diff --git a/backend/react_app.py b/backend/react_app.py
index 62ed36fb972ad695da2ab9dcc94915f5f3789417..cf06ce72a43493760f2e6b0d56d6c507ce9730d6 100644
--- a/backend/react_app.py
+++ b/backend/react_app.py
@@ -1,7 +1,6 @@
from dataclasses import dataclass
-from flask import Flask, request
+from flask import Flask, request, current_app
# from flask_cors import CORS
-
import subprocess
import os
import zipfile
@@ -19,83 +18,102 @@ from matplotlib import pyplot as plt
from numpy import save, load
from tqdm import trange
torch.manual_seed(0)
-# import agents and its functions
+# import agents and its functions
from ..library.MetaAugment import UCB1_JC_py as UCB1_JC
from ..library.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 home():
- print('in flask home')
- form_data = request.get_json()
- batch_size = 1 # size of batch the inner NN is trained with
- learning_rate = 1e-1 # fix learning rate
- ds = form_data['select_dataset'] # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
- toy_size = form_data['toy_size'] # total propeortion of training and test set we use
+ 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_leanrer = form_data["select_learner"] # augmentation methods to be excluded
+
+ print('@@@ required user input:', 'ds', ds, 'IsLeNet:', IsLeNet, 'auto_aug_leanrer:',auto_aug_leanrer)
+ # 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
+ print('@@@ advanced search: batch_size:', batch_size, 'learning_rate:', learning_rate, 'toy_size:', toy_size, 'iterations:', iterations)
+
+ # 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
- iterations = 5 # total iterations, should be more than the number of policies
- IsLeNet = form_data["network_selection"] # using LeNet or EasyNet or SimpleNet ->> default
+
# if user upload datasets and networks, save them in the database
if ds == 'Other':
- ds_folder = request.files['dataset_upload']
+ 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('./MetaAugment/datasets/'+ ds_name_zip)
- with zipfile.ZipFile('./MetaAugment/datasets/'+ ds_name_zip, 'r') as zip_ref:
- zip_ref.extractall('./MetaAugment/datasets/upload_dataset/')
- os.remove(f'./MetaAugment/datasets/{ds_name_zip}')
-
+ 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
- for (dirpath, dirnames, filenames) in os.walk(f'./MetaAugment/datasets/upload_dataset/{ds_name}/'):
+ # 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 render_template("fail_dataset.html")
- else:
- pass
-
+ 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('./MetaAugment/child_networks/'+childnetwork.filename)
+ childnetwork.save('./child_networks/'+childnetwork.filename)
# generate random policies at start
- auto_aug_leanrer = request.form.get("auto_aug_selection")
-
- if auto_aug_leanrer == '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)
- elif auto_aug_leanrer == 'Evolutionary Learner':
- learner = Evo.Evolutionary_learner(fun_num=num_funcs, p_bins=1, mag_bins=1, sub_num_pol=1, ds_name=ds_name, exclude_method=exclude_method)
- learner.run_instance()
- elif auto_aug_leanrer == 'Random Searcher':
- pass
- elif auto_aug_leanrer == 'Genetic Learner':
- pass
-
- plt.figure()
- plt.plot(q_values)
-
-
- best_q_values = np.array(best_q_values)
-
- # save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)
- # best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)
+
+ current_app.config['AAL'] = auto_aug_leanrer
+ 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("DONE")
+ print("@@@ user input has all stored in the app")
- return None
+ return {'try': 'Hello'}
@app.route('/api')
def index():
- return {'name': 'Hello'}
\ No newline at end of file
+ return {'name': 'Hello'}