def __init__(self, filename, schema, internal=False):
with tf.name_scope("AvroIOTensor") as scope:
metadata = ["schema: %s" % schema]
resource, columns = core_ops.avro_readable_init(
filename,
metadata=metadata,
container=scope,
shared_name="%s/%s" % (filename, uuid.uuid4().hex))
columns = [column.decode() for column in columns.numpy().tolist()]
elements = []
for column in columns:
shape, dtype = core_ops.avro_readable_spec(resource, column)
shape = tf.TensorShape(shape.numpy())
dtype = tf.as_dtype(dtype.numpy())
spec = tf.TensorSpec(shape, dtype, column)
function = io_tensor_ops._IOTensorComponentFunction( # pylint: disable=protected-access
core_ops.avro_readable_read, resource, column, shape,
dtype)
elements.append(
io_tensor_ops.BaseIOTensor(spec,
function,
internal=internal))
spec = tuple([e.spec for e in elements])
super(AvroIOTensor, self).__init__(spec,
columns,
elements,
internal=internal)
def __init__(self, filename, internal=False):
with tf.name_scope("FeatherIOTensor") as scope:
resource, columns = core_ops.io_feather_readable_init(
filename,
container=scope,
shared_name="{}/{}".format(filename,
uuid.uuid4().hex),
)
columns = [column.decode() for column in columns.numpy().tolist()]
elements = []
for column in columns:
shape, dtype = core_ops.io_feather_readable_spec(
resource, column)
shape = tf.TensorShape(shape.numpy())
dtype = tf.as_dtype(dtype.numpy())
spec = tf.TensorSpec(shape, dtype, column)
function = io_tensor_ops._IOTensorComponentFunction( # pylint: disable=protected-access
core_ops.io_feather_readable_read, resource, column, shape,
dtype)
elements.append(
io_tensor_ops.BaseIOTensor(spec,
function,
internal=internal))
spec = tuple([e.spec for e in elements])
super().__init__(spec, columns, elements, internal=internal)
