model

Module diagram

classDiagram
  class model {
  }
  class backbone {
  }
  class builder {
  }
  class norm {
  }
  class torchvision {
  }
  class regnet {
  }
  class resnet {
  }
  class squeezenet {
  }
  class vgg {
  }
  class van {
  }
  class benchmark {
  }
  class feature_extractor {
  }
  class neck {
  }
  class builder {
  }
  class fpem {
  }
  class fpn {
  }
  class util {
  }
  backbone --> builder
  builder --> regnet
  builder --> resnet
  builder --> squeezenet
  builder --> vgg
  builder --> van
  benchmark --> backbone
  feature_extractor --> backbone
  neck --> builder
  builder --> fpem
  builder --> fpn

model

backbone

builder

register_backbone

register_backbone(backbone, name: Optional[str] = None)

native_models에 백본을 등록해 build_backbone()에서 사용할 수 있도록 합니다

Source code in SaigeToolkit/model/backbone/builder.py

def register_backbone(backbone, name: Optional[str] = None):
    """`native_models`에 백본을 등록해 `build_backbone()`에서 사용할 수 있도록 합니다"""
    name = name or backbone.__name__
    if name in native_models and backbone != native_models[name]:
        raise ValueError(f"backbone name {name} already exists.")
    native_models[name] = backbone

norm

FrozenBatchNorm2d

FrozenBatchNorm2d(n: int)

Bases: Module

BatchNorm2d where the batch statistics and the affine parameters are fixed

Attributes:

weight (Tensor) –

batch_norm weight. not nn.Parameter for freezing values.
bias (Tensor) –

batch_norm bias. not nn.Parameter for freezing values.
running_mean (Tensor) –

batch_norm running_mean. not nn.Parameter for freezing values.
running_var (Tensor) –

batch_norm running_var. not nn.Parameter for freezing values.

Initializing FrozenBatchNorm2d

Parameters:

n (int) –

number of channels for torch.Tensor

Source code in SaigeToolkit/model/backbone/norm.py

def __init__(self, n: int) -> None:
    """Initializing FrozenBatchNorm2d

    Args:
        n (int): number of channels for torch.Tensor
    """
    super(FrozenBatchNorm2d, self).__init__()
    self.register_buffer("weight", torch.ones(n))
    self.register_buffer("bias", torch.zeros(n))
    self.register_buffer("running_mean", torch.zeros(n))
    self.register_buffer("running_var", torch.ones(n))

forward

forward(x: Tensor) -> Tensor

forward function for FrozenBatchNorm2d

Parameters:

x (Tensor) –

input tensor

Returns:

Tensor –

torch.Tensor: batch_normalized output tensor

Source code in SaigeToolkit/model/backbone/norm.py

def forward(self, x: torch.Tensor) -> torch.Tensor:
    """forward function for FrozenBatchNorm2d

    Args:
        x (torch.Tensor): input tensor

    Returns:
        torch.Tensor: batch_normalized output tensor
    """
    # Cast all fixed parameters to half() if necessary
    if x.dtype == torch.float16:
        self.weight = self.weight.half()
        self.bias = self.bias.half()
        self.running_mean = self.running_mean.half()
        self.running_var = self.running_var.half()
    scale = self.weight * self.running_var.rsqrt()
    bias = self.bias - self.running_mean * scale
    scale = scale.reshape(1, -1, 1, 1)
    bias = bias.reshape(1, -1, 1, 1)
    return x * scale + bias

get_norm

get_norm(name: str) -> Type[Module]

getting customized batch norm class

Parameters:

name (str) –

batch norm class name

Returns:

Type[Module] –

Type[nn.Module]: custom batch norm class

Source code in SaigeToolkit/model/backbone/norm.py

def get_norm(name: str) -> Type[nn.Module]:
    """getting customized batch norm class

    Args:
        name (str): batch norm class name

    Returns:
        Type[nn.Module]: custom batch norm class
    """
    return {"fixed_batch_norm": FrozenBatchNorm2d}[name]

torchvision

regnet