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43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
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Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
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frontend converts the Object Detection API frozen graph natively; the four TF
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outputs are then repacked into the single [1, 1, 100, 7] DetectionOutput-style
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tensor that Frigate's OpenVINO detector expects, and the input is flipped to
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BGR to match the legacy reverse_input_channels behavior.
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"""
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import numpy as np
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import openvino as ov
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from openvino import opset8 as ops
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from openvino.preprocess import PrePostProcessor
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model = ov.convert_model(
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"/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
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input=[("image_tensor:0", [1, 300, 300, 3])],
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)
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# rows of (image_id, class_id, score, xmin, ymin, xmax, ymax)
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boxes = model.output("detection_boxes:0").get_node().input_value(0)
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classes = model.output("detection_classes:0").get_node().input_value(0)
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scores = model.output("detection_scores:0").get_node().input_value(0)
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# (ymin,xmin,ymax,xmax) -> (xmin,ymin,xmax,ymax)
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boxes = ops.gather(boxes, [1, 0, 3, 2], 2)
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classes = ops.unsqueeze(classes, 2)
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scores = ops.unsqueeze(scores, 2)
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image_id = ops.multiply(scores, np.float32(0.0))
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detections = ops.concat([image_id, classes, scores, boxes], 2)
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detections = ops.unsqueeze(detections, 1)
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detections.output(0).get_tensor().set_names({"detection_out"})
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model = ov.Model([detections], model.get_parameters(), "ssdlite_mobilenet_v2")
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ppp = PrePostProcessor(model)
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ppp.input().tensor().set_layout(ov.Layout("NHWC"))
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ppp.input().preprocess().reverse_channels()
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model = ppp.build()
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ov.save_model(model, "/models/ssdlite_mobilenet_v2.xml", compress_to_fp16=True)
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