76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
import pytest
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import torch
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from lerobot.common.utils import init_hydra_config
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import logging
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from lerobot.common.datasets.factory import make_dataset
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from .utils import DEVICE, DEFAULT_CONFIG_PATH
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@pytest.mark.parametrize(
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"env_name,dataset_id",
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[
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("simxarm", "lift"),
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("pusht", "pusht"),
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("aloha", "sim_insertion_human"),
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("aloha", "sim_insertion_scripted"),
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("aloha", "sim_transfer_cube_human"),
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("aloha", "sim_transfer_cube_scripted"),
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],
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)
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def test_factory(env_name, dataset_id):
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cfg = init_hydra_config(
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DEFAULT_CONFIG_PATH,
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overrides=[f"env={env_name}", f"env.task={dataset_id}", f"device={DEVICE}"]
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)
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dataset = make_dataset(cfg)
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item = dataset[0]
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assert "action" in item
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assert "episode" in item
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assert "frame_id" in item
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assert "timestamp" in item
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assert "next.done" in item
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# TODO(rcadene): should we rename it agent_pos?
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assert "observation.state" in item
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for key in dataset.image_keys:
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img = item.get(key)
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assert img.dtype == torch.float32
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# TODO(rcadene): we assume for now that image normalization takes place in the model
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assert img.max() <= 1.0
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assert img.min() >= 0.0
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if "next.reward" not in item:
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logging.warning(f'Missing "next.reward" key in dataset {dataset}.')
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if "next.done" not in item:
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logging.warning(f'Missing "next.done" key in dataset {dataset}.')
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# def test_compute_stats():
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# """Check that the statistics are computed correctly according to the stats_patterns property.
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# We compare with taking a straight min, mean, max, std of all the data in one pass (which we can do
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# because we are working with a small dataset).
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# """
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# cfg = init_hydra_config(
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# DEFAULT_CONFIG_PATH, overrides=["env=aloha", "env.task=sim_transfer_cube_human"]
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# )
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# dataset = make_dataset(cfg)
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# # Get all of the data.
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# all_data = dataset.data_dict
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# # Note: we set the batch size to be smaller than the whole dataset to make sure we are testing batched
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# # computation of the statistics. While doing this, we also make sure it works when we don't divide the
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# # dataset into even batches.
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# computed_stats = buffer._compute_stats(batch_size=int(len(all_data) * 0.75))
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# for k, pattern in buffer.stats_patterns.items():
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# expected_mean = einops.reduce(all_data[k], pattern, "mean")
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# assert torch.allclose(computed_stats[k]["mean"], expected_mean)
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# assert torch.allclose(
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# computed_stats[k]["std"],
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# torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
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# )
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# assert torch.allclose(computed_stats[k]["min"], einops.reduce(all_data[k], pattern, "min"))
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# assert torch.allclose(computed_stats[k]["max"], einops.reduce(all_data[k], pattern, "max"))
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