From 5bb0d1c0b815c98048ad7100286d573d83909723 Mon Sep 17 00:00:00 2001
From: Max Ramsay King <maxramsayking@gmail.com>
Date: Wed, 20 Apr 2022 15:13:39 -0700
Subject: [PATCH] logging info
---
.../test/cifar-10-batches-py/batches.meta | Bin 0 -> 158 bytes
.../test/cifar-10-batches-py/readme.html | 1 +
.../train/cifar-10-batches-py/batches.meta | Bin 0 -> 158 bytes
.../train/cifar-10-batches-py/readme.html | 1 +
.../autoaugment_learners/evo_learner.py | 37 ++++++++++--------
5 files changed, 23 insertions(+), 16 deletions(-)
create mode 100644 MetaAugment/MetaAugment/test/cifar-10-batches-py/batches.meta
create mode 100644 MetaAugment/MetaAugment/test/cifar-10-batches-py/readme.html
create mode 100644 MetaAugment/MetaAugment/train/cifar-10-batches-py/batches.meta
create mode 100644 MetaAugment/MetaAugment/train/cifar-10-batches-py/readme.html
diff --git a/MetaAugment/MetaAugment/test/cifar-10-batches-py/batches.meta b/MetaAugment/MetaAugment/test/cifar-10-batches-py/batches.meta
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diff --git a/MetaAugment/MetaAugment/train/cifar-10-batches-py/batches.meta b/MetaAugment/MetaAugment/train/cifar-10-batches-py/batches.meta
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diff --git a/MetaAugment/MetaAugment/train/cifar-10-batches-py/readme.html b/MetaAugment/MetaAugment/train/cifar-10-batches-py/readme.html
new file mode 100644
index 00000000..e377adef
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+++ b/MetaAugment/MetaAugment/train/cifar-10-batches-py/readme.html
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+<meta HTTP-EQUIV="REFRESH" content="0; url=http://www.cs.toronto.edu/~kriz/cifar.html">
diff --git a/MetaAugment/autoaugment_learners/evo_learner.py b/MetaAugment/autoaugment_learners/evo_learner.py
index f0e3a597..b4c2e4be 100644
--- a/MetaAugment/autoaugment_learners/evo_learner.py
+++ b/MetaAugment/autoaugment_learners/evo_learner.py
@@ -29,7 +29,6 @@ class evo_learner():
batch_size=8,
toy_flag=False,
toy_size=0.1,
- sub_num_pol=5,
fun_num = 14,
exclude_method=[],
):
@@ -46,15 +45,15 @@ class evo_learner():
max_epochs=max_epochs,
early_stop_num=early_stop_num,)
-
- self.auto_aug_agent = Evo_learner(fun_num=fun_num, p_bins=p_bins, m_bins=m_bins, sub_num_pol=sub_num_pol)
+ self.num_solutions = num_solutions
+ self.auto_aug_agent = Evo_learner(fun_num=fun_num, p_bins=p_bins, m_bins=m_bins, sub_num_pol=sp_num)
self.torch_ga = torchga.TorchGA(model=self.auto_aug_agent, num_solutions=num_solutions)
self.num_parents_mating = num_parents_mating
self.initial_population = self.torch_ga.population_weights
self.train_loader = train_loader
self.child_network = child_network
self.p_bins = p_bins
- self.sub_num_pol = sub_num_pol
+ self.sub_num_pol = sp_num
self.m_bins = m_bins
self.fun_num = fun_num
self.augmentation_space = [x for x in augmentation_space if x[0] not in exclude_method]
@@ -121,15 +120,15 @@ class evo_learner():
"""
section = self.auto_aug_agent.fun_num + self.auto_aug_agent.p_bins + self.auto_aug_agent.m_bins
- y = self.auto_aug_agent.forward(x) # 1000 x 32
+ y = self.auto_aug_agent.forward(x)
- y_1 = torch.softmax(y[:,:self.auto_aug_agent.fun_num], dim = 1) # 1000 x 14
+ y_1 = torch.softmax(y[:,:self.auto_aug_agent.fun_num], dim = 1)
y[:,:self.auto_aug_agent.fun_num] = y_1
y_2 = torch.softmax(y[:,section:section+self.auto_aug_agent.fun_num], dim = 1)
y[:,section:section+self.auto_aug_agent.fun_num] = y_2
concat = torch.cat((y_1, y_2), dim = 1)
- cov_mat = torch.cov(concat.T)#[:self.auto_aug_agent.fun_num, self.auto_aug_agent.fun_num:]
+ cov_mat = torch.cov(concat.T)
cov_mat = cov_mat[:self.auto_aug_agent.fun_num, self.auto_aug_agent.fun_num:]
shape_store = cov_mat.shape
@@ -197,9 +196,16 @@ class evo_learner():
Solution_idx -> Int
"""
self.num_generations = iterations
+ self.running_best = [0 for i in range(iterations)]
+ self.running_avg = [0 for i in range(iterations)]
+ self.gen_count = 0
+ self.best_model = 0
+
self.set_up_instance()
self.ga_instance.run()
+ self.running_avg = self.running_avg / self.num_solutions
+
solution, solution_fitness, solution_idx = self.ga_instance.best_solution()
if return_weights:
return torchga.model_weights_as_dict(model=self.auto_aug_agent, weights_vector=solution)
@@ -207,14 +213,6 @@ class evo_learner():
return solution, solution_fitness, solution_idx
- def new_model(self):
- """
- Simple function to create a copy of the secondary model (used for classification)
- """
- copy_model = copy.deepcopy(self.child_network)
- return copy_model
-
-
def set_up_instance(self, train_dataset, test_dataset):
"""
Initialises GA instance, as well as fitness and on_generation functions
@@ -249,9 +247,15 @@ class evo_learner():
full_policy = self.get_full_policy(test_x)
- fit_val = ((self.test_autoaugment_policy(full_policy, train_dataset, test_dataset)[0])/
+ fit_val = ((self.test_autoaugment_policy(full_policy, train_dataset, test_dataset)[0]) /
+ self.test_autoaugment_policy(full_policy, train_dataset, test_dataset)[0]) / 2
+ if fit_val > self.running_best[self.gen_count]:
+ self.running_best[self.gen_count] = fit_val
+
+ self.running_avg[self.gen_count] += fit_val
+
+
return fit_val
def on_generation(ga_instance):
@@ -267,6 +271,7 @@ class evo_learner():
None
"""
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
+ self.gen_count += 1
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
return
--
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