diff --git a/MetaAugment/autoaugment_learners/__init__.py b/MetaAugment/autoaugment_learners/__init__.py
index 149af5b8165316ae1e00e6fc1c476338324b73cc..e844c5e0ae157061ceb54251b1fd3ecdbdea77c8 100644
--- a/MetaAugment/autoaugment_learners/__init__.py
+++ b/MetaAugment/autoaugment_learners/__init__.py
@@ -1,3 +1,4 @@
from .aa_learner import *
from .randomsearch_learner import *
from .gru_learner import *
+from .evo_learner import *
\ No newline at end of file
diff --git a/MetaAugment/UCB1_JC_py.py b/MetaAugment/autoaugment_learners/ucb_learner.py
similarity index 99%
rename from MetaAugment/UCB1_JC_py.py
rename to MetaAugment/autoaugment_learners/ucb_learner.py
index 959d55230a8ad0bfa7c4973c8b0857d93c67213f..41b8977156e9148965b0ffa6c00fe4d0a4a2595d 100644
--- a/MetaAugment/UCB1_JC_py.py
+++ b/MetaAugment/autoaugment_learners/ucb_learner.py
@@ -20,8 +20,8 @@ from matplotlib import pyplot as plt
from numpy import save, load
from tqdm import trange
-from .child_networks import *
-from .main import create_toy, train_child_network
+from ..child_networks import *
+from ..main import create_toy, train_child_network
# In[6]:
diff --git a/MetaAugment/controller_networks/__init__.py b/MetaAugment/controller_networks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6182b736f03515c352860937ce318568cb3d65f8
--- /dev/null
+++ b/MetaAugment/controller_networks/__init__.py
@@ -0,0 +1 @@
+from .evo_controller import evo_controller
\ No newline at end of file
diff --git a/auto_augmentation/training.py b/auto_augmentation/training.py
deleted file mode 100644
index 39001e9eb3bdbada40daef5cc443007e342206ac..0000000000000000000000000000000000000000
--- a/auto_augmentation/training.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from flask import Blueprint, request, render_template, flash, send_file, current_app
-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 agents and its functions
-
-from MetaAugment import UCB1_JC_py as UCB1_JC
-from MetaAugment import Evo_learner as Evo
-
-
-
-bp = Blueprint("training", __name__)
-
-
-@bp.route("/start_training", methods=["GET", "POST"])
-def response():
-
- # hyperparameters to change
-
- # auto_aug_learner = session
-
- 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 render_template("progress.html", auto_aug_learner=auto_aug_learner)
-
-
-
-
diff --git a/backend_react/react_app.py b/backend_react/react_app.py
index d61e884f224ca63d7cb7625917658adefa3f2808..21f5e8a2a9ae99d4b058931f2e912aa116d7ee0b 100644
--- a/backend_react/react_app.py
+++ b/backend_react/react_app.py
@@ -23,9 +23,9 @@ 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.autoaugment_learners import evo_learner
+# # import agents and its functions
+from MetaAugment.autoaugment_learners import ucb_learner as UCB1_JC
+from MetaAugment.autoaugment_learners import evo_learner
import MetaAugment.controller_networks as cn
import MetaAugment.autoaugment_learners as aal
print('@@@ import successful')
@@ -43,8 +43,8 @@ app = Flask(__name__)
def get_form_data():
print('@@@ in Flask Home')
# form_data = request.get_json()
- form_data = request.files['ds_upload']
- print('@@@ form_data', form_data)
+ # form_data = request.files['ds_upload']
+ # print('@@@ form_data', form_data)
# form_data = request.form.get('test')
# print('@@@ this is form data', request.get_data())
@@ -138,43 +138,59 @@ def get_form_data():
@app.route('/confirm', methods=['POST', 'GET'])
def confirm():
print('inside confirm')
+
+ # aa learner
auto_aug_learner = current_app.config.get('AAL')
+
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
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')
+ num_funcs = current_app.config.get('NUMFUN')
toy_size = current_app.config.get('TS')
- max_epochs = current_app.config.get('ME')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
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')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
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, }
- return {'data 1': 'show data'}
+ return {'batch_size': '12'}
# ========================================================================
@app.route('/training', methods=['POST', 'GET'])
def training():
+
+ # aa learner
auto_aug_learner = current_app.config.get('AAL')
+
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
+ num_funcs = current_app.config.get('NUMFUN')
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')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
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')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
if auto_aug_learner == 'UCB':
diff --git a/flask_mvp/auto_augmentation/progress.py b/flask_mvp/auto_augmentation/progress.py
index abe15fe35fb226fe94c30d513e5634b22a72c30a..4c3e96b28ca42e47eada9913c5008bafb90f5ddb 100644
--- a/flask_mvp/auto_augmentation/progress.py
+++ b/flask_mvp/auto_augmentation/progress.py
@@ -18,8 +18,8 @@ from tqdm import trange
torch.manual_seed(0)
# import agents and its functions
-from MetaAugment import UCB1_JC_py as UCB1_JC
-
+from MetaAugment.autoaugment_learners import ucb_learner
+# hi
from MetaAugment import Evo_learner as Evo
import MetaAugment.autoaugment_learners as aal
@@ -38,21 +38,28 @@ def response():
if request.method == 'POST':
+ # generate random policies at start
+ auto_aug_learner = request.form.get("auto_aug_selection")
+
+ # search space & problem setting
+ ds = request.form.get("dataset_selection") # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
+ ds_up = request.files['dataset_upload']
exclude_method = request.form.getlist("action_space")
num_funcs = 14 - len(exclude_method)
+ num_policies = 5 # fix number of policies
+ num_sub_policies = 5 # fix number of sub-policies in a policy
+ toy_size = 1 # total propeortion of training and test set we use
- batch_size = 1 # size of batch the inner NN is trained with
- learning_rate = 1e-1 # fix learning rate
- ds = request.form.get("dataset_selection") # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
- ds_up = request.files['dataset_upload']
+ # child network
+ IsLeNet = request.form.get("network_selection") # using LeNet or EasyNet or SimpleNet ->> default
nw_up = childnetwork = request.files['network_upload']
- toy_size = 1 # total propeortion of training and test set we use
- max_epochs = 10 # max number of epochs that is run if early stopping is not hit
+
+ # child network training hyperparameters
+ batch_size = 1 # size of batch the inner NN is trained with
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 = request.form.get("network_selection") # using LeNet or EasyNet or SimpleNet ->> default
+ learning_rate = 1e-1 # fix learning rate
+ max_epochs = 10 # max number of epochs that is run if early stopping is not hit
# if user upload datasets and networks, save them in the database
@@ -83,16 +90,15 @@ def response():
childnetwork = request.files['network_upload']
childnetwork.save('./MetaAugment/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':
+
+ if auto_aug_learner == 'UCB':
+ policies = ucb_learner.generate_policies(num_policies, num_sub_policies)
+ q_values, best_q_values = ucb_learner.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
+ elif auto_aug_learner == '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':
+ elif auto_aug_learner == 'Random Searcher':
# As opposed to when ucb==True, `ds` and `IsLenet` are processed outside of the agent
# This system makes more sense for the user who is not using the webapp and is instead
# using the library within their code
@@ -157,7 +163,7 @@ def response():
test_dataset,
child_network_architecture=model,
iterations=iterations)
- elif auto_aug_leanrer == 'Genetic Learner':
+ elif auto_aug_learner == 'Genetic Learner':
pass
plt.figure()
@@ -165,8 +171,8 @@ def response():
# 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)
+ # policies = ucb_learner.generate_policies(num_policies, num_sub_policies)
+ # q_values, best_q_values = ucb_learner.run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)
# # plt.figure()
# # plt.plot(q_values)
# best_q_values = np.array(best_q_values)
diff --git a/auto_augmentation/templates/intro.html b/flask_mvp/auto_augmentation/templates/intro.html
similarity index 100%
rename from auto_augmentation/templates/intro.html
rename to flask_mvp/auto_augmentation/templates/intro.html
diff --git a/auto_augmentation/templates/training.html b/flask_mvp/auto_augmentation/templates/training.html
similarity index 100%
rename from auto_augmentation/templates/training.html
rename to flask_mvp/auto_augmentation/templates/training.html
diff --git a/flask_mvp/auto_augmentation/training.py b/flask_mvp/auto_augmentation/training.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e695b58a2994efb1bdc89bb363b3eddf643d9dc
--- /dev/null
+++ b/flask_mvp/auto_augmentation/training.py
@@ -0,0 +1,99 @@
+from flask import Blueprint, request, render_template, flash, send_file, current_app
+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 agents and its functions
+
+import MetaAugment.autoaugment_learners as aal
+import MetaAugment.controller_networks as cont_n
+import MetaAugment.child_networks as cn
+
+
+
+bp = Blueprint("training", __name__)
+
+
+@bp.route("/start_training", methods=["GET", "POST"])
+def response():
+
+
+
+ # aa learner
+ auto_aug_learner = current_app.config.get('AAL')
+ # auto_aug_learner = session
+
+ # search space & problem setting
+ ds = current_app.config.get('ds')
+ ds_name = current_app.config.get('DSN')
+ exclude_method = current_app.config.get('exc_meth')
+ num_funcs = current_app.config.get('NUMFUN')
+ num_policies = current_app.config.get('NP')
+ num_sub_policies = current_app.config.get('NSP')
+ toy_size = current_app.config.get('TS')
+
+ # child network
+ IsLeNet = current_app.config.get('ISLENET')
+
+ # child network training hyperparameters
+ batch_size = current_app.config.get('BS')
+ early_stop_num = current_app.config.get('ESN')
+ iterations = current_app.config.get('IT')
+ learning_rate = current_app.config.get('LR')
+ max_epochs = current_app.config.get('ME')
+
+
+ if auto_aug_learner == 'UCB':
+ policies = aal.ucb_learner.generate_policies(num_policies, num_sub_policies)
+ q_values, best_q_values = aal.ucb_learner.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 = cont_n.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
+ child_network = cn.LeNet()
+ learner = aal.evo_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 render_template("progress.html", auto_aug_learner=auto_aug_learner)
+
+
+
+
diff --git a/src/pages/Confirm.js b/src/pages/Confirm.js
index 779b6b2ef162233ae597ed58335a5da9c4334f05..e293133a61438cde948b463ad8a4d3e581873b27 100644
--- a/src/pages/Confirm.js
+++ b/src/pages/Confirm.js
@@ -4,14 +4,19 @@ import CheckCircleOutlineRoundedIcon from '@mui/icons-material/CheckCircleOutlin
import TuneRoundedIcon from '@mui/icons-material/TuneRounded';
export default function Confirm() {
+ const [batchSize, setBatchSize] = useState(0)
// // const [myData, setMyData] = useState([{}])
-// useEffect(async () => {
-// await fetch('/confirm').then(
-// response => {console.log('response', response, 'response.json()', response.json()); response.json()}
-// )
-// // .then(data => {console.log('training', data);
-// // })
-// }, []);
+ useEffect(() => {
+ const res = fetch('/confirm').then(
+ response => response.json()
+ ).then(data => setBatchSize(data.batch_size));
+
+ console.log("batchsize", batchSize)
+ // setBatchSize(res.batch_size)
+
+ // .then(data => {console.log('training', data);
+ // })
+ }, []);
@@ -34,7 +39,7 @@ export default function Confirm() {
<ListItemAvatar>
<TuneRoundedIcon color="primary" fontSize='large'/>
</ListItemAvatar>
- <ListItemText primary="Batch size" secondary="[Batch size]" />
+ <ListItemText primary="Batch size" secondary={batchSize} />
</ListItem>
</Grid>
<Grid xs={12} sm={6} item >
diff --git a/src/pages/Home.js b/src/pages/Home.js
index 63183d191cb0a4cd8c098f9ab2c289d9d58ede88..a3057f585732b495bbd13972e39d25157eccbe67 100644
--- a/src/pages/Home.js
+++ b/src/pages/Home.js
@@ -58,6 +58,7 @@ export default function Home() {
body: formData
}).then((response) => response.json());
console.log('check if it is here')
+
navigate('/confirm', {replace:true});
//
///////// testing