Source code for archai.networks.resnet

# Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

import torch.nn as nn
import math


[docs]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)
[docs]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.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride
[docs] 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
[docs]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 * Bottleneck.expansion, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride
[docs] 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
[docs]class ResNet(nn.Module): def __init__(self, dataset, depth, n_classes, bottleneck=False): super(ResNet, self).__init__() self.dataset = dataset if self.dataset.startswith('cifar'): self.inplanes = 16 #logger.info(bottleneck) if bottleneck == True: n = int((depth - 2) / 9) block = Bottleneck else: n = int((depth - 2) / 6) block = BasicBlock self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(self.inplanes) self.relu = nn.ReLU(inplace=True) 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.avgpool = nn.AvgPool2d(8) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(64 * block.expansion, n_classes) elif dataset == 'imagenet': blocks ={18: BasicBlock, 34: BasicBlock, 50: Bottleneck, 101: Bottleneck, 152: Bottleneck, 200: Bottleneck} layers ={18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3], 200: [3, 24, 36, 3]} assert layers[depth], 'invalid detph for ResNet (depth should be one of 18, 34, 50, 101, 152, and 200)' self.inplanes = 64 self.conv1 = nn.Conv2d(3, self.inplanes, 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(blocks[depth], 64, layers[depth][0]) self.layer2 = self._make_layer(blocks[depth], 128, layers[depth][1], stride=2) self.layer3 = self._make_layer(blocks[depth], 256, layers[depth][2], stride=2) self.layer4 = self._make_layer(blocks[depth], 512, layers[depth][3], stride=2) # self.avgpool = nn.AvgPool2d(7) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * blocks[depth].expansion, n_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_() 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)
[docs] def forward(self, x): if self.dataset == 'cifar10' or self.dataset == 'cifar100': x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) elif self.dataset == 'imagenet': 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