LPRNet 车牌识别部署 rk3588（pt-onnx-rknn）包含各个步骤完整代码

48 阅读 0 评论 0 点赞

虽然车牌识别技术很成熟了，但完全没有接触过。一直想搞一下、整一下、试一下、折腾一下，工作之余找了一个简单的例子入个门。本博客简单记录一下 LPRNet 车牌识别部署 rk3588流程，训练参考 LPRNet 官方代码。

1、导出onnx
导出onnx很容易，在推理时加入保存onnx代码，但用onnx推理时发现推理失败，是有算子onnx推理时不支持，看了一下不支持的操作 nn.MaxPool3d() ，查了一下资料有等价的方法，用等价方法替换后推理结果是一致的。

import torch.nn as nn
import torch


class maxpool_3d(nn.Module):
    def __init__(self, kernel_size, stride):
        super(maxpool_3d, self).__init__()
        assert (len(kernel_size) == 3 and len(stride) == 3)
        kernel_size2d1 = kernel_size[-2:]
        stride2d1 = stride[-2:]
        kernel_size2d2 = (kernel_size[0], kernel_size[0])
        stride2d2 = (kernel_size[0], stride[0])
        self.maxpool1 = nn.MaxPool2d(kernel_size=kernel_size2d1, stride=stride2d1)
        self.maxpool2 = nn.MaxPool2d(kernel_size=kernel_size2d2, stride=stride2d2)

    def forward(self, x):
        x = self.maxpool1(x)
        x = x.transpose(1, 3)
        x = self.maxpool2(x)
        x = x.transpose(1, 3)
        return x


class small_basic_block(nn.Module):
    def __init__(self, ch_in, ch_out):
        super(small_basic_block, self).__init__()
        self.block = nn.Sequential(
            nn.Conv2d(ch_in, ch_out // 4, kernel_size=1),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(3, 1), padding=(1, 0)),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out // 4, kernel_size=(1, 3), padding=(0, 1)),
            nn.ReLU(),
            nn.Conv2d(ch_out // 4, ch_out, kernel_size=1),
        )

    def forward(self, x):
        return self.block(x)


class LPRNet(nn.Module):
    def __init__(self, lpr_max_len, phase, class_num, dropout_rate):
        super(LPRNet, self).__init__()
        self.phase = phase
        self.lpr_max_len = lpr_max_len
        self.class_num = class_num
        self.backbone = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1),  # 0
            nn.BatchNorm2d(num_features=64),
            nn.ReLU(),  # 2
            # nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)),  # 这个可以用MaxPool2d等价
            nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 1)),
            small_basic_block(ch_in=64, ch_out=128),  # *** 4 ***
            nn.BatchNorm2d(num_features=128),
            nn.ReLU(),  # 6
            # nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
            maxpool_3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)),
            small_basic_block(ch_in=64, ch_out=256),  # 8
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(),  # 10
            small_basic_block(ch_in=256, ch_out=256),  # *** 11 ***
            nn.BatchNorm2d(num_features=256),  # 12
            nn.ReLU(),
            # nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)),  # 14
            maxpool_3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)),  # 14
            nn.Dropout(dropout_rate),
            nn.Conv2d(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1),  # 16
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(),  # 18
            nn.Dropout(dropout_rate),
            nn.Conv2d(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1),  # 20
            nn.BatchNorm2d(num_features=class_num),
            nn.ReLU(),  # *** 22 ***
        )
        self.container = nn.Sequential(
            nn.Conv2d(in_channels=448 + self.class_num, out_channels=self.class_num, kernel_size=(1, 1), stride=(1, 1)),
            # nn.BatchNorm2d(num_features=self.class_num),
            # nn.ReLU(),
            # nn.Conv2d(in_channels=self.class_num, out_channels=self.lpr_max_len+1, kernel_size=3, stride=2),
            # nn.ReLU(),
        )

    def forward(self, x):
        keep_features = list()
        for i, layer in enumerate(self.backbone.children()):
            x = layer(x)
            if i in [2, 6, 13, 22]:  # [2, 4, 8, 11, 22]
                keep_features.append(x)

        global_context = list()
        for i, f in enumerate(keep_features):
            if i in [0, 1]:
                f = nn.AvgPool2d(kernel_size=5, stride=5)(f)
            if i in [2]:
                f = nn.AvgPool2d(kernel_size=(4, 10), stride=(4, 2))(f)
            f_pow = torch.pow(f, 2)
            f_mean = torch.mean(f_pow)
            f = torch.div(f, f_mean)
            global_context.append(f)

        x = torch.cat(global_context, 1)
        x = self.container(x)
        logits = torch.mean(x, dim=2)

        return logits


def build_lprnet(lpr_max_len=8, phase=False, class_num=66, dropout_rate=0.5):
    Net = LPRNet(lpr_max_len, phase, class_num, dropout_rate)

    if phase == "train":
        return Net.train()
    else:
        return Net.eval()

保存onnx代码

print("===========  onnx =========== ")
dummy_input = torch.randn(1, 3, 24, 94).cuda()
input_names = ['image']
output_names = ['output']
torch.onnx.export(lprnet, dummy_input, "./weights/LPRNet_model.onnx", verbose=False, input_names=input_names, output_names=output_names, opset_version=12)
print("======================== convert onnx Finished! .... ")

2 onnx转换rknn

onnx转rknn代码

# -*- coding: utf-8 -*-

import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
from rknn.api import RKNN
from math import exp

import math



ONNX_MODEL = './LPRNet.onnx'
RKNN_MODEL = './LPRNet.rknn'
DATASET = './images_list.txt'

QUANTIZE_ON = True

'''
CHARS = ['京', '沪', '津', '渝', '冀', '晋', '蒙', '辽', '吉', '黑',
         '苏', '浙', '皖', '闽', '赣', '鲁', '豫', '鄂', '湘', '粤',
         '桂', '琼', '川', '贵', '云', '藏', '陕', '甘', '青', '宁',
         '新',
         '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
         'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
         'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
         'W', 'X', 'Y', 'Z', 'I', 'O', '-'
         ]

'''

CHARS = ['BJ', 'SH', 'TJ', 'CQ', 'HB', 'SN', 'NM', 'LN', 'JN', 'HL',
         'JS', 'ZJ', 'AH', 'FJ', 'JX', 'SD', 'HA', 'HB', 'HN', 'GD',
         'GL', 'HI', 'SC', 'GZ', 'YN', 'XZ', 'SX', 'GS', 'QH', 'NX',
         'XJ',
         '0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
         'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K',
         'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V',
         'W', 'X', 'Y', 'Z', 'I', 'O', '-'
         ]



def export_rknn_inference(img):
    # Create RKNN object
    rknn = RKNN(verbose=True)

    # pre-process config
    print('--> Config model')
    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], quantized_algorithm='normal', quantized_method='channel', target_platform='rk3588')  # mmse
    print('done')

    # Load ONNX model
    print('--> Loading model')
    ret = rknn.load_onnx(model=ONNX_MODEL, outputs=['output'])
    if ret != 0:
        print('Load model failed!')
        exit(ret)
    print('done')

    # Build model
    print('--> Building model')
    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET, rknn_batch_size=1)
    if ret != 0:
        print('Build model failed!')
        exit(ret)
    print('done')

    # Export RKNN model
    print('--> Export rknn model')
    ret = rknn.export_rknn(RKNN_MODEL)
    if ret != 0:
        print('Export rknn model failed!')
        exit(ret)
    print('done')

    # Init runtime environment
    print('--> Init runtime environment')
    ret = rknn.init_runtime()
    # ret = rknn.init_runtime(target='rk3566')
    if ret != 0:
        print('Init runtime environment failed!')
        exit(ret)
    print('done')

    # Inference
    print('--> Running model')
    outputs = rknn.inference(inputs=[img])
    rknn.release()
    print('done')

    return outputs


if __name__ == '__main__':
    print('This is main ...')
    
    input_w = 94
    input_h = 24
    
    image_path = './test.jpg'
    origin_image = cv2.imread(image_path)
    image_height, image_width, images_channels = origin_image.shape
    
    img = cv2.resize(origin_image, (input_w, input_h), interpolation=cv2.INTER_LINEAR)
    img = np.expand_dims(img, 0)
    print(img.shape)

    preb = export_rknn_inference(img)[0][0]
    
    preb_label = []
    result = []
    for j in range(preb.shape[1]):
        preb_label.append(np.argmax(preb[:, j], axis=0))
    print(preb_label)

    pre_c = preb_label[0]
    if pre_c != len(CHARS) - 1:
        result.append(pre_c)

    for c in preb_label:
        if (pre_c == c) or (c == len(CHARS) - 1):
            if c == len(CHARS) - 1:
                pre_c = c
            continue
        result.append(c)
        pre_c = c

    ptext = ''
    for v in result:
        ptext += CHARS[v]
    print(ptext)
        
    zero_image = np.ones((image_height, image_width, images_channels), dtype=np.uint8) * 255
    cv2.putText(zero_image, ptext, (0, int(image_height / 2)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2, cv2.LINE_AA)
    combined_image = np.vstack((origin_image, zero_image))
    cv2.imwrite('./test_result.jpg', combined_image)

转换rknn测试结果
在这里插入图片描述
说明：由于中文显示出现乱码，示例代码中用拼英简写对中文进行了规避

3 部署 rk3588

在rk3588上运行的【完整代码】

板子上运行结果和时耗。
在这里插入图片描述

模型这么小在rk3588上推理时耗还是比较长的，毫无疑问是模型推理过程中有操作切换到CPU上了。如果对性能要求的比较高，可以针对切换的CPU上的操作进行规避或替换。查看转换rknn模型log可以知道是那些操作切换到CPU上了。
在这里插入图片描述

本站资源均来自互联网，仅供研究学习，禁止违法使用和商用，产生法律纠纷本站概不负责！如果侵犯了您的权益请与我们联系！

转载请注明出处：免费源码网-免费的源码资源网站 » LPRNet 车牌识别部署 rk3588（pt-onnx-rknn）包含各个步骤完整代码

点赞(0) 打赏

本文分类：文章资讯
本文标签：LPRNet 车牌识别部署 rk3588（pt-onnx-rknn）包含各个步骤完整代码
浏览次数：48 次浏览
本文链接：https://freeymw.com/article/17375.html

上一篇 > C++ 容器：pair & tuple
下一篇 > 开发不认可bug策略

评论列表共有 0 条评论

暂无评论

LPRNet 车牌识别部署 rk3588（pt-onnx-rknn）包含各个步骤完整代码

2 onnx转换rknn

3 部署 rk3588

评论列表 共有 0 条评论

发表评论 取消回复

评论列表共有 0 条评论

发表评论取消回复