Feature/ocr implement

avinda_shamal/examples/custom_cifar_dataset.py

@@ -1,6 +1,6 @@
 from torch.utils.data import DataLoader
 from torchvision import transforms
-from cvas import CustomImageDataset
+from cvas.dataclasses import CustomImageDataset
 import torch
 import matplotlib.pyplot as plt
 

avinda_shamal/examples/hf_model_example.py

@@ -1,6 +1,6 @@
 from datasets import load_dataset, load_dataset_builder
 from torchvision import transforms
-from cvas import HuggingFaceDataset
+from cvas.dataclasses import HuggingFaceDataset
 
 # check the dataset information
 ds_builder = load_dataset_builder("cifar10")

avinda_shamal/examples/training_cifar.py

@@ -5,9 +5,9 @@
 import torch.nn as nn
 import torch.optim as optim
 import numpy as np
-from model import NNModel
-from model import ModelTrainer
-from model import ModelEvaluator
+from cvas.model import NNModel
+from cvas.model import ModelTrainer
+from cvas.model import ModelEvaluator
 
 # load and normalize the Cifar10 dataset
 std = np.array([0.5, 0.5, 0.5])

avinda_shamal/src/cvas/dataclasses/__init__.py

@@ -0,0 +1,2 @@
+from cvas.dataclasses.custom_dataclasses import CustomImageDataset
+from cvas.dataclasses.huggingface_dataclass import HuggingFaceDataset

avinda_shamal/src/cvas/model/__init__.py

@@ -0,0 +1,3 @@
+from cvas.model.nn_model import NNModel
+from cvas.model.train_class import ModelTrainer
+from cvas.model.eval_class import ModelEvaluator

avinda_shamal/src/model/eval_class.py → avinda_shamal/src/cvas/model/eval_class.py

@@ -122,7 +122,7 @@ def roc_auc(self):
             plt.plot(
                 fpr["micro"],
                 tpr["micro"],
-                label=f'Micro-average (AUC = {roc_auc["micro"]:.2f})',
+                label=f"Micro-average (AUC = {roc_auc['micro']:.2f})",
                 color="navy",
                 linestyle="--",
             )

avinda_shamal/test/unit/test_dataclasses.py

@@ -36,7 +36,9 @@ def test_len_custom_dataset(datalist, expected):
 def test_get_item_custom_dataset():
     transform = transforms.Resize((32, 32))
     # Initialize the dataset and set up mock data
-    with patch("cvas.dataclasses.read_image") as mock_read_image:
+    with patch(
+        "cvas.dataclasses.custom_dataclasses.read_image"
+    ) as mock_read_image:
         mock_read_image.return_value = torch.rand(3, 44, 44)
 
         with patch(

avinda_shamal/test/unit/test_eval_model.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader, TensorDataset
-from model import ModelEvaluator
+from cvas.model import ModelEvaluator
 from unittest.mock import patch
 import numpy as np
 
@@ -19,7 +19,7 @@ def test_test_model():
     test_labels = torch.tensor([0, 1, 2, 3])
     dummy_dataset = TensorDataset(test_data, test_labels)
     test_loader = DataLoader(dummy_dataset, batch_size=1, shuffle=False)
-    with patch("model.ModelEvaluator.roc_auc") as mock_roc_auc:
+    with patch("cvas.model.eval_class.ModelEvaluator.roc_auc") as mock_roc_auc:
         mock_roc_auc.return_value = {"micro": 0.5}
         evaluator = ModelEvaluator(nn.Linear(4, 4), test_loader)
         # Test the method

avinda_shamal/test/unit/test_hf_dataclass.py

@@ -1,4 +1,4 @@
-from cvas.huggingface_dataclass import HuggingFaceDataset
+from cvas.dataclasses import HuggingFaceDataset
 import pytest
 import torch
 from torchvision import transforms

avinda_shamal/test/unit/test_model.py

@@ -1,5 +1,5 @@
 import torch
-from model.nn_model import NNModel
+from cvas.model import NNModel
 
 
 def test_forward():

avinda_shamal/test/unit/test_train_model.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader, TensorDataset
-from model import ModelTrainer
+from cvas.model import ModelTrainer
 
 dummy_dataset = TensorDataset(
     torch.randn(100, 3, 32, 32), torch.randint(0, 10, (100,))