wideresnet.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import (MetaModule, MetaConv2d, MetaBatchNorm2d, MetaLinear)


class BasicBlock(MetaModule):
  def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
    super(BasicBlock, self).__init__()
    self.bn1 = MetaBatchNorm2d(in_planes)
    self.relu1 = nn.ReLU(inplace=True)
    self.conv1 = MetaConv2d(in_planes,
                            out_planes,
                            kernel_size=3,
                            stride=stride,
                            padding=1,
                            bias=False)
    self.bn2 = MetaBatchNorm2d(out_planes)
    self.relu2 = nn.ReLU(inplace=True)
    self.conv2 = MetaConv2d(out_planes,
                            out_planes,
                            kernel_size=3,
                            stride=1,
                            padding=1,
                            bias=False)
    self.droprate = dropRate
    self.equalInOut = (in_planes == out_planes)
    self.convShortcut = (not self.equalInOut) and MetaConv2d(
        in_planes,
        out_planes,
        kernel_size=1,
        stride=stride,
        padding=0,
        bias=False) or None

  def forward(self, x):
    if not self.equalInOut:
      x = self.relu1(self.bn1(x))
    else:
      out = self.relu1(self.bn1(x))
    out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
    if self.droprate > 0:
      out = F.dropout(out, p=self.droprate, training=self.training)
    out = self.conv2(out)
    return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(MetaModule):
  def __init__(self,
               nb_layers,
               in_planes,
               out_planes,
               block,
               stride,
               dropRate=0.0):
    super(NetworkBlock, self).__init__()
    self.layer = self._make_layer(block, in_planes, out_planes, nb_layers,
                                  stride, dropRate)

  def _make_layer(self, block, in_planes, out_planes, nb_layers, stride,
                  dropRate):
    layers = []
    for i in range(int(nb_layers)):
      layers.append(
          block(i == 0 and in_planes or out_planes, out_planes,
                i == 0 and stride or 1, dropRate))
    return nn.Sequential(*layers)

  def forward(self, x):
    return self.layer(x)


class WideResNet(MetaModule):
  def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
    super(WideResNet, self).__init__()
    nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
    assert ((depth - 4) % 6 == 0)
    n = (depth - 4) / 6
    block = BasicBlock
    # 1st conv before any network block
    self.conv1 = MetaConv2d(3,
                            nChannels[0],
                            kernel_size=3,
                            stride=1,
                            padding=1,
                            bias=False)
    # 1st block
    self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1,
                               dropRate)
    # 2nd block
    self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2,
                               dropRate)
    # 3rd block
    self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2,
                               dropRate)
    # global average pooling and classifier
    self.bn1 = MetaBatchNorm2d(nChannels[3])
    self.relu = nn.ReLU(inplace=True)
    self.fc = MetaLinear(nChannels[3], num_classes)
    self.nChannels = nChannels[3]

    for m in self.modules():
      if isinstance(m, MetaConv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
      elif isinstance(m, MetaBatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
      elif isinstance(m, MetaLinear):
        m.bias.data.zero_()

  def forward(self, x):
    out = self.conv1(x)
    out = self.block1(out)
    out = self.block2(out)
    out = self.block3(out)
    out = self.relu(self.bn1(out))
    out = F.avg_pool2d(out, 8)
    out = out.view(-1, self.nChannels)
    return self.fc(out)