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248 lines
8.3 KiB
248 lines
8.3 KiB
import torch.nn as nn
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import torchvision.transforms as transforms
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import math
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from .binarized_modules import BinarizeLinear,BinarizeConv2d
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__all__ = ['resnet_binary']
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def Binaryconv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return BinarizeConv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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def conv3x3(in_planes, out_planes, stride=1):
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"3x3 convolution with padding"
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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def init_model(model):
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for m in model.modules():
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if isinstance(m, BinarizeConv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None,do_bntan=True):
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super(BasicBlock, self).__init__()
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self.conv1 = Binaryconv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.tanh1 = nn.Hardtanh(inplace=True)
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self.conv2 = Binaryconv3x3(planes, planes)
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self.tanh2 = nn.Hardtanh(inplace=True)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.do_bntan=do_bntan;
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self.stride = stride
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def forward(self, x):
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residual = x.clone()
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.tanh1(out)
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out = self.conv2(out)
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if self.downsample is not None:
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if residual.data.max()>1:
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import pdb; pdb.set_trace()
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residual = self.downsample(residual)
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out += residual
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if self.do_bntan:
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out = self.bn2(out)
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out = self.tanh2(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(Bottleneck, self).__init__()
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self.conv1 = BinarizeConv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = BinarizeConv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = BinarizeConv2d(planes, planes * 4, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * 4)
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self.tanh = nn.Hardtanh(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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import pdb; pdb.set_trace()
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.tanh(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.tanh(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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if self.do_bntan:
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out = self.bn2(out)
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out = self.tanh2(out)
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return out
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class ResNet(nn.Module):
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def __init__(self):
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super(ResNet, self).__init__()
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def _make_layer(self, block, planes, blocks, stride=1,do_bntan=True):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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BinarizeConv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks-1):
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layers.append(block(self.inplanes, planes))
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layers.append(block(self.inplanes, planes,do_bntan=do_bntan))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.maxpool(x)
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x = self.bn1(x)
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x = self.tanh1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.bn2(x)
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x = self.tanh2(x)
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x = self.fc(x)
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x = self.bn3(x)
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x = self.logsoftmax(x)
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return x
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class ResNet_imagenet(ResNet):
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def __init__(self, num_classes=1000,
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block=Bottleneck, layers=[3, 4, 23, 3]):
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super(ResNet_imagenet, self).__init__()
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self.inplanes = 64
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self.conv1 = BinarizeConv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.tanh = nn.Hardtanh(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AvgPool2d(7)
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self.fc = BinarizeLinear(512 * block.expansion, num_classes)
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init_model(self)
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self.regime = {
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0: {'optimizer': 'SGD', 'lr': 1e-1,
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'weight_decay': 1e-4, 'momentum': 0.9},
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30: {'lr': 1e-2},
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60: {'lr': 1e-3, 'weight_decay': 0},
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90: {'lr': 1e-4}
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}
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class ResNet_cifar10(ResNet):
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def __init__(self, num_classes=10,
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block=BasicBlock, depth=18):
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super(ResNet_cifar10, self).__init__()
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self.inflate = 5
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self.inplanes = 16*self.inflate
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n = int((depth - 2) / 6)
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self.conv1 = BinarizeConv2d(3, 16*self.inflate, kernel_size=3, stride=1, padding=1,
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bias=False)
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self.maxpool = lambda x: x
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self.bn1 = nn.BatchNorm2d(16*self.inflate)
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self.tanh1 = nn.Hardtanh(inplace=True)
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self.tanh2 = nn.Hardtanh(inplace=True)
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self.layer1 = self._make_layer(block, 16*self.inflate, n)
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self.layer2 = self._make_layer(block, 32*self.inflate, n, stride=2)
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self.layer3 = self._make_layer(block, 64*self.inflate, n, stride=2,do_bntan=False)
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self.layer4 = lambda x: x
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self.avgpool = nn.AvgPool2d(8)
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self.bn2 = nn.BatchNorm1d(64*self.inflate)
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self.bn3 = nn.BatchNorm1d(10)
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self.logsoftmax = nn.LogSoftmax()
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self.fc = BinarizeLinear(64*self.inflate, num_classes)
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init_model(self)
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#self.regime = {
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# 0: {'optimizer': 'SGD', 'lr': 1e-1,
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# 'weight_decay': 1e-4, 'momentum': 0.9},
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# 81: {'lr': 1e-4},
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# 122: {'lr': 1e-5, 'weight_decay': 0},
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# 164: {'lr': 1e-6}
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#}
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self.regime = {
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0: {'optimizer': 'Adam', 'lr': 5e-3},
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101: {'lr': 1e-3},
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142: {'lr': 5e-4},
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184: {'lr': 1e-4},
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220: {'lr': 1e-5}
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}
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def resnet_binary(**kwargs):
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num_classes, depth, dataset = map(
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kwargs.get, ['num_classes', 'depth', 'dataset'])
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if dataset == 'imagenet':
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num_classes = num_classes or 1000
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depth = depth or 50
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if depth == 18:
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return ResNet_imagenet(num_classes=num_classes,
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block=BasicBlock, layers=[2, 2, 2, 2])
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if depth == 34:
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return ResNet_imagenet(num_classes=num_classes,
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block=BasicBlock, layers=[3, 4, 6, 3])
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if depth == 50:
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return ResNet_imagenet(num_classes=num_classes,
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block=Bottleneck, layers=[3, 4, 6, 3])
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if depth == 101:
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return ResNet_imagenet(num_classes=num_classes,
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block=Bottleneck, layers=[3, 4, 23, 3])
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if depth == 152:
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return ResNet_imagenet(num_classes=num_classes,
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block=Bottleneck, layers=[3, 8, 36, 3])
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elif dataset == 'cifar10':
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num_classes = num_classes or 10
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depth = depth or 18
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return ResNet_cifar10(num_classes=num_classes,
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block=BasicBlock, depth=depth)
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