Format with black

alternative_loaders.py

@@ -18,8 +18,12 @@
 transform_no_aug = transforms.Compose([TT, NRM])
 
 # Downloading/Louding CIFAR10 data
-trainset = CIFAR10(root='./data', train=True, download=True)  # , transform = transform_with_aug)
-testset = CIFAR10(root='./data', train=False, download=True)  # , transform = transform_no_aug)
+trainset = CIFAR10(
+    root="./data", train=True, download=True
+)  # , transform = transform_with_aug)
+testset = CIFAR10(
+    root="./data", train=False, download=True
+)  # , transform = transform_no_aug)
 classDict = trainset.class_to_idx
 
 # Separating trainset/testset data/label
@@ -31,6 +35,7 @@
 
 # Define a function to separate CIFAR classes by class index
 
+
 def get_class_i(x, y, i):
     """
     x: trainset.train_data or testset.test_data
@@ -89,35 +94,43 @@ def index_of_which_bin(self, bin_sizes, absolute_index, verbose=False):
 
 # ================== Usage ================== #
 
-def get_n_fold_datasets_train(t, batch_size, class_names=['cat', 'dog']):
+
+def get_n_fold_datasets_train(t, batch_size, class_names=["cat", "dog"]):
 
     # Let's choose cats (class 3 of CIFAR) and dogs (class 5 of CIFAR) as trainset/testset
-    cat_dog_trainset = \
-        DatasetMaker(
-            [get_class_i(x_train, y_train, classDict[class_names[0]]), get_class_i(x_train, y_train, classDict[class_names[1]])],
-            transform_with_aug
-        )
+    cat_dog_trainset = DatasetMaker(
+        [
+            get_class_i(x_train, y_train, classDict[class_names[0]]),
+            get_class_i(x_train, y_train, classDict[class_names[1]]),
+        ],
+        transform_with_aug,
+    )
 
-    kwargs = {'num_workers': 3, 'pin_memory': False}
+    kwargs = {"num_workers": 3, "pin_memory": False}
 
     # Create datasetLoaders from trainset and testse
 
-    trainsetLoader = DataLoader(cat_dog_trainset, batch_size=batch_size, shuffle=True, **kwargs)
+    trainsetLoader = DataLoader(
+        cat_dog_trainset, batch_size=batch_size, shuffle=True, **kwargs
+    )
     return trainsetLoader
 
 
-def get_n_fold_datasets_test(t, batch_size, class_names=['cat', 'dog']):
+def get_n_fold_datasets_test(t, batch_size, class_names=["cat", "dog"]):
     # Let's choose cats (class 3 of CIFAR) and dogs (class 5 of CIFAR) as trainset/testset
-    cat_dog_testset = \
-        DatasetMaker(
-            [get_class_i(x_test, y_test, classDict[class_names[0]]),
-             get_class_i(x_test, y_test, classDict[class_names[1]])],
-            transform_no_aug
-        )
+    cat_dog_testset = DatasetMaker(
+        [
+            get_class_i(x_test, y_test, classDict[class_names[0]]),
+            get_class_i(x_test, y_test, classDict[class_names[1]]),
+        ],
+        transform_no_aug,
+    )
 
-    kwargs = {'num_workers': 3, 'pin_memory': False}
+    kwargs = {"num_workers": 3, "pin_memory": False}
 
     # Create datasetLoaders from trainset and testse
 
-    testsetLoader = DataLoader(cat_dog_testset, batch_size=batch_size, shuffle=False, **kwargs)
-    return testsetLoader
+    testsetLoader = DataLoader(
+        cat_dog_testset, batch_size=batch_size, shuffle=False, **kwargs
+    )
+    return testsetLoader

csv_logger.py

@@ -1,31 +1,45 @@
 import csv
 import pandas as pd
 
-def extract_metrics_from_ordeered_dict(ordered_dict, mode='train', result={}):
+
+def extract_metrics_from_ordeered_dict(ordered_dict, mode="train", result={}):
     for key in ordered_dict.keys():
-        name = key.split('/')[-1]
-        result[mode+'_'+name] = [ordered_dict[key]]
+        name = key.split("/")[-1]
+        result[mode + "_" + name] = [ordered_dict[key]]
     return result
+
+
 def extract_metrics_from_scalaer_dict(log_dict):
     result_dict = {}
     for key in log_dict.keys():
-        mode = key.split('-')[0]
+        mode = key.split("-")[0]
         extract_metrics_from_ordeered_dict(log_dict[key], mode, result_dict)
     return result_dict
 
+
 def log_to_csv(value_dict, savename):
     df = pd.DataFrame.from_dict(value_dict)
-    df.to_csv(savename+'.csv', sep=';')
+    df.to_csv(savename + ".csv", sep=";")
+
 
-def record_metrics(value_dict, log_dict, train_accuracy, train_loss, test_accuracy, test_loss, epoch, time):
+def record_metrics(
+    value_dict,
+    log_dict,
+    train_accuracy,
+    train_loss,
+    test_accuracy,
+    test_loss,
+    epoch,
+    time,
+):
 
     result_dict = extract_metrics_from_scalaer_dict(log_dict)
-    result_dict['train_accuracy'] = [train_accuracy]
-    result_dict['test_accuracy'] = [test_accuracy]
-    result_dict['train_loss'] = [train_loss]
-    result_dict['test_loss'] = [test_loss]
-    result_dict['epoch'] = [epoch]
-    result_dict['time_per_step'] = [time]
+    result_dict["train_accuracy"] = [train_accuracy]
+    result_dict["test_accuracy"] = [test_accuracy]
+    result_dict["train_loss"] = [train_loss]
+    result_dict["test_loss"] = [test_loss]
+    result_dict["epoch"] = [epoch]
+    result_dict["time_per_step"] = [time]
     if value_dict is None:
         return result_dict
     else:

extract_history.py

@@ -7,12 +7,15 @@
 
 
 def tabulate_events(dpath):
-    summary_iterators = [EventAccumulator(os.path.join(dpath, dname)).Reload() for dname in os.listdir(dpath)]
+    summary_iterators = [
+        EventAccumulator(os.path.join(dpath, dname)).Reload()
+        for dname in os.listdir(dpath)
+    ]
 
-    tags = summary_iterators[0].Tags()['scalars']
+    tags = summary_iterators[0].Tags()["scalars"]
 
     for it in summary_iterators:
-        assert it.Tags()['scalars'] == tags
+        assert it.Tags()["scalars"] == tags
 
     out = defaultdict(list)
     steps = []
@@ -41,13 +44,13 @@ def to_csv(dpath):
 
 
 def get_file_path(dpath, tag):
-    file_name = tag.replace("/", "_") + '.csv'
-    folder_path = os.path.join(dpath, 'csv')
+    file_name = tag.replace("/", "_") + ".csv"
+    folder_path = os.path.join(dpath, "csv")
     if not os.path.exists(folder_path):
         os.makedirs(folder_path)
     return os.path.join(folder_path, file_name)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     path = "train_run_576_1152_2304"
-    to_csv(path)
+    to_csv(path)