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基于正点原子的ATK-DLRK3568部署测试。
花卉图像分类任务#xff0c;使用使用 tf.keras.Sequential 模型#xff0c;简单构建模型#xff0c;然后转换成 RKNN 模型部署到ATK-DLRK3568板子上。
在 PC 使用 Windows 系统…若该文为原创文章转载请注明原文出处。
基于正点原子的ATK-DLRK3568部署测试。
花卉图像分类任务使用使用 tf.keras.Sequential 模型简单构建模型然后转换成 RKNN 模型部署到ATK-DLRK3568板子上。
在 PC 使用 Windows 系统安装 tensorflow并创建虚拟环境进行训练然后切换到VM下的RK3568环境使用rknn-toolkit2把模型转成rknn模型部署到RK3568板子上测试。
一、介绍 TensorFlow 是一个基于数据流编程dataflow programming的符号数学系统被广泛应用于机器学习machine learning算法的编程实现其前身是谷歌的神经网络算法库 DistBelief。
使用 tf.keras.Sequential 模型对花卉图像进行分类。
二、环境搭建
1、创建虚拟环境 conda create -n tensorflow_env python3.8 -y
2、激活环境
conda activate tensorflow_env
3、安装环境
pip install numpypip install tensorflowpip install pillow 三、训练
1、下载数据集
https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
数据集不好下载自行处理。
2、训练
tensorflow_classification.py
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential# 获取
import pathlib
#dataset_url https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
#data_dir tf.keras.utils.get_file(flower_photos, origindataset_url, untarTrue)
data_dir ./flower_photos
data_dir pathlib.Path(data_dir)batch_size 32
img_height 180
img_width 180# 划分数据
train_ds tf.keras.utils.image_dataset_from_directory(data_dir,validation_split0.2,subsettraining,seed123,image_size(img_height, img_width),batch_sizebatch_size)val_ds tf.keras.utils.image_dataset_from_directory(data_dir,validation_split0.2,subsetvalidation,seed123,image_size(img_height, img_width),batch_sizebatch_size)class_names train_ds.class_names
#print(class_names)# 处理数据
normalization_layer layers.Rescaling(1./255)
train_ds train_ds.map(lambda x, y: (normalization_layer(x), y))
val_ds val_ds.map(lambda x, y: (normalization_layer(x), y))
num_classes len(class_names)data_augmentation keras.Sequential([layers.RandomFlip(horizontal,input_shape(img_height,img_width,3)),layers.RandomRotation(0.1),layers.RandomZoom(0.1),]
)model Sequential([data_augmentation,layers.Conv2D(16, 3, paddingsame, activationrelu),layers.MaxPooling2D(),layers.Conv2D(32, 3, paddingsame, activationrelu),layers.MaxPooling2D(),layers.Conv2D(64, 3, paddingsame, activationrelu),layers.MaxPooling2D(),layers.Dropout(0.2),layers.Flatten(),layers.Dense(128, activationrelu),layers.Dense(num_classes, nameoutputs)
])model.compile(optimizeradam,losstf.keras.losses.SparseCategoricalCrossentropy(from_logitsTrue),metrics[accuracy])model.summary()# 训练模型
epochs15
history model.fit(train_ds,validation_dataval_ds,epochsepochs,
)# 测试模型
#sunflower_url https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg
#sunflower_path tf.keras.utils.get_file(Red_sunflower, originsunflower_url)
sunflower_path ./test_180.jpgimg tf.keras.utils.load_img(sunflower_path, target_size(img_height, img_width)
)
img_array tf.keras.utils.img_to_array(img)
img_array tf.expand_dims(img_array, 0) # Create a batchpredictions model.predict(img_array)
score tf.nn.softmax(predictions[0])print(This image most likely belongs to {} with a {:.2f} percent confidence..format(class_names[np.argmax(score)], 100 * np.max(score))
)# Convert the model.
converter tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model converter.convert()# Save the model.
with open(model.tflite, wb) as f:f.write(tflite_model)
代码有点需要注意代码屏蔽了下载的功能所以需要预先下载数据集如果没有下载数据集就需要把下载的代码开启。
#dataset_url https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
#data_dir tf.keras.utils.get_file(flower_photos, origindataset_url, untarTrue)
执行下面命令开始训练
python tensorflow_classification.py 等待一会会生成model.tflite模型文件。
四、RKNN模型转换
转换代码通过下面代码
rknn_transfer.py
import numpy as np
import cv2
from rknn.api import RKNN
import tensorflow as tfimg_height 180
img_width 180
IMG_PATH test.jpg
class_names [daisy, dandelion, roses, sunflowers, tulips]if __name__ __main__:# Create RKNN object#rknn RKNN(verboseDebug)rknn RKNN()# Pre-process configprint(-- Config model)rknn.config(mean_values[0, 0, 0], std_values[255, 255, 255], target_platformrk3568)print(done)# Load modelprint(-- Loading model)ret rknn.load_tflite(modelmodel.tflite)if ret ! 0:print(Load model failed!)exit(ret)print(done)# Build modelprint(-- Building model)ret rknn.build(do_quantizationFalse)#ret rknn.build(do_quantizationTrue,dataset./dataset.txt)if ret ! 0:print(Build model failed!)exit(ret)print(done)# Export rknn modelprint(-- Export rknn model)ret rknn.export_rknn(./model.rknn)if ret ! 0:print(Export rknn model failed!)exit(ret)print(done)#Init runtime environment
print(-- Init runtime environment)
ret rknn.init_runtime()
# if ret ! 0:
# print(Init runtime environment failed!)
# exit(ret)
print(done)img cv2.imread(IMG_PATH)
img cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img cv2.resize(img,(180,180))
img np.expand_dims(img, 0)#print(-- Accuracy analysis)
#rknn.accuracy_analysis(inputs[./test.jpg])
#print(done)print(-- Running model)
outputs rknn.inference(inputs[img])
print(outputs)
outputs tf.nn.softmax(outputs)
print(outputs)print(This image most likely belongs to {} with a {:.2f} percent confidence..format(class_names[np.argmax(outputs)], 100 * np.max(outputs))
)
#print(图像预测是:, class_names[np.argmax(outputs)])
print(-- done)rknn.release()运行后会生成RKNN模型 五、部署
rknnlite_inference.py
import numpy as np
import cv2
from rknnlite.api import RKNNLiteIMG_PATH test.jpg
RKNN_MODEL model.rknn
img_height 180
img_width 180
class_names [daisy, dandelion, roses, sunflowers, tulips]# Create RKNN object
rknn_lite RKNNLite()# load RKNN model
print(-- Load RKNN model)
ret rknn_lite.load_rknn(RKNN_MODEL)
if ret ! 0:print(Load RKNN model failed)exit(ret)
print(done)# Init runtime environment
print(-- Init runtime environment)
ret rknn_lite.init_runtime()
if ret ! 0:print(Init runtime environment failed!)exit(ret)
print(done)# load image
img cv2.imread(IMG_PATH)
img cv2.resize(img,(180,180))
img cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img np.expand_dims(img, 0)# runing model
print(-- Running model)
outputs rknn_lite.inference(inputs[img])
print(result: , outputs)
print(This image most likely belongs to {}..format(class_names[np.argmax(outputs)])
)rknn_lite.release()
把上面的rknnlite_inference.py和图片拷贝到开发板上终端运行即可。 如有侵权或需要完整代码请及时联系博主。
