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customize broken weight file test
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tests/test_commons.py
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46
tests/test_commons.py
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# built-in dependencies
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import os
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import pytest
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# project dependencies
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from deepface.commons import folder_utils, weight_utils, package_utils
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from deepface.commons.logger import Logger
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logger = Logger()
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tf_version = package_utils.get_tf_major_version()
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if tf_version == 1:
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from keras.models import Sequential
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from keras.layers import (
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Dropout,
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Dense,
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)
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else:
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import (
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Dropout,
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Dense,
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)
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def test_loading_broken_weights():
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home = folder_utils.get_deepface_home()
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weight_file = os.path.join(home, ".deepface/weights/vgg_face_weights.h5")
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# construct a dummy model
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model = Sequential()
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# Add layers to the model
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model.add(
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Dense(units=64, activation="relu", input_shape=(100,))
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) # Input layer with 100 features
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model.add(Dropout(0.5)) # Dropout layer to prevent overfitting
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model.add(Dense(units=32, activation="relu")) # Hidden layer
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model.add(Dense(units=10, activation="softmax")) # Output layer with 10 classes
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# vgg's weights cannot be loaded to this model
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with pytest.raises(ValueError, match="Exception while loading pre-trained weights from"):
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model = weight_utils.load_model_weights(model=model, weight_file=weight_file)
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logger.info("✅ test loading broken weight file is done")
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@ -1,13 +1,9 @@
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# built-in dependencies
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import os
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# 3rd party dependencies
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import pytest
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import cv2
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# project dependencies
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from deepface import DeepFace
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from deepface.commons import folder_utils, package_utils
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from deepface.commons.logger import Logger
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logger = Logger()
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@ -192,35 +188,3 @@ def test_verify_for_nested_embeddings():
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_ = DeepFace.verify(img1_path=img1_embeddings, img2_path=img2_path)
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logger.info("✅ test verify for nested embeddings is done")
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def test_verify_for_broken_weights():
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home = folder_utils.get_deepface_home()
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# we are not performing anything with model deepid
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weights_file = os.path.join(home, ".deepface/weights/deepid_keras_weights.h5")
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backup_file = os.path.join(home, ".deepface/weights/deepid_keras_weights_backup.h5")
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restore = False
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# backup original weight file
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if os.path.exists(weights_file) is True:
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os.rename(weights_file, backup_file)
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restore = True
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# Create a dummy vgg_face_weights.h5 file
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with open(weights_file, "w", encoding="UTF-8") as f:
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f.write("dummy content")
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with pytest.raises(ValueError, match="Exception while loading pre-trained weights from"):
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_ = DeepFace.verify(
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img1_path="dataset/img1.jpg",
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img2_path="dataset/img2.jpg",
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model_name="DeepId",
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)
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if restore:
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os.remove(weights_file)
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os.rename(backup_file, weights_file)
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logger.info("✅ test verify for broken weight file is done")
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