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resnet_bireal.py
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resnet_bireal.py
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import torch.nn as nn
import torchvision.transforms as transforms
import math
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)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=True)
def init_model(model):
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, do_bntan=False):
super(BasicBlock, self).__init__()
self.act0 = nn.Hardtanh(inplace=False)
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.act1 = nn.Hardtanh(inplace=False)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
if self.downsample is not None:
identity = self.downsample(identity)
out = self.act0(x)
out = self.conv1(out)
out = self.bn1(out)
identity2 = identity + out
out = self.act1(identity2)
out = self.conv2(out)
out = self.bn2(out)
out += identity2
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
def _make_layer(self, block, planes, blocks, stride=1, do_bntan=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.AvgPool2d(kernel_size=2, stride=2),
conv1x1(self.inplanes, planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks-1):
layers.append(block(self.inplanes, planes))
layers.append(block(self.inplanes, planes, do_bntan=do_bntan))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.maxpool(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.bn2(x)
x = self.tanh2(x)
x = self.fc(x)
x = self.bn3(x)
x = self.logsoftmax(x)
return x
class ResNet_imagenet(ResNet):
def __init__(self, num_classes=1000,
block=BasicBlock, layers=[3, 4, 23, 3]):
super(ResNet_imagenet, self).__init__()
self.inflate = 4
self.inplanes = 16*self.inflate
self.conv1 = nn.Conv2d(3, 16*self.inflate, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(16*self.inflate)
self.tanh1 = nn.Hardtanh(inplace=True)
self.tanh2 = nn.Hardtanh(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 16*self.inflate, layers[0])
self.layer2 = self._make_layer(block, 32*self.inflate, layers[1], stride=2, do_bntan=True)
self.layer3 = self._make_layer(block, 64*self.inflate, layers[2], stride=2, do_bntan=True)
self.layer4 = self._make_layer(block, 128*self.inflate, layers[3], stride=2, do_bntan=True)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.bn2 = nn.BatchNorm1d(512)
self.bn3 = nn.BatchNorm1d(num_classes)
self.logsoftmax = nn.LogSoftmax()
self.fc = nn.Linear(512 * block.expansion, num_classes)
init_model(self)
def resnet_bireal(**kwargs):
dataset = kwargs.get('dataset', 'imagenet')
if dataset == 'imagenet' or 'cifar10':
if dataset == 'cifar10':
num_classes = num_classes or 10
elif dataset == 'imagenet':
num_classes = num_classes or 1000
depth = depth or 50
if depth == 18:
return ResNet_imagenet(num_classes=num_classes,
block=BasicBlock, layers=[2, 2, 2, 2], reg_type=reg_type)
if depth == 34:
return ResNet_imagenet(num_classes=num_classes,
block=BasicBlock, layers=[3, 4, 6, 3])