pytorch实现ShuffleNet「建议收藏」

'''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
# 维度变换之后必须要使用.contiguous()使得张量在内存连续之后才能调用view函数
return x.view(N,g,int(C/g),H,W).permute(0,2,1,3,4).contiguous().view(N,C,H,W)
class Bottleneck(nn.Module):
def __init__(self, in_planes, out_planes, stride, groups):
super(Bottleneck, self).__init__()
self.stride = stride
# bottleneck层中间层的channel数变为输出channel数的1/4
mid_planes = int(out_planes/4)
g = 1 if in_planes==24 else groups
# 作者提到不在stage2的第一个pointwise层使用组卷积,因为输入channel数量太少,只有24
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):
if i == 0:
layers.append(Bottleneck(self.in_planes,
out_planes-self.in_planes,
stride=2, groups=groups))
else:
layers.append(Bottleneck(self.in_planes,
out_planes,
stride=1, 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()