update GDAS and SETN
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D-X-Y
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lib/models/cell_operations.py
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200
lib/models/cell_operations.py
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import torch
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import torch.nn as nn
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__all__ = ['OPS', 'ReLUConvBN', 'ResNetBasicblock', '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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AA_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
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SearchSpaceNames = {'connect-nas' : CONNECT_NAS_BENCHMARK,
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'aa-nas' : AA_NAS_BENCHMARK}
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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 ResNetBasicblock(nn.Module):
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def __init__(self, inplanes, planes, stride):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
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self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, 1, 1)
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self.conv_b = ReLUConvBN( planes, planes, 3, 1, 1, 1)
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if stride == 2:
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self.downsample = nn.Sequential(
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nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
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nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
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elif inplanes != planes:
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self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1)
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else:
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self.downsample = None
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self.in_dim = inplanes
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self.out_dim = planes
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self.stride = stride
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self.num_conv = 2
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def extra_repr(self):
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string = '{name}(inC={in_dim}, outC={out_dim}, stride={stride})'.format(name=self.__class__.__name__, **self.__dict__)
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return string
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def forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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return residual + basicblock
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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 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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# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
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class PartAwareOp(nn.Module):
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def __init__(self, C_in, C_out, stride, part=4):
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super().__init__()
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self.part = 4
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self.hidden = C_in // 3
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.local_conv_list = nn.ModuleList()
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for i in range(self.part):
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self.local_conv_list.append(
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nn.Sequential(nn.ReLU(), nn.Conv2d(C_in, self.hidden, 1), nn.BatchNorm2d(self.hidden, affine=True))
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)
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self.W_K = nn.Linear(self.hidden, self.hidden)
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self.W_Q = nn.Linear(self.hidden, self.hidden)
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if stride == 2 : self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
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elif stride == 1: self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
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else: raise ValueError('Invalid Stride : {:}'.format(stride))
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def forward(self, x):
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batch, C, H, W = x.size()
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assert H >= self.part, 'input size too small : {:} vs {:}'.format(x.shape, self.part)
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IHs = [0]
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for i in range(self.part): IHs.append( min(H, int((i+1)*(float(H)/self.part))) )
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local_feat_list = []
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for i in range(self.part):
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feature = x[:, :, IHs[i]:IHs[i+1], :]
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xfeax = self.avg_pool(feature)
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xfea = self.local_conv_list[i]( xfeax )
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local_feat_list.append( xfea )
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part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
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part_feature = part_feature.transpose(1,2).contiguous()
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part_K = self.W_K(part_feature)
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part_Q = self.W_Q(part_feature).transpose(1,2).contiguous()
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weight_att = torch.bmm(part_K, part_Q)
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attention = torch.softmax(weight_att, dim=2)
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aggreateF = torch.bmm(attention, part_feature).transpose(1,2).contiguous()
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features = []
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for i in range(self.part):
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feature = aggreateF[:, :, i:i+1].expand(batch, self.hidden, IHs[i+1]-IHs[i])
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feature = feature.view(batch, self.hidden, IHs[i+1]-IHs[i], 1)
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features.append( feature )
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features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
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final_fea = torch.cat((x,features), dim=1)
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outputs = self.last( final_fea )
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return outputs
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