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Re-enable batch norm on switch processing blocks

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Found out that batch norm is causing the switches to init really poorly -
not using a significant number of transforms. Might be a great time to
re-consider using the attention norm, but for now just re-enable it.
This commit is contained in:
James Betker 2020-06-24 21:15:17 -06:00
parent 4001db1ede
commit 42a10b34ce
1 changed files with 2 additions and 2 deletions
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codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -59,7 +59,7 @@ class HalvingProcessingBlock(nn.Module):
def __init__(self, filters):
super(HalvingProcessingBlock, self).__init__()
self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False)
self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=False)
self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True)
def forward(self, x):
x = self.bnconv1(x)
@ -72,7 +72,7 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_
convs = []
current_filters = filters_init
for i in range(num_convs):
convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=False))
convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=True))
current_filters += filter_growth
return nn.Sequential(*convs), current_filters