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from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Union
from torch import nn
@dataclass
class AnchorConfig:
reg_max: int
strides: List[int]
@dataclass
class LayerConfg:
args: Dict
source: Union[int, str, List[int]]
tags: str
@dataclass
class BlockConfig:
block: List[Dict[str, LayerConfg]]
@dataclass
class Model:
anchor: AnchorConfig
model: Dict[str, BlockConfig]
@dataclass
class DownloadDetail:
url: str
file_size: int
@dataclass
class DownloadOptions:
details: Dict[str, DownloadDetail]
@dataclass
class DatasetConfig:
path: str
auto_download: Optional[DownloadOptions]
@dataclass
class DataConfig:
shuffle: bool
batch_size: int
pin_memory: bool
data_augment: Dict[str, int]
@dataclass
class OptimizerArgs:
lr: float
weight_decay: float
@dataclass
class OptimizerConfig:
type: str
args: OptimizerArgs
@dataclass
class MatcherConfig:
iou: str
topk: int
factor: Dict[str, int]
@dataclass
class LossConfig:
objective: Dict[str, int]
aux: Union[bool, float]
matcher: MatcherConfig
@dataclass
class SchedulerConfig:
type: str
warmup: Dict[str, Union[int, float]]
args: Dict[str, Any]
@dataclass
class EMAConfig:
enabled: bool
decay: float
@dataclass
class TrainConfig:
task: str
dataset: DatasetConfig
epoch: int
data: DataConfig
optimizer: OptimizerConfig
loss: LossConfig
scheduler: SchedulerConfig
ema: EMAConfig
@dataclass
class NMSConfig:
min_confidence: int
min_iou: int
@dataclass
class InferenceConfig:
task: str
nms: NMSConfig
@dataclass
class Config:
task: Union[TrainConfig, InferenceConfig]
model: Model
name: str
device: Union[str, int, List[int]]
cpu_num: int
class_num: int
image_size: List[int]
out_path: str
exist_ok: bool
lucky_number: 10
use_wandb: bool
use_TensorBoard: bool
weight: Optional[str]
@dataclass
class YOLOLayer(nn.Module):
source: Union[int, str, List[int]]
output: bool
tags: str
layer_type: str
usable: bool
def __post_init__(self):
super().__init__()
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