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from dataclasses import dataclass
from typing import Dict, List, Union
@dataclass
class AnchorConfig:
reg_max: int
strides: List[int]
@dataclass
class Model:
anchor: AnchorConfig
model: Dict[str, List[Dict[str, Union[Dict, List, int]]]]
@dataclass
class Download:
auto: bool
path: str
@dataclass
class DataLoaderConfig:
batch_size: int
shuffle: bool
num_workers: int
pin_memory: bool
image_size: List[int]
class_num: int
@dataclass
class OptimizerArgs:
lr: float
weight_decay: float
@dataclass
class OptimizerConfig:
type: str
args: OptimizerArgs
@dataclass
class SchedulerArgs:
step_size: int
gamma: float
@dataclass
class SchedulerConfig:
type: str
args: SchedulerArgs
@dataclass
class EMAConfig:
enabled: bool
decay: float
@dataclass
class MatcherConfig:
iou: str
topk: int
factor: Dict[str, int]
@dataclass
class TrainConfig:
optimizer: OptimizerConfig
scheduler: SchedulerConfig
ema: EMAConfig
matcher: MatcherConfig
@dataclass
class HyperConfig:
data: DataLoaderConfig
train: TrainConfig
@dataclass
class Dataset:
file_name: str
num_files: int
@dataclass
class Datasets:
base_url: str
images: Dict[str, Dataset]
@dataclass
class Download:
auto: bool
save_path: str
datasets: Datasets
@dataclass
class Config:
model: Model
download: Download
hyper: HyperConfig
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