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- import torch.nn as nn
- import torchvision.transforms as transforms
- import math
-
- __all__ = ['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)
-
-
- def init_model(model):
- for m in model.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_()
-
-
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
- class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = 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:
- residual = self.downsample(x)
-
- out += residual
- 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):
- 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.conv1(x)
- x = self.bn1(x)
- x = self.relu(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.fc(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 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(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 = nn.Linear(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.inplanes = 16
- n = int((depth - 2) / 6)
- self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1,
- bias=False)
- self.bn1 = nn.BatchNorm2d(16)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = lambda x: x
- self.layer1 = self._make_layer(block, 16, n)
- self.layer2 = self._make_layer(block, 32, n, stride=2)
- self.layer3 = self._make_layer(block, 64, n, stride=2)
- self.layer4 = lambda x: x
- self.avgpool = nn.AvgPool2d(8)
- self.fc = nn.Linear(64, num_classes)
-
- init_model(self)
- self.regime = {
- 0: {'optimizer': 'SGD', 'lr': 1e-1,
- 'weight_decay': 1e-4, 'momentum': 0.9},
- 81: {'lr': 1e-2},
- 122: {'lr': 1e-3, 'weight_decay': 0},
- 164: {'lr': 1e-4}
- }
-
-
- def resnet(**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 #56
- return ResNet_cifar10(num_classes=num_classes,
- block=BasicBlock, depth=depth)
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