The expanded size of the tensor must match the existing size at non-singleton dimension

Trying to quantize the object detection model of EdgeNet2 but fail, using the pretrained model from here(ms coco 300x300).

    qconfig = torch.quantization.get_default_qconfig('qnnpack')
    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), "")        		
    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
    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.

Based on the error message it seems you might be passing the wrong input shape.
Is this error specific to the quantization module, i.e. is it running fine without quantization?
Could you try to resize the image array to the expected shape?

Yes, totally fine.

Already do that, it works before quantize