def __init__(self, init_net, filename, schema, num_passes=1, batch_size=1): """ Create op for building a TextFileReader instance in the workspace. Args: init_net : Net that will be run only once at startup. filename : Path to file to read from. schema : schema.Struct representing the schema of the data. Currently, only support Struct of strings. num_passes : Number of passes over the data. batch_size : Number of rows to read at a time. """ assert isinstance(schema, Struct), 'Schema must be a schema.Struct' for name, child in schema.get_children(): assert isinstance(child, Scalar), ( 'Only scalar fields are supported in TextFileReader.') field_types = [ data_type_for_dtype(dtype) for dtype in schema.field_types()] Reader.__init__(self, schema) self._reader = init_net.CreateTextFileReader( [], filename=filename, num_passes=num_passes, field_types=field_types) self._batch_size = batch_size
def __init__(self, dataset, name, batch_size=1): """Don't call this directly. Instead, use dataset.reader()""" Reader.__init__(self, dataset.content()) self.dataset = dataset self.name = name or (dataset.name + '_cursor') self.batch_size = batch_size self.cursor = None
def __init__(self, init_net, filename, schema, num_passes=1, batch_size=1): """ Create op for building a HiveReader instance in the workspace. Args: init_net : Net that will be run only once at startup. filename : Path to file to read from. schema : schema.Struct representing the schema of the data. Currently, only support Struct of strings. num_passes : Number of passes over the data. batch_size : Number of rows to read at a time. """ assert isinstance(schema, Struct), 'Schema must be a schema.Struct' for name, child in schema.get_children(): assert isinstance(child, Scalar), ( 'Only scalar fields are supported in TextFileReader.') field_types = [ data_type_for_dtype(dtype) for dtype in schema.field_types()] Reader.__init__(self, schema) self._reader = init_net.CreateTextFileReader( [], filename=filename, num_passes=num_passes, field_types=field_types) self._batch_size = batch_size
def __init__(self, content, cursor, name, indices, batch_size=1): """Don't call this directly. Instead, use dataset.random_reader()""" Reader.__init__(self, content) self._content = content self.cursor = cursor self.name = name self.indices = indices self.batch_size = batch_size
def __init__(self, content, cursor, name, batch_size=1): """Don't call this directly. Instead, use dataset.reader()""" assert isinstance(content, Field) Reader.__init__(self, content) self._content = content self.cursor = cursor self.name = name self.batch_size = batch_size
def __init__(self, dataset, name, indices, batch_size=1, loop_over=False): """Don't call this directly. Instead, use dataset.random_reader()""" Reader.__init__(self, dataset.content()) self.dataset = dataset self.cursor = None self.name = name or (dataset.name + '_cursor') self.indices = indices self.batch_size = batch_size self.loop_over = loop_over
def __init__(self, dataset, name, indices, batch_size=1, loop_over=False, enforce_batch_size=False): """Don't call this directly. Instead, use dataset.random_reader()""" Reader.__init__(self, dataset.content()) self.dataset = dataset self.cursor = None self.name = name or (dataset.name + '_cursor') self.indices = indices self.batch_size = batch_size self.loop_over = loop_over self.enforce_batch_size = enforce_batch_size
def __init__(self, reader, delay): Reader.__init__(self, schema=reader._schema) self.reader = reader self.delay = delay
def __init__(self, reader, processor): Reader.__init__(self) self.reader = reader self.processor = make_processor(processor)