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59adf7ce
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体验新版 GitCode,发现更多精彩内容 >>
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59adf7ce
编写于
8月 09, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix round(0.0) special issue
上级
938a9e4f
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
20 addition
and
11 deletion
+20
-11
python/paddle/fluid/compat.py
python/paddle/fluid/compat.py
+3
-1
python/paddle/fluid/debugger.py
python/paddle/fluid/debugger.py
+1
-0
python/paddle/fluid/graphviz.py
python/paddle/fluid/graphviz.py
+1
-0
python/paddle/fluid/profiler.py
python/paddle/fluid/profiler.py
+2
-1
python/paddle/fluid/tests/unittests/test_compat.py
python/paddle/fluid/tests/unittests/test_compat.py
+2
-0
python/paddle/fluid/tests/unittests/test_conv_shift_op.py
python/paddle/fluid/tests/unittests/test_conv_shift_op.py
+1
-1
python/paddle/fluid/tests/unittests/test_gru_op.py
python/paddle/fluid/tests/unittests/test_gru_op.py
+2
-1
python/paddle/fluid/tests/unittests/test_inference_model_io.py
...n/paddle/fluid/tests/unittests/test_inference_model_io.py
+3
-2
python/paddle/fluid/tests/unittests/test_pool_max_op.py
python/paddle/fluid/tests/unittests/test_pool_max_op.py
+5
-5
未找到文件。
python/paddle/fluid/compat.py
浏览文件 @
59adf7ce
...
...
@@ -183,9 +183,11 @@ def round(x, d=0):
if
x
>
0.0
:
p
=
10
**
d
return
float
(
math
.
floor
((
x
*
p
)
+
math
.
copysign
(
0.5
,
x
)))
/
p
el
se
:
el
if
x
<
0.0
:
p
=
10
**
d
return
float
(
math
.
ceil
((
x
*
p
)
+
math
.
copysign
(
0.5
,
x
)))
/
p
else
:
return
math
.
copysign
(
0.0
,
x
)
else
:
import
__builtin__
return
__builtin__
.
round
(
x
,
d
)
...
...
python/paddle/fluid/debugger.py
浏览文件 @
59adf7ce
...
...
@@ -13,6 +13,7 @@
# limitations under the License.
import
sys
import
six
import
re
from
.graphviz
import
GraphPreviewGenerator
from
.proto
import
framework_pb2
...
...
python/paddle/fluid/graphviz.py
浏览文件 @
59adf7ce
...
...
@@ -15,6 +15,7 @@
import
os
import
random
import
six
import
functools
import
subprocess
import
logging
...
...
python/paddle/fluid/profiler.py
浏览文件 @
59adf7ce
...
...
@@ -15,6 +15,7 @@
from
.
import
core
from
contextlib
import
contextmanager
import
os
import
six
__all__
=
[
'cuda_profiler'
,
'reset_profiler'
,
'profiler'
,
'start_profiler'
,
...
...
@@ -88,7 +89,7 @@ def cuda_profiler(output_file, output_mode=None, config=None):
config
=
NVPROF_CONFIG
if
config
is
None
else
config
config_file
=
'nvprof_config_file'
with
open
(
config_file
,
'wb'
)
as
fp
:
fp
.
writelines
([
"%s
\n
"
%
item
for
item
in
config
])
fp
.
writelines
([
six
.
b
(
"%s
\n
"
%
item
)
for
item
in
config
])
core
.
nvprof_init
(
output_file
,
output_mode
,
config_file
)
# Enables profiler collection by the active CUDA profiling tool.
core
.
nvprof_start
()
...
...
python/paddle/fluid/tests/unittests/test_compat.py
浏览文件 @
59adf7ce
...
...
@@ -440,6 +440,8 @@ class TestCompatible(unittest.TestCase):
self
.
assertEqual
(
3.0
,
cpt
.
round
(
3.4
))
self
.
assertEqual
(
4.0
,
cpt
.
round
(
3.5
))
self
.
assertEqual
(
0.0
,
cpt
.
round
(
0.1
))
self
.
assertEqual
(
0.0
,
cpt
.
round
(
0.0
))
self
.
assertEqual
(
-
0.0
,
cpt
.
round
(
-
0.0
))
self
.
assertEqual
(
-
0.0
,
cpt
.
round
(
-
0.1
))
self
.
assertEqual
(
-
3.0
,
cpt
.
round
(
-
3.4
))
self
.
assertEqual
(
-
4.0
,
cpt
.
round
(
-
3.5
))
...
...
python/paddle/fluid/tests/unittests/test_conv_shift_op.py
浏览文件 @
59adf7ce
...
...
@@ -21,7 +21,7 @@ def conv_shift_forward(x, y):
out
=
np
.
zeros_like
(
x
)
M
=
x
.
shape
[
1
]
N
=
y
.
shape
[
1
]
y_half_width
=
(
N
-
1
)
/
2
y_half_width
=
(
N
-
1
)
/
/
2
for
i
in
range
(
M
):
for
j
in
range
(
N
):
out
[:,
i
]
+=
x
[:,
(
i
+
j
+
M
-
y_half_width
)
%
M
]
*
y
[:,
j
]
...
...
python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @
59adf7ce
...
...
@@ -15,6 +15,7 @@
import
unittest
import
numpy
as
np
import
math
import
functools
from
op_test
import
OpTest
from
test_lstm_op
import
identity
,
sigmoid
,
tanh
,
relu
...
...
@@ -38,7 +39,7 @@ class TestGRUOp(OpTest):
for
i
in
range
(
len
(
seq_lens
)):
seq_starts
.
append
(
seq_starts
[
-
1
]
+
seq_lens
[
i
])
sorted_seqs
=
sorted
(
list
(
range
(
len
(
seq_lens
))),
lambda
x
,
y
:
seq_lens
[
y
]
-
seq_lens
[
x
]
)
list
(
range
(
len
(
seq_lens
))),
key
=
functools
.
cmp_to_key
(
lambda
x
,
y
:
seq_lens
[
y
]
-
seq_lens
[
x
])
)
num_batch
=
seq_lens
[
sorted_seqs
[
0
]]
for
batch_idx
in
range
(
num_batch
):
idx_in_seq
=
[]
...
...
python/paddle/fluid/tests/unittests/test_inference_model_io.py
浏览文件 @
59adf7ce
...
...
@@ -14,6 +14,7 @@
import
unittest
import
six
import
numpy
as
np
import
paddle.fluid.core
as
core
...
...
@@ -48,7 +49,7 @@ class TestBook(unittest.TestCase):
exe
.
run
(
init_program
,
feed
=
{},
fetch_list
=
[])
for
i
in
range
(
100
):
for
i
in
six
.
moves
.
x
range
(
100
):
tensor_x
=
np
.
array
(
[[
1
,
1
],
[
1
,
2
],
[
3
,
4
],
[
5
,
2
]]).
astype
(
"float32"
)
tensor_y
=
np
.
array
([[
-
2
],
[
-
3
],
[
-
7
],
[
-
7
]]).
astype
(
"float32"
)
...
...
@@ -64,7 +65,7 @@ class TestBook(unittest.TestCase):
'y'
:
tensor_y
},
fetch_list
=
[
avg_cost
])[
0
]
reload
(
executor
)
# reload to build a new scope
six
.
moves
.
reload_module
(
executor
)
# reload to build a new scope
exe
=
executor
.
Executor
(
place
)
[
infer_prog
,
feed_var_names
,
fetch_vars
]
=
load_inference_model
(
...
...
python/paddle/fluid/tests/unittests/test_pool_max_op.py
浏览文件 @
59adf7ce
...
...
@@ -24,9 +24,9 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=False):
ksize
=
[
D
,
H
,
W
]
paddings
=
[
0
,
0
,
0
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
/
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
range
(
D_out
):
...
...
@@ -63,8 +63,8 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=False):
ksize
=
[
H
,
W
]
paddings
=
[
0
,
0
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
range
(
H_out
):
...
...
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