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294 lines
8.5 KiB
294 lines
8.5 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.fluid.proto import data_feed_pb2
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from google.protobuf import text_format
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from . import core
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__all__ = ['DatasetFactory', 'InMemoryDataset', 'QueueDataset']
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class DatasetFactory(object):
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"""
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DatasetFactory is a factory which create dataset by its name,
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you can create "QueueDataset" or "InMemoryDataset",
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the default is "QueueDataset".
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Example:
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dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset")
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"""
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def __init__(self):
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"""
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Init
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"""
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pass
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def create_dataset(self, datafeed_class="QueueDataset"):
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"""
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Create "QueueDataset" or "InMemoryDataset",
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the default is "QueueDataset".
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Examples:
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import paddle.fluid as fluid
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dataset = fluid.DatasetFactory().create_dataset()
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"""
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try:
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dataset = globals()[datafeed_class]()
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return dataset
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except:
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raise ValueError("datafeed class %s does not exist" %
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datafeed_class)
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class DatasetBase(object):
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"""
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Base dataset class
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"""
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def __init__(self):
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"""
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Init
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"""
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# define class name here
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# to decide whether we need create in memory instance
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self.proto_desc = data_feed_pb2.DataFeedDesc()
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self.proto_desc.pipe_command = "cat"
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self.dataset = core.Dataset("MultiSlotDataset")
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self.thread_num = 0
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def set_pipe_command(self, pipe_command):
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"""
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Set pipe command of current dataset
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A pipe command is a UNIX pipeline command that can be used only
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Example:
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>>> dataset.set_pipe_command("python my_script.py")
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Args:
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pipe_command: pipe command
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"""
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self.proto_desc.pipe_command = pipe_command
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def set_batch_size(self, batch_size):
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"""
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Set batch size. Will be effective during training
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Example:
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>>> dataset.set_batch_size(128)
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Args:
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batch_size: batch size
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"""
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self.proto_desc.batch_size = batch_size
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def set_thread(self, thread_num):
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"""
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Set thread num, it is the num of readers.
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Example:
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>>> dataset.set_thread(12)
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Args:
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thread_num: thread num
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"""
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self.dataset.set_thread_num(thread_num)
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self.thread_num = thread_num
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def set_filelist(self, filelist):
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"""
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Set file list in current worker.
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Example:
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>>> dataset.set_filelist(['a.txt', 'b.txt'])
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Args:
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filelist: file list
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"""
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self.dataset.set_filelist(filelist)
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def set_use_var(self, var_list):
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"""
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Set Variables which you will use.
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Example:
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>>> dataset.set_use_var([data, label])
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Args:
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var_list: variable list
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"""
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multi_slot = self.proto_desc.multi_slot_desc
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for var in var_list:
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slot_var = multi_slot.slots.add()
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slot_var.is_used = True
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slot_var.name = var.name
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if var.lod_level == 0:
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slot_var.is_dense = True
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if var.dtype == core.VarDesc.VarType.FP32:
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slot_var.type = "float"
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elif var.dtype == core.VarDesc.VarType.INT64:
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slot_var.type = "uint64"
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else:
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raise ValueError(
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"Currently, fluid.dataset only supports dtype=float32 and dtype=int64"
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)
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def set_hdfs_config(self, fs_name, fs_ugi):
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"""
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Set hdfs config: fs name ad ugi
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Example:
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>>> dataset.set_hdfs_config("my_fs_name", "my_fs_ugi")
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Args:
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fs_name: fs name
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fs_ugi: fs ugi
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"""
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self.dataset.set_hdfs_config(fs_name, fs_ugi)
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def _prepare_to_run(self):
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"""
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Set data_feed_desc before load or shuffle,
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user no need to call this function.
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"""
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self.dataset.set_data_feed_desc(self.desc())
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def desc(self):
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"""
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Returns a protobuf message for this DataFeedDesc
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Example:
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>>> print(dataset.desc())
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Returns:
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A string message
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"""
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return text_format.MessageToString(self.proto_desc)
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class InMemoryDataset(DatasetBase):
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"""
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InMemoryDataset, it will load data into memory
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and shuffle data before training.
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This class should be created by DatasetFactory
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Example:
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dataset = paddle.fluid.DatasetFactory.create_dataset("InMemoryDataset")
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"""
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def __init__(self):
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"""
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Init
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"""
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super(InMemoryDataset, self).__init__()
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self.proto_desc.name = "MultiSlotInMemoryDataFeed"
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def load_into_memory(self):
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"""
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Load data into memory
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Example:
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>>> import paddle.fluid as fluid
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>>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
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>>> filelist = ["a.txt", "b.txt"]
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>>> dataset.set_filelist(filelist)
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>>> dataset.load_into_memory()
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"""
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self._prepare_to_run()
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self.dataset.load_into_memory()
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def local_shuffle(self):
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"""
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Local shuffle
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Example:
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>>> import paddle.fluid as fluid
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>>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
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>>> filelist = ["a.txt", "b.txt"]
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>>> dataset.set_filelist(filelist)
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>>> dataset.local_shuffle()
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"""
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self.dataset.local_shuffle()
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def global_shuffle(self, fleet=None):
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"""
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Global shuffle.
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Global shuffle can be used only in distributed mode. i.e. multiple
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processes on single machine or multiple machines training together.
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If you run in distributed mode, you should pass fleet instead of None.
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Examples:
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>>> import paddle.fluid as fluid
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>>> import paddle.fluid.incubate.fleet.parameter_server as fleet
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>>> dataset = fluid.DatasetFactory.create_dataset("InMemoryDataset")
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>>> filelist = ["a.txt", "b.txt"]
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>>> dataset.set_filelist(filelist)
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>>> dataset.global_shuffle(fleet)
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Args:
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fleet: fleet singleton. Default None.
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"""
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trainer_num = 1
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fleet_send_batch_size = 80000
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if fleet is not None:
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fleet.fleet_instance.role_maker_._barrier_worker()
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trainer_num = fleet.worker_num()
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self.dataset.register_client2client_msg_handler()
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self.dataset.set_trainer_num(trainer_num)
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self.dataset.set_fleet_send_batch_size(fleet_send_batch_size)
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if fleet is not None:
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fleet.fleet_instance.role_maker_._barrier_worker()
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self.dataset.global_shuffle()
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if fleet is not None:
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fleet.fleet_instance.role_maker_._barrier_worker()
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class QueueDataset(DatasetBase):
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"""
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QueueDataset, it will process data streamly.
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Example:
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import paddle.fluid as fluid
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dataset = fluid.DatasetFactory.create_dataset("QueueDataset")
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"""
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def __init__(self):
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"""
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Initialize QueueDataset
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This class should be created by DatasetFactory
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"""
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super(QueueDataset, self).__init__()
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self.proto_desc.name = "MultiSlotDataFeed"
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def local_shuffle(self):
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"""
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Local shuffle
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Local shuffle is not supported in QueueDataset
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NotImplementedError will be raised
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"""
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raise NotImplementedError(
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"QueueDataset does not support local shuffle, "
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"please use InMemoryDataset for local_shuffle")
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def global_shuffle(self, fleet=None):
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"""
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Global shuffle is not supported in QueueDataset
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NotImplementedError will be raised
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"""
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raise NotImplementedError(
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"QueueDataset does not support global shuffle, "
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"please use InMemoryDataset for global_shuffle")
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