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体验新版 GitCode,发现更多精彩内容 >>
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8a521c0b
编写于
7月 16, 2018
作者:
F
fengjiayi
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Remove buggy get_test_program and refine c++ reader demo
上级
ebe3b5e7
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
62 addition
and
159 deletion
+62
-159
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+0
-98
python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.py
...ests/demo/text_classification/convert_data_to_recordio.py
+6
-2
python/paddle/fluid/tests/demo/text_classification/train.py
python/paddle/fluid/tests/demo/text_classification/train.py
+56
-59
未找到文件。
python/paddle/fluid/io.py
浏览文件 @
8a521c0b
...
...
@@ -789,101 +789,3 @@ def get_parameter_value_by_name(name, executor, program=None):
program
=
default_main_program
()
var
=
program
.
global_block
().
var
(
name
)
return
get_parameter_value
(
var
,
executor
)
def
get_test_program
(
filelist
,
program
=
None
,
startup_program
=
None
):
"""
Transpile current train program to a program to read test dataset
if the program is using reader ops like "open_files_op".
"""
def
_copy_reader_var_
(
block
,
var
,
new_name
=
None
):
if
new_name
==
None
:
new_name
=
var
.
name
new_var
=
block
.
create_var
(
name
=
str
(
new_name
),
type
=
core
.
VarDesc
.
VarType
.
READER
)
new_var
.
desc
.
set_shapes
(
var
.
desc
.
shapes
())
new_var
.
desc
.
set_dtypes
(
var
.
desc
.
dtypes
())
new_var
.
persistable
=
True
return
new_var
def
_get_test_reader_name
(
train_reader_name
):
return
train_reader_name
+
"_test"
def
_is_reader_op
(
op
):
block
=
op
.
block
if
"Out"
in
op
.
output_names
:
reader_out
=
block
.
vars
[
op
.
output
(
"Out"
)[
0
]]
if
reader_out
.
type
==
core
.
VarDesc
.
VarType
.
READER
:
return
True
return
False
if
program
==
None
:
program
=
default_main_program
()
if
startup_program
==
None
:
startup_program
=
default_startup_program
()
startup_block
=
startup_program
.
global_block
()
# 1. find out the orignal reader var name
startup_reader_op_list
=
[]
for
op
in
startup_block
.
ops
:
if
_is_reader_op
(
op
):
startup_reader_op_list
.
append
(
op
)
if
len
(
startup_reader_op_list
)
==
0
:
return
program
root_reader_op
=
startup_reader_op_list
[
0
]
train_test_reader_map
=
{}
# 2. add operators to startup to read open and read test data files
for
op
in
startup_reader_op_list
:
assert
(
len
(
op
.
output
(
"Out"
))
==
1
)
train_reader_name
=
op
.
output
(
"Out"
)[
0
]
train_reader
=
startup_block
.
vars
[
train_reader_name
]
test_reader
=
_copy_reader_var_
(
startup_block
,
train_reader
,
new_name
=
_get_test_reader_name
(
train_reader_name
))
train_test_reader_map
[
train_reader
.
name
]
=
test_reader
test_op_inputs
=
{}
for
name
in
op
.
input_names
:
train_arg_names
=
op
.
input
(
name
)
test_arg_vars
=
[]
for
arg_name
in
train_arg_names
:
arg_var
=
train_test_reader_map
[
arg_name
]
if
name
==
"UnderlyingReader"
else
startup_block
.
vars
[
arg_name
]
test_arg_vars
.
append
(
arg_var
)
test_op_inputs
[
name
]
=
test_arg_vars
test_op
=
startup_block
.
append_op
(
type
=
op
.
type
,
inputs
=
test_op_inputs
,
outputs
=
{
'Out'
:
[
test_reader
]},
attrs
=
op
.
attrs
)
# root reader op's filelist attr for read test files
if
op
.
type
==
root_reader_op
.
type
:
test_op
.
set_attr
(
"file_names"
,
filelist
)
if
op
.
type
==
"create_multi_pass_reader"
:
test_op
.
set_attr
(
"pass_num"
,
1
)
# 3. rename reader vars in inference program to different name
# to avoid read from train data.
main_block
=
program
.
global_block
()
for
var
in
main_block
.
vars
.
values
():
if
var
.
type
==
core
.
VarDesc
.
VarType
.
READER
:
main_block
.
rename_var
(
str
(
var
.
name
),
str
(
_get_test_reader_name
(
var
.
name
)))
for
op
in
main_block
.
ops
:
if
op
.
type
==
root_reader_op
.
type
:
test_op
.
set_attr
(
"file_names"
,
filelist
)
if
op
.
type
==
"create_multi_pass_reader"
:
test_op
.
set_attr
(
"pass_num"
,
1
)
startup_program
.
sync_with_cpp
()
program
.
sync_with_cpp
()
return
program
python/paddle/fluid/tests/demo/text_classification/convert_data_to_recordio.py
浏览文件 @
8a521c0b
...
...
@@ -31,8 +31,12 @@ def load_vocab(filename):
# load word dict with paddle inner function
word_dict
=
load_vocab
(
sys
.
argv
[
1
])
word_dict
[
"<unk>"
]
=
len
(
word_dict
)
if
len
(
sys
.
argv
)
>
1
:
word_dict
=
load_vocab
(
sys
.
argv
[
1
])
word_dict
[
"<unk>"
]
=
len
(
word_dict
)
else
:
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
print
"Dict dim = "
,
len
(
word_dict
)
# input text data
...
...
python/paddle/fluid/tests/demo/text_classification/train.py
浏览文件 @
8a521c0b
...
...
@@ -19,7 +19,7 @@ import sys
TRAIN_FILES
=
[
'train.recordio'
]
TEST_FILES
=
[
'test.recordio'
]
DICT_DIM
=
89528
DICT_DIM
=
5147
# embedding dim
emb_dim
=
128
...
...
@@ -33,33 +33,24 @@ hid_dim2 = 96
# class num
class_dim
=
2
# epoch num
epoch_num
=
10
def
network_cfg
(
is_train
,
pass_num
=
100
):
with
fluid
.
unique_name
.
guard
():
train_file_obj
=
fluid
.
layers
.
open_files
(
filenames
=
TRAIN_FILES
,
pass_num
=
pass_num
,
shapes
=
[[
-
1
,
1
],
[
-
1
,
1
]],
lod_levels
=
[
1
,
0
],
dtypes
=
[
'int64'
,
'int64'
],
thread_num
=
1
)
test_file_obj
=
fluid
.
layers
.
open_files
(
filenames
=
TEST_FILES
,
pass_num
=
1
,
shapes
=
[[
-
1
,
1
],
[
-
1
,
1
]],
lod_levels
=
[
1
,
0
],
dtypes
=
[
'int64'
,
'int64'
],
thread_num
=
1
)
if
is_train
:
file_obj
=
fluid
.
layers
.
shuffle
(
train_file_obj
,
buffer_size
=
1000
)
else
:
file_obj
=
test_file_obj
def
build_program
(
is_train
):
file_obj_handle
=
fluid
.
layers
.
io
.
open_files
(
filenames
=
TRAIN_FILES
if
is_train
else
TEST_FILES
,
shapes
=
[[
-
1
,
1
],
[
-
1
,
1
]],
lod_levels
=
[
1
,
0
],
dtypes
=
[
'int64'
,
'int64'
],
thread_num
=
1
)
if
is_train
:
file_obj
=
fluid
.
layers
.
io
.
shuffle
(
file_obj_handle
,
buffer_size
=
1000
)
else
:
file_obj
=
file_obj_handle
file_obj
=
fluid
.
layers
.
io
.
double_buffer
(
file_obj
)
file_obj
=
fluid
.
layers
.
double_buffer
(
file_obj
,
name
=
"train_double_buffer"
if
is_train
else
'test_double_buffer'
)
with
fluid
.
unique_name
.
guard
():
data
,
label
=
fluid
.
layers
.
read_file
(
file_obj
)
...
...
@@ -90,58 +81,64 @@ def network_cfg(is_train, pass_num=100):
if
is_train
:
# SGD optimizer
sgd_optimizer
=
fluid
.
optimizer
.
Adagrad
(
learning_rate
=
0.01
)
sgd_optimizer
=
fluid
.
optimizer
.
Adagrad
(
learning_rate
=
0.0
0
1
)
sgd_optimizer
.
minimize
(
avg_cost
)
return
{
'loss'
:
avg_cost
,
'log'
:
[
avg_cost
,
acc
],
'file'
:
train_file_obj
if
is_train
else
test_file_obj
}
return
{
'loss'
:
avg_cost
,
'log'
:
[
avg_cost
,
acc
],
'file'
:
file_obj_handle
}
def
main
():
train
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
test
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train
,
startup
):
train_args
=
network_cfg
(
is_train
=
True
)
test
=
fluid
.
Program
()
train_args
=
build_program
(
is_train
=
True
)
with
fluid
.
program_guard
(
test
,
fluid
.
Program
()
):
test_args
=
network_cfg
(
is_train
=
False
)
with
fluid
.
program_guard
(
test
,
startup
):
test_args
=
build_program
(
is_train
=
False
)
use_cuda
=
fluid
.
core
.
is_compiled_with_cuda
()
# startup
place
=
fluid
.
CUDAPlace
(
0
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
=
place
)
exe
.
run
(
startup
)
train_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
loss_name
=
train_args
[
'loss'
].
name
,
main_program
=
train
)
use_cuda
=
use_cuda
,
loss_name
=
train_args
[
'loss'
].
name
,
main_program
=
train
)
test_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
use_cuda
,
main_program
=
test
,
share_vars_from
=
train_exe
)
fetch_var_list
=
[
var
.
name
for
var
in
train_args
[
'log'
]]
for
i
in
xrange
(
sys
.
maxint
):
result
=
map
(
numpy
.
array
,
train_exe
.
run
(
fetch_list
=
fetch_var_list
if
i
%
1000
==
0
else
[]))
if
len
(
result
)
!=
0
:
print
'Train: '
,
result
if
i
%
1000
==
0
:
test_exe
=
fluid
.
ParallelExecutor
(
use_cuda
=
True
,
main_program
=
test
,
share_vars_from
=
train_exe
)
loss
=
[]
acc
=
[]
try
:
while
True
:
loss_np
,
acc_np
=
map
(
numpy
.
array
,
test_exe
.
run
(
fetch_list
=
fetch_var_list
))
loss
.
append
(
loss_np
[
0
])
acc
.
append
(
acc_np
[
0
])
except
:
test_args
[
'file'
].
reset
()
print
'TEST: '
,
numpy
.
mean
(
loss
),
numpy
.
mean
(
acc
)
for
epoch_id
in
range
(
epoch_num
):
# train
try
:
batch_id
=
0
while
True
:
result
=
map
(
numpy
.
array
,
train_exe
.
run
(
fetch_list
=
fetch_var_list
if
batch_id
%
10
==
0
else
[]))
if
len
(
result
)
!=
0
:
print
'Train loss: '
,
result
batch_id
+=
1
except
fluid
.
core
.
EOFException
:
print
'End of epoch'
,
epoch_id
train_args
[
'file'
].
reset
()
# test
loss
=
[]
acc
=
[]
try
:
while
True
:
loss_np
,
acc_np
=
map
(
numpy
.
array
,
test_exe
.
run
(
fetch_list
=
fetch_var_list
))
loss
.
append
(
loss_np
[
0
])
acc
.
append
(
acc_np
[
0
])
except
:
test_args
[
'file'
].
reset
()
print
'TEST: '
,
numpy
.
mean
(
loss
),
numpy
.
mean
(
acc
)
if
__name__
==
'__main__'
:
...
...
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