一维浮点数据使用rknn和pb模型结果不一致 - Toybrick

<blockquote>import tensorflow as tf

from rknn.api import RKNN
if __name__ == '__main__':
# 创建RKNN执行对象
rknn = RKNN()
#初始化RKNN运行环境，指定host=’rk3399pro’，后续的操作将会运行在NPU上
print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
# 配置模型输入，用于NPU对数据输入的预处理
# channel_mean_value='0 0 0 255'，那么模型推理时，将会对RGB数据做如下转换
# (R - 0)/255, (G - 0)/255, (B - 0)/255。推理时，RKNN模型会自动做均值和归一化处理
# reorder_channel=’0 1 2’用于指定是否调整RBG顺序，设置成0 1 2即按输入的RGB顺序不做调整
# reorder_channel=’2 1 0’表示交换0和2通道，如果输入是RGB，将会被调整为BGR。如果是BGR将会被
# 调整为RGB
rknn.config(channel_mean_value='0 0 0 1', reorder_channel='0 1 2')
# 加载TensorFlow模型
# tf_pb='../digital_gesture_recognition/model_2500/digital_gesture.pb'指定待转换的TensorFlow模型
# inputs指定模型中的输入节点
# outputs指定模型中输出节点
# input_size_list指定模型输入的大小
print('--> Loading model')
rknn.load_tensorflow(tf_pb='test.pb',
inputs=['input_node'],
outputs=['logits'],
input_size_list=[[1, 1000, 1]])
print('done')
# 创建解析pb模型
# do_quantization=False指定不进行量化
# 量化会减小模型的体积和提升运算速度，但是会有精度的丢失
print('--> Building model')
rknn.build(do_quantization=False)
print('done')
# 导出保存rknn模型文件
rknn.export_rknn('test.rknn')
# Release RKNN Conte
rknn.release()

import numpy as np
import tensorflow as tf
from rknn.api import RKNN
def load_rknn():
# 创建RKNN对象
rknn = RKNN()
# 载入RKNN模型
# print('-->loading rknn model')
rknn.load_rknn('./out1/frozen_model.rknn')
# print('loading rknn model done')
# 初始化RKNN运行环境
# print('--> Init runtime environment')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('rknn prepare done\n')
return rknn
def load_pb():
with tf.gfile.GFile('test.pb', "rb") as f: # 读取模型数据
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read()) # 得到模型中的计算图和数据
with tf.Graph().as_default() as graph: # 这里的Graph()要有括号，不然会报TypeError
tf.import_graph_def(graph_def, name = "") # 导入模型中的图到现在这个新的计算图中
print("pb prepare done\n")
return graph
def rknn_infer(data,rknn):
outputs = rknn.inference(inputs=[data],data_type="float32")
return outputs
def pb_infer(data,graph):
x = graph.get_tensor_by_name("input_node:0") # 输入节点，RKNN转换模型时，input为"input_x"
y = graph.get_tensor_by_name("logits:0")
with tf.Session(graph = graph) as sess:
yout= sess.run([y], feed_dict = {x: data})
return yout
def prepare_data():
data = np.ones(1000,dtype=np.int32)
data1 = np.reshape(data,(1,1000,1))
print(data1.dtype)
data2 = np.reshape(data,(1,1,1000,1))
return data1,data2
if __name__=="__main__":
data1,data2 = prepare_data()
rknn = load_rknn()
graph = load_pb()
out1 = rknn_infer(data1,rknn)
out2 = pb_infer(data2,graph)
# print(np.sum(out2[0],axis=-1))
np.set_printoptions(suppress = True)
print("the rknn output is :{}\n".format(out1))
print("the pb output is :{}\n".format(out2))

the rknn output is :[array([[-0.00941467, 0.00408554, 0.00399399, -0.01940918, 0.00273895,
0.00243187, -0.01450348, 0.00104523, 0.00271606, 0.00832367,
0.00605774, 0.00070429]], dtype=float32)]
the pb output is :[array([[-0.02569406, 0.01786735, 0.00751904, -0.02548516, -0.02158996,
0.01359443, -0.00974836, -0.01726884, 0.02188211, 0.02064164,
-0.00169315, -0.00375702]], dtype=float32)]