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Commit e583f2e5 authored by Mia Wang's avatar Mia Wang
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MetaAugment/autoaugment_learners/evo_learner.py
+ 2
− 3
View file @ e583f2e5
...@@ -7,12 +7,11 @@ import pygad.torchga as torchga ...@@ -7,12 +7,11 @@ import pygad.torchga as torchga
import copy import copy
import torch import torch
from MetaAugment.controller_networks.evo_controller import evo_controller from MetaAugment.controller_networks.evo_controller import evo_controller
from MetaAugment.autoaugment_learners.aa_learner import aa_learner, augmentation_space
import MetaAugment.child_networks as cn import MetaAugment.child_networks as cn
from .aa_learner import aa_learner, augmentation_space
class evo_learner(): class evo_learner(aa_learner):
def __init__(self, def __init__(self,
sp_num=1, sp_num=1,
......
MetaAugment/autoaugment_learners/ucb_learner.py
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− 204
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...@@ -5,220 +5,150 @@ ...@@ -5,220 +5,150 @@
import numpy as np import numpy as np
from sklearn.covariance import log_likelihood
import torch import torch
torch.manual_seed(0)
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim import torch.optim as optim
import torch.utils.data as data_utils
import torchvision import torchvision
import torchvision.datasets as datasets
import pickle
from matplotlib import pyplot as plt
from numpy import save, load
from tqdm import trange from tqdm import trange
from ..child_networks import * from ..child_networks import *
from ..main import create_toy, train_child_network from ..main import train_child_network
from .randomsearch_learner import randomsearch_learner
from .aa_learner import augmentation_space
class ucb_learner(randomsearch_learner):
"""
Tests randomly sampled policies from the search space specified by the AutoAugment
paper. Acts as a baseline for other aa_learner's.
"""
def __init__(self,
# parameters that define the search space
sp_num=5,
fun_num=14,
p_bins=11,
m_bins=10,
discrete_p_m=True,
# hyperparameters for when training the child_network
batch_size=8,
toy_flag=False,
toy_size=0.1,
learning_rate=1e-1,
max_epochs=float('inf'),
early_stop_num=30,
# ucb_learner specific hyperparameter
num_policies=100
):
super().__init__(sp_num,
fun_num,
p_bins,
m_bins,
discrete_p_m=discrete_p_m,
batch_size=batch_size,
toy_flag=toy_flag,
toy_size=toy_size,
learning_rate=learning_rate,
max_epochs=max_epochs,
early_stop_num=early_stop_num,)
self.num_policies = num_policies
# When this learner is initialized we generate `num_policies` number
# of random policies.
# generate_new_policy is inherited from the randomsearch_learner class
self.policies = [self.generate_new_policy() for _ in self.num_policies]
# In[6]: # attributes used in the UCB1 algorithm
self.q_values = [0]*self.num_policies
self.cnts = [0]*self.num_policies
self.q_plus_cnt = [0]*self.num_policies
self.total_count = 0
def learn(self,
train_dataset,
test_dataset,
child_network_architecture,
iterations=15):
"""Randomly generate 10 policies""" #Initialize vector weights, counts and regret
"""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 best_q_values = []
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, early_stop_flag, average_validation, iterations, IsLeNet, ds_name=None):
# 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.CenterCrop(28), # <--- need to remove after finishing testing
torchvision.transforms.ToTensor()])
# open data and apply these transformations
if ds == "MNIST":
train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True, transform=transform)
elif ds == "KMNIST":
train_dataset = datasets.KMNIST(root='./datasets/kmnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.KMNIST(root='./datasets/kmnist/test', train=False, download=True, transform=transform)
elif ds == "FashionMNIST":
train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', train=False, download=True, transform=transform)
elif ds == "CIFAR10":
train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./datasets/cifar10/test', train=False, download=True, transform=transform)
elif ds == "CIFAR100":
train_dataset = datasets.CIFAR100(root='./datasets/cifar100/train', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR100(root='./datasets/cifar100/test', train=False, download=True, transform=transform)
elif ds == 'Other':
dataset = datasets.ImageFolder('./datasets/upload_dataset/'+ ds_name, transform=transform)
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])
# check output labels
if ds == 'Other':
num_labels = len(dataset.class_to_idx)
elif 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 torch.cuda.is_available():
device='cuda'
else:
device='cpu'
if IsLeNet == "LeNet":
model = LeNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
elif IsLeNet == "EasyNet":
model = EasyNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
elif IsLeNet == 'SimpleNet':
model = SimpleNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
else:
model = pickle.load(open(f'datasets/childnetwork', "rb"))
sgd = optim.SGD(model.parameters(), lr=learning_rate) for this_iter in trange(iterations):
cost = nn.CrossEntropyLoss()
best_acc = train_child_network(model, train_loader, test_loader, sgd, # get the action to try (either initially in order or using best q_plus_cnt value)
cost, max_epochs, early_stop_num, early_stop_flag, if this_iter >= self.num_policies:
average_validation, logging=False, print_every_epoch=False) this_policy = self.policies[np.argmax(self.q_plus_cnt)]
else:
this_policy = this_iter
# 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_acc = self.test_autoaugment_policy(
best_q_values.append(best_q_value) this_policy,
child_network_architecture,
train_dataset,
test_dataset,
logging=False
)
if (policy+1) % 5 == 0: # update q_values
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))))) if this_iter < self.num_policies:
self.q_values[this_policy] += best_acc
else:
self.q_values[this_policy] = (self.q_values[this_policy]*self.cnts[this_policy] + best_acc) / (self.cnts[this_policy] + 1)
# update counts best_q_value = max(self.q_values)
cnts[this_policy] += 1 best_q_values.append(best_q_value)
total_count += 1
# update q_plus_cnt values every turn after the initial sweep through if (this_iter+1) % 5 == 0:
if policy >= num_policies - 1: print("Iteration: {},\tQ-Values: {}, Best this_iter: {}".format(
for i in range(num_policies): this_iter+1,
q_plus_cnt[i] = q_values[i] + np.sqrt(2*np.log(total_count)/cnts[i]) list(np.around(np.array(self.q_values),2)),
max(list(np.around(np.array(self.q_values),2)))
)
)
# yield q_values, best_q_values # update counts
return q_values, best_q_values self.cnts[this_policy] += 1
self.total_count += 1
# update q_plus_cnt values every turn after the initial sweep through
if this_iter >= self.num_policies - 1:
for i in range(self.num_policies):
self.q_plus_cnt[i] = self.q_values[i] + np.sqrt(2*np.log(self.total_count)/self.cnts[i])
# yield q_values, best_q_values
return self.q_values, best_q_values
def run_UCB1(
policies,
batch_size,
learning_rate,
ds,
toy_size,
max_epochs,
early_stop_num,
early_stop_flag,
average_validation,
iterations,
IsLeNet
):
pass
def generate_policies(
num_policies,
self.sp_num
):
pass
# # In[9]:
if __name__=='__main__': if __name__=='__main__':
batch_size = 32 # size of batch the inner NN is trained with batch_size = 32 # size of batch the inner NN is trained with
...@@ -230,18 +160,6 @@ if __name__=='__main__': ...@@ -230,18 +160,6 @@ if __name__=='__main__':
early_stop_flag = True # implement early stopping or not early_stop_flag = True # implement early stopping or not
average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over
num_policies = 5 # fix number of policies num_policies = 5 # fix number of policies
num_sub_policies = 5 # fix number of sub-policies in a policy sp_num = 5 # fix number of sub-policies in a policy
iterations = 100 # total iterations, should be more than the number of policies iterations = 100 # total iterations, should be more than the number of policies
IsLeNet = "SimpleNet" # using LeNet or EasyNet or SimpleNet IsLeNet = "SimpleNet" # using LeNet or EasyNet or SimpleNet
\ No newline at end of file
# 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, early_stop_flag, average_validation, 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)
backend_react/react_app.py
+ 25
− 77
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from dataclasses import dataclass from dataclasses import dataclass
from flask import Flask, request, current_app, render_template from flask import Flask, request, current_app, render_template
# from flask_cors import CORS # from flask_cors import CORS
import subprocess
import os import os
import zipfile import zipfile
import numpy as np
import torch 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 numpy import save, load
from tqdm import trange
torch.manual_seed(0) torch.manual_seed(0)
import os import os
import sys import sys
sys.path.insert(0, os.path.abspath('..')) sys.path.insert(0, os.path.abspath('..'))
import wapp_util
# # 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')
# import agents and its functions # import agents and its functions
# from ..MetaAugment import UCB1_JC_py as UCB1_JC from MetaAugment import UCB1_JC_py as UCB1_JC
# from ..MetaAugment import Evo_learner as Evo from MetaAugment import Evo_learner as Evo
# print('@@@ import successful') print('@@@ import successful')
app = Flask(__name__) app = Flask(__name__)
...@@ -141,27 +126,27 @@ def confirm(): ...@@ -141,27 +126,27 @@ def confirm():
@app.route('/training', methods=['POST', 'GET']) @app.route('/training', methods=['POST', 'GET'])
def training(): def training():
# aa learner # # aa learner
auto_aug_learner = current_app.config.get('AAL') # auto_aug_learner = current_app.config.get('AAL')
# search space & problem setting # # search space & problem setting
ds = current_app.config.get('ds') # ds = current_app.config.get('ds')
ds_name = current_app.config.get('DSN') # ds_name = current_app.config.get('DSN')
exclude_method = current_app.config.get('exc_meth') # exclude_method = current_app.config.get('exc_meth')
num_funcs = current_app.config.get('NUMFUN') # num_funcs = current_app.config.get('NUMFUN')
num_policies = current_app.config.get('NP') # num_policies = current_app.config.get('NP')
num_sub_policies = current_app.config.get('NSP') # num_sub_policies = current_app.config.get('NSP')
toy_size = current_app.config.get('TS') # toy_size = current_app.config.get('TS')
# child network # # child network
IsLeNet = current_app.config.get('ISLENET') # IsLeNet = current_app.config.get('ISLENET')
# child network training hyperparameters # # child network training hyperparameters
batch_size = current_app.config.get('BS') # batch_size = current_app.config.get('BS')
early_stop_num = current_app.config.get('ESN') # early_stop_num = current_app.config.get('ESN')
iterations = current_app.config.get('IT') # iterations = current_app.config.get('IT')
learning_rate = current_app.config.get('LR') # learning_rate = current_app.config.get('LR')
max_epochs = current_app.config.get('ME') # max_epochs = current_app.config.get('ME')
# default values # default values
max_epochs = 10 # max number of epochs that is run if early stopping is not hit max_epochs = 10 # max number of epochs that is run if early stopping is not hit
...@@ -170,46 +155,9 @@ def training(): ...@@ -170,46 +155,9 @@ def training():
num_sub_policies = 5 # fix number of sub-policies in a policy num_sub_policies = 5 # fix number of sub-policies in a policy
data = current_app.config.get('data') data = current_app.config.get('data')
return {'status': 'training done!'}
if data.auto_aug_learner == 'UCB':
policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
q_values, best_q_values = UCB1_JC.run_UCB1(
policies,
data.batch_size,
data.learning_rate,
data.ds,
data.toy_size,
max_epochs,
early_stop_num,
data.iterations,
data.IsLeNet,
data.ds_name
)
best_q_values = np.array(best_q_values)
elif data.auto_aug_learner == 'Evolutionary Learner':
network = cn.evo_controller.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
child_network = aal.evo.LeNet()
learner = aal.evo.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 data.auto_aug_learner == 'Random Searcher':
pass
elif data.auto_aug_learner == 'Genetic Learner':
pass
return {'status': 'training done!'}
......
flask_mvp/app.py
+ 2
− 1
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...@@ -3,7 +3,8 @@ ...@@ -3,7 +3,8 @@
# app.run(host='0.0.0.0',port=port) # app.run(host='0.0.0.0',port=port)
from numpy import broadcast from numpy import broadcast
from auto_augmentation import home, progress,result, training from auto_augmentation import home, progress,result
from flask_mvp.auto_augmentation import training
from flask_socketio import SocketIO, send from flask_socketio import SocketIO, send
from flask import Flask, flash, request, redirect, url_for from flask import Flask, flash, request, redirect, url_for
......
flask_mvp/auto_augmentation/__init__.py
+ 2
− 1
View file @ e583f2e5
...@@ -2,7 +2,8 @@ import os ...@@ -2,7 +2,8 @@ import os
from flask import Flask, render_template, request, flash from flask import Flask, render_template, request, flash
from auto_augmentation import home, progress,result, training from auto_augmentation import home, progress,result
from flask_mvp.auto_augmentation import training
def create_app(test_config=None): def create_app(test_config=None):
# create and configure the app # create and configure the app
......
flask_mvp/auto_augmentation/progress.py
+ 15
− 115
View file @ e583f2e5
from flask import Blueprint, request, render_template, flash, send_file, current_app, g, session from flask import Blueprint, request, render_template, flash, send_file, current_app, g, session
import subprocess
import os import os
import zipfile import zipfile
import numpy as np
import torch 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) torch.manual_seed(0)
# import agents and its functions
from MetaAugment.autoaugment_learners import ucb_learner
# hi
from MetaAugment import Evo_learner as Evo
import MetaAugment.autoaugment_learners as aal
from MetaAugment.main import create_toy
import MetaAugment.child_networks as cn
import pickle
import wapp_util
bp = Blueprint("progress", __name__) bp = Blueprint("progress", __name__)
...@@ -92,100 +72,20 @@ def response(): ...@@ -92,100 +72,20 @@ def response():
if auto_aug_learner == 'UCB': learner = wapp_util.parse_users_learner_spec(auto_aug_learner,
policies = ucb_learner.generate_policies(num_policies, num_sub_policies) ds,
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) exclude_method,
elif auto_aug_learner == 'Evolutionary Learner': num_funcs,
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) num_policies,
learner.run_instance() num_sub_policies,
elif auto_aug_learner == 'Random Searcher': toy_size,
# As opposed to when ucb==True, `ds` and `IsLenet` are processed outside of the agent IsLeNet,
# This system makes more sense for the user who is not using the webapp and is instead batch_size,
# using the library within their code early_stop_num,
download = True iterations,
if ds == "MNIST": learning_rate,
train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train', train=True, download=download) max_epochs,
test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', train=False, ds_name)
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "KMNIST":
train_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/train', train=True, download=download)
test_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "FashionMNIST":
train_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=download)
test_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "CIFAR10":
train_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/cifar10/train', train=True, download=download)
test_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/cifar10/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "CIFAR100":
train_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/cifar100/train', train=True, download=download)
test_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/cifar100/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == 'Other':
dataset = datasets.ImageFolder('./MetaAugment/datasets/'+ ds_name)
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])
# check output labels
if ds == 'Other':
num_labels = len(dataset.class_to_idx)
elif 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 = cn.LeNet(img_height, img_width, num_labels, img_channels)
elif IsLeNet == "EasyNet":
model = cn.EasyNet(img_height, img_width, num_labels, img_channels)
elif IsLeNet == 'SimpleNet':
model = cn.SimpleNet(img_height, img_width, num_labels, img_channels)
else:
model = pickle.load(open(f'datasets/childnetwork', "rb"))
# use an aa_learner. in this case, a rs learner
agent = aal.randomsearch_learner(batch_size=batch_size,
toy_flag=True,
learning_rate=learning_rate,
toy_size=toy_size,
max_epochs=max_epochs,
early_stop_num=early_stop_num,
)
agent.learn(train_dataset,
test_dataset,
child_network_architecture=model,
iterations=iterations)
elif auto_aug_learner == 'Genetic Learner':
pass
plt.figure()
plt.plot(q_values)
# 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)
# # plt.figure()
# # plt.plot(q_values)
# 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
current_app.config['AAL'] = auto_aug_learner current_app.config['AAL'] = auto_aug_learner
......
flask_mvp/auto_augmentation/training.py
+ 17
− 53
View file @ e583f2e5
from flask import Blueprint, request, render_template, flash, send_file, current_app from flask import Blueprint, request, render_template, flash, send_file, current_app
import subprocess
import os import os
import zipfile
import numpy as np
import torch 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) 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
import wapp_util
bp = Blueprint("training", __name__) bp = Blueprint("training", __name__)
...@@ -56,41 +39,22 @@ def response(): ...@@ -56,41 +39,22 @@ def response():
max_epochs = current_app.config.get('ME') max_epochs = current_app.config.get('ME')
if auto_aug_learner == 'UCB': wapp_util.parse_users_learner_spec(
policies = aal.ucb_learner.generate_policies(num_policies, num_sub_policies) auto_aug_learner,
q_values, best_q_values = aal.ucb_learner.run_UCB1( ds,
policies, ds_name,
batch_size, exclude_method,
learning_rate, num_funcs,
ds, num_policies,
toy_size, num_sub_policies,
max_epochs, toy_size,
early_stop_num, IsLeNet,
iterations, batch_size,
IsLeNet, early_stop_num,
ds_name iterations,
) learning_rate,
best_q_values = np.array(best_q_values) max_epochs
)
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) return render_template("progress.html", auto_aug_learner=auto_aug_learner)
......
temp_util/parse_ds_cn_arch.py 0 → 100644
+ 59
− 0
View file @ e583f2e5
from ..child_networks import *
from ..main import create_toy, train_child_network
import torch
import torchvision.datasets as datasets
import pickle
def parse_ds_cn_arch(self, ds, ds_name, IsLeNet, transform):
# open data and apply these transformations
if ds == "MNIST":
train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True, transform=transform)
elif ds == "KMNIST":
train_dataset = datasets.KMNIST(root='./datasets/kmnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.KMNIST(root='./datasets/kmnist/test', train=False, download=True, transform=transform)
elif ds == "FashionMNIST":
train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train', train=True, download=True, transform=transform)
test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', train=False, download=True, transform=transform)
elif ds == "CIFAR10":
train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR10(root='./datasets/cifar10/test', train=False, download=True, transform=transform)
elif ds == "CIFAR100":
train_dataset = datasets.CIFAR100(root='./datasets/cifar100/train', train=True, download=True, transform=transform)
test_dataset = datasets.CIFAR100(root='./datasets/cifar100/test', train=False, download=True, transform=transform)
elif ds == 'Other':
dataset = datasets.ImageFolder('./datasets/upload_dataset/'+ ds_name, transform=transform)
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])
# check output labels
if ds == 'Other':
num_labels = len(dataset.class_to_idx)
elif 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 model
if torch.cuda.is_available():
device='cuda'
else:
device='cpu'
if IsLeNet == "LeNet":
model = LeNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
elif IsLeNet == "EasyNet":
model = EasyNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
elif IsLeNet == 'SimpleNet':
model = SimpleNet(img_height, img_width, num_labels, img_channels).to(device) # added .to(device)
else:
model = pickle.load(open(f'datasets/childnetwork', "rb"))
return train_dataset, test_dataset, model
\ No newline at end of file
temp_util/wapp_util.py 0 → 100644
+ 136
− 0
View file @ e583f2e5
"""
CONTAINS THE FUNTIONS THAT THE WEBAPP CAN USE TO INTERACT WITH
THE LIBRARY
"""
import numpy as np
import torch
import torchvision
import torchvision.datasets as datasets
# # import agents and its functions
import MetaAugment.autoaugment_learners as aal
import MetaAugment.controller_networks as cont_n
import MetaAugment.child_networks as cn
from MetaAugment.main import create_toy
import pickle
def parse_users_learner_spec(
auto_aug_learner,
ds,
ds_name,
exclude_method,
num_funcs,
num_policies,
num_sub_policies,
toy_size,
IsLeNet,
batch_size,
early_stop_num,
iterations,
learning_rate,
max_epochs
):
"""
The website receives user inputs on what they want the aa_learner
to be. We take those hyperparameters and return an aa_learner
"""
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':
# 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
download = True
if ds == "MNIST":
train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train', train=True, download=download)
test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "KMNIST":
train_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/train', train=True, download=download)
test_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "FashionMNIST":
train_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=download)
test_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "CIFAR10":
train_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/cifar10/train', train=True, download=download)
test_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/cifar10/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == "CIFAR100":
train_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/cifar100/train', train=True, download=download)
test_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/cifar100/test', train=False,
download=download, transform=torchvision.transforms.ToTensor())
elif ds == 'Other':
dataset = datasets.ImageFolder('./MetaAugment/datasets/'+ ds_name)
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])
# check output labels
if ds == 'Other':
num_labels = len(dataset.class_to_idx)
elif 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 = cn.LeNet(img_height, img_width, num_labels, img_channels)
elif IsLeNet == "EasyNet":
model = cn.EasyNet(img_height, img_width, num_labels, img_channels)
elif IsLeNet == 'SimpleNet':
model = cn.SimpleNet(img_height, img_width, num_labels, img_channels)
else:
model = pickle.load(open(f'datasets/childnetwork', "rb"))
# use an aa_learner. in this case, a rs learner
agent = aal.randomsearch_learner(batch_size=batch_size,
toy_flag=True,
learning_rate=learning_rate,
toy_size=toy_size,
max_epochs=max_epochs,
early_stop_num=early_stop_num,
)
agent.learn(train_dataset,
test_dataset,
child_network_architecture=model,
iterations=iterations)
elif auto_aug_learner == 'Genetic Learner':
pass
\ No newline at end of file
test/MetaAugment/test_ucb_learner.py 0 → 100644
+ 25
− 0
View file @ e583f2e5
import MetaAugment.autoaugment_learners as aal
import MetaAugment.child_networks as cn
import torch
import torchvision
import torchvision.datasets as datasets
import random
def test_ucb_learner():
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)
pass
\ No newline at end of file
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