target: RKNPU v2, target platform: rk3588, framework name: ONNX, framework layout: NCHW, model inference type: static_shape W RKNN: [18:43:51.375] query RKNN_QUERY_INPUT_DYNAMIC_RANGE error, rknn model is static
target: RKNPU v2, target platform: rk3588, framework name: ONNX, framework layout: NCHW, model inference type: static_shape W RKNN: [13:27:50.955] query RKNN_QUERY_INPUT_DYNAMIC_RANGE error, rknn model is static shape type, please export rknn with dynamic_shapes W Query dynamic range ...
from tensorflow.examples.tutorials.mnist import input_data from model import build_model, x, y_, keep_prob mnist = input_data.read_data_sets("../MNIST_data/", one_hot=True) def create_training_graph(is_quantify): #创建训练图,加入create_training_graph: g = tf.get_default_graph() # ...
dynamic_input:用于根据用户指定的多组输入 shape,来模拟动态输入的功能.格式 为[[input0_shapeA, input1_shapeA, ...], [input0_shapeB, input1_shapeB, ...], ...]. 默认值为 None,实验性功能. 假设原始模型只有一个输入,shape 为[1,3,224,224],或者原始模型的输入 shape 本身 就是动态的,如...
RKNN version demo of [CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search - LightTrack-rknn/3rdparty/opencv4/opencv2/videoio.hpp at master · Z-Xiong/LightTrack-rknn