File size: 8,883 Bytes
34a4bcb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
# 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
import torch.nn as nn
from monai.networks.blocks import ADN, Convolution
from monai.networks.layers.simplelayers import ChannelPad
from monai.utils import ChannelMatching
__all__ = ["HighResBlock", "HighResNet"]
DEFAULT_LAYER_PARAMS_3D = (
# initial conv layer
{"name": "conv_0", "n_features": 16, "kernel_size": 3},
# residual blocks
{"name": "res_1", "n_features": 16, "kernels": (3, 3), "repeat": 3},
{"name": "res_2", "n_features": 32, "kernels": (3, 3), "repeat": 3},
{"name": "res_3", "n_features": 64, "kernels": (3, 3), "repeat": 3},
# final conv layers
{"name": "conv_1", "n_features": 80, "kernel_size": 1},
{"name": "conv_2", "kernel_size": 1},
)
class HighResBlock(nn.Module):
def __init__(
self,
spatial_dims: int,
in_channels: int,
out_channels: int,
kernels: Sequence[int] = (3, 3),
dilation: Sequence[int] | int = 1,
norm_type: tuple | str = ("batch", {"affine": True}),
acti_type: tuple | str = ("relu", {"inplace": True}),
bias: bool = False,
channel_matching: ChannelMatching | str = ChannelMatching.PAD,
) -> None:
"""
Args:
spatial_dims: number of spatial dimensions of the input image.
in_channels: number of input channels.
out_channels: number of output channels.
kernels: each integer k in `kernels` corresponds to a convolution layer with kernel size k.
dilation: spacing between kernel elements.
norm_type: feature normalization type and arguments.
Defaults to ``("batch", {"affine": True})``.
acti_type: {``"relu"``, ``"prelu"``, ``"relu6"``}
Non-linear activation using ReLU or PReLU. Defaults to ``"relu"``.
bias: whether to have a bias term in convolution blocks. Defaults to False.
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.
channel_matching: {``"pad"``, ``"project"``}
Specifies handling residual branch and conv branch channel mismatches. Defaults to ``"pad"``.
- ``"pad"``: with zero padding.
- ``"project"``: with a trainable conv with kernel size one.
Raises:
ValueError: When ``channel_matching=pad`` and ``in_channels > out_channels``. Incompatible values.
"""
super().__init__()
self.chn_pad = ChannelPad(
spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels, mode=channel_matching
)
layers = nn.ModuleList()
_in_chns, _out_chns = in_channels, out_channels
for kernel_size in kernels:
layers.append(
ADN(ordering="NA", in_channels=_in_chns, act=acti_type, norm=norm_type, norm_dim=spatial_dims)
)
layers.append(
Convolution(
spatial_dims=spatial_dims,
in_channels=_in_chns,
out_channels=_out_chns,
kernel_size=kernel_size,
dilation=dilation,
bias=bias,
conv_only=True,
)
)
_in_chns = _out_chns
self.layers = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_conv: torch.Tensor = self.layers(x)
return x_conv + torch.as_tensor(self.chn_pad(x))
class HighResNet(nn.Module):
"""
Reimplementation of highres3dnet based on
Li et al., "On the compactness, efficiency, and representation of 3D
convolutional networks: Brain parcellation as a pretext task", IPMI '17
Adapted from:
https://github.com/NifTK/NiftyNet/blob/v0.6.0/niftynet/network/highres3dnet.py
https://github.com/fepegar/highresnet
Args:
spatial_dims: number of spatial dimensions of the input image.
in_channels: number of input channels.
out_channels: number of output channels.
norm_type: feature normalization type and arguments.
Defaults to ``("batch", {"affine": True})``.
acti_type: activation type and arguments.
Defaults to ``("relu", {"inplace": True})``.
dropout_prob: probability of the feature map to be zeroed
(only applies to the penultimate conv layer).
bias: whether to have a bias term in convolution blocks. Defaults to False.
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.
layer_params: specifying key parameters of each layer/block.
channel_matching: {``"pad"``, ``"project"``}
Specifies handling residual branch and conv branch channel mismatches. Defaults to ``"pad"``.
- ``"pad"``: with zero padding.
- ``"project"``: with a trainable conv with kernel size one.
"""
def __init__(
self,
spatial_dims: int = 3,
in_channels: int = 1,
out_channels: int = 1,
norm_type: str | tuple = ("batch", {"affine": True}),
acti_type: str | tuple = ("relu", {"inplace": True}),
dropout_prob: tuple | str | float | None = 0.0,
bias: bool = False,
layer_params: Sequence[dict] = DEFAULT_LAYER_PARAMS_3D,
channel_matching: ChannelMatching | str = ChannelMatching.PAD,
) -> None:
super().__init__()
blocks = nn.ModuleList()
# initial conv layer
params = layer_params[0]
_in_chns, _out_chns = in_channels, params["n_features"]
blocks.append(
Convolution(
spatial_dims=spatial_dims,
in_channels=_in_chns,
out_channels=_out_chns,
kernel_size=params["kernel_size"],
adn_ordering="NA",
act=acti_type,
norm=norm_type,
bias=bias,
)
)
# residual blocks
for idx, params in enumerate(layer_params[1:-2]): # res blocks except the 1st and last two conv layers.
_in_chns, _out_chns = _out_chns, params["n_features"]
_dilation = 2**idx
for _ in range(params["repeat"]):
blocks.append(
HighResBlock(
spatial_dims=spatial_dims,
in_channels=_in_chns,
out_channels=_out_chns,
kernels=params["kernels"],
dilation=_dilation,
norm_type=norm_type,
acti_type=acti_type,
bias=bias,
channel_matching=channel_matching,
)
)
_in_chns = _out_chns
# final conv layers
params = layer_params[-2]
_in_chns, _out_chns = _out_chns, params["n_features"]
blocks.append(
Convolution(
spatial_dims=spatial_dims,
in_channels=_in_chns,
out_channels=_out_chns,
kernel_size=params["kernel_size"],
adn_ordering="NAD",
act=acti_type,
norm=norm_type,
bias=bias,
dropout=dropout_prob,
)
)
params = layer_params[-1]
_in_chns = _out_chns
blocks.append(
Convolution(
spatial_dims=spatial_dims,
in_channels=_in_chns,
out_channels=out_channels,
kernel_size=params["kernel_size"],
adn_ordering="NAD",
act=acti_type,
norm=norm_type,
bias=bias,
dropout=dropout_prob,
)
)
self.blocks = nn.Sequential(*blocks)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.as_tensor(self.blocks(x))
|