From 5c3220051e377196f5a81f1b47aba96c4ba46edc Mon Sep 17 00:00:00 2001
From: Max Ramsay King <maxramsayking@gmail.com>
Date: Tue, 26 Apr 2022 21:24:34 +0100
Subject: [PATCH] fixed evo learner
---
.../autoaugment_learners/evo_learner.py | 65 +++++++++----------
1 file changed, 32 insertions(+), 33 deletions(-)
diff --git a/MetaAugment/autoaugment_learners/evo_learner.py b/MetaAugment/autoaugment_learners/evo_learner.py
index 34cc2d44..a1f21a1a 100644
--- a/MetaAugment/autoaugment_learners/evo_learner.py
+++ b/MetaAugment/autoaugment_learners/evo_learner.py
@@ -1,3 +1,4 @@
+from cgi import test
import torch
torch.manual_seed(0)
import torch.nn as nn
@@ -5,43 +6,39 @@ import pygad
import pygad.torchga as torchga
import copy
import torch
+from meta_augment.controller_networks.evo_controller import evo_controller
-from MetaAugment.autoaugment_learners.aa_learner import aa_learner
+from meta_augment.autoaugment_learners.aa_learner import aa_learner
+import meta_augment.child_networks as cn
class evo_learner(aa_learner):
def __init__(self,
- # search space settings
- sp_num=5,
- p_bins=10,
- m_bins=10,
- discrete_p_m=False,
- exclude_method=[],
- # child network settings
- learning_rate=1e-1,
+ sp_num=1,
+ num_solutions = 5,
+ num_parents_mating = 3,
+ learning_rate = 1e-1,
max_epochs=float('inf'),
early_stop_num=20,
+ p_bins = 1,
+ m_bins = 1,
batch_size=8,
- toy_size=1,
- # evolutionary learner specific settings
- num_solutions=5,
- num_parents_mating=3,
- controller=None
+ toy_size=0.1,
+ fun_num = 14,
+ exclude_method=[],
+ controller = None
):
- super().__init__(
- sp_num=sp_num,
- p_bins=p_bins,
- m_bins=m_bins,
- discrete_p_m=discrete_p_m,
- batch_size=batch_size,
- toy_size=toy_size,
- learning_rate=learning_rate,
- max_epochs=max_epochs,
- early_stop_num=early_stop_num,
- exclude_method=exclude_method
- )
+ super().__init__(sp_num,
+ fun_num,
+ p_bins,
+ m_bins,
+ batch_size=batch_size,
+ toy_size=toy_size,
+ learning_rate=learning_rate,
+ max_epochs=max_epochs,
+ early_stop_num=early_stop_num,)
self.num_solutions = num_solutions
self.controller = controller
@@ -51,6 +48,8 @@ class evo_learner(aa_learner):
self.p_bins = p_bins
self.sub_num_pol = sp_num
self.m_bins = m_bins
+ self.fun_num = fun_num
+ self.augmentation_space = [x for x in self.augmentation_space if x[0] not in exclude_method]
self.policy_dict = {}
self.policy_result = []
@@ -58,6 +57,7 @@ class evo_learner(aa_learner):
assert num_solutions > num_parents_mating, 'Number of solutions must be larger than the number of parents mating!'
+
def get_full_policy(self, x):
"""
Generates the full policy (self.num_sub_pol subpolicies). Network architecture requires
@@ -167,10 +167,10 @@ class evo_learner(aa_learner):
prob2 += torch.sigmoid(y[idx, section+self.fun_num]).item()
if mag1 is not None:
# mag1 += min(max(0, (y[idx, self.auto_aug_agent.fun_num+1]).item()), 8)
- mag1 += 10 * torch.sigmoid(y[idx, self.fun_num+1]).item()
+ mag1 += min(9, 10 * torch.sigmoid(y[idx, self.fun_num+1]).item())
if mag2 is not None:
# mag2 += min(max(0, y[idx, section+self.auto_aug_agent.fun_num+1].item()), 8)
- mag2 += 10 * torch.sigmoid(y[idx, self.fun_num+1]).item()
+ mag2 += min(9, 10 * torch.sigmoid(y[idx, self.fun_num+1]).item())
counter += 1
@@ -240,7 +240,7 @@ class evo_learner(aa_learner):
self.policy_dict[trans1][trans2].append(new_set)
return False
else:
- self.policy_dict[trans1][trans2] = [new_set]
+ self.policy_dict[trans1] = {trans2: [new_set]}
if trans2 in self.policy_dict:
if trans1 in self.policy_dict[trans2]:
for test_pol in self.policy_dict[trans2][trans1]:
@@ -249,7 +249,7 @@ class evo_learner(aa_learner):
self.policy_dict[trans2][trans1].append(new_set)
return False
else:
- self.policy_dict[trans2][trans1] = [new_set]
+ self.policy_dict[trans2] = {trans1: [new_set]}
def set_up_instance(self, train_dataset, test_dataset, child_network_architecture):
@@ -298,11 +298,11 @@ class evo_learner(aa_learner):
if len(self.policy_result) > self.sp_num:
self.policy_result = sorted(self.policy_result, key=lambda x: x[1], reverse=True)
self.policy_result = self.policy_result[:self.sp_num]
- print("Appended policy: ", self.policy_result)
+ print("appended policy: ", self.policy_result)
if fit_val > self.history_best[self.gen_count]:
- print("Best policy: ", full_policy)
+ print("best policy: ", full_policy)
self.history_best[self.gen_count] = fit_val
self.best_model = model_weights_dict
@@ -335,4 +335,3 @@ class evo_learner(aa_learner):
mutation_percent_genes = 0.1,
fitness_func=fitness_func,
on_generation = on_generation)
-
--
GitLab