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Max Ehrlich
cdcnn
Commits
cc5094a1
Verified
Commit
cc5094a1
authored
Dec 05, 2018
by
Max Ehrlich
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Batch norm training mode first attempt
parent
79824871
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35 additions
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9 deletions
+35
-9
jpeg_layers/batchnorm.py
jpeg_layers/batchnorm.py
+35
-9
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jpeg_layers/batchnorm.py
View file @
cc5094a1
...
...
@@ -5,19 +5,45 @@ class BatchNorm(torch.nn.modules.Module):
def
__init__
(
self
,
bn
):
super
(
BatchNorm
,
self
).
__init__
()
self
.
mean
=
bn
.
running_mean
self
.
var
=
bn
.
running_var
self
.
register_buffer
(
'running_mean'
,
bn
.
running_mean
)
self
.
register_buffer
(
'running_var'
,
bn
.
running_var
)
self
.
register_buffer
(
'num_batches_tracked'
,
bn
.
num_batches_tracked
)
self
.
gamma
=
bn
.
weight
self
.
beta
=
bn
.
bias
gamma_final
=
(
self
.
gamma
/
torch
.
sqrt
(
self
.
var
)).
view
(
1
,
self
.
gamma
.
shape
[
0
],
1
,
1
,
1
)
self
.
register_buffer
(
'gamma_final'
,
gamma_final
)
beta_final
=
(
self
.
beta
-
(
self
.
gamma
*
self
.
mean
)
/
torch
.
sqrt
(
self
.
var
)).
view
(
1
,
self
.
beta
.
shape
[
0
],
1
,
1
)
self
.
register_buffer
(
'beta_final'
,
beta_final
)
self
.
register_parameter
(
'gamma'
,
self
.
gamma
)
self
.
register_parameter
(
'beta'
,
self
.
beta
)
def
forward
(
self
,
input
):
input
=
input
*
self
.
gamma_final
input
[:,
:,
:,
:,
0
]
=
input
[:,
:,
:,
:,
0
]
+
self
.
beta_final
if
self
.
training
:
# Compute the batch mean for each channel
channels
=
input
.
shape
[
1
]
block_means
=
input
[:,
:,
:,
:,
0
].
permute
(
1
,
0
,
2
,
3
).
contiguous
().
view
(
channels
,
-
1
)
# channels x everything else
batch_mean
=
torch
.
mean
(
block_means
,
1
)
# Compute the batch variance for each channel
input
[:,
:,
:,
:,
0
]
=
0
# zero mean
batch_var
=
torch
.
mean
(
input
.
permute
(
1
,
0
,
2
,
3
,
4
).
contiguous
().
view
(
channels
,
-
1
)
**
2
,
1
)
# Apply parameters
bv
=
batch_var
.
view
(
1
,
batch_var
.
shape
[
0
],
1
,
1
,
1
)
b
=
self
.
beta
.
view
(
1
,
self
.
beta
.
shape
[
0
],
1
,
1
)
g
=
self
.
gamma
.
view
(
1
,
self
.
gamma
.
shape
[
0
],
1
,
1
,
1
)
input
=
(
input
/
torch
.
sqrt
(
bv
))
*
g
input
[:,
:,
:,
:,
0
]
=
input
[:,
:,
:,
:,
0
]
+
b
# Update running stats
self
.
running_mean
=
(
self
.
running_mean
*
self
.
num_batches_tracked
.
float
()
+
batch_mean
)
/
(
self
.
num_batches_tracked
.
float
()
+
1
)
self
.
running_var
=
(
self
.
running_var
*
self
.
num_batches_tracked
.
float
()
+
batch_var
)
/
(
self
.
num_batches_tracked
.
float
()
+
1
)
self
.
num_batches_tracked
+=
1
else
:
gamma_final
=
(
self
.
gamma
/
torch
.
sqrt
(
self
.
running_var
)).
view
(
1
,
self
.
gamma
.
shape
[
0
],
1
,
1
,
1
)
beta_final
=
(
self
.
beta
-
(
self
.
gamma
*
self
.
running_mean
)
/
torch
.
sqrt
(
self
.
running_var
)).
view
(
1
,
self
.
beta
.
shape
[
0
],
1
,
1
)
input
=
input
*
gamma_final
input
[:,
:,
:,
:,
0
]
=
input
[:,
:,
:,
:,
0
]
+
beta_final
return
input
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