该项目为v1.0版本的车牌识别项目，对数据集做了更新，先对车牌矫正再识别，降低任务难度，40个epoch训练达验证集98.4%精度。实现模型与batch解耦，保证推理精度不受batch影响，可与车牌检测项目搭配。包含完整训练推理过程、模型转onnx及检查推理、数据集构建等内容。
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车牌识别
LPRNet端到端训练车牌识别
本项目包括
- 完整训练推理过程
- 模型转onnx以及onnx的检查和推理
数据集构建
In [1]
# 数据解压!unzip -o -q -d /home/aistudio/data /home/aistudio/data/data17968/CCPD2019.zip
In [4]
"""
CCPD数据集的图片名称即是label:
0152-4_14-224&551_398&624-388&610_224&624_234&565_398&551-0_0_30_27_31_9_31-97-108.jpg
^ ^ ^ ^ ^ ^ ^ ^ ^ ^
| 框左上角 框右下角 右下角点 左下角点 左上角点 右上角点 车牌号码 亮度 模糊度
水平/垂直倾角
亮度数值越大，车牌越亮；模糊度数值越小，车牌越模糊。
"""import cv2import osimport numpy as npfrom tqdm.notebook import tqdm
# 参考 https://blog.csdn.net/qq_36516958/article/details/114274778from PIL import Image# 根据4顶点对图片矫正def four_point_transform(image, pts):
rect = pts.astype('float32')
br_x, br_y, bl_x, bl_y, tl_x, tl_y, tr_x, tr_y = rect
widthA = np.sqrt(((br_x - bl_x) ** 2) + ((br_y - bl_y) ** 2))
widthB = np.sqrt(((tr_x - tl_x) ** 2) + ((tr_y - tl_y) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((tr_x - br_x) ** 2) + ((tr_y - br_y) ** 2))
heightB = np.sqrt(((tl_x - bl_x) ** 2) + ((tl_y - bl_y) ** 2))
maxHeight = max(int(heightA), int(heightB))
rect = np.array([[tl_x, tl_y], [tr_x, tr_y], [br_x, br_y], [bl_x, bl_y]], dtype='float32')
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype = "float32")
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped# CCPD车牌有重复，应该是不同角度或者模糊程度path = r'data/ccpd_base' # 改成自己的车牌路径provinces = ["皖", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "京", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "警", "学", "O"]
alphabets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'O']
ads = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'O']
save_path = 'rec_images/data/'if not os.path.exists(save_path):
os.makedirs(save_path)
num = 0for filename in tqdm(os.listdir(path)):
num += 1
result = ""
_, _, box, points, plate, brightness, blurriness = filename.split('-')
list_plate = plate.split('_') # 读取车牌
result += provinces[int(list_plate[0])]
result += alphabets[int(list_plate[1])]
result += ads[int(list_plate[2])] + ads[int(list_plate[3])] + ads[int(list_plate[4])] + ads[int(list_plate[5])] + ads[int(list_plate[6])] # 新能源车牌的要求，如果不是新能源车牌可以删掉这个if
# if result[2] != 'D' and result[2] != 'F' \
# and result[-1] != 'D' and result[-1] != 'F':
# print(filename)
# print("Error label, Please check!")
# assert 0, "Error label ^~^!!!"
# print(result)
img_path = os.path.join(path, filename)
img = cv2.imread(img_path) assert os.path.exists(img_path), "image file {} dose not exist.".format(img_path)
br, bl, tl, tr = points.split('_')
br_x, br_y = [float(i) for i in br.split('&')]
bl_x, bl_y = [float(i) for i in bl.split('&')]
tl_x, tl_y = [float(i) for i in tl.split('&')]
tr_x, tr_y = [float(i) for i in tr.split('&')]
landmarks = np.array([br_x, br_y, bl_x, bl_y, tl_x, tl_y, tr_x, tr_y], dtype='float32')
img = four_point_transform(img, landmarks)
img = cv2.resize(img, (94, 24))
cv2.imencode('.jpg', img)[1].tofile(os.path.join(save_path, r"{}.jpg".format(result)))0%| | 0/95774 [00:00
数据集划分
In [5]
import osimport random
image_dir = "rec_images/data" train_file = 'rec_images/train.txt'eval_file = 'rec_images/valid.txt'dataset_list = os.listdir(image_dir)
train_num = 0valid_num = 0for img_name in dataset_list: if '.jpg' not in img_name: print(img_name) continue
probo = random.randint(1, 100) if(probo <= 80): # train
with open(train_file, 'a') as f_train:
f_train.write(img_name+'\n')
train_num+=1
else: #valid
with open(eval_file, 'a') as f_eval:
f_eval.write(img_name+'\n')
valid_num+=1print(f'train: {train_num}, val:{valid_num}').ipynb_checkpoints train: 62959, val:15937
Dataloader
数据读取
In [1]
import osfrom paddle.io import Datasetfrom PIL import Imageimport numpy as np
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_DICT = {char:i for i, char in enumerate(CHARS)}class LprnetDataloader(Dataset):
def __init__(self, target_path, label_text, transforms=None):
super().__init__()
self.transforms = transforms
self.target_path = target_path with open(label_text) as f:
self.data = f.readlines() def __getitem__(self, index):
img_name = self.data[index].strip()
img_path = os.path.join(self.target_path, img_name)
data = Image.open(img_path)
label = []
img_label = img_name.split('.', 1)[0] for c in img_label:
label.append(CHARS_DICT[c]) if len(label) == 8: if self.check(label) == False: print(imgname) assert 0, "Error label ^~^!!!"
if self.transforms is not None:
data = self.transforms(data)
data = np.array(data, dtype=np.float32)
np_label = np.array(label, dtype=np.int64) return data, np_label, len(np_label) def __len__(self):
return len(self.data)
def check(self, label):
if label[2] != CHARS_DICT['D'] and label[2] != CHARS_DICT['F'] \ and label[-1] != CHARS_DICT['D'] and label[-1] != CHARS_DICT['F']: print("Error label, Please check!") return False
else: return True组batch
将不同长度的label，padding为以最大标签长度的同一尺寸，shape为(batch_size,max_label_length)
In [2]
def collate_fn(batch):
# 图片输入已经规范到相同大小，这里只需要对标签进行padding
batch_size = len(batch) # 找出标签最长的
batch_temp = sorted(batch, key=lambda sample: len(sample[1]), reverse=True)
max_label_length = len(batch_temp[0][1]) # 以最大的长度创建0张量
labels = np.zeros((batch_size, max_label_length), dtype='int64')
label_lens = []
img_list = [] for x in range(batch_size):
sample = batch[x]
tensor = sample[0]
target = sample[1]
label_length = sample[2]
img_list.append(tensor) # 将数据插入都0张量中，实现了padding
labels[x, :label_length] = target[:]
label_lens.append(len(target))
label_lens = paddle.to_tensor(label_lens, dtype='int64') # ctcloss需要
imgs = paddle.to_tensor(img_list, dtype='float32')
labels = paddle.to_tensor(labels, dtype="int32") # ctcloss仅支持int32的labels
return imgs, labels, label_lens数据前处理
这里数据集量挺多，各种情况的数据都有（天气，角度，模糊），就不再做数据增强的操作了。
就简单做个归一化操作就好了，训练的时候对数据进行ToTensor + Normalize
import paddle.vision.transforms as T
train_transforms = T.Compose([
T.ToTensor(data_format='CHW'), # 这里的CHW是指数据的输出格式
T.Normalize(
[0.5, 0.5, 0.5], # 在totensor的时候已经将图片缩放到0-1
[0.5, 0.5, 0.5],
data_format='CHW' # 这里是数据输入格式
),
])LPRNet网络
网络结构
In [3]
import paddle.nn as nnimport paddle
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', '-'
]class small_basic_block(nn.Layer):
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 maxpool_3d(nn.Layer):
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 = (1, kernel_size[0])
stride2d2 = (1, 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((0, 3, 2, 1))
x = self.maxpool2(x)
x = x.transpose((0, 3, 2, 1)) return xclass LPRNet(nn.Layer):
def __init__(self, lpr_max_len, class_num, dropout_rate):
super(LPRNet, self).__init__()
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 [bs,3,24,94] -> [bs,64,22,92]
nn.BatchNorm2D(num_features=64), # 1 -> [bs,64,22,92]
nn.ReLU(), # 2 -> [bs,64,22,92]
maxpool_3d(kernel_size=(1, 3, 3), stride=(1, 1, 1)), # 3 -> [bs,64,20,90]
small_basic_block(ch_in=64, ch_out=128), # 4 -> [bs,128,20,90]
nn.BatchNorm2D(num_features=128), # 5 -> [bs,128,20,90]
nn.ReLU(), # 6 -> [bs,128,20,90]
maxpool_3d(kernel_size=(1, 3, 3), stride=(2, 1, 2)), # 7 -> [bs,64,18,44]
small_basic_block(ch_in=64, ch_out=256), # 8 -> [bs,256,18,44]
nn.BatchNorm2D(num_features=256), # 9 -> [bs,256,18,44]
nn.ReLU(), # 10 -> [bs,256,18,44]
small_basic_block(ch_in=256, ch_out=256), # 11 -> [bs,256,18,44]
nn.BatchNorm2D(num_features=256), # 12 -> [bs,256,18,44]
nn.ReLU(), # 13 -> [bs,256,18,44]
maxpool_3d(kernel_size=(1, 3, 3), stride=(4, 1, 2)), # 14 -> [bs,64,16,21]
nn.Dropout(dropout_rate), # 15 -> [bs,64,16,21]
nn.Conv2D(in_channels=64, out_channels=256, kernel_size=(1, 4), stride=1), # 16 -> [bs,256,16,18]
nn.BatchNorm2D(num_features=256), # 17 -> [bs,256,16,18]
nn.ReLU(), # 18 -> [bs,256,16,18]
nn.Dropout(dropout_rate), # 19 -> [bs,256,16,18]
nn.Conv2D(in_channels=256, out_channels=class_num, kernel_size=(13, 1), stride=1), # class_num=68 20 -> [bs,68,4,18]
nn.BatchNorm2D(num_features=class_num), # 21 -> [bs,68,4,18]
nn.ReLU(), # 22 -> [bs,68,4,18]
)
self.container = nn.Sequential(
nn.Conv2D(in_channels=448+self.class_num, out_channels=self.class_num, kernel_size=(1, 1), stride=(1, 1)),
) 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]:
keep_features.append(x)
global_context = list() # keep_features: [bs,64,22,92] [bs,128,20,90] [bs,256,18,44] [bs,68,4,18]
for i, f in enumerate(keep_features): if i in [0, 1]: # [bs,64,22,92] -> [bs,64,4,18]
# [bs,128,20,90] -> [bs,128,4,18]
f = nn.AvgPool2D(kernel_size=5, stride=5)(f) if i in [2]: # [bs,256,18,44] -> [bs,256,4,18]
f = nn.AvgPool2D(kernel_size=(4, 10), stride=(4, 2))(f)
f_pow = paddle.pow(f, 2) # [bs,64,4,18] 所有元素求平方
# f_mean = paddle.mean(f_pow) # 1 所有元素求平均
f_mean = paddle.mean(f_pow, axis=[1,2,3], keepdim=True)
f = paddle.divide(f, f_mean) # [bs,64,4,18] 所有元素除以这个均值
global_context.append(f)
x = paddle.concat(global_context, 1) # [bs,516,4,18]
x = self.container(x) # -> [bs, 68, 4, 18] head头
logits = paddle.mean(x, axis=2) # -> [bs, 68, 18] # 68 字符类别数 18字符序列长度
return logits权重初始化函数
In [4]
# 使用model.applay的方法，可以修改到每一个子层def init_weight(model):
for name, layer in model.named_sublayers(): if isinstance(layer, nn.Conv2D):
weight_attr = nn.initializer.KaimingNormal()
bias_attr = nn.initializer.Constant(0.)
init_bias = paddle.create_parameter(layer.bias.shape, attr=bias_attr, dtype='float32')
init_weight = paddle.create_parameter(layer.weight.shape, attr=weight_attr, dtype='float32')
layer.weight = init_weight
layer.bias = init_bias elif isinstance(layer, nn.BatchNorm2D):
weight_attr = nn.initializer.XavierUniform()
bias_attr = nn.initializer.Constant(0.)
init_bias = paddle.create_parameter(layer.bias.shape, attr=bias_attr, dtype='float32')
init_weight = paddle.create_parameter(layer.weight.shape, attr=weight_attr, dtype='float32')
layer.weight = init_weight
layer.bias = init_bias损失函数
损失函数是CTCLoss，需要传入的参数有：
-
logits: 概率序列, shape=[max_logit_length, batch_size, num_classes+1]
数据类型仅支持float32
-
lbels: padding后的标签序列，shape=[batch_size, max_label_length]
数据类型仅支持int32
-
input_lengths: 输入logits数据中的每个序列的长度，shape=[batch_size]
数据类型仅支持int64
-
label_lengths: label中每个序列的长度，shape=[batch_size]
数据类型仅支持int64
ctcloss文档：https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/CTCLoss_cn.html
准确率计算函数
In [5]
import numpy as np
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', '-'
]class ACC:
def __init__(self):
self.Tp = 0
self.Tn_1 = 0
self.Tn_2 = 0
self.acc = 0
def batch_update(self, batch_label, label_lengths, pred):
for i, label in enumerate(batch_label):
length = label_lengths[i]
label = label[:length]
pred_i = pred[i, :, :]
preb_label = [] for j in range(pred_i.shape[1]): # T
preb_label.append(np.argmax(pred_i[:, j], axis=0))
no_repeat_blank_label = []
pre_c = preb_label[0] if pre_c != len(CHARS) - 1: # 非空白
no_repeat_blank_label.append(pre_c) for c in preb_label: # dropout repeate label and blank label
if (pre_c == c) or (c == len(CHARS) - 1): if c == len(CHARS) - 1:
pre_c = c continue
no_repeat_blank_label.append(c)
pre_c = c # print('no_repeat_blank_label:', no_repeat_blank_label)
# print('gt_label:', label)
if len(label) != len(no_repeat_blank_label):
self.Tn_1 += 1
elif (np.asarray(label) == np.asarray(no_repeat_blank_label)).all():
self.Tp += 1
else:
self.Tn_2 += 1
self.acc = self.Tp * 1.0 / (self.Tp + self.Tn_1 + self.Tn_2) def clear(self):
self.Tp = 0
self.Tn_1 = 0
self.Tn_2 = 0
self.acc = 0print(len(CHARS))68
加载预训练参数
一次训练没有到位，在之前的权重参数基础上继续训练，需要加载预训练权重。
若有预训练权重可以加载预训练权重
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In [6]
# 保存的权重路径：runs/lprnet_best.pdparamsimport osdef load_pretrained(model, path=None):
print('params loading...') if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')): raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
param_state_dict = paddle.load(path + ".pdparams")
model.set_dict(param_state_dict) print(f'load {path + ".pdparams"} success...') return训练
训练得到的模型为：runs/lprnet_best_2.pdparams
矫正后的图片一定程度降低了任务难度，这里进行了40个epoch的训练，最终验证集精度98.4%
In [7]
import paddle.vision.transforms as Tfrom paddle.io import DataLoaderimport timefrom statistics import mean# 参数定义EPOCH = 40IMGSIZE = (94, 24)
IMGDIR = 'rec_images/data'TRAINFILE = 'rec_images/train.txt'VALIDFILE = 'rec_images/valid.txt'SAVEFOLDER = './runs'DROPOUT = 0.LEARNINGRATE = 0.001LPRMAXLEN = 18TRAINBATCHSIZE = 256EVALBATCHSIZE = 256NUMWORKERS = 2 # 若dataloader报错，调小该参数，或直接改为0WEIGHTDECAY = 0.001# 图片预处理train_transforms = T.Compose([
T.ColorJitter(0.2,0.2,0.2),
T.ToTensor(data_format='CHW'),
T.Normalize(
[0.5, 0.5, 0.5], # 在totensor的时候已经将图片缩放到0-1
[0.5, 0.5, 0.5],
data_format='CHW'
),
])
eval_transforms = T.Compose([
T.ToTensor(data_format='CHW'),
T.Normalize(
[0.5, 0.5, 0.5],
[0.5, 0.5, 0.5],
data_format='CHW'
),
])# 数据加载train_data_set = LprnetDataloader(IMGDIR, TRAINFILE, train_transforms)
eval_data_set = LprnetDataloader(IMGDIR, VALIDFILE, eval_transforms)
train_loader = DataLoader(
train_data_set,
batch_size=TRAINBATCHSIZE,
shuffle=True,
num_workers=NUMWORKERS,
drop_last=True,
collate_fn=collate_fn
)
eval_loader = DataLoader(
eval_data_set,
batch_size=EVALBATCHSIZE,
shuffle=False,
num_workers=NUMWORKERS,
drop_last=False,
collate_fn=collate_fn
)# 定义lossloss_func = nn.CTCLoss(len(CHARS)-1)# input_length, loss计算需要input_length = np.ones(shape=TRAINBATCHSIZE) * LPRMAXLEN
input_length = paddle.to_tensor(input_length, dtype='int64')# LPRNet网络，初始化/加载预训练参数model = LPRNet(LPRMAXLEN, len(CHARS), DROPOUT)
model.apply(init_weight) # 首次训练时初始化# 定义优化器def make_optimizer(base_lr, parameters=None):
momentum = 0.9
weight_decay = WEIGHTDECAY
scheduler = paddle.optimizer.lr.CosineAnnealingDecay(
learning_rate=base_lr, eta_min=0.01*base_lr, T_max=EPOCH, verbose=1)
scheduler = paddle.optimizer.lr.LinearWarmup( # 第一次训练的时候考虑模型权重不稳定，添加warmup策略
learning_rate=scheduler,
warmup_steps=5,
start_lr=base_lr/5,
end_lr=base_lr,
verbose=True)
optimizer = paddle.optimizer.Momentum(
learning_rate=scheduler,
weight_decay=paddle.regularizer.L2Decay(weight_decay),
momentum=momentum,
parameters=parameters) return optimizer, scheduler
optim, scheduler = make_optimizer(LEARNINGRATE, parameters=model.parameters())# accacc_train = ACC()
acc_eval = ACC()
BESTACC = 0.5# 训练流程for epoch in range(EPOCH):
start_time = time.localtime(time.time())
str_time = time.strftime("%Y-%m-%d %H:%M:%S", start_time) print(f'{str_time} || Epoch {epoch} start:')
model.train() for batch_id, bath_data in enumerate(train_loader):
img_data, label_data, label_lens = bath_data
predict = model(img_data)
logits = paddle.transpose(predict, (2,0,1)) # for ctc loss: T x N x C
loss = loss_func(logits , label_data, input_length, label_lens)
acc_train.batch_update(label_data, label_lens, predict) if batch_id % 50 == 0: print(f'epoch:{epoch}, batch_id:{batch_id}, loss:{loss.item():.4f}, \
acc:{acc_train.acc:.4f} Tp/Tn_1/Tn_2: {acc_train.Tp}/{acc_train.Tn_1}/{acc_train.Tn_2}')
loss.backward()
optim.step()
optim.clear_grad()
acc_train.clear()
# save
if epoch and epoch % 20 == 0:
paddle.save(model.state_dict(), os.path.join(SAVEFOLDER,f'lprnet_{epoch}_2.pdparams'))
paddle.save(optim.state_dict(), os.path.join(SAVEFOLDER,f'lprnet_{epoch}_2.pdopt')) print(f'Saved log ecpch-{epoch}') # eval
with paddle.no_grad():
model.eval()
loss_list = [] for batch_id, bath_data in enumerate(eval_loader):
img_data, label_data, label_lens = bath_data
predict = model(img_data)
logits = paddle.transpose(predict, (2,0,1))
loss = loss_func(logits, label_data, input_length, label_lens)
acc_eval.batch_update(label_data, label_lens, predict)
loss_list.append(loss.item()) print(f'Eval of epoch {epoch} => acc:{acc_eval.acc:.4f}, loss:{mean(loss_list):.4f}') # save best model
if acc_eval.acc > BESTACC:
paddle.save(model.state_dict(), os.path.join(SAVEFOLDER,f'lprnet_best_2.pdparams'))
paddle.save(optim.state_dict(), os.path.join(SAVEFOLDER,f'lprnet_best_2.pdopt'))
BESTACC = acc_eval.acc print(f'Saved best model of epoch{epoch}, acc {acc_eval.acc:.4f}, save path "{SAVEFOLDER}"')
acc_eval.clear()
# 学习率衰减策略
scheduler.step()Epoch 0: CosineAnnealingDecay set learning rate to 0.001. Epoch 0: LinearWarmup set learning rate to 0.0002. 2023-07-27 13:05:14 || Epoch 0 start:
W0727 13:05:14.896270 20153 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2 W0727 13:05:14.900187 20153 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2. /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance." /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:277: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.int64, the right dtype will convert to paddle.float32 .format(lhs_dtype, rhs_dtype, lhs_dtype))
epoch:0, batch_id:0, loss:71.7745, acc:0.0000 Tp/Tn_1/Tn_2: 0/242/14 epoch:0, batch_id:50, loss:4.1406, acc:0.0000 Tp/Tn_1/Tn_2: 0/11919/1137 epoch:0, batch_id:100, loss:2.7202, acc:0.0000 Tp/Tn_1/Tn_2: 0/23156/2700 epoch:0, batch_id:150, loss:2.3612, acc:0.0000 Tp/Tn_1/Tn_2: 1/34437/4218 epoch:0, batch_id:200, loss:1.9747, acc:0.0003 Tp/Tn_1/Tn_2: 15/45563/5878 Eval of epoch 0 => acc:0.0053, loss:1.7451 Epoch 1: LinearWarmup set learning rate to 0.00036. 2023-07-27 13:07:32 || Epoch 1 start: epoch:1, batch_id:0, loss:1.8066, acc:0.0000 Tp/Tn_1/Tn_2: 0/208/48 epoch:1, batch_id:50, loss:1.4053, acc:0.0102 Tp/Tn_1/Tn_2: 133/10502/2421 epoch:1, batch_id:100, loss:1.0782, acc:0.0284 Tp/Tn_1/Tn_2: 735/19852/5269 epoch:1, batch_id:150, loss:0.8829, acc:0.0548 Tp/Tn_1/Tn_2: 2119/28197/8340 epoch:1, batch_id:200, loss:0.6519, acc:0.0886 Tp/Tn_1/Tn_2: 4559/35534/11363 Eval of epoch 1 => acc:0.3610, loss:0.5038 Epoch 2: LinearWarmup set learning rate to 0.0005200000000000001. 2023-07-27 13:11:21 || Epoch 2 start: epoch:2, batch_id:0, loss:0.4789, acc:0.3516 Tp/Tn_1/Tn_2: 90/116/50 epoch:2, batch_id:50, loss:0.3491, acc:0.4154 Tp/Tn_1/Tn_2: 5423/5085/2548 epoch:2, batch_id:100, loss:0.2775, acc:0.4883 Tp/Tn_1/Tn_2: 12626/8683/4547 epoch:2, batch_id:150, loss:0.2271, acc:0.5498 Tp/Tn_1/Tn_2: 21252/11238/6166 epoch:2, batch_id:200, loss:0.1476, acc:0.5987 Tp/Tn_1/Tn_2: 30809/13097/7550 Eval of epoch 2 => acc:0.8157, loss:0.1444 Saved best model of epoch2, acc 0.8157, save path "./runs" Epoch 3: LinearWarmup set learning rate to 0.00068. 2023-07-27 13:17:25 || Epoch 3 start: epoch:3, batch_id:0, loss:0.1045, acc:0.8320 Tp/Tn_1/Tn_2: 213/28/15 epoch:3, batch_id:50, loss:0.1356, acc:0.8227 Tp/Tn_1/Tn_2: 10741/1188/1127 epoch:3, batch_id:100, loss:0.0764, acc:0.8366 Tp/Tn_1/Tn_2: 21631/2146/2079 epoch:3, batch_id:150, loss:0.1323, acc:0.8478 Tp/Tn_1/Tn_2: 32772/2902/2982 epoch:3, batch_id:200, loss:0.0691, acc:0.8577 Tp/Tn_1/Tn_2: 44132/3486/3838 Eval of epoch 3 => acc:0.9084, loss:0.0740 Saved best model of epoch3, acc 0.9084, save path "./runs" Epoch 4: LinearWarmup set learning rate to 0.00084. 2023-07-27 13:19:41 || Epoch 4 start: epoch:4, batch_id:0, loss:0.1121, acc:0.8867 Tp/Tn_1/Tn_2: 227/15/14 epoch:4, batch_id:50, loss:0.0609, acc:0.9091 Tp/Tn_1/Tn_2: 11869/419/768 epoch:4, batch_id:100, loss:0.0481, acc:0.9139 Tp/Tn_1/Tn_2: 23629/768/1459 epoch:4, batch_id:150, loss:0.0587, acc:0.9171 Tp/Tn_1/Tn_2: 35450/1082/2124124 epoch:4, batch_id:200, loss:0.0702, acc:0.9203 Tp/Tn_1/Tn_2: 47356/1337/2763 Eval of epoch 4 => acc:0.9371, loss:0.0509 Saved best model of epoch4, acc 0.9371, save path "./runs" Epoch 0: CosineAnnealingDecay set learning rate to 0.001. Epoch 5: LinearWarmup set learning rate to 0.001. 2023-07-27 13:21:59 || Epoch 5 start: epoch:5, batch_id:0, loss:0.0351, acc:0.9336 Tp/Tn_1/Tn_2: 239/6/11 epoch:5, batch_id:50, loss:0.0328, acc:0.9372 Tp/Tn_1/Tn_2: 12236/249/571 epoch:5, batch_id:100, loss:0.0846, acc:0.9387 Tp/Tn_1/Tn_2: 24270/461/1125 epoch:5, batch_id:150, loss:0.0242, acc:0.9410 Tp/Tn_1/Tn_2: 36375/657/1624 epoch:5, batch_id:200, loss:0.0518, acc:0.9416 Tp/Tn_1/Tn_2: 48453/859/2144 Eval of epoch 5 => acc:0.9513, loss:0.0396 Saved best model of epoch5, acc 0.9513, save path "./runs" Epoch 1: CosineAnnealingDecay set learning rate to 0.0009984740801978985. Epoch 6: LinearWarmup set learning rate to 0.0009984740801978985. 2023-07-27 13:24:17 || Epoch 6 start: epoch:6, batch_id:0, loss:0.0390, acc:0.9453 Tp/Tn_1/Tn_2: 242/4/10 epoch:6, batch_id:50, loss:0.0319, acc:0.9527 Tp/Tn_1/Tn_2: 12412438/155/463 epoch:6, batch_id:100, loss:0.0267, acc:0.9526 Tp/Tn_1/Tn_2: 24630/296/930 epoch:6, batch_id:150, loss:0.0330, acc:0.9526 Tp/Tn_1/Tn_2: 36825/429/1402 epoch:6, batch_id:200, loss:0.0202, acc:0.9534 Tp/Tn_1/Tn_2: 49056/560/1840 Eval of epoch 6 => acc:0.9589, loss:0.0337 Saved best model of epoch6, acc 0.9589, save path "./runs" Epoch 2: CosineAnnealingDecay set learning rate to 0.0009939057285945933. Epoch 7: LinearWarmup set learning rate to 0.0009939057285945933. 2023-07-27 13:26:33 || Epoch 7 start: epoch:7, batch_id:0, loss:0.0181, acc:0.9766 Tp/Tn_1/Tn_2: 250/0/6 epoch:7, batch_id:50, loss:0.0189, acc:0.9609 Tp/Tn_1/Tn_2: 12546/118/392 epoch:7, batch_id:100, loss:0.0424, acc:0.9590 Tp/Tn_1/Tn_2: 24795/234/827 epoch:7, batch_id:150, loss:0.0381, acc:0.9611 Tp/Tn_1/Tn_2: 37154/322/1180 epoch:7, batch_id:200, loss:0.0206, acc:0.9615 Tp/Tn_1/Tn_2: 49475/431/1550 Eval of epoch 7 => acc:0.9644, loss:0.0298 Saved best model of epoch7, acc 0.9644, save path "./runs" Epoch 3: CosineAnnealingDecay set learning rate to 0.00098632311059685. Epoch 8: LinearWarmup set learning rate to 0.00098632311059685. 2023-07-27 13:28:51 || Epoch 8 start: epoch:8, batch_id:0, loss:0.0230, acc:0.9570 Tp/Tn_1/Tn_2: 245/0/11 epoch:8, batch_id:50, loss:0.0259, acc:0.9634 Tp/Tn_1/Tn_2: 12578/91/387 epoch:8, batch_id:100, loss:0.0092, acc:0.9656 Tp/Tn_1/Tn_2: 24966/177/713 epoch:8, batch_id:150, loss:0.0376, acc:0.9657 Tp/Tn_1/Tn_2: 37332/264/1060 epoch:8, batch_id:200, loss:0.0304, acc:0.9662 Tp/Tn_1/Tn_2: 49718/344/1394 Eval of epoch 8 => acc:0.9681, loss:0.0268 Saved best model of epoch8, acc 0.9681, save path "./runs" Epoch 4: CosineAnnealingDecay set learning rate to 0.0009757729755661011. Epoch 9: LinearWarmup set learning rate to 0.0009757729755661011. 2023-07-27 13:31:10 || Epoch 9 start: epoch:9, batch_id:0, loss:0.0178, acc:0.9531 Tp/Tn_1/Tn_2: 244/1/11 epoch:9, batch_id:50, loss:0.0270, acc:0.9685 Tp/Tn_1/Tn_2: 12645/74/337 epoch:9, batch_id:100, loss:0.0288, acc:0.9699 Tp/Tn_1/Tn_2: 25078/155/623 epoch:9, batch_id:150, loss:0.0193, acc:0.9697 Tp/Tn_1/Tn_2: 37483/223/950 epoch:9, batch_id:200, loss:0.0158, acc:0.9703 Tp/Tn_1/Tn_2: 49927/287/1241242 Eval of epoch 9 => acc:0.9695, loss:0.0239 Saved best model of epoch9, acc 0.9695, save path "./runs" Epoch 5: CosineAnnealingDecay set learning rate to 0.000962320368593087. Epoch 10: LinearWarmup set learning rate to 0.000962320368593087. 2023-07-27 13:33:30 || Epoch 10 start: epoch:10, batch_id:0, loss:0.0241, acc:0.9531 Tp/Tn_1/Tn_2: 244/3/9 epoch:10, batch_id:50, loss:0.0086, acc:0.9722 Tp/Tn_1/Tn_2: 12693/66/297 epoch:10, batch_id:100, loss:0.0416, acc:0.9717 Tp/Tn_1/Tn_2: 25125/129/602 epoch:10, batch_id:150, loss:0.0239, acc:0.9724 Tp/Tn_1/Tn_2: 37588/191/877 epoch:10, batch_id:200, loss:0.0162, acc:0.9728 Tp/Tn_1/Tn_2: 50054/263/1139 Eval of epoch 10 => acc:0.9715, loss:0.0213 Saved best model of epoch10, acc 0.9715, save path "./runs" Epoch 6: CosineAnnealingDecay set learning rate to 0.0009460482294732421. Epoch 11: LinearWarmup set learning rate to 0.0009460482294732421. 2023-07-27 13:35:46 || Epoch 11 start: epoch:11, batch_id:0, loss:0.0308, acc:0.9570 Tp/Tn_1/Tn_2: 245/0/11 epoch:11, batch_id:50, loss:0.0199, acc:0.9739 Tp/Tn_1/Tn_2: 12715/77/264 epoch:11, batch_id:100, loss:0.0190, acc:0.9752 Tp/Tn_1/Tn_2: 25215/125/516 epoch:11, batch_id:150, loss:0.0100, acc:0.9751 Tp/Tn_1/Tn_2: 37692/182/782 epoch:11, batch_id:200, loss:0.0172, acc:0.9749 Tp/Tn_1/Tn_2: 50166/244/1046 Eval of epoch 11 => acc:0.9745, loss:0.0195 Saved best model of epoch11, acc 0.9745, save path "./runs" Epoch 7: CosineAnnealingDecay set learning rate to 0.0009270568813552756. Epoch 12: LinearWarmup set learning rate to 0.0009270568813552756. 2023-07-27 13:38:05 || Epoch 12 start: epoch:12, batch_id:0, loss:0.0274, acc:0.9609 Tp/Tn_1/Tn_2: 246/3/7 epoch:12, batch_id:50, loss:0.0134, acc:0.9765 Tp/Tn_1/Tn_2: 12749/46/261 epoch:12, batch_id:100, loss:0.0206, acc:0.9773 Tp/Tn_1/Tn_2: 25269/93/494 epoch:12, batch_id:150, loss:0.0182, acc:0.9766 Tp/Tn_1/Tn_2: 37753/151/752 epoch:12, batch_id:200, loss:0.0233, acc:0.9762 Tp/Tn_1/Tn_2: 50230/217/1009 Eval of epoch 12 => acc:0.9757, loss:0.0177 Saved best model of epoch12, acc 0.9757, save path "./runs" Epoch 8: CosineAnnealingDecay set learning rate to 0.000905463412215599. Epoch 13: LinearWarmup set learning rate to 0.000905463412215599. 2023-07-27 13:40:19 || Epoch 13 start: epoch:13, batch_id:0, loss:0.0058, acc:0.9922 Tp/Tn_1/Tn_2: 254/1/1 epoch:13, batch_id:50, loss:0.0078, acc:0.9788 Tp/Tn_1/Tn_2: 12779/43/234 epoch:13, batch_id:100, loss:0.0080, acc:0.9780 Tp/Tn_1/Tn_2: 25287/92/477 epoch:13, batch_id:150, loss:0.0355, acc:0.9780 Tp/Tn_1/Tn_2: 37804/141/711 epoch:13, batch_id:200, loss:0.0257, acc:0.9784 Tp/Tn_1/Tn_2: 50345/190/921 Eval of epoch 13 => acc:0.9772, loss:0.0168 Saved best model of epoch13, acc 0.9772, save path "./runs" Epoch 9: CosineAnnealingDecay set learning rate to 0.0008814009529720154. Epoch 14: LinearWarmup set learning rate to 0.0008814009529720154. 2023-07-27 13:42:34 || Epoch 14 start: epoch:14, batch_id:0, loss:0.0075, acc:0.9883 Tp/Tn_1/Tn_2: 253/0/3 epoch:14, batch_id:50, loss:0.0097, acc:0.9809 Tp/Tn_1/Tn_2: 12807/48/201 epoch:14, batch_id:100, loss:0.0088, acc:0.9803 Tp/Tn_1/Tn_2: 25346/96/414 epoch:14, batch_id:150, loss:0.0239, acc:0.9795 Tp/Tn_1/Tn_2: 37862/148/646 epoch:14, batch_id:200, loss:0.0091, acc:0.9794 Tp/Tn_1/Tn_2: 50397/194/865 Eval of epoch 14 => acc:0.9778, loss:0.0162 Saved best model of epoch14, acc 0.9778, save path "./runs" Epoch 10: CosineAnnealingDecay set learning rate to 0.000855017856687341. Epoch 15: LinearWarmup set learning rate to 0.000855017856687341. 2023-07-27 13:44:53 || Epoch 15 start: epoch:15, batch_id:0, loss:0.0075, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:15, batch_id:50, loss:0.0075, acc:0.9814 Tp/Tn_1/Tn_2: 12813/28/215 epoch:15, batch_id:100, loss:0.0178, acc:0.9817 Tp/Tn_1/Tn_2: 25383/66/407 epoch:15, batch_id:150, loss:0.0193, acc:0.9820 Tp/Tn_1/Tn_2: 37959/100/597 epoch:15, batch_id:200, loss:0.0099, acc:0.9818 Tp/Tn_1/Tn_2: 50517/147/792 Eval of epoch 15 => acc:0.9785, loss:0.0155 Saved best model of epoch15, acc 0.9785, save path "./runs" Epoch 11: CosineAnnealingDecay set learning rate to 0.0008264767839234411. Epoch 16: LinearWarmup set learning rate to 0.0008264767839234411. 2023-07-27 13:47:12 || Epoch 16 start: epoch:16, batch_id:0, loss:0.0216, acc:0.9688 Tp/Tn_1/Tn_2: 248/0/8 epoch:16, batch_id:50, loss:0.0061, acc:0.9833 Tp/Tn_1/Tn_2: 12838/35/183 epoch:16, batch_id:100, loss:0.0131, acc:0.9831 Tp/Tn_1/Tn_2: 25420/70/366 epoch:16, batch_id:150, loss:0.0040, acc:0.9834 Tp/Tn_1/Tn_2: 38013/117/526 epoch:16, batch_id:200, loss:0.0064, acc:0.9831 Tp/Tn_1/Tn_2: 50587/160/709 Eval of epoch 16 => acc:0.9786, loss:0.0148 Saved best model of epoch16, acc 0.9786, save path "./runs" Epoch 12: CosineAnnealingDecay set learning rate to 0.0007959536998847742. Epoch 17: LinearWarmup set learning rate to 0.0007959536998847742. 2023-07-27 13:49:29 || Epoch 17 start: epoch:17, batch_id:0, loss:0.0169, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:17, batch_id:50, loss:0.0070, acc:0.9845 Tp/Tn_1/Tn_2: 12853/42/161 epoch:17, batch_id:100, loss:0.0067, acc:0.9825 Tp/Tn_1/Tn_2: 25403/80/373 epoch:17, batch_id:150, loss:0.0193, acc:0.9827 Tp/Tn_1/Tn_2: 37989/129/538 epoch:17, batch_id:200, loss:0.0085, acc:0.9833 Tp/Tn_1/Tn_2: 50599/155/702 Eval of epoch 17 => acc:0.9797, loss:0.0144 Saved best model of epoch17, acc 0.9797, save path "./runs" Epoch 13: CosineAnnealingDecay set learning rate to 0.0007636367895343947. Epoch 18: LinearWarmup set learning rate to 0.0007636367895343947. 2023-07-27 13:51:45 || Epoch 18 start: epoch:18, batch_id:0, loss:0.0047, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:18, batch_id:50, loss:0.0091, acc:0.9848 Tp/Tn_1/Tn_2: 12857/32/167 epoch:18, batch_id:100, loss:0.0121, acc:0.9844 Tp/Tn_1/Tn_2: 25452/59/345 epoch:18, batch_id:150, loss:0.0130, acc:0.9846 Tp/Tn_1/Tn_2: 38059/90/507 epoch:18, batch_id:200, loss:0.0141, acc:0.9846 Tp/Tn_1/Tn_2: 50663/130/663 Eval of epoch 18 => acc:0.9804, loss:0.0138 Saved best model of epoch18, acc 0.9804, save path "./runs" Epoch 14: CosineAnnealingDecay set learning rate to 0.0007297252973710757. Epoch 19: LinearWarmup set learning rate to 0.0007297252973710757. 2023-07-27 13:54:00 || Epoch 19 start: epoch:19, batch_id:0, loss:0.0076, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:19, batch_id:50, loss:0.0050, acc:0.9849 Tp/Tn_1/Tn_2: 12859/32/165 epoch:19, batch_id:100, loss:0.0146, acc:0.9853 Tp/Tn_1/Tn_2: 25476/63/317 epoch:19, batch_id:150, loss:0.0035, acc:0.9858 Tp/Tn_1/Tn_2: 38107/87/462 epoch:19, batch_id:200, loss:0.0061, acc:0.9856 Tp/Tn_1/Tn_2: 50713/120/623 Eval of epoch 19 => acc:0.9812, loss:0.0138 Saved best model of epoch19, acc 0.9812, save path "./runs" Epoch 15: CosineAnnealingDecay set learning rate to 0.0006944282990207195. Epoch 20: LinearWarmup set learning rate to 0.0006944282990207195. 2023-07-27 13:56:15 || Epoch 20 start: epoch:20, batch_id:0, loss:0.0080, acc:0.9883 Tp/Tn_1/Tn_2: 253/0/3 epoch:20, batch_id:50, loss:0.0089, acc:0.9860 Tp/Tn_1/Tn_2: 12873/25/158 epoch:20, batch_id:100, loss:0.0161, acc:0.9858 Tp/Tn_1/Tn_2: 25488/55/313 epoch:20, batch_id:150, loss:0.0112, acc:0.9864 Tp/Tn_1/Tn_2: 38132/77/447 epoch:20, batch_id:200, loss:0.0057, acc:0.9862 Tp/Tn_1/Tn_2: 50744/107/605 Saved log ecpch-20 Eval of epoch 20 => acc:0.9813, loss:0.0134 Saved best model of epoch20, acc 0.9813, save path "./runs" Epoch 16: CosineAnnealingDecay set learning rate to 0.000657963412215599. Epoch 21: LinearWarmup set learning rate to 0.000657963412215599. 2023-07-27 13:58:31 || Epoch 21 start: epoch:21, batch_id:0, loss:0.0319, acc:0.9844 Tp/Tn_1/Tn_2: 252/3/1 epoch:21, batch_id:50, loss:0.0204, acc:0.9850 Tp/Tn_1/Tn_2: 12860/29/167 epoch:21, batch_id:100, loss:0.0085, acc:0.9858 Tp/Tn_1/Tn_2: 25488/53/315 epoch:21, batch_id:150, loss:0.0323, acc:0.9862 Tp/Tn_1/Tn_2: 38124124/79/453 epoch:21, batch_id:200, loss:0.0075, acc:0.9866 Tp/Tn_1/Tn_2: 50764/104/588 Eval of epoch 21 => acc:0.9824, loss:0.0128 Saved best model of epoch21, acc 0.9824, save path "./runs" Epoch 17: CosineAnnealingDecay set learning rate to 0.0006205554551086733. Epoch 22: LinearWarmup set learning rate to 0.0006205554551086733. 2023-07-27 14:00:46 || Epoch 22 start: epoch:22, batch_id:0, loss:0.0116, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:22, batch_id:50, loss:0.0096, acc:0.9880 Tp/Tn_1/Tn_2: 12899/23/134 epoch:22, batch_id:100, loss:0.0068, acc:0.9870 Tp/Tn_1/Tn_2: 25521/63/272 epoch:22, batch_id:150, loss:0.0063, acc:0.9876 Tp/Tn_1/Tn_2: 38175/84/397 epoch:22, batch_id:200, loss:0.0104, acc:0.9878 Tp/Tn_1/Tn_2: 50826/102/528 Eval of epoch 22 => acc:0.9822, loss:0.0129 Epoch 18: CosineAnnealingDecay set learning rate to 0.0005824350601949143. Epoch 23: LinearWarmup set learning rate to 0.0005824350601949143. 2023-07-27 14:03:02 || Epoch 23 start: epoch:23, batch_id:0, loss:0.0049, acc:0.9922 Tp/Tn_1/Tn_2: 254/1/1 epoch:23, batch_id:50, loss:0.0056, acc:0.9878 Tp/Tn_1/Tn_2: 12897/21/138 epoch:23, batch_id:100, loss:0.0058, acc:0.9876 Tp/Tn_1/Tn_2: 25535/50/271 epoch:23, batch_id:150, loss:0.0023, acc:0.9876 Tp/Tn_1/Tn_2: 38175/77/404 epoch:23, batch_id:200, loss:0.0041, acc:0.9878 Tp/Tn_1/Tn_2: 50827/99/530 Eval of epoch 23 => acc:0.9822, loss:0.0125 Epoch 19: CosineAnnealingDecay set learning rate to 0.0005438372523852833. Epoch 24: LinearWarmup set learning rate to 0.0005438372523852833. 2023-07-27 14:05:22 || Epoch 24 start: epoch:24, batch_id:0, loss:0.0029, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:24, batch_id:50, loss:0.0065, acc:0.9869 Tp/Tn_1/Tn_2: 12885/28/143 epoch:24, batch_id:100, loss:0.0044, acc:0.9877 Tp/Tn_1/Tn_2: 25537/52/267 epoch:24, batch_id:150, loss:0.0028, acc:0.9891 Tp/Tn_1/Tn_2: 38234/65/357 epoch:24, batch_id:200, loss:0.0007, acc:0.9887 Tp/Tn_1/Tn_2: 50875/83/498 Eval of epoch 24 => acc:0.9834, loss:0.0121 Saved best model of epoch24, acc 0.9834, save path "./runs" Epoch 20: CosineAnnealingDecay set learning rate to 0.000505. Epoch 25: LinearWarmup set learning rate to 0.000505. 2023-07-27 14:07:41 || Epoch 25 start: epoch:25, batch_id:0, loss:0.0111, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:25, batch_id:50, loss:0.0031, acc:0.9896 Tp/Tn_1/Tn_2: 12920/21/115 epoch:25, batch_id:100, loss:0.0046, acc:0.9896 Tp/Tn_1/Tn_2: 25586/45/225 epoch:25, batch_id:150, loss:0.0075, acc:0.9891 Tp/Tn_1/Tn_2: 38236/73/347 epoch:25, batch_id:200, loss:0.0018, acc:0.9892 Tp/Tn_1/Tn_2: 50901/97/458 Eval of epoch 25 => acc:0.9830, loss:0.0120 Epoch 21: CosineAnnealingDecay set learning rate to 0.0004661627476147168. Epoch 26: LinearWarmup set learning rate to 0.0004661627476147168. 2023-07-27 14:09:58 || Epoch 26 start: epoch:26, batch_id:0, loss:0.0084, acc:0.9922 Tp/Tn_1/Tn_2: 254/0/2 epoch:26, batch_id:50, loss:0.0024, acc:0.9897 Tp/Tn_1/Tn_2: 12922/29/105 epoch:26, batch_id:100, loss:0.0021, acc:0.9894 Tp/Tn_1/Tn_2: 25583/52/221 epoch:26, batch_id:150, loss:0.0017, acc:0.9892 Tp/Tn_1/Tn_2: 38239/66/351 epoch:26, batch_id:200, loss:0.0042, acc:0.9893 Tp/Tn_1/Tn_2: 50906/81/469 Eval of epoch 26 => acc:0.9829, loss:0.0120 Epoch 22: CosineAnnealingDecay set learning rate to 0.0004275649398050859. Epoch 27: LinearWarmup set learning rate to 0.0004275649398050859. 2023-07-27 14:12:15 || Epoch 27 start: epoch:27, batch_id:0, loss:0.0072, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:27, batch_id:50, loss:0.0022, acc:0.9881 Tp/Tn_1/Tn_2: 12900/32/124124 epoch:27, batch_id:100, loss:0.0169, acc:0.9889 Tp/Tn_1/Tn_2: 25568/57/231 epoch:27, batch_id:150, loss:0.0024, acc:0.9893 Tp/Tn_1/Tn_2: 38243/71/342 epoch:27, batch_id:200, loss:0.0031, acc:0.9894 Tp/Tn_1/Tn_2: 50909/89/458 Eval of epoch 27 => acc:0.9833, loss:0.0119 Epoch 23: CosineAnnealingDecay set learning rate to 0.0003894445448913269. Epoch 28: LinearWarmup set learning rate to 0.0003894445448913269. 2023-07-27 14:14:31 || Epoch 28 start: epoch:28, batch_id:0, loss:0.0051, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:28, batch_id:50, loss:0.0014, acc:0.9895 Tp/Tn_1/Tn_2: 12919/20/117 epoch:28, batch_id:100, loss:0.0064, acc:0.9897 Tp/Tn_1/Tn_2: 25589/44/223 epoch:28, batch_id:150, loss:0.0022, acc:0.9896 Tp/Tn_1/Tn_2: 38253/61/342 epoch:28, batch_id:200, loss:0.0049, acc:0.9895 Tp/Tn_1/Tn_2: 50918/85/453 Eval of epoch 28 => acc:0.9833, loss:0.0117 Epoch 24: CosineAnnealingDecay set learning rate to 0.0003520365877844011. Epoch 29: LinearWarmup set learning rate to 0.0003520365877844011. 2023-07-27 14:16:48 || Epoch 29 start: epoch:29, batch_id:0, loss:0.0044, acc:0.9883 Tp/Tn_1/Tn_2: 253/1/2 epoch:29, batch_id:50, loss:0.0087, acc:0.9891 Tp/Tn_1/Tn_2: 12914/22/120 epoch:29, batch_id:100, loss:0.0026, acc:0.9901 Tp/Tn_1/Tn_2: 25600/35/221 epoch:29, batch_id:150, loss:0.0053, acc:0.9900 Tp/Tn_1/Tn_2: 38271/59/326 epoch:29, batch_id:200, loss:0.0129, acc:0.9900 Tp/Tn_1/Tn_2: 50942/79/435 Eval of epoch 29 => acc:0.9838, loss:0.0116 Saved best model of epoch29, acc 0.9838, save path "./runs" Epoch 25: CosineAnnealingDecay set learning rate to 0.0003155717009792806. Epoch 30: LinearWarmup set learning rate to 0.0003155717009792806. 2023-07-27 14:19:02 || Epoch 30 start: epoch:30, batch_id:0, loss:0.0021, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:30, batch_id:50, loss:0.0052, acc:0.9897 Tp/Tn_1/Tn_2: 12921/20/115 epoch:30, batch_id:100, loss:0.0035, acc:0.9902 Tp/Tn_1/Tn_2: 25602/46/208 epoch:30, batch_id:150, loss:0.0025, acc:0.9903 Tp/Tn_1/Tn_2: 38281/73/302 epoch:30, batch_id:200, loss:0.0030, acc:0.9902 Tp/Tn_1/Tn_2: 50954/88/414 Eval of epoch 30 => acc:0.9839, loss:0.0116 Saved best model of epoch30, acc 0.9839, save path "./runs" Epoch 26: CosineAnnealingDecay set learning rate to 0.0002802747026289244. Epoch 31: LinearWarmup set learning rate to 0.0002802747026289244. 2023-07-27 14:21:17 || Epoch 31 start: epoch:31, batch_id:0, loss:0.0085, acc:0.9844 Tp/Tn_1/Tn_2: 252/0/4 epoch:31, batch_id:50, loss:0.0040, acc:0.9926 Tp/Tn_1/Tn_2: 12960/7/89 epoch:31, batch_id:100, loss:0.0104, acc:0.9915 Tp/Tn_1/Tn_2: 25636/32/188 epoch:31, batch_id:150, loss:0.0017, acc:0.9908 Tp/Tn_1/Tn_2: 38300/55/301 epoch:31, batch_id:200, loss:0.0060, acc:0.9902 Tp/Tn_1/Tn_2: 50952/81/423 Eval of epoch 31 => acc:0.9842, loss:0.0115 Saved best model of epoch31, acc 0.9842, save path "./runs" Epoch 27: CosineAnnealingDecay set learning rate to 0.0002463632104656054. Epoch 32: LinearWarmup set learning rate to 0.0002463632104656054. 2023-07-27 14:23:36 || Epoch 32 start: epoch:32, batch_id:0, loss:0.0040, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:32, batch_id:50, loss:0.0020, acc:0.9917 Tp/Tn_1/Tn_2: 12947/19/90 epoch:32, batch_id:100, loss:0.0056, acc:0.9910 Tp/Tn_1/Tn_2: 25623/36/197 epoch:32, batch_id:150, loss:0.0016, acc:0.9906 Tp/Tn_1/Tn_2: 38291/57/308 epoch:32, batch_id:200, loss:0.0043, acc:0.9906 Tp/Tn_1/Tn_2: 50974/80/402 Eval of epoch 32 => acc:0.9838, loss:0.0115 Epoch 28: CosineAnnealingDecay set learning rate to 0.00021404630011522585. Epoch 33: LinearWarmup set learning rate to 0.00021404630011522585. 2023-07-27 14:25:51 || Epoch 33 start: epoch:33, batch_id:0, loss:0.0026, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:33, batch_id:50, loss:0.0023, acc:0.9904 Tp/Tn_1/Tn_2: 12931/23/102 epoch:33, batch_id:100, loss:0.0130, acc:0.9909 Tp/Tn_1/Tn_2: 25620/41/195 epoch:33, batch_id:150, loss:0.0057, acc:0.9908 Tp/Tn_1/Tn_2: 38302/55/299 epoch:33, batch_id:200, loss:0.0029, acc:0.9911 Tp/Tn_1/Tn_2: 50999/74/383 Eval of epoch 33 => acc:0.9839, loss:0.0115 Epoch 29: CosineAnnealingDecay set learning rate to 0.00018352321607655915. Epoch 34: LinearWarmup set learning rate to 0.00018352321607655915. 2023-07-27 14:28:06 || Epoch 34 start: epoch:34, batch_id:0, loss:0.0104, acc:0.9883 Tp/Tn_1/Tn_2: 253/1/2 epoch:34, batch_id:50, loss:0.0019, acc:0.9918 Tp/Tn_1/Tn_2: 12949/21/86 epoch:34, batch_id:100, loss:0.0054, acc:0.9913 Tp/Tn_1/Tn_2: 25632/40/184 epoch:34, batch_id:150, loss:0.0043, acc:0.9911 Tp/Tn_1/Tn_2: 38312/52/292 epoch:34, batch_id:200, loss:0.0021, acc:0.9910 Tp/Tn_1/Tn_2: 50994/75/387 Eval of epoch 34 => acc:0.9836, loss:0.0114 Epoch 30: CosineAnnealingDecay set learning rate to 0.000154982143312659. Epoch 35: LinearWarmup set learning rate to 0.000154982143312659. 2023-07-27 14:30:23 || Epoch 35 start: epoch:35, batch_id:0, loss:0.0033, acc:0.9961 Tp/Tn_1/Tn_2: 255/1/0 epoch:35, batch_id:50, loss:0.0076, acc:0.9917 Tp/Tn_1/Tn_2: 12948/18/90 epoch:35, batch_id:100, loss:0.0009, acc:0.9917 Tp/Tn_1/Tn_2: 25642/38/176 epoch:35, batch_id:150, loss:0.0035, acc:0.9915 Tp/Tn_1/Tn_2: 38328/57/271 epoch:35, batch_id:200, loss:0.0037, acc:0.9914 Tp/Tn_1/Tn_2: 51013/72/371 Eval of epoch 35 => acc:0.9843, loss:0.0113 Saved best model of epoch35, acc 0.9843, save path "./runs" Epoch 31: CosineAnnealingDecay set learning rate to 0.0001285990470279847. Epoch 36: LinearWarmup set learning rate to 0.0001285990470279847. 2023-07-27 14:32:39 || Epoch 36 start: epoch:36, batch_id:0, loss:0.0057, acc:0.9883 Tp/Tn_1/Tn_2: 253/0/3 epoch:36, batch_id:50, loss:0.0112, acc:0.9913 Tp/Tn_1/Tn_2: 12942/22/92 epoch:36, batch_id:100, loss:0.0022, acc:0.9909 Tp/Tn_1/Tn_2: 25621/42/193 epoch:36, batch_id:150, loss:0.0024, acc:0.9913 Tp/Tn_1/Tn_2: 38319/55/282 epoch:36, batch_id:200, loss:0.0041, acc:0.9913 Tp/Tn_1/Tn_2: 51007/76/373 Eval of epoch 36 => acc:0.9841, loss:0.0113 Epoch 32: CosineAnnealingDecay set learning rate to 0.00010453658778440107. Epoch 37: LinearWarmup set learning rate to 0.00010453658778440107. 2023-07-27 14:34:57 || Epoch 37 start: epoch:37, batch_id:0, loss:0.0035, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:37, batch_id:50, loss:0.0025, acc:0.9913 Tp/Tn_1/Tn_2: 12943/18/95 epoch:37, batch_id:100, loss:0.0018, acc:0.9918 Tp/Tn_1/Tn_2: 25644/36/176 epoch:37, batch_id:150, loss:0.0049, acc:0.9919 Tp/Tn_1/Tn_2: 38341/52/263 epoch:37, batch_id:200, loss:0.0031, acc:0.9915 Tp/Tn_1/Tn_2: 51020/71/365 Eval of epoch 37 => acc:0.9843, loss:0.0113 Saved best model of epoch37, acc 0.9843, save path "./runs" Epoch 33: CosineAnnealingDecay set learning rate to 8.294311864472437e-05. Epoch 38: LinearWarmup set learning rate to 8.294311864472437e-05. 2023-07-27 14:37:16 || Epoch 38 start: epoch:38, batch_id:0, loss:0.0019, acc:0.9961 Tp/Tn_1/Tn_2: 255/0/1 epoch:38, batch_id:50, loss:0.0070, acc:0.9921 Tp/Tn_1/Tn_2: 12953/16/87 epoch:38, batch_id:100, loss:0.0009, acc:0.9921 Tp/Tn_1/Tn_2: 25653/41/162 epoch:38, batch_id:150, loss:0.0053, acc:0.9922 Tp/Tn_1/Tn_2: 38356/56/244 epoch:38, batch_id:200, loss:0.0121, acc:0.9921 Tp/Tn_1/Tn_2: 51048/72/336 Eval of epoch 38 => acc:0.9839, loss:0.0113 Epoch 34: CosineAnnealingDecay set learning rate to 6.395177052675794e-05. Epoch 39: LinearWarmup set learning rate to 6.395177052675794e-05. 2023-07-27 14:39:32 || Epoch 39 start: epoch:39, batch_id:0, loss:0.0059, acc:0.9883 Tp/Tn_1/Tn_2: 253/0/3 epoch:39, batch_id:50, loss:0.0037, acc:0.9926 Tp/Tn_1/Tn_2: 12960/17/79 epoch:39, batch_id:100, loss:0.0056, acc:0.9923 Tp/Tn_1/Tn_2: 25657/30/169 epoch:39, batch_id:150, loss:0.0050, acc:0.9924 Tp/Tn_1/Tn_2: 38361/48/247 epoch:39, batch_id:200, loss:0.0080, acc:0.9920 Tp/Tn_1/Tn_2: 51044/61/351 Eval of epoch 39 => acc:0.9843, loss:0.0113 Epoch 35: CosineAnnealingDecay set learning rate to 4.7679631406913064e-05. Epoch 40: LinearWarmup set learning rate to 4.7679631406913064e-05.
验证，测试
当batchsize从256设置为1时，验证集的准确率从95.23%降低到93.96%，可能是网络中如下代码的问题
# line 103f_pow = paddle.pow(f, 2)f_mean = paddle.mean(f_pow)f = paddle.divide(f, f_mean)
这里的mean方法与batch耦合，可以考虑限制维度来解耦（只在每个batch内做平均）比如：
f_mean = paddle.mean(f_pow, axis=[1,2,3], keepdim=True)
模型对batch解耦后，改变batchsize大小，不会影响最终精度
验证
In [9]
import paddle.vision.transforms as Tfrom paddle.io import DataLoaderimport timefrom statistics import mean# 参数定义IMGSIZE = (94, 24)
IMGDIR = 'rec_images/data'VALIDFILE = 'rec_images/valid.txt'LPRMAXLEN = 18EVALBATCHSIZE = 1 # batch sizeNUMWORKERS = 2# 图片预处理eval_transforms = T.Compose([
T.ToTensor(data_format='CHW'),
T.Normalize(
[0.5, 0.5, 0.5], # 在totensor的时候已经将图片缩放到0-1
[0.5, 0.5, 0.5],
data_format='CHW'
),
])# 数据加载eval_data_set = LprnetDataloader(IMGDIR, VALIDFILE, eval_transforms)
eval_loader = DataLoader(
eval_data_set,
batch_size=EVALBATCHSIZE,
shuffle=False,
num_workers=NUMWORKERS,
drop_last=False,
collate_fn=collate_fn
)# 定义lossloss_func = nn.CTCLoss(len(CHARS)-1)# input_length, loss计算需要input_length = np.ones(shape=TRAINBATCHSIZE) * LPRMAXLEN
input_length = paddle.to_tensor(input_length, dtype='int64')# LPRNet网络，添加模型权重model = LPRNet(LPRMAXLEN, len(CHARS), DROPOUT)
load_pretrained(model, 'runs/lprnet_best_2')
# accacc_eval = ACC()# 验证# evalwith paddle.no_grad():
model.eval()
loss_list = [] for batch_id, bath_data in enumerate(eval_loader):
img_data, label_data, label_lens = bath_data
predict = model(img_data)
logits = paddle.transpose(predict, (2,0,1))
loss = loss_func(logits, label_data, input_length, label_lens)
acc_eval.batch_update(label_data, label_lens, predict)
loss_list.append(loss.item()) print(f'Eval from {VALIDFILE} => acc:{acc_eval.acc:.4f}, loss:{mean(loss_list):.4f}')
acc_eval.clear()params loading... load runs/lprnet_best_2.pdparams success... Eval from rec_images/valid.txt => acc:0.9843, loss:0.0114
预测结果可视化
这里仍然是以动态图的方式进行预测，想要部署的话建议转静态图
https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/jit/index_cn.html
In [11]
"""此部分为测试的可视化代码, 后处理可参考"""import cv2import matplotlib.pyplot as pltimport numpy as npimport paddle
%matplotlib inline
img_path = 'rec_images/data/皖AF358Z.jpg'img_data = cv2.imread(img_path)
img_data = img_data[:,:,::-1] # BGR to RGBplt.imshow(img_data)
plt.axis('off')
plt.show()# 数据前处理img_data = cv2.resize(img_data,(94, 24))
img_data = (img_data - 127.5) / 127.5 # 归一化img_data = np.transpose(img_data, (2,0,1)) # HWC to CHWimg_data = np.expand_dims(img_data, 0) # to BCHWimg_tensor = paddle.to_tensor(img_data, dtype='float32') # shape == [1, 3, 24, 94]print(img_tensor.shape)# 加载模型, 预测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', '-'
]
LPRMAXLEN = 18model = LPRNet(LPRMAXLEN, len(CHARS), dropout_rate=0)
load_pretrained(model, 'runs/lprnet_best_2')
out_data = model(img_tensor) # out_data.shape == [1, 68, 18]# 后处理，单张图片数据def reprocess(pred):
pred_data = pred[0]
pred_label = np.argmax(pred_data, axis=0)
no_repeat_blank_label = []
pre_c = pred_label[0] if pre_c != len(CHARS) - 1: # 非空白
no_repeat_blank_label.append(pre_c) for c in pred_label: # dropout repeate label and blank label
if (pre_c == c) or (c == len(CHARS) - 1): if c == len(CHARS) - 1:
pre_c = c continue
no_repeat_blank_label.append(c)
pre_c = c
char_list = [CHARS[i] for i in no_repeat_blank_label] return ''.join(char_list)
rep_result = reprocess(out_data)print(rep_result) # 皖AF358Z[1, 3, 24, 94] params loading... load runs/lprnet_best_2.pdparams success... 皖AF358Z
TODO
数据集分布不均，这里只用了ccpd2019的蓝牌，可以使用ccpd2020包含绿牌数据
大部分车牌都是“皖”，可以适当添加其他省份的车牌数据
3. 车牌裁切没有做矫正，想提高精度，可考虑加上车牌的矫正算法
4. 本车牌识别网络模型与batch数据耦合，可以尝试解耦后再训练
- 网络模型已经固定了输出序列的长度18，考虑修改为能自定义长度，让模型能适用于更多场景
模型导出
导出onnx
这里将模型从动态图导出onnx文件, 直接使用api:paddle.onnx.export
https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/onnx/export_cn.html
In [12]
model = LPRNet(18, 68, dropout_rate=0) load_pretrained(model, 'runs/lprnet_best_2') save_path = 'save_onnx/lprnet' # 需要保存的路径x_spec = paddle.static.InputSpec([1, 3, 24, 94], 'float32', 'image') paddle.onnx.export(model, save_path, input_spec=[x_spec], opset_version=11)
params loading... load runs/lprnet_best_2.pdparams success...
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance." /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance." /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance." /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance." /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:712: UserWarning: When training, we now always track global mean and variance. "When training, we now always track global mean and variance."
2023-07-27 15:03:51 [INFO] Static PaddlePaddle model saved in save_onnx/paddle_model_static_onnx_temp_dir. [Paddle2ONNX] Start to parse PaddlePaddle model... [Paddle2ONNX] Model file path: save_onnx/paddle_model_static_onnx_temp_dir/model.pdmodel [Paddle2ONNX] Paramters file path: save_onnx/paddle_model_static_onnx_temp_dir/model.pdiparams [Paddle2ONNX] Start to parsing Paddle model... [Paddle2ONNX] Use opset_version = 11 for ONNX export. [Paddle2ONNX] PaddlePaddle model is exported as ONNX format now. 2023-07-27 15:03:52 [INFO] ONNX model saved in save_onnx/lprnet.onnx.
onnx测试
可以在https://netron.app/ 中查看可视化结构
更多参考：https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/advanced/model_to_onnx_cn.html
In [ ]
!pip install onnx==1.10.2!pip install onnxruntime==1.9.0
In [14]
"""检查onnx是否合理，模型的版本、图的结构、节点及其输入和输出"""import onnx
onnx_model = onnx.load("save_onnx/lprnet.onnx")
check = onnx.checker.check_model(onnx_model)print('check: ', check)check: None
In [15]
# 导入所需的库import numpy as npimport onnxruntimeimport paddle# 随机生成输入，用于验证 Paddle 和 ONNX 的推理结果是否一致x = np.random.random((1, 3, 24, 94)).astype('float32')# predict by ONNXRuntimeonnx_path = "save_onnx/lprnet.onnx"ort_sess = onnxruntime.InferenceSession(onnx_path)
ort_inputs = {ort_sess.get_inputs()[0].name: x}
ort_outs = ort_sess.run(None, ort_inputs)print("Exported model has been predicted by ONNXRuntime!")# predict by Paddlemodel = paddle.jit.load("save_onnx/paddle_model_static_onnx_temp_dir/model") # 上一步中导出onnx的时候会保存静态图文件到输出目录model.eval()
paddle_input = paddle.to_tensor(x)
paddle_outs = model(paddle_input)print("Original model has been predicted by Paddle!")# compare ONNXRuntime and Paddle resultsnp.testing.assert_allclose(ort_outs[0], paddle_outs.numpy(), rtol=1.0, atol=1e-05)print("The difference of results between ONNXRuntime and Paddle looks good!")Exported model has been predicted by ONNXRuntime! Original model has been predicted by Paddle! The difference of results between ONNXRuntime and Paddle looks good!
onnx推理
推理与上面相同，只是添加了实际数据的前处理和模型输出的后处理部分
In [16]
import onnxruntimeimport cv2import matplotlib.pyplot as pltimport numpy as np
%matplotlib inline# 数据预处理img_path = 'rec_images/data/皖AF358Z.jpg'img_data = cv2.imread(img_path)
img_data = img_data[:,:,::-1] # BGR to RGBplt.imshow(img_data)
plt.axis('off')
plt.show()
img_data = cv2.resize(img_data,(94, 24))
img_data = (img_data - 127.5) / 127.5 # 归一化img_data = np.transpose(img_data, (2,0,1)) # HWC to CHWimg_data = np.expand_dims(img_data, 0) # to BCHWnp_data = np.array(img_data, dtype=np.float32)# 加载 ONNX 模型生成推理用 sessonnx_path = "save_onnx/lprnet.onnx"sess = onnxruntime.InferenceSession(onnx_path)# 使用 ONNXRuntime 推理ort_inputs = {sess.get_inputs()[0].name: np_data}
result, = sess.run(None, ort_inputs)# 推理结果后处理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', '-'
]def reprocess(pred):
pred_data = pred[0]
pred_label = np.argmax(pred_data, axis=0)
no_repeat_blank_label = []
pre_c = pred_label[0] if pre_c != len(CHARS) - 1: # 非空白
no_repeat_blank_label.append(pre_c) for c in pred_label: # dropout repeate label and blank label
if (pre_c == c) or (c == len(CHARS) - 1): if c == len(CHARS) - 1:
pre_c = c continue
no_repeat_blank_label.append(c)
pre_c = c
char_list = [CHARS[i] for i in no_repeat_blank_label] return ''.join(char_list)
plate_str = reprocess(result)print(plate_str) # 皖AF358Z皖AF358Z
