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* fix watchdog process restarts reverting to the boot config /api/config/set parses a new FrigateConfig and swaps the API and dispatcher onto it, but FrigateApp.config was never rebound, so the watchdog factories rebuilt a crashed process from the config as of startup. Fix is to read through a ConfigHolder that the swap updates. * fix birdseye camera overrides being clobbered by a global mode change A global birdseye save published only the global object, leaving the output process to infer which cameras were inheriting by comparing against the previous global mode. That cannot tell an inherited value from an explicit one that happens to match, so it overwrote the override until a restart. Publish the per-camera values the config parse already resolved instead. * Reject non-finite numbers in GenAI review descriptions A model returning NaN for confidence or potential_threat_level slipped through the model_construct fallback, which skips validation, and was written into the review segment's JSON data. NaN is not valid JSON, so every subsequent /review request failed with "Out of range float values are not JSON compliant", blanking the review page for any time range containing the poisoned row. * restore fused DetectionOutput in the OpenVINO SSD model conversion * fix rgb swap issue for face dataset testing script
107 lines
4.1 KiB
Python
107 lines
4.1 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 translates the Object Detection API pre and post processors literally,
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producing per-class NonMaxSuppression, NonZero ops and map loops with data
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dependent shapes that the GPU plugin handles very badly. Both are cut out the
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way ssd_v2_support.json used to do it: the preprocessor is an identity at the
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native 300x300 input, and the postprocessor becomes a single fused
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DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
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OpenVINO detector expects, with the input flipped to BGR to match the legacy
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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_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
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OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
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INPUT_SHAPE = [1, 300, 300, 3]
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# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
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# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
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BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
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model = ov.convert_model(
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f"{MODEL_DIR}/frozen_inference_graph.pb",
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input=[("image_tensor:0", INPUT_SHAPE)],
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)
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nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
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parameter = model.get_parameters()[0]
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preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
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box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
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class_scores = nodes["Postprocessor/convert_scores"].output(0)
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anchors_output = nodes["Postprocessor/Reshape"].output(0)
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# The anchors only depend on the static input shape, so fold them into a
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# constant and drop the generator subgraph with the rest of the postprocessor.
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probe = ov.Core().compile_model(
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ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
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)
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probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
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anchors, resized = (out.copy() for out in probe([probe_input]).values())
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assert np.allclose(resized, probe_input, atol=1e-3), (
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"preprocessor is not an identity at 300x300, it cannot be bypassed"
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)
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image = ops.convert(parameter, "f32")
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for consumer in list(preprocessor.output(0).get_target_inputs()):
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consumer.replace_source_output(image.output(0))
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# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
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priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
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variances = np.tile(BOX_VARIANCES, len(anchors))
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proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
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# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
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box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
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class_preds = ops.reshape(class_scores, [1, -1], False)
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detections = ops.detection_output(
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box_logits,
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class_preds,
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proposals,
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{
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"background_label_id": 0,
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"top_k": 100,
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"keep_top_k": [100],
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"nms_threshold": 0.6,
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"confidence_threshold": 0.3,
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"code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
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"share_location": True,
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"variance_encoded_in_target": False,
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"normalized": True,
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"clip_before_nms": False,
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"clip_after_nms": True,
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"decrease_label_id": False,
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},
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)
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detections.output(0).get_tensor().set_names({"detection_out"})
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model = ov.Model([detections], [parameter], "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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# Fail the build rather than silently ship the dynamically shaped graph again.
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op_types = [op.get_type_name() for op in model.get_ordered_ops()]
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assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
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for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
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assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
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output_shape = model.outputs[0].get_partial_shape()
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assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
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f"unexpected detector output shape {output_shape}"
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)
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ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)
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