From 083be6414a4d4b4dfb7aaa97858cb701bd9d931c Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Wed, 16 Feb 2022 10:43:26 +0000
Subject: [PATCH] move all datasets to /datasets/mnist folder. MAKE SURE TO USE
PARAMETER DOWNLOAD=TRUE TO DOWNLOAD MNIST DATA AGAIN
---
MetaAugment/CP2_Max.py | 8 ++++----
MetaAugment/UCB1_JC.ipynb | 4 ++--
MetaAugment/__pycache__/main.cpython-38.pyc | Bin 1949 -> 2843 bytes
MetaAugment/main.py | 7 ++++---
4 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/MetaAugment/CP2_Max.py b/MetaAugment/CP2_Max.py
index df325504..1ed7b470 100644
--- a/MetaAugment/CP2_Max.py
+++ b/MetaAugment/CP2_Max.py
@@ -90,8 +90,8 @@ def train_model(transform_idx, p):
batch_size = 32
n_samples = 0.005
- train_dataset = datasets.MNIST(root='./MetaAugment/train', train=True, download=False, transform=transform_train)
- test_dataset = datasets.MNIST(root='./MetaAugment/test', train=False, download=False, transform=torchvision.transforms.ToTensor())
+ train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=False, transform=transform_train)
+ test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=False, transform=torchvision.transforms.ToTensor())
# create toy dataset from above uploaded data
train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, 0.01)
@@ -142,8 +142,8 @@ def callback_generation(ga_instance):
# ORGANISING DATA
# transforms = ['RandomResizedCrop', 'RandomHorizontalFlip', 'RandomVerticalCrop', 'RandomRotation']
-train_dataset = datasets.MNIST(root='./MetaAugment/train', train=True, download=True, transform=torchvision.transforms.ToTensor())
-test_dataset = datasets.MNIST(root='./MetaAugment/test', train=False, download=True, transform=torchvision.transforms.ToTensor())
+train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True, transform=torchvision.transforms.ToTensor())
+test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True, transform=torchvision.transforms.ToTensor())
n_samples = 0.02
# shuffle and take first n_samples %age of training dataset
shuffled_train_dataset = torch.utils.data.Subset(train_dataset, torch.randperm(len(train_dataset)).tolist())
diff --git a/MetaAugment/UCB1_JC.ipynb b/MetaAugment/UCB1_JC.ipynb
index ac88adec..d3bbda39 100644
--- a/MetaAugment/UCB1_JC.ipynb
+++ b/MetaAugment/UCB1_JC.ipynb
@@ -144,8 +144,8 @@
" torchvision.transforms.ToTensor()])\n",
"\n",
" # open data and apply these transformations\n",
- " train_dataset = datasets.MNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)\n",
- " test_dataset = datasets.MNIST(root='./MetaAugment/test', train=False, download=True, transform=torchvision.transforms.ToTensor())\n",
+ " train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True, transform=transform)\n",
+ " test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True, transform=torchvision.transforms.ToTensor())\n",
"\n",
" # create toy dataset from above uploaded data\n",
" train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, toy_size)\n",
diff --git a/MetaAugment/__pycache__/main.cpython-38.pyc b/MetaAugment/__pycache__/main.cpython-38.pyc
index 213ba2380b9f4137ea09f7c7fbea31a8aca33345..73b244741637ddc13240976d7760041aa3ac49ea 100644
GIT binary patch
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diff --git a/MetaAugment/main.py b/MetaAugment/main.py
index c68f4b1a..1f2de939 100644
--- a/MetaAugment/main.py
+++ b/MetaAugment/main.py
@@ -82,7 +82,7 @@ class AA_Learner:
def __init__(self, controller):
self.controller = controller
- def learn(self, dataset, child_network, toy_flag):
+ def learn(self, train_dataset, test_dataset, child_network, toy_flag):
'''
Deos what is seen in Figure 1 in the AutoAugment paper.
@@ -94,9 +94,10 @@ class AA_Learner:
while not good_policy_found:
policy = self.controller.pop_policy()
- train_loader, test_loader = prepare_dataset(dataset, policy, toy_flag)
+ train_loader, test_loader = create_toy(train_dataset, test_dataset,
+ batch_size=32, n_samples=0.005)
- reward = train_model(child_network, train_loader, test_loader, sgd, cost, epoch)
+ reward = train_child_network(child_network, train_loader, test_loader, sgd, cost, epoch)
self.controller.update(reward, policy)
--
GitLab