#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import OrderedDict
import torch
import torch.nn
from .backbones.vgg import vgg16_for_segmentation
from .make_layers import UpsampleCropBlock, conv_with_kaiming_uniform
[docs]class ConcatFuseBlock(torch.nn.Module):
"""
Takes in four feature maps with 16 channels each, concatenates them
and applies a 1x1 convolution with 1 output channel.
"""
def __init__(self):
super().__init__()
self.conv = conv_with_kaiming_uniform(4 * 16, 1, 1, 1, 0)
[docs] def forward(self, x1, x2, x3, x4):
x_cat = torch.cat([x1, x2, x3, x4], dim=1)
x = self.conv(x_cat)
return x
[docs]class DRIU(torch.nn.Module):
"""
DRIU head module
Based on paper by [MANINIS-2016]_.
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(DRIU, self).__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)
self.upsample4 = UpsampleCropBlock(in_upsample_4, 16, 8, 4, 0)
self.upsample8 = UpsampleCropBlock(in_upsample_8, 16, 16, 8, 0)
# 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
-------
tensor : :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(pretrained_backbone=True, progress=True):
"""Builds DRIU for vessel segmentation 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)
"""
backbone = vgg16_for_segmentation(
pretrained=pretrained_backbone,
progress=progress,
return_features=[3, 8, 14, 22],
)
head = DRIU([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"
return model