From 2e64210af6b1d72a08683f4e37235db0b8e881e6 Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Mon, 25 Apr 2022 12:49:23 +0100
Subject: [PATCH] assimilated ucb_learner into aa_learners
---
.../autoaugment_learners/parse_ds_cn_arch.py | 59 ++++
.../autoaugment_learners/ucb_learner.py | 326 +++++++-----------
2 files changed, 181 insertions(+), 204 deletions(-)
create mode 100644 MetaAugment/autoaugment_learners/parse_ds_cn_arch.py
diff --git a/MetaAugment/autoaugment_learners/parse_ds_cn_arch.py b/MetaAugment/autoaugment_learners/parse_ds_cn_arch.py
new file mode 100644
index 00000000..082711ac
--- /dev/null
+++ b/MetaAugment/autoaugment_learners/parse_ds_cn_arch.py
@@ -0,0 +1,59 @@
+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
diff --git a/MetaAugment/autoaugment_learners/ucb_learner.py b/MetaAugment/autoaugment_learners/ucb_learner.py
index 41b89771..8862e14b 100644
--- a/MetaAugment/autoaugment_learners/ucb_learner.py
+++ b/MetaAugment/autoaugment_learners/ucb_learner.py
@@ -5,220 +5,150 @@
import numpy as np
-from sklearn.covariance import log_likelihood
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
-import pickle
-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 ..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"""
-"""Each policy has 5 sub-policies"""
-"""For each sub-policy, pick 2 transformations, 2 probabilities and 2 magnitudes"""
+ #Initialize vector weights, counts and regret
-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, 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"))
+ best_q_values = []
- sgd = optim.SGD(model.parameters(), lr=learning_rate)
- cost = nn.CrossEntropyLoss()
+ for this_iter in trange(iterations):
- best_acc = train_child_network(model, train_loader, test_loader, sgd,
- cost, max_epochs, early_stop_num, early_stop_flag,
- average_validation, logging=False, print_every_epoch=False)
+ # get the action to try (either initially in order or using best q_plus_cnt value)
+ if this_iter >= self.num_policies:
+ 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_q_values.append(best_q_value)
+ best_acc = self.test_autoaugment_policy(
+ this_policy,
+ child_network_architecture,
+ train_dataset,
+ test_dataset,
+ logging=False
+ )
- if (policy+1) % 5 == 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 q_values
+ 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
- cnts[this_policy] += 1
- total_count += 1
+ best_q_value = max(self.q_values)
+ best_q_values.append(best_q_value)
- # 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])
+ if (this_iter+1) % 5 == 0:
+ print("Iteration: {},\tQ-Values: {}, Best this_iter: {}".format(
+ this_iter+1,
+ 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
- return q_values, best_q_values
+ # update counts
+ 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__':
batch_size = 32 # size of batch the inner NN is trained with
@@ -230,18 +160,6 @@ if __name__=='__main__':
early_stop_flag = True # implement early stopping or not
average_validation = [15,25] # if not implementing early stopping, what epochs are we averaging over
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
- 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, 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)
-
+ IsLeNet = "SimpleNet" # using LeNet or EasyNet or SimpleNet
\ No newline at end of file
--
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