eschmidbauer/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Another go at fixing nan

Browse Source
This commit is contained in:
James Betker 2021-06-07 11:51:43 -06:00
parent 1c574c5bd1
commit c456a60466
1 changed files with 3 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
codes/models/classifiers/cifar_resnet_branched.py
View File

@ -112,7 +112,7 @@ class ResNetTail(nn.Module):
class DropoutNorm(SwitchNorm):
def __init__(self, group_size, dropout_rate, accumulator_size=256):
def __init__(self, group_size, dropout_rate, accumulator_size=256, eps=1e-6):
super().__init__(group_size, accumulator_size)
self.accumulator_desired_size = accumulator_size
self.group_size = group_size
@ -120,6 +120,7 @@ class DropoutNorm(SwitchNorm):
self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu'))
self.register_buffer("accumulator", torch.zeros(accumulator_size, group_size))
self.eps = eps
def add_norm_to_buffer(self, x):
flatten_dims = [0] + [k+2 for k in range(len(x.shape)-2)]
@ -165,7 +166,7 @@ class DropoutNorm(SwitchNorm):
# Ensure that there is always at least one switch left un-dropped out
fix_blank = (drop.sum(dim=1, keepdim=True) == 0).repeat(1, br)
drop = drop.logical_or(fix_blank)
x = drop * x
x = drop * x + ((not drop) * x * self.eps)
return x