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seg_detector.py
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from collections import OrderedDict
import torch
import torch.nn as nn
BatchNorm2d = nn.BatchNorm2d
class SegDetector(nn.Module):
def __init__(self,
in_channels=[64, 128, 256, 512],
inner_channels=256, k=10,
bias=False, adaptive=False, smooth=False, serial=False,
*args, **kwargs):
'''
bias: Whether conv layers have bias or not.
adaptive: Whether to use adaptive threshold training or not.
smooth: If true, use bilinear instead of deconv.
serial: If true, thresh prediction will combine segmentation result as input.
'''
super(SegDetector, self).__init__()
self.k = k
self.serial = serial
self.up5 = nn.Upsample(scale_factor=2, mode='nearest')
self.up4 = nn.Upsample(scale_factor=2, mode='nearest')
self.up3 = nn.Upsample(scale_factor=2, mode='nearest')
self.in5 = nn.Conv2d(in_channels[-1], inner_channels, 1, bias=bias)
self.in4 = nn.Conv2d(in_channels[-2], inner_channels, 1, bias=bias)
self.in3 = nn.Conv2d(in_channels[-3], inner_channels, 1, bias=bias)
self.in2 = nn.Conv2d(in_channels[-4], inner_channels, 1, bias=bias)
self.out5 = nn.Sequential(
nn.Conv2d(inner_channels, inner_channels //
4, 3, padding=1, bias=bias),
nn.Upsample(scale_factor=8, mode='nearest'))
self.out4 = nn.Sequential(
nn.Conv2d(inner_channels, inner_channels //
4, 3, padding=1, bias=bias),
nn.Upsample(scale_factor=4, mode='nearest'))
self.out3 = nn.Sequential(
nn.Conv2d(inner_channels, inner_channels //
4, 3, padding=1, bias=bias),
nn.Upsample(scale_factor=2, mode='nearest'))
self.out2 = nn.Conv2d(
inner_channels, inner_channels//4, 3, padding=1, bias=bias)
self.binarize = nn.Sequential(
nn.Conv2d(inner_channels, inner_channels //
4, 3, padding=1, bias=bias),
BatchNorm2d(inner_channels//4),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(inner_channels//4, inner_channels//4, 2, 2),
BatchNorm2d(inner_channels//4),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(inner_channels//4, 1, 2, 2),
nn.Sigmoid())
self.binarize.apply(self.weights_init)
self.adaptive = adaptive
if adaptive:
self.thresh = self._init_thresh(
inner_channels, serial=serial, smooth=smooth, bias=bias)
self.thresh.apply(self.weights_init)
self.in5.apply(self.weights_init)
self.in4.apply(self.weights_init)
self.in3.apply(self.weights_init)
self.in2.apply(self.weights_init)
self.out5.apply(self.weights_init)
self.out4.apply(self.weights_init)
self.out3.apply(self.weights_init)
self.out2.apply(self.weights_init)
def weights_init(self, m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.kaiming_normal_(m.weight.data)
elif classname.find('BatchNorm') != -1:
m.weight.data.fill_(1.)
m.bias.data.fill_(1e-4)
def _init_thresh(self, inner_channels,
serial=False, smooth=False, bias=False):
in_channels = inner_channels
if serial:
in_channels += 1
self.thresh = nn.Sequential(
nn.Conv2d(in_channels, inner_channels //
4, 3, padding=1, bias=bias),
BatchNorm2d(inner_channels//4),
nn.ReLU(inplace=True),
self._init_upsample(inner_channels // 4, inner_channels//4, smooth=smooth, bias=bias),
BatchNorm2d(inner_channels//4),
nn.ReLU(inplace=True),
self._init_upsample(inner_channels // 4, 1, smooth=smooth, bias=bias),
nn.Sigmoid())
return self.thresh
def _init_upsample(self,
in_channels, out_channels,
smooth=False, bias=False):
if smooth:
inter_out_channels = out_channels
if out_channels == 1:
inter_out_channels = in_channels
module_list = [
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(in_channels, inter_out_channels, 3, 1, 1, bias=bias)]
if out_channels == 1:
module_list.append(
nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=1, padding=1, bias=True))
return nn.Sequential(module_list)
else:
return nn.ConvTranspose2d(in_channels, out_channels, 2, 2)
def forward(self, features, gt=None, masks=None, training=False):
c2, c3, c4, c5 = features
in5 = self.in5(c5)
in4 = self.in4(c4)
in3 = self.in3(c3)
in2 = self.in2(c2)
out4 = self.up5(in5) + in4 # 1/16
out3 = self.up4(out4) + in3 # 1/8
out2 = self.up3(out3) + in2 # 1/4
p5 = self.out5(in5)
p4 = self.out4(out4)
p3 = self.out3(out3)
p2 = self.out2(out2)
fuse = torch.cat((p5, p4, p3, p2), 1)
# this is the pred module, not binarization module;
# We do not correct the name due to the trained model.
binary = self.binarize(fuse)
if self.training:
result = OrderedDict(binary=binary)
else:
return binary
if self.adaptive and self.training:
if self.serial:
fuse = torch.cat(
(fuse, nn.functional.interpolate(
binary, fuse.shape[2:])), 1)
thresh = self.thresh(fuse)
thresh_binary = self.step_function(binary, thresh)
result.update(thresh=thresh, thresh_binary=thresh_binary)
return result
def step_function(self, x, y):
return torch.reciprocal(1 + torch.exp(-self.k * (x - y)))