My solution to the 华为云人工智能创新应用大赛 (Top4%).zip

 The code for "华为云人工智能创新应用大赛". The main tasks of the competition are: The task of this competition is to classify the pictures of popular scenic spots, delicacies, specialties, folklore and handicrafts in Xi'an. Many strategies were tried during the competition, with a final score of 0.979 and a ranking of 32 / 732. Although I didn't get a high ranking in the end, I have a deeper understanding of the classification problem, thanks to Huawei Cloud for organizing the competition. This repositorie is suitable for solving all kinds of classification problems, supports a variety of training skills, and has high expansibility. ## Requirements * Pytorch 1.3.0 * Torchvision 0.4.0 * Python3.7 * pretrainedmodels * efficientnet-pytorch * Chinese font-simhei.ttf, [Download Link](https://fontzone.net/download/simhei). Put simhei in `project_root/font`. ## How to run ### Clone Our Project ```bash git clone https://github.com/XiangqianMa/AI-Competition-HuaWei.git cd AI-Competition-HuaWei ``` ### Prepare Dataset Download dataset, unzip and put them into `your_data_path` directory. Structure of the `your_data_path` folder can be like: ```bash train_data label_id_name.json ``` Create soft links of datasets in the following directories: ```bash cd AI-Competition-HuaWei mkdir data cd data ln -s your_data_path huawei_data ``` It is important to note that the dataset should be organized in such a way that the following conditions are met: - The pictures and annotation files are stored in the same folder, as shown in the following figure:  - The callout file is in txt format, and its contents are as follows: ``` img_1.jpg, 0 ``` If it is your own dataset, please process it to be consistent with the above format, otherwise it may cause an error in the running of the program. ### Train ```bash cd AI-Competition-HuaWei python train_classifier.py ``` ## HuaWei Cloud Online Test We use `online-service` folder to accomplish online test on HuaWei ModelArts platform. The struct of this folder is like the following: You should put your dependencies into `online-service/model` like this:  If you want to import customized package in your code, import sentence should be like this: ```python from model import xxx ``` Also, you must specified your dependencies in `config.json`: ```python "dependencies": [ { "installer": "pip", "packages": [ { "package_name": "Pillow", "package_version": "5.0.0", "restraint": "EXACT" }, { "package_name": "torchvision", "package_version": "0.2.1", "restraint": "EXACT" }, { "package_name": "tqdm" } ] } ] ``` ## Demo First, manually create the following folder: ```bash mkdir data/demo_data ``` After the training is complete, execute the following code to get a visual classification result, and the test will be carried out on the validation set: ```bash python demo.py ``` After the test is completed, **the sample of the prediction error** will be stored in the `data/demo_data/results` folder. Examples of demos are as follows:  ## How to use obsutil ### Prepare obsutil Download obsutil from [here](https://support.huaweicloud.com/utiltg-obs/obs_11_0003.html). Unzip it to `./online-service`. After unzipping, `./online-service` folder will contain `obsutil` and `setup.sh` files ```bash cd online-service ``` Replace the following `-i=xxxxxxx` to `-i=Access Key Id?AK?` and replace the following `-k=xxxxxxxxxxxxxxxxx` to `-k=Secret Access Key?SK?` ```bash ./obsutil config -i=xxxxxxx -k=xxxxxxxxxxxxxxxxx -e=obs.cn-north-4.myhuaweicloud.com ``` Then, you will see feedback similar to the one below ```bash Config file url: /home/zdkit/.obsutilconfig Update config file successfully! ``` Then, execute the following code to check connectivity ``` ./obsutil ls -s ``` If the returned result contains "Bucket number is:", the configuration is correct. Just like these: ```bash Start at 2019-11-22 15:43:11.060016056 +0000 UTC obs://ai-competition-zdaiot obs://test-zdaiot Bucket number is: 2 ``` ### Use obsutil Please Manually change the selected model in `online-service/model/customize_service.py` file. And manually change the `model` parameters in `online-service/upload.sh`. The script will automatically find the newly modified folder from the `checkpoints/${model}` directory and copy the `model_best.pth` from it to the `online-service/model` directory. > If you want to place the `model_best.pth` file manually, please comment out the `cp ../checkpoints/${model}/${filename}/model_best.pth online-service/model` line in the `online-service/upload.sh` file. Then manually place the weight file `model_best.pth` to the `online-service/model` directory. Notice that, when you copy pyfiles from `models` to `online-service/model/deploy_models`, you should pay attention to these - change import path of Python package. For example, change `from models.custom_model import CustomModel` to `from model.deploy_models.custom_model import CustomModel` in `online-service/model/deploy_models/custom_model.py`. - modify the parameters of `prepare_model.create_model` function in the `online-service/model/customize_service.py` and `online-service/offline_service.py` according to the actual needs. When you first use it, you should run this: ```bash cd online-service ./upload.sh 0 ``` Then, this script will create a bucket called `ai-competition-$USER` in your OBS, create a new folder called `model_snapshots`, and upload the `./model` folder to the model_snapshots folder. > $USER is your Linux username If you want to update the weight file or python files. Please manually put new python files in the corresponding directory. And run this: ```bash ./upload.sh ``` ## How to train online Suppose you have built a new bucket called `ai-competition-zdaiot` ### upload dataset Create a new folder named `data` in `ai-competition-zdaiot` bucket . And upload dataset to `data`. Don't fotget upload `label_id_name.json`. For example:  ### upload project Create a new folder named `project` in`ai-competition-zdaiot` bucket . And upload Our Project to `project`. Don't fotget upload `font`. For example:  ### upload model_zoo Create a new folder named `model_zoo` in `ai-competition-zdaiot` bucket . And upload pretrained model to `model_zoo`. For example, I uploaded a pre-training model called `se_resnext101_32x4d-3b2fe3d8.pth`.  ### prepare logs folder Create a new folder named `logs` in `ai-competition-zdaiot` bucket . ### Run Open `ModelArts->训练管理->训练作业->创建`，Fill in the following parameters  If necessary, add custom parameters in the `训练作业`, and then click OK. During the training process, you can view the running logs through the `logs` folder under the `ai-competition-zdaiot` bucket root directory. After training, the inference scripts and the optimal weight will be saved in the `model_snapshots` folder under the `ai-competition-zdaiot` bucket root directory. ## Tricks Tried ### Dataset - train_data - combine - huge - adversial_samples（Not work，When the samples and their adversial samples are in training set and verification set respectively, the accuracy of the verification set is too high.） ### Data clean Delete pictures with the following characteristics - ambiguity - wrong - image resolution is too low - There are no such data characteristics, for example, there are only `石子` in `石子饼` and only `浆水` in