fixed ci and test_main.py

Author: aibekakhmetkazy

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ on:
     branches: [ main ]
 
 jobs:
-  build:
+  build-and-test:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout Code
@@ -18,10 +18,10 @@ jobs:
       - name: Cache pip
         uses: actions/cache@v3
         with:
-            path: ~/.cache/pip
-            key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
-            restore-keys: |
-              ${{ runner.os }}-pip-
+          path: ~/.cache/pip
+          key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }}
+          restore-keys: |
+            ${{ runner.os }}-pip-
 
       - name: Clean up disk space
         run: |
@@ -33,12 +33,5 @@ jobs:
       - name: Build Docker Image
         run: docker build -t classifier .
 
-  test:
-    needs: build
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v3
-
       - name: Run Tests in Docker Container
         run: docker run --rm classifier make test

test_main.py

@@ -14,26 +14,26 @@ def test_missing_input_image(self):
         with self.assertRaises(FileNotFoundError):
             main().preprocess_image('/nonexistent/image.jpg')
 
-    # def test_tensor_size(self):
-    #     image, img_shape, img_type = main().preprocess_image('/input/resized_image.jpg')
-    #     print(img_shape, img_type)
-    #     self.assertEqual(img_shape, (1,2,3), 'Not Equal Shape')
-    #
-    # def test_image_type(self):
-    #     image, img_shape, img_type = main().preprocess_image('/input/resized_image.jpg')
-    #     print(img_shape, img_type)
-    #     self.assertEqual(img_type, int, 'Not Equal Image Type')
-    #
-    # def test_image_class(self):
-    #     model = MobileNetV2()
-    #     model.load_state_dict(torch.load("./model/weights/mobilenetv2.pt", weights_only=True))  # weights ported from torchvision
-    #     model.float()
-    #
-    #     predicted_label = main().inference(model, '/input/resized_image.jpg',
-    #                      '/output_raw/output_prediction.txt')
-    #
-    #     print('Label in output:',predicted_label)
-    #     self.assertEqual(predicted_label, 'Aar', 'Wrong classification')
+    def test_tensor_size(self):
+        image, img_shape, img_type = main().preprocess_image('/input/resized_image.jpg')
+        print(img_shape, img_type)
+        self.assertEqual(img_shape, (1,2,3), 'Not Equal Shape')
+
+    def test_image_type(self):
+        image, img_shape, img_type = main().preprocess_image('/input/resized_image.jpg')
+        print(img_shape, img_type)
+        self.assertEqual(img_type, int, 'Not Equal Image Type')
+
+    def test_image_class(self):
+        model = MobileNetV2()
+        model.load_state_dict(torch.load("./model/weights/mobilenetv2.pt", weights_only=True))  # weights ported from torchvision
+        model.float()
+
+        predicted_label = main().inference(model, '/input/resized_image.jpg',
+                         '/output_raw/output_prediction.txt')
+
+        print('Label in output:',predicted_label)
+        self.assertEqual(predicted_label, 'Aar', 'Wrong classification')
 
 if __name__ == '__main__':
     unittest.main()