deep_sort/tools/generate_detections leads to Tensorflow builtins.KeyError

[jvkloc]

I'm trying to write a code for implementing YOLOv8 detection and DeepSORT tracking to mp4 video opened with OpenCV (if I get this working, I'll move to real time RealSense video stream). However, when using DeepSORT tracking tools I end up getting a Tensorflow builtins.KeyError in tensorflow/python/framework/ops.py:

 File "/home/jvkloc/Documents/TKT/Python/Sonify/realsense_main.py", line 22, in <module>
  tracker = Tracker()
File "/home/jvkloc/Documents/TKT/Python/Sonify/Tracker.py", line 22, in __init__
  self.encoder = gdet.create_box_encoder(encoder_model_filename, batch_size=1)
File "/home/jvkloc/Documents/TKT/Python/Sonify/deep_sort/tools/generate_detections.py", line 100, in create_box_encoder
  image_encoder = ImageEncoder(model_filename, input_name, output_name)
File "/home/jvkloc/Documents/TKT/Python/Sonify/deep_sort/tools/generate_detections.py", line 80, in __init__
  self.input_var = tf.compat.v1.get_default_graph().get_tensor_by_name(
File "/home/jvkloc/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 4188, in get_tensor_by_name
  return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
File "/home/jvkloc/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 4012, in as_graph_element
  return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
File "/home/jvkloc/.local/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 4052, in _as_graph_element_locked
  raise KeyError("The name %s refers to a Tensor which does not "

builtins.KeyError: "The name 'net/images:0' refers to a Tensor which does not exist. The operation, 'net/images', does not exist in the graph."

This is the Tracker class I'm trying to use:

# Tracker class for realsense_main.py object tracking script
from deep_sort.deep_sort.tracker import Tracker as DeepSortTracker
from deep_sort.tools import generate_detections as gdet
from deep_sort.deep_sort.detection import Detection
from deep_sort.deep_sort import nn_matching
import numpy as np

class Tracker():
    tracker = None
    encoder = None
    tracks = None
    
    def __init__(self):
        max_cosine_distance = 0.4
        nn_budget = None
        encoder_model_filename = 'mars-small128.pb'
        metric = nn_matching.NearestNeighborDistanceMetric('cosine', max_cosine_distance, nn_budget)
        self.tracker = DeepSortTracker(metric)
        self.encoder = gdet.create_box_encoder(encoder_model_filename, batch_size=1)
    
    def update(self, frame, detections):
        bboxes = np.asarray([d[:-1] for d in detections])
        bboxes[:, 2:] = bboxes[:, 2:] - bboxes[:, 0:2]
        scores = [d[-1] for d in detections]
        features = self.encoder(frame, bboxes)
        dets = []
        for bbox_id, bbox in enumerate(bboxes):
            dets.append(Detection(bbox, scores[bbox_id], features[bbox_id]))
        self.tracker.predict()
        self.tracker.update(dets)
        self.update_tracks()

    def update_tracks(self):
        tracks = []
        for track in self.tracker.tracks:
            if not track.is_confirmed() or track.time_since_update > 1:
                continue
            bbox = track.to_tlbr()
            id = track.track_id
            tracks.append(Track(id, bbox))
        self.tracks = tracks

class Track:
    track_id = None
    bbox = None

    def __init__(self, id, bbox):
        self.track_id = id
        self.bbox = bbox