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5e36757c
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PaddleDetection
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5e36757c
编写于
3月 17, 2018
作者:
K
Kexin Zhao
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix test
上级
151cfff9
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
88 addition
and
64 deletion
+88
-64
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
+88
-64
未找到文件。
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
浏览文件 @
5e36757c
...
...
@@ -187,74 +187,99 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
class
TestBatchNormOpInference
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"conv2d"
self
.
is_test
=
True
self
.
dtype
=
np
.
float32
self
.
data_layout
=
"NCHW"
init_dtype
()
init_data_layout
()
init_test_case
()
def
__assert_close
(
self
,
tensor
,
np_array
,
msg
,
atol
=
1e-4
):
self
.
assertTrue
(
np
.
allclose
(
np
.
array
(
tensor
),
np_array
,
atol
=
atol
),
msg
)
epsilon
=
0.00001
shape
=
self
.
shape
if
len
(
shape
)
==
2
:
x_shape
=
shape
c
=
x_shape
[
1
]
else
:
n
,
h
,
w
,
c
=
shape
[
0
],
shape
[
1
],
shape
[
2
],
shape
[
3
]
if
self
.
data_layout
==
"NHWC"
:
x_shape
=
[
n
,
h
,
w
,
c
]
elif
self
.
data_layout
==
"NCHW"
:
x_shape
=
[
n
,
c
,
h
,
w
]
def
test_inference
(
self
):
def
test_with_place
(
place
,
data_layout
,
dtype
,
shape
):
epsilon
=
0.00001
if
len
(
shape
)
==
2
:
x_shape
=
shape
c
=
x_shape
[
1
]
else
:
raise
ValueError
(
"Unknown data layout."
)
scale_shape
=
[
c
]
n
,
h
,
w
,
c
=
shape
[
0
],
shape
[
1
],
shape
[
2
],
shape
[
3
]
if
data_layout
==
"NHWC"
:
x_shape
=
[
n
,
h
,
w
,
c
]
elif
data_layout
==
"NCHW"
:
x_shape
=
[
n
,
c
,
h
,
w
]
else
:
raise
ValueError
(
"Unknown data layout."
)
scale_shape
=
[
c
]
x_val
=
np
.
random
.
random_sample
(
x_shape
).
astype
(
self
.
dtype
)
scale_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
self
.
dtype
)
bias_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
self
.
dtype
)
x_val
=
np
.
random
.
random_sample
(
x_shape
).
astype
(
dtype
)
scale_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
dtype
)
bias_val
=
np
.
random
.
random_sample
(
scale_shape
).
astype
(
dtype
)
mean
=
np
.
zeros
(
scale_shape
).
astype
(
self
.
dtype
)
variance
=
np
.
ones
(
scale_shape
).
astype
(
self
.
dtype
)
mean
=
np
.
zeros
(
scale_shape
).
astype
(
dtype
)
variance
=
np
.
ones
(
scale_shape
).
astype
(
dtype
)
saved_mean
=
np
.
zeros
(
scale_shape
).
astype
(
self
.
dtype
)
saved_variance
=
np
.
ones
(
scale_shape
).
astype
(
self
.
dtype
)
y_out
=
_reference_testing
(
x_val
,
scale_val
,
bias_val
,
mean
,
variance
,
epsilon
,
data_layout
).
astype
(
dtype
)
y_out
=
_reference_testing
(
x_val
,
scale_val
,
bias_val
,
mean
,
variance
,
epsilon
,
self
.
data_layout
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'X'
:
OpTest
.
np_dtype_to_fluid_dtype
(
x_val
),
'Scale'
:
OpTest
.
np_dtype_to_fluid_dtype
(
scale_val
),
'Bias'
:
OpTest
.
np_dtype_to_fluid_dtype
(
bias_val
),
'Mean'
:
OpTest
.
np_dtype_to_fluid_dtype
(
mean
),
'Variance'
:
OpTest
.
np_dtype_to_fluid_dtype
(
variance
)
}
self
.
attrs
=
{
'is_test'
:
self
.
is_test
,
'epsilon'
:
epsilon
,
'data_layout'
:
self
.
data_layout
}
self
.
outputs
=
{
'Y'
:
y_out
,
'MeanOut'
:
mean
,
'VarianceOut'
:
variance
,
'SavedMean'
:
saved_mean
,
'SavedVariance'
:
saved_variance
}
def
test_check_output
(
self
):
self
.
check_output
()
def
init_dtype
(
self
):
pass
def
init_data_layout
(
self
):
pass
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
4
,
5
]
scope
=
core
.
Scope
()
# create input
x_tensor
=
create_or_get_tensor
(
scope
,
"x_val"
,
OpTest
.
np_dtype_to_fluid_dtype
(
x_val
),
place
)
scale_tensor
=
create_or_get_tensor
(
scope
,
"scale_val"
,
OpTest
.
np_dtype_to_fluid_dtype
(
scale_val
),
place
)
bias_tensor
=
create_or_get_tensor
(
scope
,
"bias_val"
,
OpTest
.
np_dtype_to_fluid_dtype
(
bias_val
),
place
)
mean_tensor
=
create_or_get_tensor
(
scope
,
"mean"
,
OpTest
.
np_dtype_to_fluid_dtype
(
mean
),
place
)
variance_tensor
=
create_or_get_tensor
(
scope
,
"variance"
,
OpTest
.
np_dtype_to_fluid_dtype
(
variance
),
place
)
# create output
y_tensor
=
create_or_get_tensor
(
scope
,
"y_out"
,
None
,
place
)
saved_mean_tensor
=
create_or_get_tensor
(
scope
,
"saved_mean"
,
None
,
place
)
saved_variance_tensor
=
create_or_get_tensor
(
scope
,
"saved_variance"
,
None
,
place
)
mean_out_tensor
=
mean_tensor
variance_out_tensor
=
variance_tensor
batch_norm_op
=
Operator
(
"batch_norm"
,
# inputs
X
=
"x_val"
,
Scale
=
"scale_val"
,
Bias
=
"bias_val"
,
Mean
=
"mean"
,
Variance
=
"variance"
,
# outputs
Y
=
"y_out"
,
MeanOut
=
"mean"
,
VarianceOut
=
"variance"
,
SavedMean
=
"saved_mean"
,
SavedVariance
=
"saved_variance"
,
# attrs
is_test
=
True
,
data_layout
=
data_layout
,
epsilon
=
epsilon
)
batch_norm_op
.
run
(
scope
,
place
)
# check inference result
self
.
__assert_close
(
y_tensor
,
y_out
,
"inference output are different at "
+
str
(
place
)
+
", "
+
data_layout
+
", "
+
str
(
np
.
dtype
(
dtype
)))
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compiled_with_cuda
()
and
core
.
op_support_gpu
(
"batch_norm"
):
place
=
core
.
CUDAPlace
(
0
)
if
self
.
dtype
!=
np
.
float16
or
core
.
is_float16_supported
(
place
):
places
.
append
(
place
)
for
place
in
places
:
for
data_format
in
[
"NCHW"
,
"NHWC"
]:
for
dtype
in
[
np
.
float32
,
np
.
float16
]:
test_with_place
(
place
,
data_format
,
dtype
,
[
2
,
3
,
4
,
5
])
test_with_place
(
place
,
data_format
,
dtype
,
[
2
,
3
])
class
TestBatchNormOpTraining
(
OpTest
):
...
...
@@ -288,8 +313,7 @@ class TestBatchNormOpTraining(OpTest):
# transfer (N, C, H, W) back to (N, H, W, C)
y_out2_trans
=
np
.
transpose
(
y_out2
,
(
0
,
2
,
3
,
1
))
self
.
__assert_close
(
y_out
,
y_out2_trans
,
"inference outputs of two formats have differences"
)
self
.
__assert_close
(
y_out
,
y_out2_trans
,
"inference output"
)
print
'python: NHWC, NCHW, inference checking passed'
def
test_python_training
(
self
):
...
...
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