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import torch.nn as nn
import torchvision.transforms as transforms
import math
from .binarized_modules import BinarizeLinear,BinarizeConv2d
__all__ = ['resnet_binary']
def Binaryconv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
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 init_model(model):
for m in model.modules():
if isinstance(m, BinarizeConv2d):
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_()
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True):
super(BasicBlock, self).__init__()
self.conv1 = Binaryconv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.tanh1 = nn.Hardtanh(inplace=True)
self.conv2 = Binaryconv3x3(planes, planes)
self.tanh2 = nn.Hardtanh(inplace=True)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.do_bntan=do_bntan;
self.stride = stride
def forward(self, x):
residual = x.clone()
out = self.conv1(x)
out = self.bn1(out)
out = self.tanh1(out)
out = self.conv2(out)
if self.downsample is not None:
if residual.data.max()>1:
import pdb; pdb.set_trace()
residual = self.downsample(residual)
out += residual
if self.do_bntan:
out = self.bn2(out)
out = self.tanh2(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = BinarizeConv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = BinarizeConv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = BinarizeConv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.tanh = nn.Hardtanh(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
import pdb; pdb.set_trace()
out = self.conv1(x)
out = self.bn1(out)
out = self.tanh(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.tanh(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
if self.do_bntan:
out = self.bn2(out)
out = self.tanh2(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(
BinarizeConv2d(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-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.maxpool(x)
x = self.bn1(x)
x = self.tanh1(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=Bottleneck, layers=[3, 4, 23, 3]):
super(ResNet_imagenet, self).__init__()
self.inplanes = 64
self.conv1 = BinarizeConv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.tanh = nn.Hardtanh(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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.AvgPool2d(7)
self.fc = BinarizeLinear(512 * block.expansion, num_classes)
init_model(self)
self.regime = {
0: {'optimizer': 'SGD', 'lr': 1e-1,
'weight_decay': 1e-4, 'momentum': 0.9},
30: {'lr': 1e-2},
60: {'lr': 1e-3, 'weight_decay': 0},
90: {'lr': 1e-4}
}
class ResNet_cifar10(ResNet):
def __init__(self, num_classes=10,
block=BasicBlock, depth=18):
super(ResNet_cifar10, self).__init__()
self.inflate = 5
self.inplanes = 16*self.inflate
n = int((depth - 2) / 6)
self.conv1 = BinarizeConv2d(3, 16*self.inflate, kernel_size=3, stride=1, padding=1,
bias=False)
self.maxpool = lambda x: x
self.bn1 = nn.BatchNorm2d(16*self.inflate)
self.tanh1 = nn.Hardtanh(inplace=True)
self.tanh2 = nn.Hardtanh(inplace=True)
self.layer1 = self._make_layer(block, 16*self.inflate, n)
self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2)
self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False)
self.layer4 = lambda x: x
self.avgpool = nn.AvgPool2d(8)
self.bn2 = nn.BatchNorm1d(64*self.inflate)
self.bn3 = nn.BatchNorm1d(10)
self.logsoftmax = nn.LogSoftmax()
self.fc = BinarizeLinear(64*self.inflate, num_classes)
init_model(self)
#self.regime = {
# 0: {'optimizer': 'SGD', 'lr': 1e-1,
# 'weight_decay': 1e-4, 'momentum': 0.9},
# 81: {'lr': 1e-4},
# 122: {'lr': 1e-5, 'weight_decay': 0},
# 164: {'lr': 1e-6}
#}
self.regime = {
0: {'optimizer': 'Adam', 'lr': 5e-3},
101: {'lr': 1e-3},
142: {'lr': 5e-4},
184: {'lr': 1e-4},
220: {'lr': 1e-5}
}
def resnet_binary(**kwargs):
num_classes, depth, dataset = map(
kwargs.get, ['num_classes', 'depth', 'dataset'])
if 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])
if depth == 34:
return ResNet_imagenet(num_classes=num_classes,
block=BasicBlock, layers=[3, 4, 6, 3])
if depth == 50:
return ResNet_imagenet(num_classes=num_classes,
block=Bottleneck, layers=[3, 4, 6, 3])
if depth == 101:
return ResNet_imagenet(num_classes=num_classes,
block=Bottleneck, layers=[3, 4, 23, 3])
if depth == 152:
return ResNet_imagenet(num_classes=num_classes,
block=Bottleneck, layers=[3, 8, 36, 3])
elif dataset == 'cifar10':
num_classes = num_classes or 10
depth = depth or 18
return ResNet_cifar10(num_classes=num_classes,
block=BasicBlock, depth=depth)