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update GDAS reduction cell

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Xuanyi Dong
2020-02-22 15:08:33 +11:00
parent 1efe3cbccf
commit 2165b9c5b1
3 changed files with 187 additions and 1 deletions
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55
lib/models/cell_operations.py
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@@ -234,3 +234,58 @@ class PartAwareOp(nn.Module):
final_fea = torch.cat((x,features), dim=1)
outputs = self.last( final_fea )
return outputs
# Searching for A Robust Neural Architecture in Four GPU Hours
class GDAS_Reduction_Cell(nn.Module):
def __init__(self, C_prev_prev, C_prev, C, reduction_prev, multiplier, affine, track_running_stats):
super(GDAS_Reduction_Cell, self).__init__()
if reduction_prev:
self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
else:
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
self.multiplier = multiplier
self.reduction = True
self.ops1 = nn.ModuleList(
[nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True)),
nn.Sequential(
nn.ReLU(inplace=False),
nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False),
nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False),
nn.BatchNorm2d(C, affine=True),
nn.ReLU(inplace=False),
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(C, affine=True))])
self.ops2 = nn.ModuleList(
[nn.Sequential(
nn.MaxPool2d(3, stride=1, padding=1),
nn.BatchNorm2d(C, affine=True)),
nn.Sequential(
nn.MaxPool2d(3, stride=2, padding=1),
nn.BatchNorm2d(C, affine=True))])
def forward(self, s0, s1, drop_prob = -1):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
X0 = self.ops1[0] (s0)
X1 = self.ops1[1] (s1)
if self.training and drop_prob > 0.:
X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
X2 = self.ops2[0] (X0+X1)
X3 = self.ops2[1] (s1)
if self.training and drop_prob > 0.:
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
return torch.cat([X0, X1, X2, X3], dim=1)