mirror of
https://github.com/AntoineHX/BU_Stoch_pool.git
synced 2025-05-04 17:50:46 +02:00
87 lines
3 KiB
Python
87 lines
3 KiB
Python
|
'''MobileNetV2 in PyTorch.
|
||
|
|
|
||
|
|
See the paper "Inverted Residuals and Linear Bottlenecks:
|
||
|
|
Mobile Networks for Classification, Detection and Segmentation" for more details.
|
||
|
|
'''
|
||
|
|
import torch
|
||
|
|
import torch.nn as nn
|
||
|
|
import torch.nn.functional as F
|
||
|
|
|
||
|
|
|
||
|
|
class Block(nn.Module):
|
||
|
|
'''expand + depthwise + pointwise'''
|
||
|
|
def __init__(self, in_planes, out_planes, expansion, stride):
|
||
|
|
super(Block, self).__init__()
|
||
|
|
self.stride = stride
|
||
|
|
|
||
|
|
planes = expansion * in_planes
|
||
|
|
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False)
|
||
|
|
self.bn1 = nn.BatchNorm2d(planes)
|
||
|
|
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=False)
|
||
|
|
self.bn2 = nn.BatchNorm2d(planes)
|
||
|
|
self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
|
||
|
|
self.bn3 = nn.BatchNorm2d(out_planes)
|
||
|
|
|
||
|
|
self.shortcut = nn.Sequential()
|
||
|
|
if stride == 1 and in_planes != out_planes:
|
||
|
|
self.shortcut = nn.Sequential(
|
||
|
|
nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False),
|
||
|
|
nn.BatchNorm2d(out_planes),
|
||
|
|
)
|
||
|
|
|
||
|
|
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))
|
||
|
|
out = out + self.shortcut(x) if self.stride==1 else out
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
class MobileNetV2(nn.Module):
|
||
|
|
# (expansion, out_planes, num_blocks, stride)
|
||
|
|
cfg = [(1, 16, 1, 1),
|
||
|
|
(6, 24, 2, 1), # NOTE: change stride 2 -> 1 for CIFAR10
|
||
|
|
(6, 32, 3, 2),
|
||
|
|
(6, 64, 4, 2),
|
||
|
|
(6, 96, 3, 1),
|
||
|
|
(6, 160, 3, 2),
|
||
|
|
(6, 320, 1, 1)]
|
||
|
|
|
||
|
|
def __init__(self, num_classes=10):
|
||
|
|
super(MobileNetV2, self).__init__()
|
||
|
|
# NOTE: change conv1 stride 2 -> 1 for CIFAR10
|
||
|
|
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.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False)
|
||
|
|
self.bn2 = nn.BatchNorm2d(1280)
|
||
|
|
self.linear = nn.Linear(1280, num_classes)
|
||
|
|
|
||
|
|
def _make_layers(self, in_planes):
|
||
|
|
layers = []
|
||
|
|
for expansion, out_planes, num_blocks, stride in self.cfg:
|
||
|
|
strides = [stride] + [1]*(num_blocks-1)
|
||
|
|
for stride in strides:
|
||
|
|
layers.append(Block(in_planes, out_planes, expansion, 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.relu(self.bn2(self.conv2(out)))
|
||
|
|
# NOTE: change pooling kernel_size 7 -> 4 for CIFAR10
|
||
|
|
out = F.avg_pool2d(out, 4)
|
||
|
|
out = out.view(out.size(0), -1)
|
||
|
|
out = self.linear(out)
|
||
|
|
return out
|
||
|
|
|
||
|
|
|
||
|
|
def test():
|
||
|
|
net = MobileNetV2()
|
||
|
|
x = torch.randn(2,3,32,32)
|
||
|
|
y = net(x)
|
||
|
|
print(y.size())
|
||
|
|
|
||
|
|
# test()
|