ResUNet 3D

ConvNet Segmentation ConvNet

class lucid.models.ResUNet3d(config: UNetConfig)

ResUNet3d is a residual variant of lucid.models.UNet3d that reuses the same configurable 3D encoder-decoder structure but forces residual blocks throughout the network. It is useful for volumetric segmentation tasks where deeper stage stacks or stronger gradient flow are desirable.

Class Signature

class ResUNet3d(UNet3d):
    def __init__(self, config: UNetConfig) -> None

Parameters

  • config (UNetConfig): U-Net configuration describing the encoder/decoder stage layout, channel widths, sampling strategy, and segmentation output space. ResUNet3d reuses this config and enforces block=”res”.

Methods

ResUNet3d.forward(x: Tensor) → Tensor | dict[str, Tensor | list[Tensor]]

Examples

Build a ResUNet3d from `from_channels`

import lucid
import lucid.models as models

cfg = models.UNetConfig.from_channels(
    in_channels=1,
    out_channels=2,
    channels=(32, 64, 128, 256),
    num_blocks=(2, 2, 2, 3),
    norm="group",
    act="silu",
    upsample_mode="trilinear",
)
model = models.ResUNet3d(cfg)

x = lucid.random.randn(1, 1, 64, 128, 128)
logits = model(x)
print(logits.shape)  # (1, 2, 64, 128, 128)

Build a Custom ResUNet3d with Stage-Level Configuration

import lucid
import lucid.models as models

cfg = models.UNetConfig(
    in_channels=1,
    out_channels=4,
    encoder_stages=[
        models.UNetStageConfig(channels=32, num_blocks=2),
        models.UNetStageConfig(channels=64, num_blocks=2),
        models.UNetStageConfig(channels=128, num_blocks=3, use_attention=True),
        models.UNetStageConfig(channels=256, num_blocks=3),
    ],
    norm="group",
    act="silu",
    downsample_mode="conv",
    upsample_mode="transpose",
    deep_supervision=True,
)
model = models.ResUNet3d(cfg)

x = lucid.random.randn(1, 1, 32, 64, 64)
out = model(x)
print(out["out"].shape)  # (1, 4, 32, 64, 64)
print(len(out["aux"]))   # 2

Notes