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0803a67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | from typing import Sequence, Union
import torch
import torch.nn as nn
from monai.networks.layers.factories import Conv
from monai.networks.nets.basic_unet import Down, TwoConv, UpCat
from monai.utils import ensure_tuple_rep
__all__ = ["BasicUnetPlusPlus", "BasicunetPlusPlus", "basicunetplusplus", "BasicUNetPlusPlus"]
class BasicUNetPlusPlus(nn.Module):
def __init__(
self,
spatial_dims: int = 3,
in_channels: int = 1,
out_channels: int = 2,
features: Sequence[int] = (32, 32, 64, 128, 256, 32),
deep_supervision: bool = False,
act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
norm: Union[str, tuple] = ("instance", {"affine": True}),
bias: bool = True,
dropout: Union[float, tuple] = 0.0,
upsample: str = "deconv",
):
"""
A UNet++ implementation with 1D/2D/3D supports.
Based on:
Zhou et al. "UNet++: A Nested U-Net Architecture for Medical Image
Segmentation". 4th Deep Learning in Medical Image Analysis (DLMIA)
Workshop, DOI: https://doi.org/10.48550/arXiv.1807.10165
Args:
spatial_dims: number of spatial dimensions. Defaults to 3 for spatial 3D inputs.
in_channels: number of input channels. Defaults to 1.
out_channels: number of output channels. Defaults to 2.
features: six integers as numbers of features.
Defaults to ``(32, 32, 64, 128, 256, 32)``,
- the first five values correspond to the five-level encoder feature sizes.
- the last value corresponds to the feature size after the last upsampling.
deep_supervision: whether to prune the network at inference time. Defaults to False. If true, returns a list,
whose elements correspond to outputs at different nodes.
act: activation type and arguments. Defaults to LeakyReLU.
norm: feature normalization type and arguments. Defaults to instance norm.
bias: whether to have a bias term in convolution blocks. Defaults to True.
According to `Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html>`_,
if a conv layer is directly followed by a batch norm layer, bias should be False.
dropout: dropout ratio. Defaults to no dropout.
upsample: upsampling mode, available options are
``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
Examples::
# for spatial 2D
>>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128))
# for spatial 2D, with deep supervision enabled
>>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), deep_supervision=True)
# for spatial 2D, with group norm
>>> net = BasicUNetPlusPlus(spatial_dims=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))
# for spatial 3D
>>> net = BasicUNetPlusPlus(spatial_dims=3, features=(32, 32, 64, 128, 256, 32))
See Also
- :py:class:`monai.networks.nets.BasicUNet`
- :py:class:`monai.networks.nets.DynUNet`
- :py:class:`monai.networks.nets.UNet`
"""
super().__init__()
self.deep_supervision = deep_supervision
fea = ensure_tuple_rep(features, 6)
print(f"BasicUNetPlusPlus features: {fea}.")
self.conv_0_0 = TwoConv(spatial_dims, in_channels, fea[0], act, norm, bias, dropout)
self.conv_1_0 = Down(spatial_dims, fea[0], fea[1], act, norm, bias, dropout)
self.conv_2_0 = Down(spatial_dims, fea[1], fea[2], act, norm, bias, dropout)
self.conv_3_0 = Down(spatial_dims, fea[2], fea[3], act, norm, bias, dropout)
self.conv_4_0 = Down(spatial_dims, fea[3], fea[4], act, norm, bias, dropout)
self.upcat_0_1 = UpCat(spatial_dims, fea[1], fea[0], fea[0], act, norm, bias, dropout, upsample, halves=False)
self.upcat_1_1 = UpCat(spatial_dims, fea[2], fea[1], fea[1], act, norm, bias, dropout, upsample)
self.upcat_2_1 = UpCat(spatial_dims, fea[3], fea[2], fea[2], act, norm, bias, dropout, upsample)
self.upcat_3_1 = UpCat(spatial_dims, fea[4], fea[3], fea[3], act, norm, bias, dropout, upsample)
self.upcat_0_2 = UpCat(
spatial_dims, fea[1], fea[0] * 2, fea[0], act, norm, bias, dropout, upsample, halves=False
)
self.upcat_1_2 = UpCat(spatial_dims, fea[2], fea[1] * 2, fea[1], act, norm, bias, dropout, upsample)
self.upcat_2_2 = UpCat(spatial_dims, fea[3], fea[2] * 2, fea[2], act, norm, bias, dropout, upsample)
self.upcat_0_3 = UpCat(
spatial_dims, fea[1], fea[0] * 3, fea[0], act, norm, bias, dropout, upsample, halves=False
)
self.upcat_1_3 = UpCat(spatial_dims, fea[2], fea[1] * 3, fea[1], act, norm, bias, dropout, upsample)
self.upcat_0_4 = UpCat(
spatial_dims, fea[1], fea[0] * 4, fea[5], act, norm, bias, dropout, upsample, halves=False
)
self.final_conv_0_1 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1)
self.final_conv_0_2 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1)
self.final_conv_0_3 = Conv["conv", spatial_dims](fea[0], out_channels, kernel_size=1)
self.final_conv_0_4 = Conv["conv", spatial_dims](fea[5], out_channels, kernel_size=1)
def forward(self, x: torch.Tensor):
"""
Args:
x: input should have spatially N dimensions
``(Batch, in_channels, dim_0[, dim_1, ..., dim_N-1])``, N is defined by `dimensions`.
It is recommended to have ``dim_n % 16 == 0`` to ensure all maxpooling inputs have
even edge lengths.
Returns:
A torch Tensor of "raw" predictions in shape
``(Batch, out_channels, dim_0[, dim_1, ..., dim_N-1])``.
"""
x_0_0 = self.conv_0_0(x)
x_1_0 = self.conv_1_0(x_0_0)
x_0_1 = self.upcat_0_1(x_1_0, x_0_0)
x_2_0 = self.conv_2_0(x_1_0)
x_1_1 = self.upcat_1_1(x_2_0, x_1_0)
x_0_2 = self.upcat_0_2(x_1_1, torch.cat([x_0_0, x_0_1], dim=1))
x_3_0 = self.conv_3_0(x_2_0)
x_2_1 = self.upcat_2_1(x_3_0, x_2_0)
x_1_2 = self.upcat_1_2(x_2_1, torch.cat([x_1_0, x_1_1], dim=1))
x_0_3 = self.upcat_0_3(x_1_2, torch.cat([x_0_0, x_0_1, x_0_2], dim=1))
x_4_0 = self.conv_4_0(x_3_0)
x_3_1 = self.upcat_3_1(x_4_0, x_3_0)
x_2_2 = self.upcat_2_2(x_3_1, torch.cat([x_2_0, x_2_1], dim=1))
x_1_3 = self.upcat_1_3(x_2_2, torch.cat([x_1_0, x_1_1, x_1_2], dim=1))
x_0_4 = self.upcat_0_4(x_1_3, torch.cat([x_0_0, x_0_1, x_0_2, x_0_3], dim=1))
output_0_1 = self.final_conv_0_1(x_0_1)
output_0_2 = self.final_conv_0_2(x_0_2)
output_0_3 = self.final_conv_0_3(x_0_3)
output_0_4 = self.final_conv_0_4(x_0_4)
if self.deep_supervision:
output = [output_0_1, output_0_2, output_0_3, output_0_4]
else:
output = output_0_4
return output
BasicUnetPlusPlus = BasicunetPlusPlus = basicunetplusplus = BasicUNetPlusPlus |