diff --git a/MetaAugment/CP2_Max.py b/MetaAugment/CP2_Max.py
index 792e81e1f85932408755840fbcbc09612137d39e..a13fd207b58df0e70bdd5543a9c98bd55831a2b6 100644
--- a/MetaAugment/CP2_Max.py
+++ b/MetaAugment/CP2_Max.py
@@ -94,75 +94,56 @@ class Learner(nn.Module):
return y
+# class LeNet(nn.Module):
+# def __init__(self):
+# super().__init__()
+# self.conv1 = nn.Conv2d(1, 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(256, 120)
+# self.relu3 = nn.ReLU()
+# self.fc2 = nn.Linear(120, 84)
+# self.relu4 = nn.ReLU()
+# self.fc3 = nn.Linear(84, 10)
+# 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)
+# return y
+
+
class LeNet(nn.Module):
def __init__(self):
super().__init__()
- self.conv1 = nn.Conv2d(1, 6, 5)
+ self.fc1 = nn.Linear(784, 2048)
self.relu1 = nn.ReLU()
- self.pool1 = nn.MaxPool2d(2)
- self.conv2 = nn.Conv2d(6, 16, 5)
+ self.fc2 = nn.Linear(2048, 10)
self.relu2 = nn.ReLU()
- self.pool2 = nn.MaxPool2d(2)
- self.fc1 = nn.Linear(256, 120)
- self.relu3 = nn.ReLU()
- self.fc2 = nn.Linear(120, 84)
- self.relu4 = nn.ReLU()
- self.fc3 = nn.Linear(84, 10)
- self.relu5 = nn.ReLU()
def forward(self, x):
- y = self.conv1(x)
+ x = x.reshape((-1, 784))
+ y = self.fc1(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.relu2(y)
return y
-# code from https://github.com/ChawDoe/LeNet5-MNIST-PyTorch/blob/master/train.py
-# def train_model(full_policy, child_network):
-# """
-# Takes in the specific transformation index and probability
-# """
-
-# # transformation = generate_policy(5, ps, mags)
-
-# train_transform = transforms.Compose([
-# full_policy,
-# transforms.ToTensor()
-# ])
-
-# batch_size = 32
-# n_samples = 0.005
-
-# train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=False, transform=train_transform)
-# test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=False, transform=torchvision.transforms.ToTensor())
-
-# train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, 0.01)
-
-
-# sgd = optim.SGD(child_network.parameters(), lr=1e-1)
-# cost = nn.CrossEntropyLoss()
-# epoch = 20
-
-
-# best_acc = train_child_network(child_network, train_loader, test_loader,
-# sgd, cost, max_epochs=100, print_every_epoch=False)
-
-# return best_acc
-
-
-
-
-
# ORGANISING DATA
# transforms = ['RandomResizedCrop', 'RandomHorizontalFlip', 'RandomVerticalCrop', 'RandomRotation']
@@ -183,6 +164,7 @@ train_loader = torch.utils.data.DataLoader(reduced_train_dataset, batch_size=600
+
class Evolutionary_learner():
def __init__(self, network, num_solutions = 10, num_generations = 5, num_parents_mating = 5, train_loader = None, child_network = None, p_bins = 11, mag_bins = 10, sub_num_pol=5, fun_num = 14, augmentation_space = None, train_dataset = None, test_dataset = None):
@@ -204,28 +186,23 @@ class Evolutionary_learner():
assert num_solutions > num_parents_mating, 'Number of solutions must be larger than the number of parents mating!'
self.set_up_instance()
-
-
- def generate_policy(self, sp_num, ps, mags):
- """
-
- """
- policies = []
- for subpol in range(sp_num):
- sub = []
- for idx in range(2):
- transformation = augmentation_space[(2*subpol) + idx]
- p = ps[(2*subpol) + idx]
- mag = mags[(2*subpol) + idx]
- sub.append((transformation, p, mag))
- policies.append(tuple(sub))
-
- return policies
def get_full_policy(self, x):
"""
- Generates the full policy (5 x 2 subpolicies)
+ Generates the full policy (self.num_sub_pol subpolicies). Network architecture requires
+ output size 5 * 2 * (self.fun_num + self.p_bins + self.mag_bins)
+
+ Parameters
+ -----------
+ x -> PyTorch tensor
+ Input data for network
+
+ Returns
+ ----------
+ full_policy -> [((String, float, float), (String, float, float)), ...)
+ Full policy consisting of tuples of subpolicies. Each subpolicy consisting of
+ two transformations, with a probability and magnitude float for each
"""
section = self.auto_aug_agent.fun_num + self.auto_aug_agent.p_bins + self.auto_aug_agent.m_bins
y = self.auto_aug_agent.forward(x)
@@ -244,11 +221,26 @@ class Evolutionary_learner():
full_policy.append(tuple(int_pol))
return full_policy
-#
+
def get_policy_cov(self, x, alpha = 0.5):
"""
- Need p_bins = 1, num_sub_pol = 1, mag_bins = 1
+ Selects policy using population and covariance matrices. For this method
+ we require p_bins = 1, num_sub_pol = 1, mag_bins = 1.
+
+ Parameters
+ ------------
+ x -> PyTorch Tensor
+ Input data for the AutoAugment network
+
+ alpha -> Float
+ Proportion for covariance and population matrices
+
+ Returns
+ -----------
+ Subpolicy -> [(String, float, float), (String, float, float)]
+ Subpolicy consisting of two tuples of policies, each with a string associated
+ to a transformation, a float for a probability, and a float for a magnittude
"""
section = self.auto_aug_agent.fun_num + self.auto_aug_agent.p_bins + self.auto_aug_agent.m_bins
@@ -284,7 +276,7 @@ class Evolutionary_learner():
mag1 = None
if not self.augmentation_space[max_idx[1]][1]:
mag2 = None
-
+
for idx in range(y.shape[0]):
if (torch.argmax(y_1[idx]) == max_idx[0]) and (torch.argmax(y_2[idx]) == max_idx[1]):
prob1 += torch.sigmoid(y[idx, self.auto_aug_agent.fun_num]).item()
@@ -313,6 +305,23 @@ class Evolutionary_learner():
def run_instance(self, return_weights = False):
"""
Runs the GA instance and returns the model weights as a dictionary
+
+ Parameters
+ ------------
+ return_weights -> Bool
+ Determines if the weight of the GA network should be returned
+
+ Returns
+ ------------
+ If return_weights:
+ Network weights -> Dictionary
+
+ Else:
+ Solution -> Best GA instance solution
+
+ Solution fitness -> Float
+
+ Solution_idx -> Int
"""
self.ga_instance.run()
solution, solution_fitness, solution_idx = self.ga_instance.best_solution()
@@ -331,12 +340,25 @@ class Evolutionary_learner():
def set_up_instance(self):
+ """
+ Initialises GA instance, as well as fitness and on_generation functions
+
+ """
def fitness_func(solution, sol_idx):
"""
- Defines fitness function (accuracy of the model)
+ Defines the fitness function for the parent selection
+
+ Parameters
+ --------------
+ solution -> GA solution instance (parsed automatically)
+
+ sol_idx -> GA solution index (parsed automatically)
+
+ Returns
+ --------------
+ fit_val -> float
"""
- print("FITNESS HERE")
model_weights_dict = torchga.model_weights_as_dict(model=self.auto_aug_agent,
weights_vector=solution)
@@ -345,18 +367,23 @@ class Evolutionary_learner():
for idx, (test_x, label_x) in enumerate(train_loader):
full_policy = self.get_policy_cov(test_x)
- print("FULL POLICY: ", full_policy)
-
- fit_val = (test_autoaugment_policy(full_policy, self.train_dataset, self.test_dataset)[0]) #+ test_autoaugment_policy(full_policy, self.train_dataset, self.test_dataset)[0]) / 2
-
- print("DONE FITNESS")
+ fit_val = ((test_autoaugment_policy(full_policy, self.train_dataset, self.test_dataset)[0])/
+ + test_autoaugment_policy(full_policy, self.train_dataset, self.test_dataset)[0]) / 2
return fit_val
def on_generation(ga_instance):
"""
- Just prints stuff while running
+ Prints information of generational fitness
+
+ Parameters
+ -------------
+ ga_instance -> GA instance
+
+ Returns
+ -------------
+ None
"""
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
@@ -373,13 +400,6 @@ class Evolutionary_learner():
-
-
-
-
-
-
-
# HEREHEREHERE0
def create_toy(train_dataset, test_dataset, batch_size, n_samples, seed=100):
@@ -407,8 +427,8 @@ def create_toy(train_dataset, test_dataset, batch_size, n_samples, seed=100):
def train_child_network(child_network, train_loader, test_loader, sgd,
- cost, max_epochs=2000, early_stop_num = 5, logging=False,
- print_every_epoch=True):
+ cost, max_epochs=2000, early_stop_num = 5, logging=False,
+ print_every_epoch=True):
if torch.cuda.is_available():
device = torch.device('cuda')
else:
@@ -451,12 +471,10 @@ def train_child_network(child_network, train_loader, test_loader, sgd,
predict_y = child_network(test_x.float()).detach()
predict_ys = torch.argmax(predict_y, axis=-1)
-
- # label_np = test_label.numpy()
-
+
_ = predict_ys == test_label
correct += torch.sum(_, axis=-1)
- # correct += torch.sum(_.numpy(), axis=-1)
+
_sum += _.shape[0]
# update best validation accuracy if it was higher, otherwise increase early stop count
@@ -511,19 +529,19 @@ __all__ = ["AutoAugmentPolicy", "AutoAugment", "RandAugment", "TrivialAugmentWid
def _apply_op(img: Tensor, op_name: str, magnitude: float,
- interpolation: InterpolationMode, fill: Optional[List[float]]):
+ interpolation: InterpolationMode, fill: Optional[List[float]]):
if op_name == "ShearX":
img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[math.degrees(magnitude), 0.0],
- interpolation=interpolation, fill=fill)
+ interpolation=interpolation, fill=fill)
elif op_name == "ShearY":
img = F.affine(img, angle=0.0, translate=[0, 0], scale=1.0, shear=[0.0, math.degrees(magnitude)],
- interpolation=interpolation, fill=fill)
+ interpolation=interpolation, fill=fill)
elif op_name == "TranslateX":
img = F.affine(img, angle=0.0, translate=[int(magnitude), 0], scale=1.0,
- interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
+ interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
elif op_name == "TranslateY":
img = F.affine(img, angle=0.0, translate=[0, int(magnitude)], scale=1.0,
- interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
+ interpolation=interpolation, shear=[0.0, 0.0], fill=fill)
elif op_name == "Rotate":
img = F.rotate(img, magnitude, interpolation=interpolation, fill=fill)
elif op_name == "Brightness":
@@ -728,18 +746,6 @@ class AutoAugment(torch.nn.Module):
fill = [float(f) for f in fill]
transform_id, probs, signs = self.get_params(len(self.subpolicies))
- # print("transform_id, probs, signs : ", transform_id, probs, signs )
-
- # for i, (op_name, p, magnitude_id) in enumerate(self.subpolicies[transform_id]):
- # for i, (op_name, p, magnitude_id) in enumerate(self.subpolicies):
- # print("op_name, p, magnitude_id: ", op_name, p, magnitude_id)
- # if probs[i] <= p:
- # op_meta = self._augmentation_space(10, F.get_image_size(img))
- # magnitudes, signed = op_meta[op_name]
- # magnitude = float(magnitudes[magnitude_id].item()) if magnitude_id is not None else 0.0
- # if signed and signs[i] == 0:
- # magnitude *= -1.0
- # img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
for i, (op_name, p, magnitude) in enumerate(self.subpolicies):
img = _apply_op(img, op_name, magnitude, interpolation=self.interpolation, fill=fill)
@@ -771,8 +777,8 @@ class RandAugment(torch.nn.Module):
"""
def __init__(self, num_ops: int = 2, magnitude: int = 9, num_magnitude_bins: int = 31,
- interpolation: InterpolationMode = InterpolationMode.NEAREST,
- fill: Optional[List[float]] = None) -> None:
+ interpolation: InterpolationMode = InterpolationMode.NEAREST,
+ fill: Optional[List[float]] = None) -> None:
super().__init__()
self.num_ops = num_ops
self.magnitude = magnitude
@@ -853,7 +859,7 @@ class TrivialAugmentWide(torch.nn.Module):
"""
def __init__(self, num_magnitude_bins: int = 31, interpolation: InterpolationMode = InterpolationMode.NEAREST,
- fill: Optional[List[float]] = None) -> None:
+ fill: Optional[List[float]] = None) -> None:
super().__init__()
self.num_magnitude_bins = num_magnitude_bins
self.interpolation = interpolation
@@ -938,4 +944,4 @@ print(f"Fitness value of the best solution = {solution_fitness}")
print(f"Index of the best solution : {solution_idx}")
# Fetch the parameters of the best solution.
best_solution_weights = torchga.model_weights_as_dict(model=ev_learner.auto_aug_agent,
- weights_vector=solution)
\ No newline at end of file
+ weights_vector=solution)
diff --git a/MetaAugment/GA_results.png b/MetaAugment/GA_results.png
deleted file mode 100644
index 62449415b64500804927328ca677c4c023085436..0000000000000000000000000000000000000000
Binary files a/MetaAugment/GA_results.png and /dev/null differ
diff --git a/MetaAugment/METALEANER.py b/MetaAugment/METALEANER.py
deleted file mode 100644
index c94246d6898ccf2d316c1dae7644513bf113149e..0000000000000000000000000000000000000000
--- a/MetaAugment/METALEANER.py
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
-
-# Neural network
-# Input the dataset (same batch size, have to check if the input sizes are correc i.e. 28x28)
-# Output the hyperprameters --> weights of network, kernel size, number of layers, number of kernels
-#
\ No newline at end of file
diff --git a/MetaAugment/UCB1_JC.ipynb b/MetaAugment/UCB1_JC.ipynb
index 6d872fd7b0ec12573b775a7dc16f7b0cb3b56254..aed9567093f27cbe3a5937da7bb7057b663cb605 100644
--- a/MetaAugment/UCB1_JC.ipynb
+++ b/MetaAugment/UCB1_JC.ipynb
@@ -1,24 +1,12 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "name": "UCB1.ipynb",
- "provenance": [],
- "collapsed_sections": []
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "accelerator": "GPU"
- },
"cells": [
{
"cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "U_ZJ2LqDiu_v"
+ },
+ "outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
@@ -33,15 +21,15 @@
"from matplotlib import pyplot as plt\n",
"from numpy import save, load\n",
"from tqdm import trange"
- ],
- "metadata": {
- "id": "U_ZJ2LqDiu_v"
- },
- "execution_count": 1,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "4ksS_duLFADW"
+ },
+ "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class LeNet(nn.Module):\n",
@@ -75,15 +63,15 @@
" y = self.fc3(y)\n",
" y = self.relu5(y)\n",
" return y"
- ],
- "metadata": {
- "id": "4ksS_duLFADW"
- },
- "execution_count": 2,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "LckxnUXGfxjW"
+ },
+ "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class EasyNet(nn.Module):\n",
@@ -101,15 +89,15 @@
" y = self.fc2(y)\n",
" y = self.relu2(y)\n",
" return y"
- ],
- "metadata": {
- "id": "LckxnUXGfxjW"
- },
- "execution_count": 3,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "enaD2xbw5hew"
+ },
+ "outputs": [],
"source": [
"\"\"\"Define internal NN module that trains on the dataset\"\"\"\n",
"class SimpleNet(nn.Module):\n",
@@ -123,15 +111,15 @@
" y = self.fc1(y)\n",
" y = self.relu1(y)\n",
" return y"
- ],
- "metadata": {
- "id": "enaD2xbw5hew"
- },
- "execution_count": 4,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "xujQtvVWBgMH"
+ },
+ "outputs": [],
"source": [
"\"\"\"Make toy dataset\"\"\"\n",
"\n",
@@ -154,15 +142,15 @@
" test_loader = torch.utils.data.DataLoader(reduced_test_dataset, batch_size=batch_size)\n",
"\n",
" return train_loader, test_loader"
- ],
- "metadata": {
- "id": "xujQtvVWBgMH"
- },
- "execution_count": 5,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "Iql-c88jGGWy"
+ },
+ "outputs": [],
"source": [
"\"\"\"Randomly generate 10 policies\"\"\"\n",
"\"\"\"Each policy has 5 sub-policies\"\"\"\n",
@@ -193,15 +181,15 @@
" policies[policy, sub_policy, transformation + 4] = np.random.randint(5,15)/10\n",
"\n",
" return policies"
- ],
- "metadata": {
- "id": "Iql-c88jGGWy"
- },
- "execution_count": 6,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "QE2VWI8o731X"
+ },
+ "outputs": [],
"source": [
"\"\"\"Pick policy and sub-policy\"\"\"\n",
"\"\"\"Each row of data should have a different sub-policy but for now, this will do\"\"\"\n",
@@ -238,12 +226,7 @@
" scale = policies[policy, sub_policy][5]\n",
"\n",
" return degrees, shear, scale"
- ],
- "metadata": {
- "id": "QE2VWI8o731X"
- },
- "execution_count": 7,
- "outputs": []
+ ]
},
{
"cell_type": "code",
@@ -392,29 +375,7 @@
},
{
"cell_type": "code",
- "source": [
- "batch_size = 32 # size of batch the inner NN is trained with\n",
- "learning_rate = 1e-1 # fix learning rate\n",
- "ds = \"MNIST\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)\n",
- "toy_size = 0.02 # total propeortion of training and test set we use\n",
- "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
- "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
- "num_policies = 5 # fix number of policies\n",
- "num_sub_policies = 5 # fix number of sub-policies in a policy\n",
- "iterations = 100 # total iterations, should be more than the number of policies\n",
- "IsLeNet = \"SimpleNet\" # using LeNet or EasyNet or SimpleNet\n",
- "\n",
- "# generate random policies at start\n",
- "policies = generate_policies(num_policies, num_sub_policies)\n",
- "\n",
- "q_values, best_q_values = run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet)\n",
- "\n",
- "plt.plot(best_q_values)\n",
- "\n",
- "best_q_values = np.array(best_q_values)\n",
- "save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)\n",
- "#best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)"
- ],
+ "execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -423,168 +384,207 @@
"id": "doHUtJ_tEiA6",
"outputId": "3a7becf3-7b5d-4403-84d3-96e51bac8bf5"
},
- "execution_count": 9,
"outputs": [
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 10%|â–ˆ | 10/100 [01:09<09:26, 6.29s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 10,\tQ-Values: [0.8, 0.71, 0.79, 0.86, 0.76], Best Policy: 0.86\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 20%|██ | 20/100 [02:18<09:03, 6.80s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 20,\tQ-Values: [0.77, 0.75, 0.81, 0.86, 0.78], Best Policy: 0.86\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 30%|███ | 30/100 [03:24<06:50, 5.87s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 30,\tQ-Values: [0.81, 0.71, 0.79, 0.8, 0.78], Best Policy: 0.81\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 40%|████ | 40/100 [04:34<06:14, 6.23s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 40,\tQ-Values: [0.8, 0.7, 0.76, 0.8, 0.78], Best Policy: 0.8\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 50%|█████ | 50/100 [05:49<06:04, 7.28s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 50,\tQ-Values: [0.79, 0.72, 0.76, 0.81, 0.74], Best Policy: 0.81\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 60%|██████ | 60/100 [06:55<04:32, 6.82s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 60,\tQ-Values: [0.79, 0.72, 0.77, 0.81, 0.76], Best Policy: 0.81\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 70%|███████ | 70/100 [08:29<04:16, 8.53s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 70,\tQ-Values: [0.78, 0.7, 0.78, 0.8, 0.76], Best Policy: 0.8\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 80%|████████ | 80/100 [09:38<02:05, 6.27s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 80,\tQ-Values: [0.79, 0.72, 0.78, 0.79, 0.77], Best Policy: 0.79\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
" 90%|█████████ | 90/100 [10:41<01:04, 6.47s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 90,\tQ-Values: [0.79, 0.71, 0.78, 0.79, 0.77], Best Policy: 0.79\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"100%|██████████| 100/100 [11:51<00:00, 7.11s/it]"
]
},
{
- "output_type": "stream",
"name": "stdout",
+ "output_type": "stream",
"text": [
"Iteration: 100,\tQ-Values: [0.79, 0.72, 0.79, 0.79, 0.78], Best Policy: 0.79\n"
]
},
{
- "output_type": "stream",
"name": "stderr",
+ "output_type": "stream",
"text": [
"\n"
]
},
{
- "output_type": "display_data",
"data": {
+ "image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
- ],
- "image/png": "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\n"
+ ]
},
"metadata": {
"needs_background": "light"
- }
+ },
+ "output_type": "display_data"
}
+ ],
+ "source": [
+ "batch_size = 32 # size of batch the inner NN is trained with\n",
+ "learning_rate = 1e-1 # fix learning rate\n",
+ "ds = \"MNIST\" # pick dataset (MNIST, KMNIST, FashionMNIST, CIFAR10, CIFAR100)\n",
+ "toy_size = 0.02 # total propeortion of training and test set we use\n",
+ "max_epochs = 100 # max number of epochs that is run if early stopping is not hit\n",
+ "early_stop_num = 10 # max number of worse validation scores before early stopping is triggered\n",
+ "num_policies = 5 # fix number of policies\n",
+ "num_sub_policies = 5 # fix number of sub-policies in a policy\n",
+ "iterations = 100 # total iterations, should be more than the number of policies\n",
+ "IsLeNet = \"SimpleNet\" # using LeNet or EasyNet or SimpleNet\n",
+ "\n",
+ "# generate random policies at start\n",
+ "policies = generate_policies(num_policies, num_sub_policies)\n",
+ "\n",
+ "q_values, best_q_values = run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet)\n",
+ "\n",
+ "plt.plot(best_q_values)\n",
+ "\n",
+ "best_q_values = np.array(best_q_values)\n",
+ "save('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), best_q_values)\n",
+ "#best_q_values = load('best_q_values_{}_{}percent_{}.npy'.format(IsLeNet, int(toy_size*100), ds), allow_pickle=True)"
]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "UCB1.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MetaAugment/genetic_learner_results.py b/MetaAugment/genetic_learner_results.py
deleted file mode 100644
index 35d9de8df2e17748b34e6879d4a3ae75dca9d9fb..0000000000000000000000000000000000000000
--- a/MetaAugment/genetic_learner_results.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-# Fixed seed (same as benchmark)
-
-# Looking at last generation can make out general trends of which transformations lead to the largest accuracies
-
-
-gen_1_acc = [0.1998, 0.1405, 0.1678, 0.9690, 0.9672, 0.9540, 0.9047, 0.9730, 0.2060, 0.9260, 0.8035, 0.9715, 0.9737, 0.14, 0.9645]
-
-gen_2_acc = [0.9218, 0.9753, 0.9758, 0.1088, 0.9710, 0.1655, 0.9735, 0.9655, 0.9740, 0.9377]
-
-gen_3_acc = [0.1445, 0.9740, 0.9643, 0.9750, 0.9492, 0.9693, 0.1262, 0.9660, 0.9760, 0.9697]
-
-gen_4_acc = [0.9697, 0.1238, 0.9613, 0.9737, 0.9603, 0.8620, 0.9712, 0.9617, 0.9737, 0.1855]
-
-gen_5_acc = [0.6445, 0.9705, 0.9668, 0.9765, 0.1142, 0.9780, 0.9700, 0.2120, 0.9555, 0.9732]
-
-gen_6_acc = [0.9710, 0.9665, 0.2077, 0.9535, 0.9765, 0.9712, 0.9697, 0.2145, 0.9523, 0.9718, 0.9718, 0.9718, 0.2180, 0.9622, 0.9785]
-
-gen_acc = [gen_1_acc, gen_2_acc, gen_3_acc, gen_4_acc, gen_5_acc, gen_6_acc]
-
-gen_acc_means = []
-gen_acc_stds = []
-
-for val in gen_acc:
- gen_acc_means.append(np.mean(val))
- gen_acc_stds.append(np.std(val))
-
-
-
-# Vary seed
-
-gen_1_vary = [0.1998, 0.9707, 0.9715, 0.9657, 0.8347, 0.9655, 0.1870, 0.0983, 0.3750, 0.9765, 0.9712, 0.9705, 0.9635, 0.9718, 0.1170]
-
-gen_2_vary = [0.9758, 0.9607, 0.9597, 0.9753, 0.1165, 0.1503, 0.9747, 0.1725, 0.9645, 0.2290]
-
-gen_3_vary = [0.1357, 0.9725, 0.1708, 0.9607, 0.2132, 0.9730, 0.9743, 0.9690, 0.0850, 0.9755]
-
-gen_4_vary = [0.9722, 0.9760, 0.9697, 0.1155, 0.9715, 0.9688, 0.1785, 0.9745, 0.2362, 0.9765]
-
-gen_5_vary = [0.9705, 0.2280, 0.9745, 0.1875, 0.9735, 0.9735, 0.9720, 0.9678, 0.9770, 0.1155]
-
-gen_6_vary = [0.9685, 0.9730, 0.9735, 0.9760, 0.1495, 0.9707, 0.9700, 0.9747, 0.9750, 0.1155, 0.9732, 0.9745, 0.9758, 0.9768, 0.1155]
-
-gen_vary = [gen_1_vary, gen_2_vary, gen_3_vary, gen_4_vary, gen_5_vary, gen_6_vary]
-
-gen_vary_means = []
-gen_vary_stds = []
-
-for val in gen_vary:
- gen_vary_means.append(np.mean(val))
- gen_vary_stds.append(np.std(val))
-
-
-
-
-
-# Multiple runs
-
-gen_1_mult = [0.1762, 0.9575, 0.1200, 0.9660, 0.9650, 0.9570, 0.9745, 0.9700, 0.15, 0.23, 0.16, 0.186, 0.9640, 0.9650]
-
-gen_2_mult = [0.17, 0.1515, 0.1700, 0.9625, 0.9630, 0.9732, 0.9680, 0.9633, 0.9530, 0.9640]
-
-gen_3_mult = [0.9750, 0.9720, 0.9655, 0.9530, 0.9623, 0.9730, 0.9748, 0.9625, 0.9716, 0.9672]
-
-gen_4_mult = [0.9724, 0.9755, 0.9657, 0.9718, 0.9690, 0.9735, 0.9715, 0.9300, 0.9725, 0.9695]
-
-gen_5_mult = [0.9560, 0.9750, 0.8750, 0.9717, 0.9731, 0.9741, 0.9747, 0.9726, 0.9729, 0.9727]
-
-gen_6_mult = [0.9730, 0.9740, 0.9715, 0.9755, 0.9761, 0.9700, 0.9755, 0.9750, 0.9726, 0.9748, 0.9705, 0.9745, 0.9752, 0.9740, 0.9744]
-
-
-
-gen_mult = [gen_1_mult, gen_2_mult, gen_3_mult, gen_4_mult, gen_5_mult, gen_6_mult]
-
-gen_mult_means = []
-gen_mult_stds = []
-
-for val in gen_mult:
- gen_mult_means.append(np.mean(val))
- gen_mult_stds.append(np.std(val))
-
-num_gen = [i for i in range(len(gen_mult))]
-
-
-# Baseline
-baseline = [0.7990 for i in range(len(gen_mult))]
-
-
-
-# plt.errorbar(num_gen, gen_acc_means, yerr=gen_acc_stds, linestyle = 'dotted', label = 'Fixed seed GA')
-# plt.errorbar(num_gen, gen_vary_means, linestyle = 'dotted', yerr=gen_vary_stds, label = 'Varying seed GA')
-# plt.errorbar(num_gen, gen_mult_means, linestyle = 'dotted', yerr=gen_mult_stds, label = 'Varying seed GA 2')
-
-plt.plot(num_gen, gen_acc_means, linestyle = 'dotted', label = 'Fixed seed GA')
-plt.plot(num_gen, gen_vary_means, linestyle = 'dotted', label = 'Varying seed GA')
-plt.plot(num_gen, gen_mult_means, linestyle = 'dotted', label = 'Varying seed GA 2')
-
-plt.plot(num_gen, baseline, label = 'Fixed seed baseline')
-
-
-plt.xlabel('Generation', fontsize = 16)
-plt.ylabel('Validation Accuracy', fontsize = 16)
-
-plt.legend()
-
-plt.savefig('GA_results.png')
\ No newline at end of file
diff --git a/app.py b/app.py
index e0f2a3ca2891816f160d1d7bdab85e14bcbf659a..3de6b5b0c5285e24d774b12606894c2a4e6f0f88 100644
--- a/app.py
+++ b/app.py
@@ -1,9 +1,49 @@
-from flask import Flask
+# from flask import Flask
+# from auto_augmentation import create_app
+# import os
+
+# app = create_app()
+# port = int(os.environ.get("PORT", 5000))
+
+# if __name__ == '__main__':
+# app.run(host='0.0.0.0',port=port)
+
+
+from flask import Flask, flash, request, redirect, url_for
+from werkzeug.utils import secure_filename
from auto_augmentation import create_app
import os
-
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__':
- app.run(host='0.0.0.0',port=port)
\ No newline at end of file
+ app.run(debug=True)
\ No newline at end of file
diff --git a/auto_augmentation/progress.py b/auto_augmentation/progress.py
index 03d33fadf5894115ddbf2c20ccc10f5f2ccc214f..77845a0260cf6c2494e8111f69fce2fdbf3124a8 100644
--- a/auto_augmentation/progress.py
+++ b/auto_augmentation/progress.py
@@ -1,5 +1,6 @@
from flask import Blueprint, request, render_template, flash, send_file
import subprocess
+import os
import numpy as np
import torch
@@ -15,7 +16,10 @@ from numpy import save, load
from tqdm import trange
torch.manual_seed(0)
# import agents and its functions
-from MetaAugment import UCB1_JC
+
+from MetaAugment import UCB1_JC_py as UCB1_JC
+
+
bp = Blueprint("progress", __name__)
diff --git a/auto_augmentation/templates/home.html b/auto_augmentation/templates/home.html
index 7e4fc0670a7f7cddb9ed59c80586505a0160b70a..c40f23f825448e20ef0bf2c30dbb2377a264345c 100644
--- a/auto_augmentation/templates/home.html
+++ b/auto_augmentation/templates/home.html
@@ -59,77 +59,86 @@
<h3>Advanced Search</h3>
<!-- action(data augmentation) space -->
Which data augmentation method you would like exclude? <br>
- <input type="radio" id="ShearX"
+ <input type="checkbox" id="ShearX"
name="action_space" value="ShearX">
<label for="ShearX">ShearX</label><br>
- <input type="radio" id="ShearY"
+ <input type="checkbox" id="ShearY"
name="action_space" value="ShearY">
<label for="ShearY">ShearY</label><br>
- <input type="radio" id="TranslateX"
+ <input type="checkbox" id="TranslateX"
name="action_space" value="TranslateX">
<label for="TranslateX">TranslateX</label><br>
- <input type="radio" id="TranslateY"
+ <input type="checkbox" id="TranslateY"
name="action_space" value="TranslateY">
<label for="TranslateY">TranslateY</label><br>
- <input type="radio" id="Rotate"
+ <input type="checkbox" id="Rotate"
name="Rotate" value="Rotate">
<label for="Rotate">Rotate</label><br>
- <input type="radio" id="Brightness"
+ <input type="checkbox" id="Brightness"
name="action_space" value="Brightness">
<label for="Brightness">Brightness</label><br>
- <input type="radio" id="Color"
+ <input type="checkbox" id="Color"
name="action_space" value="Color">
<label for="Color">Color</label><br>
- <input type="radio" id="Contrast"
+ <input type="checkbox" id="Contrast"
name="action_space" value="Contrast">
<label for="Contrast">Contrast</label><br>
- <input type="radio" id="Sharpness"
+ <input type="checkbox" id="Sharpness"
name="action_space" value="Sharpness">
<label for="Sharpness">Sharpness</label><br>
- <input type="radio" id="Posterize"
+ <input type="checkbox" id="Posterize"
name="action_space" value="Posterize">
<label for="Posterize">Posterize</label><br>
- <input type="radio" id="Solarize"
+ <input type="checkbox" id="Solarize"
name="action_space" value="Solarize">
<label for="Solarize">Solarize</label><br>
- <input type="radio" id="AutoContrast"
+ <input type="checkbox" id="AutoContrast"
name="action_space" value="AutoContrast">
<label for="AutoContrast">AutoContrast</label><br>
- <input type="radio" id="Equalize"
+ <input type="checkbox" id="Equalize"
name="action_space" value="Equalize">
<label for="Equalize">Equalize</label><br>
- <input type="radio" id="Invert"
+ <input type="checkbox" id="Invert"
name="action_space" value="Invert">
<label for="Invert">Invert</label><br><br><br>
+ <!-- <div id="exclude_augments" class="dropdown-check-list" tabindex="100">
+ <span class="anchor">Select data augmentation method(s) to exclude:</span>
+ <ul class="items">
+ <input type="checkbox" />Translate
+ <input type="checkbox" />Rotate
+ <input type="checkbox" />AutoContrast
+ <input type="checkbox" />Equalize
+ <br>
+ <input type="checkbox" />Solarize
+ <input type="checkbox" />Posterize
+ <input type="checkbox" />Contrast
+ <input type="checkbox" />Brightness
+ </ul>
+ </div> -->
+
+ <div id="exclude_augments">
+ <span class="anchor">Hyperparameter (Learning Rate):</span>
+ <ul class="items">
+ Automatic: <input type="checkbox" /> <div></div>
+ Manual: <input type="number" />
+ </ul>
+ </div>
- <!-- action space -->
- <!-- <label for="data_aug_method">Which data augmentation method you would like exclude?</label>
- <select id="data_aug_method" name="data_aug_method">
- <option value="Translate">Translate</option>
- <option value="Rotate">Rotate</option>
- <option value="AutoContrast">AutoContrast</option>
- <option value="Equalize">Equalize</option>
- <option value="Solarize">Solarize</option>
- <option value="Posterize">Posterize</option>
- <option value="Contrast">Contrast</option>
- <option value="Brightness">Brightness</option>
-
- </select><br><br> -->
<input type="submit">