From 5fadf7f72aa1115de66e61c980d24cdbb44aa3a0 Mon Sep 17 00:00:00 2001
From: codingwithsurya <codingwithsurya@users.noreply.github.com>
Date: Fri, 13 Oct 2023 02:55:45 +0000
Subject: [PATCH] :art: Format Python code with psf/black

---
 training/training/core/dataset.py       | 12 ++++++------
 training/training/core/dl_model.py      |  2 +-
 training/training/routes/image/image.py |  6 ++----
 training/training/urls.py               |  1 +
 4 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/training/training/core/dataset.py b/training/training/core/dataset.py
index fa1bf6259..dd06a859b 100644
--- a/training/training/core/dataset.py
+++ b/training/training/core/dataset.py
@@ -130,7 +130,7 @@ def __init__(
         self.train_transform = train_transform or transforms.Compose(
             [transforms.ToTensor()]
         )
-        
+
         self.test_transform = test_transform or transforms.Compose(
             [transforms.ToTensor()]
         )
@@ -139,10 +139,10 @@ def __init__(
 
         # Ensure the directory exists
         os.makedirs(self.dataset_dir, exist_ok=True)
-        print(f'train transform: {train_transform}')
-        print(f'test transform: {test_transform}')
+        print(f"train transform: {train_transform}")
+        print(f"test transform: {test_transform}")
         # Load the datasets
-        
+
         self.train_set = datasets.__dict__[dataset_name](
             root=self.dataset_dir,
             train=True,
@@ -182,7 +182,7 @@ def createTrainDataset(self) -> DataLoader:
             shuffle=self.shuffle,
             drop_last=True,
         )
-        self.delete_datasets_from_directory() # Delete datasets after loading
+        self.delete_datasets_from_directory()  # Delete datasets after loading
         return train_loader
 
     def createTestDataset(self) -> DataLoader:
@@ -192,7 +192,7 @@ def createTestDataset(self) -> DataLoader:
             shuffle=self.shuffle,
             drop_last=True,
         )
-        self.delete_datasets_from_directory() # Delete datasets after loading
+        self.delete_datasets_from_directory()  # Delete datasets after loading
         return test_loader
 
     def getCategoryList(self) -> list[str]:
diff --git a/training/training/core/dl_model.py b/training/training/core/dl_model.py
index eb9625326..73cfc2095 100644
--- a/training/training/core/dl_model.py
+++ b/training/training/core/dl_model.py
@@ -17,7 +17,7 @@ class DLModel(nn.Module):
         "SIGMOID": nn.Sigmoid,
         "LOGSOFTMAX": nn.LogSoftmax,
         "CONV2D": nn.Conv2d,
-        "DROPOUT": nn.Dropout
+        "DROPOUT": nn.Dropout,
     }
 
     def __init__(self, layer_list: list[nn.Module]):
diff --git a/training/training/routes/image/image.py b/training/training/routes/image/image.py
index 0e74a01d5..8db19c803 100644
--- a/training/training/routes/image/image.py
+++ b/training/training/routes/image/image.py
@@ -16,9 +16,7 @@
 @router.post("", auth=FirebaseAuth())
 def imageTrain(request: HttpRequest, imageParams: ImageParams):
     if imageParams.default:
-        dataCreator = ImageDefaultDatasetCreator.fromDefault(
-            imageParams.default
-        )
+        dataCreator = ImageDefaultDatasetCreator.fromDefault(imageParams.default)
         print(vars(dataCreator))
         train_loader = dataCreator.createTrainDataset()
         test_loader = dataCreator.createTestDataset()
@@ -37,7 +35,7 @@ def imageTrain(request: HttpRequest, imageParams: ImageParams):
         # )
 
         model = DLModel.fromLayerParamsList(imageParams.user_arch)
-        print(f'model is: {model}')
+        print(f"model is: {model}")
         optimizer = getOptimizer(model, imageParams.optimizer_name, 0.05)
         criterionHandler = getCriterionHandler(imageParams.criterion)
         if imageParams.problem_type == "CLASSIFICATION":
diff --git a/training/training/urls.py b/training/training/urls.py
index 2e6e6c048..f834bf4f5 100644
--- a/training/training/urls.py
+++ b/training/training/urls.py
@@ -23,6 +23,7 @@
 from training.routes.datasets.default.columns import router as default_dataset_router
 from training.routes.tabular.tabular import router as tabular_router
 from training.routes.image.image import router as image_router
+
 # from training.routes.datasets.default import get_default_datasets_router
 # from training.routes.tabular import get_tabular_router
 # from training.routes.image import image_router