Merge pull request #1329 from serengil/feat-task-3108-unit-test-for-broken-weight-file

unit test for broken weight file

deepface/commons/package_utils.py

@@ -1,3 +1,6 @@
+# built-in dependencies
+import hashlib
+
 # 3rd party dependencies
 import tensorflow as tf
 
@@ -44,3 +47,19 @@ def validate_for_keras3():
             "tf-keras package. Please run `pip install tf-keras` "
             "or downgrade your tensorflow."
         ) from err
+
+
+def find_file_hash(file_path: str, hash_algorithm: str = "sha256") -> str:
+    """
+    Find the hash of a given file with its content
+    Args:
+        file_path (str): exact path of a given file
+        hash_algorithm (str): hash algorithm
+    Returns:
+        hash (str)
+    """
+    hash_func = hashlib.new(hash_algorithm)
+    with open(file_path, "rb") as f:
+        while chunk := f.read(8192):
+            hash_func.update(chunk)
+    return hash_func.hexdigest()

tests/test_commons.py

@@ -0,0 +1,46 @@
+# built-in dependencies
+import os
+import pytest
+
+# project dependencies
+from deepface.commons import folder_utils, weight_utils, package_utils
+from deepface.commons.logger import Logger
+
+logger = Logger()
+
+tf_version = package_utils.get_tf_major_version()
+
+if tf_version == 1:
+    from keras.models import Sequential
+    from keras.layers import (
+        Dropout,
+        Dense,
+    )
+else:
+    from tensorflow.keras.models import Sequential
+    from tensorflow.keras.layers import (
+        Dropout,
+        Dense,
+    )
+
+
+def test_loading_broken_weights():
+    home = folder_utils.get_deepface_home()
+    weight_file = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
+
+    # construct a dummy model
+    model = Sequential()
+
+    # Add layers to the model
+    model.add(
+        Dense(units=64, activation="relu", input_shape=(100,))
+    )  # Input layer with 100 features
+    model.add(Dropout(0.5))  # Dropout layer to prevent overfitting
+    model.add(Dense(units=32, activation="relu"))  # Hidden layer
+    model.add(Dense(units=10, activation="softmax"))  # Output layer with 10 classes
+
+    # vgg's weights cannot be loaded to this model
+    with pytest.raises(ValueError, match="Exception while loading pre-trained weights from"):
+        model = weight_utils.load_model_weights(model=model, weight_file=weight_file)
+
+    logger.info("✅ test loading broken weight file is done")

tests/test_verify.py

@@ -186,3 +186,5 @@ def test_verify_for_nested_embeddings():
         match="When passing img1_path as a list, ensure that all its items are of type float",
     ):
         _ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_path)
+
+    logger.info("✅ test verify for nested embeddings is done")