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Pepijn
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for training-time RTC helpers."""
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import torch
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from lerobot.configs.types import RTCTrainingDelayDistribution
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from lerobot.policies.rtc.configuration_rtc import RTCTrainingConfig
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from lerobot.policies.rtc.training_time import apply_rtc_training_time, sample_rtc_delay
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def test_rtc_training_config_defaults():
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config = RTCTrainingConfig()
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assert config.enabled is False
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assert config.min_delay == 0
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assert config.max_delay == 0
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assert config.delay_distribution == RTCTrainingDelayDistribution.UNIFORM
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assert config.exp_decay == 1.0
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def test_sample_rtc_delay_uniform_range():
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cfg = RTCTrainingConfig(enabled=True, min_delay=1, max_delay=4)
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delays = sample_rtc_delay(cfg, batch_size=100, device=torch.device("cpu"))
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assert delays.min().item() >= 1
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assert delays.max().item() <= 4
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def test_apply_rtc_training_time_prefix_mask():
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time = torch.tensor([0.5])
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delays = torch.tensor([2])
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time_tokens, postfix_mask = apply_rtc_training_time(time, delays, seq_len=4)
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assert time_tokens.shape == (1, 4)
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assert postfix_mask.shape == (1, 4)
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# Delay=2 means the first two steps are prefix (time forced to 0.0) and only the last two are postfix.
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assert torch.allclose(time_tokens[0], torch.tensor([0.0, 0.0, 0.5, 0.5]))
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assert torch.equal(postfix_mask[0], torch.tensor([False, False, True, True]))
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