Tests

.github/workflows/pytest.yml

@@ -0,0 +1,23 @@
+name: Pytest
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest
+    - name: Testing the code with pytest
+      run: |
+        pytest -v

utils.py → src/utils.py

@@ -3,7 +3,7 @@
 import numpy as np
 import tensorflow as tf
 
-from cGAN import conditionalGAN
+from src.cGAN import conditionalGAN
 
 batch_size = 64
 num_channels = 1

test/test_cgan.py

@@ -0,0 +1,38 @@
+import tensorflow as tf
+from src.cGAN import conditionalGAN
+
+class TestConditionalGAN:
+    @classmethod
+    def setup_class(cls):
+        discriminator = tf.keras.models.Sequential()
+        generator = tf.keras.models.Sequential()
+        latent_dim = 100
+        image_size = 28
+        num_classes = 10
+        cls.cgan = conditionalGAN(discriminator, generator, latent_dim, image_size, num_classes)
+        cls.cgan.compile(
+            d_optimizer=tf.keras.optimizers.Adam(),
+            g_optimizer=tf.keras.optimizers.Adam(),
+            loss_fn=tf.keras.losses.BinaryCrossentropy(),
+        )
+
+    def test_train_step(self):
+        # Create dummy data for testing
+        real_images = tf.random.normal((32, 28, 28, 1))
+        one_hot_labels = tf.one_hot([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], depth=10)
+
+        # Perform a single train step
+        train_logs = self.cgan.train_step((real_images, one_hot_labels))
+
+        # Check if the loss values are updated
+        assert "g_loss" in train_logs
+        assert "d_loss" in train_logs
+        assert train_logs["g_loss"] != 0.0
+        assert train_logs["d_loss"] != 0.0
+
+    def test_metrics(self):
+        # Check if the metrics are correctly initialized
+        metrics = self.cgan.metrics
+        assert len(metrics) == 2
+        assert isinstance(metrics[0], tf.keras.metrics.Mean)
+        assert isinstance(metrics[1], tf.keras.metrics.Mean)