'''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
debug = False #True
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
self.dense_depth = dense_depth
self.last_planes = last_planes
self.in_planes = in_planes
self.conv1 = nn.Conv3d(last_planes, in_planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm3d(in_planes)
self.conv2 = nn.Conv3d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
self.bn2 = nn.BatchNorm3d(in_planes)
self.conv3 = nn.Conv3d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(out_planes+dense_depth)
self.shortcut = nn.Sequential()
if first_layer:
self.shortcut = nn.Sequential(
nn.Conv3d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm3d(out_planes+dense_depth)
)
def forward(self, x):
# print 'bottleneck_0', x.size(), self.last_planes, self.in_planes, 1
out = F.relu(self.bn1(self.conv1(x)))
# print 'bottleneck_1', out.size(), self.in_planes, self.in_planes, 3
out = F.relu(self.bn2(self.conv2(out)))
# print 'bottleneck_2', out.size(), self.in_planes, self.out_planes+self.dense_depth, 1
out = self.bn3(self.conv3(out))
# print 'bottleneck_3', out.size()
x = self.shortcut(x)
d = self.out_planes
# print 'bottleneck_4', x.size(), self.last_planes, self.out_planes+self.dense_depth, d
out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
# print 'bottleneck_5', out.size()
out = F.relu(out)
return out
class DPN(nn.Module):
def __init__(self, cfg):
super(DPN, self).__init__()
in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
# self.in_planes = in_planes
# self.out_planes = out_planes
# self.num_blocks = num_blocks
# self.dense_depth = dense_depth
self.conv1 = nn.Conv3d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.last_planes = 64
self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], 2)#10)
def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for i,stride in enumerate(strides):
layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
self.last_planes = out_planes + (i+2) * dense_depth
# print '_make_layer', i, layers[-1].size()
return nn.Sequential(*layers)
def forward(self, x):
if debug: print '0', x.size(), 64
out = F.relu(self.bn1(self.conv1(x)))
if debug: print '1', out.size()
out = self.layer1(out)
if debug: print '2', out.size()
out = self.layer2(out)
if debug: print '3', out.size()
out = self.layer3(out)
if debug: print '4', out.size()
out = self.layer4(out)
if debug: print '5', out.size()
out = F.avg_pool3d(out, 4)
if debug: print '6', out.size()
out_1 = out.view(out.size(0), -1)
if debug: print '7', out_1.size()
out = self.linear(out_1)
if debug: print '8', out.size()
return out, out_1
def DPN26():
cfg = {
'in_planes': (96,192,384,768),
'out_planes': (256,512,1024,2048),
'num_blocks': (2,2,2,2),
'dense_depth': (16,32,24,128)
}
return DPN(cfg)
def DPN92_3D():
cfg = {
'in_planes': (96,192,384,768),
'out_planes': (256,512,1024,2048),
'num_blocks': (3,4,20,3),
'dense_depth': (16,32,24,128)
}
return DPN(cfg)
def test():
debug = True
net = DPN92_3D()
x = Variable(torch.randn(1,1,32,32,32))
y = net(x)
print(y)
# test()
