Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Paddle
提交
9ba231d3
P
Paddle
项目概览
PaddlePaddle
/
Paddle
大约 1 年 前同步成功
通知
2298
Star
20931
Fork
5422
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1423
列表
看板
标记
里程碑
合并请求
543
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1,423
Issue
1,423
列表
看板
标记
里程碑
合并请求
543
合并请求
543
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
9ba231d3
编写于
3月 01, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Complete inferencer.
上级
4c24ac1a
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
61 addition
and
28 deletion
+61
-28
demo/mnist/api_train_v2.py
demo/mnist/api_train_v2.py
+13
-0
python/paddle/v2/dataset/mnist.py
python/paddle/v2/dataset/mnist.py
+15
-14
python/paddle/v2/inferencer.py
python/paddle/v2/inferencer.py
+16
-11
python/paddle/v2/reader/decorator.py
python/paddle/v2/reader/decorator.py
+17
-3
未找到文件。
demo/mnist/api_train_v2.py
浏览文件 @
9ba231d3
...
...
@@ -44,6 +44,19 @@ def main():
batch_size
=
32
),
event_handler
=
event_handler
)
# output is a softmax layer. It returns probabilities.
# Shape should be (100, 10)
probs
=
paddle
.
infer
(
output
=
inference
,
parameters
=
parameters
,
reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
limited
(
paddle
.
reader
.
map_readers
(
lambda
item
:
(
item
[
0
],
),
paddle
.
dataset
.
mnist
.
test
()),
limit
=
100
),
batch_size
=
32
))
print
probs
.
shape
if
__name__
==
'__main__'
:
main
()
python/paddle/v2/dataset/mnist.py
浏览文件 @
9ba231d3
...
...
@@ -35,24 +35,25 @@ def reader_creator(image_filename, label_filename, buffer_size):
l
=
subprocess
.
Popen
([
zcat_cmd
,
label_filename
],
stdout
=
subprocess
.
PIPE
)
l
.
stdout
.
read
(
8
)
# skip some magic bytes
while
True
:
labels
=
numpy
.
fromfile
(
l
.
stdout
,
'ubyte'
,
count
=
buffer_size
).
astype
(
"int"
)
try
:
# reader could be break.
while
True
:
labels
=
numpy
.
fromfile
(
l
.
stdout
,
'ubyte'
,
count
=
buffer_size
).
astype
(
"int"
)
if
labels
.
size
!=
buffer_size
:
break
# numpy.fromfile returns empty slice after EOF.
if
labels
.
size
!=
buffer_size
:
break
# numpy.fromfile returns empty slice after EOF.
images
=
numpy
.
fromfile
(
m
.
stdout
,
'ubyte'
,
count
=
buffer_size
*
28
*
28
).
reshape
(
(
buffer_size
,
28
*
28
)).
astype
(
'float32'
)
images
=
numpy
.
fromfile
(
m
.
stdout
,
'ubyte'
,
count
=
buffer_size
*
28
*
28
).
reshape
(
(
buffer_size
,
28
*
28
)).
astype
(
'float32'
)
images
=
images
/
255.0
*
2.0
-
1.0
images
=
images
/
255.0
*
2.0
-
1.0
for
i
in
xrange
(
buffer_size
):
yield
images
[
i
,
:],
int
(
labels
[
i
])
m
.
terminate
()
l
.
terminate
()
for
i
in
xrange
(
buffer_size
):
yield
images
[
i
,
:],
int
(
labels
[
i
])
finally
:
m
.
terminate
()
l
.
terminate
()
return
reader
...
...
python/paddle/v2/inferencer.py
浏览文件 @
9ba231d3
...
...
@@ -16,18 +16,18 @@ class InferenceEngine(object):
for
param
in
gm
.
getParameters
():
val
=
param
.
getBuf
(
api
.
PARAMETER_VALUE
)
name
=
param
.
getName
()
assert
isinstance
(
val
,
api
.
Matrix
)
val
.
copyFromNumpy
Mat
(
parameters
.
get
(
name
))
assert
isinstance
(
val
,
api
.
Vector
)
val
.
copyFromNumpy
Array
(
parameters
.
get
(
name
).
flatten
(
))
self
.
__gradient_machine__
=
gm
self
.
__data_types__
=
topo
.
data_type
()
def
iter_infer
(
self
,
reader
,
reader_dict
=
None
):
if
reader_dict
is
None
:
reader_dict
=
self
.
default_reader_dict
()
feeder
=
DataFeeder
(
self
.
__data_types__
,
reader_dict
)
out_args
=
api
.
Arguments
.
createArguments
(
0
)
self
.
__gradient_machine__
.
start
()
for
data_batch
in
reader
():
yield
self
.
__gradient_machine__
.
forwardTest
(
feeder
(
data_batch
),
out_args
,
api
.
PASS_TEST
)
yield
self
.
__gradient_machine__
.
forwardTest
(
feeder
(
data_batch
))
self
.
__gradient_machine__
.
finish
()
def
iter_infer_field
(
self
,
field
,
**
kwargs
):
...
...
@@ -35,12 +35,17 @@ class InferenceEngine(object):
yield
[
each_result
[
field
]
for
each_result
in
result
]
def
infer
(
self
,
field
=
'value'
,
**
kwargs
):
retv
=
[]
for
result
in
itertools
.
izip
(
self
.
iter_infer_field
(
field
=
field
,
**
kwargs
)):
retv
.
append
(
numpy
.
concatenate
(
result
))
return
retv
retv
=
None
for
result
in
self
.
iter_infer_field
(
field
=
field
,
**
kwargs
):
if
retv
is
None
:
retv
=
[[]]
*
len
(
result
)
for
i
,
item
in
enumerate
(
result
):
retv
[
i
].
append
(
item
)
retv
=
[
numpy
.
concatenate
(
out
)
for
out
in
retv
]
if
len
(
retv
)
==
1
:
return
retv
[
0
]
else
:
return
retv
def
default_reader_dict
(
self
):
reader_dict
=
dict
()
...
...
python/paddle/v2/reader/decorator.py
浏览文件 @
9ba231d3
...
...
@@ -14,13 +14,13 @@
__all__
=
[
'map_readers'
,
'buffered'
,
'compose'
,
'chain'
,
'shuffle'
,
'ComposeNotAligned'
,
'batched'
'ComposeNotAligned'
,
'batched'
,
'limited'
]
from
Queue
import
Queue
from
threading
import
Thread
import
itertools
import
random
from
Queue
import
Queue
from
threading
import
Thread
def
map_readers
(
func
,
*
readers
):
...
...
@@ -213,3 +213,17 @@ def batched(reader, batch_size):
yield
batch
return
batched_reader
def
limited
(
reader
,
limit
):
"""
Limit the max number of samples that reader could return.
"""
def
limited_reader
():
for
i
,
item
in
enumerate
(
reader
()):
if
i
==
limit
:
break
yield
item
return
limited_reader
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录