morpheus.messages.multi_response_message.MultiResponseMessage
- class MultiResponseMessage(*, meta, mess_offset=0, mess_count=- 1, memory=None, offset=0, count=- 1, id_tensor_name='seq_ids', probs_tensor_name='probs')[source]
Bases:
morpheus.messages.multi_tensor_message.MultiTensorMessage
This class contains several inference responses as well as the cooresponding message metadata.
- Attributes
id
id_col
outputs
tensors
timestamp
Returns ID column values from
morpheus.pipeline.messages.MessageMeta.df
as list.Returns ID column values from
morpheus.pipeline.messages.MessageMeta.df
.Get outputs stored in the TensorMemory container.
Get tensors stored in the TensorMemory container sliced according to
offset
andcount
.Returns timestamp column values from morpheus.messages.MessageMeta.df as list.
Methods
copy_meta_ranges
(ranges[, mask])Perform a copy of the underlying dataframe for the given ranges
of rows.copy_ranges
(ranges)Perform a copy of the current message, dataframe and tensors for the given ranges
of rows.copy_tensor_ranges
(ranges[, mask])Perform a copy of the underlying tensor tensors for the given ranges
of rows.from_message
(message, *[, meta, ...])Creates a new instance of a derived class from MultiMessage
using an existing message as the template.get_id_tensor
()Get the tensor that holds message ID information. get_meta
([columns])Return column values from morpheus.pipeline.messages.MessageMeta.df
.get_meta_column_names
()Return column names available in the underlying DataFrame. get_meta_list
([col_name])Return a column values from morpheus.messages.MessageMeta.df as a list. get_output
(name)Get output stored in the TensorMemory container. get_probs_tensor
()Get the tensor that holds output probabilities. get_slice
(start, stop)Perform a slice of the current message from start
:stop
(excludingstop
)get_tensor
(name)Get tensor stored in the TensorMemory container. set_meta
(columns, value)Set column values to morpheus.pipelines.messages.MessageMeta.df
.- copy_meta_ranges(ranges, mask=None)[source]
Perform a copy of the underlying dataframe for the given
ranges
of rows.- Parameters
- ranges
- mask
Rows to include in the copy in the form of
[(`start_row
,stop_row
),…]` Thestop_row
isn’t included. For example to copy rows 1-2 & 5-7ranges=[(1, 3), (5, 8)]
Optionally specify rows as a cupy array (when using cudf Dataframes) or a numpy array (when using pandas Dataframes) of booleans. When not-None
ranges
will be ignored. This is useful as an optimization as this avoids needing to generate the mask on it’s own.- Returns
Dataframe
- copy_ranges(ranges)[source]
Perform a copy of the current message, dataframe and tensors for the given
ranges
of rows.- Parameters
- ranges
- ——-
- `MultiTensorMessage`
Rows to include in the copy in the form of
[(`start_row
,stop_row
),…]` Thestop_row
isn’t included. For example to copy rows 1-2 & 5-7ranges=[(1, 3), (5, 8)]
- copy_tensor_ranges(ranges, mask=None)[source]
Perform a copy of the underlying tensor tensors for the given
ranges
of rows.- Parameters
- ranges
- mask
Rows to include in the copy in the form of
[(`start_row
,stop_row
),…]` Thestop_row
isn’t included. For example to copy rows 1-2 & 5-7ranges=[(1, 3), (5, 8)]
Optionally specify rows as a cupy array (when using cudf Dataframes) or a numpy array (when using pandas Dataframes) of booleans. When not-None
ranges
will be ignored. This is useful as an optimization as this avoids needing to generate the mask on it’s own.- Returns
- typing.Dict[str, cupy.ndarray]
- classmethod from_message(message, *, meta=None, mess_offset=- 1, mess_count=- 1, memory=None, offset=- 1, count=- 1, **kwargs)[source]
Creates a new instance of a derived class from
MultiMessage
using an existing message as the template. This is very useful when a new message needs to be created with a single change to an existingMessageMeta
.When creating the new message, all required arguments for the class specified by
cls
will be pulled frommessage
unless otherwise specified in thekwargs
. Special handling is performed depending on whether or not a newmeta
object is supplied. If one is supplied, the offset and count defaults will be 0 andmeta.count
respectively. Otherwise offset and count will be pulled from the inputmessage
.- Parameters
- cls
- message
- meta
- mess_offset
- mess_count
- memory
- offset
- count
-
**kwargs :
dict
The class to create
An existing message to use as a template. Can be a base or derived from
cls
as long as all arguments can be pulled frommessage
or proveded inkwargs
A new
MessageMeta
to use, by default NoneA new
mess_offset
to use, by default -1A new
mess_count
to use, by default -1A new
TensorMemory
to use. If supplied,offset
andcount
default to0
andmemory.count
respectively. By default NoneA new
offset
to use, by default -1A new
count
to use, by default -1Keyword arguments to use when creating the new instance.
- Returns
- Self
A new instance of type
cls
- Raises
- ValueError
If the incoming
message
is None
- get_id_tensor()[source]
Get the tensor that holds message ID information. Equivalent to
get_tensor(id_tensor_name)
- Returns
- cupy.ndarray
Array containing the ID information
- Raises
- KeyError
If
self.id_tensor_name
is not found in the tensors
- get_meta(columns=None)[source]
Return column values from
morpheus.pipeline.messages.MessageMeta.df
.- Parameters
- columns
Input column names. Returns all columns if
None
is specified. When a string is passed, aSeries
is returned. Otherwise, aDataframe
is returned.- Returns
- Series or Dataframe
Column values from the dataframe.
- get_meta_column_names()[source]
Return column names available in the underlying DataFrame.
- Returns
- list[str]
Column names from the dataframe.
- get_meta_list(col_name=None)[source]
Return a column values from morpheus.messages.MessageMeta.df as a list.
- Parameters
- col_name
Column name in the dataframe.
- Returns
- List[str]
Column values from the dataframe.
- get_output(name)[source]
Get output stored in the TensorMemory container. Alias for
MultiResponseMessage.get_tensor
.- Parameters
- name
Output key name.
- Returns
- cupy.ndarray
Inference output.
- get_probs_tensor()[source]
Get the tensor that holds output probabilities. Equivalent to
get_tensor(probs_tensor_name)
- Returns
- cupy.ndarray
The probabilities tensor
- Raises
- KeyError
If
self.probs_tensor_name
is not found in the tensors
- get_slice(start, stop)[source]
Perform a slice of the current message from
start
:stop
(excludingstop
)For example to slice from rows 1-3 use
m.get_slice(1, 4)
. The returnedMultiTensorMessage
will contain references to the same underlying Dataframe and tensor tensors, and this calling this method is reletively low cost compared toMultiTensorMessage.copy_ranges
- Parameters
- start
- stop
- ——-
- `MultiTensorMessage`
Starting row of the slice
Stop of the slice
- get_tensor(name)[source]
Get tensor stored in the TensorMemory container.
- Parameters
- name
tensor key name.
- Returns
- cupy.ndarray
Inference tensor.
- property id: List[int]
Returns ID column values from
morpheus.pipeline.messages.MessageMeta.df
as list.- Returns
- List[int]
ID column values from the dataframe as list.
- property id_col
Returns ID column values from
morpheus.pipeline.messages.MessageMeta.df
.- Returns
- pandas.Series
ID column values from the dataframe.
- property outputs
Get outputs stored in the TensorMemory container. Alias for
MultiResponseMessage.tensors
.- Returns
- cupy.ndarray
Inference outputs.
- probs_tensor_name: ClassVar[str] = 'probs'
Name of the tensor that holds output probabilities
- set_meta(columns, value)[source]
Set column values to
morpheus.pipelines.messages.MessageMeta.df
.- Parameters
- columns
- value
Input column names. Sets the value for the corresponding column names. If
None
is specified, all columns will be used. If the column does not exist, a new one will be created.Value to apply to the specified columns. If a single value is passed, it will be broadcast to all rows. If a
Series
orDataframe
is passed, rows will be matched by index.
- property tensors
Get tensors stored in the TensorMemory container sliced according to
offset
andcount
.- Returns
- cupy.ndarray
Inference tensors.
- property timestamp: List[int]
Returns timestamp column values from morpheus.messages.MessageMeta.df as list.
- Returns
- List[int]
Timestamp column values from the dataframe as list.