diff --git a/MindEnergy/application/DAE-PINNs/DAE-PINN.ipynb b/MindEnergy/application/DAE-PINNs/DAE-PINN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bd5257f88f2d1e910d7974a9c96cb0e782a53f42 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/DAE-PINN.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# DAE-PINN\n", + "\n", + "\n", + "## 环境安装\n", + "\n", + "本案例要求 **MindSpore >= 2.5.0** 版本以调用如下接口: *mindspore.jit, mindspore.jit_class, mindspore.data_sink*。具体请查看[MindSpore安装](https://www.mindspore.cn/install)。\n", + "\n", + "此外，你需要安装 **MindEnergy >=0.1.0** 版本。如果当前环境还没有安装，请按照下列方式选择后端和版本进行安装。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mindenergy_version = \"0.1.0\" # update if needed\n", + "# Comment out the following code if you are using NPU.\n", + "!pip uninstall -y mindscience-ascend\n", + "!pip install mindscience-ascend==$mindscience_version\n", + "\n", + "# NPU Uncomment if needed.\n", + "# !pip uninstall -y mindscience-ascend\n", + "# !pip install mindscience-ascend==$mindscience_version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 概述\n", + "\n", + "\n", + "* **电力网络动态安全评估需求** ：随着电力网络中分布式能源资源的整合、市场自由化以及复杂通信和控制算法的采用，电力网络的运行条件和潜在故障场景变得更加多样化，影响其安全性。为了评估电力网络的动态安全性，需要模拟其在面对单一故障时的动态响应，这需要求解一组非线性微分代数方程（DAE），而传统显式积分方案在求解 DAE 时会失败，商业求解器计算成本高、内存需求大，限制了动态安全评估的在线部署。\n", + "* **深度学习在科学和工程领域的潜力与挑战** ：尽管深度学习在计算机视觉和自然语言处理等领域取得了巨大成功，但在学习科学和工程动态系统方面应用有限，因为数据收集成本高昂，且大多数传统深度学习方法在数据量有限的情况下缺乏鲁棒性和泛化能力。\n", + "\n", + "## 工作原理\n", + "\n", + " * DAE-PINNs 框架结合了隐式龙格 - 库塔时间步进方案（专为求解 DAE 设计）和物理信息神经网络（PINN）。在时间步进过程中，假设已积分至 $(t_n, y_n, z_n)$，目标是推进至 $(t_{n+1}, y_{n+1}, z_{n+1})$，应用隐式龙格 - 库塔方案后，得到一系列方程，包括内部阶段的更新公式和最终状态的更新公式。\n", + " * 通过惩罚方法强制神经网络满足 DAE 作为近似硬约束。在训练过程中，将 DAE 的残差作为损失函数的一部分，使得网络在学习过程中不仅拟合数据，还能满足物理定律所描述的 DAE 方程，从而将物理信息融入到神经网络的学习过程中。\n", + "\n", + "## 方法细节\n", + "\n", + " * **问题设置** ：DAE 以半显式形式给出，包括动态状态 y 和代数变量 z，以及描述微分方程的 f 和代数方程的 g。假设 f 和 g 具有足够高的可微性，并且 DAE 的索引为 1，即雅可比矩阵 g_z 的逆存在且在精确解附近有界，这使得代数方程在局部有唯一解 $z = G(y)$，从而 DAE 可以转化为普通微分方程系统。\n", + " * **网络结构** ：与标准的 PINN 类似，DAE-PINNs 通常由输入层、多个隐藏层和输出层组成。输入层接收时间和动态状态等信息，隐藏层通过非线性激活函数进行特征提取和转换，输出层预测代数变量的值。\n", + " * **损失函数** ：损失函数由两部分组成，一部分是数据损失，用于拟合初始条件、边界条件等已知点的数据；另一部分是物理损失，即 DAE 的残差损失，通过自动微分计算网络输出对时间和状态变量的导数，代入 DAE 方程得到残差，并将其作为物理损失的一部分。通过优化这两个部分的损失函数，使得网络既能拟合数据，又能满足物理方程。\n", + "\n", + "![model](./images/model.png)\n", + "\n", + "[DAE-PINN](https://arxiv.org/abs/2109.04304)的网络结构如上图。\n", + "\n", + "与传统神经网络不同，DAE-PINN 在网络结构中融入了物理信息，通过构造特定的损失函数，使网络在学习过程中不仅拟合数据，还能满足物理定律所描述的 DAE 方程，从而提高了模型的准确性和泛化能力。整体的网络架构如上，分为两个网络分别处理动态状态和代数状态，网络的输入是包括时间信息和动态状态信息，网络的输出是对动态状态和代数状态的预测值，具体来说，会输出动态状态 y 和代数变量 z 的预测结果，如在电力网络案例中，输出动态状态和代数变量的预测以实现对电力网络动态行为的模拟。网络支持使用`fnn`、`attention`、`conv1d`3种backbone。`fnn`为多层感知机网络，`attention`为采用类似transformer attention形式的FFN网络，`conv1d`为使用了`Conv1D`的FFN网络。\n", + "\n", + "\n", + "\n", + "## 准备环节\n", + "\n", + "实践前，确保已经正确安装最新版本的MindSpore与mindscience。如果没有，可以通过：\n", + "\n", + "* [MindSpore安装页面](https://www.mindspore.cn/install) 安装MindSpore。\n", + "\n", + "* [mindscience安装页面](https://www.mindspore.cn/mindscience/docs/zh-CN/master/mindscience_install.html) 安装mindscience。" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DAE-PINN实现\n", + "\n", + "DAE-PINN实现分为以下5个步骤：\n", + "\n", + "1. 配置网络与训练参数\n", + "2. 数据集制作与加载\n", + "3. 模型构建\n", + "4. 模型训练\n", + "5. 结果可视化" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "from mindspore import ops, jit\n", + "from mindspore import context\n", + "from mindspore.experimental import optim\n", + "import numpy as np\n", + "\n", + "from mindscience.utils import load_yaml_config\n", + "\n", + "from src.utils import dotdict\n", + "from src.model import three_bus_PN\n", + "from src.data import get_dataset\n", + "from src.trainer import DaeTrainer\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "context.set_context(mode=context.PYNATIVE_MODE, device_target='Ascend', device_id=1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 配置网络与训练参数\n", + "\n", + "从配置文件中读取模型相关参数（model）、数据相关参数（data）、优化器相关参数（optimizer），设置模型初始化、特征维度、子网类型等参数。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "config = load_yaml_config('./configs/config.yaml')\n", + "model_params, data_params, optim_params, ode_params, summary_params = config[\n", + " 'model'], config['data'], config['optimizer'], config['ode'], config['summary']\n", + "\n", + "dynamic = dotdict()\n", + "dynamic.num_IRK_stages = model_params['num_IRK_stages']\n", + "dynamic.state_dim = 4\n", + "dynamic.activation = model_params['dyn_activation']\n", + "dynamic.initializer = \"Glorot normal\"\n", + "dynamic.dropout_rate = 0\n", + "dynamic.batch_normalization = None if model_params['dyn_bn'] == \"no-bn\" else model_params['dyn_bn']\n", + "dynamic.layer_normalization = None if model_params['dyn_ln'] == \"no-ln\" else model_params['dyn_ln']\n", + "dynamic.type = model_params['dyn_type']\n", + "\n", + "if model_params['unstacked']:\n", + " dim_out = dynamic.state_dim * (dynamic.num_IRK_stages + 1)\n", + "else:\n", + " dim_out = dynamic.num_IRK_stages + 1\n", + "\n", + "if model_params['use_input_layer']:\n", + " dynamic.layer_size = [dynamic.state_dim * 5] + \\\n", + " [model_params['dyn_width']] * model_params['dyn_depth'] + [dim_out]\n", + "else:\n", + " dynamic.layer_size = [dynamic.state_dim] + \\\n", + " [model_params['dyn_width']] * model_params['dyn_depth'] + [dim_out]\n", + "\n", + "algebraic = dotdict()\n", + "algebraic.num_IRK_stages = model_params['num_IRK_stages']\n", + "dim_out_alg = algebraic.num_IRK_stages + 1\n", + "algebraic.layer_size = [dynamic.state_dim] + \\\n", + " [model_params['alg_width']] * model_params['alg_depth'] + [dim_out_alg]\n", + "algebraic.activation = model_params['alg_activation']\n", + "algebraic.initializer = \"Glorot normal\"\n", + "algebraic.dropout_rate = 0\n", + "algebraic.batch_normalization = None if model_params['alg_bn'] == \"no-bn\" else model_params['alg_bn']\n", + "algebraic.layer_normalization = None if model_params['alg_ln'] == \"no-ln\" else model_params['alg_ln']\n", + "algebraic.type = model_params['alg_type']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 数据集制作与加载\n", + "\n", + "数据集下载地址：[data_driven/airfoil/2D_steady](https://download.mindspore.cn/mindscience/mindscience/dataset/applications/data_driven/airfoil/2D_steady/)\n", + "该文件包含6000条HyperCube数据集。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset, test_dataset, val_dataset = get_dataset(data_params)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 模型构建\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "net = three_bus_PN(\n", + " dynamic,\n", + " algebraic,\n", + " use_input_layer=model_params['use_input_layer'],\n", + " stacked=not model_params['unstacked'],\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 损失函数与优化器\n", + "\n", + "损失函数采用了MSE函数。优化器选用了Adam，学习率调度采用了ReduceLROnPlateau。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = DaeTrainer(net, irk_dir=data_params['IRK_dir'], num_IRK_stages=model_params['num_IRK_stages'],\n", + " h=ode_params['h'], dyn_weight=model_params['dyn_weight'],\n", + " alg_weight=model_params['alg_weight'])\n", + "\n", + "optimizer = optim.Adam(net.trainable_params(), lr=optim_params['lr'])\n", + "scheduler_type = optim_params['scheduler_type']\n", + "use_scheduler = optim_params['use_scheduler']\n", + "if use_scheduler:\n", + " if scheduler_type == \"plateau\":\n", + " scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n", + " optimizer,\n", + " mode='min',\n", + " patience=optim_params['patience'],\n", + " factor=optim_params['factor'],\n", + " )\n", + " elif scheduler_type == \"step\":\n", + " scheduler = optim.lr_scheduler.StepLR(\n", + " optimizer, step_size=optim_params['patience'], gamma=optim_params['factor']\n", + " )\n", + " else:\n", + " scheduler = None\n", + "else:\n", + " scheduler = None\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 训练函数\n", + "\n", + "使用**MindSpore>= 2.5.0**的版本，可以使用函数式编程范式训练神经网络，单步训练函数使用jit装饰。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def forward_fn(x):\n", + " loss = trainer.get_loss(x)\n", + " return loss\n", + "\n", + "grad_fn = ops.value_and_grad(\n", + " forward_fn, None, optimizer.parameters, has_aux=False)\n", + "\n", + "@jit\n", + "def train_step(x):\n", + " loss, grads = grad_fn(x)\n", + " optimizer(grads)\n", + " return loss" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 模型训练\n", + "\n", + "模型训练过程中边训练边推理。用户可以直接加载测试数据集，每训练n个epoch后输出一次测试集上的推理精度。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "test_interval = summary_params['test_interval']\n", + "\n", + "for epoch in range(1, 1 + optim_params['epochs']):\n", + " # train\n", + " time_beg = time.time()\n", + " net.set_train(True)\n", + " loss_val = []\n", + " for data, in train_dataset:\n", + " step_train_loss = train_step(data)\n", + " loss_val.append(step_train_loss.numpy())\n", + " time_end = time.time()\n", + "\n", + " print(\n", + " f\"epoch: {epoch} train loss: {np.mean(loss_val)} epoch time: {time_end-time_beg:.3f}s\")\n", + " if use_scheduler:\n", + " if scheduler_type == \"plateau\":\n", + " net.set_train(False)\n", + " loss_val = trainer.get_loss(val_dataset)\n", + " scheduler.step(loss_val)\n", + " else:\n", + " scheduler.step()\n", + " # test\n", + " if epoch % test_interval == 0:\n", + " net.set_train(False)\n", + " loss_test = trainer.get_loss(test_dataset)\n", + " print(f'test loss: {loss_test}')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 结果可视化\n", + "\n", + "模型训练的loss下降曲线如下：\n", + "\n", + "![model](images/loss.png)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.16 64-bit ('gbq_2.0': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "vscode": { + "interpreter": { + "hash": "b9063439a3781aed32d6b0dd4804a0c8b51ecec7893a0f31b99846bc91ef39eb" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/MindEnergy/application/DAE-PINNs/README.md b/MindEnergy/application/DAE-PINNs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6e10241e90974f82e0728505d35564a980681542 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/README.md @@ -0,0 +1,76 @@ +# DAE-PINN + +## 概述 + +* **电力网络动态安全评估需求** ：随着电力网络中分布式能源资源的整合、市场自由化以及复杂通信和控制算法的采用，电力网络的运行条件和潜在故障场景变得更加多样化，影响其安全性。为了评估电力网络的动态安全性，需要模拟其在面对单一故障时的动态响应，这需要求解一组非线性微分代数方程（DAE），而传统显式积分方案在求解 DAE 时会失败，商业求解器计算成本高、内存需求大，限制了动态安全评估的在线部署。 +* **深度学习在科学和工程领域的潜力与挑战** ：尽管深度学习在计算机视觉和自然语言处理等领域取得了巨大成功，但在学习科学和工程动态系统方面应用有限，因为数据收集成本高昂，且大多数传统深度学习方法在数据量有限的情况下缺乏鲁棒性和泛化能力。 + +## 工作原理 + + * DAE-PINNs 框架结合了隐式龙格 - 库塔时间步进方案（专为求解 DAE 设计）和物理信息神经网络（PINN）。在时间步进过程中，假设已积分至 $(t_n, y_n, z_n)$，目标是推进至 $(t_{n+1}, y_{n+1}, z_{n+1})$，应用隐式龙格 - 库塔方案后，得到一系列方程，包括内部阶段的更新公式和最终状态的更新公式。 + * 通过惩罚方法强制神经网络满足 DAE 作为近似硬约束。在训练过程中，将 DAE 的残差作为损失函数的一部分，使得网络在学习过程中不仅拟合数据，还能满足物理定律所描述的 DAE 方程，从而将物理信息融入到神经网络的学习过程中。 + +## 方法细节 + + * **问题设置** ：DAE 以半显式形式给出，包括动态状态 y 和代数变量 z，以及描述微分方程的 f 和代数方程的 g。假设 f 和 g 具有足够高的可微性，并且 DAE 的索引为 1，即雅可比矩阵 g_z 的逆存在且在精确解附近有界，这使得代数方程在局部有唯一解 $z = G(y)$，从而 DAE 可以转化为普通微分方程系统。 + * **网络结构** ：与标准的 PINN 类似，DAE-PINNs 通常由输入层、多个隐藏层和输出层组成。输入层接收时间和动态状态等信息，隐藏层通过非线性激活函数进行特征提取和转换，输出层预测代数变量的值。 + * **损失函数** ：损失函数由两部分组成，一部分是数据损失，用于拟合初始条件、边界条件等已知点的数据；另一部分是物理损失，即 DAE 的残差损失，通过自动微分计算网络输出对时间和状态变量的导数，代入 DAE 方程得到残差，并将其作为物理损失的一部分。通过优化这两个部分的损失函数，使得网络既能拟合数据，又能满足物理方程。 + +![model](./images/model.png) + +[DAE-PINN](https://arxiv.org/abs/2109.04304)的网络结构如上图。 + +与传统神经网络不同，DAE-PINN 在网络结构中融入了物理信息，通过构造特定的损失函数，使网络在学习过程中不仅拟合数据，还能满足物理定律所描述的 DAE 方程，从而提高了模型的准确性和泛化能力。整体的网络架构如上，分为两个网络分别处理动态状态和代数状态，网络的输入是包括时间信息和动态状态信息，网络的输出是对动态状态和代数状态的预测值，具体来说，会输出动态状态 y 和代数变量 z 的预测结果，如在电力网络案例中，输出动态状态和代数变量的预测以实现对电力网络动态行为的模拟。网络支持使用`fnn`、`attention`、`conv1d`3种backbone。`fnn`为多层感知机网络，`attention`为采用类似transformer attention形式的FFN网络，`conv1d`为使用了`Conv1D`的FFN网络。 + +## 优势与贡献 + + * **优势** ：DAE-PINNs 能够有效地学习和模拟具有一定程度刚性的通用电力系统的解轨迹，生成的模拟适用于长时间范围的 DAE 模拟，填补了深度学习方法在处理刚性动力学方面的空白，为解决复杂工程系统中的 DAE 问题提供了一种新的高效方法。 + * **贡献** ：作者通过三节点电力网络的案例，验证了 DAE-PINN 在短时间内学习初始条件分布到解轨迹的映射以及长时间模拟 DAE 的能力，展示了其有效性和准确性，为电力系统动态安全评估提供了一种潜在的在线工具。 + +## 快速开始 + +### 训练方式一：在命令行中调用`train.py`脚本 + +```shell +python -u train.py --config_file ./configs/config.yaml --device_target Ascend --device_id 1 --mode PYNATIVE +``` + +其中， + +`--config_file`表示配置文件的路径，默认值'./configs/vit.yaml'； + +`--device_target`表示使用的计算平台类型，可以选择'Ascend'或'CPU'，默认值'Ascend'； + +`--device_id`表示使用的计算卡编号，可按照实际情况填写，默认值 1； + +`--mode`表示运行的模式，'GRAPH'表示静态图模式, 'PYNATIVE'表示动态图模式，默认值'PYNATIVE'。 + +### 训练方式二：运行 Jupyter Notebook + +您可以使用[Jupyter Notebook](./DAE-PINN.ipynb)逐行运行训练和验证代码。 + +## 结果展示 + +训练的loss曲线如下图： + +![loss](images/loss.png) + +## 性能 + +| 参数 | 指标 | +| :-----------: | :-----------------------------------------------: | +| 硬件资源 | Atlas 800T A2 | +| MindSpore版本 | >=2.5.0 | +| 数据集 | HyperCube | +| 参数量 | 6e4 | +| 训练参数 | batch_size=1048, steps_per_epoch=6, epochs=30000 | +| 优化器 | Adam | +| 训练损失(MSE) | 5e-3 | +| 验证损失(MSE) | 5e-3 | +| 速度(ms/step) | 140 | + +## 贡献者 + +gitee id: [Brian-K](https://gitee.com/b_rookie) + +email: brian_k2023@163.com diff --git a/MindEnergy/application/DAE-PINNs/configs/config.yaml b/MindEnergy/application/DAE-PINNs/configs/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97b2905e2a6d6686850177603c95e60155147692 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/configs/config.yaml @@ -0,0 +1,39 @@ +ode: + h: 0.1 + N: 80 + method: 'BDF' +model: + dyn_bn: 'no-bn' + dyn-ln: 'no-ln' + dyn-type: 'attention' # fnn, attention, Conv1D + unstacked: True + use-input-layer: False + dyn-width: 100 + dyn-depth: 4 + dyn-activation: 'sin' + dyn-weight: 64.0 + alg-bn: 'no-bn' + alg-ln: 'no-ln' + alg-type: 'attention' # fnn, attention, Conv1D + alg-width: 40 + alg-depth: 2 + alg-activation: 'sin' + alg-weight: 1.0 + num_IRK_stages: 100 +data: + data_path: 'data.npz' + IRK_dir: './IRK_weights' + num_train: 6000 + num_val: 100 + num_test: 500 + batch_size: 1048 +summary: + summary_dir: "./logs/dae-pinns-example-2/" + test_interval: 1000 +optimizer: + epochs: 30000 + lr: 1e-4 + use-scheduler: False + scheduler-type: 'plateau' + patience: 2000 + factor: .8 diff --git a/MindEnergy/application/DAE-PINNs/data/IRK_weights/Butcher_IRK100.txt b/MindEnergy/application/DAE-PINNs/data/IRK_weights/Butcher_IRK100.txt new file mode 100644 index 0000000000000000000000000000000000000000..21a14275f0fcfe606f0ec4c9f9658d9ed4cea089 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/data/IRK_weights/Butcher_IRK100.txt @@ -0,0 +1,10200 @@ +0.0001836586226264179326 +-0.000070015687539637576411 +0.000053853534327657867977 +-0.000045579488369569833163 +0.000040256727870382627455 +-0.000036438909940913561807 +0.000033515737964206391559 +-0.000031176687543938895126 +0.000029244171170672623136 +-0.000027608074902721408391 +0.000026195928094101058988 +-0.000024957792936842614555 +0.000023857977539740400648 +-0.000022870200053157452552 +0.000021974621920126745673 +-0.000021155951182348004089 +0.000020402187156195263671 +-0.000019703764697955525162 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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. +# ============================================================================ \ No newline at end of file diff --git a/MindEnergy/application/DAE-PINNs/src/data.py b/MindEnergy/application/DAE-PINNs/src/data.py new file mode 100644 index 0000000000000000000000000000000000000000..9a415667235ba08f5280b8b21f55146106831af0 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/data.py @@ -0,0 +1,16 @@ +import abc +import numpy as np +from mindspore import dataset as ds +from mindspore import Tensor + + +def get_dataset(data_params, shuffle=False, num_val=100): + data = np.load('../data/data.npz') + x_train, x_test = data['X_train'], data['X_test'] + batch_size = data_params['batch_size'] + + train_dataset = ds.NumpySlicesDataset(x_train, shuffle=shuffle) + train_dataset = train_dataset.batch(batch_size) + val_dataset = Tensor(x_train[:num_val]) + test_dataset = Tensor(x_test) + return train_dataset, test_dataset, val_dataset diff --git a/MindEnergy/application/DAE-PINNs/src/layer.py b/MindEnergy/application/DAE-PINNs/src/layer.py new file mode 100644 index 0000000000000000000000000000000000000000..d6b5a570253839e033c7446721eb448e56db1e16 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/layer.py @@ -0,0 +1,511 @@ +# Copyright 2025 Huawei Technologies Co., Ltd +# +# 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 numpy as np +import mindspore as ms +import mindspore.nn as nn +import mindspore.ops as ops +from mindspore import Tensor + + +class fnn(nn.Cell): + """ + feed-forward neural network. + """ + + def __init__(self, + layer_size, + activation, + kernel_initializer, + dropout_rate=0.0, + batch_normalization=None, + layer_normalization=None, + input_transform=None, + output_transform=None, + use_bias=True, + print_net=False): + super().__init__() + self.layer_size = layer_size + self.activation = activation + self.initializer = kernel_initializer + self.dropout_rate = dropout_rate + self.batch_normalization = batch_normalization + self.layer_normalization = layer_normalization + self.input_transform = input_transform + self.output_transform = output_transform + self.use_bias = use_bias + # build neural network + if self.batch_normalization and self.layer_normalization: + raise ValueError( + "cannot apply batch normalization and layer normalization at the same time") + self.net = nn.CellList() + if (self.batch_normalization is None) and (self.layer_normalization is None): + self.build_standard() + elif (self.batch_normalization == "before") or (self.layer_normalization == "before"): + self.build_before() + elif (self.batch_normalization == "after") or (self.layer_normalization == "after"): + self.build_after() + else: + raise ValueError("Neural net was not built") + # initialize parameters + self.net.init_parameters_data() + if print_net: + print("NN built...\n") + print(self.net) + + def construct(self, y): + """ + FNN forward pass. + Args: + :input (Tensor): \in [B, d_in] + Returns: + :y (Tensor): \in [B, d_out] + """ + if self.input_transform is not None: + y = self.input_transform(y) + for i in range(len(self.net)): + y = self.net[i](y) + if self.output_transform is not None: + y = self.output_transform(y) + return y + + def _init_weights(self, m): + """ + initializes layer parameters. + """ + if isinstance(m, nn.Dense): + if self.initializer == "Glorot normal": + m.weight.set_data( + ms.common.initializer.XavierUniform()(m.weight.shape)) + elif self.initializer == "Glorot uniform": + m.weight.set_data( + ms.common.initializer.XavierUniform()(m.weight.shape)) + else: + raise ValueError( + "initializer {} not implemented".format(self.initializer)) + if m.bias is not None: + m.bias.data.fill(0.0) + elif isinstance(m, nn.LayerNorm): + m.bias.data.fill(0.0) + m.gamma.data.fill(1.0) + + def build_standard(self): + # FC - activation + # input layer + self.net.append( + nn.Dense(self.layer_size[0], self.layer_size[1], has_bias=self.use_bias)) + for i in range(1, len(self.layer_size)-1): + self.net.append(nn.Dense(self.layer_size[i], self.layer_size[i+1], + has_bias=self.use_bias, activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + + def build_before(self): + # FC - BN or LN - activation + self.net.append( + nn.Dense(self.layer_size[0], self.layer_size[1], has_bias=self.use_bias)) + for i in range(1, len(self.layer_size)-1): + if self.batch_normalization is not None: + self.net.append(nn.BatchNorm1d(self.layer_size[i])) + elif self.layer_normalization is not None: + self.net.append(nn.LayerNorm((self.layer_size[i],))) + self.net.append(nn.Dense(self.layer_size[i], self.layer_size[i+1], + has_bias=self.use_bias, activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + + def build_after(self): + # FC - activation - BN or LN + self.net.append(nn.Dense(self.layer_size[0], self.layer_size[1], + has_bias=self.use_bias, activation=ops.Sin())) + for i in range(1, len(self.layer_size)-1): + if self.batch_normalization is not None: + self.net.append(nn.BatchNorm1d(self.layer_size[i])) + elif self.layer_normalization is not None: + self.net.append(nn.LayerNorm((self.layer_size[i],))) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + self.net.append( + nn.Dense(self.layer_size[i], self.layer_size[i+1], has_bias=self.use_bias)) + + +class Sin(nn.Cell): + def __init__(self, ): + super().__init__() + + def construct(self, x): + return ops.sin(x) + + +def get_activation(act): + act_list = {'sin': Sin()} + return act_list[act] + + +class Conv1D(nn.Cell): + """ + feed-forward neural networks with Conv1D dense layers. + """ + + def __init__(self, + layer_size, + activation, + dropout_rate=0.0, + batch_normalization=None, + layer_normalization=None, + input_transform=None, + output_transform=None): + super().__init__() + self.layer_size = layer_size + self.activation = activation + self.dropout_rate = dropout_rate + self.batch_normalization = batch_normalization + self.layer_normalization = layer_normalization + self.input_transform = input_transform + self.output_transform = output_transform + if self.batch_normalization and self.layer_normalization: + raise ValueError( + "Can not apply batch_normalization and layer_normalization at the same time.") + self.net = nn.CellList() + if (self.batch_normalization is None) and (self.layer_normalization is None): + self.build_standard() + elif (self.batch_normalization == "before") or (self.layer_normalization == "before"): + self.build_before() + elif (self.batch_normalization == "after") or (self.layer_normalization == "after"): + self.build_after() + else: + raise ValueError("Neural net was not built") + print("NN built...\n") + print(self.net) + + def construct(self, y): + """ + FNN forward pass + Args: + :y (Tensor): \in [B, d_in] + Returns: + :y (Tensor): \in [B, d_out] + """ + if self.input_transform is not None: + y = self.input_transform(y) + for i in range(len(self.net)): + y = self.net[i](y) + if self.output_transform is not None: + y = self.output_transform(y) + return y + + def build_standard(self): + # FC - activation + # input layer + self.net.append( + nn.Dense(self.layer_size[0], self.layer_size[1], activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + for i in range(1, len(self.layer_size)-2): + self.net.append( + nn.Dense(self.layer_size[i], self.layer_size[i+1], activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + self.net.append(nn.Dense(self.layer_size[-2], self.layer_size[-1])) + + def build_before(self): + # FC - BN or LN - activation + self.net.append(nn.Dense(self.layer_size[0], self.layer_size[1])) + for i in range(1, len(self.layer_size)-1): + if self.batch_normalization is not None: + self.net.append(nn.BatchNorm1d(self.layer_size[i])) + elif self.layer_normalization is not None: + self.net.append(nn.LayerNorm((self.layer_size[i],))) + self.net.append( + nn.Dense(self.layer_size[i], self.layer_size[i+1], activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + + def build_after(self): + # FC - activation - BN or LN + self.net.append( + nn.Dense(self.layer_size[0], self.layer_size[1], activation=ops.Sin())) + if self.batch_normalization is not None: + self.net.append(nn.BatchNorm1d(self.layer_size[1])) + elif self.layer_normalization is not None: + self.net.append(nn.LayerNorm((self.layer_size[1],))) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + for i in range(1, len(self.layer_size)-2): + self.net.append( + nn.Dense(self.layer_size[i], self.layer_size[i+1], activation=ops.Sin())) + if self.batch_normalization is not None: + self.net.append(nn.BatchNorm1d(self.layer_size[i+1])) + elif self.layer_normalization is not None: + self.net.append(nn.LayerNorm((self.layer_size[i+1],))) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + self.net.append(nn.Dense(self.layer_size[-2], self.layer_size[-1])) + + +class attention(nn.Cell): + """ + feed-forward neural network with attention-like architecture. + """ + + def __init__(self, + layer_size, + activation, + kernel_initializer, + dropout_rate=0.0, + batch_normalization=None, + layer_normalization=None, + input_transform=None, + output_transform=None, + use_bias=True, + print_net=False): + super().__init__() + self.layer_size = layer_size + self.activation = activation + self.initializer = kernel_initializer + self.dropout_rate = dropout_rate + self.batch_normalization = batch_normalization + self.layer_normalization = layer_normalization + self.input_transform = input_transform + self.output_transform = output_transform + self.use_bias = use_bias + # build neural networks + if self.batch_normalization and self.layer_normalization: + raise ValueError( + "cannot apply batch normalization and layer normalization at the same time") + self.net = nn.CellList() + if (self.batch_normalization is None) and (self.layer_normalization is None): + self.build_standard() + elif self.batch_normalization == "before": + self.build_beforeBN() + elif self.layer_normalization == "before": + self.build_beforeLN() + elif self.batch_normalization == "after": + self.build_afterBN() + elif self.layer_normalization == "after": + self.build_afterLN() + else: + raise ValueError("Neural net was not built") + # initialize parameters + self.net.init_parameters_data() + self.U.init_parameters_data() + self.V.init_parameters_data() + if print_net: + print(self.net) + print(self.U) + print(self.V) + + def construct(self, y): + """ + FNN forward pass + Args: + :y (Tensor): \in [B, d_in] + Returns: + :y (Tensor): \in [B, d_out] + """ + if self.input_transform is not None: + y = self.input_transform(y) + u = self.U(y) + v = self.V(y) + + for i in range(len(self.net)-1): + y = self.net[i](y) + y = (1 - y) * u + y * v + y = self.net[-1](y) + if self.output_transform is not None: + y = self.output_transform(y) + return y + + def build_standard(self): + # build U and V nets + self.U = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.V = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.net = nn.CellList() + for k in range(len(self.layer_size)-2): + self.net.append(nn.Dense(self.layer_size[k], self.layer_size[k+1], + has_bias=self.use_bias, activation=ops.Sin())) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + # output layer + self.net.append( + nn.Dense(self.layer_size[-2], self.layer_size[-1], has_bias=self.use_bias)) + + def build_beforeBN(self): + # FC -BN - activation + # build U and V nets + self.U = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + nn.BatchNorm1d(self.layer_size[1]), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.V = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + nn.BatchNorm1d(self.layer_size[1]), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.net = nn.CellList() + for k in range(len(self.layer_size)-2): + self.net.append( + nn.Dense(self.layer_size[k], self.layer_size[k+1], has_bias=self.use_bias)) + self.net.append(nn.BatchNorm1d(self.layer_size[k+1])) + self.net.append(get_activation(self.activation)) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + # output layer + self.net.append( + nn.Dense(self.layer_size[-2], self.layer_size[-1], has_bias=self.use_bias)) + + def build_afterBN(self): + # FC - activation -BN + # build U and V nets + self.U = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.BatchNorm1d(self.layer_size[1]), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.V = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.BatchNorm1d(self.layer_size[1]), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.net = nn.CellList() + for k in range(len(self.layer_size)-2): + self.net.append(nn.Dense(self.layer_size[k], self.layer_size[k+1], + has_bias=self.use_bias, activation=ops.Sin())) + self.net.append(nn.BatchNorm1d(self.layer_size[k+1])) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + # output layer + self.net.append( + nn.Dense(self.layer_size[-2], self.layer_size[-1], has_bias=self.use_bias)) + + def build_beforeLN(self): + # FC - LN - activation + # build U and V nets + self.U = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + nn.LayerNorm((self.layer_size[1],)), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.V = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + nn.LayerNorm((self.layer_size[1],)), + get_activation(self.activation), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.net = nn.CellList() + for k in range(len(self.layer_size)-2): + self.net.append( + nn.Dense(self.layer_size[k], self.layer_size[k+1], has_bias=self.use_bias)) + self.net.append(nn.LayerNorm((self.layer_size[k+1],))) + self.net.append(get_activation(self.activation)) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + # output layer + self.net.append( + nn.Dense(self.layer_size[-2], self.layer_size[-1], has_bias=self.use_bias)) + + def build_afterLN(self): + # FC - activation - LN + # build U and V nets + self.U = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.LayerNorm((self.layer_size[1],)), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.V = nn.SequentialCell([ + nn.Dense(self.layer_size[0], + self.layer_size[1], has_bias=self.use_bias), + get_activation(self.activation), + nn.LayerNorm((self.layer_size[1],)), + nn.Dropout( + keep_prob=1 - self.dropout_rate) if self.dropout_rate > 0 else nn.Identity() + ]) + self.net = nn.CellList() + for k in range(len(self.layer_size)-2): + self.net.append(nn.Dense(self.layer_size[k], self.layer_size[k+1], + has_bias=self.use_bias, activation=ops.Sin())) + self.net.append(nn.LayerNorm((self.layer_size[k+1],))) + if self.dropout_rate > 0.0: + self.net.append(nn.Dropout(keep_prob=1 - self.dropout_rate)) + # output layer + self.net.append( + nn.Dense(self.layer_size[-2], self.layer_size[-1], has_bias=self.use_bias)) + + +class DenseConv1D(nn.Cell): + """ + 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). + Basically works like a linear layer but the weights are transposed. + Args: + :inputs (int): number of input features. + :outputs (out): number of output features. + code adopted from: https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_utils.py + """ + + def __init__(self, inputs, outputs, activation=None): + super().__init__() + self.n_out = outputs + self.weight = ms.Parameter(Tensor(np.random.normal( + 0, 0.02, (inputs, outputs)), dtype=ms.float32)) + self.bias = ms.Parameter(Tensor(np.zeros(outputs), dtype=ms.float32)) + self.activation = activation + + def construct(self, x): + """ + forward pass + Args: + :x (Tensor): \in [batch_size, inputs] + Returns: + :(Tensor): \in [batch_size, outputs] + """ + size_out = x.shape[:-1] + (self.n_out,) + y = ops.add(ops.matmul( + x.view(-1, x.shape[-1]), self.weight), self.bias) + y = y.view(*size_out) + if self.activation is not None: + y = self.activation(y) + return y diff --git a/MindEnergy/application/DAE-PINNs/src/metrics.py b/MindEnergy/application/DAE-PINNs/src/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..7a49663277bf25776047f43bed7583bc5522f053 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/metrics.py @@ -0,0 +1,68 @@ +import numpy as np +from sklearn import metrics + +# numpy metrics + + +def accuracy(y_true, y_pred): + return np.mean(np.equal(np.argmax(y_pred, axis=-1), np.argmax(y_true, axis=-1))) + + +def l2_relative_error(y_true, y_pred): + return np.linalg.norm(y_true - y_pred) / np.linalg.norm(y_true) + + +def nanl2_relative_error(y_true, y_pred): + """Return the L2 relative error treating Not a Numbers (NaNs) as zero.""" + err = y_true - y_pred + err = np.nan_to_num(err) + y_true = np.nan_to_num(y_true) + return np.linalg.norm(err) / np.linalg.norm(y_true) + + +def _absolute_percentage_error(y_true, y_pred): + return 100 * np.abs( + (y_true - y_pred) / np.clip(np.abs(y_true), np.finfo(np.float32).eps, None) + ) + + +def mean_absolute_percentage_error(y_true, y_pred): + return np.mean(_absolute_percentage_error(y_true, y_pred)) + + +def max_absolute_percentage_error(y_true, y_pred): + return np.amax(_absolute_percentage_error(y_true, y_pred)) + + +def absolute_percentage_error_std(y_true, y_pred): + return np.std(_absolute_percentage_error(y_true, y_pred)) + + +def mean_squared_error_outlier(y_true, y_pred): + error = np.ravel((y_true - y_pred) ** 2) + error = np.sort(error)[: -len(error) // 1000] + return np.mean(error) + + +mean_squared_error = metrics.mean_squared_error + + +def get(identifier): + metric_identifier = { + "accuracy": accuracy, + "l2 relative error": l2_relative_error, + "nanl2 relative error": nanl2_relative_error, + "mean squared error": mean_squared_error, + "MSE": mean_squared_error, + "mse": mean_squared_error, + "MAPE": mean_absolute_percentage_error, + "max APE": max_absolute_percentage_error, + "APE SD": absolute_percentage_error_std, + "mse outlier": mean_squared_error_outlier, + } + if isinstance(identifier, str): + return metric_identifier[identifier] + elif callable(identifier): + return identifier + else: + raise ValueError("Could not interpret metric function identifier:", identifier) diff --git a/MindEnergy/application/DAE-PINNs/src/model.py b/MindEnergy/application/DAE-PINNs/src/model.py new file mode 100644 index 0000000000000000000000000000000000000000..cc1fa7de752bc7f737584fc8ee74aad8834574ff --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/model.py @@ -0,0 +1,132 @@ +# Copyright 2025 Huawei Technologies Co., Ltd +# +# 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. +# ============================================================================ +from mindspore import nn, ops +import numpy as np +from .layer import fnn, attention, Conv1D + + +def dyn_input_feature_layer(x): + return ops.cat((x, ops.cos(np.pi * x), ops.sin(np.pi * x), ops.cos(2 * np.pi * x), ops.sin(2 * np.pi * x)), axis=-1) + + +class three_bus_PN(nn.Cell): + def __init__(self, + dynamic, + algebraic, + use_input_layer=None, + stacked=False): + super().__init__() + self.stacked = stacked + self.dim = 4 + self.num_IRK_stages = dynamic.num_IRK_stages + if stacked: + num_layer = self.dim + else: + num_layer = 1 + dyn_in_transform = None if use_input_layer else dyn_input_feature_layer + dyn_out_transform = None + alg_out_transform = ops.Softplus() + alg_in_transform = None + if dynamic.type == "fnn": + self.Y = nn.CellList([ + fnn( + dynamic.layer_size, + dynamic.activation, + dynamic.initializer, + dropout_rate=dynamic.dropout_rate, + batch_normalization=dynamic.batch_normalization, + layer_normalization=dynamic.layer_normalization, + input_transform=dyn_in_transform, + output_transform=dyn_out_transform + ) for _ in range(num_layer) + ]) + + elif dynamic.type == "attention": + self.Y = nn.CellList([ + attention( + dynamic.layer_size, + dynamic.activation, + dynamic.initializer, + dropout_rate=dynamic.dropout_rate, + batch_normalization=dynamic.batch_normalization, + layer_normalization=dynamic.layer_normalization, + input_transform=dyn_in_transform, + output_transform=dyn_out_transform + ) for _ in range(num_layer) + ]) + + elif dynamic.type == "Conv1D": + self.Y = nn.CellList([ + Conv1D( + dynamic.layer_size, + dynamic.activation, + dropout_rate=dynamic.dropout_rate, + batch_normalization=dynamic.batch_normalization, + layer_normalization=dynamic.layer_normalization, + input_transform=dyn_in_transform, + output_transform=dyn_out_transform + ) for _ in range(num_layer) + ]) + + if algebraic.type == "fnn": + self.Z = fnn( + algebraic.layer_size, + algebraic.activation, + algebraic.initializer, + dropout_rate=algebraic.dropout_rate, + batch_normalization=algebraic.batch_normalization, + layer_normalization=algebraic.layer_normalization, + input_transform=alg_in_transform, + output_transform=alg_out_transform + ) + elif algebraic.type == "attention": + self.Z = attention( + algebraic.layer_size, + algebraic.activation, + algebraic.initializer, + dropout_rate=algebraic.dropout_rate, + batch_normalization=algebraic.batch_normalization, + layer_normalization=algebraic.layer_normalization, + input_transform=alg_in_transform, + output_transform=alg_out_transform + ) + elif algebraic.type == "Conv1D": + self.Z = Conv1D( + algebraic.layer_size, + algebraic.activation, + dropout_rate=algebraic.dropout_rate, + batch_normalization=algebraic.batch_normalization, + layer_normalization=algebraic.layer_normalization, + input_transform=alg_in_transform, + output_transform=alg_out_transform + ) + else: + raise ValueError(f"{algebraic.type} type on NN not implemented") + + def construct(self, inputs): + if self.stacked: + Y0 = self.Y[0](inputs) + Y1 = self.Y[1](inputs) + Y2 = self.Y[2](inputs) + Y3 = self.Y[3](inputs) + else: + dim_out = self.num_IRK_stages + 1 + Y = self.Y(inputs) + Y0 = Y[..., :dim_out] + Y1 = Y[..., dim_out:2 * dim_out] + Y2 = Y[..., 2 * dim_out:3 * dim_out] + Y3 = Y[..., 3 * dim_out:4 * dim_out] + Z = self.Z(inputs) + return Y0, Y1, Y2, Y3, Z diff --git a/MindEnergy/application/DAE-PINNs/src/plots.py b/MindEnergy/application/DAE-PINNs/src/plots.py new file mode 100644 index 0000000000000000000000000000000000000000..2ec407c1cd752d69fdd4b8bca9dcd1d9f7eb52f6 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/plots.py @@ -0,0 +1,394 @@ +import os +import matplotlib.pyplot as plt +from matplotlib import rc + +import numpy as np +from scipy.integrate import solve_ivp +from matplotlib.pyplot import plot + +from src.metrics import l2_relative_error + +# Set the global font and size +rc('font', **{'family': 'sans-serif', + 'sans-serif': ['DejaVu Sans'], 'size': 25}) +# Set the font used for math +rc('mathtext', **{'default': 'regular'}) + + +def power_net_dae_plot(t, x): + eps = 0.0001 + # parameters + m_1, m_2, d, d_d, b = .052, .0531, .05, .005, 10. + v_1, v_2, p_g, p_l, q_l = 1.02, 1.05, -2.0, 3.0, .1 + + w1, w2, d2, d3, v3 = x + + f_1 = b * v_1 * v_2 * np.sin(d2) + b * v_2 * v3 * np.sin(d2 - d3) + p_g + f_2 = b * v_1 * v3 * np.sin(d3) + b * v_2 * v3 * np.sin(d3 - d2) + p_l + g = 2 * b * (v3 ** 2) - b * v3 * v_1 * np.cos(d3) - \ + b * v3 * v_2 * np.cos(d3 - d2) + q_l + + F0 = (1 / m_1) * (- d * w1 + f_1 + f_2) + F1 = (1 / m_2) * (- d * w2 - f_1) + F2 = (w2 - w1) + F3 = (- w1 - (1 / d_d) * f_2) + F4 = (- (1 / (eps * v3)) * g) + + return F0, F1, F2, F3, F4 + + +def scipy_integrate(func, X0, h, IRK_times, method='BDF', N=0): + """ + integrates stiff power network dynamics using scipy for N time steps of size h + """ + V0 = 0.7 # we fix the volatge initial condition + t_span = [0.0, h * N] + t_sim = np.array([t_span[0]]) + for k in range(1, N + 1): + temp = (k - 1) * h + IRK_times * h + t_sim = np.vstack((t_sim, temp)) + t_next = np.array([k * h]) + t_sim = np.vstack((t_sim, t_next)) + del temp, t_next + sol = solve_ivp(func, t_span, [ + X0[0], X0[1], X0[2], X0[3], V0], method=method, t_eval=t_sim.reshape(-1,)) + y_test = sol.y + return t_sim[1:, :], y_test[:, 1:] + + +def stylize_axes(ax, size=25, legend=True, xlabel=None, ylabel=None, title=None, xticks=None, yticks=None, xticklabels=None, yticklabels=None, top_spine=True, right_spine=True): + """ + stylizes the axes of our plots. + """ + ax.spines['top'].set_visible(top_spine) + ax.spines['right'].set_visible(right_spine) + + ax.xaxis.set_tick_params(top='off', direction='out', width=1) + ax.yaxis.set_tick_params(right='off', direction='out', width=1) + + if title is not None: + ax.set_title(title) + + if xlabel is not None: + ax.set_xlabel(xlabel) + + if ylabel is not None: + ax.set_ylabel(ylabel) + + if xticks is not None: + ax.set_xticks(xticks) + + if yticks is not None: + ax.set_yticks(yticks) + + if xticklabels is not None: + ax.set_xticklabels(xticklabels) + + if yticklabels is not None: + ax.set_yticklabels(yticklabels) + + if legend: + ax.legend(fontsize=size) + return ax + + +def custom_logplot(ax, x, y, label="loss", xlims=None, ylims=None, color='red', linestyle='solid', marker=None): + """ + Customized plot with log scale on y axis. + """ + if marker is None: + ax.semilogy(x, y, color=color, label=label, linestyle=linestyle) + else: + ax.semilogy(x, y, color=color, label=label, + linestyle=linestyle, marker=marker) + if xlims is not None: + ax.set_xlim(xlims) + if ylims is not None: + ax.set_ylim(ylims) + return ax + + +def custom_scatterplot(ax, x, y, xlims=None, ylims=None, error=1.0, color='green', markerscale=10): + """ + Customized scatter plot where marker size is proportional to error measure. + """ + markersize = error * markerscale + ax.scatter(x, y, color=color, marker='o', s=markersize, alpha=0.5) + if xlims is not None: + ax.set_xlim(xlims) + if ylims is not None: + ax.set_ylim(ylims) + return ax + + +def custom_lineplot(ax, x, y, label=None, xlims=None, ylims=None, color="red", linestyle="solid", linewidth=2.0, marker=None): + """ + Customized line plot. + """ + if label is not None: + if marker is None: + ax.plot(x, y, color=color, label=label, + linestyle=linestyle, linewidth=linewidth) + else: + ax.plot(x, y, color=color, label=label, linestyle=linestyle, + linewidth=linewidth, marker=marker) + else: + if marker is None: + ax.plot(x, y, color=color, linestyle=linestyle, linewidth=linewidth) + else: + ax.plot(x, y, color=color, linestyle=linestyle, + linewidth=linewidth, marker=marker) + if xlims is not None: + ax.set_xlim(xlims) + if ylims is not None: + ax.set_ylim(ylims) + return ax + + +def custom_barchart(ax, x, y, error, xlims=None, ylims=None, color='blue', width=1.0, label=None): + """ + Customized bar chart with positive error bars only. + """ + error_kw = {'capsize': 5, 'capthick': 1, 'ecolor': 'black'} + error = [np.zeros(len(error)), error] + + ax.bar(x, y, color=color, width=width, yerr=error, + error_kw=error_kw, align='center', label=label) + + if xlims is not None: + ax.set_xlim(xlims) + if ylims is not None: + ax.set_ylim(ylims) + + return ax + + +def custom_loglogplot(ax, x, y, label="loss", xlims=None, ylims=None, color='red', linestyle='solid', marker=None): + """ + Customized plot with log scale on both axis + """ + if marker is None: + ax.loglog(x, y, color=color, label=label, linestyle=linestyle) + else: + ax.loglog(x, y, color=color, label=label, + linestyle=linestyle, marker=marker) + + if xlims is not None: + ax.set_xlim(xlims) + if ylims is not None: + ax.set_ylim(ylims) + return ax + + +def plot_loss_history(loss_history, fname="./logs/loss.png", size=25, figsize=(8, 6)): + """ + plots the loss history. + """ + loss_train = np.array(loss_history.loss_train) + loss_test = np.array(loss_history.loss_test) + + fig, ax = plt.subplots(figsize=figsize) + custom_logplot(ax, loss_history.steps, loss_train, + label="Train loss", color='blue', linestyle='solid') + custom_logplot(ax, loss_history.steps, loss_test, + label="Train loss", linestyle='dashed') + stylize_axes(ax, size=size, xlabel="No. of iterations", ylabel="M.s.e.") + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_three_bus(t, y_eval, y_pred, fname="./logs/test-trajectory.png", size=25, figsize=(8, 6)): + """ + plots the exact and predicted power network trajectories. + """ + t = t.reshape(-1,) + ylims = [None, None, None, None, (0.2, 1.2)] + xlabel = [None, None, None, None, 'time (s)'] + ylabel = ['$\omega_1(t)$', '$\omega_2(t)$', + '$\delta_2(t)$', '$\delta_3(t)$', '$V_3(t)$'] + fig, ax = plt.subplots(nrows=5, ncols=1, figsize=figsize) + for i in range(5): + custom_lineplot(ax[i], t, y_eval[i, ...].reshape(-1,), + label="Exact", ylims=ylims[i]) + custom_lineplot(ax[i], t, y_pred[i, ...].reshape(-1,), color="blue", + linestyle="dashed", label="Predicted", ylims=ylims[i]) + stylize_axes(ax[i], size=size, xlabel=xlabel[i], ylabel=ylabel[i]) + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_regression(predicted, y, fname="./log/regression.png", size=20, figsize=(8, 6), x_line=None, y_line=None): + """ + Parity plot. + """ + predicted = predicted.reshape(-1,) + y = y.reshape(-1,) + if x_line is None: + x_line = [y.min(), y.max()] + y_line = [y.min(), y.max()] + + fig, ax = plt.subplots(figsize=figsize) + custom_lineplot(ax, x_line, y_line, color="yellow", + linestyle="dashed", linewidth=3.0) + custom_scatterplot(ax, predicted, y, color='blue', markerscale=10) + stylize_axes(ax, size=size, xlabel="Predicted", + ylabel="Exact", legend=False) + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_barchart(train, test, fname="./log/regression.png", size=20, figsize=(8, 6)): + """ + Plots a a bar chart. + """ + # train \in [num, 2] + # test \in [num, 2] + + train = train.reshape(-1, 2) + test = test.reshape(-1, 2) + + mean_train = train.mean(axis=0) + mean_test = test.mean(axis=0) + error_train = train.std(axis=0) + error_test = test.std(axis=0) + print("mean train...", mean_train) + print("mean_test...", mean_test) + print("error_train...", error_train) + print("error_test...", error_test) + width = .25 + + x = np.arange(len(mean_train)) + fig, ax = plt.subplots(figsize=figsize) + custom_barchart(ax, x, mean_train, error_train, + color='blue', width=width, label='Train') + custom_barchart(ax, x + width, mean_test, error_test, + color='red', width=width, label='Test') + xticks = x + width/2 + xticklabels = ['Stacked', 'Unstacked'] + stylize_axes(ax, size=size, ylabel="M.s.e.", xticks=xticks, + xticklabels=xticklabels, legend=True) + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_width_analysis(width, train, test, fname="./log/width_analysis.png", size=20, figsize=(8, 6)): + """ + Plots losses as a function of the nn width. + """ + train = train.reshape(-1,) + test = test.reshape(-1,) + + fig, ax = plt.subplots(figsize=figsize) + custom_loglogplot(ax, width, train, linestyle='dashed', + marker="s", color='red', label='Train') + custom_loglogplot(ax, width, test, linestyle='dashed', + marker="o", color='blue', label='Test') + stylize_axes(ax, size=size, xlabel="Width", ylabel="M.s.e") + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_depth_analysis(depth, train, test, fname="./log/width_analysis.png", size=20, figsize=(8, 6)): + """ + Plots losses as a function of the nn depth. + """ + train = train.reshape(-1,) + test = test.reshape(-1,) + + fig, ax = plt.subplots(figsize=figsize) + custom_logplot(ax, depth, train, linestyle='dashed', + marker="s", color='red', label='Train') + custom_logplot(ax, depth, test, linestyle='dashed', + marker="o", color='blue', label='Test') + stylize_axes(ax, size=size, xlabel="Depth", ylabel="M.s.e") + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_num_train_analysis(num_train, train, test, fname="./log/num_train.png", size=20, figsize=(8, 6)): + """ + Plots losses as a function of the number of training examples. + """ + train = train.reshape(-1,) + test = test.reshape(-1,) + + fig, ax = plt.subplots(figsize=figsize) + custom_loglogplot(ax, num_train, train, linestyle='dashed', + marker="s", color='red', label='Train') + custom_loglogplot(ax, num_train, test, linestyle='dashed', + marker="o", color='blue', label='Test') + stylize_axes(ax, size=size, xlabel="No. of training examples", + ylabel="M.s.e") + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def plot_L2relative_error(N, error, fname="./log/num_train.png", size=20, figsize=(8, 6)): + """ + Plots the L_2-relative error as a function of the number of integration steps. + """ + error = error.reshape(-1,) + + fig, ax = plt.subplots(figsize=figsize) + custom_lineplot(ax, N, error, color="blue", + linestyle="dashed", linewidth=3.0, marker='s') + stylize_axes(ax, size=size, xlabel="No. of time steps $N$", + ylabel="L$_2$-relative error", legend=False) + + fig.tight_layout() + fig.savefig(fname, dpi=300, bbox_inches='tight', transparent=True) + + +def visualize(trainer, summary_dir, ode_params): + # Test one trajectory + X0 = [0., 0., .1, .1] + X0_npy = np.array(X0).astype(np.float32) + h, N, method = ode_params['h'], ode_params['N'], ode_params['method'] + y_pred = trainer.integrate( + X0_npy, N=N, dyn_state_dim=4, model_restore_path=None) + + IRK_times = trainer.IRK_times + t, y_eval = scipy_integrate( + power_net_dae_plot, X0, h, IRK_times, method, N) + print("plotting trajectory...\n") + plot_three_bus(t, y_eval, y_pred, fname=os.path.join( + summary_dir, 'trajectories.png'), size=25, figsize=(16, 24)) + + # compute metrics for long-time integration + l2_error = [] + for i in range(y_eval.shape[0]): + l2_error.append(l2_relative_error(y_pred[i, ...], y_eval[i, ...])) + print("L2relative error:", l2_error[i]) + + # compute the L_2 relative error as a function of the number of time steps + error_data = np.empty((N, 5)) + for k in range(1, N+1): + y_pred_k = trainer.integrate( + X0_npy, N=k, dyn_state_dim=4, model_restore_path=None) + _, y_eval_k = scipy_integrate( + power_net_dae_plot, X0, h, IRK_times, N=k) + for i in range(5): + error_data[k-1, i] = l2_relative_error(y_pred_k[i, ...], y_eval_k[i, ...]) + + # plot L2 relative error for dynamic and algebraic variables + N_vec = np.arange(1, N + 0.1) + for k in range(5): + fname_k = f'L2relative_error_{k}.png' + fname = os.path.join(summary_dir, fname_k) + plot_L2relative_error( + N_vec, error_data[:, k], fname=fname, size=20, figsize=(8, 6)) + + # save data for future use + np.savez(os.path.join(summary_dir, "L2Relative_error"), + N=N_vec, error=error_data) + + # regression plot for voltage + x_line = [-.5, .5] + y_line = [-.5, .5] + plot_regression(y_pred[-2, ...], y_eval[-2, ...], fname=os.path.join(summary_dir, + 'regression-voltage.png'), size=20, figsize=(8, 6), x_line=x_line, y_line=y_line) diff --git a/MindEnergy/application/DAE-PINNs/src/trainer.py b/MindEnergy/application/DAE-PINNs/src/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..4aa61c888e499b776901df7bf9d29d1c8a75e682 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/trainer.py @@ -0,0 +1,113 @@ +import os +import numpy as np +from mindspore import ops, Tensor, load_param_into_net, load_checkpoint + + +def power_net_dae(yn, pred, h, IRK_weights): + T = 1.0 + # parameters + M_1, M_2, D, D_d, b = .052, .0531, .05, .005, 10. + V_1, V_2, P_g, P_l, Q_l = 1.02, 1.05, -2.0, 3.0, .1 + # pinn + w1, w2, d2, d3, v3 = pred + + xi_w1 = w1[..., :-1] + xi_w2 = w2[..., :-1] + xi_d2 = d2[..., :-1] + xi_d3 = d3[..., :-1] + zeta_v3 = v3[..., :-1] + + f_1 = b * V_1 * V_2 * ops.sin(xi_d2) + b * \ + V_2 * zeta_v3 * ops.sin(xi_d2 - xi_d3) + P_g + f_2 = b * V_1 * zeta_v3 * \ + ops.sin(xi_d3) + b * V_2 * zeta_v3 * ops.sin(xi_d3 - xi_d2) + P_l + + # compute dynamic residuals + F0 = T * (1 / M_1) * (- D * xi_w1 + f_1 + f_2) + F1 = T * (1 / M_2) * (- D * xi_w2 - f_1) + F2 = T * (xi_w2 - xi_w1) + + F3 = T * (- xi_w1 - (1 / D_d) * f_2) + + f0 = yn[..., 0:1] - (w1 - h*F0.mm(IRK_weights.T)) + f1 = yn[..., 1:2] - (w2 - h*F1.mm(IRK_weights.T)) + f2 = yn[..., 2:3] - (d2 - h*F2.mm(IRK_weights.T)) + f3 = yn[..., 3:4] - (d3 - h*F3.mm(IRK_weights.T)) + + # compute algebrtaic residuals + G = 2 * b * (v3 ** 2) - b * v3 * V_1 * ops.cos(d3) - \ + b * v3 * V_2 * ops.cos(d3 - d2) + Q_l + g = - (T/v3) * G + + return [f0, f1, f2, f3], [g] + + +def MSE(y_pred, y_true=None): + """ + computes MSE error. + """ + if y_true is None: + return ops.mean(y_pred ** 2) + else: + return ops.mean((y_pred - y_true) ** 2) + + +class DaeTrainer: + def __init__(self, net, irk_dir, num_IRK_stages=100, h=0.1, dyn_weight=64.0, alg_weight=1.0): + self.net = net + self.optimizer = None + self.scheduler = None + self.batch_size = None + self.stop_training = False + self.events = None + self.nu = num_IRK_stages + self.h = Tensor([h]) + # collecting RK data + tmp = np.loadtxt(os.path.join(irk_dir, f'Butcher_IRK{self.nu}.txt'), ndmin=2, dtype=np.float32) + IRK_weights = np.reshape( + tmp[0:self.nu**2+self.nu], (self.nu+1, self.nu)) + self.IRK_weights = Tensor(IRK_weights) # \in [nu + 1, nu] + self.IRK_times = tmp[self.nu**2 + self.nu:] # \in [nu, 1] + self.loss_weights = [dyn_weight, alg_weight] + + def get_pred(self, x): + return self.net(x) + + def sum_loss(self, loss_list): + for k, weight in enumerate(self.loss_weights): + loss_list[k] *= weight + return sum(loss_list) + + def get_loss(self, inputs): + """ compute losses """ + loss_list = [] + pred = self.get_pred(inputs) + f, g = power_net_dae(inputs, pred, self.h, self.IRK_weights) + # losses from dynamic equations + losses_dyn = [MSE(fi) for fi in f] + loss_list.append(sum(losses_dyn)) + # losses from algebraic/perturbed/stiff equations + losses_alg = [MSE(gi) for gi in g] + loss_list.append(sum(losses_alg)) + return self.sum_loss(loss_list) + + def predict(self, inputs, model_restore_path=None): + if model_restore_path is not None: + param_dict = load_checkpoint(model_restore_path) + load_param_into_net(self.net, param_dict) + self.net.set_train(False) + inputs = Tensor(inputs) + vel1, vel2, ang2, ang3, v3 = self.net(inputs) + y_pred = np.vstack((vel1.numpy(), vel2.numpy(), + ang2.numpy(), ang3.numpy(), v3.numpy())) + return y_pred + + def integrate(self, X0, N=1, dyn_state_dim=4, model_restore_path=None): + yn = X0 + soln = [] + for _ in range(N): + y_pred_n = self.predict(yn.reshape( + 1, -1), model_restore_path=model_restore_path) + soln.append(y_pred_n) + yn = y_pred_n[:dyn_state_dim, -1] + return np.hstack(soln) diff --git a/MindEnergy/application/DAE-PINNs/src/utils.py b/MindEnergy/application/DAE-PINNs/src/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1cbf2f12ad137da1b2ef6d1e483c83f2f66cea98 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/src/utils.py @@ -0,0 +1,77 @@ +import time +import sys +from functools import wraps + +import numpy as np + + +def timing(f): + """ + Decorator for measuring the execution time of methods. + """ + @wraps(f) + def wrapper(*args, **kwargs): + ts = time.time() + result = f(*args, **kwargs) + te = time.time() + print("%r took %f s\n" % (f.__name__, te - ts)) + sys.stdout.flush() + return result + return wrapper + + +class dotdict(dict): + """ + dot.notation access to dictionary attributes. + """ + __getattr__ = dict.get + __setattr__ = dict.__setitem__ + __delattr__ = dict.__delitem__ + + +def list_to_str(nums, precision=3): + """ + list to str for displaying errors and metrics. + """ + if nums is None: + return "" + if not isinstance(nums, (list, tuple, np.ndarray)): + return "{:.{}e}".format(nums, precision) + return "[{:s}]".format(", ".join(["{:.{}e}".format(x, precision) for x in nums])) + + +def make_config(model_params): + dynamic = dotdict() + dynamic.num_IRK_stages = model_params['num_IRK_stages'] + dynamic.state_dim = 4 + dynamic.activation = model_params['dyn_activation'] + dynamic.initializer = "Glorot normal" + dynamic.dropout_rate = 0 + dynamic.batch_normalization = None if model_params['dyn_bn'] == "no-bn" else model_params['dyn_bn'] + dynamic.layer_normalization = None if model_params['dyn_ln'] == "no-ln" else model_params['dyn_ln'] + dynamic.type = model_params['dyn_type'] + + if model_params['unstacked']: + dim_out = dynamic.state_dim * (dynamic.num_IRK_stages + 1) + else: + dim_out = dynamic.num_IRK_stages + 1 + + if model_params['use_input_layer']: + dynamic.layer_size = [dynamic.state_dim * 5] + \ + [model_params['dyn_width']] * model_params['dyn_depth'] + [dim_out] + else: + dynamic.layer_size = [dynamic.state_dim] + \ + [model_params['dyn_width']] * model_params['dyn_depth'] + [dim_out] + + algebraic = dotdict() + algebraic.num_IRK_stages = model_params['num_IRK_stages'] + dim_out_alg = algebraic.num_IRK_stages + 1 + algebraic.layer_size = [dynamic.state_dim] + \ + [model_params['alg_width']] * model_params['alg_depth'] + [dim_out_alg] + algebraic.activation = model_params['alg_activation'] + algebraic.initializer = "Glorot normal" + algebraic.dropout_rate = 0 + algebraic.batch_normalization = None if model_params['alg_bn'] == "no-bn" else model_params['alg_bn'] + algebraic.layer_normalization = None if model_params['alg_ln'] == "no-ln" else model_params['alg_ln'] + algebraic.type = model_params['alg_type'] + return dynamic, algebraic diff --git a/MindEnergy/application/DAE-PINNs/train.py b/MindEnergy/application/DAE-PINNs/train.py new file mode 100644 index 0000000000000000000000000000000000000000..687996b9544d55ac7e71ec093f1740c65373cdd8 --- /dev/null +++ b/MindEnergy/application/DAE-PINNs/train.py @@ -0,0 +1,114 @@ +import time +import os +import argparse + +from mindspore import ops, jit +from mindspore import context +from mindspore.experimental import optim +import numpy as np + +from mindscience.utils import load_yaml_config + +from src.utils import dotdict, make_config +from src.model import three_bus_PN +from src.data import get_dataset +from src.trainer import DaeTrainer + + +def main(args): + print("starting...\n") + # create log dir + os.makedirs(summary_dir, exist_ok=True) + config = load_yaml_config(args.config_file) + model_params, data_params, optim_params, ode_params, summary_params = config[ + 'model'], config['data'], config['optimizer'], config['ode'], config['summary'] + summary_dir = summary_params['summary_dir'] + + + dynamic, algebraic = make_config(model_params) + net = three_bus_PN( + dynamic, + algebraic, + use_input_layer=model_params['use_input_layer'] + stacked=not model_params['unstacked'], + ) + + np.random.seed(1234) + + train_dataset, test_dataset, val_dataset = get_dataset(data_params) + trainer = DaeTrainer(net, irk_dir=data_params['IRK_dir'], num_IRK_stages=model_params['num_IRK_stages'], + h=ode_params['h'], dyn_weight=model_params['dyn_weight'], + alg_weight=model_params['alg_weight']) + + optimizer = optim.Adam(net.trainable_params(), lr=optim_params['lr']) + scheduler_type = optim_params['scheduler_type'] + use_scheduler = optim_params['use_scheduler'] + if use_scheduler: + if scheduler_type == "plateau": + scheduler = optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode='min', + patience=optim_params['patience'], + factor=optim_params['factor'], + ) + elif scheduler_type == "step": + scheduler = optim.lr_scheduler.StepLR( + optimizer, step_size=optim_params['patience'], gamma=optim_params['factor'] + ) + else: + scheduler = None + else: + scheduler = None + + def forward_fn(x): + loss = trainer.get_loss(x) + return loss + + grad_fn = ops.value_and_grad( + forward_fn, None, optimizer.parameters, has_aux=False) + + @jit + def train_step(x): + loss, grads = grad_fn(x) + optimizer(grads) + return loss + test_interval = summary_params['test_interval'] + + for epoch in range(1, 1 + optim_params['epochs']): + time_beg = time.time() + net.set_train(True) + loss_val = [] + for data, in train_dataset: + step_train_loss = train_step(data) + loss_val.append(step_train_loss.numpy()) + time_end = time.time() + + print( + f"epoch: {epoch} train loss: {np.mean(loss_val)} epoch time: {time_end-time_beg:.3f}s") + if use_scheduler: + if scheduler_type == "plateau": + net.set_train(False) + loss_val = trainer.get_loss(val_dataset) + scheduler.step(loss_val) + else: + scheduler.step() + if epoch % test_interval == 0: + net.set_train(False) + loss_test = trainer.get_loss(test_dataset) + print(f'test loss: {loss_test}') + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="dae-pinns-example") + parser.add_argument('--device_id', type=int, default=1) + parser.add_argument('--method', type=str, default='BDF', + help="integration method") + parser.add_argument('--config_file', type=str, default='config.yaml', + help="config yaml path") + parser.add_argument('--mode', type=str, default='PYNATIVE', + help="mindspore context mode") + args = parser.parse_args() + mode = context.PYNATIVE_MODE if args.mode.lower().startswith('graph') else context.GRAPH_MODE + context.set_context(mode=mode, device_target=args.target, + device_id=args.device_id) + main(args)