benchmark code

benchmark/scripts/04_22_ci_gru.py

+import torchvision.datasets as datasets
+import torchvision
+import torch
+
+import MetaAugment.child_networks as cn
+import MetaAugment.autoaugment_learners as aal
+
+from .util_04_22 import *
+
+
+# aa_learner config
+config = {
+        'sp_num' : 3,
+        'learning_rate' : 1e-1,
+        'toy_flag' : False,
+#         'toy_flag' : True,
+#         'toy_size' : 0.001,
+        'batch_size' : 32,
+        'max_epochs' : 100,
+        'early_stop_num' : 10,
+        }
+
+
+# CIFAR10 with LeNet
+train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                        train=True, download=True, transform=None)
+test_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                        train=False, download=True, 
+                        transform=torchvision.transforms.ToTensor())
+child_network_architecture = cn.LeNet(
+                                    img_height=32,
+                                    img_width=32,
+                                    num_labels=10,
+                                    img_channels=3
+                                    )
+
+
+# gru
+run_benchmark(
+    save_file='./benchmark/pickles/04_22_cf_ln_gru',
+    train_dataset=train_dataset,
+    test_dataset=test_dataset,
+    child_network_architecture=child_network_architecture,
+    agent_arch=aal.gru_learner,
+    config=config,
+    )
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benchmark/scripts/04_22_ci_rs.py

+import torchvision.datasets as datasets
+import torchvision
+import torch
+
+import MetaAugment.child_networks as cn
+import MetaAugment.autoaugment_learners as aal
+
+from .util_04_22 import *
+
+
+# aa_learner config
+config = {
+        'sp_num' : 3,
+        'learning_rate' : 1e-1,
+        'toy_flag' : False,
+#         'toy_flag' : True,
+#         'toy_size' : 0.001,
+        'batch_size' : 32,
+        'max_epochs' : 100,
+        'early_stop_num' : 10,
+        }
+
+
+# CIFAR10 with LeNet
+train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                        train=True, download=True, transform=None)
+test_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                        train=False, download=True, 
+                        transform=torchvision.transforms.ToTensor())
+child_network_architecture = cn.LeNet(
+                                    img_height=32,
+                                    img_width=32,
+                                    num_labels=10,
+                                    img_channels=3
+                                    )
+
+
+# rs
+run_benchmark(
+    save_file='./benchmark/pickles/04_22_cf_ln_rs',
+    train_dataset=train_dataset,
+    test_dataset=test_dataset,
+    child_network_architecture=child_network_architecture,
+    agent_arch=aal.randomsearch_learner,
+    config=config,
+    )

benchmark/scripts/04_22_fm_gru.py

+import torchvision.datasets as datasets
+import torchvision
+import torch
+
+import MetaAugment.child_networks as cn
+import MetaAugment.autoaugment_learners as aal
+
+from .util_04_22 import *
+
+
+# aa_learner config
+config = {
+        'sp_num' : 3,
+        'learning_rate' : 1e-1,
+        'toy_flag' : False,
+#         'toy_flag' : True,
+#         'toy_size' : 0.001,
+        'batch_size' : 32,
+        'max_epochs' : 100,
+        'early_stop_num' : 10,
+        }
+
+
+# FashionMNIST with SimpleNet
+train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+                            train=True, download=True, transform=None)
+test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+                        train=False, download=True,
+                        transform=torchvision.transforms.ToTensor())
+child_network_architecture = cn.SimpleNet
+
+
+# gru
+run_benchmark(
+    save_file='./benchmark/pickles/04_22_fm_sn_gru.pkl',
+    train_dataset=train_dataset,
+    test_dataset=test_dataset,
+    child_network_architecture=child_network_architecture,
+    agent_arch=aal.gru_learner,
+    config=config,
+    )
\ No newline at end of file

benchmark/scripts/04_22_fm_rs.py

+import torchvision.datasets as datasets
+import torchvision
+import torch
+
+import MetaAugment.child_networks as cn
+import MetaAugment.autoaugment_learners as aal
+
+from .util_04_22 import *
+
+
+# aa_learner config
+config = {
+        'sp_num' : 3,
+        'learning_rate' : 1e-1,
+        'toy_flag' : False,
+#         'toy_flag' : True,
+#         'toy_size' : 0.001,
+        'batch_size' : 32,
+        'max_epochs' : 100,
+        'early_stop_num' : 10,
+        }
+
+
+# FashionMNIST with SimpleNet
+train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+                            train=True, download=True, transform=None)
+test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+                        train=False, download=True,
+                        transform=torchvision.transforms.ToTensor())
+child_network_architecture = cn.SimpleNet
+
+
+# rs
+run_benchmark(
+    save_file='./benchmark/pickles/04_22_fm_sn_rs.pkl',
+    train_dataset=train_dataset,
+    test_dataset=test_dataset,
+    child_network_architecture=child_network_architecture,
+    agent_arch=aal.randomsearch_learner,
+    config=config,
+    )
\ No newline at end of file

benchmark/scripts/04_22_gru_rs.py → benchmark/scripts/util_04_22.py

@@ -5,7 +5,7 @@ import torch
 import MetaAugment.child_networks as cn
 import MetaAugment.autoaugment_learners as aal
 
-from pathlib import Path
+
 
 """
 testing gru_learner and randomsearch_learner on
@@ -25,12 +25,12 @@ else:
 
 def run_benchmark(
     save_file,
-    total_iter,
     train_dataset,
     test_dataset,
     child_network_architecture,
     agent_arch,
     config,
+    total_iter=150,
     ):
     try:
         # try to load agent
@@ -56,82 +56,4 @@ def run_benchmark(
         with open(save_file, 'wb+') as f:
             torch.save(agent, f)
 
-    print('run_benchmark closing')
-
-
-# aa_learner config
-config = {
-        'sp_num' : 3,
-        'learning_rate' : 1e-1,
-        'toy_flag' : False,
-#         'toy_flag' : True,
-#         'toy_size' : 0.001,
-        'batch_size' : 32,
-        'max_epochs' : 100,
-        'early_stop_num' : 10,
-        }
-total_iter=150
-
-
-# FashionMNIST with SimpleNet
-train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
-                            train=True, download=True, transform=None)
-test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
-                        train=False, download=True,
-                        transform=torchvision.transforms.ToTensor())
-child_network_architecture = cn.SimpleNet
-
-
-# gru
-run_benchmark(
-    save_file='./benchmark/pickles/04_22_fm_sn_gru.pkl',
-    total_iter=total_iter,
-    train_dataset=train_dataset,
-    test_dataset=test_dataset,
-    child_network_architecture=child_network_architecture,
-    agent_arch=aal.gru_learner,
-    config=config,
-    )
-
-# rs
-run_benchmark(
-    save_file='./benchmark/pickles/04_22_fm_sn_rs.pkl',
-    total_iter=total_iter,
-    train_dataset=train_dataset,
-    test_dataset=test_dataset,
-    child_network_architecture=child_network_architecture,
-    agent_arch=aal.randomsearch_learner,
-    config=config,
-    )
-
-
-# CIFAR10 with LeNet
-train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
-                        train=True, download=True, transform=None)
-test_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
-                        train=False, download=True, 
-                        transform=torchvision.transforms.ToTensor())
-child_network_architecture = cn.SimpleNet
-
-
-# gru
-run_benchmark(
-    save_file='./benchmark/pickles/04_22_cf_ln_gru',
-    total_iter=total_iter,
-    train_dataset=train_dataset,
-    test_dataset=test_dataset,
-    child_network_architecture=child_network_architecture,
-    agent_arch=aal.gru_learner,
-    config=config,
-    )
-
-# rs
-run_benchmark(
-    save_file='./benchmark/pickles/04_22_cf_ln_rs',
-    total_iter=total_iter,
-    train_dataset=train_dataset,
-    test_dataset=test_dataset,
-    child_network_architecture=child_network_architecture,
-    agent_arch=aal.randomsearch_learner,
-    config=config,
-    )
+    print('run_benchmark closing')
\ No newline at end of file