update: actpred 默认图像分辨率 64→224
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@@ -9,7 +9,7 @@ Transformer(pre-LN)自注意力融合,取 [PRED] token 回归下一步动
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## 架构
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```
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F 帧图像 (F×3×64×64) ─► 共享 CNN 编码器 ─► F 个 token ─► proj → d_model ┐
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F 帧图像 (F×3×224×224) ─► 共享 CNN 编码器 ─► F 个 token ─► proj → d_model ┐
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├─► [img×F, act×H, PRED] + 位置编码
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动作历史 (H×D) ─► Linear proj → d_model ─► H 个 token ────────┤ │
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可学习 [PRED] token ────────────────────────────────────────┘ ▼
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@@ -110,7 +110,7 @@ recorder.flush()
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| 参数 | 默认值 | 说明 |
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|------|--------|------|
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| `--image-size` | 64 | 图像缩放尺寸 |
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| `--image-size` | 224 | 图像缩放尺寸 |
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| `--action-dim` | 18 | 动作向量维度(对齐 bot4cj 18 列:10 舵机 + 2 云台 + 扬声器频率 + 延时 + 4 预留)|
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| `--history-len` | 5 | 历史动作窗口长度 |
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| `--frame-len` | 3 | 输入图像帧数(含当前帧,旧帧在前)|
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@@ -5,7 +5,7 @@ from typing import Optional
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@dataclass
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class DataConfig:
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image_size: int = 64
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image_size: int = 224
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action_dim: int = 18
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history_len: int = 5
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frame_len: int = 3
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@@ -16,7 +16,7 @@ def _natural_key(p):
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class ActionDataset(Dataset):
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def __init__(self, data_dir: str, history_len: int, action_dim: int,
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image_size: int = 64, normalize_actions: bool = False,
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image_size: int = 224, normalize_actions: bool = False,
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action_stats_path: str = None, episode_indices: list = None,
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frame_len: int = 3):
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self.history_len = history_len
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@@ -263,7 +263,7 @@ class TestConfig(unittest.TestCase):
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def test_default_config(self):
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cfg = Config()
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self.assertEqual(cfg.data.image_size, 64)
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self.assertEqual(cfg.data.image_size, 224)
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self.assertEqual(cfg.data.action_dim, 18)
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self.assertEqual(cfg.data.history_len, 5)
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self.assertEqual(cfg.data.frame_len, 3)
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