Skip to content

P
Paddle

PaddlePaddle / Paddle
1 年多 前同步成功

通知 2302
Star 20931
Fork 5422
P
Paddle
未验证 提交 a3989b5e 编写于 作者: Z zqw_1997 提交者： GitHub
浏览文件
操作

remove incubate.data_generator (#50325) 

* remove incubate.data_generator

* modify the setup.py

* modifyt the setup.py.in
上级 b08c91ab
隐藏空白更改
内联 并排
Showing with 6 addition and 364 deletion
python/paddle/fluid/incubate/data_generator/__init__.py 已删除 100644 → 0
浏览文件 @ b08c91ab
# Copyright (c) 2019 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
import sys
__all__ = ['MultiSlotDataGenerator', 'MultiSlotStringDataGenerator']
class DataGenerator:
"""
DataGenerator is a general Base class for user to inherit
A user who wants to define his/her own python processing logic
with paddle.fluid.dataset should inherit this class
"""
def __init__(self):
self._proto_info = None
self.batch_size_ = 32
def _set_line_limit(self, line_limit):
if not isinstance(line_limit, int):
raise ValueError(
"line_limit%s must be in int type" % type(line_limit)
)
if line_limit < 1:
raise ValueError("line_limit can not less than 1")
self._line_limit = line_limit
def set_batch(self, batch_size):
'''
Set batch size of current DataGenerator
This is necessary only if a user wants to define generator_batch
Example:
.. code-block:: python
import paddle.fluid.incubate.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", int_words)
return local_iter
def generate_batch(self, samples):
def local_iter():
for s in samples:
yield ("words", s[1].extend([s[1][0]]))
mydata = MyData()
mydata.set_batch(128)
'''
self.batch_size_ = batch_size
def run_from_memory(self):
'''
This function generator data from memory, it is usually used for
debug and benchmarking
Example:
.. code-block:: python
import paddle.fluid.incubate.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
yield ("words", [1, 2, 3, 4])
return local_iter
mydata = MyData()
mydata.run_from_memory()
'''
batch_samples = []
line_iter = self.generate_sample(None)
for user_parsed_line in line_iter():
if user_parsed_line is None:
continue
batch_samples.append(user_parsed_line)
if len(batch_samples) == self.batch_size_:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample))
batch_samples = []
if len(batch_samples) > 0:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample))
def run_from_stdin(self):
'''
This function reads the data row from stdin, parses it with the
process function, and further parses the return value of the
process function with the _gen_str function. The parsed data will
be wrote to stdout and the corresponding protofile will be
generated.
Example:
.. code-block:: python
import paddle.fluid.incubate.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", [int_words])
return local_iter
mydata = MyData()
mydata.run_from_stdin()
'''
batch_samples = []
for line in sys.stdin:
line_iter = self.generate_sample(line)
for user_parsed_line in line_iter():
if user_parsed_line is None:
continue
batch_samples.append(user_parsed_line)
if len(batch_samples) == self.batch_size_:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample))
batch_samples = []
if len(batch_samples) > 0:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample))
def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the datafeed,and
updating proto_info information.
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the datafeed.
'''
raise NotImplementedError(
"pls use MultiSlotDataGenerator or PairWiseDataGenerator"
)
def generate_sample(self, line):
'''
This function needs to be overridden by the user to process the
original data row into a list or tuple.
Args:
line(str): the original data row
Returns:
Returns the data processed by the user.
The data format is list or tuple:
[(name, [feasign, ...]), ...]
or ((name, [feasign, ...]), ...)
For example:
[("words", [1926, 08, 17]), ("label", [1])]
or (("words", [1926, 08, 17]), ("label", [1]))
Note:
The type of feasigns must be in int or float. Once the float
element appears in the feasign, the type of that slot will be
processed into a float.
Example:
.. code-block:: python
import paddle.fluid.incubate.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", [int_words])
return local_iter
'''
raise NotImplementedError(
"Please rewrite this function to return a list or tuple: "
+ "[(name, [feasign, ...]), ...] or ((name, [feasign, ...]), ...)"
)
def generate_batch(self, samples):
'''
This function needs to be overridden by the user to process the
generated samples from generate_sample(self, str) function
It is usually used as batch processing when a user wants to
do preprocessing on a batch of samples, e.g. padding according to
the max length of a sample in the batch
Args:
samples(list tuple): generated sample from generate_sample
Returns:
a python generator, the same format as return value of generate_sample
Example:
.. code-block:: python
import paddle.fluid.incubate.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", int_words)
return local_iter
def generate_batch(self, samples):
def local_iter():
for s in samples:
yield ("words", s[1].extend([s[1][0]]))
mydata = MyData()
mydata.set_batch(128)
'''
def local_iter():
for sample in samples:
yield sample
return local_iter
# TODO: guru4elephant
# add more generalized DataGenerator that can adapt user-defined slot
# for example, [(name, float_list), (name, str_list), (name, int_list)]
class MultiSlotStringDataGenerator(DataGenerator):
def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info information.
The input line will be in this format:
>>> [(name, [str(feasign), ...]), ...]
>>> or ((name, [str(feasign), ...]), ...)
The output will be in this format:
>>> [ids_num id1 id2 ...] ...
For example, if the input is like this:
>>> [("words", ["1926", "08", "17"]), ("label", ["1"])]
>>> or (("words", ["1926", "08", "17"]), ("label", ["1"]))
the output will be:
>>> 3 1234 2345 3456 1 1
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the MultiSlotDataFeed.
'''
if not isinstance(line, list) and not isinstance(line, tuple):
raise ValueError(
"the output of process() must be in list or tuple type"
"Examples: [('words', ['1926', '08', '17']), ('label', ['1'])]"
)
output = ""
for index, item in enumerate(line):
name, elements = item
if output:
output += " "
out_str = []
out_str.append(str(len(elements)))
out_str.extend(elements)
output += " ".join(out_str)
return output + "\n"
class MultiSlotDataGenerator(DataGenerator):
def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info information.
The input line will be in this format:
>>> [(name, [feasign, ...]), ...]
>>> or ((name, [feasign, ...]), ...)
The output will be in this format:
>>> [ids_num id1 id2 ...] ...
The proto_info will be in this format:
>>> [(name, type), ...]
For example, if the input is like this:
>>> [("words", [1926, 08, 17]), ("label", [1])]
>>> or (("words", [1926, 08, 17]), ("label", [1]))
the output will be:
>>> 3 1234 2345 3456 1 1
the proto_info will be:
>>> [("words", "uint64"), ("label", "uint64")]
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the MultiSlotDataFeed.
'''
if not isinstance(line, list) and not isinstance(line, tuple):
raise ValueError(
"the output of process() must be in list or tuple type"
"Example: [('words', [1926, 08, 17]), ('label', [1])]"
)
output = ""
if self._proto_info is None:
self._proto_info = []
for item in line:
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError(
"elements%s must be in list type" % type(elements)
)
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
self._proto_info.append((name, "uint64"))
if output:
output += " "
output += str(len(elements))
for elem in elements:
if isinstance(elem, float):
self._proto_info[-1] = (name, "float")
elif not isinstance(elem, int) and not isinstance(
elem, long
):
raise ValueError(
"the type of element%s must be in int or float"
% type(elem)
)
output += " " + str(elem)
else:
if len(line) != len(self._proto_info):
raise ValueError(
"the complete field set of two given line are inconsistent."
)
for index, item in enumerate(line):
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError(
"elements%s must be in list type" % type(elements)
)
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
if name != self._proto_info[index][0]:
raise ValueError(
"the field name of two given line are not match: require<%s>, get<%s>."
% (self._proto_info[index][0], name)
)
if output:
output += " "
output += str(len(elements))
for elem in elements:
if self._proto_info[index][1] != "float":
if isinstance(elem, float):
self._proto_info[index] = (name, "float")
elif not isinstance(elem, int) and not isinstance(
elem, long
):
raise ValueError(
"the type of element%s must be in int or float"
% type(elem)
)
output += " " + str(elem)
return output + "\n"
python/paddle/fluid/tests/unittests/ps/dataset_generator_A.py
浏览文件 @ a3989b5e
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.incubate.data_generator as dg
import paddle.distributed.fleet as fleet
cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
......@@ -22,7 +22,7 @@ continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
class CriteoDataset(dg.MultiSlotDataGenerator):
class CriteoDataset(fleet.MultiSlotDataGenerator):
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
......
python/paddle/fluid/tests/unittests/ps/dataset_generator_B.py
浏览文件 @ a3989b5e
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.incubate.data_generator as dg
import paddle.distributed.fleet as fleet
cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
......@@ -22,7 +22,7 @@ continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
class CriteoDataset(dg.MultiSlotDataGenerator):
class CriteoDataset(fleet.MultiSlotDataGenerator):
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
......
python/paddle/fluid/tests/unittests/test_dataset_consistency_inspection.py
浏览文件 @ a3989b5e
......@@ -21,8 +21,8 @@ import tempfile
import unittest
import paddle
import paddle.distributed.fleet as fleet
import paddle.fluid as fluid
import paddle.fluid.incubate.data_generator as dg
# paddle.enable_static()
# fluid.disable_dygraph()
......@@ -51,7 +51,7 @@ query_schema = [
]
class CTRDataset(dg.MultiSlotDataGenerator):
class CTRDataset(fleet.MultiSlotDataGenerator):
def __init__(self, mode):
self.test = mode
......
python/setup.py.in
浏览文件 @ a3989b5e
......@@ -401,7 +401,6 @@ packages=['paddle',
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details',
'paddle.fluid.incubate',
'paddle.fluid.incubate.data_generator',
'paddle.fluid.incubate.fleet',
'paddle.fluid.incubate.checkpoint',
'paddle.fluid.incubate.fleet.base',
......
setup.py
浏览文件 @ a3989b5e
......@@ -1287,7 +1287,6 @@ def get_setup_parameters():
'paddle.fluid.transpiler',
'paddle.fluid.transpiler.details',
'paddle.fluid.incubate',
'paddle.fluid.incubate.data_generator',
'paddle.fluid.incubate.fleet',
'paddle.fluid.incubate.checkpoint',
'paddle.fluid.incubate.fleet.base',
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或