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light_cnn.py
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light_cnn.py
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'''
implement Light CNN
@author: Alfred Xiang Wu
@date: 2017.07.04
'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, type=1):
super(mfm, self).__init__()
self.out_channels = out_channels
if type == 1:
self.filter = nn.Conv2d(in_channels, 2*out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
else:
self.filter = nn.Linear(in_channels, 2*out_channels)
def forward(self, x):
x = self.filter(x)
out = torch.split(x, self.out_channels, 1)
return torch.max(out[0], out[1])
class group(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
super(group, self).__init__()
self.conv_a = mfm(in_channels, in_channels, 1, 1, 0)
self.conv = mfm(in_channels, out_channels, kernel_size, stride, padding)
def forward(self, x):
x = self.conv_a(x)
x = self.conv(x)
return x
class resblock(nn.Module):
def __init__(self, in_channels, out_channels):
super(resblock, self).__init__()
self.conv1 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = mfm(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
def forward(self, x):
res = x
out = self.conv1(x)
out = self.conv2(out)
out = out + res
return out
class network_9layers(nn.Module):
def __init__(self, num_classes=79077):
super(network_9layers, self).__init__()
self.features = nn.Sequential(
mfm(1, 48, 5, 1, 2),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
group(48, 96, 3, 1, 1),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
group(96, 192, 3, 1, 1),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
group(192, 128, 3, 1, 1),
group(128, 128, 3, 1, 1),
nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True),
)
self.fc1 = mfm(8*8*128, 256, type=0)
self.fc2 = nn.Linear(256, num_classes)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.fc1(x)
x = F.dropout(x, training=self.training)
out = self.fc2(x)
return out, x
class network_29layers(nn.Module):
def __init__(self, block, layers, num_classes=79077):
super(network_29layers, self).__init__()
self.conv1 = mfm(1, 48, 5, 1, 2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.block1 = self._make_layer(block, layers[0], 48, 48)
self.group1 = group(48, 96, 3, 1, 1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.block2 = self._make_layer(block, layers[1], 96, 96)
self.group2 = group(96, 192, 3, 1, 1)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.block3 = self._make_layer(block, layers[2], 192, 192)
self.group3 = group(192, 128, 3, 1, 1)
self.block4 = self._make_layer(block, layers[3], 128, 128)
self.group4 = group(128, 128, 3, 1, 1)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
self.fc = mfm(8*8*128, 256, type=0)
self.fc2 = nn.Linear(256, num_classes)
def _make_layer(self, block, num_blocks, in_channels, out_channels):
layers = []
for i in range(0, num_blocks):
layers.append(block(in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.pool1(x)
x = self.block1(x)
x = self.group1(x)
x = self.pool2(x)
x = self.block2(x)
x = self.group2(x)
x = self.pool3(x)
x = self.block3(x)
x = self.group3(x)
x = self.block4(x)
x = self.group4(x)
x = self.pool4(x)
x = x.view(x.size(0), -1)
fc = self.fc(x)
fc = F.dropout(fc, training=self.training)
out = self.fc2(fc)
return out, fc
class network_29layers_v2(nn.Module):
def __init__(self, block, layers, num_classes=79077):
super(network_29layers_v2, self).__init__()
self.conv1 = mfm(1, 48, 5, 1, 2)
self.block1 = self._make_layer(block, layers[0], 48, 48)
self.group1 = group(48, 96, 3, 1, 1)
self.block2 = self._make_layer(block, layers[1], 96, 96)
self.group2 = group(96, 192, 3, 1, 1)
self.block3 = self._make_layer(block, layers[2], 192, 192)
self.group3 = group(192, 128, 3, 1, 1)
self.block4 = self._make_layer(block, layers[3], 128, 128)
self.group4 = group(128, 128, 3, 1, 1)
self.fc = nn.Linear(8*8*128, 256)
self.fc2 = nn.Linear(256, num_classes, bias=False)
def _make_layer(self, block, num_blocks, in_channels, out_channels):
layers = []
for i in range(0, num_blocks):
layers.append(block(in_channels, out_channels))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block1(x)
x = self.group1(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block2(x)
x = self.group2(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = self.block3(x)
x = self.group3(x)
x = self.block4(x)
x = self.group4(x)
x = F.max_pool2d(x, 2) + F.avg_pool2d(x, 2)
x = x.view(x.size(0), -1)
fc = self.fc(x)
x = F.dropout(fc, training=self.training)
out = self.fc2(x)
return out, fc
def LightCNN_9Layers(**kwargs):
model = network_9layers(**kwargs)
return model
def LightCNN_29Layers(**kwargs):
model = network_29layers(resblock, [1, 2, 3, 4], **kwargs)
return model
def LightCNN_29Layers_v2(**kwargs):
model = network_29layers_v2(resblock, [1, 2, 3, 4], **kwargs)
return model