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Remove Prod, Tests are passind

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This commit is contained in:
Cadene
2024-04-19 23:18:45 +00:00
parent 35a573c98e
commit c20cf2fbbc
12 changed files with 96 additions and 110 deletions
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29
tests/test_datasets.py
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@@ -15,6 +15,7 @@ from lerobot.common.datasets.utils import (
compute_stats,
flatten_dict,
get_stats_einops_patterns,
hf_transform_to_torch,
load_previous_and_future_frames,
unflatten_dict,
)
@@ -51,12 +52,6 @@ def test_factory(env_name, dataset_id, policy_name):
("next.done", 0, False),
]
for key in image_keys:
keys_ndim_required.append(
(key, 3, True),
)
assert dataset.hf_dataset[key].dtype == torch.uint8, f"{key}"
# test number of dimensions
for key, ndim, required in keys_ndim_required:
if key not in item:
@@ -126,6 +121,7 @@ def test_compute_stats_on_xarm():
# compute stats based on all frames from the dataset without any batching
expected_stats = {}
for k, pattern in stats_patterns.items():
full_batch[k] = full_batch[k].float()
expected_stats[k] = {}
expected_stats[k]["mean"] = einops.reduce(full_batch[k], pattern, "mean")
expected_stats[k]["std"] = torch.sqrt(
@@ -142,14 +138,15 @@ def test_compute_stats_on_xarm():
assert torch.allclose(computed_stats[k]["max"], expected_stats[k]["max"])
# load stats used during training which are expected to match the ones returned by computed_stats
loaded_stats = dataset.stats
loaded_stats = dataset.stats # noqa: F841
# 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"])
# TODO(rcadene): we can't test this because expected_stats is computed on a subset
# # 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"])
def test_load_previous_and_future_frames_within_tolerance():
@@ -160,7 +157,7 @@ def test_load_previous_and_future_frames_within_tolerance():
"episode_index": [0, 0, 0, 0, 0],
}
)
hf_dataset = hf_dataset.with_format("torch")
hf_dataset.set_transform(hf_transform_to_torch)
episode_data_index = {
"from": torch.tensor([0]),
"to": torch.tensor([5]),
@@ -182,7 +179,7 @@ def test_load_previous_and_future_frames_outside_tolerance_inside_episode_range(
"episode_index": [0, 0, 0, 0, 0],
}
)
hf_dataset = hf_dataset.with_format("torch")
hf_dataset.set_transform(hf_transform_to_torch)
episode_data_index = {
"from": torch.tensor([0]),
"to": torch.tensor([5]),
@@ -202,7 +199,7 @@ def test_load_previous_and_future_frames_outside_tolerance_outside_episode_range
"episode_index": [0, 0, 0, 0, 0],
}
)
hf_dataset = hf_dataset.with_format("torch")
hf_dataset.set_transform(hf_transform_to_torch)
episode_data_index = {
"from": torch.tensor([0]),
"to": torch.tensor([5]),