tensor:
|
||||
if not isinstance(t, torch.Tensor):
|
||||
t = torch.as_tensor(t)
|
||||
# cast floats to model dtype, keep integral/bool as-is
|
||||
return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)
|
||||
|
||||
|
||||
inputs = {k: to_model(v) for k, v in inputs.items()}
|
||||
inputs.update(
|
||||
{
|
||||
"proprio": to_model(proprio),
|
||||
"domain_id": domain_id.to(device),
|
||||
}
|
||||
)
|
||||
|
||||
# Inference
|
||||
action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
|
||||
|
||||
|
||||
#### now for lerobot model #####################################################
|
||||
|
||||
cfg = PreTrainedConfig.from_pretrained("/raid/jade/models/xvla-libero-og_migrated")
|
||||
env_cfg = make_env_config("libero", task="libero_spatial")
|
||||
cfg.pretrained_path = "/raid/jade/models/xvla-libero-og_migrated"
|
||||
policy = make_policy(cfg=cfg, env_cfg=env_cfg)
|
||||
policy.eval()
|
||||
policy.to("cuda")
|
||||
|
||||
action_1 = policy.model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
|
||||
Reference in New Issue
Block a user