LaNAS/one-shot_LaNAS/Evaluate/operations.py [10:132]:
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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),
  'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, 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),
  'conv_7x1_1x7' : lambda C, stride, affine: nn.Sequential(
    nn.ReLU(inplace=False),
    nn.Conv2d(C, C, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
    nn.Conv2d(C, C, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
    nn.BatchNorm2d(C, affine=affine)
    ),
  'conv_1x1' : lambda C, stride, affine: nn.Conv2d(C, C, (1,1), stride=(stride, stride), padding=(0,0), bias=False),
  'conv_3x3' : lambda C, stride, affine: nn.Conv2d(C, C, (3,3), stride=(stride, stride), padding=(1,1), bias=False),
  'conv_5x5' : lambda C, stride, affine: nn.Conv2d(C, C, (5,5), stride=(stride, stride), padding=(2,2), bias=False),
}

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_in, kernel_size=kernel_size, stride=stride, 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),
      )

  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_in, kernel_size=1, padding=0, bias=False),
      nn.BatchNorm2d(C_in, affine=affine),
      nn.ReLU(inplace=False),
      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, 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 Conv2d(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
                 padding, dilation, groups, bias)

    def forward(self, x):
        weight = self.weight
        weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
                                  keepdim=True).mean(dim=3, keepdim=True)
        weight = weight - weight_mean
        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
        weight = weight / std.expand_as(weight)
        return F.conv2d(x, weight, self.bias, self.stride,
                        self.padding, self.dilation, self.groups)



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
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LaNAS/one-shot_LaNAS/supernet/operations.py [10:132]:
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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),
  'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, 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),
  'conv_7x1_1x7' : lambda C, stride, affine: nn.Sequential(
    nn.ReLU(inplace=False),
    nn.Conv2d(C, C, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
    nn.Conv2d(C, C, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
    nn.BatchNorm2d(C, affine=affine)
    ),
  'conv_1x1' : lambda C, stride, affine: nn.Conv2d(C, C, (1,1), stride=(stride, stride), padding=(0,0), bias=False),
  'conv_3x3' : lambda C, stride, affine: nn.Conv2d(C, C, (3,3), stride=(stride, stride), padding=(1,1), bias=False),
  'conv_5x5' : lambda C, stride, affine: nn.Conv2d(C, C, (5,5), stride=(stride, stride), padding=(2,2), bias=False),
}

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_in, kernel_size=kernel_size, stride=stride, 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),
      )

  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_in, kernel_size=1, padding=0, bias=False),
      nn.BatchNorm2d(C_in, affine=affine),
      nn.ReLU(inplace=False),
      nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, 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 Conv2d(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
                 padding, dilation, groups, bias)

    def forward(self, x):
        weight = self.weight
        weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
                                  keepdim=True).mean(dim=3, keepdim=True)
        weight = weight - weight_mean
        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
        weight = weight / std.expand_as(weight)
        return F.conv2d(x, weight, self.bias, self.stride,
                        self.padding, self.dilation, self.groups)



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
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