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• Contents
• Wukong Dataset
• Environment requirements
• Quick Start
  • Prepare Dataset
  • Prepare files required for tokenizer
  • Propare prompt files
  • Prepare pretrained model checkpoint
  • Zero-shot Classification
• Wukong Dataset

This project provides the zero-shot classification task on ILSVRC dataset using multi-modality large-scale model pretrained on Noah-Wukong dataset. Model structure as follows:

More benchmark of the multi-modality modal please refer to Noah-Wukong Benchmark

• Hardware
  • Ascend processor
• Framework
  • Mindspore
• Tutorial
  • Mindspore Tutorial
  • Mindspore Python API

• Download ILSVRC dataset and organize the file as follows:

.
└── data_root
     ├── class1
     │    ├── 000000000001.jpg
     │    ├── 000000000002.jpg
     │    ├── ...
     ├── class2
     │    ├── 000000000001.jpg
     │    ├── 000000000002.jpg
     │    ├── ...
     ├── class3
     │    ├── 000000000001.jpg
     │    ├── 000000000002.jpg
     │    ├── ...
     ├── classN
     ├── ...

• Download corresponding Chinese class name file imagenet_class_name_zh.json and place it the same folder with eval.py .

Download following files and place them under src/tools/

• English: bpe_simple_vocab_16e6.txt.gz
• Chinese: vocab_zh.txt

Download prompt filezh_templates.txtto src/tools/.This file defines the prompts used in zero-shot classification task. The number of prompts can be modified according to time/performance balance. Custom prompts are also allowed.

Download corresponding pretrained checkpoint files following links in the table.

Run eval.py to do zero-shot classification, each model has its config file under src/config/ folder.

python eval.py --config_path [config_path] --ckpt_path [ckpt_path] --dataset_path [/path/to/data_root] --batch_size [batch size]

evaluation result is something like this

INFO:main:correct @1: 51.51; correct @5: 78.33

Detailed zero-shot classification performance is as below:

Wukong 100m dataset files can be downloaed from Wukong, file structure should be like this:

.
└── data_root
    └─wukong_release
        ├─ wukong_100m_0.csv
        ├─ wukong_100m_1.csv
        ├─ wukong_100m_2.csv
        ├─ ....
        └─ wukong_100m_255.csv

We provide a multi-threaded python script for downloading the images through annotation files.

cd models/research/mm/wukong/src/dataset/
python wukong_download.py --csv_dir /path/to/data_root/wukong_release/ --img_dir IMG_DIR [--start_id 0] [--end_id -1] [--thread_num 4]

where IMG_DIR refer to the downloaded image dir, option start_id and end_id defines the start and end id for csv files to be downloaded, thread_num defines the number of threads used for parallel downloading. If not provided, default setting will download images in all csv files. Each csv file corresponds to a subdir under IMG_DIR and the final structure is like this:

.
└── IMG_DIR
    ├─000
    │   ├─ 00000.jpg
    │   ├─ 00001.jpg
    │   ├─ 00002.jpg
    │   └─ ......
    ├─001
    ├─002
    ├─...

In order to be used in Mindspore, we convert the raw data into MindRecord format. To do this, run code

cd models/research/mm/wukong/
python -m src.dataset.generate_dataset --csv_dir /path/to/data_root/wukong_release/ --img_dir IMG_DIR --data_record_dir DATA_RECORD_DIR [--shard_num 10] [--worker_num 4] [--block_size 2000]

Here DATA_RECORD_DIR refer to the path where mindrecord files will be generated into; shared_num refer to the number of files mindrecord is splited; worker_num refer to the number of workers to convert mindrecord and block size defines the block size of each write. After execution the mindrecord files should be like this

└─DATA_RECORD_DIR
        ├─ wukong100m.mindrecord0
        ├─ wukong100m.mindrecord0.db
        ├─ ....
        ├─ wukong100m.mindrecord9
        └─ wukong100m.mindrecord9.db

Then you can load the dataset in a standard format like get_wukong_dataset function in models/research/mm/wukong/src/dataset/dataset.py.