From d646941f33f933a623b4b842e24bf191b23b6190 Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Sat, 16 Apr 2022 15:17:30 +0100
Subject: [PATCH] refactor UCB1_JC_py.py
---
MetaAugment/UCB1_JC_py.py | 102 +-------------------------------------
MetaAugment/main.py | 2 +-
2 files changed, 3 insertions(+), 101 deletions(-)
diff --git a/MetaAugment/UCB1_JC_py.py b/MetaAugment/UCB1_JC_py.py
index 252a2551..9c16382a 100644
--- a/MetaAugment/UCB1_JC_py.py
+++ b/MetaAugment/UCB1_JC_py.py
@@ -20,106 +20,8 @@ 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
+from MetaAugment.child_networks import *
+from MetaAugment.main import create_toy
# In[6]:
diff --git a/MetaAugment/main.py b/MetaAugment/main.py
index 0c5cdeae..9a43c222 100644
--- a/MetaAugment/main.py
+++ b/MetaAugment/main.py
@@ -19,7 +19,7 @@ def create_toy(train_dataset, test_dataset, batch_size, n_samples, seed=100):
reduced_train_dataset = torch.utils.data.Subset(shuffled_train_dataset, indices_train)
# shuffle and take first n_samples %age of test dataset
- shuffle_order_test = np.random.RandomState(seed=seed).permutation(len(test_dataset))
+ shuffle_order_test = np.random.RandomState(seed=10*seed).permutation(len(test_dataset))
shuffled_test_dataset = torch.utils.data.Subset(test_dataset, shuffle_order_test)
big = 1 # how much bigger is the test set
--
GitLab