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- '''Dual Path Networks in PyTorch.'''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- 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.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
- self.bn2 = nn.BatchNorm2d(in_planes)
- self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(out_planes+dense_depth)
-
- self.shortcut = nn.Sequential()
- if first_layer:
- self.shortcut = nn.Sequential(
- nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(out_planes+dense_depth)
- )
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = F.relu(self.bn2(self.conv2(out)))
- out = self.bn3(self.conv3(out))
- x = self.shortcut(x)
- d = self.out_planes
- out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
- 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.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(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], 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
- return nn.Sequential(*layers)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.layer1(out)
- out = self.layer2(out)
- out = self.layer3(out)
- out = self.layer4(out)
- out = F.avg_pool2d(out, 4)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
-
- 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():
- 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():
- net = DPN92()
- x = torch.randn(1,3,32,32)
- y = net(x)
- print(y)
-
- # test()
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