# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
#
# SPDX-License-Identifier: GPL-3.0-or-later
from collections import OrderedDict
import torch
import torch.nn
from .backbones.vgg import vgg16_for_segmentation
from .driu import ConcatFuseBlock
from .make_layers import UpsampleCropBlock
[docs]
class DRIUPIX(torch.nn.Module):
"""DRIUPIX head module. DRIU with pixelshuffle instead of ConvTrans2D.
Parameters
----------
in_channels_list : list
number of channels for each feature map that is returned from backbone
"""
def __init__(self, in_channels_list=None):
super().__init__()
(
in_conv_1_2_16,
in_upsample2,
in_upsample_4,
in_upsample_8,
) = in_channels_list
self.conv1_2_16 = torch.nn.Conv2d(in_conv_1_2_16, 16, 3, 1, 1)
# Upsample layers
self.upsample2 = UpsampleCropBlock(
in_upsample2, 16, 4, 2, 0, pixelshuffle=True
)
self.upsample4 = UpsampleCropBlock(
in_upsample_4, 16, 8, 4, 0, pixelshuffle=True
)
self.upsample8 = UpsampleCropBlock(
in_upsample_8, 16, 16, 8, 0, pixelshuffle=True
)
# Concat and Fuse
self.concatfuse = ConcatFuseBlock()
[docs]
def forward(self, x):
"""
Parameters
----------
x : list
list of tensors as returned from the backbone network.
First element: height and width of input image.
Remaining elements: feature maps for each feature level.
Returns
-------
:py:class:`torch.Tensor`
"""
hw = x[0]
conv1_2_16 = self.conv1_2_16(x[1]) # conv1_2_16
upsample2 = self.upsample2(x[2], hw) # side-multi2-up
upsample4 = self.upsample4(x[3], hw) # side-multi3-up
upsample8 = self.upsample8(x[4], hw) # side-multi4-up
out = self.concatfuse(conv1_2_16, upsample2, upsample4, upsample8)
return out
[docs]
def driu_pix(pretrained_backbone=True, progress=True):
"""Builds DRIU with pixelshuffle by adding backbone and head together.
Parameters
----------
pretrained_backbone : :py:class:`bool`, Optional
If set to ``True``, then loads a pre-trained version of the backbone
(not the head) for the DRIU network using VGG-16 trained for ImageNet
classification.
progress : :py:class:`bool`, Optional
If set to ``True``, and you decided to use a ``pretrained_backbone``,
then, shows a progress bar of the backbone model downloading if
download is necesssary.
Returns
-------
module : :py:class:`torch.nn.Module`
Network model for DRIU (vessel segmentation) with pixelshuffle
"""
backbone = vgg16_for_segmentation(
pretrained=pretrained_backbone,
progress=progress,
return_features=[3, 8, 14, 22],
)
head = DRIUPIX([64, 128, 256, 512])
order = [("backbone", backbone), ("head", head)]
if pretrained_backbone:
from .normalizer import TorchVisionNormalizer
order = [("normalizer", TorchVisionNormalizer())] + order
model = torch.nn.Sequential(OrderedDict(order))
model.name = "driu-pix"
return model