Add unit test on example scripts.

aidge_export_cpp/unit_tests/test_examples.py

+import subprocess
+import sys
+import os
+import pytest
+from pathlib import Path
+
+CURRENT_DIR = Path(__file__).parent
+EXAMPLES_DIR = CURRENT_DIR / "../../examples"
+
+# Dictionary of test cases: {id: (script_name, script_args)}
+TEST_CASES = {
+    "lenet-int8-no-args": ("export_LeNet/lenet.py", []),
+    "resnet18-int8-no-args": ("export_ResNet18/resnet18.py", ["--mock_db"])
+}
+
+def generate_test_cases():
+    """Parse TEST_CASES to provide valid pytest params.
+    """
+    for test_id, (script, args) in TEST_CASES.items():
+        yield pytest.param(script, args, id=test_id)
+
+@pytest.mark.parametrize(("script_name", "script_args"), generate_test_cases())
+def test_example_scripts_run_without_error(script_name, script_args):
+    """Basic test to verify that examples script run withoput raising an Error.
+    This test DO NOT check that the examples are working only that they are not broken.
+    """
+    script_path = os.path.join(EXAMPLES_DIR, script_name)
+    result = subprocess.run(
+        [sys.executable, script_path] + script_args,  # Or any lightweight args
+        capture_output=True,
+        text=True
+    )
+    assert result.returncode == 0, f"{script_name} failed with error: {result.stderr}"
+
+
+def main():
+    import sys
+
+    print(
+        f"{sys.argv[0]}: Warning: skipped: run with: pytest {sys.argv[0]}",
+        file=sys.stderr,
+)
+
+
+if __name__ == "__main__":
+    main()

examples/export_ResNet18/resnet18.py

@@ -16,6 +16,7 @@ from PIL import Image
 import requests
 from pathlib import Path
 import subprocess
+from random import randint
 # Aidge Modules
 import aidge_core
 import aidge_onnx
@@ -63,6 +64,10 @@ parser.add_argument(
         "DEBUG < INFO < NOTICE < WARN < ERROR < FATAL."
     )
 )
+
+parser.add_argument("--mock_db", action="store_true", help="Use a mock database instead of real one (TEST ONLY).")
+
+
 parser.add_argument(
     "--imagenet_path",
     type=str,
@@ -187,6 +192,7 @@ def print_cfg():
     print(' DEV_MODE         = ', DEV_MODE)
     print(' IMAGENET_PATH    = ', IMAGENET_PATH)
     print(' LABEL_PATH       = ', LABEL_PATH)
+    print(' MOCK_DB          = ', args.mock_db)
 
 print_cfg()
 
@@ -194,59 +200,68 @@ torch.manual_seed(RNG_SEED)
 random.seed(RNG_SEED)
 np.random.seed(RNG_SEED)
 
-backend = "cuda" if USE_CUDA else "cpu"
-
-image_label_pairs = []
-with open(LABEL_PATH, 'r') as f:
-    for line in f:
-        parts = line.strip().split()
-        if len(parts) == 2:
-            image_name, label = parts
-            image_label_pairs.append((image_name, int(label)))
-
-np.random.seed(RNG_SEED)
-NB_SELECT = max(NB_TEST, NB_CALIB)  # Check that NB_TEST and NB_CALIB are fixed
-selected_pairs = image_label_pairs[:NB_SELECT]
-
-# --------------------------------------------------------------
-# CREATE THE SAMPLES
-# --------------------------------------------------------------
-
-transform_val = transforms.Compose([transforms.Resize(256),
-                                    transforms.CenterCrop(224),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-                                    ])
-
-tensors = []
-labels  = []
-paths   = []
-index   = 0
-
-for image_name, label in selected_pairs:
-    image_path = os.path.join(IMAGENET_PATH, image_name)
-    if os.path.exists(image_path):
-        try:
-            image = Image.open(image_path)
-            if image.mode != 'RGB':
-                image = image.convert('RGB')
-            tensor = transform_val(image)
-            tensors.append(tensor)
-            labels.append(label)
-            paths.append(image_path)
-        except Exception as e:
-            print(f"Error with image {image_path}: {e}")
-
 backend = "cuda" if USE_CUDA else "cpu"
 aidge_tensors = []
-for tensor in tensors:
-    array = tensor.numpy()
-    array = np.reshape(array, (1, 3, 224, 224))
-    array = normalize(array)
-    aidge_tensor = aidge_core.Tensor(array)
-    aidge_tensor.set_backend(backend)
-    aidge_tensor.set_datatype(aidge_core.dtype.float32)
-    aidge_tensors.append(aidge_tensor)
+labels  = []
+if args.mock_db:
+    for i in range(NB_TEST):
+        aidge_tensor = aidge_core.Tensor(dims=(1, 3, 224, 224))
+        aidge_tensor.set_backend(backend)
+        aidge_tensor.set_datatype(aidge_core.dtype.float32)
+        aidge_core.uniform_filler(aidge_tensor, -1.0, 1.0)
+        aidge_tensors.append(aidge_tensor)
+        labels.append(randint(1, 1000))
+else:
+    image_label_pairs = []
+    with open(LABEL_PATH, 'r') as f:
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) == 2:
+                image_name, label = parts
+                image_label_pairs.append((image_name, int(label)))
+
+    np.random.seed(RNG_SEED)
+    NB_SELECT = max(NB_TEST, NB_CALIB)  # Check that NB_TEST and NB_CALIB are fixed
+    selected_pairs = image_label_pairs[:NB_SELECT]
+
+    # --------------------------------------------------------------
+    # CREATE THE SAMPLES
+    # --------------------------------------------------------------
+
+    transform_val = transforms.Compose([transforms.Resize(256),
+                                        transforms.CenterCrop(224),
+                                        transforms.ToTensor(),
+                                        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                                        ])
+
+    tensors = []
+    labels  = []
+    paths   = []
+    index   = 0
+
+    for image_name, label in selected_pairs:
+        image_path = os.path.join(IMAGENET_PATH, image_name)
+        if os.path.exists(image_path):
+            try:
+                image = Image.open(image_path)
+                if image.mode != 'RGB':
+                    image = image.convert('RGB')
+                tensor = transform_val(image)
+                tensors.append(tensor)
+                labels.append(label)
+                paths.append(image_path)
+            except Exception as e:
+                print(f"Error with image {image_path}: {e}")
+
+
+    for tensor in tensors:
+        array = tensor.numpy()
+        array = np.reshape(array, (1, 3, 224, 224))
+        array = normalize(array)
+        aidge_tensor = aidge_core.Tensor(array)
+        aidge_tensor.set_backend(backend)
+        aidge_tensor.set_datatype(aidge_core.dtype.float32)
+        aidge_tensors.append(aidge_tensor)
 
 
 # --------------------------------------------------------------
@@ -260,7 +275,6 @@ Load the .onnx model and perform some usual graph modifications :
     - Expand the metaOperators to perform the desired fusions.
 """
 
-
 # Define the target path and filename
 file_url = "https://huggingface.co/EclipseAidge/resnet18/resolve/main/resnet18_imagenet_1k.onnx?download=true"
 file_path = Path(MODEL_NAME + "_imagenet_1k.onnx")