enable test_compute_stats

enable test_compute_stats

tests/test_datasets.py

@@ -1,6 +1,12 @@
+import os
+from pathlib import Path
+import einops
 import pytest
 import torch
 
+from lerobot.common.datasets.utils import compute_stats, get_stats_einops_patterns
+from lerobot.common.datasets.xarm import XarmDataset
+from lerobot.common.transforms import Prod
 from lerobot.common.utils import init_hydra_config
 import logging
 from lerobot.common.datasets.factory import make_dataset
@@ -81,28 +87,58 @@ def test_factory(env_name, dataset_id, policy_name):
             assert key in item, f"{key}"
 
 
-# def test_compute_stats():
-#     """Check that the statistics are computed correctly according to the stats_patterns property.
+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"]
-#     )
-#     dataset = make_dataset(cfg)
-#     # Get all of the data.
-#     all_data = dataset.data_dict
-#     # 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)
-#         assert torch.allclose(
-#             computed_stats[k]["std"],
-#             torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
-#         )
-#         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"))
+    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).
+    """
+    DATA_DIR = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
+
+    # get transform to convert images from uint8 [0,255] to float32 [0,1]
+    transform = Prod(in_keys=XarmDataset.image_keys, prod=1 / 255.0)
+
+    dataset = XarmDataset(
+        dataset_id="xarm_lift_medium",
+        root=DATA_DIR,
+        transform=transform,
+    )
+
+    # 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 = compute_stats(dataset, batch_size=int(len(dataset) * 0.25))
+
+    # get einops patterns to aggregate batches and compute statistics
+    stats_patterns = get_stats_einops_patterns(dataset)
+
+    # get all frames from the dataset in the same dtype and range as during compute_stats
+    data_dict = transform(dataset.data_dict)
+
+    # compute stats based on all frames from the dataset without any batching
+    expected_stats = {}
+    for k, pattern in stats_patterns.items():
+        expected_stats[k] = {}
+        expected_stats[k]["mean"] = einops.reduce(data_dict[k], pattern, "mean")
+        expected_stats[k]["std"] = torch.sqrt(einops.reduce((data_dict[k] - expected_stats[k]["mean"]) ** 2, pattern, "mean"))
+        expected_stats[k]["min"] = einops.reduce(data_dict[k], pattern, "min")
+        expected_stats[k]["max"] = einops.reduce(data_dict[k], pattern, "max")
+
+    # test computed stats match expected stats
+    for k in stats_patterns:
+        assert torch.allclose(computed_stats[k]["mean"], expected_stats[k]["mean"])
+        assert torch.allclose(computed_stats[k]["std"], expected_stats[k]["std"])
+        assert torch.allclose(computed_stats[k]["min"], expected_stats[k]["min"])
+        assert torch.allclose(computed_stats[k]["max"], expected_stats[k]["max"])
+
+    # TODO(rcadene): check that the stats used for training are correct too
+    # # load stats that are expected to match the ones returned by computed_stats
+    # assert (dataset.data_dir / "stats.pth").exists()
+    # loaded_stats = torch.load(dataset.data_dir / "stats.pth")
+
+    # # test loaded stats match expected stats
+    # for k in stats_patterns:
+    #     assert torch.allclose(loaded_stats[k]["mean"], expected_stats[k]["mean"])
+    #     assert torch.allclose(loaded_stats[k]["std"], expected_stats[k]["std"])
+    #     assert torch.allclose(loaded_stats[k]["min"], expected_stats[k]["min"])
+    #     assert torch.allclose(loaded_stats[k]["max"], expected_stats[k]["max"])