From bd840c0d8ce43cb87855bf6b7a42a342ace79620 Mon Sep 17 00:00:00 2001
From: Sun Jin Kim <sk2521@ic.ac.uk>
Date: Tue, 26 Apr 2022 22:40:39 +0100
Subject: [PATCH] privatise certain methods in aa_learner's
---
.../autoaugment_learners/aa_learner.py | 16 +++++------
.../autoaugment_learners/autoaugment.py | 6 ++---
.../autoaugment_learners/gru_learner.py | 12 ++++-----
.../randomsearch_learner.py | 16 +++++------
.../autoaugment_learners/ucb_learner.py | 8 +++---
benchmark/scripts/util_04_22.py | 2 +-
.../autoaugment_learners/aa_learners.rst | 4 ++-
...ugment.autoaugment_learners.aa_learner.rst | 4 ---
...gment.autoaugment_learners.evo_learner.rst | 27 +++++++++++++++++++
...gment.autoaugment_learners.gru_learner.rst | 4 ---
...oaugment_learners.randomsearch_learner.rst | 6 -----
...gment.autoaugment_learners.ucb_learner.rst | 24 +++++++++++++++++
docs/source/usage/autoaugment_helperclass.rst | 2 +-
test/MetaAugment/test_aa_learner.py | 18 ++++++-------
test/MetaAugment/test_gru_learner.py | 6 ++---
test/MetaAugment/test_randomsearch_learner.py | 6 ++---
16 files changed, 100 insertions(+), 61 deletions(-)
create mode 100644 docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.evo_learner.rst
create mode 100644 docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.ucb_learner.rst
diff --git a/MetaAugment/autoaugment_learners/aa_learner.py b/MetaAugment/autoaugment_learners/aa_learner.py
index 756ae33e..48c05b95 100644
--- a/MetaAugment/autoaugment_learners/aa_learner.py
+++ b/MetaAugment/autoaugment_learners/aa_learner.py
@@ -101,7 +101,7 @@ class aa_learner:
self.op_tensor_length = self.fun_num + p_bins + m_bins if discrete_p_m else self.fun_num +2
- def translate_operation_tensor(self, operation_tensor, return_log_prob=False, argmax=False):
+ def _translate_operation_tensor(self, operation_tensor, return_log_prob=False, argmax=False):
"""
takes in a tensor representing an operation and returns an actual operation which
is in the form of:
@@ -220,7 +220,7 @@ class aa_learner:
return operation
- def generate_new_policy(self):
+ def _generate_new_policy(self):
"""
Generate a new policy which can be fed into an AutoAugment object
by calling:
@@ -243,7 +243,7 @@ class aa_learner:
by calling: AutoAugment.subpolicies = policy
"""
- raise NotImplementedError('generate_new_policy not implemented in aa_learner')
+ raise NotImplementedError('_generate_new_policy not implemented in aa_learner')
def learn(self, train_dataset, test_dataset, child_network_architecture, iterations=15):
@@ -290,10 +290,10 @@ class aa_learner:
different policies
for _ in range(15):
- policy = self.generate_new_policy()
+ policy = self._generate_new_policy()
pprint(policy)
- reward = self.test_autoaugment_policy(policy,
+ reward = self._test_autoaugment_policy(policy,
child_network_architecture,
train_dataset,
test_dataset)
@@ -302,7 +302,7 @@ class aa_learner:
"""
- def test_autoaugment_policy(self,
+ def _test_autoaugment_policy(self,
policy,
child_network_architecture,
train_dataset,
@@ -396,10 +396,10 @@ class aa_learner:
# # This is dummy code
# # test out `n` random policies
# for _ in range(n):
- # policy = self.generate_new_policy()
+ # policy = self._generate_new_policy()
# pprint(policy)
- # reward, acc_list = self.test_autoaugment_policy(policy,
+ # reward, acc_list = self._test_autoaugment_policy(policy,
# child_network_architecture,
# train_dataset,
# test_dataset,
diff --git a/MetaAugment/autoaugment_learners/autoaugment.py b/MetaAugment/autoaugment_learners/autoaugment.py
index 5a8ecbcf..6baa28cd 100644
--- a/MetaAugment/autoaugment_learners/autoaugment.py
+++ b/MetaAugment/autoaugment_learners/autoaugment.py
@@ -446,7 +446,7 @@ if __name__=='__main__':
- def test_autoaugment_policy(subpolicies, train_dataset, test_dataset):
+ def _test_autoaugment_policy(subpolicies, train_dataset, test_dataset):
aa_transform = AutoAugment()
aa_transform.subpolicies = subpolicies
@@ -470,8 +470,8 @@ if __name__=='__main__':
return best_acc, acc_log
- _, acc_log1 = test_autoaugment_policy(subpolicies1, train_dataset, test_dataset)
- _, acc_log2 = test_autoaugment_policy(subpolicies2, train_dataset, test_dataset)
+ _, acc_log1 = _test_autoaugment_policy(subpolicies1, train_dataset, test_dataset)
+ _, acc_log2 = _test_autoaugment_policy(subpolicies2, train_dataset, test_dataset)
plt.plot(acc_log1, label='subpolicies1')
plt.plot(acc_log2, label='subpolicies2')
diff --git a/MetaAugment/autoaugment_learners/gru_learner.py b/MetaAugment/autoaugment_learners/gru_learner.py
index 5c15a4a4..7ca8088c 100644
--- a/MetaAugment/autoaugment_learners/gru_learner.py
+++ b/MetaAugment/autoaugment_learners/gru_learner.py
@@ -85,7 +85,7 @@ class gru_learner(aa_learner):
self.softmax = torch.nn.Softmax(dim=0)
- def generate_new_policy(self):
+ def _generate_new_policy(self):
"""
The GRU controller pops out a new policy.
@@ -101,7 +101,7 @@ class gru_learner(aa_learner):
choice of function, prob, and mag seperately, so that the
resulting tensor's values sums up to 3.
- Then we input each tensor into self.translate_operation_tensor
+ Then we input each tensor into self._translate_operation_tensor
with parameter (return_log_prob=True), which outputs a tuple
in the form of ('img_function_name', prob, mag) and a float
representing the log probability that we chose the chosen
@@ -150,8 +150,8 @@ class gru_learner(aa_learner):
op2 = softmaxed_vectors[2*subpolicy_idx+1]
# translate both vectors
- op1, log_prob1 = self.translate_operation_tensor(op1, return_log_prob=True)
- op2, log_prob2 = self.translate_operation_tensor(op2, return_log_prob=True)
+ op1, log_prob1 = self._translate_operation_tensor(op1, return_log_prob=True)
+ op2, log_prob2 = self._translate_operation_tensor(op2, return_log_prob=True)
new_policy.append((op1,op2))
log_prob += (log_prob1+log_prob2)
@@ -177,10 +177,10 @@ class gru_learner(aa_learner):
for k in range(self.cont_mb_size):
# log_prob is $\sum_{t=1}^T log(P(a_t|a_{(t-1):1};\theta_c))$, used in PPO
- policy, log_prob = self.generate_new_policy()
+ policy, log_prob = self._generate_new_policy()
pprint(policy)
- reward = self.test_autoaugment_policy(policy,
+ reward = self._test_autoaugment_policy(policy,
child_network_architecture,
train_dataset,
test_dataset)
diff --git a/MetaAugment/autoaugment_learners/randomsearch_learner.py b/MetaAugment/autoaugment_learners/randomsearch_learner.py
index 2c35fb80..25fbd112 100644
--- a/MetaAugment/autoaugment_learners/randomsearch_learner.py
+++ b/MetaAugment/autoaugment_learners/randomsearch_learner.py
@@ -46,7 +46,7 @@ class randomsearch_learner(aa_learner):
)
- def generate_new_discrete_operation(self):
+ def _generate_new_discrete_operation(self):
"""
generate a new random operation in the form of a tensor of dimension:
(fun_num + 11 + 10)
@@ -74,7 +74,7 @@ class randomsearch_learner(aa_learner):
return torch.cat([fun_t, prob_t, mag_t])
- def generate_new_continuous_operation(self):
+ def _generate_new_continuous_operation(self):
"""
Returns operation_tensor, which is a tensor representation of a random operation with
dimension:
@@ -101,7 +101,7 @@ class randomsearch_learner(aa_learner):
return fun_p_m
- def generate_new_policy(self):
+ def _generate_new_policy(self):
"""
Generates a new policy, with the elements chosen at random
(unifom random distribution).
@@ -115,10 +115,10 @@ class randomsearch_learner(aa_learner):
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()
+ new_op = self._generate_new_discrete_operation()
else:
- new_op = self.generate_new_continuous_operation()
- new_op = self.translate_operation_tensor(new_op)
+ new_op = self._generate_new_continuous_operation()
+ new_op = self._translate_operation_tensor(new_op)
ops.append(new_op)
new_subpolicy = tuple(ops)
@@ -135,10 +135,10 @@ class randomsearch_learner(aa_learner):
iterations=15):
# test out `iterations` number of random policies
for _ in range(iterations):
- policy = self.generate_new_policy()
+ policy = self._generate_new_policy()
pprint(policy)
- reward = self.test_autoaugment_policy(policy,
+ reward = self._test_autoaugment_policy(policy,
child_network_architecture,
train_dataset,
test_dataset)
diff --git a/MetaAugment/autoaugment_learners/ucb_learner.py b/MetaAugment/autoaugment_learners/ucb_learner.py
index fdf735be..6ed010fc 100644
--- a/MetaAugment/autoaugment_learners/ucb_learner.py
+++ b/MetaAugment/autoaugment_learners/ucb_learner.py
@@ -47,7 +47,7 @@ class ucb_learner(randomsearch_learner):
# attributes used in the UCB1 algorithm
self.num_policies = num_policies
- self.policies = [self.generate_new_policy() for _ in range(num_policies)]
+ self.policies = [self._generate_new_policy() for _ in range(num_policies)]
self.avg_accs = [None]*self.num_policies
self.best_avg_accs = []
@@ -67,7 +67,7 @@ class ucb_learner(randomsearch_learner):
and add to our list of policies
"""
- self.policies += [self.generate_new_policy() for _ in range(n)]
+ self.policies += [self._generate_new_policy() for _ in range(n)]
# all the below need to be lengthened to store information for the
# new policies
@@ -96,7 +96,7 @@ class ucb_learner(randomsearch_learner):
# test that one
this_policy_idx = self.avg_accs.index(None)
this_policy = self.policies[this_policy_idx]
- acc = self.test_autoaugment_policy(
+ acc = self._test_autoaugment_policy(
this_policy,
child_network_architecture,
train_dataset,
@@ -111,7 +111,7 @@ class ucb_learner(randomsearch_learner):
# one with the best q_plus_cnt value
this_policy_idx = np.argmax(self.q_plus_cnt)
this_policy = self.policies[this_policy_idx]
- acc = self.test_autoaugment_policy(
+ acc = self._test_autoaugment_policy(
this_policy,
child_network_architecture,
train_dataset,
diff --git a/benchmark/scripts/util_04_22.py b/benchmark/scripts/util_04_22.py
index 62c0456a..8d7aa6b1 100644
--- a/benchmark/scripts/util_04_22.py
+++ b/benchmark/scripts/util_04_22.py
@@ -111,7 +111,7 @@ def rerun_best_policy(
print(f'{_}/{repeat_num}')
temp_agent = aal.aa_learner(**config)
accs.append(
- temp_agent.test_autoaugment_policy(megapol,
+ temp_agent._test_autoaugment_policy(megapol,
child_network_architecture,
train_dataset,
test_dataset,
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/aa_learners.rst b/docs/source/MetaAugment_library/autoaugment_learners/aa_learners.rst
index 4fc99146..db9d7b9f 100644
--- a/docs/source/MetaAugment_library/autoaugment_learners/aa_learners.rst
+++ b/docs/source/MetaAugment_library/autoaugment_learners/aa_learners.rst
@@ -6,5 +6,7 @@ AutoAugment learners
:toctree: generated
MetaAugment.autoaugment_learners.aa_learner
+ MetaAugment.autoaugment_learners.evo_learner
MetaAugment.autoaugment_learners.gru_learner
- MetaAugment.autoaugment_learners.randomsearch_learner
\ No newline at end of file
+ MetaAugment.autoaugment_learners.randomsearch_learner
+ MetaAugment.autoaugment_learners.ucb_learner
\ No newline at end of file
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.aa_learner.rst b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.aa_learner.rst
index 010241cb..85be0d01 100644
--- a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.aa_learner.rst
+++ b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.aa_learner.rst
@@ -14,11 +14,7 @@
.. autosummary::
~aa_learner.__init__
- ~aa_learner.demo_plot
- ~aa_learner.generate_new_policy
~aa_learner.learn
- ~aa_learner.test_autoaugment_policy
- ~aa_learner.translate_operation_tensor
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.evo_learner.rst b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.evo_learner.rst
new file mode 100644
index 00000000..37f06b00
--- /dev/null
+++ b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.evo_learner.rst
@@ -0,0 +1,27 @@
+MetaAugment.autoaugment\_learners.evo\_learner
+==============================================
+
+.. currentmodule:: MetaAugment.autoaugment_learners
+
+.. autoclass:: evo_learner
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~evo_learner.__init__
+ ~evo_learner.get_full_policy
+ ~evo_learner.get_single_policy_cov
+ ~evo_learner.in_pol_dict
+ ~evo_learner.learn
+ ~evo_learner.set_up_instance
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.gru_learner.rst b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.gru_learner.rst
index f5cbe442..23eb306c 100644
--- a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.gru_learner.rst
+++ b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.gru_learner.rst
@@ -14,11 +14,7 @@
.. autosummary::
~gru_learner.__init__
- ~gru_learner.demo_plot
- ~gru_learner.generate_new_policy
~gru_learner.learn
- ~gru_learner.test_autoaugment_policy
- ~gru_learner.translate_operation_tensor
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.randomsearch_learner.rst b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.randomsearch_learner.rst
index 5bfb303d..72903e47 100644
--- a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.randomsearch_learner.rst
+++ b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.randomsearch_learner.rst
@@ -14,13 +14,7 @@
.. autosummary::
~randomsearch_learner.__init__
- ~randomsearch_learner.demo_plot
- ~randomsearch_learner.generate_new_continuous_operation
- ~randomsearch_learner.generate_new_discrete_operation
- ~randomsearch_learner.generate_new_policy
~randomsearch_learner.learn
- ~randomsearch_learner.test_autoaugment_policy
- ~randomsearch_learner.translate_operation_tensor
diff --git a/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.ucb_learner.rst b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.ucb_learner.rst
new file mode 100644
index 00000000..83f80f48
--- /dev/null
+++ b/docs/source/MetaAugment_library/autoaugment_learners/generated/MetaAugment.autoaugment_learners.ucb_learner.rst
@@ -0,0 +1,24 @@
+MetaAugment.autoaugment\_learners.ucb\_learner
+==============================================
+
+.. currentmodule:: MetaAugment.autoaugment_learners
+
+.. autoclass:: ucb_learner
+
+
+ .. automethod:: __init__
+
+
+ .. rubric:: Methods
+
+ .. autosummary::
+
+ ~ucb_learner.__init__
+ ~ucb_learner.learn
+ ~ucb_learner.make_more_policies
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/source/usage/autoaugment_helperclass.rst b/docs/source/usage/autoaugment_helperclass.rst
index 75f080fb..cc361da4 100644
--- a/docs/source/usage/autoaugment_helperclass.rst
+++ b/docs/source/usage/autoaugment_helperclass.rst
@@ -11,7 +11,7 @@ we use as a helper class to help us apply AutoAugment policies to datasets.
This is a tutorial (in the sense describe in https://documentation.divio.com/structure/).
For an example of how the material is used in our library, see the source code of
-:meth:`aa_learner.test_autoaugment_policy <MetaAugment.autoaugment_learners.aa_learner>`.
+:meth:`aa_learner._test_autoaugment_policy <MetaAugment.autoaugment_learners.aa_learner>`.
Let's say we have a policy within the search space specified by the original
AutoAugment paper:
diff --git a/test/MetaAugment/test_aa_learner.py b/test/MetaAugment/test_aa_learner.py
index 8960746c..64517cd0 100644
--- a/test/MetaAugment/test_aa_learner.py
+++ b/test/MetaAugment/test_aa_learner.py
@@ -7,13 +7,13 @@ import torchvision.datasets as datasets
import random
-def test_translate_operation_tensor():
+def test__translate_operation_tensor():
"""
- See if aa_learner class's translate_operation_tensor works
+ See if aa_learner class's _translate_operation_tensor works
by feeding many (valid) inputs in it.
We make a lot of (fun_num+p_bins_m_bins,) size tensors, softmax
- them, and feed them through the translate_operation_tensor method
+ them, and feed them through the _translate_operation_tensor method
to see if it doesn't break
"""
@@ -44,7 +44,7 @@ def test_translate_operation_tensor():
mag_t = softmax(mag_t * alpha)
softmaxed_vector = torch.cat((fun_t, prob_t, mag_t))
- agent.translate_operation_tensor(softmaxed_vector)
+ agent._translate_operation_tensor(softmaxed_vector)
# discrete_p_m=False
@@ -73,10 +73,10 @@ def test_translate_operation_tensor():
softmaxed_vector = torch.cat((fun_t, prob_t, mag_t))
- agent.translate_operation_tensor(softmaxed_vector)
+ agent._translate_operation_tensor(softmaxed_vector)
-def test_test_autoaugment_policy():
+def test__test_autoaugment_policy():
agent = aal.aa_learner(
sp_num=5,
p_bins=11,
@@ -107,7 +107,7 @@ def test_test_autoaugment_policy():
train=False, download=True,
transform=torchvision.transforms.ToTensor())
- acc = agent.test_autoaugment_policy(
+ acc = agent._test_autoaugment_policy(
policy,
child_network_architecture,
train_dataset,
@@ -134,7 +134,7 @@ def test_exclude_method():
exclude_method=exclude_method
)
for _ in range(200):
- new_pol, _ = agent.generate_new_policy()
+ new_pol, _ = agent._generate_new_policy()
print(new_pol)
for (op1, op2) in new_pol:
image_function_1 = op1[0]
@@ -146,7 +146,7 @@ def test_exclude_method():
exclude_method=exclude_method
)
for _ in range(200):
- new_pol, _ = agent.generate_new_policy()
+ new_pol= agent._generate_new_policy()
print(new_pol)
for (op1, op2) in new_pol:
image_function_1 = op1[0]
diff --git a/test/MetaAugment/test_gru_learner.py b/test/MetaAugment/test_gru_learner.py
index cd52b0e9..b2ea8930 100644
--- a/test/MetaAugment/test_gru_learner.py
+++ b/test/MetaAugment/test_gru_learner.py
@@ -6,9 +6,9 @@ import torchvision.datasets as datasets
import random
-def test_generate_new_policy():
+def test__generate_new_policy():
"""
- make sure gru_learner.generate_new_policy() is robust
+ make sure gru_learner._generate_new_policy() is robust
with respect to different values of sp_num, fun_num,
p_bins, and m_bins
"""
@@ -24,7 +24,7 @@ def test_generate_new_policy():
cont_mb_size=2
)
for _ in range(4):
- new_policy = agent.generate_new_policy()
+ new_policy = agent._generate_new_policy()
assert isinstance(new_policy[0], list), new_policy
diff --git a/test/MetaAugment/test_randomsearch_learner.py b/test/MetaAugment/test_randomsearch_learner.py
index 61e9f9cd..6c5a9350 100644
--- a/test/MetaAugment/test_randomsearch_learner.py
+++ b/test/MetaAugment/test_randomsearch_learner.py
@@ -6,9 +6,9 @@ import torchvision.datasets as datasets
import random
-def test_generate_new_policy():
+def test__generate_new_policy():
"""
- make sure randomsearch_learner.generate_new_policy() is robust
+ make sure randomsearch_learner._generate_new_policy() is robust
with respect to different values of sp_num, fun_num,
p_bins, and m_bins
"""
@@ -27,7 +27,7 @@ def test_generate_new_policy():
discrete_p_m=discrete_p_m
)
for _ in range(4):
- new_policy = agent.generate_new_policy()
+ new_policy = agent._generate_new_policy()
assert isinstance(new_policy, list), new_policy
discrete_p_m = True
--
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