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import torch
from torch import nn
import torch.nn.functional as F
from lib_v5 import spec_utils
class Conv2DBNActiv(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
super(Conv2DBNActiv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
nin, nout,
kernel_size=ksize,
stride=stride,
padding=pad,
dilation=dilation,
bias=False),
nn.BatchNorm2d(nout),
activ()
)
def __call__(self, x):
return self.conv(x)
class SeperableConv2DBNActiv(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
super(SeperableConv2DBNActiv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
nin, nin,
kernel_size=ksize,
stride=stride,
padding=pad,
dilation=dilation,
groups=nin,
bias=False),
nn.Conv2d(
nin, nout,
kernel_size=1,
bias=False),
nn.BatchNorm2d(nout),
activ()
)
def __call__(self, x):
return self.conv(x)
class Encoder(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
super(Encoder, self).__init__()
self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
def __call__(self, x):
skip = self.conv1(x)
h = self.conv2(skip)
return h, skip
class Decoder(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
super(Decoder, self).__init__()
self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
self.dropout = nn.Dropout2d(0.1) if dropout else None
def __call__(self, x, skip=None):
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
if skip is not None:
skip = spec_utils.crop_center(skip, x)
x = torch.cat([x, skip], dim=1)
h = self.conv(x)
if self.dropout is not None:
h = self.dropout(h)
return h
class ASPPModule(nn.Module):
def __init__(self, nn_architecture, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU):
super(ASPPModule, self).__init__()
self.conv1 = nn.Sequential(
nn.AdaptiveAvgPool2d((1, None)),
Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
)
self.nn_architecture = nn_architecture
self.six_layer = [129605]
self.seven_layer = [537238, 537227, 33966]
extra_conv = SeperableConv2DBNActiv(
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
self.conv3 = SeperableConv2DBNActiv(
nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
self.conv4 = SeperableConv2DBNActiv(
nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
self.conv5 = SeperableConv2DBNActiv(
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
if self.nn_architecture in self.six_layer:
self.conv6 = extra_conv
nin_x = 6
elif self.nn_architecture in self.seven_layer:
self.conv6 = extra_conv
self.conv7 = extra_conv
nin_x = 7
else:
nin_x = 5
self.bottleneck = nn.Sequential(
Conv2DBNActiv(nin * nin_x, nout, 1, 1, 0, activ=activ),
nn.Dropout2d(0.1)
)
def forward(self, x):
_, _, h, w = x.size()
feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
feat5 = self.conv5(x)
if self.nn_architecture in self.six_layer:
feat6 = self.conv6(x)
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
elif self.nn_architecture in self.seven_layer:
feat6 = self.conv6(x)
feat7 = self.conv7(x)
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
else:
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
bottle = self.bottleneck(out)
return bottle
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