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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.autoaugment_learners import ucb_learner as UCB1_JC
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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)