def __init__(self, in_channels):
super(MMFNet, self).__init__()
self.do = nn.Dropout(p=0.5)
# these next blocks are the same for both RGB and HHA because
# the input volumes are exactly the same dimensions (including # channels)
# conv3 is also used for the convolution before and after fusion
# pre-fusion RGB blocks
self.conv1_rgb = conv1x1(in_planes=in_channels, out_planes=in_channels)
self.RCUs_rgb = nn.Sequential( # 2 RCU blocks
BasicBlock(inplanes=in_channels,
planes=in_channels), # expansion=1, no downsampling
BasicBlock(inplanes=in_channels,
planes=in_channels) # expansion=1, no downsampling
)
self.conv3_rgb = conv3x3(in_planes=in_channels, out_planes=in_channels)
# pre-fusion HHA blocks
self.conv1_hha = conv1x1(in_planes=in_channels, out_planes=in_channels)
self.RCUs_hha = nn.Sequential( # 2 RCU blocks
BasicBlock(inplanes=in_channels,
planes=in_channels), # expansion=1, no downsampling
BasicBlock(inplanes=in_channels,
planes=in_channels) # expansion=1, no downsampling
)
self.conv3_hha = conv3x3(in_planes=in_channels, out_planes=in_channels)
# post-fusion block
#self.maxpool = nn.MaxPool2d(kernel_size=5, stride=1)
#self.conv3 = conv3x3(in_planes=in_channels, out_planes=in_channels)
self.crp = CRPBlock(in_planes=in_channels,
out_planes=in_channels,
n_stages=1)
def _make_crp(self, in_planes, out_planes, stages):
layers = [CRPBlock(in_planes, out_planes, stages)]
return nn.Sequential(*layers)