pytorch-MobileNet
Simple Code Implementation of "MobileNet" architecture using PyTorch.
For simplicity, i write codes in ipynb. So, you can easliy test my code.
Last update : 2018/12/19
Contributor
- hoya012
Requirements
Python 3.5
numpy
matplotlib
torch=1.0.0
torchvision
Usage
You only run MobileNet-pytorch.ipynb.
For test, i used CIFAR-10 Dataset and resize image scale from 32x32 to 224x224.
If you want to use own dataset, you can simply resize images.
depthwise convolution and other blocks impelemtation.
In MobileNet, there are many depthwise convolution operation. This is my simple implemenatation.
depthwise convolution operation
class depthwise_conv(nn.Module):
def __init__(self, nin, kernel_size, padding, bias=False, stride=1):
super(depthwise_conv, self).__init__()
self.depthwise = nn.Conv2d(nin, nin, kernel_size=kernel_size, stride=stride, padding=padding, groups=nin, bias=bias)
def forward(self, x):
out = self.depthwise(x)
return out
depthwise block
class dw_block(nn.Module):
def __init__(self, nin, kernel_size, padding=1, bias=False, stride=1):
super(dw_block, self).__init__()
self.dw_block = nn.Sequential(
depthwise_conv(nin, kernel_size, padding, bias, stride),
nn.BatchNorm2d(nin),
nn.ReLU(True)
)
def forward(self, x):
out = self.dw_block(x)
return out
1x1 block
class one_by_one_block(nn.Module):
def __init__(self, nin, nout, padding=1, bias=False, stride=1):
super(one_by_one_block, self).__init__()
self.one_by_one_block = nn.Sequential(
nn.Conv2d(nin, nout, kernel_size=1, stride=stride, padding=padding, bias=bias),
nn.BatchNorm2d(nout),
nn.ReLU(True)
)
def forward(self, x):
out = self.one_by_one_block(x)
return out