Specifications for Importing Data from an OBS Directory

When a dataset is imported, the data storage directory and file name must comply with the ModelArts specifications if the data to be used is stored in OBS.

Only the following types of dataset support the OBS path import mode: Image classification, Object detection, Text classification, Table, and Sound classification. A table dataset can be imported from data sources such as OBS, DWS, DLI, and MRS.

To import data from an OBS directory, you must have the read permission on the OBS directory.

Image Classification

Image classification data can be in two modes. The first mode (directory mode) supports only single labels. The second mode (.txt label files) supports multiple labels.
- Images with the same label must be stored in the same directory, and the label name is the directory name. If there are multiple levels of directories, the last level is used as the label name.
  In the following example, Cat and Dog are label names.
```
dataset-import-example 
├─Cat 
│      10.jpg 
│      11.jpg 
│      12.jpg 
│ 
└─Dog 
        1.jpg 
        2.jpg 
        3.jpg
```
- If .txt files exist in the directory, the content in the .txt files is used as the image label. This mode is better than the previous one.
  In the following example, import-dir-1 and import-dir-2 are the imported subdirectories:
```
dataset-import-example 
├─import-dir-1
│      10.jpg
│      10.txt    
│      11.jpg 
│      11.txt
│      12.jpg 
│      12.txt
└─import-dir-2
        1.jpg 
        1.txt
        2.jpg 
        2.txt
```
  The following shows a label file for a single label, for example, the 1.txt file:
```
Cat
```
  The following shows a label file for multiple labels, for example, the 1.txt file:
```
Cat
Dog
```
Only images in JPG, JPEG, PNG, and BMP formats are supported. The size of a single image cannot exceed 5 MB, and the total size of all images uploaded at a time cannot exceed 8 MB.

Object Detection

The simple mode of object detection requires users store labeled objects and their label files (in one-to-one relationship with the labeled objects) in the same directory. For example, if the name of the labeled object file is IMG_20180919_114745.jpg, the name of the label file must be IMG_20180919_114745.xml.

The label files for object detection must be in PASCAL VOC format. For details about the format, see Table 8.

Example:

├─dataset-import-example 
│      IMG_20180919_114732.jpg 
│      IMG_20180919_114732.xml 
│      IMG_20180919_114745.jpg 
│      IMG_20180919_114745.xml 
│      IMG_20180919_114945.jpg 
│      IMG_20180919_114945.xml

A label file example is as follows:

      
       
         
         
           <?xml version="1.0" encoding="UTF-8" standalone="no"?>
<annotation>
    <folder>NA</folder>
    <filename>bike_1_1593531469339.png</filename>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>554</width>
        <height>606</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>Dog</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <occluded>0</occluded>
        <bndbox>
            <xmin>279</xmin>
            <ymin>52</ymin>
            <xmax>474</xmax>
            <ymax>278</ymax>
        </bndbox>
    </object>
    <object>
        <name>Cat</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <occluded>0</occluded>
        <bndbox>
            <xmin>279</xmin>
            <ymin>198</ymin>
            <xmax>456</xmax>
            <ymax>421</ymax>
        </bndbox>
    </object>
</annotation>

          

        

      
     

Only images in JPG, JPEG, PNG, and BMP formats are supported. The size of a single image cannot exceed 5 MB, and the total size of all images uploaded at a time cannot exceed 8 MB.

Image Segmentation

The simple mode of image segmentation requires users store labeled objects and their label files (in one-to-one relationship with the labeled objects) in the same directory. For example, if the name of the labeled object file is IMG_20180919_114746.jpg, the name of the label file must be IMG_20180919_114746.xml.

Fields mask_source and mask_color are added to the label file in PASCAL VOC format. For details about the format, see Table 4.

Example:

├─dataset-import-example 
│      IMG_20180919_114732.jpg 
│      IMG_20180919_114732.xml 
│      IMG_20180919_114745.jpg 
│      IMG_20180919_114745.xml 
│      IMG_20180919_114945.jpg 
│      IMG_20180919_114945.xml

A label file example is as follows:

      
       
         
         
           <?xml version="1.0" encoding="UTF-8" standalone="no"?>
<annotation>
    <folder>NA</folder>
    <filename>image_0006.jpg</filename>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>230</width>
        <height>300</height>
        <depth>3</depth>
    </size>
    <segmented>1</segmented>
    <mask_source>obs://xianao/out/dataset-8153-Jmf5ylLjRmSacj9KevS/annotation/V001/segmentationClassRaw/image_0006.png</mask_source>
    <object>
        <name>bike</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <mask_color>193,243,53</mask_color>
        <occluded>0</occluded>
        <polygon>
            <x1>71</x1>
            <y1>48</y1>
            <x2>75</x2>
            <y2>73</y2>
            <x3>49</x3>
            <y3>69</y3>
            <x4>68</x4>
            <y4>92</y4>
            <x5>90</x5>
            <y5>101</y5>
            <x6>45</x6>
            <y6>110</y6>
            <x7>71</x7>
            <y7>48</y7>
        </polygon>
    </object>
</annotation>

          

        

      
     

Text Classification

txt and csv files can be imported for text classification, with the text encoding format of UTF-8 or GBK.

Labeled objects and labels for text classification can be stored in two modes:

The labeled objects and labels for text classification are in the same text file. You can specify a separator to separate the labeled objects and labels, as well as multiple labels.

For example, the following shows an example text file. The Tab key is used to separate the labeled object from the label.

It touches good and responds quickly. I don't know how it performs in the future.   positive
Three months ago, I bought a very good phone and replaced my old one with it. It can operate longer between charges.  positive
Why does my phone heat up if I charge it for a while? The volume button stuck after being pressed down.  negative
It's a gift for Father's Day. The logistics is fast and I received it in 24 hours. I like the earphones because the bass sounds feel good and they would not fall off.  positive

The labeled objects and labels for text classification are text files, and correspond to each other based on the rows. For example, the first row in a label file indicates the label of the first row in the file of the labeled object.

For example, the content of labeled object COMMENTS_20180919_114745.txt is as follows:

It touches good and responds quickly. I don't know how it performs in the future.
Three months ago, I bought a very good phone and replaced my old one with it. It can operate longer between charges.
Why does my phone heat up if I charge it for a while? The volume button stuck after being pressed down.
It's a gift for Father's Day. The logistics is fast and I received it in 24 hours. I like the earphones because the bass sounds feel good and they would not fall off.

The content of label file COMMENTS_20180919_114745_result.txt is as follows:

positive
negative
negative 
positive

The data format requires users to store labeled objects and their label files (in one-to-one relationship with the labeled objects) in the same directory. For example, if the name of the labeled object file is COMMENTS_20180919_114745.txt, the name of the label file must be COMMENTS _20180919_114745_result.txt.

Example of data file storage:

├─dataset-import-example 
│      COMMENTS_20180919_114732.txt 
│      COMMENTS _20180919_114732_result.txt 
│      COMMENTS _20180919_114745.txt 
│      COMMENTS _20180919_114745_result.txt 
│      COMMENTS _20180919_114945.txt 
│      COMMENTS _20180919_114945_result.txt

Sound Classification

For sound classification, sound files with the same label must be stored in the same directory, and the label name is the directory name.

Example:

dataset-import-example 
├─Cat 
│      10.wav 
│      11.wav 
│      12.wav 
│ 
└─Dog 
        1.wav 
        2.wav 
        3.wav

Table

You can import data from OBS, DWS, DLI, and MRS.

Import description:

The prerequisite for successful import is that the schema of the data source must be the same as that specified during dataset creation. The schema indicates column names and types of a table. Once specified during dataset creation, the values cannot be changed.
If the data format is invalid, the data is set to null values. For details, see Table 4.
When a CSV file is imported from OBS or MRS, the data type is not verified, but the number of columns must be the same as that in the schema of the dataset.

The following describes how to import data from different data sources:

From OBS
CSV files can be imported from OBS. You need to select the directory where the files are stored. The number of columns in the CSV file must be the same as that in the dataset schema. The schema of the CSV file can be automatically obtained.
```
├─dataset-import-example 
│      table_import_1.csv 
│      table_import_2.csv
│      table_import_3.csv
│      table_import_4.csv
```

From DWS
To import data from DWS, you need to select a DWS cluster and enter the database name, table name, username, and password. The schema (column name and type) of the imported table must be the same as that of the dataset.
From DLI
To import data from DLI, you need to select the DLI queue, database, and table name. The schema (column name and type) of the selected table must be the same as that of the dataset. The schema of the selected table can be automatically obtained. The default queue of DLI is used only for experience. Different accounts may preempt resources. Therefore, resources need to be queued. You may not be able to obtain required resources each time to perform related operations. DLI supports schema mapping. That is, the schema field name of the imported table can be different from that of the dataset, but the type must be the same.
From MRS
Data can be imported only from the analysis cluster. To import data in CSV format stored on HDFS from MRS, select an existing MRS cluster and select the file name or directory from the HDFS file list. The number of columns in the imported file must be the same as that of the dataset schema.

Parent topic: Importing Data

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Next topic: Specifications for Importing the Manifest File