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complete edgetpu refactoring

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This commit is contained in:
YS 2022-01-02 18:44:20 +03:00
parent c7b3330265
commit f8118adf6b
2 changed files with 56 additions and 59 deletions
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2
frigate/config.py
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@ -960,7 +960,7 @@ class FrigateConfig(FrigateBaseModel):
# check runtime config
decoder_config = (
camera_config.ffmpeg
if "ffmpeg" in camera_config
if camera_config.ffmpeg is not None
else camera_config.gstreamer
)
assigned_roles = list(

113
frigate/detection/edgetpu.py
View File

@ -9,10 +9,10 @@ from typing import Dict
import numpy as np
# import tflite_runtime.interpreter as tflite
import tflite_runtime.interpreter as tflite
# from tflite_runtime.interpreter import load_delegate
from tflite_runtime.interpreter import load_delegate
from frigate.util import EventsPerSecond
from .object_detector import ObjectDetector
@ -27,7 +27,7 @@ def object_detector_factory(detector_config: DetectorConfig, model_path: str):
):
return None
object_detector = LocalObjectDetector(
tf_device=detector_config.device,
tf_device=detector_config.type,
model_path=model_path,
num_threads=detector_config.num_threads,
)
@ -49,77 +49,74 @@ class LocalObjectDetector(ObjectDetector):
edge_tpu_delegate = None
# if tf_device != "cpu":
# try:
# logger.info(f"Attempting to load TPU as {device_config['device']}")
# edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
# logger.info("TPU found")
# self.interpreter = tflite.Interpreter(
# model_path=model_path or "/edgetpu_model.tflite",
# experimental_delegates=[edge_tpu_delegate],
# )
# except ValueError:
# logger.error(
# "No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
# )
# raise
# else:
# logger.warning(
# "CPU detectors are not recommended and should only be used for testing or for trial purposes."
# )
# self.interpreter = tflite.Interpreter(
# model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
# )
if tf_device != "cpu":
try:
logger.info(f"Attempting to load TPU as {device_config['device']}")
edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
logger.info("TPU found")
self.interpreter = tflite.Interpreter(
model_path=model_path or "/edgetpu_model.tflite",
experimental_delegates=[edge_tpu_delegate],
)
except ValueError:
logger.error(
"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
)
raise
else:
logger.warning(
"CPU detectors are not recommended and should only be used for testing or for trial purposes."
)
self.interpreter = tflite.Interpreter(
model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
)
# self.interpreter.allocate_tensors()
self.interpreter.allocate_tensors()
# self.tensor_input_details = self.interpreter.get_input_details()
# self.tensor_output_details = self.interpreter.get_output_details()
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
def detect(self, tensor_input, threshold=0.4):
# TODO: called from process_clip
# TODO: process_clip
detections = []
assert False, "implement detect() for process_clip.py"
# raw_detections = self.detect_raw(tensor_input)
raw_detections = self.detect_raw(tensor_input)
# for d in raw_detections:
# if d[1] < threshold:
# break
# detections.append(
# (self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
# )
# self.fps.update()
for d in raw_detections:
if d[1] < threshold:
break
detections.append(
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
)
self.fps.update()
return detections
def detect_raw(self, tensor_input):
logger.error(">>>>>>>>>> detect raw")
# Expand dimensions [height, width, 3] ince the model expects images to have shape [1, height, width, 3]
tensor_input = np.expand_dims(tensor_input, axis=0)
# self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
# self.interpreter.invoke()
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
self.interpreter.invoke()
# boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
# class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
# scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
# count = int(
# self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
# )
boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
count = int(
self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
)
detections = np.zeros((20, 6), np.float32)
# for i in range(count):
# if scores[i] < 0.4 or i == 20:
# break
# detections[i] = [
# class_ids[i],
# float(scores[i]),
# boxes[i][0],
# boxes[i][1],
# boxes[i][2],
# boxes[i][3],
# ]
for i in range(count):
if scores[i] < 0.4 or i == 20:
break
detections[i] = [
class_ids[i],
float(scores[i]),
boxes[i][0],
boxes[i][1],
boxes[i][2],
boxes[i][3],
]
return detections