# 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.
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# 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.
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# limitations under the License.
from typing import List, Optional, Sequence, Tuple, Union
import torch
from torch import nn
from monai.networks.blocks import UpSample
from monai.networks.layers.factories import Conv
from monai.networks.layers.utils import get_act_layer
from monai.networks.nets import EfficientNetBNFeatures
from monai.networks.nets.basic_unet import UpCat
from monai.utils import InterpolateMode
__all__ = ["FlexibleUNet"]
encoder_feature_channel = {
"efficientnet-b0": (16, 24, 40, 112, 320),
"efficientnet-b1": (16, 24, 40, 112, 320),
"efficientnet-b2": (16, 24, 48, 120, 352),
"efficientnet-b3": (24, 32, 48, 136, 384),
"efficientnet-b4": (24, 32, 56, 160, 448),
"efficientnet-b5": (24, 40, 64, 176, 512),
"efficientnet-b6": (32, 40, 72, 200, 576),
"efficientnet-b7": (32, 48, 80, 224, 640),
"efficientnet-b8": (32, 56, 88, 248, 704),
"efficientnet-l2": (72, 104, 176, 480, 1376),
}
def _get_encoder_channels_by_backbone(backbone: str, in_channels: int = 3) -> tuple:
"""
Get the encoder output channels by given backbone name.
Args:
backbone: name of backbone to generate features, can be from [efficientnet-b0, ..., efficientnet-b7].
in_channels: channel of input tensor, default to 3.
Returns:
A tuple of output feature map channels' length .
"""
encoder_channel_tuple = encoder_feature_channel[backbone]
encoder_channel_list = [in_channels] + list(encoder_channel_tuple)
encoder_channel = tuple(encoder_channel_list)
return encoder_channel
class UNetDecoder(nn.Module):
"""
UNet Decoder.
This class refers to `segmentation_models.pytorch
<https://github.com/qubvel/segmentation_models.pytorch>`_.
Args:
spatial_dims: number of spatial dimensions.
encoder_channels: number of output channels for all feature maps in encoder.
`len(encoder_channels)` should be no less than 2.
decoder_channels: number of output channels for all feature maps in decoder.
`len(decoder_channels)` should equal to `len(encoder_channels) - 1`.
act: activation type and arguments.
norm: feature normalization type and arguments.
dropout: dropout ratio.
bias: whether to have a bias term in convolution blocks in this decoder.
upsample: upsampling mode, available options are
``"deconv"``, ``"pixelshuffle"``, ``"nontrainable"``.
pre_conv: a conv block applied before upsampling.
Only used in the "nontrainable" or "pixelshuffle" mode.
interp_mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``}
Only used in the "nontrainable" mode.
align_corners: set the align_corners parameter for upsample. Defaults to True.
Only used in the "nontrainable" mode.
is_pad: whether to pad upsampling features to fit the encoder spatial dims.
"""
def __init__(
self,
spatial_dims: int,
encoder_channels: Sequence[int],
decoder_channels: Sequence[int],
act: Union[str, tuple],
norm: Union[str, tuple],
dropout: Union[float, tuple],
bias: bool,
upsample: str,
pre_conv: Optional[str],
interp_mode: str,
align_corners: Optional[bool],
is_pad: bool,
):
super().__init__()
if len(encoder_channels) < 2:
raise ValueError("the length of `encoder_channels` should be no less than 2.")
if len(decoder_channels) != len(encoder_channels) - 1:
raise ValueError("`len(decoder_channels)` should equal to `len(encoder_channels) - 1`.")
in_channels = [encoder_channels[-1]] + list(decoder_channels[:-1])
skip_channels = list(encoder_channels[1:-1][::-1]) + [0]
halves = [True] * (len(skip_channels) - 1)
halves.append(False)
blocks = []
for in_chn, skip_chn, out_chn, halve in zip(in_channels, skip_channels, decoder_channels, halves):
blocks.append(
UpCat(
spatial_dims=spatial_dims,
in_chns=in_chn,
cat_chns=skip_chn,
out_chns=out_chn,
act=act,
norm=norm,
dropout=dropout,
bias=bias,
upsample=upsample,
pre_conv=pre_conv,
interp_mode=interp_mode,
align_corners=align_corners,
halves=halve,
is_pad=is_pad,
)
)
self.blocks = nn.ModuleList(blocks)
def forward(self, features: List[torch.Tensor], skip_connect: int = 4):
skips = features[:-1][::-1]
features = features[1:][::-1]
x = features[0]
for i, block in enumerate(self.blocks):
if i < skip_connect:
skip = skips[i]
else:
skip = None
x = block(x, skip)
return x
class SegmentationHead(nn.Sequential):
"""
Segmentation head.
This class refers to `segmentation_models.pytorch
<https://github.com/qubvel/segmentation_models.pytorch>`_.
Args:
spatial_dims: number of spatial dimensions.
in_channels: number of input channels for the block.
out_channels: number of output channels for the block.
kernel_size: kernel size for the conv layer.
act: activation type and arguments.
scale_factor: multiplier for spatial size. Has to match input size if it is a tuple.
"""
def __init__(
self,
spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: int = 3,
act: Optional[Union[Tuple, str]] = None,
scale_factor: float = 1.0,
):
conv_layer = Conv[Conv.CONV, spatial_dims](
in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, padding=kernel_size // 2
)
up_layer: nn.Module = nn.Identity()
if scale_factor > 1.0:
up_layer = UpSample(
spatial_dims=spatial_dims,
scale_factor=scale_factor,
mode="nontrainable",
pre_conv=None,
interp_mode=InterpolateMode.LINEAR,
)
if act is not None:
act_layer = get_act_layer(act)
else:
act_layer = nn.Identity()
super().__init__(conv_layer, up_layer, act_layer)
[docs]class FlexibleUNet(nn.Module):
"""
A flexible implementation of UNet-like encoder-decoder architecture.
"""
[docs] def __init__(
self,
in_channels: int,
out_channels: int,
backbone: str,
pretrained: bool = False,
decoder_channels: Tuple = (256, 128, 64, 32, 16),
spatial_dims: int = 2,
norm: Union[str, tuple] = ("batch", {"eps": 1e-3, "momentum": 0.1}),
act: Union[str, tuple] = ("relu", {"inplace": True}),
dropout: Union[float, tuple] = 0.0,
decoder_bias: bool = False,
upsample: str = "nontrainable",
interp_mode: str = "nearest",
is_pad: bool = True,
) -> None:
"""
A flexible implement of UNet, in which the backbone/encoder can be replaced with
any efficient network. Currently the input must have a 2 or 3 spatial dimension
and the spatial size of each dimension must be a multiple of 32 if is pad parameter
is False
Args:
in_channels: number of input channels.
out_channels: number of output channels.
backbone: name of backbones to initialize, only support efficientnet right now,
can be from [efficientnet-b0,..., efficientnet-b8, efficientnet-l2].
pretrained: whether to initialize pretrained ImageNet weights, only available
for spatial_dims=2 and batch norm is used, default to False.
decoder_channels: number of output channels for all feature maps in decoder.
`len(decoder_channels)` should equal to `len(encoder_channels) - 1`,default
to (256, 128, 64, 32, 16).
spatial_dims: number of spatial dimensions, default to 2.
norm: normalization type and arguments, default to ("batch", {"eps": 1e-3,
"momentum": 0.1}).
act: activation type and arguments, default to ("relu", {"inplace": True}).
dropout: dropout ratio, default to 0.0.
decoder_bias: whether to have a bias term in decoder's convolution blocks.
upsample: upsampling mode, available options are``"deconv"``, ``"pixelshuffle"``,
``"nontrainable"``.
interp_mode: {``"nearest"``, ``"linear"``, ``"bilinear"``, ``"bicubic"``, ``"trilinear"``}
Only used in the "nontrainable" mode.
is_pad: whether to pad upsampling features to fit features from encoder. Default to True.
If this parameter is set to "True", the spatial dim of network input can be arbitary
size, which is not supported by TensorRT. Otherwise, it must be a multiple of 32.
"""
super().__init__()
if backbone not in encoder_feature_channel:
raise ValueError(f"invalid model_name {backbone} found, must be one of {encoder_feature_channel.keys()}.")
if spatial_dims not in (2, 3):
raise ValueError("spatial_dims can only be 2 or 3.")
adv_prop = "ap" in backbone
self.backbone = backbone
self.spatial_dims = spatial_dims
model_name = backbone
encoder_channels = _get_encoder_channels_by_backbone(backbone, in_channels)
self.encoder = EfficientNetBNFeatures(
model_name=model_name,
pretrained=pretrained,
in_channels=in_channels,
spatial_dims=spatial_dims,
norm=norm,
adv_prop=adv_prop,
)
self.decoder = UNetDecoder(
spatial_dims=spatial_dims,
encoder_channels=encoder_channels,
decoder_channels=decoder_channels,
act=act,
norm=norm,
dropout=dropout,
bias=decoder_bias,
upsample=upsample,
interp_mode=interp_mode,
pre_conv=None,
align_corners=None,
is_pad=is_pad,
)
self.segmentation_head = SegmentationHead(
spatial_dims=spatial_dims,
in_channels=decoder_channels[-1],
out_channels=out_channels,
kernel_size=3,
act=None,
)
[docs] def forward(self, inputs: torch.Tensor):
"""
Do a typical encoder-decoder-header inference.
Args:
inputs: input should have spatially N dimensions ``(Batch, in_channels, dim_0[, dim_1, ..., dim_N])``,
N is defined by `dimensions`.
Returns:
A torch Tensor of "raw" predictions in shape ``(Batch, out_channels, dim_0[, dim_1, ..., dim_N])``.
"""
x = inputs
enc_out = self.encoder(x)
decoder_out = self.decoder(enc_out)
x_seg = self.segmentation_head(decoder_out)
return x_seg