# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Sequence, Union
import torch
import torch.nn as nn
from monai.networks.blocks import Convolution, UpSample
from monai.networks.layers.factories import Conv, Pool
from monai.utils import ensure_tuple_rep
__all__ = ["BasicUNet", "BasicUnet", "Basicunet"]
class TwoConv(nn.Sequential):
"""two convolutions."""
def __init__(
self,
dim: int,
in_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
dropout: Union[float, tuple] = 0.0,
):
"""
Args:
dim: number of spatial dimensions.
in_chns: number of input channels.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
dropout: dropout ratio. Defaults to no dropout.
"""
super().__init__()
conv_0 = Convolution(dim, in_chns, out_chns, act=act, norm=norm, dropout=dropout, padding=1)
conv_1 = Convolution(dim, out_chns, out_chns, act=act, norm=norm, dropout=dropout, padding=1)
self.add_module("conv_0", conv_0)
self.add_module("conv_1", conv_1)
class Down(nn.Sequential):
"""maxpooling downsampling and two convolutions."""
def __init__(
self,
dim: int,
in_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
dropout: Union[float, tuple] = 0.0,
):
"""
Args:
dim: number of spatial dimensions.
in_chns: number of input channels.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
dropout: dropout ratio. Defaults to no dropout.
"""
super().__init__()
max_pooling = Pool["MAX", dim](kernel_size=2)
convs = TwoConv(dim, in_chns, out_chns, act, norm, dropout)
self.add_module("max_pooling", max_pooling)
self.add_module("convs", convs)
class UpCat(nn.Module):
"""upsampling, concatenation with the encoder feature map, two convolutions"""
def __init__(
self,
dim: int,
in_chns: int,
cat_chns: int,
out_chns: int,
act: Union[str, tuple],
norm: Union[str, tuple],
dropout: Union[float, tuple] = 0.0,
upsample: str = "deconv",
halves: bool = True,
):
"""
Args:
dim: number of spatial dimensions.
in_chns: number of input channels to be upsampled.
cat_chns: number of channels from the decoder.
out_chns: number of output channels.
act: activation type and arguments.
norm: feature normalization type and arguments.
dropout: dropout ratio. Defaults to no dropout.
upsample: upsampling mode, available options are
``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
halves: whether to halve the number of channels during upsampling.
"""
super().__init__()
up_chns = in_chns // 2 if halves else in_chns
self.upsample = UpSample(dim, in_chns, up_chns, 2, mode=upsample)
self.convs = TwoConv(dim, cat_chns + up_chns, out_chns, act, norm, dropout)
def forward(self, x: torch.Tensor, x_e: torch.Tensor):
"""
Args:
x: features to be upsampled.
x_e: features from the encoder.
"""
x_0 = self.upsample(x)
# handling spatial shapes due to the 2x maxpooling with odd edge lengths.
dimensions = len(x.shape) - 2
sp = [0] * (dimensions * 2)
for i in range(dimensions):
if x_e.shape[-i - 1] != x_0.shape[-i - 1]:
sp[i * 2 + 1] = 1
x_0 = torch.nn.functional.pad(x_0, sp, "replicate")
x = self.convs(torch.cat([x_e, x_0], dim=1)) # input channels: (cat_chns + up_chns)
return x
[docs]class BasicUNet(nn.Module):
def __init__(
self,
dimensions: int = 3,
in_channels: int = 1,
out_channels: int = 2,
features: Sequence[int] = (32, 32, 64, 128, 256, 32),
act: Union[str, tuple] = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True}),
norm: Union[str, tuple] = ("instance", {"affine": True}),
dropout: Union[float, tuple] = 0.0,
upsample: str = "deconv",
):
"""
A UNet implementation with 1D/2D/3D supports.
Based on:
Falk et al. "U-Net – Deep Learning for Cell Counting, Detection, and
Morphometry". Nature Methods 16, 67–70 (2019), DOI:
http://dx.doi.org/10.1038/s41592-018-0261-2
Args:
dimensions: 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.
act: activation type and arguments. Defaults to LeakyReLU.
norm: feature normalization type and arguments. Defaults to instance norm.
dropout: dropout ratio. Defaults to no dropout.
upsample: upsampling mode, available options are
``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
Examples::
# for spatial 2D
>>> net = BasicUNet(dimensions=2, features=(64, 128, 256, 512, 1024, 128))
# for spatial 2D, with group norm
>>> net = BasicUNet(dimensions=2, features=(64, 128, 256, 512, 1024, 128), norm=("group", {"num_groups": 4}))
# for spatial 3D
>>> net = BasicUNet(dimensions=3, features=(32, 32, 64, 128, 256, 32))
See Also
- :py:class:`monai.networks.nets.DynUNet`
- :py:class:`monai.networks.nets.UNet`
"""
super().__init__()
fea = ensure_tuple_rep(features, 6)
print(f"BasicUNet features: {fea}.")
self.conv_0 = TwoConv(dimensions, in_channels, features[0], act, norm, dropout)
self.down_1 = Down(dimensions, fea[0], fea[1], act, norm, dropout)
self.down_2 = Down(dimensions, fea[1], fea[2], act, norm, dropout)
self.down_3 = Down(dimensions, fea[2], fea[3], act, norm, dropout)
self.down_4 = Down(dimensions, fea[3], fea[4], act, norm, dropout)
self.upcat_4 = UpCat(dimensions, fea[4], fea[3], fea[3], act, norm, dropout, upsample)
self.upcat_3 = UpCat(dimensions, fea[3], fea[2], fea[2], act, norm, dropout, upsample)
self.upcat_2 = UpCat(dimensions, fea[2], fea[1], fea[1], act, norm, dropout, upsample)
self.upcat_1 = UpCat(dimensions, fea[1], fea[0], fea[5], act, norm, dropout, upsample, halves=False)
self.final_conv = Conv["conv", dimensions](fea[5], out_channels, kernel_size=1)
[docs] def forward(self, x: torch.Tensor):
"""
Args:
x: input should have spatially N dimensions
``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``, 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])``.
"""
x0 = self.conv_0(x)
x1 = self.down_1(x0)
x2 = self.down_2(x1)
x3 = self.down_3(x2)
x4 = self.down_4(x3)
u4 = self.upcat_4(x4, x3)
u3 = self.upcat_3(u4, x2)
u2 = self.upcat_2(u3, x1)
u1 = self.upcat_1(u2, x0)
logits = self.final_conv(u1)
return logits
BasicUnet = Basicunet = basicunet = BasicUNet