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