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"""Contains classes concerning VisionEncoderDecoderForImageCaptioning."""
from sparknlp.common import *
[docs]class VisionEncoderDecoderForImageCaptioning(AnnotatorModel,
HasBatchedAnnotateImage,
HasImageFeatureProperties,
HasGeneratorProperties,
HasRescaleFactor,
HasEngine):
"""VisionEncoderDecoder model that converts images into text captions. It allows for the use of
pretrained vision auto-encoding models, such as ViT, BEiT, or DeiT as the encoder, in
combination with pretrained language models, like RoBERTa, GPT2, or BERT as the decoder.
Pretrained models can be loaded with ``pretrained`` of the companion object:
.. code-block:: python
imageClassifier = VisionEncoderDecoderForImageCaptioning.pretrained() \\
.setInputCols(["image_assembler"]) \\
.setOutputCol("caption")
The default model is ``"image_captioning_vit_gpt2"``, if no name is provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?task=Image+Captioning>`__.
Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To
see which models are compatible and how to import them see
https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended
examples, see
`VisionEncoderDecoderTestSpec <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/VisionEncoderDecoderForImageCaptioningTestSpec.scala>`__.
Notes
-----
This is a very computationally expensive module especially on larger
batch sizes. The use of an accelerator such as GPU is recommended.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``IMAGE`` ``DOCUMENT``
====================== ======================
Parameters
----------
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
doResize
Whether to resize the input to a certain size
doNormalize
Whether to normalize the input with mean and standard deviation
featureExtractorType
Name of model's architecture for feature extraction
imageMean
The sequence of means for each channel, to be used when normalizing images
imageStd
The sequence of standard deviations for each channel, to be used when normalizing images
resample
An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BILINEAR` or
`PIL.Image.BICUBIC`. Only has an effect if do_resize is set to True.
size
Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is
provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True.
doRescale
Whether to rescale the image values by rescaleFactor
rescaleFactor
Factor to scale the image values
minOutputLength
Minimum length of the sequence to be generated
maxOutputLength
Maximum length of output text
doSample
Whether or not to use sampling; use greedy decoding otherwise
temperature
The value used to module the next token probabilities
topK
The number of highest probability vocabulary tokens to keep for top-k-filtering
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
repetitionPenalty
The parameter for repetition penalty. 1.0 means no penalty.
See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once
beamSize
The Number of beams for beam search
nReturnSequences
The number of sequences to return from the beam search
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> imageDF = spark.read \\
... .format("image") \\
... .option("dropInvalid", value = True) \\
... .load("src/test/resources/image/")
>>> imageAssembler = ImageAssembler() \\
... .setInputCol("image") \\
... .setOutputCol("image_assembler")
>>> imageCaptioning = VisionEncoderDecoderForImageCaptioning \\
... .pretrained() \\
... .setBeamSize(2) \\
... .setDoSample(False) \\
... .setInputCols(["image_assembler"]) \\
... .setOutputCol("caption")
>>> pipeline = Pipeline().setStages([imageAssembler, imageCaptioning])
>>> pipelineDF = pipeline.fit(imageDF).transform(imageDF)
>>> pipelineDF \\
... .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "caption.result") \\
... .show(truncate = False)
+-----------------+---------------------------------------------------------+
|image_name |result |
+-----------------+---------------------------------------------------------+
|palace.JPEG |[a large room filled with furniture and a large window] |
|egyptian_cat.jpeg|[a cat laying on a couch next to another cat] |
|hippopotamus.JPEG|[a brown bear in a body of water] |
|hen.JPEG |[a flock of chickens standing next to each other] |
|ostrich.JPEG |[a large bird standing on top of a lush green field] |
|junco.JPEG |[a small bird standing on a wet ground] |
|bluetick.jpg |[a small dog standing on a wooden floor] |
|chihuahua.jpg |[a small brown dog wearing a blue sweater] |
|tractor.JPEG |[a man is standing in a field with a tractor] |
|ox.JPEG |[a large brown cow standing on top of a lush green field]|
+-----------------+---------------------------------------------------------+
"""
name = "VisionEncoderDecoderForImageCaptioning"
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)
[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)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.cv.VisionEncoderDecoderForImageCaptioning",
java_model=None):
super(VisionEncoderDecoderForImageCaptioning, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=2,
beamSize=1,
doNormalize=True,
doRescale=True,
doResize=True,
doSample=True,
imageMean=[0.5, 0.5, 0.5],
imageStd=[0.5, 0.5, 0.5],
maxOutputLength=50,
minOutputLength=0,
nReturnSequences=1,
noRepeatNgramSize=0,
repetitionPenalty=1.0,
resample=2,
rescaleFactor=1 / 255.0,
size=224,
temperature=1.0,
topK=50,
topP=1.0)
@staticmethod
[docs] def loadSavedModel(folder, spark_session):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
VisionEncoderDecoderForImageCaptioning
The restored model
"""
from sparknlp.internal import _VisionEncoderDecoderForImageCaptioning
jModel = _VisionEncoderDecoderForImageCaptioning(folder, spark_session._jsparkSession)._java_obj
return VisionEncoderDecoderForImageCaptioning(java_model=jModel)
@staticmethod
[docs] def pretrained(name="image_captioning_vit_gpt2", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"image_captioning_vit_gpt2"
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
-------
VisionEncoderDecoderForImageCaptioning
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(VisionEncoderDecoderForImageCaptioning, name, lang, remote_loc)