{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://authoring-modelarts-cnnorth4.huaweicloud.com/console/lab?share-url-b64=aHR0cHM6Ly9taW5kc3BvcmUtd2Vic2l0ZS5vYnMuY24tbm9ydGgtNC5teWh1YXdlaWNsb3VkLmNvbS9ub3RlYm9vay9yMi4yL3R1dG9yaWFscy96aF9jbi9iZWdpbm5lci9taW5kc3BvcmVfc2F2ZV9sb2FkLmlweW5i&imageid=4c43b3ad-9df7-4b83-a096-c775dc4ba243) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.2/tutorials/zh_cn/beginner/mindspore_save_load.ipynb) \n",
"[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/r2.2/tutorials/zh_cn/beginner/mindspore_save_load.py) \n",
"[](https://gitee.com/mindspore/docs/blob/r2.2/tutorials/source_zh_cn/beginner/save_load.ipynb)\n",
"\n",
"[基本介绍](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/introduction.html) || [快速入门](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/quick_start.html) || [张量 Tensor](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/tensor.html) || [数据集 Dataset](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/dataset.html) || [数据变换 Transforms](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/transforms.html) || [网络构建](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/model.html) || [函数式自动微分](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/autograd.html) || [模型训练](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/train.html) || **保存与加载** || [使用静态图加速](https://www.mindspore.cn/tutorials/zh-CN/r2.2/beginner/accelerate_with_static_graph.html)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"# 保存与加载\n",
"\n",
"上一章节主要介绍了如何调整超参数，并进行网络模型训练。在训练网络模型的过程中，实际上我们希望保存中间和最后的结果，用于微调（fine-tune）和后续的模型推理与部署，本章节我们将介绍如何保存与加载模型。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import mindspore\n",
"from mindspore import nn\n",
"from mindspore import Tensor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def network():\n",
" model = nn.SequentialCell(\n",
" nn.Flatten(),\n",
" nn.Dense(28*28, 512),\n",
" nn.ReLU(),\n",
" nn.Dense(512, 512),\n",
" nn.ReLU(),\n",
" nn.Dense(512, 10))\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## 保存和加载模型权重\n",
"\n",
"保存模型使用`save_checkpoint`接口，传入网络和指定的保存路径："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"model = network()\n",
"mindspore.save_checkpoint(model, \"model.ckpt\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"要加载模型权重，需要先创建相同模型的实例，然后使用`load_checkpoint`和`load_param_into_net`方法加载参数。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = network()\n",
"param_dict = mindspore.load_checkpoint(\"model.ckpt\")\n",
"param_not_load, _ = mindspore.load_param_into_net(model, param_dict)\n",
"print(param_not_load)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"> `param_not_load`是未被加载的参数列表，为空时代表所有参数均加载成功。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 保存和加载MindIR\n",
"\n",
"除Checkpoint外，MindSpore提供了云侧（训练）和端侧（推理）统一的[中间表示（Intermediate Representation，IR）](https://www.mindspore.cn/docs/zh-CN/r2.2/design/all_scenarios.html#中间表示mindir)。可使用`export`接口直接将模型保存为MindIR。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"model = network()\n",
"inputs = Tensor(np.ones([1, 1, 28, 28]).astype(np.float32))\n",
"mindspore.export(model, inputs, file_name=\"model\", file_format=\"MINDIR\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> MindIR同时保存了Checkpoint和模型结构，因此需要定义输入Tensor来获取输入shape。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"已有的MindIR模型可以方便地通过`load`接口加载，传入`nn.GraphCell`即可进行推理。\n",
"\n",
"> `nn.GraphCell`仅支持图模式。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mindspore.set_context(mode=mindspore.GRAPH_MODE)\n",
"\n",
"graph = mindspore.load(\"model.mindir\")\n",
"model = nn.GraphCell(graph)\n",
"outputs = model(inputs)\n",
"print(outputs.shape)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "MindSpore",
"language": "python",
"name": "mindspore"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
},
"vscode": {
"interpreter": {
"hash": "8c9da313289c39257cb28b126d2dadd33153d4da4d524f730c81a4aaccbd2ca7"
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