ResNeXtConfig¶

class lucid.models.ResNeXtConfig(layers: tuple[int, int, int, int] | list[int], cardinality: int, base_width: int, num_classes: int = 1000, in_channels: int = 3, stem_width: int = 64, stem_type: Literal['deep'] | None = None, avg_down: bool = False, channels: tuple[int, int, int, int] | list[int] = (64, 128, 256, 512), block_args: dict[str, typing.Any] = <factory>)¶

ResNeXtConfig stores the grouped-convolution settings used by lucid.models.ResNeXt. It defines the stage depths, cardinality, base width, classifier size, and the shared ResNet stem options.

Class Signature¶

@dataclass
class ResNeXtConfig:
    layers: tuple[int, int, int, int] | list[int]
    cardinality: int
    base_width: int
    num_classes: int = 1000
    in_channels: int = 3
    stem_width: int = 64
    stem_type: Literal["deep"] | None = None
    avg_down: bool = False
    channels: tuple[int, int, int, int] | list[int] = (64, 128, 256, 512)
    block_args: dict[str, Any] = field(default_factory=dict)

Parameters¶

layers (tuple[int, int, int, int] | list[int]): Number of bottleneck blocks in each of the four stages.
cardinality (int): Number of groups used by the grouped bottleneck convolution.
base_width (int): Width per group used to derive the bottleneck channel count.
num_classes (int): Number of output classes.
in_channels (int): Number of channels in the input image tensor.
stem_width (int): Width parameter used by the deep stem variant.
stem_type (Literal[“deep”] | None): Stem style. None uses the classic single 7x7 stem, while “deep” uses a three-layer stem.
avg_down (bool): Whether projection shortcuts should downsample using average pooling before the 1x1 projection.
channels (tuple[int, int, int, int] | list[int]): Output width of each residual stage.
block_args (dict[str, Any]): Extra keyword arguments forwarded to each grouped bottleneck block, in addition to the preset cardinality and base_width.

Validation¶

layers and channels must each contain exactly four positive integers.
cardinality, base_width, num_classes, in_channels, and stem_width must be greater than 0.
stem_type must be None or “deep”.
block_args must be a dictionary.

Usage¶

import lucid.models as models

config = models.ResNeXtConfig(
    layers=[3, 4, 23, 3],
    cardinality=32,
    base_width=8,
    num_classes=10,
    avg_down=True,
)

model = models.ResNeXt(config)