/
data_feed_desc.py
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data_feed_desc.py
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# Copyright (c) 2018 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.
from paddle.fluid.proto import data_feed_pb2
from google.protobuf import text_format
__all__ = ['DataFeedDesc']
class DataFeedDesc(object):
"""
Datafeed descriptor, describing input training data format. This class is
currently only used for AsyncExecutor (See comments for class AsyncExecutor
for a brief introduction)
DataFeedDesc shall be initialized from a valid protobuf message from disk.
See :code:`paddle/fluid/framework/data_feed.proto` for message definition.
A typical message might look like:
.. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2'
print >> f, 'multi_slot_desc {'
print >> f, ' slots {'
print >> f, ' name: "words"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, ' slots {'
print >> f, ' name: "label"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, '}'
f.close()
data_feed = fluid.DataFeedDesc('data.proto')
However, users usually shouldn't care about the message format; instead,
they are encouragd to use :code:`Data Generator` as a tool to generate a
valid data description, in the process of converting their raw log files to
training files acceptable to AsyncExecutor.
DataFeedDesc can also be changed during runtime. Once you got familiar with
what each field mean, you can modify it to better suit your need. E.g.:
.. code-block:: python
import paddle.fluid as fluid
data_feed = fluid.DataFeedDesc('data.proto')
data_feed.set_batch_size(128)
data_feed.set_dense_slots('wd') # The slot named 'wd' will be dense
data_feed.set_use_slots('wd') # The slot named 'wd' will be used
Finally, the content can be dumped out for debugging purpose:
.. code-block:: python
print(data_feed.desc())
Args:
proto_file(string): Disk file containing a data feed description.
"""
def __init__(self, proto_file):
self.proto_desc = data_feed_pb2.DataFeedDesc()
self.proto_desc.pipe_command = "cat"
with open(proto_file, 'r') as f:
text_format.Parse(f.read(), self.proto_desc)
if self.proto_desc.name == "MultiSlotDataFeed":
self.__name_to_index = {
slot.name: i
for i, slot in enumerate(self.proto_desc.multi_slot_desc.slots)
}
def set_batch_size(self, batch_size):
"""
Set :attr:`batch_size` in :ref:`api_fluid_DataFeedDesc` . :attr:`batch_size` can be changed during training.
Example:
.. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2'
print >> f, 'multi_slot_desc {'
print >> f, ' slots {'
print >> f, ' name: "words"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, ' slots {'
print >> f, ' name: "label"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, '}'
f.close()
data_feed = fluid.DataFeedDesc('data.proto')
data_feed.set_batch_size(128)
Args:
batch_size (int): The number of batch size.
Returns:
None.
"""
self.proto_desc.batch_size = batch_size
def set_dense_slots(self, dense_slots_name):
"""
Set slots in :attr:`dense_slots_name` as dense slots. **Note: In default, all slots are sparse slots.**
Features for a dense slot will be fed into a Tensor, while those for a
sparse slot will be fed into a LoDTensor.
Example:
.. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2'
print >> f, 'multi_slot_desc {'
print >> f, ' slots {'
print >> f, ' name: "words"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, ' slots {'
print >> f, ' name: "label"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, '}'
f.close()
data_feed = fluid.DataFeedDesc('data.proto')
data_feed.set_dense_slots(['words'])
Args:
dense_slots_name (list(str)): a list of slot names which will be set dense.
Returns:
None.
"""
if self.proto_desc.name != "MultiSlotDataFeed":
raise ValueError(
"Only MultiSlotDataFeed needs set_dense_slots, please check your datafeed.proto"
)
for name in dense_slots_name:
self.proto_desc.multi_slot_desc.slots[self.__name_to_index[
name]].is_dense = True
def set_use_slots(self, use_slots_name):
"""
Set if a specific slot will be used for training. A dataset shall
contain a lot of features, through this function one can select which
ones will be used for a specific model.
Example:
.. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2'
print >> f, 'multi_slot_desc {'
print >> f, ' slots {'
print >> f, ' name: "words"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, ' slots {'
print >> f, ' name: "label"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, '}'
f.close()
data_feed = fluid.DataFeedDesc('data.proto')
data_feed.set_use_slots(['words'])
Args:
use_slots_name: a list of slot names which will be used in training
Note:
Default is not used for all slots
"""
if self.proto_desc.name != "MultiSlotDataFeed":
raise ValueError(
"Only MultiSlotDataFeed needs set_use_slots, please check your datafeed.proto"
)
for name in use_slots_name:
self.proto_desc.multi_slot_desc.slots[self.__name_to_index[
name]].is_used = True
def desc(self):
"""
Returns a protobuf message for this DataFeedDesc
Example:
.. code-block:: python
import paddle.fluid as fluid
f = open("data.proto", "w")
print >> f, 'name: "MultiSlotDataFeed"'
print >> f, 'batch_size: 2'
print >> f, 'multi_slot_desc {'
print >> f, ' slots {'
print >> f, ' name: "words"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, ' slots {'
print >> f, ' name: "label"'
print >> f, ' type: "uint64"'
print >> f, ' is_dense: false'
print >> f, ' is_used: true'
print >> f, ' }'
print >> f, '}'
f.close()
data_feed = fluid.DataFeedDesc('data.proto')
print(data_feed.desc())
Returns:
A string message
"""
return text_format.MessageToString(self.proto_desc)