mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-17 17:50:09 +00:00
refactor(pipeline): Transition from tuple to dictionary format for EnvTransition
- Updated the EnvTransition structure to use a dictionary format instead of a tuple, enhancing readability and maintainability. - Replaced instances of TransitionIndex with TransitionKey for accessing transition components. - Adjusted related processing functions and tests to accommodate the new dictionary format, ensuring consistent handling of transitions across the codebase.
This commit is contained in:
Adil Zouitine
@@ -18,7 +18,7 @@ import json
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import tempfile
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Dict
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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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@@ -26,6 +26,22 @@ import torch
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import torch.nn as nn
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from lerobot.processor import EnvTransition, ProcessorStepRegistry, RobotProcessor
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from lerobot.processor.pipeline import TransitionKey
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def create_transition(
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observation=None, action=None, reward=0.0, done=False, truncated=False, info=None, complementary_data=None
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):
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"""Helper to create an EnvTransition dictionary."""
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return {
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TransitionKey.OBSERVATION: observation,
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TransitionKey.ACTION: action,
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TransitionKey.REWARD: reward,
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TransitionKey.DONE: done,
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TransitionKey.TRUNCATED: truncated,
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TransitionKey.INFO: info if info is not None else {},
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TransitionKey.COMPLEMENTARY_DATA: complementary_data if complementary_data is not None else {},
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}
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@dataclass
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@@ -45,14 +61,16 @@ class MockStep:
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Add a counter to the complementary_data."""
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obs, action, reward, done, truncated, info, comp_data = transition
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comp_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
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comp_data = {} if comp_data is None else dict(comp_data) # Make a copy
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comp_data[f"{self.name}_counter"] = self.counter
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self.counter += 1
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return (obs, action, reward, done, truncated, info, comp_data)
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# Create a new transition with updated complementary_data
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new_transition = transition.copy()
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp_data
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return new_transition
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def get_config(self) -> dict[str, Any]:
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# Return all JSON-serializable attributes that should be persisted
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@@ -79,12 +97,14 @@ class MockStepWithoutOptionalMethods:
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Multiply reward by multiplier."""
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obs, action, reward, done, truncated, info, comp_data = transition
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reward = transition.get(TransitionKey.REWARD)
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if reward is not None:
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reward = reward * self.multiplier
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new_transition = transition.copy()
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new_transition[TransitionKey.REWARD] = reward * self.multiplier
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return new_transition
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return (obs, action, reward, done, truncated, info, comp_data)
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return transition
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@dataclass
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@@ -105,7 +125,7 @@ class MockStepWithTensorState:
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Update running statistics."""
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obs, action, reward, done, truncated, info, comp_data = transition
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reward = transition.get(TransitionKey.REWARD)
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if reward is not None:
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# Update running mean
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@@ -143,7 +163,7 @@ def test_empty_pipeline():
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"""Test pipeline with no steps."""
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pipeline = RobotProcessor()
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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result = pipeline(transition)
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assert result == transition
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@@ -155,15 +175,15 @@ def test_single_step_pipeline():
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step = MockStep("test_step")
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pipeline = RobotProcessor([step])
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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result = pipeline(transition)
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assert len(pipeline) == 1
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assert result[6]["test_step_counter"] == 0 # complementary_data
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assert result[TransitionKey.COMPLEMENTARY_DATA]["test_step_counter"] == 0
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# Call again to test counter increment
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result = pipeline(transition)
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assert result[6]["test_step_counter"] == 1
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assert result[TransitionKey.COMPLEMENTARY_DATA]["test_step_counter"] == 1
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def test_multiple_steps_pipeline():
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@@ -172,46 +192,46 @@ def test_multiple_steps_pipeline():
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step2 = MockStep("step2")
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pipeline = RobotProcessor([step1, step2])
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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result = pipeline(transition)
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assert len(pipeline) == 2
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assert result[6]["step1_counter"] == 0
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assert result[6]["step2_counter"] == 0
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assert result[TransitionKey.COMPLEMENTARY_DATA]["step1_counter"] == 0
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assert result[TransitionKey.COMPLEMENTARY_DATA]["step2_counter"] == 0
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def test_invalid_transition_format():
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"""Test pipeline with invalid transition format."""
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pipeline = RobotProcessor([MockStep()])
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# Test with wrong number of elements
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with pytest.raises(ValueError, match="EnvTransition must be a 7-tuple"):
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pipeline((None, None, 0.0)) # Only 3 elements
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# Test with wrong type (tuple instead of dict)
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with pytest.raises(ValueError, match="EnvTransition must be a dictionary"):
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pipeline((None, None, 0.0, False, False, {}, {})) # Tuple instead of dict
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# Test with wrong type
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with pytest.raises(ValueError, match="EnvTransition must be a 7-tuple"):
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pipeline("not a tuple")
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# Test with wrong type (string)
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with pytest.raises(ValueError, match="EnvTransition must be a dictionary"):
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pipeline("not a dict")
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def test_step_through():
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"""Test step_through method with tuple input."""
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"""Test step_through method with dict input."""
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step1 = MockStep("step1")
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step2 = MockStep("step2")
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pipeline = RobotProcessor([step1, step2])
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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results = list(pipeline.step_through(transition))
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assert len(results) == 3 # Original + 2 steps
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assert results[0] == transition # Original
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assert "step1_counter" in results[1][6] # After step1
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assert "step2_counter" in results[2][6] # After step2
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assert "step1_counter" in results[1][TransitionKey.COMPLEMENTARY_DATA] # After step1
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assert "step2_counter" in results[2][TransitionKey.COMPLEMENTARY_DATA] # After step2
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# Ensure all results are tuples (same format as input)
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# Ensure all results are dicts (same format as input)
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for result in results:
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assert isinstance(result, tuple)
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assert len(result) == 7
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assert isinstance(result, dict)
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assert all(isinstance(k, TransitionKey) for k in result.keys())
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def test_step_through_with_dict():
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@@ -279,7 +299,7 @@ def test_hooks():
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pipeline.register_before_step_hook(before_hook)
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pipeline.register_after_step_hook(after_hook)
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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pipeline(transition)
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assert before_calls == [0]
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@@ -292,15 +312,16 @@ def test_hook_modification():
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pipeline = RobotProcessor([step])
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def modify_reward_hook(idx: int, transition: EnvTransition):
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obs, action, reward, done, truncated, info, comp_data = transition
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return (obs, action, 42.0, done, truncated, info, comp_data)
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new_transition = transition.copy()
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new_transition[TransitionKey.REWARD] = 42.0
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return new_transition
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pipeline.register_before_step_hook(modify_reward_hook)
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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result = pipeline(transition)
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assert result[2] == 42.0 # reward modified by hook
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assert result[TransitionKey.REWARD] == 42.0 # reward modified by hook
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def test_reset():
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@@ -316,7 +337,7 @@ def test_reset():
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pipeline.register_reset_hook(reset_hook)
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# Make some calls to increment counter
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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pipeline(transition)
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pipeline(transition)
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@@ -335,7 +356,7 @@ def test_profile_steps():
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step2 = MockStep("step2")
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pipeline = RobotProcessor([step1, step2])
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transition = (None, None, 0.0, False, False, {}, {})
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transition = create_transition()
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profile_results = pipeline.profile_steps(transition, num_runs=10)
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@@ -397,10 +418,10 @@ def test_step_without_optional_methods():
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step = MockStepWithoutOptionalMethods(multiplier=3.0)
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pipeline = RobotProcessor([step])
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transition = (None, None, 2.0, False, False, {}, {})
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transition = create_transition(reward=2.0)
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result = pipeline(transition)
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assert result[2] == 6.0 # 2.0 * 3.0
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assert result[TransitionKey.REWARD] == 6.0 # 2.0 * 3.0
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# Reset should work even if step doesn't implement reset
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pipeline.reset()
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@@ -419,7 +440,7 @@ def test_mixed_json_and_tensor_state():
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# Process some transitions with rewards
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for i in range(10):
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transition = (None, None, float(i), False, False, {}, {})
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transition = create_transition(reward=float(i))
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pipeline(transition)
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# Check state
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@@ -466,7 +487,7 @@ class MockModuleStep(nn.Module):
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Process transition and update running mean."""
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obs, action, reward, done, truncated, info, comp_data = transition
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obs = transition.get(TransitionKey.OBSERVATION)
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if obs is not None and isinstance(obs, torch.Tensor):
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# Process observation through linear layer
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@@ -509,7 +530,7 @@ def test_to_device_with_state_dict():
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# Process some transitions to populate state
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for i in range(10):
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transition = (None, None, float(i), False, False, {}, {})
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transition = create_transition(reward=float(i))
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pipeline(transition)
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# Check initial device (should be CPU)
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@@ -551,7 +572,7 @@ def test_to_device_with_module():
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# Process some data
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obs = torch.randn(2, 5)
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transition = (obs, None, 1.0, False, False, {}, {})
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transition = create_transition(observation=obs, reward=1.0)
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pipeline(transition)
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# Check initial device
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@@ -575,7 +596,7 @@ def test_to_device_with_module():
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# Verify the module still works after transfer
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obs_cuda = torch.randn(2, 5, device="cuda:0")
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transition = (obs_cuda, None, 1.0, False, False, {}, {})
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transition = create_transition(observation=obs_cuda, reward=1.0)
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pipeline(transition) # Should not raise an error
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@@ -589,7 +610,7 @@ def test_to_device_mixed_steps():
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# Process some data
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for i in range(5):
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transition = (torch.randn(2, 10), None, float(i), False, False, {}, {})
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transition = create_transition(observation=torch.randn(2, 10), reward=float(i))
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pipeline(transition)
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# Check initial state
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@@ -630,7 +651,7 @@ def test_to_device_preserves_functionality():
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# Process initial data
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rewards = [1.0, 2.0, 3.0]
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for r in rewards:
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transition = (None, None, r, False, False, {}, {})
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transition = create_transition(reward=r)
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pipeline(transition)
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# Check state before transfer
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@@ -645,7 +666,7 @@ def test_to_device_preserves_functionality():
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assert step.running_count == initial_count
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# Process more data to ensure functionality
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transition = (None, None, 4.0, False, False, {}, {})
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transition = create_transition(reward=4.0)
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_ = pipeline(transition)
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assert step.running_count == 4
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@@ -700,7 +721,8 @@ class MockNonModuleStepWithState:
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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"""Process transition using tensor operations."""
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obs, action, reward, done, truncated, info, comp_data = transition
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obs = transition.get(TransitionKey.OBSERVATION)
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comp_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
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if obs is not None and isinstance(obs, torch.Tensor) and obs.numel() >= self.feature_dim:
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# Perform some tensor operations
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@@ -718,7 +740,12 @@ class MockNonModuleStepWithState:
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comp_data[f"{self.name}_mean_output"] = output.mean().item()
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comp_data[f"{self.name}_steps"] = self.step_count.item()
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return (obs, action, reward, done, truncated, info, comp_data)
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# Return updated transition
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new_transition = transition.copy()
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp_data
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return new_transition
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return transition
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def get_config(self) -> dict[str, Any]:
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return {
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@@ -763,9 +790,9 @@ def test_to_device_non_module_class():
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# Process some data to populate state
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for i in range(3):
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obs = torch.randn(2, 5)
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transition = (obs, None, float(i), False, False, {}, {})
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transition = create_transition(observation=obs, reward=float(i))
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result = pipeline(transition)
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comp_data = result[6]
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comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
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assert f"{non_module_step.name}_steps" in comp_data
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# Verify all tensors are on CPU initially
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@@ -811,9 +838,9 @@ def test_to_device_non_module_class():
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# Test that step still works on GPU
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obs_gpu = torch.randn(2, 5, device="cuda")
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transition = (obs_gpu, None, 1.0, False, False, {}, {})
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transition = create_transition(observation=obs_gpu, reward=1.0)
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result = pipeline(transition)
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comp_data = result[6]
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comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
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# Verify processing worked
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assert comp_data[f"{non_module_step.name}_steps"] == 4
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@@ -835,7 +862,7 @@ def test_to_device_module_vs_non_module():
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# Process some data
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obs = torch.randn(2, 5)
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transition = (obs, None, 1.0, False, False, {}, {})
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transition = create_transition(observation=obs, reward=1.0)
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_ = pipeline(transition)
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# Check initial devices
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@@ -860,7 +887,7 @@ def test_to_device_module_vs_non_module():
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# Process data on GPU
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obs_gpu = torch.randn(2, 5, device="cuda")
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transition = (obs_gpu, None, 2.0, False, False, {}, {})
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transition = create_transition(observation=obs_gpu, reward=2.0)
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_ = pipeline(transition)
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# Verify both steps processed the data
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@@ -889,7 +916,8 @@ class MockStepWithNonSerializableParam:
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self.env = env # Non-serializable parameter (like gym.Env)
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs, action, reward, done, truncated, info, comp_data = transition
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reward = transition.get(TransitionKey.REWARD)
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comp_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
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# Use the env parameter if provided
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if self.env is not None:
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@@ -897,10 +925,14 @@ class MockStepWithNonSerializableParam:
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comp_data[f"{self.name}_env_info"] = str(self.env)
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# Apply multiplier to reward
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new_transition = transition.copy()
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if reward is not None:
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reward = reward * self.multiplier
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new_transition[TransitionKey.REWARD] = reward * self.multiplier
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return (obs, action, reward, done, truncated, info, comp_data)
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if comp_data:
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp_data
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return new_transition
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def get_config(self) -> dict[str, Any]:
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# Note: env is intentionally NOT included here as it's not serializable
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@@ -928,13 +960,15 @@ class RegisteredMockStep:
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device: str = "cpu"
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def __call__(self, transition: EnvTransition) -> EnvTransition:
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obs, action, reward, done, truncated, info, comp_data = transition
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comp_data = transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
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comp_data = {} if comp_data is None else dict(comp_data)
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comp_data["registered_step_value"] = self.value
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comp_data["registered_step_device"] = self.device
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return (obs, action, reward, done, truncated, info, comp_data)
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new_transition = transition.copy()
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new_transition[TransitionKey.COMPLEMENTARY_DATA] = comp_data
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return new_transition
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def get_config(self) -> dict[str, Any]:
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return {
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@@ -993,18 +1027,18 @@ def test_from_pretrained_with_overrides():
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assert loaded_pipeline.name == "TestOverrides"
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# Test the loaded steps
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transition = (None, None, 1.0, False, False, {}, {})
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transition = create_transition(reward=1.0)
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result = loaded_pipeline(transition)
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# Check that overrides were applied
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comp_data = result[6]
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comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
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assert "env_step_env_info" in comp_data
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assert comp_data["env_step_env_info"] == "MockEnvironment(test_env)"
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assert comp_data["registered_step_value"] == 200
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assert comp_data["registered_step_device"] == "cuda"
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# Check that multiplier override was applied
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assert result[2] == 3.0 # 1.0 * 3.0 (overridden multiplier)
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assert result[TransitionKey.REWARD] == 3.0 # 1.0 * 3.0 (overridden multiplier)
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def test_from_pretrained_with_partial_overrides():
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@@ -1024,13 +1058,13 @@ def test_from_pretrained_with_partial_overrides():
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# Both steps will get the override
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loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides)
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transition = (None, None, 1.0, False, False, {}, {})
|
||||
transition = create_transition(reward=1.0)
|
||||
result = loaded_pipeline(transition)
|
||||
|
||||
# The reward should be affected by both steps, both getting the override
|
||||
# First step: 1.0 * 5.0 = 5.0 (overridden)
|
||||
# Second step: 5.0 * 5.0 = 25.0 (also overridden)
|
||||
assert result[2] == 25.0
|
||||
assert result[TransitionKey.REWARD] == 25.0
|
||||
|
||||
|
||||
def test_from_pretrained_invalid_override_key():
|
||||
@@ -1082,10 +1116,10 @@ def test_from_pretrained_registered_step_override():
|
||||
loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides)
|
||||
|
||||
# Test that overrides were applied
|
||||
transition = (None, None, 0.0, False, False, {}, {})
|
||||
transition = create_transition()
|
||||
result = loaded_pipeline(transition)
|
||||
|
||||
comp_data = result[6]
|
||||
comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
|
||||
assert comp_data["registered_step_value"] == 999
|
||||
assert comp_data["registered_step_device"] == "cuda"
|
||||
|
||||
@@ -1110,13 +1144,13 @@ def test_from_pretrained_mixed_registered_and_unregistered():
|
||||
loaded_pipeline = RobotProcessor.from_pretrained(tmp_dir, overrides=overrides)
|
||||
|
||||
# Test both steps
|
||||
transition = (None, None, 2.0, False, False, {}, {})
|
||||
transition = create_transition(reward=2.0)
|
||||
result = loaded_pipeline(transition)
|
||||
|
||||
comp_data = result[6]
|
||||
comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
|
||||
assert comp_data["unregistered_env_info"] == "MockEnvironment(mixed_test)"
|
||||
assert comp_data["registered_step_value"] == 777
|
||||
assert result[2] == 8.0 # 2.0 * 4.0
|
||||
assert result[TransitionKey.REWARD] == 8.0 # 2.0 * 4.0
|
||||
|
||||
|
||||
def test_from_pretrained_no_overrides():
|
||||
@@ -1133,10 +1167,10 @@ def test_from_pretrained_no_overrides():
|
||||
assert len(loaded_pipeline) == 1
|
||||
|
||||
# Test that the step works (env will be None)
|
||||
transition = (None, None, 1.0, False, False, {}, {})
|
||||
transition = create_transition(reward=1.0)
|
||||
result = loaded_pipeline(transition)
|
||||
|
||||
assert result[2] == 3.0 # 1.0 * 3.0
|
||||
assert result[TransitionKey.REWARD] == 3.0 # 1.0 * 3.0
|
||||
|
||||
|
||||
def test_from_pretrained_empty_overrides():
|
||||
@@ -1153,10 +1187,10 @@ def test_from_pretrained_empty_overrides():
|
||||
assert len(loaded_pipeline) == 1
|
||||
|
||||
# Test that the step works normally
|
||||
transition = (None, None, 1.0, False, False, {}, {})
|
||||
transition = create_transition(reward=1.0)
|
||||
result = loaded_pipeline(transition)
|
||||
|
||||
assert result[2] == 2.0
|
||||
assert result[TransitionKey.REWARD] == 2.0
|
||||
|
||||
|
||||
def test_from_pretrained_override_instantiation_error():
|
||||
@@ -1185,7 +1219,7 @@ def test_from_pretrained_with_state_and_overrides():
|
||||
|
||||
# Process some data to create state
|
||||
for i in range(10):
|
||||
transition = (None, None, float(i), False, False, {}, {})
|
||||
transition = create_transition(reward=float(i))
|
||||
pipeline(transition)
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
|
||||
Reference in New Issue
Block a user