Memory leak caused by incomplete executeAsync() function execution - tensorflow

Question

As the title suggests, I was inferring using executeAsync() for an object detection task. The function works well when I have a detection continuously in my live video and there's no memory leak. However, if I have no detection in one particular frame, there will be an error while it executes the executeAsync() function.
TypeError: Cannot read property '0' of undefined
And here's the memory leak that happened when there's no detection.
{unreliable: false, numBytesInGPU: 422861990, numTensors: 1111, numDataBuffers: 673, numBytes: 320663096}
{unreliable: false, numBytesInGPU: 490830799, numTensors: 1263, numDataBuffers: 752, numBytes: 371606568}
{unreliable: false, numBytesInGPU: 558799608, numTensors: 1415, numDataBuffers: 831, numBytes: 422550040}
I am thinking the issue lies in the dangling tensors that are created in executeAsync() since if there's a detection I would dispose the tensors, but if there's no detection, I couldn't get a reference of the dangling tensors since the test_result variable hasn't been created yet due to the aforementioned error.
I wanted to use tf.tidy() but I did some research on it and apparently it can't be used with asynchronous functions.
How can I eradicate the memory leak? Or is there a problem with my model inference? Really appreciate any help.
async detect(video){
console.log("detecting...");
var example = tf.browser.fromPixels(video);
var tf4d = example.expandDims(0);
try{
// error if there's no detection
const test_result = await this.model.executeAsync({ image_tensor: tf4d }, ['detection_boxes', 'num_detections', 'detection_classes', 'detection_scores']) as tf.Tensor[];
this.no_detection = false;
var detection_boxes = test_result[0].dataSync();
var num_detections = test_result[1].dataSync();
var detection_classes = test_result[2].dataSync();
var detection_scores = test_result[3].dataSync();
// dispose tensors to avoid memory leak
await Promise.all(test_result.map(t => t.data()));
test_result.map(t => t.dispose());
tf.dispose(test_result);
tf4d.dispose();
example.dispose();
console.log(tf.memory());
}
catch(error){
console.log("No detection");
console.log(error)
this.no_detection = true;
// dispose of tensor created from images if theres no detection
tf4d.dispose();
example.dispose();
console.log(tf.memory());
}
}
Edited: I think it's also worth mentioning that I retrained my own model using the Tensorflow Object Detection API following this tutorial.
I think there might also be some problem with the exporting of inference graph using export_inference_graph.pb since I also got this
23 ops no flops stats due to incomplete shapes.
while converting.
I tried converting from a pretrained inference graph to a tfjs model and it works fine - if there's no detection executeAsync() would just return an empty array.
That's the reason why I think maybe there's a problem with the exporting of inference graph of my own model..?
Here's the command (I tried both with and without the --input_shape argument):
python export_inference_graph.py --input_type image_tensor --input_shape 1,300,300,3 --pipeline_config_path training/ssd_mobilenet_v2.config --trained_checkpoint_prefix training/model.ckpt-38153 --output_directory trained-inference-graphs/output_inference_graph