From 71673f0c70747d20b3df9d5c96b7196e3e695862 Mon Sep 17 00:00:00 2001
From: Max Ramsay King <maxramsayking@gmail.com>
Date: Fri, 29 Apr 2022 12:46:52 +0100
Subject: [PATCH] doc strings
---
04_22_evo.py | 2 +-
autoaug/autoaugment_learners/GenLearner.py | 104 ++++++++++++++++++---
benchmark/pickles/04_22_cf_ln_rssad.pkl | Bin 0 -> 241 bytes
3 files changed, 90 insertions(+), 16 deletions(-)
create mode 100644 benchmark/pickles/04_22_cf_ln_rssad.pkl
diff --git a/04_22_evo.py b/04_22_evo.py
index 6b84237c..53f765d6 100644
--- a/04_22_evo.py
+++ b/04_22_evo.py
@@ -76,7 +76,7 @@ def run_benchmark(
train_dataset=train_dataset,
test_dataset=test_dataset,
child_network_architecture=child_network_architecture,
- iterations=1
+ iterations=5
)
# save agent every iteration
with open(save_file, 'wb+') as f:
diff --git a/autoaug/autoaugment_learners/GenLearner.py b/autoaug/autoaugment_learners/GenLearner.py
index b93d106b..b3d70720 100644
--- a/autoaug/autoaugment_learners/GenLearner.py
+++ b/autoaug/autoaugment_learners/GenLearner.py
@@ -83,6 +83,13 @@ class Genetic_learner(AaLearner):
def gen_random_subpol(self):
+ """
+ Generates a random subpolicy using the reduced augmentation_space
+
+ Returns
+ --------
+ subpolicy -> ((transformation, probability, magnitude), (trans., prob., mag.))
+ """
choose_items = [x[0] for x in self.augmentation_space]
trans1 = str(random.choice(choose_items))
trans2 = str(random.choice(choose_items))
@@ -104,30 +111,50 @@ class Genetic_learner(AaLearner):
def gen_random_policy(self):
+ """
+ Generates a random policy, consisting of sp_num subpolicies
+
+ Returns
+ ------------
+ policy -> [subpolicy, subpolicy, ...]
+ """
pol = []
for _ in range(self.sp_num):
pol.append(self.gen_random_subpol())
return pol
- def bin_to_subpol(self, subpol):
+ def bin_to_subpol(self, subpol_bin):
+ """
+ Converts a binary string representation of a subpolicy to a subpolicy
+
+ Parameters
+ ------------
+ subpol_bin -> str
+ Binary representation of a subpolicy
+
+
+ Returns
+ -----------
+ policy -> [(subpolicy)]
+ """
pol = []
for idx in range(2):
- if subpol[idx*12:(idx*12)+4] in self.bin_to_aug:
- trans = self.bin_to_aug[subpol[idx*12:(idx*12)+4]]
+ if subpol_bin[idx*12:(idx*12)+4] in self.bin_to_aug:
+ trans = self.bin_to_aug[subpol_bin[idx*12:(idx*12)+4]]
else:
trans = random.choice(self.just_augs)
mag_is_none = not self.aug_space_dict[trans]
- if subpol[(idx*12)+4: (idx*12)+8] in self.bin_to_prob:
- prob = float(self.bin_to_prob[subpol[(idx*12)+4: (idx*12)+8]])
+ if subpol_bin[(idx*12)+4: (idx*12)+8] in self.bin_to_prob:
+ prob = float(self.bin_to_prob[subpol_bin[(idx*12)+4: (idx*12)+8]])
else:
prob = float(random.randrange(0, 11, 1) / 10)
- if subpol[(idx*12)+8:(idx*12)+12] in self.bin_to_mag:
- mag = int(self.bin_to_mag[subpol[(idx*12)+8:(idx*12)+12]])
+ if subpol_bin[(idx*12)+8:(idx*12)+12] in self.bin_to_mag:
+ mag = int(self.bin_to_mag[subpol_bin[(idx*12)+8:(idx*12)+12]])
else:
mag = int(random.randrange(0, 10, 1))
@@ -139,28 +166,54 @@ class Genetic_learner(AaLearner):
def subpol_to_bin(self, subpol):
- pol = ''
+ """
+ Converts a subpolicy to its binary representation
+
+ Parameters
+ ------------
+ subpol -> ((transforamtion, probability, magnitude), (trans., prob., mag.))
+
+ Returns
+ ------------
+ bin_pol -> str
+ Binary representation of the subpolicy
+ """
+ bin_pol = ''
trans1, prob1, mag1 = subpol[0]
trans2, prob2, mag2 = subpol[1]
- pol += self.aug_to_bin[trans1] + self.prob_to_bin[str(prob1)]
+ bin_pol += self.aug_to_bin[trans1] + self.prob_to_bin[str(prob1)]
if mag1 == None:
- pol += '1111'
+ bin_pol += '1111'
else:
- pol += self.mag_to_bin[str(mag1)]
+ bin_pol += self.mag_to_bin[str(mag1)]
- pol += self.aug_to_bin[trans2] + self.prob_to_bin[str(prob2)]
+ bin_pol += self.aug_to_bin[trans2] + self.prob_to_bin[str(prob2)]
if mag2 == None:
- pol += '1111'
+ bin_pol += '1111'
else:
- pol += self.mag_to_bin[str(mag2)]
+ bin_pol += self.mag_to_bin[str(mag2)]
- return pol
+ return bin_pol
def choose_parents(self, parents, parents_weights):
+ """
+ Chooses parents from which the next policy will be generated from
+
+ Parameters
+ ------------
+ parents -> [policy, policy, ...]
+
+ parents_weights -> [float, float, ...]
+
+ Returns
+ ------------
+ (parent1, parent2) -> (policy, policy)
+
+ """
parent1 = random.choices(parents, parents_weights, k=1)[0][0]
parent2 = random.choices(parents, parents_weights, k=1)[0][0]
while parent2 == parent1:
@@ -171,6 +224,13 @@ class Genetic_learner(AaLearner):
def generate_children(self):
+ """
+ Generates children via the random crossover method
+
+ Returns
+ ------------
+ new_pols -> [child_policy, child_policy, ...]
+ """
parent_acc = sorted(self.history, key = lambda x: x[1], reverse=True)
parents = [x[0] for x in parent_acc]
parents_weights = [x[1] for x in parent_acc]
@@ -188,6 +248,20 @@ class Genetic_learner(AaLearner):
def learn(self, train_dataset, test_dataset, child_network_architecture, iterations = 100):
+ """
+ Generates policies through a genetic algorithm.
+
+ Parameters
+ ------------
+ train_dataset -> torchvision.dataset
+
+ test_dataset -> torchvision.dataset
+
+ child_network_architecture ->
+
+ iterations -> int
+ number of iterations to run the instance for
+ """
for idx in range(iterations):
print("ITERATION: ", idx)
diff --git a/benchmark/pickles/04_22_cf_ln_rssad.pkl b/benchmark/pickles/04_22_cf_ln_rssad.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..29086397fdb56e3b236de68ebcf742d2ebda228b
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