connected with learners

autoaug/autoaugment_learners/AaLearner.py

@@ -462,3 +462,22 @@ class AaLearner:
             megapol += pol[0]
 
         return megapol
+    
+
+    def get_n_best_policies(self, number_policies=5):
+        """
+        returns the n best policies
+
+        
+        Args: 
+            number_policies (int): Number of (sub)policies to return
+
+        Returns:
+            list of best n policies
+        """
+
+        number_policies = max(number_policies, len(self.history))
+
+        inter_pol = sorted(self.history, key=lambda x: x[1], reverse = True)[:number_policies]
+
+        return inter_pol[n]

autoaug/autoaugment_learners/UcbLearner.py

@@ -153,8 +153,7 @@ class UcbLearner(RsLearner):
             train_dataset, 
             test_dataset, 
             child_network_architecture, 
-            iterations=15,
-            print_every_epoch=False):
+            iterations=15,):
         """continue the UCB algorithm for ``iterations`` number of turns
 
         """
@@ -173,7 +172,6 @@ class UcbLearner(RsLearner):
                                 train_dataset,
                                 test_dataset,
                                 logging=False,
-                                print_every_epoch=print_every_epoch
                                 )
                 # update q_values (average accuracy)
                 self.avg_accs[this_policy_idx] = acc
@@ -188,7 +186,6 @@ class UcbLearner(RsLearner):
                                 train_dataset,
                                 test_dataset,
                                 logging=False,
-                                print_every_epoch=print_every_epoch
                                 )
                 # update q_values (average accuracy)
                 self.avg_accs[this_policy_idx] = (self.avg_accs[this_policy_idx]*self.cnts[this_policy_idx] + acc) / (self.cnts[this_policy_idx] + 1)
@@ -246,6 +243,30 @@ class UcbLearner(RsLearner):
 
         return megapol
 
+
+    def get_n_best_policies(self, number_policies=5):
+        """
+        returns the n best policies
+
+        
+        Args: 
+            number_policies (int): Number of (sub)policies to return
+
+        Returns:
+            list of best n policies
+        """
+
+        temp_avg_accs = [x if x is not None  else 0 for x in self.avg_accs]
+
+        temp_history = list(zip(self.policies, temp_avg_accs))
+
+        number_policies = max(number_policies, len(temp_history))
+
+        inter_pol = sorted(temp_history, key=lambda x: x[1], reverse = True)[:number_policies]
+
+        return inter_pol[n]
+
+
        
 
 

flask_mvp/auto_augmentation/progress.py

@@ -6,7 +6,7 @@ import torch
 torch.manual_seed(0)
 
 
-import temp_util.wapp_util as wapp_util
+import react_backend.wapp_util as wapp_util
 
 bp = Blueprint("progress", __name__)
 

flask_mvp/auto_augmentation/training.py

@@ -5,7 +5,7 @@ import torch
 torch.manual_seed(0)
 
 
-import temp_util.wapp_util as wapp_util
+import react_backend.wapp_util as wapp_util
 
 bp = Blueprint("training", __name__)
 

react_app.py

 from dataclasses import dataclass
 from flask import Flask, request, current_app, send_file, send_from_directory, redirect, url_for, session
 from flask_cors import CORS, cross_origin
-
 import os
 import zipfile
-
 import torch
-from numpy import save, load
-# import temp_util.wapp_util as wapp_util
-import time
-
+from numpy import int0, save, load
+from react_backend.wapp_util import parse_users_learner_spec
+import pprint
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
 import os
 import sys
 sys.path.insert(0, os.path.abspath('..'))
@@ -39,22 +39,22 @@ def get_form_data():
 
         print('@@@ required user input:', 'ds', ds, 'IsLeNet:', IsLeNet, 'auto_aug_leanrer:',auto_aug_learner)
         # advanced input
-        if form_data['batch_size'] != 'undefined': 
-            batch_size = form_data['batch_size']       # size of batch the inner NN is trained with
+        if form_data['batch_size'] not in ['undefined', ""]: 
+            batch_size = int(form_data['batch_size']    )   # size of batch the inner NN is trained with
         else: 
-            batch_size = 1 # this is for demonstration purposes
-        if form_data['learning_rate'] != 'undefined': 
-            learning_rate =  form_data['learning_rate']  # fix learning rate
+            batch_size = 16 # this is for demonstration purposes
+        if form_data['learning_rate'] not in ['undefined', ""]: 
+            learning_rate =  float(form_data['learning_rate'])  # fix learning rate
         else: 
-            learning_rate = 10-1
-        if form_data['toy_size'] != 'undefined': 
-            toy_size = form_data['toy_size']      # total propeortion of training and test set we use
+            learning_rate = 1e-2
+        if form_data['toy_size'] not in ['undefined', ""]: 
+            toy_size = float(form_data['toy_size'])      # total propeortion of training and test set we use
         else: 
-            toy_size = 1 # this is for demonstration purposes
-        if form_data['iterations'] != 'undefined': 
-            iterations = form_data['iterations']      # total iterations, should be more than the number of policies
+            toy_size = 0.01 # this is for demonstration purposes
+        if form_data['iterations'] not in ['undefined', ""]: 
+            iterations = int(form_data['iterations'])      # total iterations, should be more than the number of policies
         else: 
-            iterations = 10
+            iterations = 2
         exclude_method = form_data['select_action']
         print('@@@ advanced search: batch_size:', batch_size, 'learning_rate:', learning_rate, 'toy_size:', toy_size, 'iterations:', iterations, 'exclude_method', exclude_method)
         
@@ -156,15 +156,54 @@ def training():
     num_sub_policies = 5  # fix number of sub-policies in a policy
     data = current_app.config.get('data')
 
-    # fake training
-    print('pretend it is training')
-    time.sleep(1)
-    print('epoch: 1')
-    time.sleep(1)
-    print('epoch: 2')
-    time.sleep(1) 
-    print('epoch: 3')
-    print('it has finished training')
+    # parse the settings given by the user to obtain tools we need
+    train_dataset, test_dataset, child_archi, agent = parse_users_learner_spec(
+                                            max_epochs=max_epochs,
+                                            early_stop_num=early_stop_num,
+                                            num_policies=num_policies,
+                                            num_sub_policies=num_sub_policies,
+                                            **data
+                                        )
+
+    # train the autoaugment learner for number of `iterations`
+    agent.learn(
+        train_dataset=train_dataset, 
+        test_dataset=test_dataset, 
+        child_network_architecture=child_archi,
+        iterations=data['iterations']
+        )
+    
+    print('the history of all the policies the agent has tested:')
+    pprint.pprint(agent.history)
+
+    # get acc graph and best acc graph
+    acc_list = [acc for (policy,acc) in agent.history]
+    best_acc_list = []
+    best_til_now = 0
+    for acc in acc_list:
+        if acc>best_til_now:
+            best_til_now=acc
+        best_acc_list.append(best_til_now)
+    
+    # plot both here
+    fig = plt.figure()
+    ax = fig.add_subplot(1, 1, 1)
+    ax.plot(acc_list)
+    ax.plot(best_acc_list)
+    ax.set_xlabel('Number of Iterations')
+    ax.set_ylabel('Accuracy')
+    ax.set_title('Auto-augmentation Learner Performance Curve')
+    with open("./react_frontend/src/pages/output.png", 'wb') as f:
+        fig.savefig(f)
+
+    print("best policies:")
+    best_policy = agent.get_mega_policy(number_policies=4)
+    print(best_policy)
+    with open("./react_backend/policy.txt", 'w') as f:
+        # save the best_policy in pretty_print string format
+        f.write(pprint.pformat(best_policy, indent=4))
+
+    print('')
 
     return {'status': 'Training is done!'}
 

temp_util/parse_ds_cn_arch.py → react_backend/parse_ds_cn_arch.py

@@ -36,12 +36,15 @@ def parse_ds_cn_arch(ds, ds_name, IsLeNet):
         len_train = int(0.8*len(dataset))
         train_dataset, test_dataset = torch.utils.data.random_split(dataset, [len_train, len(dataset)-len_train])
 
-        # check sizes of images
-    img_height = len(train_dataset[0][0][0])
-    img_width = len(train_dataset[0][0][0][0])
-    img_channels = len(train_dataset[0][0])
-
+    my_image = train_dataset[0][0]
+    my_transform = torchvision.transforms.ToTensor()
+    my_image = my_transform(my_image)
+    # check sizes of images
+    img_height = len(my_image[0])
+    img_width = len(my_image[0][0])
+    img_channels = len(my_image)
 
+ 
         # check output labels
     if ds == 'Other':
         num_labels = len(dataset.class_to_idx)

react_backend/policy.txt

-0.7018545454545454,0.6530636363636364,0.6565090909090909,0.7029727272727273,0.6615000000000001,0.6610181818181818,0.6333545454545454,0.6617909090909091,0.6584636363636364,0.6933909090909091
\ No newline at end of file
+[   (('Brightness', 0.4, 4), ('Color', 0.0, 8)),
+    (('Posterize', 0.1, 0), ('Rotate', 0.3, 3)),
+    (('Equalize', 0.8, None), ('TranslateX', 0.7, 4)),
+    (('Equalize', 0.1, None), ('Posterize', 0.4, 1)),
+    (('AutoContrast', 0.3, None), ('Solarize', 0.2, 3)),
+    (('Sharpness', 1.0, 9), ('ShearY', 0.9, 9)),
+    (('Posterize', 0.6, 0), ('Color', 1.0, 0)),
+    (('TranslateX', 0.9, 0), ('Solarize', 0.1, 2)),
+    (('Rotate', 1.0, 9), ('Equalize', 0.3, None)),
+    (('ShearX', 0.3, 5), ('TranslateX', 0.0, 7))]
\ No newline at end of file

temp_util/wapp_util.py → react_backend/wapp_util.py

@@ -26,7 +26,6 @@ def parse_users_learner_spec(
             auto_aug_learner, 
             # search space settings
             exclude_method, 
-            num_funcs, 
             num_policies, 
             num_sub_policies, 
             # child network settings
@@ -35,7 +34,9 @@ def parse_users_learner_spec(
             early_stop_num, 
             iterations, 
             learning_rate, 
-            max_epochs
+            max_epochs,
+            # dummy variable which does nothing
+            network_name,
             ):
     train_dataset, test_dataset, child_archi = parse_ds_cn_arch(
                                                     ds, 
@@ -111,8 +112,4 @@ def parse_users_learner_spec(
                         early_stop_num=early_stop_num,
                         )
 
-
-    agent.learn(train_dataset,
-                test_dataset,
-                child_network_architecture=child_archi,
-                iterations=iterations)
\ No newline at end of file
+    return train_dataset, test_dataset, child_archi, agent
\ No newline at end of file

react_frontend/src/pages/Confirm.js

@@ -8,17 +8,21 @@ export default function Confirm() {
     const [myData, setMyData] = useState([])
     const [dataset, setDataset] = useState()
     const [network, setNetwork] = useState()
+    const [yes, setYes] = useState()
 
-    // console.log('already in confirm react')
+    console.log('already in confirm react')
   useEffect(() => {
     const res = fetch('/home').then(
       response => response.json()
       ).then(data => {setMyData(data);
         if (data.ds == 'Other'){setDataset(data.ds_name)} else {setDataset(data.ds)};
         if (data.IsLeNet == 'Other'){setNetwork(data.network_name)} else {setNetwork(data.IsLeNet)};
+        setYes('hey');
+        console.log('setYes', yes);
     });
   }, []);
 
+
   let navigate = useNavigate();
   const onSubmit = async () => {
     navigate('/progress', {replace:true});
@@ -27,7 +31,7 @@ export default function Confirm() {
     return (
         <div className="App" style={{padding:"60px"}}>
             <Typography gutterBottom variant="h3" align="center" >
-            Data Auto-Augmentation
+            Data Auto-Augmentation {yes}
             </Typography>
             <Grid>
                 <Card style={{ maxWidth: 900, padding: "10px 5px", margin: "0 auto" }}>

react_frontend/src/pages/Home.js

@@ -27,7 +27,6 @@ const ExpandMore = styled((props) => {
 
 export default function Home() {
     const [selectAction, setSelectAction] = useState([]);
-    const [validation, setValidation] = useState([]);
 
     // for form submission  
     const {register, control, handleSubmit, setValue, watch, formState: { errors, dirtyFields}} = useForm();
@@ -66,31 +65,8 @@ export default function Home() {
             response => response.json()
             ).then(data => {
                 if ('error' in data){navigate('/error', data)} else {navigate('/confirm', {replace:true})}
-            });
-        
-        // 
-        ///////// testing
-        // .then((response)=> {
-        //     responseClone = response.clone(); // 2
-        //     return response.json();
-        // })
-        // .then(function (data) {
-        //     console.log('data from flask', data)
-        // }, function (rejectionReason) { // 3
-        //     console.log('Error parsing JSON from response:', rejectionReason, responseClone); // 4
-        //     responseClone.text() // 5
-        //     .then(function (bodyText) {
-        //         console.log('Received the following instead of valid JSON:', bodyText); // 6
-        //     });
-        // });
-        
+            });     
     };
-
-    
-    // body: JSON.stringify(data)
-    // console.log('errors', errors); 
-    // console.log('handleSubmit', handleSubmit)
-
     
     // handling action selection
     const handleActionSelect = (value) => {
@@ -328,10 +304,10 @@ export default function Home() {
                                     <TextField type="number" inputProps={{step: "0.000000001",min: 0}} {...register("learning_rate")} name="learning_rate" placeholder="Learning Rate" label="Learning Rate" variant="outlined" fullWidth />
                                 </Grid>
                                 <Grid xs={12} sm={6} item>
-                                    <TextField type="number" InputProps={{step:"1", inputProps: { min: 0, max: 1} }} {...register("iterations")} name="iterations" placeholder="Number of Iterations" label="Iterations" variant="outlined" fullWidth />
+                                    <TextField type="number" InputProps={{step:"1", inputProps: { min: 0} }} {...register("iterations")} name="iterations" placeholder="Number of Iterations" label="Iterations" variant="outlined" fullWidth />
                                 </Grid>
                                 <Grid xs={12} sm={6} item>
-                                    <TextField type="number" inputProps={{step: "0.01", min: 0, max: 1}} {...register("toy_size")} name="toy_size" placeholder="Dataset Proportion" label="Dataset Proportion" variant="outlined" fullWidth />
+                                    <TextField type="number" inputProps={{step: "0.0001", min: 0, max: 1}} {...register("toy_size")} name="toy_size" placeholder="Dataset Proportion" label="Dataset Proportion" variant="outlined" fullWidth />
                                 </Grid>
                                 <FormLabel variant="h8" align='centre'>
                                     * Dataset Proportion defines the percentage of original dataset our auto-augment learner will use to find the 

react_frontend/src/pages/Result.js

 import React, { useState, useEffect } from "react";
 import { Grid, List, ListItem, Avatar, ListItemAvatar, ListItemText, Card, CardContent, Typography, Button, CardMedia } from '@mui/material';
-import output from './pytest.png'
+import output from './output.png'
 import {useNavigate, Route} from "react-router-dom";
 import axios from 'axios'
 import fileDownload from 'js-file-download'
@@ -28,10 +28,10 @@ export default function Result() {
                         Here are the results from our auto-augment agent:
                     </Typography>
                     <Grid style={{padding:"30px"}} container spacing={4} alignItems="center">
-                        <Grid xs={7} item> 
+                        <Grid xs={6} item> 
                             <img src={output} alt='output' />
                         </Grid>
-                        <Grid xs={5} item> 
+                        <Grid xs={6} item> 
                             <Typography>
                                 write something here to explain the meaning of the graph to the user
                             </Typography>
@@ -41,6 +41,7 @@ export default function Result() {
                     <Typography gutterBottom variant='h6' align='center'>
                         You can download the augentation policy here
                     </Typography>
+
                     <Button
                             type="submit"
                             variant="contained"
@@ -50,9 +51,13 @@ export default function Result() {
                         >
                             Download
                     </Button>
-                    <Typography>
-                        Please follow our documentation to apply this policy to your dataset.
-                    </Typography>
+
+                    <Grid style={{padding:'10px'}}>
+                        <Typography>
+                            Please follow our documentation to apply this policy to your dataset.
+                        </Typography>
+                    </Grid>
+                    
                 </CardContent>
             </Card>