paddle2.0模型转onnx

自己开始用paddle时就是2.0版本了，所以不关心之前的版本。

1、查看本地有没有安装onnx

进入paddle 环境

conda list

或者

pip list

如果没有安装onnx则先安装onnx，onnx版本匹配是一个令人头疼的问题。
暂且不考虑版本问题直接使用下面的命令安装。

pip install onnx

2、安装paddle2onnx

pip install paddle2onnx

3、导出onnx模型

导出onnx模型的大致步骤如下：

import os
import time
import paddle

# 从模型代码中导入模型

# 实例化模型

# 加载预训练模型参数

# 将模型设置为评估状态

# 定义输入数据

# ONNX模型导出
paddle.onnx.export(model, [path to the save onnx model], input_spec=[input_spec], opset_version=[opset version])

3.1 导出静态图与onnx推理

导出静态图

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))

import argparse

import paddle
from paddle.jit import to_static

from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.logging import get_logger
from tools.program import load_config, merge_config, ArgsParser


def main():
    FLAGS = ArgsParser().parse_args()
    config = load_config(FLAGS.config)
    merge_config(FLAGS.opt)
    logger = get_logger()
    # build post process
    post_process_class = build_post_process(config['PostProcess'], config['Global'])

    # build model
    # for rec algorithm
    if hasattr(post_process_class, 'character'):
        char_num = len(getattr(post_process_class, 'character'))
        config['Architecture']["Head"]['out_channels'] = char_num
    model = build_model(config['Architecture'])

    init_model(config, model, logger)

    model.eval()

    # 定义输入数据
    input_spec = paddle.static.InputSpec(shape=[1, 3, 32, 320], dtype='float32', name='data')

    # ONNX模型导出
    paddle.onnx.export(model, "ocr_rec", input_spec=[input_spec], opset_version=10)


if __name__ == '__main__':
    main()

onnx推理

import onnx
import numpy as np
import onnxruntime
import cv2

onnx_file = './ocr_rec.onnx'
onnx_model = onnx.load(onnx_file)
onnx.checker.check_model(onnx_model)
print('The model is checked!')

x = np.random.random((1, 3, 32, 320)).astype('float32')
print("x:", x)

img = cv2.imread("./00000.jpg")
resized_image = cv2.resize(img, (320, 32))
img_in = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)
img_in = np.expand_dims(img_in, axis=0)
img_in /= 255.0
# predict by ONNX Runtime
ort_sess = onnxruntime.InferenceSession(onnx_file)
ort_inputs = {ort_sess.get_inputs()[0].name: img_in}
ort_outs = ort_sess.run(None, ort_inputs)

print("Exported model has been predicted by ONNXRuntime!")

具体代码还不完善，后面会修改。

参考：PaddleOCR转ONNX推理
参考：PaddleOCR模型转ONNX__PaddlePaddle模型导出ONNX协议
参考：Paddle2.0：ONNX模型的导出和部署
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