From 2bd466b63dd3abdf32b6b6eb52c87deaeedbdf15 Mon Sep 17 00:00:00 2001
From: Keon Sanavandi <keonsanavandijoon@gmail.com>
Date: Tue, 6 Feb 2024 18:15:27 -0500
Subject: [PATCH 1/4] Added backend application of transforms

---
 training/training/routes/image/image.py   | 92 +++++++++++++++++++++++
 training/training/routes/image/schemas.py |  1 +
 2 files changed, 93 insertions(+)

diff --git a/training/training/routes/image/image.py b/training/training/routes/image/image.py
index 92ccc369..1986a4d7 100644
--- a/training/training/routes/image/image.py
+++ b/training/training/routes/image/image.py
@@ -9,12 +9,18 @@
 from training.core.trainer import ClassificationTrainer
 from training.routes.image.schemas import ImageParams
 from training.core.authenticator import FirebaseAuth
+import torchvision.transforms as transforms
+import json
 
 router = Router()
 
 
 @router.post("", auth=FirebaseAuth())
 def imageTrain(request: HttpRequest, imageParams: ImageParams):
+    transforms = json.loads(imageParams.transforms)
+    train_transorms = transformParser(transforms["train_transforms"]) if transforms["train_transforms"] else transforms.ToTensor()
+    test_transforms = transformParser(transforms["test_transforms"]) if transforms["test_transforms"] else transforms.ToTensor()
+    
     if imageParams.default:
         dataCreator = ImageDefaultDatasetCreator.fromDefault(imageParams.default)
         train_loader = dataCreator.createTrainDataset()
@@ -38,3 +44,89 @@ def imageTrain(request: HttpRequest, imageParams: ImageParams):
             print(trainer.generate_confusion_matrix())
             print(trainer.generate_AUC_ROC_CURVE())
             return trainer.generate_AUC_ROC_CURVE()
+
+def transformParser(transformArray):
+    transformsToReturn = transforms.ToTensor()
+    for x in transformArray:
+        if (x["type"] == "CenterCrop"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.CenterCrop(x["parameters"]["size"])
+            )
+        elif (x["type"] == "ColorJitter"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.ColorJitter(x["parameters"]["brightness"], x["parameters"]["contrast"], x["parameters"]["saturation"], x["parameters"]["hue"])
+            )
+        elif (x["type"] == "FiveCrop"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.FiveCrop(x["parameters"]["size"])
+            )
+        elif (x["type"] == "Grayscale"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.Grayscale(x["parameters"]["num_output_channels"])
+            )
+        elif (x["type"] == "Pad"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.Pad(x["parameters"]["padding"])
+            )
+        elif (x["type"] == "RandomAffine"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomAffine(x["parameters"]["degrees"], x["parameters"]["translate"], x["parameters"]["scale"], x["parameters"]["shear"])
+            )
+        elif (x["type"] == "RandomCrop"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomCrop(x["parameters"]["size"], x["parameters"]["padding"])
+            )
+        elif (x["type"] == "RandomGrayscale"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomGrayscale(x["parameters"]["p"])
+            )
+        elif (x["type"] == "RandomHorizontalFlip"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomHorizontalFlip(x["parameters"]["p"])
+            )
+        elif (x["type"] == "Resize"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.Resize(x["parameters"]["size"])
+            )
+        elif (x["type"] == "RandomPerspective"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomPerspective(x["parameters"]["distortion_scale"], x["parameters"]["p"])
+            )
+        elif (x["type"] == "RandomResizedCrop"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                #What do we want to do for other non-single value params
+                transforms.RandomResizedCrop(x["parameters"]["size"])
+            )
+        elif (x["type"] == "RandomVerticalFlip"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomVerticalFlip(x["parameters"]["p"])
+            )
+        elif (x["type"] == "RandomRotation"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.RandomRotation(x["parameters"]["degrees"])
+            )
+        elif (x["type"] == "TenCrop"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.TenCrop(x["parameters"]["size"])
+            )
+        elif (x["type"] == "GaussianBlur"):
+            transformsToReturn = transforms.Compose(
+                transformsToReturn,
+                transforms.GaussianBlur(x["parameters"]["kernel_size"])
+            )
+    return transformsToReturn
\ No newline at end of file
diff --git a/training/training/routes/image/schemas.py b/training/training/routes/image/schemas.py
index 441f1fa0..255f8df0 100644
--- a/training/training/routes/image/schemas.py
+++ b/training/training/routes/image/schemas.py
@@ -18,3 +18,4 @@ class ImageParams(Schema):
     test_size: float
     batch_size: int
     user_arch: list[LayerParams]
+    transforms: str

From f28638c80f4d73918ce718b11f78ca1e1949b33f Mon Sep 17 00:00:00 2001
From: Keon-San <keonsanavandijoon@gmail.com>
Date: Thu, 7 Mar 2024 16:07:11 -0500
Subject: [PATCH 2/4] Fixed a couple bugs and got it working

---
 training/training/routes/image/image.py   | 74 ++++++++++++-----------
 training/training/routes/image/schemas.py |  2 +-
 2 files changed, 39 insertions(+), 37 deletions(-)

diff --git a/training/training/routes/image/image.py b/training/training/routes/image/image.py
index 1986a4d7..1e0233cf 100644
--- a/training/training/routes/image/image.py
+++ b/training/training/routes/image/image.py
@@ -17,9 +17,11 @@
 
 @router.post("", auth=FirebaseAuth())
 def imageTrain(request: HttpRequest, imageParams: ImageParams):
-    transforms = json.loads(imageParams.transforms)
-    train_transorms = transformParser(transforms["train_transforms"]) if transforms["train_transforms"] else transforms.ToTensor()
-    test_transforms = transformParser(transforms["test_transforms"]) if transforms["test_transforms"] else transforms.ToTensor()
+    transform = {}
+    if (imageParams.transforms != ""):
+        transform = json.loads(imageParams.transforms)
+    train_transorms = transformParser(transform["train_transforms"]) if "train_transforms" in transform else transforms.ToTensor()
+    test_transforms = transformParser(transform["test_transforms"]) if "test_transforms" in transform else transforms.ToTensor()
     
     if imageParams.default:
         dataCreator = ImageDefaultDatasetCreator.fromDefault(imageParams.default)
@@ -27,7 +29,7 @@ def imageTrain(request: HttpRequest, imageParams: ImageParams):
         test_loader = dataCreator.createTestDataset()
         model = DLModel.fromLayerParamsList(imageParams.user_arch)
         optimizer = getOptimizer(model, imageParams.optimizer_name, 0.05)
-        criterionHandler = getCriterionHandler(imageParams.criterion)
+        criterionHandler = getCriterionHandler(imageParams.criterion) 
         if imageParams.problem_type == "CLASSIFICATION":
             trainer = ClassificationTrainer(
                 train_loader,
@@ -49,84 +51,84 @@ def transformParser(transformArray):
     transformsToReturn = transforms.ToTensor()
     for x in transformArray:
         if (x["type"] == "CenterCrop"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.CenterCrop(x["parameters"]["size"])
+                transforms.CenterCrop(x["parameters"]["size"])]
             )
         elif (x["type"] == "ColorJitter"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.ColorJitter(x["parameters"]["brightness"], x["parameters"]["contrast"], x["parameters"]["saturation"], x["parameters"]["hue"])
+                transforms.ColorJitter(x["parameters"]["brightness"], x["parameters"]["contrast"], x["parameters"]["saturation"], x["parameters"]["hue"])]
             )
         elif (x["type"] == "FiveCrop"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.FiveCrop(x["parameters"]["size"])
+                transforms.FiveCrop(x["parameters"]["size"])]
             )
         elif (x["type"] == "Grayscale"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.Grayscale(x["parameters"]["num_output_channels"])
+                transforms.Grayscale(x["parameters"]["num_output_channels"])]
             )
         elif (x["type"] == "Pad"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.Pad(x["parameters"]["padding"])
+                transforms.Pad(x["parameters"]["padding"])]
             )
         elif (x["type"] == "RandomAffine"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomAffine(x["parameters"]["degrees"], x["parameters"]["translate"], x["parameters"]["scale"], x["parameters"]["shear"])
+                transforms.RandomAffine(x["parameters"]["degrees"], x["parameters"]["translate"], x["parameters"]["scale"], x["parameters"]["shear"])]
             )
         elif (x["type"] == "RandomCrop"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomCrop(x["parameters"]["size"], x["parameters"]["padding"])
+                transforms.RandomCrop(x["parameters"]["size"], x["parameters"]["padding"])]
             )
         elif (x["type"] == "RandomGrayscale"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomGrayscale(x["parameters"]["p"])
+                transforms.RandomGrayscale(x["parameters"]["p"])]
             )
         elif (x["type"] == "RandomHorizontalFlip"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomHorizontalFlip(x["parameters"]["p"])
+                transforms.RandomHorizontalFlip(x["parameters"]["p"])]
             )
         elif (x["type"] == "Resize"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.Resize(x["parameters"]["size"])
+                transforms.Resize(x["parameters"]["size"])]
             )
         elif (x["type"] == "RandomPerspective"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomPerspective(x["parameters"]["distortion_scale"], x["parameters"]["p"])
+                transforms.RandomPerspective(x["parameters"]["distortion_scale"], x["parameters"]["p"])]
             )
         elif (x["type"] == "RandomResizedCrop"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
                 #What do we want to do for other non-single value params
-                transforms.RandomResizedCrop(x["parameters"]["size"])
+                transforms.RandomResizedCrop(x["parameters"]["size"])]
             )
         elif (x["type"] == "RandomVerticalFlip"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomVerticalFlip(x["parameters"]["p"])
+                transforms.RandomVerticalFlip(x["parameters"]["p"])]
             )
         elif (x["type"] == "RandomRotation"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.RandomRotation(x["parameters"]["degrees"])
+                transforms.RandomRotation(x["parameters"]["degrees"])]
             )
         elif (x["type"] == "TenCrop"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.TenCrop(x["parameters"]["size"])
+                transforms.TenCrop(x["parameters"]["size"])]
             )
         elif (x["type"] == "GaussianBlur"):
-            transformsToReturn = transforms.Compose(
+            transformsToReturn = transforms.Compose([
                 transformsToReturn,
-                transforms.GaussianBlur(x["parameters"]["kernel_size"])
+                transforms.GaussianBlur(x["parameters"]["kernel_size"])]
             )
     return transformsToReturn
\ No newline at end of file
diff --git a/training/training/routes/image/schemas.py b/training/training/routes/image/schemas.py
index 255f8df0..b1e8751d 100644
--- a/training/training/routes/image/schemas.py
+++ b/training/training/routes/image/schemas.py
@@ -18,4 +18,4 @@ class ImageParams(Schema):
     test_size: float
     batch_size: int
     user_arch: list[LayerParams]
-    transforms: str
+    transforms: str = ""

From d154ac8e4b818aa266d23868c38b8d2dabffc550 Mon Sep 17 00:00:00 2001
From: Keon-San <Keon-San@users.noreply.github.com>
Date: Fri, 8 Mar 2024 00:08:02 +0000
Subject: [PATCH 3/4] :art: Auto-generated directory tree for repository in
 Architecture.md

---
 .github/Architecture.md | 302 ++++++++++++++++++++--------------------
 1 file changed, 151 insertions(+), 151 deletions(-)

diff --git a/.github/Architecture.md b/.github/Architecture.md
index c381f867..e1be534c 100644
--- a/.github/Architecture.md
+++ b/.github/Architecture.md
@@ -5,24 +5,16 @@
 ```
 📦 training
 |  |- 📂 training:
-|  |  |- 📂 core:
-|  |  |  |- 📜 dataset.py : read in the dataset through URL or file upload
-|  |  |  |- 📜 criterion.py
-|  |  |  |- 📜 optimizer.py : what optimizer to use (ie: SGD or Adam for now)
-|  |  |  |- 📜 __init__.py
-|  |  |  |- 📜 dl_model.py : torch model based on user specifications from drag and drop
-|  |  |  |- 📜 trainer.py
-|  |  |  |- 📜 authenticator.py
 |  |  |- 📂 routes:
+|  |  |  |- 📂 image:
+|  |  |  |  |- 📜 schemas.py
+|  |  |  |  |- 📜 __init__.py
+|  |  |  |  |- 📜 image.py
 |  |  |  |- 📂 datasets:
 |  |  |  |  |- 📂 default:
 |  |  |  |  |  |- 📜 schemas.py
-|  |  |  |  |  |- 📜 __init__.py
 |  |  |  |  |  |- 📜 columns.py
-|  |  |  |  |- 📜 __init__.py
-|  |  |  |- 📂 image:
-|  |  |  |  |- 📜 image.py
-|  |  |  |  |- 📜 schemas.py
+|  |  |  |  |  |- 📜 __init__.py
 |  |  |  |  |- 📜 __init__.py
 |  |  |  |- 📂 tabular:
 |  |  |  |  |- 📜 tabular.py
@@ -33,198 +25,206 @@
 |  |  |- 📂 middleware:
 |  |  |  |- 📜 health_check_middleware.py
 |  |  |  |- 📜 __init__.py
+|  |  |- 📂 core:
+|  |  |  |- 📜 optimizer.py : what optimizer to use (ie: SGD or Adam for now)
+|  |  |  |- 📜 criterion.py
+|  |  |  |- 📜 dl_model.py : torch model based on user specifications from drag and drop
+|  |  |  |- 📜 dataset.py : read in the dataset through URL or file upload
+|  |  |  |- 📜 authenticator.py
+|  |  |  |- 📜 __init__.py
+|  |  |  |- 📜 trainer.py
+|  |  |- 📜 wsgi.py
+|  |  |- 📜 settings.py
 |  |  |- 📜 urls.py
 |  |  |- 📜 asgi.py
 |  |  |- 📜 __init__.py
-|  |  |- 📜 wsgi.py
-|  |  |- 📜 settings.py
-|  |- 📜 README.md
-|  |- 📜 docker-compose.prod.yml
 |  |- 📜 pytest.ini
-|  |- 📜 poetry.lock
 |  |- 📜 cli.py
+|  |- 📜 README.md
+|  |- 📜 Dockerfile.prod
 |  |- 📜 pyproject.toml
-|  |- 📜 environment.yml
+|  |- 📜 docker-compose.yml
+|  |- 📜 poetry.lock
 |  |- 📜 Dockerfile
-|  |- 📜 Dockerfile.prod
 |  |- 📜 manage.py
-|  |- 📜 docker-compose.yml
+|  |- 📜 docker-compose.prod.yml
+|  |- 📜 environment.yml
 ```
 
 ## Frontend Architecture
 
 ```
 📦 frontend
+|  |- 📂 public:
+|  |  |- 📂 images:
+|  |  |  |- 📂 learn_mod_images:
+|  |  |  |  |- 📜 LeakyReLUactivation.png
+|  |  |  |  |- 📜 neuralnet.png
+|  |  |  |  |- 📜 tanhactivation.png
+|  |  |  |  |- 📜 lossExample.png
+|  |  |  |  |- 📜 sigmoidfunction.png
+|  |  |  |  |- 📜 neuron.png
+|  |  |  |  |- 📜 ReLUactivation.png
+|  |  |  |  |- 📜 sigmoidactivation.png
+|  |  |  |  |- 📜 lossExampleEquation.png
+|  |  |  |  |- 📜 neuronWithEquation.png
+|  |  |  |  |- 📜 robotImage.jpg
+|  |  |  |  |- 📜 binarystepactivation.png
+|  |  |  |  |- 📜 lossExampleTable.png
+|  |  |  |- 📂 wiki_images:
+|  |  |  |  |- 📜 avgpool_maxpool.gif
+|  |  |  |  |- 📜 tanh_equation.png
+|  |  |  |  |- 📜 maxpool2d.gif
+|  |  |  |  |- 📜 dropout_diagram.png
+|  |  |  |  |- 📜 batchnorm_diagram.png
+|  |  |  |  |- 📜 softmax_equation.png : PNG file of Softmax equation
+|  |  |  |  |- 📜 tanh_plot.png
+|  |  |  |  |- 📜 sigmoid_equation.png
+|  |  |  |  |- 📜 conv2d.gif
+|  |  |  |  |- 📜 conv2d2.gif
+|  |  |  |- 📂 logos:
+|  |  |  |  |- 📂 dlp_branding:
+|  |  |  |  |  |- 📜 dlp-logo.svg : DLP Logo, duplicate of files in public, but essential as the frontend can't read public
+|  |  |  |  |  |- 📜 dlp-logo.png : DLP Logo, duplicate of files in public, but essential as the frontend can't read public
+|  |  |  |  |- 📜 pytorch-logo.png
+|  |  |  |  |- 📜 dsgt-logo-dark.png
+|  |  |  |  |- 📜 flask-logo.png
+|  |  |  |  |- 📜 dsgt-logo-light.png
+|  |  |  |  |- 📜 aws-logo.png
+|  |  |  |  |- 📜 github.png
+|  |  |  |  |- 📜 google.png
+|  |  |  |  |- 📜 pandas-logo.png
+|  |  |  |  |- 📜 python-logo.png
+|  |  |  |  |- 📜 dsgt-logo-white-back.png
+|  |  |  |  |- 📜 react-logo.png
+|  |  |  |- 📜 demo_video.gif : GIF tutorial of a simple classification training session
+|  |  |- 📜 dlp-logo.ico : DLP Logo
+|  |  |- 📜 manifest.json : Default React file for choosing icon based on
+|  |  |- 📜 index.html : Base HTML file that will be initially rendered
+|  |  |- 📜 robots.txt
+|  |- 📂 layer_docs:
+|  |  |- 📜 Linear.md : Doc for Linear layer
+|  |  |- 📜 Softmax.md : Doc for Softmax layer
+|  |  |- 📜 ReLU.md : Doc for ReLU later
+|  |  |- 📜 softmax_equation.png : PNG file of Softmax equation
 |  |- 📂 src:
-|  |  |- 📂 pages:
-|  |  |  |- 📂 train:
-|  |  |  |  |- 📜 [train_space_id].tsx
-|  |  |  |  |- 📜 index.tsx
-|  |  |  |- 📜 settings.tsx
-|  |  |  |- 📜 feedback.tsx
-|  |  |  |- 📜 forgot.tsx
-|  |  |  |- 📜 learn.tsx
-|  |  |  |- 📜 dashboard.tsx
-|  |  |  |- 📜 login.tsx
-|  |  |  |- 📜 _document.tsx
-|  |  |  |- 📜 _app.tsx
-|  |  |  |- 📜 about.tsx
-|  |  |  |- 📜 wiki.tsx
-|  |  |  |- 📜 LearnContent.tsx
 |  |  |- 📂 features:
-|  |  |  |- 📂 Feedback:
-|  |  |  |  |- 📂 redux:
-|  |  |  |  |  |- 📜 feedbackApi.ts
 |  |  |  |- 📂 Dashboard:
+|  |  |  |  |- 📂 redux:
+|  |  |  |  |  |- 📜 dashboardApi.ts
 |  |  |  |  |- 📂 components:
 |  |  |  |  |  |- 📜 TrainDataGrid.tsx
 |  |  |  |  |  |- 📜 TrainBarChart.tsx
 |  |  |  |  |  |- 📜 TrainDoughnutChart.tsx
+|  |  |  |- 📂 LearnMod:
+|  |  |  |  |- 📜 FRQuestion.tsx
+|  |  |  |  |- 📜 MCQuestion.tsx
+|  |  |  |  |- 📜 ClassCard.tsx
+|  |  |  |  |- 📜 ImageComponent.tsx
+|  |  |  |  |- 📜 LearningModulesContent.tsx
+|  |  |  |  |- 📜 ModulesSideBar.tsx
+|  |  |  |  |- 📜 Exercise.tsx
+|  |  |  |- 📂 Feedback:
 |  |  |  |  |- 📂 redux:
-|  |  |  |  |  |- 📜 dashboardApi.ts
+|  |  |  |  |  |- 📜 feedbackApi.ts
 |  |  |  |- 📂 Train:
-|  |  |  |  |- 📂 constants:
-|  |  |  |  |  |- 📜 trainConstants.ts
-|  |  |  |  |- 📂 components:
-|  |  |  |  |  |- 📜 DatasetStepLayout.tsx
-|  |  |  |  |  |- 📜 CreateTrainspace.tsx
-|  |  |  |  |  |- 📜 TrainspaceLayout.tsx
 |  |  |  |  |- 📂 redux:
 |  |  |  |  |  |- 📜 trainspaceApi.ts
 |  |  |  |  |  |- 📜 trainspaceSlice.ts
 |  |  |  |  |- 📂 features:
-|  |  |  |  |  |- 📂 Image:
-|  |  |  |  |  |  |- 📂 constants:
-|  |  |  |  |  |  |  |- 📜 imageConstants.ts
-|  |  |  |  |  |  |- 📂 components:
-|  |  |  |  |  |  |  |- 📜 ImageTrainspace.tsx
-|  |  |  |  |  |  |  |- 📜 ImageFlow.tsx
-|  |  |  |  |  |  |  |- 📜 ImageReviewStep.tsx
-|  |  |  |  |  |  |  |- 📜 ImageParametersStep.tsx
-|  |  |  |  |  |  |  |- 📜 ImageDatasetStep.tsx
-|  |  |  |  |  |  |- 📂 redux:
-|  |  |  |  |  |  |  |- 📜 imageApi.ts
-|  |  |  |  |  |  |  |- 📜 imageActions.ts
-|  |  |  |  |  |  |- 📂 types:
-|  |  |  |  |  |  |  |- 📜 imageTypes.ts
-|  |  |  |  |  |  |- 📜 index.ts
 |  |  |  |  |  |- 📂 Tabular:
+|  |  |  |  |  |  |- 📂 redux:
+|  |  |  |  |  |  |  |- 📜 tabularActions.ts
+|  |  |  |  |  |  |  |- 📜 tabularApi.ts
 |  |  |  |  |  |  |- 📂 constants:
 |  |  |  |  |  |  |  |- 📜 tabularConstants.ts
+|  |  |  |  |  |  |- 📂 types:
+|  |  |  |  |  |  |  |- 📜 tabularTypes.ts
 |  |  |  |  |  |  |- 📂 components:
-|  |  |  |  |  |  |  |- 📜 TabularTrainspace.tsx
+|  |  |  |  |  |  |  |- 📜 TabularFlow.tsx
 |  |  |  |  |  |  |  |- 📜 TabularParametersStep.tsx
+|  |  |  |  |  |  |  |- 📜 TabularTrainspace.tsx
 |  |  |  |  |  |  |  |- 📜 TabularDatasetStep.tsx
-|  |  |  |  |  |  |  |- 📜 TabularFlow.tsx
 |  |  |  |  |  |  |  |- 📜 TabularReviewStep.tsx
+|  |  |  |  |  |  |- 📜 index.ts
+|  |  |  |  |  |- 📂 Image:
 |  |  |  |  |  |  |- 📂 redux:
-|  |  |  |  |  |  |  |- 📜 tabularActions.ts
-|  |  |  |  |  |  |  |- 📜 tabularApi.ts
+|  |  |  |  |  |  |  |- 📜 imageApi.ts
+|  |  |  |  |  |  |  |- 📜 imageActions.ts
+|  |  |  |  |  |  |- 📂 constants:
+|  |  |  |  |  |  |  |- 📜 imageConstants.ts
 |  |  |  |  |  |  |- 📂 types:
-|  |  |  |  |  |  |  |- 📜 tabularTypes.ts
+|  |  |  |  |  |  |  |- 📜 imageTypes.ts
+|  |  |  |  |  |  |- 📂 components:
+|  |  |  |  |  |  |  |- 📜 ImageReviewStep.tsx
+|  |  |  |  |  |  |  |- 📜 ImageFlow.tsx
+|  |  |  |  |  |  |  |- 📜 ImageParametersStep.tsx
+|  |  |  |  |  |  |  |- 📜 ImageDatasetStep.tsx
+|  |  |  |  |  |  |  |- 📜 ImageTrainspace.tsx
 |  |  |  |  |  |  |- 📜 index.ts
+|  |  |  |  |- 📂 constants:
+|  |  |  |  |  |- 📜 trainConstants.ts
 |  |  |  |  |- 📂 types:
 |  |  |  |  |  |- 📜 trainTypes.ts
+|  |  |  |  |- 📂 components:
+|  |  |  |  |  |- 📜 CreateTrainspace.tsx
+|  |  |  |  |  |- 📜 DatasetStepLayout.tsx
+|  |  |  |  |  |- 📜 TrainspaceLayout.tsx
 |  |  |  |- 📂 OpenAi:
 |  |  |  |  |- 📜 openAiUtils.ts
-|  |  |  |- 📂 LearnMod:
-|  |  |  |  |- 📜 ModulesSideBar.tsx
-|  |  |  |  |- 📜 Exercise.tsx
-|  |  |  |  |- 📜 LearningModulesContent.tsx
-|  |  |  |  |- 📜 FRQuestion.tsx
-|  |  |  |  |- 📜 ImageComponent.tsx
-|  |  |  |  |- 📜 ClassCard.tsx
-|  |  |  |  |- 📜 MCQuestion.tsx
-|  |  |- 📂 backend_outputs:
-|  |  |  |- 📜 model.pkl
-|  |  |  |- 📜 my_deep_learning_model.onnx : Last ONNX file output
-|  |  |  |- 📜 model.pt : Last model.pt output
 |  |  |- 📂 common:
-|  |  |  |- 📂 components:
-|  |  |  |  |- 📜 Spacer.tsx
-|  |  |  |  |- 📜 Footer.tsx
-|  |  |  |  |- 📜 DlpTooltip.tsx
-|  |  |  |  |- 📜 EmailInput.tsx
-|  |  |  |  |- 📜 HtmlTooltip.tsx
-|  |  |  |  |- 📜 NavBarMain.tsx
-|  |  |  |  |- 📜 TitleText.tsx
-|  |  |  |  |- 📜 ClientOnlyPortal.tsx
+|  |  |  |- 📂 styles:
+|  |  |  |  |- 📜 globals.css
+|  |  |  |  |- 📜 Home.module.css
 |  |  |  |- 📂 redux:
-|  |  |  |  |- 📜 store.ts
-|  |  |  |  |- 📜 train.ts
 |  |  |  |  |- 📜 userLogin.ts
+|  |  |  |  |- 📜 store.ts
 |  |  |  |  |- 📜 hooks.ts
 |  |  |  |  |- 📜 backendApi.ts
+|  |  |  |  |- 📜 train.ts
 |  |  |  |- 📂 utils:
-|  |  |  |  |- 📜 dateFormat.ts
 |  |  |  |  |- 📜 firebase.ts
 |  |  |  |  |- 📜 dndHelpers.ts
-|  |  |  |- 📂 styles:
-|  |  |  |  |- 📜 Home.module.css
-|  |  |  |  |- 📜 globals.css
-|  |  |- 📜 next-env.d.ts
-|  |  |- 📜 GlobalStyle.ts
-|  |  |- 📜 iris.csv : Sample CSV data
+|  |  |  |  |- 📜 dateFormat.ts
+|  |  |  |- 📂 components:
+|  |  |  |  |- 📜 NavBarMain.tsx
+|  |  |  |  |- 📜 Footer.tsx
+|  |  |  |  |- 📜 ClientOnlyPortal.tsx
+|  |  |  |  |- 📜 Spacer.tsx
+|  |  |  |  |- 📜 EmailInput.tsx
+|  |  |  |  |- 📜 TitleText.tsx
+|  |  |  |  |- 📜 HtmlTooltip.tsx
+|  |  |  |  |- 📜 DlpTooltip.tsx
+|  |  |- 📂 backend_outputs:
+|  |  |  |- 📜 model.pkl
+|  |  |  |- 📜 my_deep_learning_model.onnx : Last ONNX file output
+|  |  |  |- 📜 model.pt : Last model.pt output
+|  |  |- 📂 pages:
+|  |  |  |- 📂 train:
+|  |  |  |  |- 📜 [train_space_id].tsx
+|  |  |  |  |- 📜 index.tsx
+|  |  |  |- 📜 login.tsx
+|  |  |  |- 📜 feedback.tsx
+|  |  |  |- 📜 _app.tsx
+|  |  |  |- 📜 forgot.tsx
+|  |  |  |- 📜 learn.tsx
+|  |  |  |- 📜 LearnContent.tsx
+|  |  |  |- 📜 settings.tsx
+|  |  |  |- 📜 dashboard.tsx
+|  |  |  |- 📜 about.tsx
+|  |  |  |- 📜 wiki.tsx
+|  |  |  |- 📜 _document.tsx
 |  |  |- 📜 constants.ts
-|  |- 📂 layer_docs:
-|  |  |- 📜 softmax_equation.png : PNG file of Softmax equation
-|  |  |- 📜 Linear.md : Doc for Linear layer
-|  |  |- 📜 Softmax.md : Doc for Softmax layer
-|  |  |- 📜 ReLU.md : Doc for ReLU later
-|  |- 📂 public:
-|  |  |- 📂 images:
-|  |  |  |- 📂 wiki_images:
-|  |  |  |  |- 📜 softmax_equation.png : PNG file of Softmax equation
-|  |  |  |  |- 📜 tanh_plot.png
-|  |  |  |  |- 📜 conv2d.gif
-|  |  |  |  |- 📜 conv2d2.gif
-|  |  |  |  |- 📜 avgpool_maxpool.gif
-|  |  |  |  |- 📜 sigmoid_equation.png
-|  |  |  |  |- 📜 batchnorm_diagram.png
-|  |  |  |  |- 📜 maxpool2d.gif
-|  |  |  |  |- 📜 tanh_equation.png
-|  |  |  |  |- 📜 dropout_diagram.png
-|  |  |  |- 📂 learn_mod_images:
-|  |  |  |  |- 📜 neuronWithEquation.png
-|  |  |  |  |- 📜 robotImage.jpg
-|  |  |  |  |- 📜 neuralnet.png
-|  |  |  |  |- 📜 sigmoidactivation.png
-|  |  |  |  |- 📜 lossExample.png
-|  |  |  |  |- 📜 binarystepactivation.png
-|  |  |  |  |- 📜 tanhactivation.png
-|  |  |  |  |- 📜 LeakyReLUactivation.png
-|  |  |  |  |- 📜 sigmoidfunction.png
-|  |  |  |  |- 📜 lossExampleTable.png
-|  |  |  |  |- 📜 lossExampleEquation.png
-|  |  |  |  |- 📜 neuron.png
-|  |  |  |  |- 📜 ReLUactivation.png
-|  |  |  |- 📂 logos:
-|  |  |  |  |- 📂 dlp_branding:
-|  |  |  |  |  |- 📜 dlp-logo.svg : DLP Logo, duplicate of files in public, but essential as the frontend can't read public
-|  |  |  |  |  |- 📜 dlp-logo.png : DLP Logo, duplicate of files in public, but essential as the frontend can't read public
-|  |  |  |  |- 📜 dsgt-logo-white-back.png
-|  |  |  |  |- 📜 pytorch-logo.png
-|  |  |  |  |- 📜 google.png
-|  |  |  |  |- 📜 flask-logo.png
-|  |  |  |  |- 📜 dsgt-logo-dark.png
-|  |  |  |  |- 📜 pandas-logo.png
-|  |  |  |  |- 📜 dsgt-logo-light.png
-|  |  |  |  |- 📜 github.png
-|  |  |  |  |- 📜 react-logo.png
-|  |  |  |  |- 📜 python-logo.png
-|  |  |  |  |- 📜 aws-logo.png
-|  |  |  |- 📜 demo_video.gif : GIF tutorial of a simple classification training session
-|  |  |- 📜 manifest.json : Default React file for choosing icon based on
-|  |  |- 📜 dlp-logo.ico : DLP Logo
-|  |  |- 📜 index.html : Base HTML file that will be initially rendered
-|  |  |- 📜 robots.txt
-|  |- 📜 .eslintrc.json
+|  |  |- 📜 iris.csv : Sample CSV data
+|  |  |- 📜 GlobalStyle.ts
+|  |  |- 📜 next-env.d.ts
 |  |- 📜 .eslintignore
+|  |- 📜 tsconfig.json
+|  |- 📜 .eslintrc.json
 |  |- 📜 pnpm-lock.yaml
+|  |- 📜 jest.config.js
 |  |- 📜 package.json
 |  |- 📜 next.config.js
-|  |- 📜 jest.config.js
 |  |- 📜 next-env.d.ts
-|  |- 📜 tsconfig.json
 ```
 

From d0f27cfb5b259f54feabf9d371c3466a4e5cf058 Mon Sep 17 00:00:00 2001
From: Keon-San <Keon-San@users.noreply.github.com>
Date: Fri, 8 Mar 2024 00:08:16 +0000
Subject: [PATCH 4/4] :art: Format Python code with psf/black

---
 training/training/routes/image/image.py | 211 ++++++++++++++----------
 1 file changed, 124 insertions(+), 87 deletions(-)

diff --git a/training/training/routes/image/image.py b/training/training/routes/image/image.py
index 1e0233cf..178703a8 100644
--- a/training/training/routes/image/image.py
+++ b/training/training/routes/image/image.py
@@ -18,18 +18,26 @@
 @router.post("", auth=FirebaseAuth())
 def imageTrain(request: HttpRequest, imageParams: ImageParams):
     transform = {}
-    if (imageParams.transforms != ""):
+    if imageParams.transforms != "":
         transform = json.loads(imageParams.transforms)
-    train_transorms = transformParser(transform["train_transforms"]) if "train_transforms" in transform else transforms.ToTensor()
-    test_transforms = transformParser(transform["test_transforms"]) if "test_transforms" in transform else transforms.ToTensor()
-    
+    train_transorms = (
+        transformParser(transform["train_transforms"])
+        if "train_transforms" in transform
+        else transforms.ToTensor()
+    )
+    test_transforms = (
+        transformParser(transform["test_transforms"])
+        if "test_transforms" in transform
+        else transforms.ToTensor()
+    )
+
     if imageParams.default:
         dataCreator = ImageDefaultDatasetCreator.fromDefault(imageParams.default)
         train_loader = dataCreator.createTrainDataset()
         test_loader = dataCreator.createTestDataset()
         model = DLModel.fromLayerParamsList(imageParams.user_arch)
         optimizer = getOptimizer(model, imageParams.optimizer_name, 0.05)
-        criterionHandler = getCriterionHandler(imageParams.criterion) 
+        criterionHandler = getCriterionHandler(imageParams.criterion)
         if imageParams.problem_type == "CLASSIFICATION":
             trainer = ClassificationTrainer(
                 train_loader,
@@ -47,88 +55,117 @@ def imageTrain(request: HttpRequest, imageParams: ImageParams):
             print(trainer.generate_AUC_ROC_CURVE())
             return trainer.generate_AUC_ROC_CURVE()
 
+
 def transformParser(transformArray):
     transformsToReturn = transforms.ToTensor()
     for x in transformArray:
-        if (x["type"] == "CenterCrop"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.CenterCrop(x["parameters"]["size"])]
-            )
-        elif (x["type"] == "ColorJitter"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.ColorJitter(x["parameters"]["brightness"], x["parameters"]["contrast"], x["parameters"]["saturation"], x["parameters"]["hue"])]
-            )
-        elif (x["type"] == "FiveCrop"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.FiveCrop(x["parameters"]["size"])]
-            )
-        elif (x["type"] == "Grayscale"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.Grayscale(x["parameters"]["num_output_channels"])]
-            )
-        elif (x["type"] == "Pad"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.Pad(x["parameters"]["padding"])]
-            )
-        elif (x["type"] == "RandomAffine"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomAffine(x["parameters"]["degrees"], x["parameters"]["translate"], x["parameters"]["scale"], x["parameters"]["shear"])]
-            )
-        elif (x["type"] == "RandomCrop"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomCrop(x["parameters"]["size"], x["parameters"]["padding"])]
-            )
-        elif (x["type"] == "RandomGrayscale"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomGrayscale(x["parameters"]["p"])]
-            )
-        elif (x["type"] == "RandomHorizontalFlip"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomHorizontalFlip(x["parameters"]["p"])]
-            )
-        elif (x["type"] == "Resize"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.Resize(x["parameters"]["size"])]
-            )
-        elif (x["type"] == "RandomPerspective"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomPerspective(x["parameters"]["distortion_scale"], x["parameters"]["p"])]
-            )
-        elif (x["type"] == "RandomResizedCrop"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                #What do we want to do for other non-single value params
-                transforms.RandomResizedCrop(x["parameters"]["size"])]
-            )
-        elif (x["type"] == "RandomVerticalFlip"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomVerticalFlip(x["parameters"]["p"])]
-            )
-        elif (x["type"] == "RandomRotation"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.RandomRotation(x["parameters"]["degrees"])]
-            )
-        elif (x["type"] == "TenCrop"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.TenCrop(x["parameters"]["size"])]
-            )
-        elif (x["type"] == "GaussianBlur"):
-            transformsToReturn = transforms.Compose([
-                transformsToReturn,
-                transforms.GaussianBlur(x["parameters"]["kernel_size"])]
-            )
-    return transformsToReturn
\ No newline at end of file
+        if x["type"] == "CenterCrop":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.CenterCrop(x["parameters"]["size"])]
+            )
+        elif x["type"] == "ColorJitter":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.ColorJitter(
+                        x["parameters"]["brightness"],
+                        x["parameters"]["contrast"],
+                        x["parameters"]["saturation"],
+                        x["parameters"]["hue"],
+                    ),
+                ]
+            )
+        elif x["type"] == "FiveCrop":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.FiveCrop(x["parameters"]["size"])]
+            )
+        elif x["type"] == "Grayscale":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.Grayscale(x["parameters"]["num_output_channels"]),
+                ]
+            )
+        elif x["type"] == "Pad":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.Pad(x["parameters"]["padding"])]
+            )
+        elif x["type"] == "RandomAffine":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomAffine(
+                        x["parameters"]["degrees"],
+                        x["parameters"]["translate"],
+                        x["parameters"]["scale"],
+                        x["parameters"]["shear"],
+                    ),
+                ]
+            )
+        elif x["type"] == "RandomCrop":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomCrop(
+                        x["parameters"]["size"], x["parameters"]["padding"]
+                    ),
+                ]
+            )
+        elif x["type"] == "RandomGrayscale":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.RandomGrayscale(x["parameters"]["p"])]
+            )
+        elif x["type"] == "RandomHorizontalFlip":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomHorizontalFlip(x["parameters"]["p"]),
+                ]
+            )
+        elif x["type"] == "Resize":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.Resize(x["parameters"]["size"])]
+            )
+        elif x["type"] == "RandomPerspective":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomPerspective(
+                        x["parameters"]["distortion_scale"], x["parameters"]["p"]
+                    ),
+                ]
+            )
+        elif x["type"] == "RandomResizedCrop":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    # What do we want to do for other non-single value params
+                    transforms.RandomResizedCrop(x["parameters"]["size"]),
+                ]
+            )
+        elif x["type"] == "RandomVerticalFlip":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomVerticalFlip(x["parameters"]["p"]),
+                ]
+            )
+        elif x["type"] == "RandomRotation":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.RandomRotation(x["parameters"]["degrees"]),
+                ]
+            )
+        elif x["type"] == "TenCrop":
+            transformsToReturn = transforms.Compose(
+                [transformsToReturn, transforms.TenCrop(x["parameters"]["size"])]
+            )
+        elif x["type"] == "GaussianBlur":
+            transformsToReturn = transforms.Compose(
+                [
+                    transformsToReturn,
+                    transforms.GaussianBlur(x["parameters"]["kernel_size"]),
+                ]
+            )
+    return transformsToReturn