import torch import torch.nn as nn OPS = { 'none' : lambda C, stride, affine: Zero(stride), 'avg_pool_3x3' : lambda C, stride, affine: nn.Sequential( nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False), nn.BatchNorm2d(C, affine=False) ), 'max_pool_3x3' : lambda C, stride, affine: nn.Sequential( nn.MaxPool2d(3, stride=stride, padding=1), nn.BatchNorm2d(C, affine=False) ), '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: Conv717(C, C, stride, affine), } class Conv717(nn.Module): def __init__(self, C_in, C_out, stride, affine): super(Conv717, self).__init__() self.op = nn.Sequential( nn.ReLU(inplace=False), nn.Conv2d(C_in , C_out, (1,7), stride=(1, stride), padding=(0, 3), bias=False), nn.Conv2d(C_out, C_out, (7,1), stride=(stride, 1), padding=(3, 0), bias=False), nn.BatchNorm2d(C_out, affine=affine) ) def forward(self, x): return self.op(x) 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 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) self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0) def forward(self, x): x = self.relu(x) y = self.pad(x) out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1) out = self.bn(out) return out