import torch import torch.nn as nn from torch.autograd import Variable OPS = { 'noise': lambda C, stride, affine: NoiseOp(stride, 0., 1.), '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) ), 'sep_conv_3x3_skip': lambda C, stride, affine: SepConvSkip(C, C, 3, stride, 1, affine=affine), 'sep_conv_5x5_skip': lambda C, stride, affine: SepConvSkip(C, C, 5, stride, 2, affine=affine), 'dil_conv_3x3_skip': lambda C, stride, affine: DilConvSkip(C, C, 3, stride, 2, 2, affine=affine), 'dil_conv_5x5_skip': lambda C, stride, affine: DilConvSkip(C, C, 5, stride, 4, 2, affine=affine), } class NoiseOp(nn.Module): def __init__(self, stride, mean, std): super(NoiseOp, self).__init__() self.stride = stride self.mean = mean self.std = std def forward(self, x, block_input=False): if block_input: x = x*0 if self.stride != 1: x_new = x[:,:,::self.stride,::self.stride] else: x_new = x noise = Variable(x_new.data.new(x_new.size()).normal_(self.mean, self.std)) return noise 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, block_input=False): if block_input: x = x*0 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, block_input=False): if block_input: x = x*0 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, block_input=False): if block_input: x = x*0 return self.op(x) class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, x, block_input=False): if block_input: x = x*0 return x class Zero(nn.Module): def __init__(self, stride): super(Zero, self).__init__() self.stride = stride def forward(self, x, block_input=False): if block_input: x = x*0 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, block_input=False): if block_input: x = x*0 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 #### operations with skip class DilConvSkip(nn.Module): def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True): super(DilConvSkip, 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, block_input=False): if block_input: x = x*0 return self.op(x) + x class SepConvSkip(nn.Module): def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True): super(SepConvSkip, 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, block_input=False): if block_input: x = x*0 return self.op(x) + x