Trying to quantize the object detection model of EdgeNet2 1 but fail, using the pretrained model from here 1(ms coco 300x300).
    qconfig = torch.quantization.get_default_qconfig('qnnpack')
    print(qconfig)		
    model.eval()
    model.qconfig = qconfig
    torch.quantization.prepare(model, inplace=True)		        
	
    predictor = BoxPredictor(cfg=cfg, device=device)
    #works fine if I do not run the main_images
    main_images(predictor=predictor, model=model, object_names=object_names,
                in_dir=args.im_dir, out_dir=args.save_dir, device=device)
	
    # Convert to quantized model
    torch.quantization.convert(model, inplace=True)
    torch.save(model.state_dict(), "espnet2v_300.pt")        		
    print("save as quantize model")		       


def main_images(predictor, model, object_names, in_dir, out_dir, device='cuda'):
    png_file_names = glob.glob(in_dir + os.sep + '*.png')
    jpg_file_names = glob.glob(in_dir + os.sep + '*.jpg')
    file_names = png_file_names + jpg_file_names

    if len(file_names) == 0:
        print_error_message('No image files in the folder')

    # model in eval mode
    model.eval()
    with torch.no_grad():
        for img_name in file_names:
            image = cv2.imread(img_name)
            predictor.predict(model, image, is_scaling=False)                

If I do not run the main_images function, I can quantize the model, If I  run the main_images function, I received error message.
The expanded size of the tensor (243076) must match the existing size (262144) at non-singleton dimension 0.  Target sizes: [243076].  Tensor sizes: [262144]
The main_images function works fine if I do not quantize it, any idea how should I fix this?Or do anyone know an object detection project of pytorch suit for quantization? Thanks
ps : Tried with the model of tflite, but accuracy is not that good.