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detect: remove old models

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This commit is contained in:
Koushik Dutta
2024-04-08 14:57:47 -07:00
parent 267a53e84b
commit 7514ccf804
10 changed files with 215 additions and 181 deletions
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4
plugins/coreml/package-lock.json generated
View File

@@ -1,12 +1,12 @@
{
"name": "@scrypted/coreml",
"version": "0.1.33",
"version": "0.1.34",
"lockfileVersion": 2,
"requires": true,
"packages": {
"": {
"name": "@scrypted/coreml",
"version": "0.1.33",
"version": "0.1.34",
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
}

2
plugins/coreml/package.json
View File

@@ -42,5 +42,5 @@
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
},
"version": "0.1.33"
"version": "0.1.34"
}

79
plugins/coreml/src/coreml/__init__.py
View File

@@ -18,9 +18,18 @@ from predict import Prediction, PredictPlugin, Rectangle
predictExecutor = concurrent.futures.ThreadPoolExecutor(8, "CoreML-Predict")
availableModels = [
"Default",
"scrypted_yolov9c_320",
"scrypted_yolov9c",
"yolov9c_320",
"yolov9c",
"ssdlite_mobilenet_v2",
"yolov4-tiny",
]
def parse_label_contents(contents: str):
lines = contents.split(',')
lines = contents.split(",")
ret = {}
for row_number, content in enumerate(lines):
pair = re.split(r"[:\s]+", content.strip(), maxsplit=1)
@@ -45,23 +54,21 @@ def parse_labels(userDefined):
raise Exception("no classes found in model metadata")
return parse_label_contents(classes)
class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProvider):
def __init__(self, nativeId: str | None = None):
super().__init__(nativeId=nativeId)
model = self.storage.getItem("model") or "Default"
if model == "Default":
# if arch is arm64, use yolov9c, otherwise use yolov8n
if platform.machine() == 'arm64':
model = "scrypted_yolov9c_320"
else:
model = "scrypted_yolov8n_320"
if model == "Default" or model not in availableModels:
if model != "Default":
self.storage.setItem("model", "Default")
model = "scrypted_yolov9c_320"
self.yolo = "yolo" in model
self.yolov8 = "yolov8" in model
self.yolov9 = "yolov9" in model
self.scrypted_model = "scrypted" in model
model_version = "v4"
mlmodel = 'model' if self.yolov8 or self.yolov9 else model
model_version = "v5"
mlmodel = "model" if self.yolov9 else model
print(f"model: {model}")
@@ -72,7 +79,7 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
f"{model}.mlmodel",
)
else:
if self.yolov8 or self.yolov9:
if self.yolov9:
files = [
f"{model}/{model}.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
f"{model}/{model}.mlpackage/Data/com.apple.CoreML/{mlmodel}.mlmodel",
@@ -112,30 +119,34 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
self.loop = asyncio.get_event_loop()
self.minThreshold = 0.2
asyncio.ensure_future(self.prepareVisionFramework(), loop = self.loop)
asyncio.ensure_future(self.prepareVisionFramework(), loop=self.loop)
async def prepareVisionFramework(self):
try:
from vision import VisionPlugin
if not VisionPlugin:
raise Exception("no vision plugin")
await scrypted_sdk.deviceManager.onDevicesChanged({
"devices": [
{
"nativeId": "vision",
"type": scrypted_sdk.ScryptedDeviceType.Builtin.value,
"interfaces": [
scrypted_sdk.ScryptedInterface.ObjectDetection.value,
],
"name": "Vision Framework",
}
]
})
await scrypted_sdk.deviceManager.onDevicesChanged(
{
"devices": [
{
"nativeId": "vision",
"type": scrypted_sdk.ScryptedDeviceType.Builtin.value,
"interfaces": [
scrypted_sdk.ScryptedInterface.ObjectDetection.value,
],
"name": "Vision Framework",
}
]
}
)
except:
pass
async def getDevice(self, nativeId: str) -> Any:
from vision import VisionPlugin
return VisionPlugin(nativeId)
async def getSettings(self) -> list[Setting]:
@@ -145,19 +156,7 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
"key": "model",
"title": "Model",
"description": "The detection model used to find objects.",
"choices": [
"Default",
"scrypted_yolov9c_320",
"scrypted_yolov8n_320",
"scrypted_yolov9c",
"scrypted_yolov8n",
"yolov8n_320",
"yolov9c_320",
"yolov8n",
"yolov9c",
"ssdlite_mobilenet_v2",
"yolov4-tiny",
],
"choices": availableModels,
"value": model,
},
]
@@ -179,7 +178,7 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
# run in executor if this is the plugin loop
if self.yolo:
input_name = "image" if self.yolov8 or self.yolov9 else "input_1"
input_name = "image" if self.yolov9 else "input_1"
if asyncio.get_event_loop() is self.loop:
out_dict = await asyncio.get_event_loop().run_in_executor(
predictExecutor, lambda: self.model.predict({input_name: input})
@@ -187,9 +186,9 @@ class CoreMLPlugin(PredictPlugin, scrypted_sdk.Settings, scrypted_sdk.DeviceProv
else:
out_dict = self.model.predict({input_name: input})
if self.yolov8 or self.yolov9:
if self.yolov9:
results = list(out_dict.values())[0][0]
objs = yolo.parse_yolov8(results)
objs = yolo.parse_yolov9(results)
ret = self.create_detection_result(objs, src_size, cvss)
return ret

4
plugins/openvino/package-lock.json generated
View File

@@ -1,12 +1,12 @@
{
"name": "@scrypted/openvino",
"version": "0.1.57",
"version": "0.1.58",
"lockfileVersion": 2,
"requires": true,
"packages": {
"": {
"name": "@scrypted/openvino",
"version": "0.1.57",
"version": "0.1.58",
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
}

2
plugins/openvino/package.json
View File

@@ -41,5 +41,5 @@
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
},
"version": "0.1.57"
"version": "0.1.58"
}

237
plugins/openvino/src/ov/__init__.py
View File

@@ -14,39 +14,58 @@ from predict import PredictPlugin, Prediction, Rectangle
import numpy as np
import yolo
availableModels = [
"Default",
"scrypted_yolov9c_320",
"scrypted_yolov9c",
"ssd_mobilenet_v1_coco",
"ssdlite_mobilenet_v2",
"yolo-v3-tiny-tf",
"yolo-v4-tiny-tf",
]
def parse_label_contents(contents: str):
lines = contents.splitlines()
ret = {}
for row_number, content in enumerate(lines):
pair = re.split(r'[:\s]+', content.strip(), maxsplit=1)
pair = re.split(r"[:\s]+", content.strip(), maxsplit=1)
if len(pair) == 2 and pair[0].strip().isdigit():
ret[int(pair[0])] = pair[1].strip()
else:
ret[row_number] = content.strip()
return ret
def param_to_string(parameters) -> str:
"""Convert a list / tuple of parameters returned from IE to a string."""
if isinstance(parameters, (list, tuple)):
return ', '.join([str(x) for x in parameters])
return ", ".join([str(x) for x in parameters])
else:
return str(parameters)
def dump_device_properties(core):
print('Available devices:')
print("Available devices:")
for device in core.available_devices:
print(f'{device} :')
print('\tSUPPORTED_PROPERTIES:')
for property_key in core.get_property(device, 'SUPPORTED_PROPERTIES'):
if property_key not in ('SUPPORTED_METRICS', 'SUPPORTED_CONFIG_KEYS', 'SUPPORTED_PROPERTIES'):
print(f"{device} :")
print("\tSUPPORTED_PROPERTIES:")
for property_key in core.get_property(device, "SUPPORTED_PROPERTIES"):
if property_key not in (
"SUPPORTED_METRICS",
"SUPPORTED_CONFIG_KEYS",
"SUPPORTED_PROPERTIES",
):
try:
property_val = core.get_property(device, property_key)
except TypeError:
property_val = 'UNSUPPORTED TYPE'
print(f'\t\t{property_key}: {param_to_string(property_val)}')
print('')
property_val = "UNSUPPORTED TYPE"
print(f"\t\t{property_key}: {param_to_string(property_val)}")
print("")
class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.Settings):
class OpenVINOPlugin(
PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.Settings
):
def __init__(self, nativeId: str | None = None):
super().__init__(nativeId=nativeId)
@@ -54,75 +73,94 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
dump_device_properties(self.core)
available_devices = self.core.available_devices
self.available_devices = available_devices
print('available devices: %s' % available_devices)
print("available devices: %s" % available_devices)
mode = self.storage.getItem('mode')
if mode == 'Default':
mode = 'AUTO'
mode = mode or 'AUTO'
mode = self.storage.getItem("mode")
if mode == "Default":
mode = "AUTO"
mode = mode or "AUTO"
precision = self.storage.getItem('precision') or 'Default'
if precision == 'Default':
precision = self.storage.getItem("precision") or "Default"
if precision == "Default":
using_mode = mode
if using_mode == 'AUTO':
if 'GPU' in available_devices:
using_mode = 'GPU'
if using_mode == 'GPU':
precision = 'FP16'
if using_mode == "AUTO":
if "GPU" in available_devices:
using_mode = "GPU"
if using_mode == "GPU":
precision = "FP16"
else:
precision = 'FP32'
precision = "FP32"
model = self.storage.getItem('model') or 'Default'
if model == 'Default':
model = 'scrypted_yolov9c_320'
self.yolo = 'yolo' in model
self.yolov8 = "yolov8" in model
model = self.storage.getItem("model") or "Default"
if model == "Default" or model not in availableModels:
if model != "Default":
self.storage.setItem("model", "Default")
model = "scrypted_yolov9c_320"
self.yolo = "yolo" in model
self.yolov9 = "yolov9" in model
self.scrypted_model = "scrypted" in model
self.sigmoid = model == 'yolo-v4-tiny-tf'
self.sigmoid = model == "yolo-v4-tiny-tf"
print(f'model/mode/precision: {model}/{mode}/{precision}')
print(f"model/mode/precision: {model}/{mode}/{precision}")
ovmodel = 'best' if self.scrypted_model else model
ovmodel = "best" if self.scrypted_model else model
model_version = 'v4'
xmlFile = self.downloadFile(f'https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.xml', f'{model_version}/{precision}/{ovmodel}.xml')
binFile = self.downloadFile(f'https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.bin', f'{model_version}/{precision}/{ovmodel}.bin')
model_version = "v4"
xmlFile = self.downloadFile(
f"https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.xml",
f"{model_version}/{precision}/{ovmodel}.xml",
)
binFile = self.downloadFile(
f"https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.bin",
f"{model_version}/{precision}/{ovmodel}.bin",
)
if self.scrypted_model:
labelsFile = self.downloadFile('https://raw.githubusercontent.com/koush/openvino-models/main/scrypted_labels.txt', 'scrypted_labels.txt')
labelsFile = self.downloadFile(
"https://raw.githubusercontent.com/koush/openvino-models/main/scrypted_labels.txt",
"scrypted_labels.txt",
)
elif self.yolo:
labelsFile = self.downloadFile('https://raw.githubusercontent.com/koush/openvino-models/main/coco_80cl.txt', 'coco_80cl.txt')
labelsFile = self.downloadFile(
"https://raw.githubusercontent.com/koush/openvino-models/main/coco_80cl.txt",
"coco_80cl.txt",
)
else:
labelsFile = self.downloadFile('https://raw.githubusercontent.com/koush/openvino-models/main/coco_labels.txt', 'coco_labels.txt')
labelsFile = self.downloadFile(
"https://raw.githubusercontent.com/koush/openvino-models/main/coco_labels.txt",
"coco_labels.txt",
)
print(xmlFile, binFile, labelsFile)
try:
self.compiled_model = self.core.compile_model(xmlFile, mode)
print("EXECUTION_DEVICES", self.compiled_model.get_property("EXECUTION_DEVICES"))
print(
"EXECUTION_DEVICES",
self.compiled_model.get_property("EXECUTION_DEVICES"),
)
except:
import traceback
traceback.print_exc()
print("Reverting all settings.")
self.storage.removeItem('mode')
self.storage.removeItem('model')
self.storage.removeItem('precision')
self.storage.removeItem("mode")
self.storage.removeItem("model")
self.storage.removeItem("precision")
self.requestRestart()
# mobilenet 1,300,300,3
# yolov3/4 1,416,416,3
# yolov8 1,3,320,320
# yolov9 1,3,320,320
# second dim is always good.
self.model_dim = self.compiled_model.inputs[0].shape[2]
labels_contents = open(labelsFile, 'r').read()
labels_contents = open(labelsFile, "r").read()
self.labels = parse_label_contents(labels_contents)
async def getSettings(self) -> list[Setting]:
mode = self.storage.getItem('mode') or 'Default'
model = self.storage.getItem('model') or 'Default'
precision = self.storage.getItem('precision') or 'Default'
mode = self.storage.getItem("mode") or "Default"
model = self.storage.getItem("model") or "Default"
precision = self.storage.getItem("precision") or "Default"
return [
{
"title": "Available Devices",
@@ -132,52 +170,38 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
"key": "available_devices",
},
{
'key': 'model',
'title': 'Model',
'description': 'The detection model used to find objects.',
'choices': [
'Default',
'scrypted_yolov9c_320',
'scrypted_yolov8n_320',
'scrypted_yolov9c',
'scrypted_yolov8n',
'yolov9c_320',
'yolov8n_320',
'yolov9c',
'yolov8n',
'ssd_mobilenet_v1_coco',
'ssdlite_mobilenet_v2',
'yolo-v3-tiny-tf',
'yolo-v4-tiny-tf',
],
'value': model,
"key": "model",
"title": "Model",
"description": "The detection model used to find objects.",
"choices": availableModels,
"value": model,
},
{
'key': 'mode',
'title': 'Mode',
'description': 'AUTO, CPU, or GPU mode to use for detections. Requires plugin reload. Use CPU if the system has unreliable GPU drivers.',
'choices': [
'Default',
'AUTO',
'CPU',
'GPU',
"key": "mode",
"title": "Mode",
"description": "AUTO, CPU, or GPU mode to use for detections. Requires plugin reload. Use CPU if the system has unreliable GPU drivers.",
"choices": [
"Default",
"AUTO",
"CPU",
"GPU",
],
'value': mode,
'combobox': True,
"value": mode,
"combobox": True,
},
{
'key': 'precision',
'title': 'Precision',
'description': 'The model floating point precision. FP16 is recommended for GPU. FP32 is recommended for CPU.',
'choices': [
'Default',
'FP16',
'FP32',
"key": "precision",
"title": "Precision",
"description": "The model floating point precision. FP16 is recommended for GPU. FP32 is recommended for CPU.",
"choices": [
"Default",
"FP16",
"FP32",
],
'value': precision,
}
"value": precision,
},
]
async def putSetting(self, key: str, value: SettingValue):
self.storage.setItem(key, value)
await self.onDeviceEvent(scrypted_sdk.ScryptedInterface.Settings.value, None)
@@ -195,7 +219,7 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
infer_request = self.compiled_model.create_infer_request()
# the input_tensor can be created with the shared_memory=True parameter,
# but that seems to cause issues on some platforms.
if self.yolov8 or self.yolov9:
if self.yolov9:
im = np.stack([input])
im = im.transpose((0, 3, 1, 2)) # BHWC to BCHW, (n, 3, h, w)
im = im.astype(np.float32) / 255.0
@@ -203,7 +227,9 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
im = ov.Tensor(array=im)
input_tensor = im
elif self.yolo:
input_tensor = ov.Tensor(array=np.expand_dims(np.array(input), axis=0).astype(np.float32))
input_tensor = ov.Tensor(
array=np.expand_dims(np.array(input), axis=0).astype(np.float32)
)
else:
input_tensor = ov.Tensor(array=np.expand_dims(np.array(input), axis=0))
# Set input tensor for model with one input
@@ -213,8 +239,8 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
objs = []
if self.yolov8 or self.yolov9:
objs = yolo.parse_yolov8(infer_request.output_tensors[0].data[0])
if self.yolov9:
objs = yolo.parse_yolov9(infer_request.output_tensors[0].data[0])
return objs
if self.yolo:
@@ -224,17 +250,21 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
out_blob = infer_request.outputs[0]
else:
out_blob = infer_request.outputs[1]
# 13 13
objects = yolo.parse_yolo_region(out_blob.data, (input.width, input.height),(81,82, 135,169, 344,319), self.sigmoid)
objects = yolo.parse_yolo_region(
out_blob.data,
(input.width, input.height),
(81, 82, 135, 169, 344, 319),
self.sigmoid,
)
for r in objects:
obj = Prediction(r['classId'], r['confidence'], Rectangle(
r['xmin'],
r['ymin'],
r['xmax'],
r['ymax']
))
obj = Prediction(
r["classId"],
r["confidence"],
Rectangle(r["xmin"], r["ymin"], r["xmax"], r["ymax"]),
)
objs.append(obj)
# what about output[1]?
@@ -243,7 +273,6 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
return objs
output = infer_request.get_output_tensor(0)
for values in output.data[0][0].astype(float):
valid, index, confidence, l, t, r, b = values
@@ -258,12 +287,7 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
r = torelative(r)
b = torelative(b)
obj = Prediction(index - 1, confidence, Rectangle(
l,
t,
r,
b
))
obj = Prediction(index - 1, confidence, Rectangle(l, t, r, b))
objs.append(obj)
return objs
@@ -272,6 +296,7 @@ class OpenVINOPlugin(PredictPlugin, scrypted_sdk.BufferConverter, scrypted_sdk.S
objs = await predict()
except:
import traceback
traceback.print_exc()
raise

2
plugins/openvino/src/yolo/__init__.py
View File

@@ -6,7 +6,7 @@ from predict import Prediction, Rectangle
defaultThreshold = .2
def parse_yolov8(results, threshold = defaultThreshold, scale = None, confidence_scale = None):
def parse_yolov9(results, threshold = defaultThreshold, scale = None, confidence_scale = None):
objs = []
keep = np.argwhere(results[4:] > threshold)
for indices in keep:

4
plugins/tensorflow-lite/package-lock.json generated
View File

@@ -1,12 +1,12 @@
{
"name": "@scrypted/tensorflow-lite",
"version": "0.1.52",
"version": "0.1.54",
"lockfileVersion": 2,
"requires": true,
"packages": {
"": {
"name": "@scrypted/tensorflow-lite",
"version": "0.1.52",
"version": "0.1.54",
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
}

2
plugins/tensorflow-lite/package.json
View File

@@ -53,5 +53,5 @@
"devDependencies": {
"@scrypted/sdk": "file:../../sdk"
},
"version": "0.1.52"
"version": "0.1.54"
}

60
plugins/tensorflow-lite/src/tflite/__init__.py
View File

@@ -29,6 +29,18 @@ from scrypted_sdk.types import Setting, SettingValue
import yolo
from predict import PredictPlugin
availableModels = [
"Default",
"ssd_mobilenet_v2_coco_quant_postprocess",
"tf2_ssd_mobilenet_v2_coco17_ptq",
"ssdlite_mobiledet_coco_qat_postprocess",
"scrypted_yolov9c_320",
"efficientdet_lite0_320_ptq",
"efficientdet_lite1_384_ptq",
"efficientdet_lite2_448_ptq",
"efficientdet_lite3_512_ptq",
"efficientdet_lite3x_640_ptq",
]
def parse_label_contents(contents: str):
lines = contents.splitlines()
@@ -59,26 +71,32 @@ class TensorFlowLitePlugin(
edge_tpus = None
pass
model_version = "v12"
model_version = "v13"
model = self.storage.getItem("model") or "Default"
if model not in availableModels:
self.storage.setItem("model", "Default")
model = "Default"
defaultModel = model == "Default"
branch = "main"
labelsFile = None
def configureModel():
nonlocal labelsFile
nonlocal model
if defaultModel:
if edge_tpus and next((obj for obj in edge_tpus if obj['type'] == 'usb'), None):
if edge_tpus and next(
(obj for obj in edge_tpus if obj["type"] == "usb"), None
):
model = "ssdlite_mobiledet_coco_qat_postprocess"
else:
model = "efficientdet_lite0_320_ptq"
self.yolo = "yolo" in model
self.yolov8 = "yolov8" in model
self.yolov9 = "yolov9" in model
self.scrypted_model = "scrypted" in model
print(f'model: {model}')
print(f"model: {model}")
if self.scrypted_model:
labelsFile = self.downloadFile(
@@ -172,20 +190,7 @@ class TensorFlowLitePlugin(
"key": "model",
"title": "Model",
"description": "The detection model used to find objects.",
"choices": [
"Default",
"ssd_mobilenet_v2_coco_quant_postprocess",
"tf2_ssd_mobilenet_v2_coco17_ptq",
"ssdlite_mobiledet_coco_qat_postprocess",
"yolov8n_full_integer_quant",
"yolov8n_full_integer_quant_320",
"scrypted_yolov8n_320",
"efficientdet_lite0_320_ptq",
"efficientdet_lite1_384_ptq",
"efficientdet_lite2_448_ptq",
"efficientdet_lite3_512_ptq",
"efficientdet_lite3x_640_ptq",
],
"choices": availableModels,
"value": model,
},
]
@@ -202,12 +207,12 @@ class TensorFlowLitePlugin(
interpreter = self.interpreters.get()
try:
if self.yolo:
tensor_index = input_details(interpreter, 'index')
tensor_index = input_details(interpreter, "index")
im = np.stack([input])
i = interpreter.get_input_details()[0]
if i['dtype'] == np.int8:
scale, zero_point = i['quantization']
if i["dtype"] == np.int8:
scale, zero_point = i["quantization"]
if scale == 0.003986024297773838 and zero_point == -128:
# fast path for quantization 1/255 = 0.003986024297773838
im = im.view(np.int8)
@@ -222,16 +227,21 @@ class TensorFlowLitePlugin(
interpreter.invoke()
output_details = interpreter.get_output_details()
output = output_details[0]
x = interpreter.get_tensor(output['index'])
x = interpreter.get_tensor(output["index"])
input_scale = self.get_input_details()[0]
if x.dtype == np.int8:
scale, zero_point = output['quantization']
scale, zero_point = output["quantization"]
threshold = yolo.defaultThreshold / scale + zero_point
combined_scale = scale * input_scale
objs = yolo.parse_yolov8(x[0], threshold, scale=lambda v: (v - zero_point) * combined_scale, confidence_scale=lambda v: (v - zero_point) * scale)
objs = yolo.parse_yolov9(
x[0],
threshold,
scale=lambda v: (v - zero_point) * combined_scale,
confidence_scale=lambda v: (v - zero_point) * scale,
)
else:
# this code path is unused.
objs = yolo.parse_yolov8(x[0], scale=lambda v: v * input_scale)
objs = yolo.parse_yolov9(x[0], scale=lambda v: v * input_scale)
else:
common.set_input(interpreter, input)
interpreter.invoke()