+1; we should consider something similar for multi-dimensional tensors too.
Matei > On Sep 23, 2017, at 7:27 AM, Yanbo Liang <yblia...@gmail.com> wrote: > > +1 > > On Sat, Sep 23, 2017 at 7:08 PM, Noman Khan <nomanbp...@live.com> wrote: > +1 > > Regards > Noman > From: Denny Lee <denny.g....@gmail.com> > Sent: Friday, September 22, 2017 2:59:33 AM > To: Apache Spark Dev; Sean Owen; Tim Hunter > Cc: Danil Kirsanov; Joseph Bradley; Reynold Xin; Sudarshan Sudarshan > Subject: Re: [VOTE][SPIP] SPARK-21866 Image support in Apache Spark > > +1 > > On Thu, Sep 21, 2017 at 11:15 Sean Owen <so...@cloudera.com> wrote: > Am I right that this doesn't mean other packages would use this > representation, but that they could? > > The representation looked fine to me w.r.t. what DL frameworks need. > > My previous comment was that this is actually quite lightweight. It's kind of > like how I/O support is provided for CSV and JSON, so makes enough sense to > add to Spark. It doesn't really preclude other solutions. > > For those reasons I think it's fine. +1 > > On Thu, Sep 21, 2017 at 6:32 PM Tim Hunter <timhun...@databricks.com> wrote: > Hello community, > > I would like to call for a vote on SPARK-21866. It is a short proposal that > has important applications for image processing and deep learning. Joseph > Bradley has offered to be the shepherd. > > JIRA ticket: https://issues.apache.org/jira/browse/SPARK-21866 > PDF version: > https://issues.apache.org/jira/secure/attachment/12884792/SPIP%20-%20Image%20support%20for%20Apache%20Spark%20V1.1.pdf > > Background and motivation > As Apache Spark is being used more and more in the industry, some new use > cases are emerging for different data formats beyond the traditional SQL > types or the numerical types (vectors and matrices). Deep Learning > applications commonly deal with image processing. A number of projects add > some Deep Learning capabilities to Spark (see list below), but they struggle > to communicate with each other or with MLlib pipelines because there is no > standard way to represent an image in Spark DataFrames. We propose to > federate efforts for representing images in Spark by defining a > representation that caters to the most common needs of users and library > developers. > This SPIP proposes a specification to represent images in Spark DataFrames > and Datasets (based on existing industrial standards), and an interface for > loading sources of images. It is not meant to be a full-fledged image > processing library, but rather the core description that other libraries and > users can rely on. Several packages already offer various processing > facilities for transforming images or doing more complex operations, and each > has various design tradeoffs that make them better as standalone solutions. > This project is a joint collaboration between Microsoft and Databricks, which > have been testing this design in two open source packages: MMLSpark and Deep > Learning Pipelines. > The proposed image format is an in-memory, decompressed representation that > targets low-level applications. It is significantly more liberal in memory > usage than compressed image representations such as JPEG, PNG, etc., but it > allows easy communication with popular image processing libraries and has no > decoding overhead. > Targets users and personas: > Data scientists, data engineers, library developers. > The following libraries define primitives for loading and representing > images, and will gain from a common interchange format (in alphabetical > order): > • BigDL > • DeepLearning4J > • Deep Learning Pipelines > • MMLSpark > • TensorFlow (Spark connector) > • TensorFlowOnSpark > • TensorFrames > • Thunder > Goals: > • Simple representation of images in Spark DataFrames, based on > pre-existing industrial standards (OpenCV) > • This format should eventually allow the development of > high-performance integration points with image processing libraries such as > libOpenCV, Google TensorFlow, CNTK, and other C libraries. > • The reader should be able to read popular formats of images from > distributed sources. > Non-Goals: > Images are a versatile medium and encompass a very wide range of formats and > representations. This SPIP explicitly aims at the most common use case in the > industry currently: multi-channel matrices of binary, int32, int64, float or > double data that can fit comfortably in the heap of the JVM: > • the total size of an image should be restricted to less than 2GB > (roughly) > • the meaning of color channels is application-specific and is not > mandated by the standard (in line with the OpenCV standard) > • specialized formats used in meteorology, the medical field, etc. are > not supported > • this format is specialized to images and does not attempt to solve > the more general problem of representing n-dimensional tensors in Spark > Proposed API changes > We propose to add a new package in the package structure, under the MLlib > project: > org.apache.spark.image > Data format > We propose to add the following structure: > imageSchema = StructType([ > • StructField("mode", StringType(), False), > • The exact representation of the data. > • The values are described in the following OpenCV convention. > Basically, the type has both "depth" and "number of channels" info: in > particular, type "CV_8UC3" means "3 channel unsigned bytes". BGRA format > would be CV_8UC4 (value 32 in the table) with the channel order specified by > convention. > • The exact channel ordering and meaning of each channel is > dictated by convention. By default, the order is RGB (3 channels) and BGRA (4 > channels). > If the image failed to load, the value is the empty string "". > • StructField("origin", StringType(), True), > • Some information about the origin of the image. The content > of this is application-specific. > • When the image is loaded from files, users should expect to > find the file name in this field. > • StructField("height", IntegerType(), False), > • the height of the image, pixels > • If the image fails to load, the value is -1. > • StructField("width", IntegerType(), False), > • the width of the image, pixels > • If the image fails to load, the value is -1. > • StructField("nChannels", IntegerType(), False), > • The number of channels in this image: it is typically a value > of 1 (B&W), 3 (RGB), or 4 (BGRA) > • If the image fails to load, the value is -1. > • StructField("data", BinaryType(), False) > • packed array content. Due to implementation limitation, it > cannot currently store more than 2 billions of pixels. > • The data is stored in a pixel-by-pixel BGR row-wise order. > This follows the OpenCV convention. > • If the image fails to load, this array is empty. > For more information about image types, here is an OpenCV guide on types: > http://docs.opencv.org/2.4/modules/core/doc/intro.html#fixed-pixel-types-limited-use-of-templates > The reference implementation provides some functions to convert popular > formats (JPEG, PNG, etc.) to the image specification above, and some > functions to verify if an image is valid. > Image ingest API > We propose the following function to load images from a remote distributed > source as a DataFrame. Here is the signature in Scala. The python interface > is similar. For compatibility with java, this function should be made > available through a builder pattern or through the DataSource API. The exact > mechanics can be discussed during implementation; the goal of the proposal > below is to propose a specification of the behavior and of the options: > def readImages( > path: > String > , > session: SparkSession = > null > , > recursive: > Boolean = false > , > numPartitions: Int = 0, > dropImageFailures: > Boolean = false > , > > // Experimental options > > sampleRatio: Double > = 1.0): DataFrame > > The type of the returned DataFrame should be the structure type above, with > the expectation that all the file names be filled. > Mandatory parameters: > • path: a directory for a file system that contains images > Optional parameters: > • session (SparkSession, default null): the Spark Session to use to > create the dataframe. If not provided, it will use the current default Spark > session via SparkSession.getOrCreate(). > • recursive (bool, default false): take the top-level images or look > into directory recursively > • numPartitions (int, default null): the number of partitions of the > final dataframe. By default uses the default number of partitions from Spark. > • dropImageFailures (bool, default false): drops the files that failed > to load. If false (do not drop), some invalid images are kept. > Parameters that are experimental/may be quickly deprecated. These would be > useful to have but are not critical for a first cut: > • sampleRatio (float, in (0,1), default 1): if less than 1, returns a > fraction of the data. There is no statistical guarantee about how the > sampling is performed. This proved to be very helpful for fast prototyping. > Marked as experimental since it should be pushed to the Spark core. > The implementation is expected to be in Scala for performance, with a wrapper > for python. > This function should be lazy to the extent possible: it should not trigger > access to the data when called. Ideally, any file system supported by Spark > should be supported when loading images. There may be restrictions for some > options such as zip files, etc. > The reference implementation has also some experimental options (undocumented > here). > Reference implementation > A reference implementation is available as an open-source Spark package in > this repository (Apache 2.0 license): > https://github.com/Microsoft/spark-images > This Spark package will also be published in a binary form on > spark-packages.org . > Comments about the API should be addressed in this ticket. > Optional Rejected Designs > The use of User-Defined Types was considered. It adds some burden to the > implementation of various languages and does not provide significant > advantages. > --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
