refactor files.

add a notebook module.
add config package.

test/conftest.py

@@ -1,20 +0,0 @@
-# conftest.py
-import pytest
-import os
-from dotenv import load_dotenv
-from pathlib import Path
-
-
-@pytest.fixture(autouse=True)
-def load_env():
-    # Set up your environment variables here
-    env = Path(__file__).parent / ".env.test"
-    if not load_dotenv(env):
-        raise RuntimeError(".env not loaded")
-    # os.environ['MY_ENV_VAR'] = 'some_value'
-    # You can add more setup code here if needed
-
-    yield
-
-    # Optional: Cleanup code after test (if needed)
-    # e.g., unset environment variables if they should not persist after test

test/test_cnn.py

@@ -0,0 +1,6 @@
+from ml_pipeline import config
+from ml_pipeline.model.cnn import VGG11
+
+def test_in_channels():
+    assert config.model.name == 'vgg11' 
+

test/test_inputs.py

@@ -0,0 +1,28 @@
+from ml_pipeline.data.dataset import MnistDataset
+from ml_pipeline import config
+from pathlib import Path
+import pytest
+
+@pytest.mark.skip()
+def test_init():
+    pass
+
+
+def test_getitem():
+    train_set = MnistDataset(config.data.train_path)
+
+    assert train_set[0][1].item() == 5
+    repeated = 8
+    length = 28
+    channels = 1
+    assert train_set[0][0].shape == (channels, length * repeated, length * repeated)
+
+@pytest.mark.skip()
+def test_loader():
+    from torch.utils.data import DataLoader
+    train_set = MnistDataset(config.data.train_path)
+    # train_loader = DataLoader(train_set, batch_size=config.training.batch_size, shuffle=True)
+    # for sample, target in train_loader:
+    #     assert len(sample) == config.training.batch_size
+    # len(sample)
+    # len(target)

test/test_pipeline.py

@@ -1,17 +0,0 @@
-from src.model.linear import DNN
-from src.data.dataset import MnistDataset
-import os
-
-
-def test_size_of_dataset():
-    examples = 500
-    os.environ["TRAINING_EXAMPLES"] = str(examples)
-    channels = 1
-    width, height = 224, 224
-    dataset = MnistDataset(os.getenv("TRAIN_PATH"))
-    # label = dataset[0][1].item()
-    image = dataset[0][0].shape
-    assert channels == image[0]
-    assert width == image[1]
-    assert height == image[2]
-    assert len(dataset) == examples