Tests for factories closes #25

tests/test_callback_factory.py

@@ -0,0 +1,22 @@
+import pytest
+from callbacks import CSVLogging, EarlyStopping
+from factories.callback_factory import CallbackFactory
+
+def test_callback_factory_creation():
+    factory = CallbackFactory()
+
+    # Test CSVLogging creation
+    params = {"csv_path": "./logs"}
+    csv_logger = factory.create("CSVLogging", **params)
+    assert isinstance(csv_logger, CSVLogging), "Failed to create CSVLogging"
+
+    # Test EarlyStopping creation
+    params = {"patience": 5}
+    early_stopper = factory.create("EarlyStopping", **params)
+    assert isinstance(early_stopper, EarlyStopping), "Failed to create EarlyStopping"
+
+def test_callback_factory_unknown():
+    factory = CallbackFactory()
+    with pytest.raises(ValueError) as e:
+        factory.create("UnknownCallback")
+    assert "Unknown callback" in str(e.value)

tests/test_checkpoints.py

@@ -17,7 +17,7 @@
 def test_checkpoint():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    transforms = get_transforms(CONFIG_TEST)
+    transforms = get_transforms(CONFIG_TEST['data']['transforms'])
     data = get_dataset(
         name=CONFIG_TEST['data']['name'],
         root_dir=CONFIG_TEST['data']['dataset_path'],

tests/test_early_stopping.py

@@ -16,7 +16,7 @@
 def test_early_stopping():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    transforms = get_transforms(CONFIG_TEST)
+    transforms = get_transforms(CONFIG_TEST['data']['transforms'])
     data = get_dataset(
         name=CONFIG_TEST['data']['name'],
         root_dir=CONFIG_TEST['data']['dataset_path'],

tests/test_fine_tuning_pipeline.py

@@ -37,7 +37,7 @@ def test_fine_tuning_loop():
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    transforms = get_transforms(CONFIG_TEST)
+    transforms = get_transforms(CONFIG_TEST['data']['transforms'])
 
     data = get_dataset(
         name=CONFIG_TEST['data']['name'],

tests/test_loss_factory.py

@@ -0,0 +1,20 @@
+import pytest
+from torch.nn import CrossEntropyLoss, MSELoss
+from factories.loss_factory import LossFactory
+
+def test_loss_factory_creation():
+    factory = LossFactory()
+
+    # Test CrossEntropyLoss creation
+    loss = factory.create("CrossEntropyLoss")
+    assert isinstance(loss, CrossEntropyLoss), "Failed to create CrossEntropyLoss"
+
+    # Test MSELoss creation
+    loss = factory.create("MSELoss")
+    assert isinstance(loss, MSELoss), "Failed to create MSELoss"
+
+def test_loss_factory_unknown():
+    factory = LossFactory()
+    with pytest.raises(ValueError) as e:
+        factory.create("UnknownLoss")
+    assert "Unknown configuration" in str(e.value)

tests/test_model_factory.py

@@ -0,0 +1,20 @@
+import pytest
+from torchvision.models import efficientnet_b0, resnet18
+from factories.model_factory import ModelFactory
+
+def test_model_factory_creation():
+    factory = ModelFactory()
+
+    # Test EfficientNet B0 creation
+    model = factory.create("efficientnet_b0", num_classes=10, pretrained=False)
+    assert isinstance(model, efficientnet_b0().__class__), "Failed to create EfficientNet B0 with correct class"
+
+    # Test ResNet18 creation
+    model = factory.create("resnet18", num_classes=10, pretrained=False)
+    assert isinstance(model, resnet18().__class__), "Failed to create ResNet18 with correct class"
+
+def test_model_factory_unknown_model():
+    factory = ModelFactory()
+    with pytest.raises(ValueError) as e:
+        factory.create("unknown_model", num_classes=10, pretrained=True)
+    assert "Unknown configuration" in str(e.value)

tests/test_optimizer_factory.py

@@ -0,0 +1,25 @@
+import pytest
+import torch
+from torch.optim import Adam, SGD
+from factories.optimizer_factory import OptimizerFactory
+
+def test_optimizer_factory_creation():
+    factory = OptimizerFactory()
+
+    # Simulate model parameters
+    params = [torch.tensor([1.0, 2.0], requires_grad=True)]
+
+    # Test Adam creation with a learning rate
+    optimizer = factory.create("Adam")
+    assert isinstance(optimizer(lr=0.01, params=params), Adam), "Failed to create Adam optimizer"
+
+    # Test SGD creation with a learning rate
+    optimizer = factory.create("SGD")
+    assert isinstance(optimizer(lr=0.01, params=params), SGD), "Failed to create SGD optimizer"
+
+def test_optimizer_factory_unknown():
+    factory = OptimizerFactory()
+    with pytest.raises(ValueError) as excinfo:
+        # Even though this will fail, you should still simulate correct usage
+        factory.create("UnknownOptimizer")
+    assert "Unknown configuration" in str(excinfo.value)

tests/test_training_pipeline.py

@@ -36,7 +36,7 @@ def test_training_loop():
 
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-    transforms = get_transforms(CONFIG_TEST)
+    transforms = get_transforms(CONFIG_TEST['data']['transforms'])
 
     data = get_dataset(
         name=CONFIG_TEST['data']['name'],