tf serving部署
转onnx再pb再tf serving使用
a、转onnx
import torch
import torchvision
import torch.onnx
import torch.nn as nn
import clip
device = "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
text1 = clip.tokenize(["hello"]).to(device)
print(text1)
print(type(text1))
# 加载
model_txt = torch.load('./single_model_text1.pkl')
torch.onnx.export(model_txt, text1, "./single_model_text.onnx")
b、onnx再转pb
tf2版本里,安装环境:
pip install onnx onnx_tf
pip install -U tensorflow-probability
导出代码:
import onnx
import numpy as np
from onnx_tf.backend import prepare
model = onnx.load(r'aaa_simp.onnx')
tf_model = prepare(model)
tf_model.export_graph(r'.\1')
报错:
No module named 'tensorflow_probability'
解决方法:
pip install -U tensorflow-probability
然后导出测试ok,会自动创建1目录并导出pb文件。
原文链接:https://blog.csdn.net/weixin_42357472/article/details/118491846
tf加载pb文件,是1.x版本,不是2.x版本:
def load_pb_model(sess, save_path):
with tf.gfile.FastGFile(save_path + 'model.pb', 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='') # 导入计算图
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原文链接:https://blog.csdn.net/qq_41959920/article/details/115737188