🎨 Format Python code with psf/black

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]:

training/training/core/dl_model.py

@@ -17,7 +17,7 @@ class DLModel(nn.Module):
         "CONV2D": nn.Conv2d,
         "DROPOUT": nn.Dropout,
         "MAXPOOL2D": nn.MaxPool2d,
-        "FLATTEN": nn.Flatten
+        "FLATTEN": nn.Flatten,
     }
 
     def __init__(self, layer_list: list[nn.Module]):

training/training/core/trainer.py

@@ -62,10 +62,10 @@ def _train_step(self, inputs: torch.Tensor, labels: torch.Tensor):
         self.optimizer.zero_grad()  # zero out gradient for each batch
         self.model.forward(inputs)  # make prediction on input
         self._outputs: torch.Tensor = self.model(inputs)  # make prediction on input
-        print('MODEL FORWARD PASS DONE!!!!')
-        print(f'output dim: {self._outputs.shape}')
-        print(f'label dim: {labels.shape}')
-        print(f'loss function used: {self.criterionHandler}')
+        print("MODEL FORWARD PASS DONE!!!!")
+        print(f"output dim: {self._outputs.shape}")
+        print(f"label dim: {labels.shape}")
+        print(f"loss function used: {self.criterionHandler}")
         loss = self.criterionHandler.compute_loss(self._outputs, labels)
         loss.backward()  # backpropagation
         self.optimizer.step()  # adjust optimizer weights

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":

training/training/urls.py

@@ -22,6 +22,7 @@
 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
@@ -41,4 +42,4 @@ def test(request: HttpRequest):
 urlpatterns = [
     path("admin/", admin.site.urls),
     path("api/", api.urls),  # type: ignore
-]
+]