CrbnsCat10n
484643409d
modified: checkpoints/forward/best_tcn_model.pt modified: checkpoints/forward/training_history.csv new file: checkpoints_mlp/task1_feature_mlp/best_feature_mlp.pt new file: checkpoints_mlp/task1_feature_mlp/training_history.csv new file: checkpoints_rms/forward_rms/best_rms_model.pt new file: checkpoints_rms/forward_rms/training_history.csv modified: evaluation_outputs/forward/evaluation_forward_test_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b0_s0.png new file: evaluation_outputs/forward/evaluation_forward_val_b3_s0.png new file: evaluation_outputs/forward_rms/evaluation_test_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_test_s0.png new file: evaluation_outputs/forward_rms/evaluation_train_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_all_samples.csv new file: evaluation_outputs/forward_rms/evaluation_val_s0.png new file: evaluation_outputs/forward_rms/evaluation_val_s0_waveform.png new file: evaluation_outputs/task1_feature_mlp/evaluation_train_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_train_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_all_samples.csv new file: evaluation_outputs/task1_feature_mlp/evaluation_val_curve.png new file: evaluation_outputs/task1_feature_mlp/evaluation_val_s0.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_0.75Hz_prediction.png new file: evaluation_outputs/task1_feature_mlp/harmonic_5mm_1.55Hz_prediction.png new file: scripts/__pycache__/config.cpython-310.pyc new file: scripts/__pycache__/dataset.cpython-310.pyc new file: scripts/__pycache__/model.cpython-310.pyc new file: scripts/config.py new file: scripts/dataset.py new file: scripts/evaluate.py new file: scripts/model.py new file: scripts/predict_single.py new file: scripts/train.py modified: src/__pycache__/config.cpython-310.pyc modified: src/__pycache__/dataset.cpython-310.pyc modified: src/__pycache__/model.cpython-310.pyc modified: src/config.py modified: src/dataset.py modified: src/model.py new file: src_new/__pycache__/config.cpython-310.pyc new file: src_new/__pycache__/dataset.cpython-310.pyc new file: src_new/__pycache__/evaluate.cpython-310.pyc new file: src_new/__pycache__/model.cpython-310.pyc new file: src_new/__pycache__/train.cpython-310.pyc new file: src_new/config.py new file: src_new/dataset.py new file: src_new/evaluate.py new file: src_new/model.py new file: src_new/train.py
104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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@dataclass
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class DataConfig:
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project_root: Path = field(default_factory=lambda: Path(__file__).resolve().parents[1])
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scenario: str = "Non_TMD"
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train_split_name: str = "train"
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val_split_name: str = "val"
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test_split_name: str = "test"
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csv_pattern: str = "*.csv"
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code_column: str = "code"
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time_column: str = "time"
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base_sensor_code: str = "WSMS00012"
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base_axis: str = "value1"
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response_sensor_code: str = "WSMS00007"
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response_axis: str = "value3"
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max_sequence_length: int = 4096
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min_sequence_length: int = 512
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batch_size: int = 8
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num_workers: int = 0
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pin_memory: bool = True
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use_weighted_train_sampler: bool = True
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train_weight_power: float = 1.0
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train_weight_min: float = 0.5
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train_weight_max: float = 8.0
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low_frequency_emphasis_power: float = 1.25
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low_frequency_reference_hz: float = 1.0
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use_steady_state_only: bool = True
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steady_state_start_ratio: float = 0.50
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steady_state_min_samples: int = 256
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interpolation_method: str = "linear"
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normalization_eps: float = 1e-6
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downloads_dir: Path = field(init=False)
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scenario_dir: Path = field(init=False)
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train_dir: Path = field(init=False)
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val_dir: Path = field(init=False)
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test_dir: Path = field(init=False)
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def __post_init__(self) -> None:
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self.project_root = Path(self.project_root).resolve()
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self.downloads_dir = self.project_root / "downloads"
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self.scenario_dir = self.downloads_dir / self.scenario
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self.train_dir = self.scenario_dir / self.train_split_name
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self.val_dir = self.scenario_dir / self.val_split_name
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self.test_dir = self.scenario_dir / self.test_split_name
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@dataclass
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class ModelConfig:
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input_channels: int = 1
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tcn_channels: tuple[int, ...] = (32, 32, 64, 64)
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kernel_size: int = 7
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dropout: float = 0.15
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dilation_base: int = 2
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pooled_feature_dim: int = 128
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@dataclass
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class TrainConfig:
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epochs: int = 120
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learning_rate: float = 1e-3
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weight_decay: float = 1e-4
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seed: int = 42
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device: str = "cuda"
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grad_clip_norm: float = 1.0
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use_amp: bool = True
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lr_scheduler_patience: int = 8
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lr_scheduler_factor: float = 0.5
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min_learning_rate: float = 1e-6
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early_stop_patience: int = 15
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checkpoint_dir: str = "checkpoints_rms"
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best_model_name: str = "best_rms_model.pt"
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history_name: str = "training_history.csv"
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@dataclass
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class LossConfig:
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relative_rms_weight: float = 1.0
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log_rms_weight: float = 0.5
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mae_weight: float = 0.25
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waveform_l1_weight: float = 0.03
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waveform_huber_weight: float = 0.05
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@dataclass
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class ExperimentConfig:
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data: DataConfig = field(default_factory=DataConfig)
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model: ModelConfig = field(default_factory=ModelConfig)
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train: TrainConfig = field(default_factory=TrainConfig)
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loss: LossConfig = field(default_factory=LossConfig)
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def make_rms_forward_config() -> ExperimentConfig:
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return ExperimentConfig()
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