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- '''ShuffleNet in PyTorch.
-
- See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
- '''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class ShuffleBlock(nn.Module):
- def __init__(self, groups):
- super(ShuffleBlock, self).__init__()
- self.groups = groups
-
- def forward(self, x):
- '''Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]'''
- N,C,H,W = x.size()
- g = self.groups
- return x.view(N,g,C//g,H,W).permute(0,2,1,3,4).reshape(N,C,H,W)
-
-
- class Bottleneck(nn.Module):
- def __init__(self, in_planes, out_planes, stride, groups):
- super(Bottleneck, self).__init__()
- self.stride = stride
-
- mid_planes = out_planes/4
- g = 1 if in_planes==24 else groups
- self.conv1 = nn.Conv2d(in_planes, mid_planes, kernel_size=1, groups=g, bias=False)
- self.bn1 = nn.BatchNorm2d(mid_planes)
- self.shuffle1 = ShuffleBlock(groups=g)
- self.conv2 = nn.Conv2d(mid_planes, mid_planes, kernel_size=3, stride=stride, padding=1, groups=mid_planes, bias=False)
- self.bn2 = nn.BatchNorm2d(mid_planes)
- self.conv3 = nn.Conv2d(mid_planes, out_planes, kernel_size=1, groups=groups, bias=False)
- self.bn3 = nn.BatchNorm2d(out_planes)
-
- self.shortcut = nn.Sequential()
- if stride == 2:
- self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1))
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.shuffle1(out)
- out = F.relu(self.bn2(self.conv2(out)))
- out = self.bn3(self.conv3(out))
- res = self.shortcut(x)
- out = F.relu(torch.cat([out,res], 1)) if self.stride==2 else F.relu(out+res)
- return out
-
-
- class ShuffleNet(nn.Module):
- def __init__(self, cfg):
- super(ShuffleNet, self).__init__()
- out_planes = cfg['out_planes']
- num_blocks = cfg['num_blocks']
- groups = cfg['groups']
-
- self.conv1 = nn.Conv2d(3, 24, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(24)
- self.in_planes = 24
- self.layer1 = self._make_layer(out_planes[0], num_blocks[0], groups)
- self.layer2 = self._make_layer(out_planes[1], num_blocks[1], groups)
- self.layer3 = self._make_layer(out_planes[2], num_blocks[2], groups)
- self.linear = nn.Linear(out_planes[2], 10)
-
- def _make_layer(self, out_planes, num_blocks, groups):
- layers = []
- for i in range(num_blocks):
- stride = 2 if i == 0 else 1
- cat_planes = self.in_planes if i == 0 else 0
- layers.append(Bottleneck(self.in_planes, out_planes-cat_planes, stride=stride, groups=groups))
- self.in_planes = out_planes
- 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 = F.avg_pool2d(out, 4)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
-
- def ShuffleNetG2():
- cfg = {
- 'out_planes': [200,400,800],
- 'num_blocks': [4,8,4],
- 'groups': 2
- }
- return ShuffleNet(cfg)
-
- def ShuffleNetG3():
- cfg = {
- 'out_planes': [240,480,960],
- 'num_blocks': [4,8,4],
- 'groups': 3
- }
- return ShuffleNet(cfg)
-
-
- def test():
- net = ShuffleNetG2()
- x = torch.randn(1,3,32,32)
- y = net(x)
- print(y)
-
- # test()
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