|
- '''MobileNet in PyTorch.
-
- See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
- for more details.
- '''
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
-
- class Block(nn.Module):
- '''Depthwise conv + Pointwise conv'''
- def __init__(self, in_planes, out_planes, stride=1):
- super(Block, self).__init__()
- self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
- self.bn2 = nn.BatchNorm2d(out_planes)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = F.relu(self.bn2(self.conv2(out)))
- return out
-
-
- class MobileNet(nn.Module):
- # (128,2) means conv planes=128, conv stride=2, by default conv stride=1
- cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), 1024]
-
- def __init__(self, num_classes=10):
- super(MobileNet, self).__init__()
- self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(32)
- self.layers = self._make_layers(in_planes=32)
- self.linear = nn.Linear(1024, num_classes)
-
- def _make_layers(self, in_planes):
- layers = []
- for x in self.cfg:
- out_planes = x if isinstance(x, int) else x[0]
- stride = 1 if isinstance(x, int) else x[1]
- layers.append(Block(in_planes, out_planes, stride))
- in_planes = out_planes
- return nn.Sequential(*layers)
-
- def forward(self, x):
- out = F.relu(self.bn1(self.conv1(x)))
- out = self.layers(out)
- out = F.avg_pool2d(out, 2)
- out = out.view(out.size(0), -1)
- out = self.linear(out)
- return out
-
-
- def test():
- net = MobileNet()
- x = torch.randn(1,3,32,32)
- y = net(x)
- print(y.size())
-
- # test()
|