模型推理时长宽不一样，就不能正常得到结果吗

model_input_size可以和rgb888p_size不一样，但是model_input_size需要和你训练的分辨率一致

我训练时的分别率就是高224，宽160。这是我的代码，但是查看res = ob_det.run(img)，中的res结果为空

# 导入必要的库 Import necessary libraries
from libs.PipeLine import PipeLine, ScopedTiming  # 导入Pipeline处理和计时器类 Import pipeline processing and timer classes
from libs.AIBase import AIBase  # 导入AI基类 Import AI base class
from libs.AI2D import Ai2d  # 导入2D图像处理类 Import 2D image processing class
from libs.Utils import *  # 导入工具函数 Import utility functions
import os
import ujson
from media.media import *
from time import *
import nncase_runtime as nn
import ulab.numpy as np
import time
import utime
import image
import random
import gc  # 垃圾回收模块 Garbage collection module
import sys
import aidemo

from libs.YbProtocol import YbProtocol
from ybUtils.YbUart import YbUart
# uart = None
uart = YbUart(baudrate=115200)
pto = YbProtocol()


# 自定义YOLOv8检测类 Custom YOLOv8 detection class
class ObjectDetectionApp(AIBase):
    def __init__(self,kmodel_path,labels,model_input_size,max_boxes_num,confidence_threshold=0.5,nms_threshold=0.2,rgb888p_size=[224,224],display_size=[1920,1080],debug_mode=0):
        """
        初始化函数 Initialization function
        kmodel_path: 模型路径 Model path
        labels: 标签列表 Label list
        model_input_size: 模型输入大小 Model input size
        max_boxes_num: 最大检测框数量 Maximum number of detection boxes
        confidence_threshold: 置信度阈值 Confidence threshold
        nms_threshold: 非极大值抑制阈值 Non-maximum suppression threshold
        rgb888p_size: RGB图像大小 RGB image size
        display_size: 显示大小 Display size
        debug_mode: 调试模式 Debug mode
        """
        super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
        self.kmodel_path = kmodel_path
        self.labels = labels
        # 模型输入分辨率 Model input resolution
        self.model_input_size = model_input_size
        # 阈值设置 Threshold settings
        self.confidence_threshold = confidence_threshold
        self.nms_threshold = nms_threshold
        self.max_boxes_num = max_boxes_num
        # sensor给到AI的图像分辨率 Image resolution from sensor to AI
        self.rgb888p_size = [ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
        # 显示分辨率 Display resolution
        self.display_size = [ALIGN_UP(display_size[0],16),display_size[1]]
        self.debug_mode = debug_mode
        # 检测框预置颜色值 Preset colors for detection boxes
        self.color_four = get_colors(len(self.labels))
        # 宽高缩放比例 Width and height scaling ratios
        self.x_factor = float(self.rgb888p_size[0])/self.model_input_size[0]
        self.y_factor = float(self.rgb888p_size[1])/self.model_input_size[1]
        # Ai2d实例，用于实现模型预处理 Ai2d instance for model preprocessing
        self.ai2d = Ai2d(debug_mode)
        # 设置Ai2d的输入输出格式和类型 Set Ai2d input/output format and type
        self.ai2d.set_ai2d_dtype(nn.ai2d_format.NCHW_FMT,nn.ai2d_format.NCHW_FMT,np.uint8, np.uint8)

    # 配置预处理操作 Configure preprocessing operations
    def config_preprocess(self,input_image_size=None):
        """
        配置图像预处理参数 Configure image preprocessing parameters
        input_image_size: 输入图像大小(可选) Input image size (optional)
        """
        with ScopedTiming("set preprocess config",self.debug_mode > 0):
            # 初始化ai2d预处理配置 Initialize ai2d preprocessing configuration
            ai2d_input_size = input_image_size if input_image_size else self.rgb888p_size
            top,bottom,left,right,self.scale = letterbox_pad_param(self.rgb888p_size,self.model_input_size)
            # 配置padding预处理 Configure padding preprocessing
            self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [128,128,128])
            self.ai2d.resize(nn.interp_method.tf_bilinear, nn.interp_mode.half_pixel)
            self.ai2d.build([1,3,ai2d_input_size[1],ai2d_input_size[0]],[1,3,self.model_input_size[1],self.model_input_size[0]])

    # 预处理函数 Preprocessing function
    def preprocess(self,input_np):
        with ScopedTiming("preprocess",self.debug_mode > 0):
            return [nn.from_numpy(input_np)]

    # 后处理函数 Postprocessing function
    def postprocess(self,results):
        """
        处理模型输出结果 Process model output results
        results: 模型输出 Model output
        """
        with ScopedTiming("postprocess",self.debug_mode > 0):
            new_result = results[0][0].transpose()
            det_res = aidemo.yolov8_det_postprocess(new_result.copy(),
                [self.rgb888p_size[1],self.rgb888p_size[0]],
                [self.model_input_size[1],self.model_input_size[0]],
                [self.display_size[1],self.display_size[0]],
                len(self.labels),
                self.confidence_threshold,
                self.nms_threshold,
                self.max_boxes_num)
            return det_res

    # 绘制结果函数 Draw results function
    def draw_result(self,pl,dets):
        """
        绘制检测结果 Draw detection results
        pl: PipeLine实例 PipeLine instance
        dets: 检测结果 Detection results
        """
        with ScopedTiming("display_draw",self.debug_mode >0):
            if dets:
                pl.osd_img.clear()
                for i in range(len(dets[0])):
                    x, y, w, h = map(lambda x: int(round(x, 0)), dets[0][i])
                    # 绘制矩形框和标签 Draw rectangle box and label
                    pl.osd_img.draw_rectangle(x,y, w, h, color=self.color_four[dets[1][i]],thickness=4)
                    pl.osd_img.draw_string_advanced( x , y-50,32,
                        " " + self.labels[dets[1][i]] ,
                        color=self.color_four[dets[1][i]])

                    pto_data = pto.get_object_detect_data(x, y, w, h, self.labels[dets[1][i]])
                    uart.send(pto_data)
                    print(pto_data)
            else:
                pl.osd_img.clear()

# 性能监控类
class PerformanceMonitor:
    def __init__(self):
        self.frame_count = 0
        self.start_time = time.time_ns()  # 开始时间(纳秒)
        self.last_time = self.start_time  # 上一帧时间(纳秒)
        self.frame_times = []  # 帧处理时间列表(纳秒)

    def update(self):
        current_time = time.time_ns()
        frame_time = current_time - self.last_time  # 计算当前帧处理时间(纳秒)
        self.frame_times.append(frame_time)

        if len(self.frame_times) > 30:  # 保留最近30帧的数据
            self.frame_times.pop(0)

        self.frame_count += 1
        self.last_time = current_time

    def get_fps(self):
        if self.frame_count <= 1:
            return 0

        elapsed_time = time.time_ns() - self.start_time  # 总运行时间(纳秒)

        # 避免除零错误，添加最小时间阈值(1微秒)
        if elapsed_time < 1000:  # 1微秒 = 1000纳秒
            return 0

        # 计算FPS(总帧数 / 总时间(秒))
        return self.frame_count / (elapsed_time / 1e9)

    def get_avg_frame_time(self):
        if not self.frame_times:
            return 0

        # 计算平均帧处理时间(毫秒)
        avg_ns = sum(self.frame_times) / len(self.frame_times)
        return avg_ns / 1e6  # 转换为毫秒

    def get_current_frame_time(self):
        if not self.frame_times:
            return 0

        # 返回当前帧处理时间(毫秒)
        return self.frame_times[-1] / 1e6  # 转换为毫秒

# 主程序入口 Main program entry
if __name__=="__main__":
    # 显示模式，默认"hdmi" Display mode, default "hdmi"
    display_mode="lcd"
    rgb888p_size=[224, 64]

    # 根据显示模式设置分辨率 Set resolution based on display mode
    if display_mode=="hdmi":
        display_size=[1920,1080]
    else:
        display_size=[640,480]
    # 模型路径 Model path
    kmodel_path="/data/yolov8_runner_bib_int8.kmodel"
    # 标签列表 Label list
    labels = ["bib"]

    # 参数设置 Parameter settings
    confidence_threshold = 0.3  # 置信度阈值 Confidence threshold
    nms_threshold = 0.4  # NMS阈值 NMS threshold
    max_boxes_num = 10  # 最大检测框数量 Maximum number of detection boxes

    # 初始化PipeLine Initialize PipeLine
    pl = PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
    pl.create()
    # 初始化性能监控器
    perf_monitor = PerformanceMonitor()

    # 初始化目标检测实例 Initialize object detection instance
    ob_det = ObjectDetectionApp(kmodel_path,labels=labels,
                              model_input_size=[224, 64],
                              max_boxes_num=max_boxes_num,
                              confidence_threshold=confidence_threshold,
                              nms_threshold=nms_threshold,
                              rgb888p_size=rgb888p_size,
                              display_size=display_size,
                              debug_mode=0)
    ob_det.config_preprocess()
    fps = time.clock()

    # 主循环 Main loop
    while True:
        with ScopedTiming("total",1):
            fps.tick()
            # 获取当前帧数据 Get current frame data
            img = pl.get_frame()
            print(img.shape)
            # 推理当前帧 Inference current frame
            res = ob_det.run(img)
            print(res)
            # 绘制结果 Draw results
            ob_det.draw_result(pl,res)
            # 更新性能数据
            perf_monitor.update()
            # 在屏幕上显示性能数据
            # fps = perf_monitor.get_fps()
            # avg_frame_time = perf_monitor.get_avg_frame_time()  # 转换为毫秒
            current_inference_time = perf_monitor.get_current_frame_time()  # 转换为毫秒
            # 显示性能指标
            pl.osd_img.draw_string_advanced(10, 10, 24,
                            f"FPS: {fps.fps():.1f} | Inference: {current_inference_time:.1f}ms",
                            color=(255, 255, 0))  # 黄色文字
            # 显示结果 Display results
            pl.show_image()
            # 垃圾回收 Garbage collection
            gc.collect()

    # 释放资源 Release resources
    ob_det.deinit()
    pl.destroy()