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5ccd75a | 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 | # Copyright 2020 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.
import torch.nn as nn
from monai.networks.blocks.convolutions import Convolution
from monai.networks.blocks.upsample import UpSample
from monai.networks.layers.factories import Act, Norm
def get_norm_layer(spatial_dims: int, in_channels: int, norm_name: str, num_groups: int = 8):
if norm_name not in ["batch", "instance", "group"]:
raise ValueError(f"Unsupported normalization mode: {norm_name}")
else:
if norm_name == "group":
norm = Norm[norm_name](num_groups=num_groups, num_channels=in_channels)
else:
norm = Norm[norm_name, spatial_dims](in_channels)
if norm.bias is not None:
nn.init.zeros_(norm.bias)
if norm.weight is not None:
nn.init.ones_(norm.weight)
return norm
def get_conv_layer(
spatial_dims: int, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, bias: bool = False
):
return Convolution(
spatial_dims,
in_channels,
out_channels,
strides=stride,
kernel_size=kernel_size,
bias=bias,
conv_only=True,
)
def get_upsample_layer(spatial_dims: int, in_channels: int, upsample_mode: str = "trilinear", scale_factor: int = 2):
up_module: nn.Module
if upsample_mode == "transpose":
up_module = UpSample(
spatial_dims,
in_channels,
scale_factor=scale_factor,
with_conv=True,
)
else:
upsample_mode = "bilinear" if spatial_dims == 2 else "trilinear"
up_module = nn.Upsample(scale_factor=scale_factor, mode=upsample_mode, align_corners=False)
return up_module
class ResBlock(nn.Module):
"""
ResBlock employs skip connection and two convolution blocks and is used
in SegResNet based on `3D MRI brain tumor segmentation using autoencoder regularization
<https://arxiv.org/pdf/1810.11654.pdf>`_.
"""
def __init__(
self,
spatial_dims: int,
in_channels: int,
kernel_size: int = 3,
stride: int = 1,
bias: bool = False,
norm_name: str = "group",
num_groups: int = 8,
) -> None:
"""
Args:
spatial_dims: number of spatial dimensions, could be 1, 2 or 3.
in_channels: number of input channels.
kernel_size: convolution kernel size, the value should be an odd number. Defaults to 3.
stride: convolution stride. Defaults to 1.
bias: whether to have a bias term in convolution layer. Defaults to ``True``.
norm_name: feature normalization type, this module only supports group norm,
batch norm and instance norm. Defaults to ``group``.
num_groups: number of groups to separate the channels into, in this module,
in_channels should be divisible by num_groups. Defaults to 8.
"""
super().__init__()
assert kernel_size % 2 == 1, "kernel_size should be an odd number."
assert in_channels % num_groups == 0, "in_channels should be divisible by num_groups."
self.norm1 = get_norm_layer(spatial_dims, in_channels, norm_name, num_groups=num_groups)
self.norm2 = get_norm_layer(spatial_dims, in_channels, norm_name, num_groups=num_groups)
self.relu = Act[Act.RELU](inplace=True)
self.conv1 = get_conv_layer(spatial_dims, in_channels, in_channels)
self.conv2 = get_conv_layer(spatial_dims, in_channels, in_channels)
def forward(self, x):
identity = x
x = self.norm1(x)
x = self.relu(x)
x = self.conv1(x)
x = self.norm2(x)
x = self.relu(x)
x = self.conv2(x)
x += identity
return x
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