react_app.py

from dataclasses import dataclass
from flask import Flask, request, current_app, render_template
# from flask_cors import CORS
import subprocess
import os
import zipfile

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data_utils
import torchvision
import torchvision.datasets as datasets

from matplotlib import pyplot as plt
from numpy import save, load
from tqdm import trange
torch.manual_seed(0)

import os
import sys
sys.path.insert(0, os.path.abspath('..'))

# import agents and its functions
from MetaAugment import UCB1_JC_py as UCB1_JC
from MetaAugment import Evo_learner as Evo
print('@@@ import successful')

# import agents and its functions
# from ..MetaAugment import UCB1_JC_py as UCB1_JC
# from ..MetaAugment import Evo_learner as Evo
# print('@@@ import successful')

app = Flask(__name__)


# it is used to collect user input and store them in the app
@app.route('/home', methods=["GET", "POST"])
def get_form_data():
    print('@@@ in Flask Home')
    form_data = request.get_json() 
    # form_data = request.files
    # form_data = request.form.get('test')
    print('@@@ this is form data', form_data)

    # required input
    ds = form_data['select_dataset'] # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
    IsLeNet = form_data["select_network"]   # using LeNet or EasyNet or SimpleNet ->> default 
    auto_aug_learner = form_data["select_learner"] # augmentation methods to be excluded

    print('@@@ required user input:', 'ds', ds, 'IsLeNet:', IsLeNet, 'auto_aug_leanrer:',auto_aug_learner)
    # advanced input
    if 'batch_size' in form_data.keys(): 
        batch_size = form_data['batch_size']       # size of batch the inner NN is trained with
    else: 
        batch_size = 1 # this is for demonstration purposes
    if 'learning_rate' in form_data.keys(): 
        learning_rate =  form_data['learning_rate']  # fix learning rate
    else: 
        learning_rate = 10-1
    if 'toy_size' in form_data.keys(): 
        toy_size = form_data['toy_size']      # total propeortion of training and test set we use
    else: 
        toy_size = 1 # this is for demonstration purposes
    if 'iterations' in form_data.keys(): 
        iterations = form_data['iterations']      # total iterations, should be more than the number of policies
    else: 
        iterations = 10
    exclude_method = form_data['select_action']
    num_funcs = 14 - len(exclude_method)
    print('@@@ advanced search: batch_size:', batch_size, 'learning_rate:', learning_rate, 'toy_size:', toy_size, 'iterations:', iterations, 'exclude_method', exclude_method, 'num_funcs', num_funcs)
    

    # default values 
    max_epochs = 10      # max number of epochs that is run if early stopping is not hit
    early_stop_num = 10   # max number of worse validation scores before early stopping is triggered
    num_policies = 5      # fix number of policies
    num_sub_policies = 5  # fix number of sub-policies in a policy
    
    
    # if user upload datasets and networks, save them in the database
    if ds == 'Other':
        ds_folder = request.files['ds_upload'] 
        print('!!!ds_folder', ds_folder)
        ds_name_zip = ds_folder.filename
        ds_name = ds_name_zip.split('.')[0]
        ds_folder.save('./datasets/'+ ds_name_zip)
        with zipfile.ZipFile('./datasets/'+ ds_name_zip, 'r') as zip_ref:
            zip_ref.extractall('./datasets/upload_dataset/')
        if not current_app.debug:
            os.remove(f'./datasets/{ds_name_zip}')
    else: 
        ds_name = None

    # test if uploaded dataset meets the criteria 
    for (dirpath, dirnames, filenames) in os.walk(f'./datasets/upload_dataset/{ds_name}/'):
        for dirname in dirnames:
            if dirname[0:6] != 'class_':
                return None # neet to change render to a 'failed dataset webpage'

    # save the user uploaded network
    if IsLeNet == 'Other':
        childnetwork = request.files['network_upload']
        childnetwork.save('./child_networks/'+childnetwork.filename)
        network_name = childnetwork.filename

    
    # generate random policies at start
    current_app.config['AAL'] = auto_aug_learner
    current_app.config['NP'] = num_policies
    current_app.config['NSP'] = num_sub_policies
    current_app.config['BS'] = batch_size
    current_app.config['LR'] = learning_rate
    current_app.config['TS'] = toy_size
    current_app.config['ME'] = max_epochs
    current_app.config['ESN'] = early_stop_num
    current_app.config['IT'] = iterations
    current_app.config['ISLENET'] = IsLeNet
    current_app.config['DSN'] = ds_name
    current_app.config['ds'] = ds

    
    print("@@@ user input has all stored in the app")

    data = {'ds': ds, 'ds_name': ds_name, 'IsLeNet': IsLeNet, 'ds_folder.filename': ds_name,
            'auto_aug_learner':auto_aug_learner, 'batch_size': batch_size, 'learning_rate': learning_rate, 
            'toy_size':toy_size, 'iterations':iterations, }
    
    print('@@@ all data sent', data)
    return {'data': 'show training data'}



# ========================================================================
@app.route('/training', methods=['POST', 'GET'])
def training():
    auto_aug_learner = current_app.config.get('AAL')
    num_policies = current_app.config.get('NP')
    num_sub_policies = current_app.config.get('NSP')
    batch_size = current_app.config.get('BS')
    learning_rate = current_app.config.get('LR')
    toy_size = current_app.config.get('TS')
    max_epochs = current_app.config.get('ME')
    early_stop_num = current_app.config.get('ESN')
    iterations = current_app.config.get('IT')
    IsLeNet = current_app.config.get('ISLENET')
    ds_name = current_app.config.get('DSN')
    num_funcs = current_app.config.get('NUMFUN')
    ds = current_app.config.get('ds')
    exclude_method = current_app.config.get('exc_meth')


    if auto_aug_learner == 'UCB':
        policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
        q_values, best_q_values = UCB1_JC.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)     
        best_q_values = np.array(best_q_values)

    elif auto_aug_learner == 'Evolutionary Learner':
        network = Evo.Learner(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
        child_network = Evo.LeNet()
        learner = Evo.Evolutionary_learner(network=network, fun_num=num_funcs, p_bins=1, mag_bins=1, sub_num_pol=1, ds = ds, ds_name=ds_name, exclude_method=exclude_method, child_network=child_network)
        learner.run_instance()
    elif auto_aug_learner == 'Random Searcher':
        pass 
    elif auto_aug_learner == 'Genetic Learner':
        pass

    return {'status': 'training'}



# ========================================================================
@app.route('/results')
def show_result():
    return {'status': 'results'}

@app.route('/api')
def index():
    return {'status': 'api test'}


if __name__ == '__main__':
    app.run(debug=True)