重现步骤
- PipeLine的例程可以跑, 但是用yolo大作战里的教程转换出来的Kmodel模型替换上去就有这个报错: ValueError: array is too big, 也尝试过用yolo大作战里的代码跑,但是还是不行.
错误日志
Traceback (most recent call last):
File "", line 213, in
File "/sdcard/libs/AIBase.py", line 73, in run
File "/sdcard/libs/AIBase.py", line 59, in inference
ValueError: array is too big
尝试解决过程
后面照着这个链接里的提问改了下sdcard/libs/AIBase.py里面的内容(https://developer.canaan-creative.com/answer/questions/10010000000001213) ,又会有这个报错: IndexError: list index out of range
第一次解决后错误日志
Traceback (most recent call last):
File "", line 138, in
File "/sdcard/app/libs/AIBase.py", line 75, in run
File "", line 60, in postprocess
IndexError: list index out of range
第二次尝试解决过程
查了GPT它这样回答: det[5] 的值超出了 self.labels 列表的范围。具体来说，在这行代码中，程序试图通过索引访问 labels 列表中的元素，但是传递的索引值大于 labels 列表的长度。
尝试解决后的结果:
照着它给的解决方案加了一个判断条件, 虽然报错解决了, 但是又出现了画面左上角一直会绘制目标框,然后想正常检测的目标又不会绘制出目标框的问题。
实际效果图片: 
软硬件版本信息
实验名称：车型识别和行人检测（基于yolov5s）
nncase版本: nncase_kpu-2.8.3-py2.py3-none-win_amd64.whl
开发板信息: CanMW-K230-V1.1
镜像版本信息: CanMV-K230_micropython_v1.0_sdcard_v1.6_nncase_v2.8.3.img
补充材料
我的运行代码:
from libs.PipeLine import PipeLine, ScopedTiming
from libs.AIBase import AIBase
from libs.AI2D import Ai2d
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
import sys
import aicube
#自定义人体检测类
class PersonDetectionApp(AIBase):
def __init__(self,kmodel_path,model_input_size,labels,anchors,confidence_threshold=0.2,nms_threshold=0.5,nms_option=False,strides=[8,16,32],rgb888p_size=[224,224],display_size=[1920,1080],debug_mode=0):
super().__init__(kmodel_path,model_input_size,rgb888p_size,debug_mode)
self.kmodel_path=kmodel_path
# 模型输入分辨率
self.model_input_size=model_input_size
# 标签
self.labels=labels
# 检测anchors设置
self.anchors=anchors
# 特征图降采样倍数
self.strides=strides
# 置信度阈值设置
self.confidence_threshold=confidence_threshold
# nms阈值设置
self.nms_threshold=nms_threshold
self.nms_option=nms_option
# sensor给到AI的图像分辨率
self.rgb888p_size=[ALIGN_UP(rgb888p_size[0],16),rgb888p_size[1]]
# 显示分辨率
self.display_size=[ALIGN_UP(display_size[0],16),display_size[1]]
self.debug_mode=debug_mode
# Ai2d实例，用于实现模型预处理
self.ai2d=Ai2d(debug_mode)
# 设置Ai2d的输入输出格式和类型
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，这里使用了pad和resize，Ai2d支持crop/shift/pad/resize/affine，具体代码请打开/sdcard/app/libs/AI2D.py查看
def config_preprocess(self,input_image_size=None):
with ScopedTiming("set preprocess config",self.debug_mode > 0):
# 初始化ai2d预处理配置，默认为sensor给到AI的尺寸，您可以通过设置input_image_size自行修改输入尺寸
ai2d_input_size=input_image_size if input_image_size else self.rgb888p_size
top,bottom,left,right=self.get_padding_param()
self.ai2d.pad([0,0,0,0,top,bottom,left,right], 0, [0,0,0])
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]])
# 自定义当前任务的后处理
def postprocess(self,results):
with ScopedTiming("postprocess",self.debug_mode > 0):
# 这里使用了aicube模型的后处理接口anchorbasedet_post_preocess
dets = aicube.anchorbasedet_post_process(results[0], results[1], results[2], self.model_input_size, self.rgb888p_size, self.strides, len(self.labels), self.confidence_threshold, self.nms_threshold, self.anchors, self.nms_option)
return dets
# 绘制结果
def draw_result(self,pl,dets):
with ScopedTiming("display_draw",self.debug_mode >0):
if dets:
pl.osd_img.clear()
for det_box in dets:
x1, y1, x2, y2 = det_box[2],det_box[3],det_box[4],det_box[5]
w = float(x2 - x1) * self.display_size[0] // self.rgb888p_size[0]
h = float(y2 - y1) * self.display_size[1] // self.rgb888p_size[1]
x1 = int(x1 * self.display_size[0] // self.rgb888p_size[0])
y1 = int(y1 * self.display_size[1] // self.rgb888p_size[1])
x2 = int(x2 * self.display_size[0] // self.rgb888p_size[0])
y2 = int(y2 * self.display_size[1] // self.rgb888p_size[1])
if (h<(0.1*self.display_size[0])):
continue
if (w<(0.25*self.display_size[0]) and ((x1<(0.03*self.display_size[0])) or (x2>(0.97*self.display_size[0])))):
continue
if (w<(0.15*self.display_size[0]) and ((x1<(0.01*self.display_size[0])) or (x2>(0.99*self.display_size[0])))):
continue
pl.osd_img.draw_rectangle(x1 , y1 , int(w) , int(h), color=(255, 0, 255, 0), thickness = 2)
pl.osd_img.draw_string_advanced( x1 , y1-50,32, " " + self.labels[det_box[0]] + " " + str(round(det_box[1],2)), color=(255,0, 255, 0))
else:
pl.osd_img.clear()
# 计算padding参数
def get_padding_param(self):
dst_w = self.model_input_size[0]
dst_h = self.model_input_size[1]
input_width = self.rgb888p_size[0]
input_high = self.rgb888p_size[1]
ratio_w = dst_w / input_width
ratio_h = dst_h / input_high
if ratio_w < ratio_h:
ratio = ratio_w
else:
ratio = ratio_h
new_w = (int)(ratio * input_width)
new_h = (int)(ratio * input_high)
dw = (dst_w - new_w) / 2
dh = (dst_h - new_h) / 2
top = int(round(dh - 0.1))
bottom = int(round(dh + 0.1))
left = int(round(dw - 0.1))
right = int(round(dw - 0.1))
return top, bottom, left, right
if __name__=="__main__":
# 显示模式，默认"hdmi",可以选择"hdmi"和"lcd"
display_mode="hdmi"
if display_mode=="hdmi":
display_size=[1920,1080]
else:
display_size=[800,480]
# 模型路径
kmodel_path="/sdcard/app/tests/kmodel/best.kmodel"
# 其它参数设置
confidence_threshold = 0.2
nms_threshold = 0.6
rgb888p_size=[1920,1080]
labels = ["people"]
anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
# 初始化PipeLine
pl=PipeLine(rgb888p_size=rgb888p_size,display_size=display_size,display_mode=display_mode)
pl.create()
# 初始化自定义人体检测实例
person_det=PersonDetectionApp(kmodel_path,model_input_size=[640,640],labels=labels,anchors=anchors,confidence_threshold=confidence_threshold,nms_threshold=nms_threshold,nms_option=False,strides=[8,16,32],rgb888p_size=rgb888p_size,display_size=display_size,debug_mode=0)
person_det.config_preprocess()
try:
while True:
os.exitpoint()
with ScopedTiming("total",1):
# 获取当前帧数据
img=pl.get_frame()
# 推理当前帧
res=person_det.run(img)
# 绘制结果到PipeLine的osd图像
person_det.draw_result(pl,res)
# 显示当前的绘制结果
pl.show_image()
gc.collect()
except Exception as e:
sys.print_exception(e)
finally:
person_det.deinit()
pl.destroy()
`AIBase.py中的代码:`
from libs.PipeLine import ScopedTiming
import os
import ujson
from media.sensor import *
from media.display import *
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
import sys
#AIBase类别主要抽象的是AI任务推理流程
class AIBase:
def __init__(self,kmodel_path,model_input_size=None,rgb888p_size=None,debug_mode=0):
# kmodel路径
self.kmodel_path=kmodel_path
# 模型输入分辨率
self.model_input_size=model_input_size
# sensor给到AI的图像分辨率
self.rgb888p_size=rgb888p_size
# 调试模式
self.debug_mode=debug_mode
# kpu对象
self.kpu=nn.kpu()
self.kpu.load_kmodel(self.kmodel_path)
self.cur_img=None
self.tensors=[]
# 推理结果列表
self.results=[]
def get_kmodel_inputs_num(self):
return self.kpu.inputs_size()
def get_kmodel_outputs_num(self):
return self.kpu.outputs_size()
def preprocess(self,input_np):
with ScopedTiming("preprocess",self.debug_mode > 0):
return [self.ai2d.run(input_np)]
def inference(self,tensors):
with ScopedTiming("kpu run & get output",self.debug_mode > 0):
self.results.clear()
for i in range(self.kpu.inputs_size()):
# 将ai2d的输出tensor绑定为kmodel的输入数据
self.kpu.set_input_tensor(i, tensors[i])
# 运行kmodel做推理
self.kpu.run()
# 获取kmodel的推理输出tensor,输出可能为多个，因此返回的是一个列表
for i in range(self.kpu.outputs_size()):
output_data = self.kpu.get_output_tensor(i)
result = output_data.to_numpy()
self.results.appendz(result)
del output_data
return self.results
# 基类后处理接口
def postprocess(self,results):
return
# kmodel运行pipe，包括预处理+推理+后处理，后处理在单独的任务类中实现
def run(self,input_np):
self.cur_img=input_np
self.tensors.clear()
self.tensors=self.preprocess(input_np)
self.results=self.inference(self.tensors)
return self.postprocess(self.results)
# AIBase销毁函数
def deinit(self):
with ScopedTiming("deinit",self.debug_mode > 0):
del self.kpu
if hasattr(self,"ai2d"):
del self.ai2d
self.tensors.clear()
del self.tensors
gc.collect()
nn.shrink_memory_pool()
os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
time.sleep_ms(100)
