operations.py

import torch
import torch.nn as nn
import torch.nn.functional as F

OPS = {
  'none'        : lambda C, stride, affine: Zero(stride),
  'avg_pool_3x3': lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
  'max_pool_3x3': lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
  'skip_connect': lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
  'sep_conv_3x3': lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
  'sep_conv_5x5': lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
  'dil_conv_3x3': lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
  'dil_conv_5x5': lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
}

class ReLUConvBN(nn.Module):

  def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
    super(ReLUConvBN, self).__init__()
    self.op = nn.Sequential(
      nn.ReLU(inplace=False),
      nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
      nn.BatchNorm2d(C_out, affine=affine)
    )

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

class DilConv(nn.Module):

  def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
    super(DilConv, self).__init__()
    self.op = nn.Sequential(
      nn.ReLU(inplace=False),
      nn.Conv2d(C_in, C_out, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=False),
      nn.BatchNorm2d(C_out, affine=affine),
      )

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


class SepConv(nn.Module):

  def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
    super(SepConv, self).__init__()
    self.op = nn.Sequential(
      nn.ReLU(inplace=False),
      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
      nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
      nn.BatchNorm2d(C_out, affine=affine),
      )

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


class Identity(nn.Module):

  def __init__(self):
    super(Identity, self).__init__()

  def forward(self, x):
    return x


class Zero(nn.Module):

  def __init__(self, stride):
    super(Zero, self).__init__()
    self.stride = stride

  def forward(self, x):
    if self.stride == 1:
      return x.mul(0.)
    return x[:, :, ::self.stride, ::self.stride].mul(0.)


class FactorizedReduce(nn.Module):

  def __init__(self, C_in, C_out, affine=True):
    super(FactorizedReduce, self).__init__()
    assert C_out % 2 == 0
    self.relu = nn.ReLU(inplace=False)
    self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
    self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
    self.bn = nn.BatchNorm2d(C_out, affine=affine)

  def forward(self, x):
    x = self.relu(x)
    out = torch.cat([self.conv_1(x), self.conv_2(x[:, :, 1:, 1:])], dim=1)
    out = self.bn(out)
    return out

class FactorizedIncrease(nn.Module):
    def __init__(self, C_in, C_out, affine=True):
        super(FactorizedIncrease, self).__init__()
        self.op = nn.Sequential(
            nn.Upsample(scale_factor=2, mode="bilinear"),
            nn.ReLU(inplace=False),
            nn.Conv2d(C_in, C_out, 1, stride=1, padding=0),
            nn.BatchNorm2d(C_out, affine=affine)
        )

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



class ASPP(nn.Module):
    def __init__(self, C_in, C_out, padding, dilation, affine=True):
        super(ASPP, self).__init__()
        self.conv_normal = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.Conv2d(C_in, C_out, 1, padding=0, bias=False),
            nn.BatchNorm2d(C_out, affine=affine),
        )
        self.conv_dilation = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.Conv2d(C_in, C_in, kernel_size=3, padding=padding, dilation=dilation, groups=C_in, bias=False),
            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
            nn.BatchNorm2d(C_out, affine=affine),
        )
        self.global_avg_pool_pre = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.AdaptiveAvgPool2d(1),
        )
        self.global_avg_pool_post = nn.Sequential(
            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
            nn.BatchNorm2d(C_out, affine=affine),
        )
        self.conv_concat = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.Conv2d(C_out * 3, C_out, 1, padding=0, bias=False),
            nn.BatchNorm2d(C_out, affine=affine),
        )

    def forward(self, x):
        x1 = self.conv_normal(x)
        x2 = self.conv_dilation(x)
        x3 = self.global_avg_pool_pre(x)
        x3 = F.interpolate(x3, size=x.size()[2:], mode='bilinear', align_corners=True)
        x3 = self.global_avg_pool_post(x3)
        x4 = torch.cat([x1, x2, x3], dim=1)
        out = self.conv_concat(x4)
        return out