Table of Contents outline true
Link to Dev list discussion
Feature Shepherd
Shepherd - Anirudh Acharya
Problem
Data IO is often a bottleneck for training and inference workflows with image data. And as data size gets larger and is unable to fit in the main memory, data loading can bring down the performance of the workflow. Which is why it would be beneficial to have the image stored in the binary recordIO format, which is much more compact than raw image files, occupies less memory and more efficient while data loading.
The goal of this project is to have an easy to use and intuitive interface to pre-process image data and create recordIO files. Currently our customers have to clone the whole MXNet repository to use a command line tool to pre-process and create recordIO files from image datasets. This is inconvenient for our customers, with the proposed change the customers will be able to use this functionality straight out of the PyPi package.
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As a user, I’d like to have an API to convert a dataset of raw images into binary format and pack them as RecordIO files.
Open Questions
- Why is RecordIO a preferred format for image data in MXNet. Are there alternatives to it like Apache Parquet or Avro etc.. ?
- What are the options for editing an already created .rec file?
the ideal solution is to rewrite the file as files are always read and written as streams of data and it would not be possible to add records in the middle of a file in-place. This cannot be seen as a drawback of the API as this limitation is shared by the reading/writing of any generic text file in Python or other programming languages. But reading, writing and editing of record files can be accomplished by
read_idx()
andwrite_idx()
methods of theMXIndexedRecordIO
object.Code Block language py theme Eclipse linenumbers true ''' Existing RecordIO files can perused and edited and rewritten using the below two code snippets of reading and writing RecordIO files. ''' # Write a record label1 = [2,3] id1 = 2 header1 = mx.recordio.IRHeader(0, label1, id1, 0) with open('img.jpg', 'rb') as fin: img = fin.read() s1 = mx.recordio.pack(header1, img) write_record = mx.recordio.MXIndexedRecordIO('img.idx', 'img.rec', 'w') write_record.write_idx(id1, s1) # Read record read_record = mx.recordio.MXIndexedRecordIO('img.idx', 'img.rec', 'r') item = read_record.read_idx(2) header, img = mx.recordio.unpack_img(item) print(header.label)
Proposed Approach
Implement a new API in MXNet's Data IO API that accepts an image list file or a numpy array, and converts that data into recordIO file format and stores the file. The proposed approach will also parallelize and user will be given the option to set the number of threads he/she can use to perform this function. The proposed API will have the same functionality as an existing CLI tool, which is currently used by customers for creating .rec files, but customers will have the convenience of using this functionality from the PyPi package itself.
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He/She should be able to stack the desired image transformations into a gluon.transform.Compose and pass that object to the im2rec API which will apply those transforms and then pack the transformed images into recordIO files.
Multi-Reader Single-Writer Design for Creating .rec file
Addition of new APIs
im2rec API Specification
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def mx.io.im2rec(list_file, transforms, dataset_params, output_path):
"""
Convert image.list file containing the path to the raw images to binary files.
Input Parameters -
----------
list_file - str object containing the path to the list file
transforms - gluon.transforms.Compose object
dataset_params - dict object whose description is given in the appendix
output_path - string object containing the path to the output location
Return type -
----------
rec_file_path - str object depicting the path of the output rec file
"""
return rec_file_path |
arr2rec API Specification
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def mx.io.np2rec(data, labels, transforms, dataset_params): """ Convert numpy representation of images to binary files. Input Parameters - ---------- data - numpy array holding all the image data. Supported array shapes - (N,H,W) - image with uint8 data (N,3,H,W) - image with RGB values (float or uint8) (N,4,H,W) - image with RGBA values. N is the number of the images. H and W are the rows and columns of the image. The pixel values should be in the range of [0...1] for float data type and [0...255] for int data type. Values outside this range will be clipped. labels - numpy array holding labels for each of the images. Should be of length N. transforms - gluon.transforms.Compose object dataset_params - dict object whose description is given in the appendix output_path - string object containing the path to the output location Return type - ---------- rec_file_path - str object depicting the path of the output rec file """ return rec_file_path |
Backward Compatibility
Post implementing the API the existing CLI tool will continue to exist, but users will also be directed to the new API and its accompanying documentation/tutorials.
Performance Benchmarks
<>
Alternative Approach
One of the initial approaches I came up with involved having each of the image transforms and dataset_params as a parameter to the API. This will end up creating an API with potentially 10-15 parameters and adding/removing more transforms or parameters might be difficult and could lead to API breakage. Hence using gluon.transforms was preferred.
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Parameter | Default Value/Optional | Description | |
num_workers | 1 | Have multiple workers doing the job. This option will imply shuffling the dataset. | |
labelbatch_widthsize | 14096specify the | ||
pass_through | False label_width in the list, by default set to 1 | ||
pack_label | 0 | ||
whether to also pack multi dimensional label in the record filensplitparts | 1 | used for part generation, logically split the .lst file to NSPLIT parts by position |
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