hexone2086
479c2b1488
- CNNEncoder: stride=2 convs replace Conv2d+MaxPool2d pattern - 3 layers (32,64,128) instead of 4 (32,64,128,256), GRU input 192 - DecodeSample resizes grayscale frames to 80x60 via INTER_AREA - Model params: 227K (was 1.5M), input 80x60 (was 320x240) Generated by Mistral Vibe. Co-Authored-By: Mistral Vibe <vibe@mistral.ai>
125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
"""
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Global configuration for velocity prediction.
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"""
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from dataclasses import dataclass, field
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from pathlib import Path
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# ──────────────────────────── Dataset paths ────────────────────────────
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DATASET_ROOT = Path(__file__).resolve().parents[2] / "dataset"
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# Velocity normalization stats (computed from forward scenes only, 28363 frames)
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# Yaw-compensated horizontal velocity: [v_right, v_forward]
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VELOCITY_MEAN = [-2.902497, 3.837231] # [v_right, v_forward]
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VELOCITY_STD = [3.453774, 3.722085] # [v_right, v_forward]
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# TRAIN_SCENES = [
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# "indoor_forward_3", "indoor_forward_5", "indoor_forward_6",
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# "indoor_forward_7", "indoor_forward_9", "indoor_forward_10",
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# "indoor_45_2", "indoor_45_4", "indoor_45_9", "indoor_45_12",
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# ]
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# VAL_SCENES = [
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# "indoor_45_13", "indoor_45_14",
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# ]
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# TEST_SCENES = [
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# "outdoor_forward_1", "outdoor_forward_3", "outdoor_forward_5",
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# "outdoor_45_1",
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# ]
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TRAIN_SCENES = [
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"indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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"indoor_forward_5", "indoor_forward_6", # Medium
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"outdoor_forward_3" # Medium 室外
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]
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VAL_SCENES = [
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"indoor_forward_7", # Hard 室内
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"outdoor_forward_1" # Easy 室外
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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TEST_SCENES = [
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# "indoor_forward_9",
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# "indoor_forward_9","indoor_forward_3",
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# "indoor_forward_7", # Hard 室内
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"outdoor_forward_1", # Easy 室外
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# "outdoor_forward_5" # Hard 室外
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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# ──────────────────────────── Model architecture ────────────────────────────
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@dataclass
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class CNNConfig:
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in_channels: int = 1
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channels: tuple = (32, 64, 128) # per-layer output channels
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kernel_size: int = 3
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stride: int = 2 # strided conv replaces conv+pool
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use_bn: bool = True
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@dataclass
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class PoseMLPConfig:
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input_dim: int = 3
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hidden_dim: int = 32
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output_dim: int = 64
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@dataclass
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class GRUConfig:
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input_size: int = 192 # CNN(128) + PoseMLP(64)
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hidden_size: int = 128
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num_layers: int = 1
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dropout: float = 0.0
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@dataclass
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class HeadConfig:
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input_dim: int = 128 # GRU hidden_size
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hidden_dim: int = 64
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output_dim: int = 2 # [v_right, v_forward]
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@dataclass
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class ModelConfig:
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cnn: CNNConfig = field(default_factory=CNNConfig)
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pose_mlp: PoseMLPConfig = field(default_factory=PoseMLPConfig)
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gru: GRUConfig = field(default_factory=GRUConfig)
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head: HeadConfig = field(default_factory=HeadConfig)
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# ──────────────────────────── Training ────────────────────────────
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@dataclass
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class TrainConfig:
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seq_len: int = 128 # frames per training sequence
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batch_size: int = 4
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epochs: int = 300
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lr: float = 1e-3
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weight_decay: float = 1e-5
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lr_scheduler_step: int = 30
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lr_scheduler_gamma: float = 0.5
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num_workers: int = 4
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seed: int = 42
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# Sliding window: stride=1 → full overlap, stride=seq_len → non-overlapping
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sliding_window_stride: int = 64
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# Event simulation
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event_threshold: float = 0.1
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event_use_log: bool = True
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event_auto_threshold: bool = False
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# Logging / checkpoint
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log_dir: str = "logs"
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checkpoint_dir: str = "checkpoints"
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log_interval: int = 10
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save_interval: int = 10
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# ──────────────────────────── Singleton instances ────────────────────────────
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model_cfg = ModelConfig()
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train_cfg = TrainConfig()
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