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- 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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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
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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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