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From "wangwei (JIRA)" <>
Subject [jira] [Updated] (SINGA-82) Refactor input layers using data store abstraction
Date Mon, 05 Oct 2015 03:26:26 GMT


wangwei updated SINGA-82:
    Summary: Refactor input layers using data store abstraction  (was: Refactor input layers
use data store abstraction)

> Refactor input layers using data store abstraction
> --------------------------------------------------
>                 Key: SINGA-82
>                 URL:
>             Project: Singa
>          Issue Type: Improvement
>            Reporter: wangwei
>            Assignee: wangwei
> 1. Separate the data storage from Layer. Currently, SINGA creates one layer to read data
from one storage, e.g., ShardData, CSV, LMDB. One problem is that only read operations are
provided. When users prepare the training data, they have to get familiar with the read/write
operations for each storage. Inspired from caffe::db::DB, we can provide a storage  abstraction
with simple read/write operation interfaces. Then users call these operations to prepare their
training data. Particularly, training data is stored as (string key, string value) tuples.
The base Store class 
> {code}
> // open the store for reading, writing or appending
> virtual bool Open(const string& source, Mode mode);
> // for reading tuples
> virtual bool Read(string*key, string*value) = 0;
> // for writing tuples
> virtual bool Write(const string& key, const string& value) = 0;
> {code}
> The specific storage, e.g., CSV, LMDB, image folder or HDFS (will be supported soon),
inherits Store and overrides the functions. 
> Consequently, a single KVInputLayer (like the SequenceFile.Reader from Hadoop) can read
from different sources by configuring *store* field (e.g., store=csv). 
> With the Store class, we can implement a KVInputLayer to read batchsize tuples in its
ComputeFeature function. The tuple is parsed by a virtual function depending on the application
(or the format of the tuple). 
> {code}
> // parse the tuple as the k-th instance for one mini-batch
> virtual bool Parse(int k, const string& key, const string& tuple) = 0;
> {code}
> For example, a CSVKVInputLayer may parse the key into a line ID, and parse the label
and feature from the value field. An ImageKVInputLayer may parse a SingleLabelImageRecord
from the value field.
> 2. The will be a set of layers doing data preprocessing, e.g., normalization and image

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