diff --git a/MetaAugment/UCB1_JC.py b/MetaAugment/UCB1_JC.py
deleted file mode 100644
index 1986368aff7f5d42e966f61e0cf17424d0f2fb7e..0000000000000000000000000000000000000000
--- a/MetaAugment/UCB1_JC.py
+++ /dev/null
@@ -1,361 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[1]:
-
-
-import numpy as np
-import torch
-torch.manual_seed(0)
-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
-
-
-# In[2]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class LeNet(nn.Module):
- def __init__(self, img_height, img_width, num_labels, img_channels):
- super().__init__()
- self.conv1 = nn.Conv2d(img_channels, 6, 5)
- self.relu1 = nn.ReLU()
- self.pool1 = nn.MaxPool2d(2)
- self.conv2 = nn.Conv2d(6, 16, 5)
- self.relu2 = nn.ReLU()
- self.pool2 = nn.MaxPool2d(2)
- self.fc1 = nn.Linear(int((((img_height-4)/2-4)/2)*(((img_width-4)/2-4)/2)*16), 120)
- self.relu3 = nn.ReLU()
- self.fc2 = nn.Linear(120, 84)
- self.relu4 = nn.ReLU()
- self.fc3 = nn.Linear(84, num_labels)
- self.relu5 = nn.ReLU()
-
- def forward(self, x):
- y = self.conv1(x)
- y = self.relu1(y)
- y = self.pool1(y)
- y = self.conv2(y)
- y = self.relu2(y)
- y = self.pool2(y)
- y = y.view(y.shape[0], -1)
- y = self.fc1(y)
- y = self.relu3(y)
- y = self.fc2(y)
- y = self.relu4(y)
- y = self.fc3(y)
- y = self.relu5(y)
- return y
-
-
-# In[3]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class EasyNet(nn.Module):
- def __init__(self, img_height, img_width, num_labels, img_channels):
- super().__init__()
- self.fc1 = nn.Linear(img_height*img_width*img_channels, 2048)
- self.relu1 = nn.ReLU()
- self.fc2 = nn.Linear(2048, num_labels)
- self.relu2 = nn.ReLU()
-
- def forward(self, x):
- y = x.view(x.shape[0], -1)
- y = self.fc1(y)
- y = self.relu1(y)
- y = self.fc2(y)
- y = self.relu2(y)
- return y
-
-
-# In[4]:
-
-
-"""Define internal NN module that trains on the dataset"""
-class SimpleNet(nn.Module):
- def __init__(self, img_height, img_width, num_labels, img_channels):
- super().__init__()
- self.fc1 = nn.Linear(img_height*img_width*img_channels, num_labels)
- self.relu1 = nn.ReLU()
-
- def forward(self, x):
- y = x.view(x.shape[0], -1)
- y = self.fc1(y)
- y = self.relu1(y)
- return y
-
-
-# In[5]:
-
-
-"""Make toy dataset"""
-
-def create_toy(train_dataset, test_dataset, batch_size, n_samples):
-
- # shuffle and take first n_samples %age of training dataset
- shuffle_order_train = np.random.RandomState(seed=100).permutation(len(train_dataset))
- shuffled_train_dataset = torch.utils.data.Subset(train_dataset, shuffle_order_train)
- indices_train = torch.arange(int(n_samples*len(train_dataset)))
- reduced_train_dataset = data_utils.Subset(shuffled_train_dataset, indices_train)
-
- # shuffle and take first n_samples %age of test dataset
- shuffle_order_test = np.random.RandomState(seed=1000).permutation(len(test_dataset))
- shuffled_test_dataset = torch.utils.data.Subset(test_dataset, shuffle_order_test)
- indices_test = torch.arange(int(n_samples*len(test_dataset)))
- reduced_test_dataset = data_utils.Subset(shuffled_test_dataset, indices_test)
-
- # push into DataLoader
- train_loader = torch.utils.data.DataLoader(reduced_train_dataset, batch_size=batch_size)
- test_loader = torch.utils.data.DataLoader(reduced_test_dataset, batch_size=batch_size)
-
- return train_loader, test_loader
-
-
-# In[6]:
-
-
-"""Randomly generate 10 policies"""
-"""Each policy has 5 sub-policies"""
-"""For each sub-policy, pick 2 transformations, 2 probabilities and 2 magnitudes"""
-
-def generate_policies(num_policies, num_sub_policies):
-
- policies = np.zeros([num_policies,num_sub_policies,6])
-
- # Policies array will be 10x5x6
- for policy in range(num_policies):
- for sub_policy in range(num_sub_policies):
- # pick two sub_policy transformations (0=rotate, 1=shear, 2=scale)
- policies[policy, sub_policy, 0] = np.random.randint(0,3)
- policies[policy, sub_policy, 1] = np.random.randint(0,3)
- while policies[policy, sub_policy, 0] == policies[policy, sub_policy, 1]:
- policies[policy, sub_policy, 1] = np.random.randint(0,3)
-
- # pick probabilities
- policies[policy, sub_policy, 2] = np.random.randint(0,11) / 10
- policies[policy, sub_policy, 3] = np.random.randint(0,11) / 10
-
- # pick magnitudes
- for transformation in range(2):
- if policies[policy, sub_policy, transformation] <= 1:
- policies[policy, sub_policy, transformation + 4] = np.random.randint(-4,5)*5
- elif policies[policy, sub_policy, transformation] == 2:
- policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10
-
- return policies
-
-
-# In[7]:
-
-
-"""Pick policy and sub-policy"""
-"""Each row of data should have a different sub-policy but for now, this will do"""
-
-def sample_sub_policy(policies, policy, num_sub_policies):
- sub_policy = np.random.randint(0,num_sub_policies)
-
- degrees = 0
- shear = 0
- scale = 1
-
- # check for rotations
- if policies[policy, sub_policy][0] == 0:
- if np.random.uniform() < policies[policy, sub_policy][2]:
- degrees = policies[policy, sub_policy][4]
- elif policies[policy, sub_policy][1] == 0:
- if np.random.uniform() < policies[policy, sub_policy][3]:
- degrees = policies[policy, sub_policy][5]
-
- # check for shears
- if policies[policy, sub_policy][0] == 1:
- if np.random.uniform() < policies[policy, sub_policy][2]:
- shear = policies[policy, sub_policy][4]
- elif policies[policy, sub_policy][1] == 1:
- if np.random.uniform() < policies[policy, sub_policy][3]:
- shear = policies[policy, sub_policy][5]
-
- # check for scales
- if policies[policy, sub_policy][0] == 2:
- if np.random.uniform() < policies[policy, sub_policy][2]:
- scale = policies[policy, sub_policy][4]
- elif policies[policy, sub_policy][1] == 2:
- if np.random.uniform() < policies[policy, sub_policy][3]:
- scale = policies[policy, sub_policy][5]
-
- return degrees, shear, scale
-
-
-# In[8]:
-
-
-"""Sample policy, open and apply above transformations"""
-def run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet):
-
- # get number of policies and sub-policies
- num_policies = len(policies)
- num_sub_policies = len(policies[0])
-
- #Initialize vector weights, counts and regret
- q_values = [0]*num_policies
- cnts = [0]*num_policies
- q_plus_cnt = [0]*num_policies
- total_count = 0
-
- best_q_values = []
-
- for policy in trange(iterations):
-
- # get the action to try (either initially in order or using best q_plus_cnt value)
- if policy >= num_policies:
- this_policy = np.argmax(q_plus_cnt)
- else:
- this_policy = policy
-
- # get info of transformation for this sub-policy
- degrees, shear, scale = sample_sub_policy(policies, this_policy, num_sub_policies)
-
- # create transformations using above info
- transform = torchvision.transforms.Compose(
- [torchvision.transforms.RandomAffine(degrees=(degrees,degrees), shear=(shear,shear), scale=(scale,scale)),
- torchvision.transforms.ToTensor()])
-
- # open data and apply these transformations
- if ds == "MNIST":
- train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train', train=True, download=True, transform=transform)
- test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', train=False, download=True, transform=transform)
- elif ds == "KMNIST":
- train_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/train', train=True, download=True, transform=transform)
- test_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/test', train=False, download=True, transform=transform)
- elif ds == "FashionMNIST":
- train_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
- test_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
- elif ds == "CIFAR10":
- train_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
- test_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
- elif ds == "CIFAR100":
- train_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
- test_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
-
- # 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])
-
- # check output labels
- if ds == "CIFAR10" or ds == "CIFAR100":
- num_labels = (max(train_dataset.targets) - min(train_dataset.targets) + 1)
- else:
- num_labels = (max(train_dataset.targets) - min(train_dataset.targets) + 1).item()
-
- # create toy dataset from above uploaded data
- train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, toy_size)
-
- # create model
- if IsLeNet == "LeNet":
- model = LeNet(img_height, img_width, num_labels, img_channels)
- elif IsLeNet == "EasyNet":
- model = EasyNet(img_height, img_width, num_labels, img_channels)
- else:
- model = SimpleNet(img_height, img_width, num_labels, img_channels)
- sgd = optim.SGD(model.parameters(), lr=1e-1)
- cost = nn.CrossEntropyLoss()
-
- # set variables for best validation accuracy and early stop count
- best_acc = 0
- early_stop_cnt = 0
-
- # train model and check validation accuracy each epoch
- for _epoch in range(max_epochs):
-
- # train model
- model.train()
- for idx, (train_x, train_label) in enumerate(train_loader):
- label_np = np.zeros((train_label.shape[0], num_labels))
- sgd.zero_grad()
- predict_y = model(train_x.float())
- loss = cost(predict_y, train_label.long())
- loss.backward()
- sgd.step()
-
- # check validation accuracy on validation set
- correct = 0
- _sum = 0
- model.eval()
- for idx, (test_x, test_label) in enumerate(test_loader):
- predict_y = model(test_x.float()).detach()
- predict_ys = np.argmax(predict_y, axis=-1)
- label_np = test_label.numpy()
- _ = predict_ys == test_label
- correct += np.sum(_.numpy(), axis=-1)
- _sum += _.shape[0]
-
- # update best validation accuracy if it was higher, otherwise increase early stop count
- acc = correct / _sum
- if acc > best_acc :
- best_acc = acc
- early_stop_cnt = 0
- else:
- early_stop_cnt += 1
-
- # exit if validation gets worse over 10 runs
- if early_stop_cnt >= early_stop_num:
- break
-
- # update q_values
- if policy < num_policies:
- q_values[this_policy] += best_acc
- else:
- q_values[this_policy] = (q_values[this_policy]*cnts[this_policy] + best_acc) / (cnts[this_policy] + 1)
-
- best_q_value = max(q_values)
- best_q_values.append(best_q_value)
-
- if (policy+1) % 10 == 0:
- print("Iteration: {},\tQ-Values: {}, Best Policy: {}".format(policy+1, list(np.around(np.array(q_values),2)), max(list(np.around(np.array(q_values),2)))))
-
- # update counts
- cnts[this_policy] += 1
- total_count += 1
-
- # update q_plus_cnt values every turn after the initial sweep through
- if policy >= num_policies - 1:
- for i in range(num_policies):
- q_plus_cnt[i] = q_values[i] + np.sqrt(2*np.log(total_count)/cnts[i])
-
- return q_values, best_q_values
-
-
-# # In[9]:
-
-
-# batch_size = 32 # size of batch the inner NN is trained with
-# learning_rate = 1e-1 # fix learning rate
-# ds = "MNIST" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
-# toy_size = 0.02 # total propeortion of training and test set we use
-# max_epochs = 100 # 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 = 100 # total iterations, should be more than the number of policies
-# IsLeNet = "SimpleNet" # using LeNet or EasyNet or SimpleNet
-
-# # generate random policies at start
-# policies = generate_policies(num_policies, num_sub_policies)
-
-# q_values, best_q_values = run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet)
-
-# plt.plot(best_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)
-
diff --git a/app.py b/app.py
index 3de6b5b0c5285e24d774b12606894c2a4e6f0f88..7ce0ae47eee0afa3e6d978ae7e59557407d6af1a 100644
--- a/app.py
+++ b/app.py
@@ -17,32 +17,7 @@ app = create_app()
port = int(os.environ.get("PORT", 5000))
-UPLOAD_FOLDER = '/datasets'
-
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-ALLOWED_EXTENSIONS = {'pdf', 'py'}
-
-def allowed_file(filename):
- return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-@app.route('/user_input', methods = ['GET', 'POST'])
-def upload_file():
- print("HELLoasdjsadojsadojsaodjsaoij")
- if request.method == 'POST':
- if 'file' not in request.files:
- flash('No file part')
- return redirect(request.url)
- file = request.files['file']
- if file.filename == '':
- flash('No selected file')
- return redirect(request.url)
- if file and allowed_file(file.filename):
- filename = secure_filename(file.filename)
- file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
- return redirect(url_for('uploaded_file', filename=filename))
- return '''
-
- '''
+
if __name__ == '__main__':
diff --git a/auto_augmentation/progress.py b/auto_augmentation/progress.py
index 411e8c5551a7580641008f1943c3d67d248f7629..c6fa0081a0b0457a41849affe51e984a0c45e1a7 100644
--- a/auto_augmentation/progress.py
+++ b/auto_augmentation/progress.py
@@ -1,6 +1,7 @@
from flask import Blueprint, request, render_template, flash, send_file
import subprocess
import os
+import zipfile
import numpy as np
import torch
@@ -17,39 +18,92 @@ from tqdm import trange
torch.manual_seed(0)
# import agents and its functions
-from MetaAugment import UCB1_JC as UCB1_JC
+from MetaAugment import UCB1_JC_py as UCB1_JC
bp = Blueprint("progress", __name__)
+
@bp.route("/user_input", methods=["GET", "POST"])
def response():
# hyperparameters to change
- batch_size = 32 # size of batch the inner NN is trained with
- learning_rate = 1e-1 # fix learning rate
- ds = request.args["dataset_selection"] # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)
- toy_size = 0.02 # total propeortion of training and test set we use
- max_epochs = 100 # 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 = 100 # total iterations, should be more than the number of policies
- IsLeNet = request.args["network_selection"] # using LeNet or EasyNet or SimpleNet ->> default
+ # print("thing: ", request.files['dataset_upload'] )
+ if request.method == 'POST':
+ 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)
+ 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
+ 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 = 10 # total iterations, should be more than the number of policies
+ IsLeNet = request.form.get("network_selection") # using LeNet or EasyNet or SimpleNet ->> default
+
+ print(f'@@@@@ dataset is: {ds}, network is :{IsLeNet}')
+
+ # if user upload datasets and networks, save them in the database
+ if ds == 'Other':
+ ds_folder = request.files['dataset_upload']
+ print('@@@ ds_folder', ds_folder)
+ ds_name_zip = ds_folder.filename
+ 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/')
+ ds_name = ds_name_zip.split('.')[0]
+
+ else:
+ ds_name = None
+
+
+ if IsLeNet == 'Other':
+ childnetwork = request.files['network_upload']
+ childnetwork.save('./MetaAugment/child_networks/'+childnetwork.filename)
+
+
+ # generate random policies at start
+ 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)
+
+ # plt.plot(best_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)
+ print("DONE")
+
+
+ return render_template("progress.html")
+
+
- print(f'@@@@@ dataset is: {ds}, network is :{IsLeNet}')
- # generate random policies at start
- 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)
+########### TESTING STUFF
- plt.plot(best_q_values)
+# UPLOAD_FOLDER = '/datasets'
- 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)
+# app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+# def allowed_file(filename):
+# return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
- return render_template("progress.html")
\ No newline at end of file
+# @app.route('/user_input', methods = ['GET', 'POST'])
+# def upload_file():
+# if request.method == 'POST':
+# if 'file' not in request.files:
+# flash('No file part')
+# return redirect(request.url)
+# file = request.files['file']
+# if file.filename == '':
+# flash('No selected file')
+# return redirect(request.url)
+# if file and allowed_file(file.filename):
+# filename = secure_filename(file.filename)
+# file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+# return redirect(url_for('uploaded_file', filename=filename))
+# return '''
+
+# '''
\ No newline at end of file
diff --git a/auto_augmentation/templates/home.html b/auto_augmentation/templates/home.html
index 99a9ecb50b272d08098c28b1c91499c21f7faa20..010cdfd89c7e2681926467af0f2444cd922c9992 100644
--- a/auto_augmentation/templates/home.html
+++ b/auto_augmentation/templates/home.html
@@ -4,11 +4,15 @@
<h1>Meta Reinforcement Learning for Data Augmentation</h1>
<h3>Choose your dataset</h3>
-<form action="/user_input">
+<form action="/user_input" method="POST" enctype="multipart/form-data">
<!-- upload dataset -->
<label for="dataset_upload">You can upload your dataset folder here:</label>
- <!-- <input type="file" name="dataset_upload" class="upload"><br><br> -->
- <input type="file" webkitdirectory mozdirectory /><br><br>
+ <input type="file" id='dataset_upload' name="dataset_upload" class="upload"><br><br>
+
+ <!-- <label for="dataset_upload">Upload your train dataset folder here:</label> -->
+ <!-- <input type="file" id='dataset_upload' name="dataset_upload" webkitdirectory mozdirectory /><br> -->
+ <!-- <label for="dataset_upload">Upload your test dataset folder here:</label>
+ <input type="file" id='test_upload' name="test_upload" webkitdirectory mozdirectory /><br><br> -->
<!-- dataset radio button -->
Or you can select a dataset from our database: <br>
@@ -30,15 +34,19 @@
<input type="radio" id="dataset5"
name="dataset_selection" value="CIFAR100">
- <label for="dataset5">CIFAR100 dataset</label><br><br>
+ <label for="dataset5">CIFAR100 dataset</label><br>
+
+ <input type="radio" id="dataset6"
+ name="dataset_selection" value="Other">
+ <label for="dataset6">Other dataset DIFFERENT</label><br><br>
<!-- --------------------------------------------------------------- -->
<h3>Choose the network the dataset is trained on</h3>
<!-- upload network -->
- <label for="network_upload">Please upload your network here:</label>
- <input type="file" name="network_upload" class="upload"><br><br>
+ <label for="network_upload">Please upload your network here as a .pkl file:</label>
+ <input type="file" id='network_upload' name="network_upload" class="upload"><br><br>
<!-- network selection -->
@@ -53,7 +61,11 @@
<input type="radio" id="network3"
name="network_selection" value="SimpleNet">
- <label for="network3">SimpleNet</label><br><br>
+ <label for="network3">SimpleNet</label><br>
+
+ <input type="radio" id="network4"
+ name="network_selection" value="Other">
+ <label for="network4">Other</label><br><br>