Source code for archai.networks.wideresnet

import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import numpy as np


[docs]def conv3x3(in_planes, out_planes, stride=1): return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
[docs]def conv_init(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: init.xavier_uniform_(m.weight, gain=np.sqrt(2)) init.constant_(m.bias, 0) elif classname.find('BatchNorm') != -1: init.constant_(m.weight, 1) init.constant_(m.bias, 0)
[docs]class WideBasic(nn.Module): def __init__(self, in_planes, planes, dropout_rate, stride=1): super(WideBasic, self).__init__() self.bn1 = nn.BatchNorm2d(in_planes, momentum=0.9) self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True) self.dropout = nn.Dropout(p=dropout_rate) self.bn2 = nn.BatchNorm2d(planes, momentum=0.9) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True) self.shortcut = nn.Sequential() if stride != 1 or in_planes != planes: self.shortcut = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=True), )
[docs] def forward(self, x): out = self.dropout(self.conv1(F.relu(self.bn1(x)))) out = self.conv2(F.relu(self.bn2(out))) out += self.shortcut(x) return out
[docs]class WideResNet(nn.Module): def __init__(self, depth, widen_factor, dropout_rate, n_classes): super(WideResNet, self).__init__() self.in_planes = 16 assert ((depth - 4) % 6 == 0), 'Wide-resnet depth should be 6n+4' n = int((depth - 4) / 6) k = widen_factor nStages = [16, 16*k, 32*k, 64*k] self.conv1 = conv3x3(3, nStages[0]) self.layer1 = self._wide_layer(WideBasic, nStages[1], n, dropout_rate, stride=1) self.layer2 = self._wide_layer(WideBasic, nStages[2], n, dropout_rate, stride=2) self.layer3 = self._wide_layer(WideBasic, nStages[3], n, dropout_rate, stride=2) self.bn1 = nn.BatchNorm2d(nStages[3], momentum=0.9) self.linear = nn.Linear(nStages[3], n_classes) # self.apply(conv_init) def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride): strides = [stride] + [1]*(num_blocks-1) layers = [] for stride in strides: layers.append(block(self.in_planes, planes, dropout_rate, stride)) self.in_planes = planes return nn.Sequential(*layers)
[docs] def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = F.relu(self.bn1(out)) # out = F.avg_pool2d(out, 8) out = F.adaptive_avg_pool2d(out, (1, 1)) out = out.view(out.size(0), -1) out = self.linear(out) return out