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96a2e44a
编写于
7年前
作者:
Q
qiaolongfei
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差异文件
optimize seq2seq-dataset
上级
37806792
develop
1.8.5
2.0.1-rocm-post
2.4.1
Ligoml-patch-1
OliverLPH-patch-1
OliverLPH-patch-2
PaddlePM-patch-1
PaddlePM-patch-2
ZHUI-patch-1
add_default_att
add_kylinv10
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addfile
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ascendrc
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cp_2.4_fix_numpy
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incubate/frl_train_eval
incubate/infrt
incubate/lite
incubate/new_frl
incubate/new_frl_rc
incubate/stride
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layer_norm
make_flag_adding_easier
master
matmul_double_grad
move_embedding_to_phi
move_histogram_to_pten
move_sgd_to_phi
move_slice_to_pten
move_temporal_shift_to_phi
move_yolo_box_to_phi
npu_fix_alloc
numel
operator_opt
paddle_tiny_install
paralleltest
pass-compile-eval-frame
preln_ernie
prv-disable-more-cache
prv-md-even-more
prv-onednn-2.5
prv-reshape-mkldnn-ut2
pten_tensor_refactor
release-deleted/2.5
release-rc/2.5
release/0.10.0
release/0.11.0
release/0.12.0
release/0.13.0
release/0.14.0
release/0.15.0
release/1.0.0
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release/2.0-rc1
release/2.1
release/2.2
release/2.3
release/2.3-fc-ernie-fix
release/2.4
release/2.5
release/lite-0.1
release/llm_2.5
revert-24981-add_device_attr_for_regulization
revert-26856-strategy_example2
revert-27520-disable_pr
revert-31068-fix_conv3d_windows
revert-31562-mean
revert-32290-develop-hardlabel
revert-33037-forci
revert-33475-fix_cifar_label_dimension
revert-33630-bug-fix
revert-34159-add_npu_bce_logical_dev
revert-34406-add_copy_from_tensor
revert-34910-spinlocks_for_allocator
revert-35069-revert-34910-spinlocks_for_allocator
revert-36057-dev/read_flags_in_ut
revert-36201-refine_fast_threaded_ssa_graph_executor
revert-36985-add_license
revert-37318-refactor_dygraph_to_eager
revert-37926-eager_coreops_500
revert-37956-revert-37727-pylayer_support_tuple
revert-38100-mingdong
revert-38301-allocation_rearrange_pr
revert-38703-numpy_bf16_package_reupload
revert-38732-remove_useless_header_in_elementwise_mul_grad
revert-38959-Reduce_Grad
revert-39143-adjust_empty
revert-39227-move_trace_op_to_pten
revert-39268-dev/remove_concat_fluid_kernel
revert-40170-support_partial_grad
revert-41056-revert-40727-move_some_activaion_to_phi
revert-41065-revert-40993-mv_ele_floordiv_pow
revert-41068-revert-40790-phi_new
revert-41944-smaller_inference_api_test
revert-42149-do-not-reset-default-stream-for-stream-safe-cuda-allocator
revert-43155-fix_ut_tempfile
revert-43882-revert-41944-smaller_inference_api_test
revert-45808-phi/simplify_size_op
revert-46827-deform_comment
revert-47325-remove_cudnn_hardcode
revert-47645-add_npu_storage_dims
revert-48815-set_free_when_no_cache_hit_default_value_true
revert-49499-test_ninja_on_ci
revert-49654-prim_api_gen
revert-49673-modify_get_single_cov
revert-49763-fix_static_composite_gen
revert-50158-fix_found_inf_bug_for_custom_optimizer
revert-50188-refine_optimizer_create_accumulators
revert-50335-fix_optminizer_set_auxiliary_var_bug
revert-51676-flag_delete
revert-51850-fix_softmaxce_dev
revert-52175-dev_peak_memory
revert-52186-deve
revert-52523-test_py38
revert-52912-develop
revert-53248-set_cmake_policy
revert-54029-fix_windows_compile_bug
revert-54068-support_translating_op_attribute
revert-54214-modify_cmake_dependencies
revert-54370-offline_pslib
revert-54391-fix_cmake_md5error
revert-54411-fix_cpp17_compile
revert-54466-offline_pslib
revert-54480-cmake-rocksdb
revert-55568-fix_BF16_bug1
revert-56328-new_ir_support_vector_type_place_transfer
revert-56366-fix_openssl_bug
revert-56545-revert-56366-fix_openssl_bug
revert-56620-fix_new_ir_ocr_bug
revert-56925-check_inputs_grad_semantic
revert-57005-refine_stride_flag
rocm_dev_0217
sd_conv_linear_autocast
semi-auto/rule-base
support-0D-sort
support_weight_transpose
test_benchmark_ci
test_feature_precision_test_c
test_for_Filtetfiles
test_model_benchmark
test_model_benchmark_ci
zhiqiu-patch-1
v2.5.1
v2.5.0
v2.5.0-rc1
v2.5.0-rc0
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lite-v0.1
3 合并请求
!11636
[IMPORTANT] MKLDNN layout: Support for sum operator
,
!2081
Release/0.10.0
,
!1560
optimize Seq2seq dataset
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
229 addition
and
258 deletion
+229
-258
demo/sentiment/preprocess.py
demo/sentiment/preprocess.py
+160
-6
python/paddle/v2/dataset/wmt14.py
python/paddle/v2/dataset/wmt14.py
+69
-80
python/paddle/v2/dataset/wmt14_util.py
python/paddle/v2/dataset/wmt14_util.py
+0
-172
未找到文件。
demo/sentiment/preprocess.py
浏览文件 @
96a2e44a
...
@@ -12,22 +12,176 @@
...
@@ -12,22 +12,176 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
import
os
import
sys
import
random
import
operator
import
operator
import
numpy
as
np
from
subprocess
import
Popen
,
PIPE
from
os.path
import
join
as
join_path
from
optparse
import
OptionParser
from
optparse
import
OptionParser
from
os.path
import
join
as
join_path
from
subprocess
import
Popen
,
PIPE
import
numpy
as
np
from
paddle.utils.preprocess_util
import
*
from
paddle.utils.preprocess_util
import
*
from
paddle.utils.preprocess_util
import
save_list
,
DatasetCreater
"""
"""
Usage: run following command to show help message.
Usage: run following command to show help message.
python preprocess.py -h
python preprocess.py -h
"""
"""
class
SeqToSeqDatasetCreater
(
DatasetCreater
):
"""
A class to process data for sequence to sequence application.
"""
def
__init__
(
self
,
data_path
,
output_path
):
"""
data_path: the path to store the train data, test data and gen data
output_path: the path to store the processed dataset
"""
DatasetCreater
.
__init__
(
self
,
data_path
)
self
.
gen_dir_name
=
'gen'
self
.
gen_list_name
=
'gen.list'
self
.
output_path
=
output_path
def
concat_file
(
self
,
file_path
,
file1
,
file2
,
output_path
,
output
):
"""
Concat file1 and file2 to be one output file
The i-th line of output = i-th line of file1 + '
\t
' + i-th line of file2
file_path: the path to store file1 and file2
output_path: the path to store output file
"""
file1
=
os
.
path
.
join
(
file_path
,
file1
)
file2
=
os
.
path
.
join
(
file_path
,
file2
)
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
'paste '
+
file1
+
' '
+
file2
+
' > '
+
output
)
def
cat_file
(
self
,
dir_path
,
suffix
,
output_path
,
output
):
"""
Cat all the files in dir_path with suffix to be one output file
dir_path: the base directory to store input file
suffix: suffix of file name
output_path: the path to store output file
"""
cmd
=
'cat '
file_list
=
os
.
listdir
(
dir_path
)
file_list
.
sort
()
for
file
in
file_list
:
if
file
.
endswith
(
suffix
):
cmd
+=
os
.
path
.
join
(
dir_path
,
file
)
+
' '
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
cmd
+
'> '
+
output
)
def
build_dict
(
self
,
file_path
,
dict_path
,
dict_size
=-
1
):
"""
Create the dictionary for the file, Note that
1. Valid characters include all printable characters
2. There is distinction between uppercase and lowercase letters
3. There is 3 special token:
<s>: the start of a sequence
<e>: the end of a sequence
<unk>: a word not included in dictionary
file_path: the path to store file
dict_path: the path to store dictionary
dict_size: word count of dictionary
if is -1, dictionary will contains all the words in file
"""
if
not
os
.
path
.
exists
(
dict_path
):
dictory
=
dict
()
with
open
(
file_path
,
"r"
)
as
fdata
:
for
line
in
fdata
:
line
=
line
.
split
(
'
\t
'
)
for
line_split
in
line
:
words
=
line_split
.
strip
().
split
()
for
word
in
words
:
if
word
not
in
dictory
:
dictory
[
word
]
=
1
else
:
dictory
[
word
]
+=
1
output
=
open
(
dict_path
,
"w+"
)
output
.
write
(
'<s>
\n
<e>
\n
<unk>
\n
'
)
count
=
3
for
key
,
value
in
sorted
(
dictory
.
items
(),
key
=
lambda
d
:
d
[
1
],
reverse
=
True
):
output
.
write
(
key
+
"
\n
"
)
count
+=
1
if
count
==
dict_size
:
break
self
.
dict_size
=
count
def
create_dataset
(
self
,
dict_size
=-
1
,
mergeDict
=
False
,
suffixes
=
[
'.src'
,
'.trg'
]):
"""
Create seqToseq dataset
"""
# dataset_list and dir_list has one-to-one relationship
train_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
train_dir_name
)
test_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
test_dir_name
)
gen_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
gen_dir_name
)
dataset_list
=
[
train_dataset
,
test_dataset
,
gen_dataset
]
train_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
train_dir_name
)
test_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
test_dir_name
)
gen_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
gen_dir_name
)
dir_list
=
[
train_dir
,
test_dir
,
gen_dir
]
# create directory
for
dir
in
dir_list
:
if
not
os
.
path
.
exists
(
dir
):
os
.
makedirs
(
dir
)
# checkout dataset should be parallel corpora
suffix_len
=
len
(
suffixes
[
0
])
for
dataset
in
dataset_list
:
file_list
=
os
.
listdir
(
dataset
)
if
len
(
file_list
)
%
2
==
1
:
raise
RuntimeError
(
"dataset should be parallel corpora"
)
file_list
.
sort
()
for
i
in
range
(
0
,
len
(
file_list
),
2
):
if
file_list
[
i
][:
-
suffix_len
]
!=
file_list
[
i
+
1
][:
-
suffix_len
]:
raise
RuntimeError
(
"source and target file name should be equal"
)
# cat all the files with the same suffix in dataset
for
suffix
in
suffixes
:
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
+
suffix
self
.
cat_file
(
dataset
,
suffix
,
dataset
,
outname
)
# concat parallel corpora and create file.list
print
'concat parallel corpora for dataset'
id
=
0
list
=
[
'train.list'
,
'test.list'
,
'gen.list'
]
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
self
.
concat_file
(
dataset
,
outname
+
suffixes
[
0
],
outname
+
suffixes
[
1
],
dir_list
[
id
],
outname
)
save_list
([
os
.
path
.
join
(
dir_list
[
id
],
outname
)],
os
.
path
.
join
(
self
.
output_path
,
list
[
id
]))
id
+=
1
# build dictionary for train data
dict
=
[
'src.dict'
,
'trg.dict'
]
dict_path
=
[
os
.
path
.
join
(
self
.
output_path
,
dict
[
0
]),
os
.
path
.
join
(
self
.
output_path
,
dict
[
1
])
]
if
mergeDict
:
outname
=
os
.
path
.
join
(
train_dir
,
train_dataset
.
split
(
'/'
)[
-
1
])
print
'build src dictionary for train data'
self
.
build_dict
(
outname
,
dict_path
[
0
],
dict_size
)
print
'build trg dictionary for train data'
os
.
system
(
'cp '
+
dict_path
[
0
]
+
' '
+
dict_path
[
1
])
else
:
outname
=
os
.
path
.
join
(
train_dataset
,
self
.
train_dir_name
)
for
id
in
range
(
0
,
2
):
suffix
=
suffixes
[
id
]
print
'build '
+
suffix
[
1
:]
+
' dictionary for train data'
self
.
build_dict
(
outname
+
suffix
,
dict_path
[
id
],
dict_size
)
print
'dictionary size is'
,
self
.
dict_size
def
save_dict
(
dict
,
filename
,
is_reverse
=
True
):
def
save_dict
(
dict
,
filename
,
is_reverse
=
True
):
"""
"""
Save dictionary into file.
Save dictionary into file.
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/dataset/wmt14.py
浏览文件 @
96a2e44a
...
@@ -14,103 +14,92 @@
...
@@ -14,103 +14,92 @@
"""
"""
wmt14 dataset
wmt14 dataset
"""
"""
import
os
import
os.path
import
tarfile
import
tarfile
import
paddle.v2.dataset.common
import
paddle.v2.dataset.common
from
wmt14_util
import
SeqToSeqDatasetCreater
__all__
=
[
'train'
,
'test'
,
'build_dict'
]
__all__
=
[
'train'
,
'test'
,
'build_dict'
]
URL_DEV_TEST
=
'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
URL_DEV_TEST
=
'http://www-lium.univ-lemans.fr/~schwenk/cslm_joint_paper/data/dev+test.tgz'
MD5_DEV_TEST
=
'7d7897317ddd8ba0ae5c5fa7248d3ff5'
MD5_DEV_TEST
=
'7d7897317ddd8ba0ae5c5fa7248d3ff5'
# this is a small set of data for test. The original data is too large and will be add later.
# this is a small set of data for test. The original data is too large and will be add later.
URL_TRAIN
=
'http://
paddlepaddle.bj.bcebos.com/demo/wmt_shrinked_data
/wmt14.tgz'
URL_TRAIN
=
'http://
localhost:8989
/wmt14.tgz'
MD5_TRAIN
=
'
7373473f86016f1f48037c9c340a2d5b
'
MD5_TRAIN
=
'
a755315dd01c2c35bde29a744ede23a6
'
START
=
"<s>"
START
=
"<s>"
END
=
"<e>"
END
=
"<e>"
UNK
=
"<unk>"
UNK
=
"<unk>"
UNK_IDX
=
2
UNK_IDX
=
2
DEFAULT_DATA_DIR
=
"./data"
ORIGIN_DATA_DIR
=
"wmt14"
def
__read_to_dict__
(
tar_file
,
dict_size
):
INNER_DATA_DIR
=
"pre-wmt14"
def
__to_dict__
(
fd
,
size
):
SRC_DICT
=
INNER_DATA_DIR
+
"/src.dict"
TRG_DICT
=
INNER_DATA_DIR
+
"/trg.dict"
TRAIN_FILE
=
INNER_DATA_DIR
+
"/train/train"
def
__process_data__
(
data_path
,
dict_size
=
None
):
downloaded_data
=
os
.
path
.
join
(
data_path
,
ORIGIN_DATA_DIR
)
if
not
os
.
path
.
exists
(
downloaded_data
):
# 1. download and extract tgz.
with
tarfile
.
open
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
))
as
tf
:
tf
.
extractall
(
data_path
)
# 2. process data file to intermediate format.
processed_data
=
os
.
path
.
join
(
data_path
,
INNER_DATA_DIR
)
if
not
os
.
path
.
exists
(
processed_data
):
dict_size
=
dict_size
or
-
1
data_creator
=
SeqToSeqDatasetCreater
(
downloaded_data
,
processed_data
)
data_creator
.
create_dataset
(
dict_size
,
mergeDict
=
False
)
def
__read_to_dict__
(
dict_path
,
count
):
with
open
(
dict_path
,
"r"
)
as
fin
:
out_dict
=
dict
()
out_dict
=
dict
()
for
line_count
,
line
in
enumerate
(
f
in
):
for
line_count
,
line
in
enumerate
(
f
d
):
if
line_count
<
=
count
:
if
line_count
<
size
:
out_dict
[
line
.
strip
()]
=
line_count
out_dict
[
line
.
strip
()]
=
line_count
else
:
else
:
break
break
return
out_dict
return
out_dict
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
def
__reader__
(
file_name
,
src_dict
,
trg_dict
):
names
=
[
with
open
(
file_name
,
'r'
)
as
f
:
each_item
.
name
for
each_item
in
f
for
line_count
,
line
in
enumerate
(
f
):
if
each_item
.
name
.
endswith
(
"src.dict"
)
line_split
=
line
.
strip
().
split
(
'
\t
'
)
]
if
len
(
line_split
)
!=
2
:
assert
len
(
names
)
==
1
continue
src_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
src_seq
=
line_split
[
0
]
# one source sequence
names
=
[
src_words
=
src_seq
.
split
()
each_item
.
name
for
each_item
in
f
src_ids
=
[
if
each_item
.
name
.
endswith
(
"trg.dict"
)
src_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
[
START
]
+
src_words
+
[
END
]
]
assert
len
(
names
)
==
1
trg_dict
=
__to_dict__
(
f
.
extractfile
(
names
[
0
]),
dict_size
)
return
src_dict
,
trg_dict
def
reader_creator
(
tar_file
,
file_name
,
dict_size
):
def
reader
():
src_dict
,
trg_dict
=
__read_to_dict__
(
tar_file
,
dict_size
)
with
tarfile
.
open
(
tar_file
,
mode
=
'r'
)
as
f
:
names
=
[
each_item
.
name
for
each_item
in
f
if
each_item
.
name
.
endswith
(
file_name
)
]
]
for
name
in
names
:
trg_seq
=
line_split
[
1
]
# one target sequence
for
line
in
f
.
extractfile
(
name
):
trg_words
=
trg_seq
.
split
()
line_split
=
line
.
strip
().
split
(
'
\t
'
)
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
if
len
(
line_split
)
!=
2
:
continue
# remove sequence whose length > 80 in training mode
src_seq
=
line_split
[
0
]
# one source sequence
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
src_words
=
src_seq
.
split
()
continue
src_ids
=
[
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
src_dict
.
get
(
w
,
UNK_IDX
)
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
for
w
in
[
START
]
+
src_words
+
[
END
]
]
yield
src_ids
,
trg_ids
,
trg_ids_next
trg_seq
=
line_split
[
1
]
# one target sequence
trg_words
=
trg_seq
.
split
()
def
train
(
data_dir
=
None
,
dict_size
=
None
):
trg_ids
=
[
trg_dict
.
get
(
w
,
UNK_IDX
)
for
w
in
trg_words
]
data_dir
=
data_dir
or
DEFAULT_DATA_DIR
__process_data__
(
data_dir
,
dict_size
)
# remove sequence whose length > 80 in training mode
src_lang_dict
=
os
.
path
.
join
(
data_dir
,
SRC_DICT
)
if
len
(
src_ids
)
>
80
or
len
(
trg_ids
)
>
80
:
trg_lang_dict
=
os
.
path
.
join
(
data_dir
,
TRG_DICT
)
continue
train_file_name
=
os
.
path
.
join
(
data_dir
,
TRAIN_FILE
)
trg_ids_next
=
trg_ids
+
[
trg_dict
[
END
]]
trg_ids
=
[
trg_dict
[
START
]]
+
trg_ids
default_dict_size
=
len
(
open
(
src_lang_dict
,
"r"
).
readlines
())
yield
src_ids
,
trg_ids
,
trg_ids_next
if
dict_size
>
default_dict_size
:
raise
ValueError
(
"dict_dim should not be larger then the "
return
reader
"length of word dict"
)
real_dict_dim
=
dict_size
or
default_dict_size
def
train
(
dict_size
):
return
reader_creator
(
src_dict
=
__read_to_dict__
(
src_lang_dict
,
real_dict_dim
)
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
trg_dict
=
__read_to_dict__
(
trg_lang_dict
,
real_dict_dim
)
'train/train'
,
dict_size
)
return
lambda
:
__reader__
(
train_file_name
,
src_dict
,
trg_dict
)
def
test
(
dict_size
):
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
URL_TRAIN
,
'wmt14'
,
MD5_TRAIN
),
'test/test'
,
dict_size
)
This diff is collapsed.
Click to expand it.
python/paddle/v2/dataset/wmt14_util.py
已删除
100644 → 0
浏览文件 @
37806792
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
os
from
paddle.utils.preprocess_util
import
save_list
,
DatasetCreater
class
SeqToSeqDatasetCreater
(
DatasetCreater
):
"""
A class to process data for sequence to sequence application.
"""
def
__init__
(
self
,
data_path
,
output_path
):
"""
data_path: the path to store the train data, test data and gen data
output_path: the path to store the processed dataset
"""
DatasetCreater
.
__init__
(
self
,
data_path
)
self
.
gen_dir_name
=
'gen'
self
.
gen_list_name
=
'gen.list'
self
.
output_path
=
output_path
def
concat_file
(
self
,
file_path
,
file1
,
file2
,
output_path
,
output
):
"""
Concat file1 and file2 to be one output file
The i-th line of output = i-th line of file1 + '
\t
' + i-th line of file2
file_path: the path to store file1 and file2
output_path: the path to store output file
"""
file1
=
os
.
path
.
join
(
file_path
,
file1
)
file2
=
os
.
path
.
join
(
file_path
,
file2
)
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
'paste '
+
file1
+
' '
+
file2
+
' > '
+
output
)
def
cat_file
(
self
,
dir_path
,
suffix
,
output_path
,
output
):
"""
Cat all the files in dir_path with suffix to be one output file
dir_path: the base directory to store input file
suffix: suffix of file name
output_path: the path to store output file
"""
cmd
=
'cat '
file_list
=
os
.
listdir
(
dir_path
)
file_list
.
sort
()
for
file
in
file_list
:
if
file
.
endswith
(
suffix
):
cmd
+=
os
.
path
.
join
(
dir_path
,
file
)
+
' '
output
=
os
.
path
.
join
(
output_path
,
output
)
if
not
os
.
path
.
exists
(
output
):
os
.
system
(
cmd
+
'> '
+
output
)
def
build_dict
(
self
,
file_path
,
dict_path
,
dict_size
=-
1
):
"""
Create the dictionary for the file, Note that
1. Valid characters include all printable characters
2. There is distinction between uppercase and lowercase letters
3. There is 3 special token:
<s>: the start of a sequence
<e>: the end of a sequence
<unk>: a word not included in dictionary
file_path: the path to store file
dict_path: the path to store dictionary
dict_size: word count of dictionary
if is -1, dictionary will contains all the words in file
"""
if
not
os
.
path
.
exists
(
dict_path
):
dictory
=
dict
()
with
open
(
file_path
,
"r"
)
as
fdata
:
for
line
in
fdata
:
line
=
line
.
split
(
'
\t
'
)
for
line_split
in
line
:
words
=
line_split
.
strip
().
split
()
for
word
in
words
:
if
word
not
in
dictory
:
dictory
[
word
]
=
1
else
:
dictory
[
word
]
+=
1
output
=
open
(
dict_path
,
"w+"
)
output
.
write
(
'<s>
\n
<e>
\n
<unk>
\n
'
)
count
=
3
for
key
,
value
in
sorted
(
dictory
.
items
(),
key
=
lambda
d
:
d
[
1
],
reverse
=
True
):
output
.
write
(
key
+
"
\n
"
)
count
+=
1
if
count
==
dict_size
:
break
self
.
dict_size
=
count
def
create_dataset
(
self
,
dict_size
=-
1
,
mergeDict
=
False
,
suffixes
=
[
'.src'
,
'.trg'
]):
"""
Create seqToseq dataset
"""
# dataset_list and dir_list has one-to-one relationship
train_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
train_dir_name
)
test_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
test_dir_name
)
gen_dataset
=
os
.
path
.
join
(
self
.
data_path
,
self
.
gen_dir_name
)
dataset_list
=
[
train_dataset
,
test_dataset
,
gen_dataset
]
train_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
train_dir_name
)
test_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
test_dir_name
)
gen_dir
=
os
.
path
.
join
(
self
.
output_path
,
self
.
gen_dir_name
)
dir_list
=
[
train_dir
,
test_dir
,
gen_dir
]
# create directory
for
dir
in
dir_list
:
if
not
os
.
path
.
exists
(
dir
):
os
.
makedirs
(
dir
)
# checkout dataset should be parallel corpora
suffix_len
=
len
(
suffixes
[
0
])
for
dataset
in
dataset_list
:
file_list
=
os
.
listdir
(
dataset
)
if
len
(
file_list
)
%
2
==
1
:
raise
RuntimeError
(
"dataset should be parallel corpora"
)
file_list
.
sort
()
for
i
in
range
(
0
,
len
(
file_list
),
2
):
if
file_list
[
i
][:
-
suffix_len
]
!=
file_list
[
i
+
1
][:
-
suffix_len
]:
raise
RuntimeError
(
"source and target file name should be equal"
)
# cat all the files with the same suffix in dataset
for
suffix
in
suffixes
:
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
+
suffix
self
.
cat_file
(
dataset
,
suffix
,
dataset
,
outname
)
# concat parallel corpora and create file.list
print
'concat parallel corpora for dataset'
id
=
0
list
=
[
'train.list'
,
'test.list'
,
'gen.list'
]
for
dataset
in
dataset_list
:
outname
=
os
.
path
.
basename
(
dataset
)
self
.
concat_file
(
dataset
,
outname
+
suffixes
[
0
],
outname
+
suffixes
[
1
],
dir_list
[
id
],
outname
)
save_list
([
os
.
path
.
join
(
dir_list
[
id
],
outname
)],
os
.
path
.
join
(
self
.
output_path
,
list
[
id
]))
id
+=
1
# build dictionary for train data
dict
=
[
'src.dict'
,
'trg.dict'
]
dict_path
=
[
os
.
path
.
join
(
self
.
output_path
,
dict
[
0
]),
os
.
path
.
join
(
self
.
output_path
,
dict
[
1
])
]
if
mergeDict
:
outname
=
os
.
path
.
join
(
train_dir
,
train_dataset
.
split
(
'/'
)[
-
1
])
print
'build src dictionary for train data'
self
.
build_dict
(
outname
,
dict_path
[
0
],
dict_size
)
print
'build trg dictionary for train data'
os
.
system
(
'cp '
+
dict_path
[
0
]
+
' '
+
dict_path
[
1
])
else
:
outname
=
os
.
path
.
join
(
train_dataset
,
self
.
train_dir_name
)
for
id
in
range
(
0
,
2
):
suffix
=
suffixes
[
id
]
print
'build '
+
suffix
[
1
:]
+
' dictionary for train data'
self
.
build_dict
(
outname
+
suffix
,
dict_path
[
id
],
dict_size
)
print
'dictionary size is'
,
self
.
dict_size
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