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"""Contains classes concerning SwinForImageClassification."""
from sparknlp.common import *
[docs]class SwinForImageClassification(AnnotatorModel,
HasBatchedAnnotateImage,
HasImageFeatureProperties,
HasRescaleFactor,
HasEngine):
"""SwinImageClassification is an image classifier based on Swin.
The Swin Transformer was proposed in Swin Transformer: Hierarchical Vision
Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan
Wei, Zheng Zhang, Stephen Lin, Baining Guo.
It is basically a hierarchical Transformer whose representation is computed with
shifted windows. The shifted windowing scheme brings greater efficiency by limiting
self-attention computation to non-overlapping local windows while also allowing for
cross-window connection.
.. code-block:: python
imageClassifier = SwinForImageClassification.pretrained() \\
.setInputCols(["image_assembler"]) \\
.setOutputCol("class")
The default model is ``"image_classifier_swin_base_patch4_window7_224"``, if no name is
provided.
For available pretrained models please see the
`Models Hub <https://sparknlp.org/models?task=Image+Classification>`__.
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
`SwinForImageClassificationTest <https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/annotators/cv/SwinForImageClassificationTest.scala>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``IMAGE`` ``CATEGORY``
====================== ======================
**Paper Abstract:**
*This paper presents a new vision Transformer, called Swin Transformer, that capably
serves as a general-purpose backbone for computer vision. Challenges in adapting
Transformer from language to vision arise from differences between the two domains,
such as large variations in the scale of visual entities and the high resolution of
pixels in images compared to words in text. To address these differences, we
propose a hierarchical Transformer whose representation is computed with Shifted
windows. The shifted windowing scheme brings greater efficiency by limiting
self-attention computation to non-overlapping local windows while also allowing for
cross-window connection. This hierarchical architecture has the flexibility to
model at various scales and has linear computational complexity with respect to
image size. These qualities of Swin Transformer make it compatible with a broad
range of vision tasks, including image classification (87.3 top-1 accuracy on
ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and
51.1 mask AP on COCO test- dev) and semantic segmentation (53.5 mIoU on ADE20K
val). Its performance surpasses the previous state-of-the- art by a large margin of
+2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the
potential of Transformer-based models as vision backbones. The hierarchical design
and the shifted window approach also prove beneficial for all-MLP architectures.*
References
----------
`Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
<https://arxiv.org/pdf/2103.14030.pdf>`__
Parameters
----------
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
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
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")
>>> imageClassifier = SwinForImageClassification \\
... .pretrained() \\
... .setInputCols(["image_assembler"]) \\
... .setOutputCol("class")
>>> pipeline = Pipeline().setStages([imageAssembler, imageClassifier])
>>> pipelineDF = pipeline.fit(imageDF).transform(imageDF)
>>> pipelineDF \\
... .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result") \\
... .show(truncate=False)
+-----------------+----------------------------------------------------------+
|image_name |result |
+-----------------+----------------------------------------------------------+
|palace.JPEG |[palace] |
|egyptian_cat.jpeg|[tabby, tabby cat] |
|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]|
|hen.JPEG |[hen] |
|ostrich.JPEG |[ostrich, Struthio camelus] |
|junco.JPEG |[junco, snowbird] |
|bluetick.jpg |[bluetick] |
|chihuahua.jpg |[Chihuahua] |
|tractor.JPEG |[tractor] |
|ox.JPEG |[ox] |
+-----------------+----------------------------------------------------------+
"""
name = "SwinForImageClassification"
inputAnnotatorTypes = [AnnotatorType.IMAGE]
outputAnnotatorType = AnnotatorType.CATEGORY
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with "
"config_proto.SerializeToString()",
TypeConverters.toListInt)
[docs] def getClasses(self):
"""
Returns labels used to train this model
"""
return self._call_java("getClasses")
[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.SwinForImageClassification",
java_model=None):
super(SwinForImageClassification, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
batchSize=2,
doNormalize=True,
doRescale=True,
doResize=True,
imageMean=[0.485, 0.456, 0.406],
imageStd=[0.229, 0.224, 0.225],
resample=3,
size=224,
rescaleFactor=1 / 255.0
)
@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
-------
SwinForImageClassification
The restored model
"""
from sparknlp.internal import _SwinForImageClassification
jModel = _SwinForImageClassification(folder,
spark_session._jsparkSession)._java_obj
return SwinForImageClassification(java_model=jModel)
@staticmethod
[docs] def pretrained(name="image_classifier_swin_base_patch4_window7_224", lang="en",
remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default
"image_classifier_swin_base_patch4_window7_224"
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
-------
SwinForImageClassification
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(SwinForImageClassification, name, lang,
remote_loc)