Source code for sparknlp.annotator.cv.llava_for_multimodal
# Copyright 2017-2024 John Snow Labs
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from sparknlp.common import *
[docs]class LLAVAForMultiModal(AnnotatorModel,
HasBatchedAnnotateImage,
HasImageFeatureProperties,
HasEngine,
HasCandidateLabelsProperties,
HasRescaleFactor):
"""LLAVAForMultiModal can load LLAVA models for visual question answering.
The model consists of a vision encoder, a text encoder as well as a text decoder.
The vision encoder will encode the input image, the text encoder will encode the input question together
with the encoding of the image, and the text decoder will output the answer to the question.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> visualQAClassifier = LLAVAForMultiModal.pretrained() \\
... .setInputCols(["image_assembler"]) \\
... .setOutputCol("answer")
The default model is ``"llava_1_5_7b_hf"``, if no name is
provided.
For available pretrained models please see the `Models Hub
<https://sparknlp.org/models?task=Question+Answering>`__.
To see which models are compatible and how to import them see
`Import Transformers into Spark NLP 🚀
<https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``IMAGE`` ``DOCUMENT``
====================== ======================
Parameters
----------
batchSize
Batch size. Large values allows faster processing but requires more
memory, by default 2
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
maxSentenceLength
Max sentence length to process, by default 50
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> image_df = SparkSessionForTest.spark.read.format("image").load(path=images_path)
>>> test_df = image_df.withColumn("text", lit("USER: \n <|image|> \n What's this picture about? \n ASSISTANT:\n"))
>>> imageAssembler = ImageAssembler() \\
... .setInputCol("image") \\
... .setOutputCol("image_assembler")
>>> visualQAClassifier = LLAVAForMultiModal.pretrained() \\
... .setInputCols("image_assembler") \\
... .setOutputCol("answer")
>>> pipeline = Pipeline().setStages([
... imageAssembler,
... visualQAClassifier
... ])
>>> result = pipeline.fit(test_df).transform(test_df)
>>> result.select("image_assembler.origin", "answer.result").show(false)
+--------------------------------------+------+
|origin |result|
+--------------------------------------+------+
|[file:///content/images/cat_image.jpg]|[The unusual aspect of this picture is the presence of two cats lying on a pink couch]|
+--------------------------------------+------+
"""
name = "LLAVAForMultiModal"
inputAnnotatorTypes = [AnnotatorType.IMAGE]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with "
"config_proto.SerializeToString()",
TypeConverters.toListInt)
minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated",
typeConverter=TypeConverters.toInt)
maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text",
typeConverter=TypeConverters.toInt)
doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise",
typeConverter=TypeConverters.toBoolean)
temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities",
typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering",
typeConverter=TypeConverters.toInt)
topP = Param(Params._dummy(), "topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation",
typeConverter=TypeConverters.toFloat)
repetitionPenalty = Param(Params._dummy(), "repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty. See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details",
typeConverter=TypeConverters.toFloat)
noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once",
typeConverter=TypeConverters.toInt)
ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds",
"A list of token ids which are ignored in the decoder's output",
typeConverter=TypeConverters.toListInt)
beamSize = Param(Params._dummy(), "beamSize",
"The Number of beams for beam search.",
typeConverter=TypeConverters.toInt)
[docs] def setMaxSentenceSize(self, value):
"""Sets Maximum sentence length that the annotator will process, by
default 50.
Parameters
----------
value : int
Maximum sentence length that the annotator will process
"""
return self._set(maxSentenceLength=value)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output.
Parameters
----------
value : List[int]
The words to be filtered out
"""
return self._set(ignoreTokenIds=value)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setMinOutputLength(self, value):
"""Sets minimum length of the sequence to be generated.
Parameters
----------
value : int
Minimum length of the sequence to be generated
"""
return self._set(minOutputLength=value)
[docs] def setMaxOutputLength(self, value):
"""Sets maximum length of output text.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise.
Parameters
----------
value : bool
Whether or not to use sampling; use greedy decoding otherwise
"""
return self._set(doSample=value)
[docs] def setTemperature(self, value):
"""Sets the value used to module the next token probabilities.
Parameters
----------
value : float
The value used to module the next token probabilities
"""
return self._set(temperature=value)
[docs] def setTopK(self, value):
"""Sets the number of highest probability vocabulary tokens to keep for
top-k-filtering.
Parameters
----------
value : int
Number of highest probability vocabulary tokens to keep
"""
return self._set(topK=value)
[docs] def setTopP(self, value):
"""Sets the top cumulative probability for vocabulary tokens.
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
Parameters
----------
value : float
Cumulative probability for vocabulary tokens
"""
return self._set(topP=value)
[docs] def setRepetitionPenalty(self, value):
"""Sets the parameter for repetition penalty. 1.0 means no penalty.
Parameters
----------
value : float
The repetition penalty
References
----------
See `Ctrl: A Conditional Transformer Language Model For Controllable
Generation <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
"""
return self._set(repetitionPenalty=value)
[docs] def setNoRepeatNgramSize(self, value):
"""Sets size of n-grams that can only occur once.
If set to int > 0, all ngrams of that size can only occur once.
Parameters
----------
value : int
N-gram size can only occur once
"""
return self._set(noRepeatNgramSize=value)
[docs] def setBeamSize(self, value):
"""Sets the number of beam size for beam search, by default `4`.
Parameters
----------
value : int
Number of beam size for beam search
"""
return self._set(beamSize=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.cv.LLAVAForMultiModal",
java_model=None):
super(LLAVAForMultiModal, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=2,
minOutputLength=0,
maxOutputLength=200,
doSample=False,
temperature=1,
topK=50,
topP=1,
repetitionPenalty=1.0,
noRepeatNgramSize=0,
ignoreTokenIds=[],
beamSize=1,
)
[docs] @staticmethod
def loadSavedModel(folder, spark_session, use_openvino=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
CLIPForZeroShotClassification
The restored model
"""
from sparknlp.internal import _LLAVAForMultiModalLoader
jModel = _LLAVAForMultiModalLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return LLAVAForMultiModal(java_model=jModel)
[docs] @staticmethod
def pretrained(name="llava_1_5_7b_hf", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"llava_1_5_7b_hf"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
LLAVAForMultiModal
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(LLAVAForMultiModal, name, lang, remote_loc)