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
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。
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。
