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体验新版 GitCode,发现更多精彩内容 >>
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f456a4e9
编写于
10月 27, 2017
作者:
Z
zchen0211
浏览文件
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电子邮件补丁
差异文件
batch-norm forward backward nchw, nhwc passed
上级
03789a7d
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
44 addition
and
45 deletion
+44
-45
python/paddle/v2/framework/tests/test_batch_norm_op.py
python/paddle/v2/framework/tests/test_batch_norm_op.py
+44
-45
未找到文件。
python/paddle/v2/framework/tests/test_batch_norm_op.py
浏览文件 @
f456a4e9
...
...
@@ -184,47 +184,47 @@ class TestBatchNormOp(OpTest):
print
'python: NHWC, NCHW, backward checking passed'
def
test_forward_backward
(
self
):
# attr
data_format
=
"NCHW"
epsilon
=
0.00001
momentum
=
0.9
# N, H, W, C: 12, 3, 4, 2
n
,
h
,
w
,
c
=
2
,
3
,
4
,
2
if
data_format
==
"NHWC"
:
x_shape
=
[
n
,
h
,
w
,
c
]
elif
data_format
==
"NCHW"
:
x_shape
=
[
n
,
c
,
h
,
w
]
else
:
raise
ValueError
(
"Unknown data type."
)
scale_shape
=
[
c
]
x_val
=
np
.
random
.
random_sample
(
x_shape
).
astype
(
np
.
float32
)
scale_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
bias_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
mean
=
np
.
zeros
(
scale_shape
).
astype
(
np
.
float32
)
variance
=
np
.
ones
(
scale_shape
).
astype
(
np
.
float32
)
# run forward
y_out
,
saved_mean
,
var_ref
=
_reference_training
(
x_val
,
scale_val
,
bias_val
,
epsilon
,
data_format
)
# update moving mean and variance
mean_out
=
saved_mean
*
(
1.
-
momentum
)
+
momentum
*
mean
variance_out
=
var_ref
*
(
1.
-
momentum
)
+
momentum
*
variance
saved_variance
=
1.
/
np
.
sqrt
(
var_ref
+
epsilon
)
# for gradient test
# y_grad = np.ones(x_shape).astype(np.float32)
y_grad
=
np
.
zeros
(
x_shape
).
astype
(
np
.
float32
)
y_grad
[
0
,
0
,
0
,
0
]
=
1.
# y_grad = np.random.random_sample(x_shape).astype(np.float32)
x_grad_ref
,
scale_grad_ref
,
bias_grad_ref
=
_reference_grad
(
x_val
,
y_grad
,
scale_val
,
saved_mean
,
var_ref
,
epsilon
,
data_format
)
def
test_with_place
(
place
,
tensor_format
):
# attr
epsilon
=
0.00001
momentum
=
0.9
# N, H, W, C: 12, 3, 4, 2
n
,
h
,
w
,
c
=
2
,
3
,
4
,
2
if
data_format
==
"NHWC"
:
x_shape
=
[
n
,
h
,
w
,
c
]
elif
data_format
==
"NCHW"
:
x_shape
=
[
n
,
c
,
h
,
w
]
else
:
raise
ValueError
(
"Unknown data type."
)
scale_shape
=
[
c
]
x_val
=
np
.
random
.
random_sample
(
x_shape
).
astype
(
np
.
float32
)
scale_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
bias_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
np
.
float32
)
mean
=
np
.
zeros
(
scale_shape
).
astype
(
np
.
float32
)
variance
=
np
.
ones
(
scale_shape
).
astype
(
np
.
float32
)
# run forward
y_out
,
saved_mean
,
var_ref
=
_reference_training
(
x_val
,
scale_val
,
bias_val
,
epsilon
,
data_format
)
# update moving mean and variance
mean_out
=
saved_mean
*
(
1.
-
momentum
)
+
momentum
*
mean
variance_out
=
var_ref
*
(
1.
-
momentum
)
+
momentum
*
variance
saved_variance
=
1.
/
np
.
sqrt
(
var_ref
+
epsilon
)
# for gradient test
# y_grad = np.ones(x_shape).astype(np.float32)
y_grad
=
np
.
zeros
(
x_shape
).
astype
(
np
.
float32
)
y_grad
[
0
,
0
,
0
,
0
]
=
1.
# y_grad = np.random.random_sample(x_shape).astype(np.float32)
x_grad_ref
,
scale_grad_ref
,
bias_grad_ref
=
_reference_grad
(
x_val
,
y_grad
,
scale_val
,
saved_mean
,
var_ref
,
epsilon
,
data_format
)
def
test_with_place
(
place
,
tensor_format
=
data_format
):
scope
=
core
.
Scope
()
# create input
...
...
@@ -275,14 +275,13 @@ class TestBatchNormOp(OpTest):
self
.
__assert_close
(
saved_variance_tensor
,
saved_variance
,
"saved_variance"
)
self
.
__assert_close
(
mean_out_tensor
,
mean_out
,
"mean_out"
)
# FIXME(qiao) figure out why with cuDNN variance_out have a higher error rate
if
isinstance
(
place
,
core
.
GPUPlace
):
atol
=
5e-2
else
:
atol
=
1e-4
self
.
__assert_close
(
variance_out_tensor
,
variance_out
,
"variance_out"
,
atol
)
print
"op test forward passed: "
,
tensor_format
print
"op test forward passed: "
,
str
(
place
),
tensor_format
# run backward
batch_norm_op_grad
=
get_backward_op
(
scope
,
batch_norm_op
,
set
())
...
...
@@ -307,14 +306,14 @@ class TestBatchNormOp(OpTest):
self
.
__assert_close
(
x_grad_tensor
,
x_grad_ref
,
"x_grad"
)
self
.
__assert_close
(
scale_grad_tensor
,
scale_grad_ref
,
"scale_grad"
)
self
.
__assert_close
(
bias_grad_tensor
,
bias_grad_ref
,
"bias_grad"
)
print
"op test backward passed: "
,
tensor_format
print
"op test backward passed: "
,
str
(
place
),
tensor_format
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compile_gpu
()
and
core
.
op_support_gpu
(
"batch_norm"
):
places
.
append
(
core
.
GPUPlace
(
0
))
for
place
in
places
:
test_with_place
(
place
)
print
"test forward passed"
for
data_format
in
[
"NCHW"
,
"NHWC"
]:
test_with_place
(
place
,
data_format
)
if
__name__
==
'__main__'
:
...
...
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