# Copyright (c) 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 __future__ import annotations
from collections.abc import Sequence
import torch
from torch import nn
from torch.nn import functional as F
from monai.networks.blocks import Convolution
from monai.networks.layers import Conv, Norm, Pool, same_padding
def get_conv_block(
spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: Sequence[int] | int = 3,
strides: int = 1,
padding: tuple[int, ...] | int | None = None,
act: tuple | str | None = "RELU",
norm: tuple | str | None = "BATCH",
initializer: str | None = "kaiming_uniform",
) -> nn.Module:
if padding is None:
padding = same_padding(kernel_size)
conv_block: nn.Module = Convolution(
spatial_dims,
in_channels,
out_channels,
kernel_size=kernel_size,
strides=strides,
act=act,
norm=norm,
bias=False,
conv_only=False,
padding=padding,
)
conv_type: type[nn.Conv1d | nn.Conv2d | nn.Conv3d] = Conv[Conv.CONV, spatial_dims]
for m in conv_block.modules():
if isinstance(m, conv_type):
if initializer == "kaiming_uniform":
nn.init.kaiming_normal_(torch.as_tensor(m.weight))
elif initializer == "zeros":
nn.init.zeros_(torch.as_tensor(m.weight))
else:
raise ValueError(
f"initializer {initializer} is not supported, " "currently supporting kaiming_uniform and zeros"
)
return conv_block
def get_conv_layer(
spatial_dims: int, in_channels: int, out_channels: int, kernel_size: Sequence[int] | int = 3
) -> nn.Module:
padding = same_padding(kernel_size)
mod: nn.Module = Convolution(
spatial_dims, in_channels, out_channels, kernel_size=kernel_size, bias=False, conv_only=True, padding=padding
)
return mod
[docs]
class RegistrationResidualConvBlock(nn.Module):
"""
A block with skip links and layer - norm - activation.
Only changes the number of channels, the spatial size is kept same.
"""
[docs]
def __init__(
self, spatial_dims: int, in_channels: int, out_channels: int, num_layers: int = 2, kernel_size: int = 3
):
"""
Args:
spatial_dims: number of spatial dimensions
in_channels: number of input channels
out_channels: number of output channels
num_layers: number of layers inside the block
kernel_size: kernel_size
"""
super().__init__()
self.num_layers = num_layers
self.layers = nn.ModuleList(
[
get_conv_layer(
spatial_dims=spatial_dims,
in_channels=in_channels if i == 0 else out_channels,
out_channels=out_channels,
kernel_size=kernel_size,
)
for i in range(num_layers)
]
)
self.norms = nn.ModuleList([Norm[Norm.BATCH, spatial_dims](out_channels) for _ in range(num_layers)])
self.acts = nn.ModuleList([nn.ReLU() for _ in range(num_layers)])
[docs]
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Tensor in shape (batch, ``in_channels``, insize_1, insize_2, [insize_3])
Returns:
Tensor in shape (batch, ``out_channels``, insize_1, insize_2, [insize_3]),
with the same spatial size as ``x``
"""
skip = x
for i, (conv, norm, act) in enumerate(zip(self.layers, self.norms, self.acts)):
x = conv(x)
x = norm(x)
if i == self.num_layers - 1:
# last block
x = x + skip
x = act(x)
return x
[docs]
class RegistrationDownSampleBlock(nn.Module):
"""
A down-sample module used in RegUNet to half the spatial size.
The number of channels is kept same.
Adapted from:
DeepReg (https://github.com/DeepRegNet/DeepReg)
"""
[docs]
def __init__(self, spatial_dims: int, channels: int, pooling: bool) -> None:
"""
Args:
spatial_dims: number of spatial dimensions.
channels: channels
pooling: use MaxPool if True, strided conv if False
"""
super().__init__()
if pooling:
self.layer = Pool[Pool.MAX, spatial_dims](kernel_size=2)
else:
self.layer = get_conv_block(
spatial_dims=spatial_dims,
in_channels=channels,
out_channels=channels,
kernel_size=2,
strides=2,
padding=0,
)
[docs]
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Halves the spatial dimensions and keeps the same channel.
output in shape (batch, ``channels``, insize_1 / 2, insize_2 / 2, [insize_3 / 2]),
Args:
x: Tensor in shape (batch, ``channels``, insize_1, insize_2, [insize_3])
Raises:
ValueError: when input spatial dimensions are not even.
"""
for i in x.shape[2:]:
if i % 2 != 0:
raise ValueError("expecting x spatial dimensions be even, " f"got x of shape {x.shape}")
out: torch.Tensor = self.layer(x)
return out
def get_deconv_block(spatial_dims: int, in_channels: int, out_channels: int) -> nn.Module:
mod: nn.Module = Convolution(
spatial_dims=spatial_dims,
in_channels=in_channels,
out_channels=out_channels,
strides=2,
act="RELU",
norm="BATCH",
bias=False,
is_transposed=True,
padding=1,
output_padding=1,
)
return mod
