70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
import einops
|
|
import pytest
|
|
import torch
|
|
from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
|
|
from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
|
|
|
|
from lerobot.common.datasets.factory import make_offline_buffer
|
|
from lerobot.common.utils import init_hydra_config
|
|
|
|
from .utils import DEVICE, DEFAULT_CONFIG_PATH
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"env_name,dataset_id",
|
|
[
|
|
("simxarm", "lift"),
|
|
("pusht", "pusht"),
|
|
("aloha", "sim_insertion_human"),
|
|
("aloha", "sim_insertion_scripted"),
|
|
("aloha", "sim_transfer_cube_human"),
|
|
("aloha", "sim_transfer_cube_scripted"),
|
|
],
|
|
)
|
|
def test_factory(env_name, dataset_id):
|
|
cfg = init_hydra_config(
|
|
DEFAULT_CONFIG_PATH,
|
|
overrides=[f"env={env_name}", f"env.task={dataset_id}", f"device={DEVICE}"]
|
|
)
|
|
offline_buffer = make_offline_buffer(cfg)
|
|
for key in offline_buffer.image_keys:
|
|
img = offline_buffer[0].get(key)
|
|
assert img.dtype == torch.float32
|
|
# TODO(rcadene): we assume for now that image normalization takes place in the model
|
|
assert img.max() <= 1.0
|
|
assert img.min() >= 0.0
|
|
|
|
|
|
def test_compute_stats():
|
|
"""Check that the statistics are computed correctly according to the stats_patterns property.
|
|
|
|
We compare with taking a straight min, mean, max, std of all the data in one pass (which we can do
|
|
because we are working with a small dataset).
|
|
"""
|
|
cfg = init_hydra_config(
|
|
DEFAULT_CONFIG_PATH, overrides=["env=aloha", "env.task=sim_transfer_cube_human"]
|
|
)
|
|
buffer = make_offline_buffer(cfg)
|
|
# Get all of the data.
|
|
all_data = TensorDictReplayBuffer(
|
|
storage=buffer._storage,
|
|
batch_size=len(buffer),
|
|
sampler=SamplerWithoutReplacement(),
|
|
).sample().float()
|
|
# Note: we set the batch size to be smaller than the whole dataset to make sure we are testing batched
|
|
# computation of the statistics. While doing this, we also make sure it works when we don't divide the
|
|
# dataset into even batches.
|
|
computed_stats = buffer._compute_stats(batch_size=int(len(all_data) * 0.75))
|
|
for k, pattern in buffer.stats_patterns.items():
|
|
expected_mean = einops.reduce(all_data[k], pattern, "mean")
|
|
assert torch.allclose(computed_stats[k]["mean"], expected_mean)
|
|
try:
|
|
assert torch.allclose(
|
|
computed_stats[k]["std"],
|
|
torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
|
|
)
|
|
except:
|
|
breakpoint()
|
|
assert torch.allclose(computed_stats[k]["min"], einops.reduce(all_data[k], pattern, "min"))
|
|
assert torch.allclose(computed_stats[k]["max"], einops.reduce(all_data[k], pattern, "max"))
|