From 6e49635fa37c1d537ee9be7edc33390560519d3d Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Thu, 7 Apr 2022 22:52:41 +0900
Subject: [PATCH] cleaned up imports in /autoaugment_learners
---
.../autoaugment_learners/aa_learner.py | 14 +++---
.../autoaugment_learners/gru_learner.py | 47 ++++++-------------
.../randomsearch_learner.py | 11 +----
MetaAugment/main.py | 2 -
4 files changed, 22 insertions(+), 52 deletions(-)
diff --git a/MetaAugment/autoaugment_learners/aa_learner.py b/MetaAugment/autoaugment_learners/aa_learner.py
index 4b7cd86a..60d463c2 100644
--- a/MetaAugment/autoaugment_learners/aa_learner.py
+++ b/MetaAugment/autoaugment_learners/aa_learner.py
@@ -1,14 +1,12 @@
-# The parent class for all other autoaugment learners``
+# The parent class for all other autoaugment learners
import torch
-import numpy as np
-from MetaAugment.main import *
-import MetaAugment.child_networks as cn
-import torchvision.transforms as transforms
-from MetaAugment.autoaugment_learners.autoaugment import *
+import torch.nn as nn
+import torch.optim as optim
+from MetaAugment.main import train_child_network, create_toy
+from MetaAugment.autoaugment_learners.autoaugment import AutoAugment
-import torchvision.transforms.autoaugment as torchaa
-from torchvision.transforms import functional as F, InterpolationMode
+import torchvision.transforms as transforms
from pprint import pprint
diff --git a/MetaAugment/autoaugment_learners/gru_learner.py b/MetaAugment/autoaugment_learners/gru_learner.py
index 58970e68..709fdabc 100644
--- a/MetaAugment/autoaugment_learners/gru_learner.py
+++ b/MetaAugment/autoaugment_learners/gru_learner.py
@@ -1,13 +1,8 @@
import torch
-import numpy as np
-import torchvision.transforms as transforms
-import torchvision.transforms.autoaugment as torchaa
-from torchvision.transforms import functional as F, InterpolationMode
-from MetaAugment.main import *
import MetaAugment.child_networks as cn
-from MetaAugment.autoaugment_learners.autoaugment import *
-from MetaAugment.autoaugment_learners.aa_learner import *
+from MetaAugment.autoaugment_learners.aa_learner import aa_learner
+from MetaAugment.controller_networks.rnn_controller import RNNModel
from pprint import pprint
@@ -33,6 +28,7 @@ augmentation_space = [
("Invert", False),
]
+
class gru_learner(aa_learner):
# Uses a GRU controller which is updated via Proximal Polixy Optimization
# It is the same model use in
@@ -50,8 +46,9 @@ class gru_learner(aa_learner):
'''
super().__init__(sp_num, fun_num, p_bins, m_bins, discrete_p_m)
- # TODO: We should probably use a different way to store results than self.history
- self.history = []
+ # input_size of the RNNModel can be chosen arbitrarily as we don't put any inputs in it.
+ self.controller = RNNModel(mode='GRU', input_size=1, hidden_size=40, num_layers=1,
+ bias=True, output_size=fun_num+p_bins+m_bins)
def generate_new_policy(self):
@@ -68,25 +65,7 @@ class gru_learner(aa_learner):
(("ShearY", 0.5, 8), ("Invert", 0.7, None)),
]
'''
- new_policy = []
-
- for _ in range(self.sp_num): # generate sp_num subpolicies for each policy
- ops = []
- # generate 2 operations for each subpolicy
- for i in range(2):
- # if our agent uses discrete representations of probability and magnitude
- if self.discrete_p_m:
- new_op = self.generate_new_discrete_operation()
- else:
- new_op = self.generate_new_continuous_operation()
- new_op = self.translate_operation_tensor(new_op)
- ops.append(new_op)
-
- new_subpolicy = tuple(ops)
-
- new_policy.append(new_subpolicy)
-
- return new_policy
+ new_policy = self.controller(input=torch.rand(1))
def learn(self, train_dataset, test_dataset, child_network_architecture, toy_flag):
@@ -114,13 +93,15 @@ if __name__=='__main__':
# We can initialize the train_dataset with its transform as None.
# Later on, we will change this object's transform attribute to the policy
# that we want to test
- train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=False,
+ train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=True,
transform=None)
- test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=False,
+ test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=True,
transform=torchvision.transforms.ToTensor())
child_network = cn.lenet
- rs_learner = randomsearch_learner(discrete_p_m=False)
- rs_learner.learn(train_dataset, test_dataset, child_network, toy_flag=True)
- pprint(rs_learner.history)
\ No newline at end of file
+ learner = gru_learner(discrete_p_m=False)
+ print(learner.generate_new_policy())
+ breakpoint()
+ learner.learn(train_dataset, test_dataset, child_network, toy_flag=True)
+ pprint(learner.history)
\ No newline at end of file
diff --git a/MetaAugment/autoaugment_learners/randomsearch_learner.py b/MetaAugment/autoaugment_learners/randomsearch_learner.py
index 3657224f..02798927 100644
--- a/MetaAugment/autoaugment_learners/randomsearch_learner.py
+++ b/MetaAugment/autoaugment_learners/randomsearch_learner.py
@@ -1,13 +1,8 @@
import torch
import numpy as np
-import torchvision.transforms as transforms
-import torchvision.transforms.autoaugment as torchaa
-from torchvision.transforms import functional as F, InterpolationMode
-from MetaAugment.main import *
import MetaAugment.child_networks as cn
-from MetaAugment.autoaugment_learners.autoaugment import *
-from MetaAugment.autoaugment_learners.aa_learner import *
+from MetaAugment.autoaugment_learners.aa_learner import aa_learner
from pprint import pprint
@@ -43,9 +38,7 @@ class randomsearch_learner(aa_learner):
m_bins: number of bins we divide the magnitude space
'''
super().__init__(sp_num, fun_num, p_bins, m_bins, discrete_p_m)
-
- # TODO: We should probably use a different way to store results than self.history
- self.history = []
+
def generate_new_discrete_operation(self):
'''
diff --git a/MetaAugment/main.py b/MetaAugment/main.py
index 0fd76bcf..5b0e04e4 100644
--- a/MetaAugment/main.py
+++ b/MetaAugment/main.py
@@ -2,11 +2,9 @@ import numpy as np
import torch
torch.manual_seed(0)
import torch.nn as nn
-import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.datasets as datasets
-import torchvision.transforms.autoaugment as autoaugment
#import MetaAugment.AutoAugmentDemo.ops as ops #
# code from https://github.com/ChawDoe/LeNet5-MNIST-PyTorch/blob/master/train.py
--
GitLab