SoundGame Dev
f0f0a9b50e
- 共享引擎（app/src/main）：GestureEngine 6 手势识别、AudioEngine 三层音频 （ExoPlayer BGM 交叉淡入 / 对白 ducking / SoundPool 低延迟）、Game/GameEngine 状态机接口、MainActivity 全屏封面 + TalkBack - 《声命线》storyline：末世互动广播剧，3 幕 8 抉择点 4 结局 - 《声东击西》reflex：听声辨位反应，12 波战斗 + BGM 三档 BPM 变速 - 《暗室》escape：3x3 网格密室逃脱，钥匙→铁柜→保险丝→电箱→大门谜题链 - 文档：setup（从零搭建）/ architecture（架构）/ engine-api（引擎 API）/ asset-pipeline（资产流水线）/ games/*（三款游戏设计文档） - 资产管线：Qwen3-TTS 对白 + 程序合成音效/BGM + Mureka BGM 的 gen 脚本与台账， 全部 ogg 资产入库可复现 - env.d 环境脚本：android/tts 入库；sfx/mureka 含密钥不入库，仅提交 .example.sh 模板
91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
import numpy as np
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import pytest
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pytest.importorskip("mlx.core")
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import mlx.core as mx
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from optimized.mlx.models.defs.training import (
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rectified_flow_loss,
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sample_training_timesteps,
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shift_training_timesteps,
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)
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def test_truncated_logit_normal_sampler_matches_sa3_distribution():
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values = sample_training_timesteps(
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"trunc_logit_normal",
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20_000,
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rng=np.random.default_rng(17),
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)
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assert values.dtype == np.float32
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assert np.all((values >= 0.0) & (values <= 1.0))
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assert 0.52 < float(values.mean()) < 0.55
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assert 0.52 < float(np.median(values)) < 0.56
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def test_full_training_shift_matches_sa3_defaults():
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shifted = shift_training_timesteps(
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[0.5],
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507,
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shift_type="full",
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)
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assert shifted[0] == pytest.approx(0.6323907626)
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def test_rectified_flow_loss_uses_velocity_target_and_mask():
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clean = mx.array([[[1.0, 2.0, 3.0]]], dtype=mx.float32)
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noise = mx.array([[[4.0, 5.0, 6.0]]], dtype=mx.float32)
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timesteps = mx.array([0.25], dtype=mx.float32)
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mask = mx.array([[True, False, True]])
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def perfect_model(noised, timestep):
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del noised, timestep
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return noise - clean
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def zero_model(noised, timestep):
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del timestep
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return mx.zeros_like(noised)
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assert float(
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rectified_flow_loss(
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perfect_model,
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clean,
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timesteps,
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noise=noise,
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loss_mask=mask,
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)
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) == pytest.approx(0.0)
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assert float(
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rectified_flow_loss(
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zero_model,
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clean,
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timesteps,
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noise=noise,
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loss_mask=mask,
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)
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) == pytest.approx(9.0)
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def test_rectified_flow_loss_averages_masked_loss_per_sample():
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clean = mx.zeros((2, 1, 3), dtype=mx.float32)
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noise = mx.array([[[1.0, 1.0, 1.0]], [[3.0, 3.0, 3.0]]], dtype=mx.float32)
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timesteps = mx.array([0.25, 0.25], dtype=mx.float32)
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mask = mx.array([[True, False, False], [True, True, True]])
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def zero_model(noised, timestep):
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del noised, timestep
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return mx.zeros_like(clean)
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loss = rectified_flow_loss(
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zero_model,
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clean,
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timesteps,
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noise=noise,
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loss_mask=mask,
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)
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assert float(loss) == pytest.approx(5.0)
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