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import torch
import torch.nn as nn
import math
from collections import OrderedDict
################## AlexNet ##################
def bn_relu(inplanes):
return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True))
def bn_relu_pool(inplanes, kernel_size=3, stride=2):
return nn.Sequential(nn.BatchNorm2d(inplanes), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=kernel_size, stride=stride))
class AlexNet(nn.Module):
def __init__(self, num_classes=1):
super(AlexNet, self).__init__()
self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, bias=False)
self.relu_pool1 = bn_relu_pool(inplanes=96)
self.conv2 = nn.Conv2d(96, 192, kernel_size=5, padding=2, groups=2, bias=False)
self.relu_pool2 = bn_relu_pool(inplanes=192)
self.conv3 = nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2, bias=False)
self.relu3 = bn_relu(inplanes=384)
self.conv4 = nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2, bias=False)
self.relu4 = bn_relu(inplanes=384)
self.conv5 = nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2, bias=False)
self.relu_pool5 = bn_relu_pool(inplanes=256)
# classifier
self.conv6 = nn.Conv2d(256, 256, kernel_size=5, groups=2, bias=False)
self.relu6 = bn_relu(inplanes=256)
self.conv7 = nn.Conv2d(256, num_classes, kernel_size=1, bias=False)
def forward(self, x):
x = self.conv1(x)
x = self.relu_pool1(x)
x = self.conv2(x)
x = self.relu_pool2(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.conv5(x)
x = self.relu_pool5(x)
x = self.conv6(x)
x = self.relu6(x)
x = self.conv7(x)
x = x.view(x.size(0), -1)
return x
################## ResNet ##################
def conv3x3(in_planes, out_planes, stride=1):
# 3x3 convolution with padding
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
m = OrderedDict()
m['conv1'] = conv3x3(inplanes, planes, stride)
m['bn1'] = nn.BatchNorm2d(planes)
m['relu1'] = nn.ReLU(inplace=True)
m['conv2'] = conv3x3(planes, planes)
m['bn2'] = nn.BatchNorm2d(planes)
self.group1 = nn.Sequential(m)
self.relu = nn.Sequential(nn.ReLU(inplace=True))
self.downsample = downsample
def forward(self, x):
if self.downsample is not None:
residual = self.downsample(x)
else:
residual = x
out = self.group1(x) + residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
m = OrderedDict()
m['conv1'] = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
m['bn1'] = nn.BatchNorm2d(64)
m['relu1'] = nn.ReLU(inplace=True)
m['maxpool'] = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.group1= nn.Sequential(m)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.Sequential(nn.AvgPool2d(7))
self.group2 = nn.Sequential(
OrderedDict([
('fullyconnected', nn.Linear(512 * block.expansion, num_classes))
])
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight.data)
torch.nn.init.constant_(m.bias.data, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.group1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.group2(x)
return x
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