114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
import torch
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import torch.nn as nn
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__all__ = ['OPS', 'ReLUConvBN', 'SearchSpaceNames']
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OPS = {
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'none' : lambda C_in, C_out, stride: Zero(C_in, C_out, stride),
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'avg_pool_3x3' : lambda C_in, C_out, stride: POOLING(C_in, C_out, stride, 'avg'),
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'max_pool_3x3' : lambda C_in, C_out, stride: POOLING(C_in, C_out, stride, 'max'),
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'nor_conv_7x7' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1)),
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'nor_conv_3x3' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1)),
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'nor_conv_1x1' : lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1)),
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'skip_connect' : lambda C_in, C_out, stride: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride),
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}
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CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
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SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK}
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class POOLING(nn.Module):
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def __init__(self, C_in, C_out, stride, mode):
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super(POOLING, self).__init__()
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if C_in == C_out:
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self.preprocess = None
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else:
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self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0)
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if mode == 'avg' : self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
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elif mode == 'max': self.op = nn.MaxPool2d(3, stride=stride, padding=1)
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else : raise ValueError('Invalid mode={:} in POOLING'.format(mode))
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def forward(self, inputs):
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if self.preprocess: x = self.preprocess(inputs)
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else : x = inputs
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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, dilation):
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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, dilation=dilation, bias=False),
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nn.BatchNorm2d(C_out)
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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, C_in, C_out, stride):
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super(Zero, self).__init__()
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self.C_in = C_in
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self.C_out = C_out
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self.stride = stride
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self.is_zero = True
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def forward(self, x):
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if self.C_in == self.C_out:
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if self.stride == 1: return x.mul(0.)
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else : return x[:,:,::self.stride,::self.stride].mul(0.)
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else:
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shape = list(x.shape)
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shape[1] = self.C_out
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zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
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return zeros
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def extra_repr(self):
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return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
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class FactorizedReduce(nn.Module):
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def __init__(self, C_in, C_out, stride):
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super(FactorizedReduce, self).__init__()
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self.stride = stride
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self.C_in = C_in
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self.C_out = C_out
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self.relu = nn.ReLU(inplace=False)
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if stride == 2:
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#assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
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C_outs = [C_out // 2, C_out - C_out // 2]
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self.convs = nn.ModuleList()
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for i in range(2):
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self.convs.append( nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=False) )
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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else:
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raise ValueError('Invalid stride : {:}'.format(stride))
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self.bn = nn.BatchNorm2d(C_out)
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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.convs[0](x), self.convs[1](y[:,:,1:,1:])], dim=1)
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out = self.bn(out)
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return out
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def extra_repr(self):
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return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
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