Source code for paddle.fluid.data_feed_desc

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from paddle.fluid.proto import data_feed_pb2
from google.protobuf import text_format

__all__ = ['DataFeedDesc']


[docs]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: >>> data_feed = fluid.DataFeedDesc('data.proto') See :code:`paddle/fluid/framework/data_feed.proto` for message definition. A typical message might look like: >>> name: "MultiSlotDataFeed" >>> batch_size: 2 >>> multi_slot_desc { >>> slots { >>> name: "words" >>> type: "uint64" >>> is_dense: false >>> is_used: true >>> } >>> slots { >>> name: "label" >>> type: "uint64" >>> is_dense: false >>> is_used: true >>> } >>> } 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.: >>> 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: >>> 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) }
[docs] def set_batch_size(self, batch_size): """ Set batch size. Will be effective during training Example: >>> data_feed = fluid.DataFeedDesc('data.proto') >>> data_feed.set_batch_size(128) Args: batch_size: batch size """ self.proto_desc.batch_size = batch_size
[docs] def set_dense_slots(self, dense_slots_name): """ Set if a specific slot will be dense. Will be effective during training. features for a dense slot will be fed into a Tensor, while those for a sparse slot will be fed into a LoDTensor Example: >>> data_feed = fluid.DataFeedDesc('data.proto') >>> data_feed.set_dense_slots(['words']) Args: dense_slots_name: a list of slot names which will be set dense Note: Default is sparse for all slots """ if self.proto_desc.name != "MultiSlotDataFeed": raise ValueError( "Only MultiSlotDataFeed need set_dense_slots, pls 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
[docs] 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: >>> 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 need set_use_slots, pls 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
[docs] def desc(self): """ Returns a protobuf message for this DataFeedDesc Example: >>> data_feed = fluid.DataFeedDesc('data.proto') >>> print(data_feed.desc()) Returns: A string message """ return text_format.MessageToString(self.proto_desc)