123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
import torch
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import torch.nn as nn
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OPS = {
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'none' : lambda C, stride, affine: Zero(stride),
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'avg_pool_3x3' : lambda C, stride, affine: nn.Sequential(
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nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
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nn.BatchNorm2d(C, affine=False) ),
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'max_pool_3x3' : lambda C, stride, affine: nn.Sequential(
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nn.MaxPool2d(3, stride=stride, padding=1),
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nn.BatchNorm2d(C, affine=False) ),
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'skip_connect' : lambda C, stride, affine: Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
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'sep_conv_3x3' : lambda C, stride, affine: SepConv(C, C, 3, stride, 1, affine=affine),
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'sep_conv_5x5' : lambda C, stride, affine: SepConv(C, C, 5, stride, 2, affine=affine),
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'sep_conv_7x7' : lambda C, stride, affine: SepConv(C, C, 7, stride, 3, affine=affine),
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'dil_conv_3x3' : lambda C, stride, affine: DilConv(C, C, 3, stride, 2, 2, affine=affine),
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'dil_conv_5x5' : lambda C, stride, affine: DilConv(C, C, 5, stride, 4, 2, affine=affine),
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'conv_7x1_1x7' : lambda C, stride, affine: Conv717(C, C, stride, affine),
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}
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class Conv717(nn.Module):
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def __init__(self, C_in, C_out, stride, affine):
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super(Conv717, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C_in , C_out, (1,7), stride=(1, stride), padding=(0, 3), bias=False),
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nn.Conv2d(C_out, C_out, (7,1), stride=(stride, 1), padding=(3, 0), bias=False),
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nn.BatchNorm2d(C_out, affine=affine)
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)
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def forward(self, x):
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return self.op(x)
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class ReLUConvBN(nn.Module):
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def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
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super(ReLUConvBN, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False),
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nn.BatchNorm2d(C_out, affine=affine)
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)
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def forward(self, x):
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return self.op(x)
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class DilConv(nn.Module):
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def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
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super(DilConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_out, affine=affine),
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)
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def forward(self, x):
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return self.op(x)
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class SepConv(nn.Module):
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def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
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super(SepConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, groups=C_in, bias=False),
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nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_in, affine=affine),
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nn.ReLU(inplace=False),
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nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=1, padding=padding, groups=C_in, bias=False),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
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nn.BatchNorm2d(C_out, affine=affine),
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)
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def forward(self, x):
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return self.op(x)
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class Identity(nn.Module):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, x):
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return x
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class Zero(nn.Module):
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def __init__(self, stride):
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super(Zero, self).__init__()
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self.stride = stride
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def forward(self, x):
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if self.stride == 1:
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return x.mul(0.)
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return x[:,:,::self.stride,::self.stride].mul(0.)
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class FactorizedReduce(nn.Module):
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def __init__(self, C_in, C_out, affine=True):
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super(FactorizedReduce, self).__init__()
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assert C_out % 2 == 0
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self.relu = nn.ReLU(inplace=False)
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self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
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self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
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self.bn = nn.BatchNorm2d(C_out, affine=affine)
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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def forward(self, x):
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x = self.relu(x)
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y = self.pad(x)
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out = torch.cat([self.conv_1(x), self.conv_2(y[:,:,1:,1:])], dim=1)
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out = self.bn(out)
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return out
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