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feat: test case support
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@ -84,7 +84,7 @@ class FaceNet512dONNXClient(FacialRecognition):
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input_name = self.model.get_inputs()[0].name
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input_name = self.model.get_inputs()[0].name
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output_name = self.model.get_outputs()[0].name
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output_name = self.model.get_outputs()[0].name
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result = self.model.run([output_name], {input_name: img})
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result = self.model.run([output_name], {input_name: img})
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return result[0]
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return result[0][0]
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def scaling(x, scale):
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def scaling(x, scale):
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@ -1734,7 +1734,7 @@ def load_facenet512d_model(
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def load_facenet512d_onnx_model(
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def load_facenet512d_onnx_model(
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url="https://github.com/serengil/deepface_models/releases/download/v1.0/facenet512_weights.h5",
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url="https://github.com/ShivamSinghal1/deepface/releases/download/v1/facenet512_fp32.onnx",
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) -> Model:
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) -> Model:
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"""
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"""
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Download Facenet512d ONNX model weights and load
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Download Facenet512d ONNX model weights and load
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@ -1743,14 +1743,14 @@ def load_facenet512d_onnx_model(
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"""
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"""
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try:
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try:
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import onnxruntime as ort
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import onnxruntime as ort
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import torch
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except ModuleNotFoundError as e:
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except ModuleNotFoundError as e:
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raise ImportError(
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raise ImportError(
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"FaceNet512dONNX is an optional model, ensure the library is installed. "
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"FaceNet512dONNX is an optional model, ensure the library is installed. "
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"Please install using 'pip install onnxruntime' or 'pip install onnxruntime-gpu' to use gpu"
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"Please install using 'pip install onnxruntime' or 'pip install onnxruntime-gpu' to use gpu"
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) from e
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) from e
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if torch.cuda.is_available():
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if ort.get_device() == "GPU":
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logger.info(f"using onnx GPU for inference")
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
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else:
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else:
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providers = ['CPUExecutionProvider']
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providers = ['CPUExecutionProvider']
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@ -4,3 +4,4 @@ dlib>=19.20.0
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ultralytics>=8.0.122
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ultralytics>=8.0.122
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facenet-pytorch>=2.5.3
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facenet-pytorch>=2.5.3
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torch>=2.1.2
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torch>=2.1.2
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onnxruntime>=1.19.0
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@ -8,7 +8,7 @@ from deepface.commons.logger import Logger
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logger = Logger()
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logger = Logger()
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models = ["VGG-Face", "Facenet", "Facenet512", "ArcFace", "GhostFaceNet"]
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models = ["VGG-Face", "Facenet", "Facenet512", "ArcFace", "GhostFaceNet", "Facenet512ONNX"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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metrics = ["cosine", "euclidean", "euclidean_l2"]
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detectors = ["opencv", "mtcnn"]
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detectors = ["opencv", "mtcnn"]
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