65 lines
2.6 KiB
Python
65 lines
2.6 KiB
Python
import einops
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import pytest
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import torch
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from torchrl.data.replay_buffers.replay_buffers import TensorDictReplayBuffer
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from torchrl.data.replay_buffers.samplers import SamplerWithoutReplacement
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from lerobot.common.datasets.factory import make_offline_buffer
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from lerobot.common.utils import init_hydra_config
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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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offline_buffer = make_offline_buffer(cfg)
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for key in offline_buffer.image_keys:
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img = offline_buffer[0].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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def test_compute_stats():
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"""Check that the correct statistics are computed.
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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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This test does not check that the stats_patterns are correct (instead, it relies on them).
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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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buffer = make_offline_buffer(cfg)
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# Get all of the data.
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all_data = TensorDictReplayBuffer(
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storage=buffer._storage,
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batch_size=len(buffer),
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sampler=SamplerWithoutReplacement(),
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).sample().float()
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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.
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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(computed_stats[k]["std"], torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean")))
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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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