diff --git a/MetaAugment/autoaugment_learners/evo_learner.py b/MetaAugment/autoaugment_learners/evo_learner.py
index 34cc2d44555423475914a1ba2528cfddb71aad57..c1ba5ed47f54b86d2a11ce4dac887608733eda7c 100644
--- a/MetaAugment/autoaugment_learners/evo_learner.py
+++ b/MetaAugment/autoaugment_learners/evo_learner.py
@@ -1,12 +1,12 @@
import torch
-torch.manual_seed(0)
import torch.nn as nn
import pygad
import pygad.torchga as torchga
-import copy
+import torchvision
import torch
from MetaAugment.autoaugment_learners.aa_learner import aa_learner
+import MetaAugment.controller_networks as cont_n
class evo_learner(aa_learner):
@@ -14,7 +14,7 @@ class evo_learner(aa_learner):
def __init__(self,
# search space settings
sp_num=5,
- p_bins=10,
+ p_bins=11,
m_bins=10,
discrete_p_m=False,
exclude_method=[],
@@ -27,7 +27,7 @@ class evo_learner(aa_learner):
# evolutionary learner specific settings
num_solutions=5,
num_parents_mating=3,
- controller=None
+ controller=cont_n.evo_controller
):
super().__init__(
@@ -43,14 +43,19 @@ class evo_learner(aa_learner):
exclude_method=exclude_method
)
+ # evolutionary algorithm settings
+ self.controller = controller(
+ fun_num=self.fun_num,
+ p_bins=self.p_bins,
+ m_bins=self.m_bins,
+ sub_num_pol=self.sp_num
+ )
self.num_solutions = num_solutions
- self.controller = controller
self.torch_ga = torchga.TorchGA(model=self.controller, num_solutions=num_solutions)
self.num_parents_mating = num_parents_mating
self.initial_population = self.torch_ga.population_weights
- self.p_bins = p_bins
- self.sub_num_pol = sp_num
- self.m_bins = m_bins
+
+ # store our logs
self.policy_dict = {}
self.policy_result = []
@@ -77,7 +82,7 @@ class evo_learner(aa_learner):
section = self.fun_num + self.p_bins + self.m_bins
y = self.controller.forward(x)
full_policy = []
- for pol in range(self.sub_num_pol):
+ for pol in range(self.sp_num):
int_pol = []
for _ in range(2):
idx_ret = torch.argmax(y[:, (pol * section):(pol*section) + self.fun_num].mean(dim = 0))
@@ -277,6 +282,7 @@ class evo_learner(aa_learner):
weights_vector=solution)
self.controller.load_state_dict(model_weights_dict)
+ train_dataset.transform = torchvision.transforms.ToTensor()
self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size)
for idx, (test_x, label_x) in enumerate(self.train_loader):
diff --git a/temp_util/wapp_util.py b/temp_util/wapp_util.py
index cde572f7f1e0ba1590fb5685b7212f4d0b3b173a..bb10113311f77d32f3f0b996e76d71f16fa0d184 100644
--- a/temp_util/wapp_util.py
+++ b/temp_util/wapp_util.py
@@ -54,6 +54,7 @@ def parse_users_learner_spec(
p_bins=11,
m_bins=10,
discrete_p_m=True,
+ exclude_method=exclude_method,
# hyperparameters for when training the child_network
batch_size=batch_size,
toy_size=toy_size,
@@ -63,52 +64,55 @@ def parse_users_learner_spec(
# ucb_learner specific hyperparameter
num_policies=num_policies
)
- pprint(learner.policies)
-
- learner.learn(
- train_dataset=train_dataset,
- test_dataset=test_dataset,
- child_network_architecture=child_archi,
- iterations=5
- )
elif auto_aug_learner == 'Evolutionary Learner':
- network = cont_n.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
- child_network = cn.LeNet()
learner = aal.evo_learner(
- network=network,
- fun_num=num_funcs,
- p_bins=1,
- mag_bins=1,
- sub_num_pol=1,
- ds = ds,
- ds_name=ds_name,
- exclude_method=exclude_method,
- child_network=child_network
- )
+ # parameters that define the search space
+ sp_num=num_sub_policies,
+ p_bins=11,
+ m_bins=10,
+ discrete_p_m=True,
+ exclude_method=exclude_method,
+ # hyperparameters for when training the child_network
+ batch_size=batch_size,
+ toy_size=toy_size,
+ learning_rate=learning_rate,
+ max_epochs=max_epochs,
+ early_stop_num=early_stop_num,
+ )
learner.run_instance()
elif auto_aug_learner == 'Random Searcher':
agent = aal.randomsearch_learner(
- sp_num=num_sub_policies,
- batch_size=batch_size,
- learning_rate=learning_rate,
- toy_size=toy_size,
- max_epochs=max_epochs,
- early_stop_num=early_stop_num,
- )
- agent.learn(train_dataset,
- test_dataset,
- child_network_architecture=child_archi,
- iterations=iterations)
+ # parameters that define the search space
+ sp_num=num_sub_policies,
+ p_bins=11,
+ m_bins=10,
+ discrete_p_m=True,
+ exclude_method=exclude_method,
+ # hyperparameters for when training the child_network
+ batch_size=batch_size,
+ toy_size=toy_size,
+ learning_rate=learning_rate,
+ max_epochs=max_epochs,
+ early_stop_num=early_stop_num,
+ )
elif auto_aug_learner == 'GRU Learner':
agent = aal.gru_learner(
- sp_num=num_sub_policies,
- batch_size=batch_size,
- learning_rate=learning_rate,
- toy_size=toy_size,
- max_epochs=max_epochs,
- early_stop_num=early_stop_num,
- )
- agent.learn(train_dataset,
- test_dataset,
- child_network_architecture=child_archi,
- iterations=iterations)
\ No newline at end of file
+ # parameters that define the search space
+ sp_num=num_sub_policies,
+ p_bins=11,
+ m_bins=10,
+ discrete_p_m=True,
+ exclude_method=exclude_method,
+ # hyperparameters for when training the child_network
+ batch_size=batch_size,
+ toy_size=toy_size,
+ learning_rate=learning_rate,
+ max_epochs=max_epochs,
+ early_stop_num=early_stop_num,
+ )
+
+
+ agent.learn(train_dataset,
+ test_dataset,
+ child_network_architecture=child_archi,
+ iterations=iterations)
\ No newline at end of file