5231752487
The test was inadvertently comparing uninitialized parts of the array, which could lead to inconsistent or undefined results. This fix ensures only the relevant, properly initialized sections are checked. Co-authored-by: Eugene Mironov <helper2424@gmail.com>
600 lines
22 KiB
Python
600 lines
22 KiB
Python
import sys
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from typing import Callable, Optional
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import pytest
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import torch
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from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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from lerobot.scripts.server.buffer import BatchTransition, ReplayBuffer, random_crop_vectorized
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from tests.fixtures.constants import DUMMY_REPO_ID
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def state_dims() -> list[str]:
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return ["observation.image", "observation.state"]
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@pytest.fixture
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def replay_buffer() -> ReplayBuffer:
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return create_empty_replay_buffer()
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def clone_state(state: dict) -> dict:
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return {k: v.clone() for k, v in state.items()}
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def create_empty_replay_buffer(
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optimize_memory: bool = False,
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use_drq: bool = False,
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image_augmentation_function: Optional[Callable] = None,
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) -> ReplayBuffer:
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buffer_capacity = 10
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device = "cpu"
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return ReplayBuffer(
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buffer_capacity,
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device,
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state_dims(),
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optimize_memory=optimize_memory,
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use_drq=use_drq,
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image_augmentation_function=image_augmentation_function,
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)
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def create_random_image() -> torch.Tensor:
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return torch.rand(3, 84, 84)
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def create_dummy_transition() -> dict:
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return {
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"observation.image": create_random_image(),
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"action": torch.randn(4),
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"reward": torch.tensor(1.0),
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"observation.state": torch.randn(
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10,
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),
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"done": torch.tensor(False),
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"truncated": torch.tensor(False),
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"complementary_info": {},
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}
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def create_dataset_from_replay_buffer(tmp_path) -> tuple[LeRobotDataset, ReplayBuffer]:
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dummy_state_1 = create_dummy_state()
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dummy_action_1 = create_dummy_action()
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dummy_state_2 = create_dummy_state()
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dummy_action_2 = create_dummy_action()
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dummy_state_3 = create_dummy_state()
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dummy_action_3 = create_dummy_action()
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dummy_state_4 = create_dummy_state()
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dummy_action_4 = create_dummy_action()
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replay_buffer = create_empty_replay_buffer()
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replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_1, False, False)
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replay_buffer.add(dummy_state_2, dummy_action_2, 1.0, dummy_state_2, False, False)
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replay_buffer.add(dummy_state_3, dummy_action_3, 1.0, dummy_state_3, True, True)
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replay_buffer.add(dummy_state_4, dummy_action_4, 1.0, dummy_state_4, True, True)
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root = tmp_path / "test"
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return (replay_buffer.to_lerobot_dataset(DUMMY_REPO_ID, root=root), replay_buffer)
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def create_dummy_state() -> dict:
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return {
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"observation.image": create_random_image(),
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"observation.state": torch.randn(
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10,
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),
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}
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def get_tensor_memory_consumption(tensor):
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return tensor.nelement() * tensor.element_size()
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def get_tensors_memory_consumption(obj, visited_addresses):
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total_size = 0
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address = id(obj)
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if address in visited_addresses:
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return 0
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visited_addresses.add(address)
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if isinstance(obj, torch.Tensor):
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return get_tensor_memory_consumption(obj)
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elif isinstance(obj, (list, tuple)):
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for item in obj:
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total_size += get_tensors_memory_consumption(item, visited_addresses)
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elif isinstance(obj, dict):
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for value in obj.values():
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total_size += get_tensors_memory_consumption(value, visited_addresses)
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elif hasattr(obj, "__dict__"):
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# It's an object, we need to get the size of the attributes
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for _, attr in vars(obj).items():
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total_size += get_tensors_memory_consumption(attr, visited_addresses)
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return total_size
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def get_object_memory(obj):
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# Track visited addresses to avoid infinite loops
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# and cases when two properties point to the same object
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visited_addresses = set()
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# Get the size of the object in bytes
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total_size = sys.getsizeof(obj)
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# Get the size of the tensor attributes
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total_size += get_tensors_memory_consumption(obj, visited_addresses)
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return total_size
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def create_dummy_action() -> torch.Tensor:
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return torch.randn(4)
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def dict_properties() -> list:
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return ["state", "next_state"]
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@pytest.fixture
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def dummy_state() -> dict:
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return create_dummy_state()
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@pytest.fixture
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def next_dummy_state() -> dict:
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return create_dummy_state()
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@pytest.fixture
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def dummy_action() -> torch.Tensor:
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return torch.randn(4)
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def test_empty_buffer_sample_raises_error(replay_buffer):
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assert len(replay_buffer) == 0, "Replay buffer should be empty."
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assert replay_buffer.capacity == 10, "Replay buffer capacity should be 10."
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with pytest.raises(RuntimeError, match="Cannot sample from an empty buffer"):
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replay_buffer.sample(1)
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def test_zero_capacity_buffer_raises_error():
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with pytest.raises(ValueError, match="Capacity must be greater than 0."):
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ReplayBuffer(0, "cpu", ["observation", "next_observation"])
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def test_add_transition(replay_buffer, dummy_state, dummy_action):
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replay_buffer.add(dummy_state, dummy_action, 1.0, dummy_state, False, False)
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assert len(replay_buffer) == 1, "Replay buffer should have one transition after adding."
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assert torch.equal(replay_buffer.actions[0], dummy_action), (
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"Action should be equal to the first transition."
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)
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assert replay_buffer.rewards[0] == 1.0, "Reward should be equal to the first transition."
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assert not replay_buffer.dones[0], "Done should be False for the first transition."
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assert not replay_buffer.truncateds[0], "Truncated should be False for the first transition."
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for dim in state_dims():
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assert torch.equal(replay_buffer.states[dim][0], dummy_state[dim]), (
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"Observation should be equal to the first transition."
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)
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assert torch.equal(replay_buffer.next_states[dim][0], dummy_state[dim]), (
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"Next observation should be equal to the first transition."
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)
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def test_add_over_capacity():
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replay_buffer = ReplayBuffer(2, "cpu", ["observation", "next_observation"])
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dummy_state_1 = create_dummy_state()
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dummy_action_1 = create_dummy_action()
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dummy_state_2 = create_dummy_state()
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dummy_action_2 = create_dummy_action()
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dummy_state_3 = create_dummy_state()
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dummy_action_3 = create_dummy_action()
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replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_1, False, False)
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replay_buffer.add(dummy_state_2, dummy_action_2, 1.0, dummy_state_2, False, False)
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replay_buffer.add(dummy_state_3, dummy_action_3, 1.0, dummy_state_3, True, True)
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assert len(replay_buffer) == 2, "Replay buffer should have 2 transitions after adding 3."
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for dim in state_dims():
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assert torch.equal(replay_buffer.states[dim][0], dummy_state_3[dim]), (
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"Observation should be equal to the first transition."
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)
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assert torch.equal(replay_buffer.next_states[dim][0], dummy_state_3[dim]), (
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"Next observation should be equal to the first transition."
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)
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assert torch.equal(replay_buffer.actions[0], dummy_action_3), (
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"Action should be equal to the last transition."
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)
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assert replay_buffer.rewards[0] == 1.0, "Reward should be equal to the last transition."
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assert replay_buffer.dones[0], "Done should be True for the first transition."
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assert replay_buffer.truncateds[0], "Truncated should be True for the first transition."
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def test_sample_from_empty_buffer(replay_buffer):
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with pytest.raises(RuntimeError, match="Cannot sample from an empty buffer"):
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replay_buffer.sample(1)
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def test_sample_with_1_transition(replay_buffer, dummy_state, next_dummy_state, dummy_action):
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replay_buffer.add(dummy_state, dummy_action, 1.0, next_dummy_state, False, False)
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got_batch_transition = replay_buffer.sample(1)
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expected_batch_transition = BatchTransition(
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state=clone_state(dummy_state),
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action=dummy_action.clone(),
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reward=1.0,
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next_state=clone_state(next_dummy_state),
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done=False,
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truncated=False,
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)
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for buffer_property in dict_properties():
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for k, v in expected_batch_transition[buffer_property].items():
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got_state = got_batch_transition[buffer_property][k]
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assert got_state.shape[0] == 1, f"{k} should have 1 transition."
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assert got_state.device.type == "cpu", f"{k} should be on cpu."
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assert torch.equal(got_state[0], v), f"{k} should be equal to the expected batch transition."
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for key, _value in expected_batch_transition.items():
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if key in dict_properties():
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continue
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got_value = got_batch_transition[key]
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v_tensor = expected_batch_transition[key]
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if not isinstance(v_tensor, torch.Tensor):
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v_tensor = torch.tensor(v_tensor)
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assert got_value.shape[0] == 1, f"{key} should have 1 transition."
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assert got_value.device.type == "cpu", f"{key} should be on cpu."
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assert torch.equal(got_value[0], v_tensor), f"{key} should be equal to the expected batch transition."
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def test_sample_with_batch_bigger_than_buffer_size(
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replay_buffer, dummy_state, next_dummy_state, dummy_action
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):
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replay_buffer.add(dummy_state, dummy_action, 1.0, next_dummy_state, False, False)
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got_batch_transition = replay_buffer.sample(10)
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expected_batch_transition = BatchTransition(
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state=dummy_state,
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action=dummy_action,
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reward=1.0,
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next_state=next_dummy_state,
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done=False,
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truncated=False,
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)
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for buffer_property in dict_properties():
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for k in expected_batch_transition[buffer_property]:
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got_state = got_batch_transition[buffer_property][k]
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assert got_state.shape[0] == 1, f"{k} should have 1 transition."
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for key in expected_batch_transition:
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if key in dict_properties():
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continue
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got_value = got_batch_transition[key]
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assert got_value.shape[0] == 1, f"{key} should have 1 transition."
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def test_sample_batch(replay_buffer):
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dummy_state_1 = create_dummy_state()
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dummy_action_1 = create_dummy_action()
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dummy_state_2 = create_dummy_state()
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dummy_action_2 = create_dummy_action()
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dummy_state_3 = create_dummy_state()
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dummy_action_3 = create_dummy_action()
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dummy_state_4 = create_dummy_state()
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dummy_action_4 = create_dummy_action()
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replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_1, False, False)
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replay_buffer.add(dummy_state_2, dummy_action_2, 2.0, dummy_state_2, False, False)
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replay_buffer.add(dummy_state_3, dummy_action_3, 3.0, dummy_state_3, True, True)
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replay_buffer.add(dummy_state_4, dummy_action_4, 4.0, dummy_state_4, True, True)
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dummy_states = [dummy_state_1, dummy_state_2, dummy_state_3, dummy_state_4]
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dummy_actions = [dummy_action_1, dummy_action_2, dummy_action_3, dummy_action_4]
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got_batch_transition = replay_buffer.sample(3)
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for buffer_property in dict_properties():
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for k in got_batch_transition[buffer_property]:
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got_state = got_batch_transition[buffer_property][k]
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assert got_state.shape[0] == 3, f"{k} should have 3 transition."
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for got_state_item in got_state:
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assert any(torch.equal(got_state_item, dummy_state[k]) for dummy_state in dummy_states), (
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f"{k} should be equal to one of the dummy states."
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)
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for got_action_item in got_batch_transition["action"]:
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assert any(torch.equal(got_action_item, dummy_action) for dummy_action in dummy_actions), (
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"Actions should be equal to the dummy actions."
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)
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for k in got_batch_transition:
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if k in dict_properties() or k == "complementary_info":
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continue
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got_value = got_batch_transition[k]
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assert got_value.shape[0] == 3, f"{k} should have 3 transition."
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def test_to_lerobot_dataset_with_empty_buffer(replay_buffer):
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with pytest.raises(ValueError, match="The replay buffer is empty. Cannot convert to a dataset."):
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replay_buffer.to_lerobot_dataset("dummy_repo")
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def test_to_lerobot_dataset(tmp_path):
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ds, buffer = create_dataset_from_replay_buffer(tmp_path)
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assert len(ds) == len(buffer), "Dataset should have the same size as the Replay Buffer"
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assert ds.fps == 1, "FPS should be 1"
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assert ds.repo_id == "dummy/repo", "The dataset should have `dummy/repo` repo id"
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for dim in state_dims():
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assert dim in ds.features
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assert ds.features[dim]["shape"] == buffer.states[dim][0].shape
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assert ds.num_episodes == 2
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assert ds.num_frames == 4
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for j, value in enumerate(ds):
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print(torch.equal(value["observation.image"], buffer.next_states["observation.image"][j]))
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for i in range(len(ds)):
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for feature, value in ds[i].items():
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if feature == "action":
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assert torch.equal(value, buffer.actions[i])
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elif feature == "next.reward":
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assert torch.equal(value, buffer.rewards[i])
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elif feature == "next.done":
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assert torch.equal(value, buffer.dones[i])
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elif feature == "observation.image":
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# Tenssor -> numpy is not precise, so we have some diff there
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# TODO: Check and fix it
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torch.testing.assert_close(value, buffer.states["observation.image"][i], rtol=0.3, atol=0.003)
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elif feature == "observation.state":
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assert torch.equal(value, buffer.states["observation.state"][i])
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def test_from_lerobot_dataset(tmp_path):
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dummy_state_1 = create_dummy_state()
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dummy_action_1 = create_dummy_action()
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dummy_state_2 = create_dummy_state()
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dummy_action_2 = create_dummy_action()
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dummy_state_3 = create_dummy_state()
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dummy_action_3 = create_dummy_action()
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dummy_state_4 = create_dummy_state()
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dummy_action_4 = create_dummy_action()
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replay_buffer = create_empty_replay_buffer()
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replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_1, False, False)
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replay_buffer.add(dummy_state_2, dummy_action_2, 1.0, dummy_state_2, False, False)
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replay_buffer.add(dummy_state_3, dummy_action_3, 1.0, dummy_state_3, True, True)
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replay_buffer.add(dummy_state_4, dummy_action_4, 1.0, dummy_state_4, True, True)
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root = tmp_path / "test"
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ds = replay_buffer.to_lerobot_dataset(DUMMY_REPO_ID, root=root)
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reconverted_buffer = ReplayBuffer.from_lerobot_dataset(
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ds, state_keys=list(state_dims()), device="cpu", capacity=replay_buffer.capacity, use_drq=False
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)
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# Check only the part of the buffer that's actually filled with data
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assert torch.equal(
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reconverted_buffer.actions[: len(replay_buffer)],
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replay_buffer.actions[: len(replay_buffer)],
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), "Actions from converted buffer should be equal to the original replay buffer."
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assert torch.equal(
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reconverted_buffer.rewards[: len(replay_buffer)], replay_buffer.rewards[: len(replay_buffer)]
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), "Rewards from converted buffer should be equal to the original replay buffer."
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assert torch.equal(
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reconverted_buffer.dones[: len(replay_buffer)], replay_buffer.dones[: len(replay_buffer)]
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), "Dones from converted buffer should be equal to the original replay buffer."
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# Lerobot DS haven't supported truncateds yet
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expected_truncateds = torch.zeros(len(replay_buffer)).bool()
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assert torch.equal(reconverted_buffer.truncateds[: len(replay_buffer)], expected_truncateds), (
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"Truncateds from converted buffer should be equal False"
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)
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assert torch.equal(
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replay_buffer.states["observation.state"][: len(replay_buffer)],
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reconverted_buffer.states["observation.state"][: len(replay_buffer)],
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), "State should be the same after converting to dataset and return back"
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for i in range(4):
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torch.testing.assert_close(
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replay_buffer.states["observation.image"][i],
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reconverted_buffer.states["observation.image"][i],
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rtol=0.4,
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atol=0.004,
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)
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# The 2, 3 frames have done flag, so their values will be equal to the current state
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for i in range(2):
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# In the current implementation we take the next state from the `states` and ignore `next_states`
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next_index = (i + 1) % 4
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torch.testing.assert_close(
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replay_buffer.states["observation.image"][next_index],
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reconverted_buffer.next_states["observation.image"][i],
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rtol=0.4,
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atol=0.004,
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)
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for i in range(2, 4):
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assert torch.equal(
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replay_buffer.states["observation.state"][i],
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reconverted_buffer.next_states["observation.state"][i],
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)
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def test_buffer_sample_alignment():
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# Initialize buffer
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buffer = ReplayBuffer(capacity=100, device="cpu", state_keys=["state_value"], storage_device="cpu")
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# Fill buffer with patterned data
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for i in range(100):
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signature = float(i) / 100.0
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state = {"state_value": torch.tensor([[signature]]).float()}
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action = torch.tensor([[2.0 * signature]]).float()
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reward = 3.0 * signature
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is_end = (i + 1) % 10 == 0
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if is_end:
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next_state = {"state_value": torch.tensor([[signature]]).float()}
|
|
done = True
|
|
else:
|
|
next_signature = float(i + 1) / 100.0
|
|
next_state = {"state_value": torch.tensor([[next_signature]]).float()}
|
|
done = False
|
|
|
|
buffer.add(state, action, reward, next_state, done, False)
|
|
|
|
# Sample and verify
|
|
batch = buffer.sample(50)
|
|
|
|
for i in range(50):
|
|
state_sig = batch["state"]["state_value"][i].item()
|
|
action_val = batch["action"][i].item()
|
|
reward_val = batch["reward"][i].item()
|
|
next_state_sig = batch["next_state"]["state_value"][i].item()
|
|
is_done = batch["done"][i].item() > 0.5
|
|
|
|
# Verify relationships
|
|
assert abs(action_val - 2.0 * state_sig) < 1e-4, (
|
|
f"Action {action_val} should be 2x state signature {state_sig}"
|
|
)
|
|
|
|
assert abs(reward_val - 3.0 * state_sig) < 1e-4, (
|
|
f"Reward {reward_val} should be 3x state signature {state_sig}"
|
|
)
|
|
|
|
if is_done:
|
|
assert abs(next_state_sig - state_sig) < 1e-4, (
|
|
f"For done states, next_state {next_state_sig} should equal state {state_sig}"
|
|
)
|
|
else:
|
|
# Either it's the next sequential state (+0.01) or same state (for episode boundaries)
|
|
valid_next = (
|
|
abs(next_state_sig - state_sig - 0.01) < 1e-4 or abs(next_state_sig - state_sig) < 1e-4
|
|
)
|
|
assert valid_next, (
|
|
f"Next state {next_state_sig} should be either state+0.01 or same as state {state_sig}"
|
|
)
|
|
|
|
|
|
def test_memory_optimization():
|
|
dummy_state_1 = create_dummy_state()
|
|
dummy_action_1 = create_dummy_action()
|
|
|
|
dummy_state_2 = create_dummy_state()
|
|
dummy_action_2 = create_dummy_action()
|
|
|
|
dummy_state_3 = create_dummy_state()
|
|
dummy_action_3 = create_dummy_action()
|
|
|
|
dummy_state_4 = create_dummy_state()
|
|
dummy_action_4 = create_dummy_action()
|
|
|
|
replay_buffer = create_empty_replay_buffer()
|
|
replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_2, False, False)
|
|
replay_buffer.add(dummy_state_2, dummy_action_2, 1.0, dummy_state_3, False, False)
|
|
replay_buffer.add(dummy_state_3, dummy_action_3, 1.0, dummy_state_4, False, False)
|
|
replay_buffer.add(dummy_state_4, dummy_action_4, 1.0, dummy_state_4, True, True)
|
|
|
|
optimized_replay_buffer = create_empty_replay_buffer(True)
|
|
optimized_replay_buffer.add(dummy_state_1, dummy_action_1, 1.0, dummy_state_2, False, False)
|
|
optimized_replay_buffer.add(dummy_state_2, dummy_action_2, 1.0, dummy_state_3, False, False)
|
|
optimized_replay_buffer.add(dummy_state_3, dummy_action_3, 1.0, dummy_state_4, False, False)
|
|
optimized_replay_buffer.add(dummy_state_4, dummy_action_4, 1.0, None, True, True)
|
|
|
|
assert get_object_memory(optimized_replay_buffer) < get_object_memory(replay_buffer), (
|
|
"Optimized replay buffer should be smaller than the original replay buffer"
|
|
)
|
|
|
|
|
|
def test_check_image_augmentations_with_drq_and_dummy_image_augmentation_function(dummy_state, dummy_action):
|
|
def dummy_image_augmentation_function(x):
|
|
return torch.ones_like(x) * 10
|
|
|
|
replay_buffer = create_empty_replay_buffer(
|
|
use_drq=True, image_augmentation_function=dummy_image_augmentation_function
|
|
)
|
|
|
|
replay_buffer.add(dummy_state, dummy_action, 1.0, dummy_state, False, False)
|
|
|
|
sampled_transitions = replay_buffer.sample(1)
|
|
assert torch.all(sampled_transitions["state"]["observation.image"] == 10), (
|
|
"Image augmentations should be applied"
|
|
)
|
|
assert torch.all(sampled_transitions["next_state"]["observation.image"] == 10), (
|
|
"Image augmentations should be applied"
|
|
)
|
|
|
|
|
|
def test_check_image_augmentations_with_drq_and_default_image_augmentation_function(
|
|
dummy_state, dummy_action
|
|
):
|
|
replay_buffer = create_empty_replay_buffer(use_drq=True)
|
|
|
|
replay_buffer.add(dummy_state, dummy_action, 1.0, dummy_state, False, False)
|
|
|
|
# Let's check that it doesn't fail and shapes are correct
|
|
sampled_transitions = replay_buffer.sample(1)
|
|
assert sampled_transitions["state"]["observation.image"].shape == (1, 3, 84, 84)
|
|
assert sampled_transitions["next_state"]["observation.image"].shape == (1, 3, 84, 84)
|
|
|
|
|
|
def test_random_crop_vectorized_basic():
|
|
# Create a batch of 2 images with known patterns
|
|
batch_size, channels, height, width = 2, 3, 10, 8
|
|
images = torch.zeros((batch_size, channels, height, width))
|
|
|
|
# Fill with unique values for testing
|
|
for b in range(batch_size):
|
|
images[b] = b + 1
|
|
|
|
crop_size = (6, 4) # Smaller than original
|
|
cropped = random_crop_vectorized(images, crop_size)
|
|
|
|
# Check output shape
|
|
assert cropped.shape == (batch_size, channels, *crop_size)
|
|
|
|
# Check that values are preserved (should be either 1s or 2s for respective batches)
|
|
assert torch.all(cropped[0] == 1)
|
|
assert torch.all(cropped[1] == 2)
|
|
|
|
|
|
def test_random_crop_vectorized_invalid_size():
|
|
images = torch.zeros((2, 3, 10, 8))
|
|
|
|
# Test crop size larger than image
|
|
with pytest.raises(ValueError, match="Requested crop size .* is bigger than the image size"):
|
|
random_crop_vectorized(images, (12, 8))
|
|
|
|
with pytest.raises(ValueError, match="Requested crop size .* is bigger than the image size"):
|
|
random_crop_vectorized(images, (10, 10))
|