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From "wangwei (JIRA)" <>
Subject [jira] [Created] (SINGA-82) Refactor input layers use data store abstraction
Date Mon, 05 Oct 2015 03:25:26 GMT
wangwei created SINGA-82:

             Summary: Refactor input layers use data store abstraction
                 Key: SINGA-82
             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 
// 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;
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). 
// parse the tuple as the k-th instance for one mini-batch
virtual bool Parse(int k, const string& key, const string& tuple) = 0;
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

2. The will be a set of layers doing data preprocessing, e.g., normalization and image augmentation.

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