Converting Dataset to MindRecord

 

Overview

Users can convert non-standard datasets and common datasets into the MindSpore data format, MindRecord, so that they can be easily loaded to MindSpore for training. In addition, the performance of MindSpore in some scenarios is optimized, which delivers better user experience when you use datasets in the MindSpore data format.

The MindSpore data format has the following features:

1. Unified storage and access of user data are implemented, simplifying training data loading.

2. Data is aggregated for storage, which can be efficiently read, managed and moved.

3. Data encoding and decoding are efficient and transparent to users.

4. The partition size is flexibly controlled to implement distributed training.

The mindspore data format aims to normalize the datasets of users to MindRecord, which can be further loaded through the MindDataset and used in the training procedure (Please refer to the API for detailed use).

A MindRecord file consists of data files and index files. Data files and index files do not support renaming for now.

• Data file

  A data file contains a file header, scalar data pages and block data pages for storing normalized training data. It is recommended that the size of a single MindRecord file does not exceed 20 GB. Users can break up a large dataset and store the dataset into multiple MindRecord files.

• Index file

  An index file contains the index information generated based on scalar data (such as image labels and image file names), used for convenient data fetching and storing statistical data about the dataset.

A data file consists of the following key parts:

• File Header

  The file header stores the file header size, scalar data page size, block data page size, schema, index fields, statistics, file partition information, and mapping between scalar data and block data. It is the metadata of the MindRecord file.

• Scalar data page

  The scalar data page is used to store integer, string and floating point data, such as the label of an image, file name of an image, and length, width of an image. The information suitable for storage with scalars is stored here.

• Block data page

  The block data page is used to store data such as binary strings and NumPy arrays. Additional examples include converted python dictionaries generated from texts and binary image files.

Converting Dataset to MindRecord

The following tutorial demonstrates how to convert image data and its annotations to MindRecord. For more instructions on MindSpore data format conversion.

Example 1: Show how to convert data into a MindRecord data file according to the defined dataset structure.

1. Import the FileWriter class for file writing.

from mindspore.mindrecord import FileWriter

2. Define a dataset schema which defines dataset fields and field types.

cv_schema_json = {"file_name": {"type": "string"}, "label": {"type": "int32"}, "data": {"type": "bytes"}}

Schema mainly contains `name`, `type` and `shape`:
- `name`: field names, consist of letters, digits and underscores.
- `type`: field types, include int32, int64, float32, float64, string and bytes.
- `shape`: [-1] for one-dimensional array, [m, n, ...] for higher dimensional array in which m and n represent the dimensions.

> - The type of a field with the `shape` attribute can only be int32, int64, float32, or float64.
> - If the field has the `shape` attribute, only data in `numpy.ndarray` type can be transferred to the `write_raw_data` API.

3. Prepare the data sample list to be written based on the user-defined schema format. Binary data of the images is transferred below.

data = [{"file_name": "1.jpg", "label": 0, "data": b"\x10c\xb3w\xa8\xee$o&<q\x8c\x8e(\xa2\x90\x90\x96\xbc\xb1\x1e\xd4QER\x13?\xff\xd9"},
        {"file_name": "2.jpg", "label": 56, "data": b"\xe6\xda\xd1\xae\x07\xb8>\xd4\x00\xf8\x129\x15\xd9\xf2q\xc0\xa2\x91YFUO\x1dsE1\x1ep"},
        {"file_name": "3.jpg", "label": 99, "data": b"\xaf\xafU<\xb8|6\xbd}\xc1\x99[\xeaj+\x8f\x84\xd3\xcc\xa0,i\xbb\xb9-\xcdz\xecp{T\xb1\xdb"}]

4. Adding index fields can accelerate data loading. This step is optional.

indexes = ["file_name", "label"]

5. Create a FileWriter object, transfer the file name and number of slices, add the schema and index, call the write_raw_data API to write data, and call the commit API to generate a local data file.

writer = FileWriter(file_name="test.mindrecord", shard_num=4)
writer.add_schema(cv_schema_json, "test_schema")
writer.add_index(indexes)
writer.write_raw_data(data)
writer.commit()

MSRStatus.SUCCESS

This example will generate `test.mindrecord0`, `test.mindrecord0.db`, `test.mindrecord1`, `test.mindrecord1.db`, `test.mindrecord2`, `test.mindrecord2.db`, `test.mindrecord3`, `test.mindrecord3.db`, totally eight files, called MindRecord datasets. `test.mindrecord0` and `test.mindrecord0.db` are collectively referred to as a MindRecord file, where `test.mindrecord0` is the data file and `test.mindrecord0.db` is the index file.

**Interface Description:**
- `FileWriter`: If the parameter shard_num > 1, the original dataset will be saved to shard_num of mindrecord files and each mindrecord file will save the metadata information of adjacent mindrecord files. Then, when using the `MindDataset` interface to read the mindrecord dataset, you can read all shard_num of mindrecord files through `MindDataset(dataset_files="./test.mindrecord0")` and you can read only `test.mindrecord0` mindrecord file through `MindDataset(dataset_files=["./test.mindrecord0"])`.
- `write_raw_data`: write data to memory.
- `commit`: write data in memory to disk.

6. For adding data to the existing data format file, call the open_for_append API to open the existing data file, call the write_raw_data API to write new data, and then call the commit API to generate a local data file.

writer = FileWriter.open_for_append("test.mindrecord0")
writer.write_raw_data(data)
writer.commit()

MSRStatus.SUCCESS

Example 2: Convert a picture in jpg format into a MindRecord dataset according to the method in Example 1.

Download the image data transform.jpg that needs to be processed as the raw data to be processed.

Create a folder directory ./datasets/convert_dataset_to_mindrecord/data_to_mindrecord/ to store all the converted datasets in this experience.

Create a folder directory ./datasets/convert_dataset_to_mindrecord/images/ to store the downloaded image data.

The following example code downloads and unzips the dataset to the specified location.

import os
import requests
import tarfile
import zipfile

requests.packages.urllib3.disable_warnings()

def download_dataset(url, target_path):
    """download and decompress dataset"""
    if not os.path.exists(target_path):
        os.makedirs(target_path)
    download_file = url.split("/")[-1]
    if not os.path.exists(download_file):
        res = requests.get(url, stream=True, verify=False)
        if download_file.split(".")[-1] not in ["tgz", "zip", "tar", "gz"]:
            download_file = os.path.join(target_path, download_file)
        with open(download_file, "wb") as f:
            for chunk in res.iter_content(chunk_size=512):
                if chunk:
                    f.write(chunk)
    if download_file.endswith("zip"):
        z = zipfile.ZipFile(download_file, "r")
        z.extractall(path=target_path)
        z.close()
    if download_file.endswith(".tar.gz") or download_file.endswith(".tar") or download_file.endswith(".tgz"):
        t = tarfile.open(download_file)
        names = t.getnames()
        for name in names:
            t.extract(name, target_path)
        t.close()
    print("The {} file is downloaded and saved in the path {} after processing".format(os.path.basename(url), target_path))

download_dataset("https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/transform.jpg", "./datasets/convert_dataset_to_mindrecord/images/")
if not os.path.exists("./datasets/convert_dataset_to_mindrecord/data_to_mindrecord/"):
    os.makedirs("./datasets/convert_dataset_to_mindrecord/data_to_mindrecord/")

Execute the following code to convert the downloaded transform.jpg into a MindRecord dataset.

# step 1 import class FileWriter
import os
from mindspore.mindrecord import FileWriter

# clean up old run files before in Linux
data_path = './datasets/convert_dataset_to_mindrecord/data_to_mindrecord/'
os.system('rm -f {}test.*'.format(data_path))

# import FileWriter class ready to write data
data_record_path = './datasets/convert_dataset_to_mindrecord/data_to_mindrecord/test.mindrecord'
writer = FileWriter(file_name=data_record_path, shard_num=4)

# define the data type
data_schema = {"file_name": {"type": "string"}, "label": {"type": "int32"}, "data": {"type": "bytes"}}
writer.add_schema(data_schema, "test_schema")

# prepeare the data contents
file_name = "./datasets/convert_dataset_to_mindrecord/images/transform.jpg"
with open(file_name, "rb") as f:
    bytes_data = f.read()
data = [{"file_name": "transform.jpg", "label": 1, "data": bytes_data}]

# add index field
indexes = ["file_name", "label"]
writer.add_index(indexes)

# save data to the files
writer.write_raw_data(data)
writer.commit()

MSRStatus.SUCCESS

This example will generate 8 files, which become the MindRecord dataset. test.mindrecord0 and test.mindrecord0.db are called 1 MindRecord files, where test.mindrecord0 is the data file, and test.mindrecord0.db is the index file. The generated files are as follows:

./datasets/convert_dataset_to_mindrecord/data_to_mindrecord/
├── test.mindrecord0
├── test.mindrecord0.db
├── test.mindrecord1
├── test.mindrecord1.db
├── test.mindrecord2
├── test.mindrecord2.db
├── test.mindrecord3
└── test.mindrecord3.db

Loading MindRecord Dataset

The following tutorial briefly demonstrates how to load the MindRecord dataset using the MindDataset.

1. Import the dataset for dataset loading.

import mindspore.dataset as ds

2. Use the MindDataset to load the MindRecord dataset.

data_set = ds.MindDataset(dataset_files="test.mindrecord0")     # read full dataset
count = 0
for item in data_set.create_dict_iterator(output_numpy=True):
    print("sample: {}".format(item))
    count += 1
print("Got {} samples".format(count))

sample: {'data': array([175, 175,  85,  60, 184, 124,  54, 189, 125, 193, 153,  91, 234,
       106,  43, 143, 132, 211, 204, 160,  44, 105, 187, 185,  45, 205,
       122, 236, 112, 123,  84, 177, 219], dtype=uint8), 'file_name': array(b'3.jpg', dtype='|S5'), 'label': array(99, dtype=int32)}
sample: {'data': array([ 16,  99, 179, 119, 168, 238,  36, 111,  38,  60, 113, 140, 142,
        40, 162, 144, 144, 150, 188, 177,  30, 212,  81,  69,  82,  19,
        63, 255, 217], dtype=uint8), 'file_name': array(b'1.jpg', dtype='|S5'), 'label': array(0, dtype=int32)}
sample: {'data': array([175, 175,  85,  60, 184, 124,  54, 189, 125, 193, 153,  91, 234,
       106,  43, 143, 132, 211, 204, 160,  44, 105, 187, 185,  45, 205,
       122, 236, 112, 123,  84, 177, 219], dtype=uint8), 'file_name': array(b'3.jpg', dtype='|S5'), 'label': array(99, dtype=int32)}
sample: {'data': array([230, 218, 209, 174,   7, 184,  62, 212,   0, 248,  18,  57,  21,
       217, 242, 113, 192, 162, 145,  89,  70,  85,  79,  29, 115,  69,
        49,  30, 112], dtype=uint8), 'file_name': array(b'2.jpg', dtype='|S5'), 'label': array(56, dtype=int32)}
sample: {'data': array([ 16,  99, 179, 119, 168, 238,  36, 111,  38,  60, 113, 140, 142,
        40, 162, 144, 144, 150, 188, 177,  30, 212,  81,  69,  82,  19,
        63, 255, 217], dtype=uint8), 'file_name': array(b'1.jpg', dtype='|S5'), 'label': array(0, dtype=int32)}
sample: {'data': array([230, 218, 209, 174,   7, 184,  62, 212,   0, 248,  18,  57,  21,
       217, 242, 113, 192, 162, 145,  89,  70,  85,  79,  29, 115,  69,
        49,  30, 112], dtype=uint8), 'file_name': array(b'2.jpg', dtype='|S5'), 'label': array(56, dtype=int32)}
Got 6 samples