{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# 基于GraphCast中期降水模块\n", "\n", "[](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindearth/zh_cn/medium-range/mindspore_graphcast_tp.ipynb) [](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/master/mindearth/zh_cn/medium-range/mindspore_graphcast_tp.py) [](https://gitee.com/mindspore/docs/blob/master/docs/mindearth/docs/source_zh_cn/medium-range/graphcast_tp.ipynb)\n", "\n", "## 概述\n", "\n", "本模块基于GraphCast预训练模型,在下游承接可训练的GraphCast backbone进行微调,最终实现中期降水的预报。模型框架可见下图\n", "\n", "\n", "\n", "## 技术路径\n", "\n", "中期降水模型具体流程如下:\n", "\n", "1. 创建数据集\n", "2. 模型构建\n", "3. 损失函数\n", "4. 模型训练\n", "5. 模型评估与可视化\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "训练和测试所用数据集可以在: [dataset](https://download-mindspore.osinfra.cn/mindscience/mindearth/dataset/medium_precipitation/tiny_datasets/)下载" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from mindspore import set_seed\n", "from mindspore import context, ops\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "下述`src`可以在[graphcast/src](https://gitee.com/mindspore/mindscience/tree/master/MindEarth/applications/medium-range/graphcast/src)下载" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from mindearth.utils import load_yaml_config\n", "from mindearth.data import Dataset\n", "\n", "from src import get_coe, get_logger, init_tp_model\n", "from src import LossNet, GraphCastTrainerTp, CustomWithLossCell, InferenceModuleTp\n", "from src import Era5DataTp" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "set_seed(0)\n", "np.random.seed(0)\n", "random.seed(0)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "模型涉及的参数、优化器、数据配置见configs。执行降水代码时,[GraphCastTp.yaml](https://gitee.com/mindspore/mindscience/blob/master/MindEarth/applications/medium-range/graphcast/configs/GraphCastTp.yaml)文件中的`tp`需要设置为`True`。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "config = load_yaml_config(\"./GraphCastTp.yaml\")\n", "context.set_context(mode=context.GRAPH_MODE, device_target=\"Ascend\", device_id=5)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 创建数据集\n", "\n", "在[dataset](https://download-mindspore.osinfra.cn/mindscience/mindearth/dataset/medium_precipitation/tiny_datasets/)路径下,下载正则化参数、训练数据集到`./dataset`目录。\n", "修改`./configs/GraphCastTp.yaml`配置文件中的`root_dir`以及`tp_dir`参数,这两个参数分别设置了数据集和降水标签的路径。\n", "`./dataset`中的目录结构如下所示:\n", "\n", "```text\n", "├── statistic\n", "│ ├── mean.npy\n", "│ ├── mean_s.npy\n", "│ ├── std.npy\n", "│ └── std_s.npy\n", "│ └── climate_0.5_tp.npy\n", "├── train\n", "│ └── 2018\n", "├── train_static\n", "│ └── 2018\n", "├── train_surface\n", "│ └── 2018\n", "├── train_surface_static\n", "│ └── 2018\n", "├── valid\n", "│ └── 2021\n", "├── valid_static\n", "│ └── 2021\n", "├── valid_surface\n", "│ └── 2021\n", "├── valid_surface_static\n", "│ └── 2021\n", "```\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 模型构建\n", "\n", "若是训练代码,设置`run_mode='train'`,还需要一个训练好的`GraphCast`模型ckpt,可以在这里[ckpt](https://download-mindspore.osinfra.cn/mindscience/mindearth/dataset/medium_precipitation/tiny_datasets/ckpt/)下载,ckpt的路径在`./GraphCastTp.yaml`中的`backbone_ckpt_path`配置。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2023-12-05 06:21:28,126 - utils.py[line:165] - INFO: {'name': 'GraphCastTp', 'latent_dims': 512, 'processing_steps': 10, 'recompute': True, 'vm_in_channels': 3, 'em_in_channels': 4, 'eg2m_in_channels': 4, 'em2g_in_channels': 4}\n", "2023-12-05 06:21:28,126 - utils.py[line:165] - INFO: {'name': 'GraphCastTp', 'latent_dims': 512, 'processing_steps': 10, 'recompute': True, 'vm_in_channels': 3, 'em_in_channels': 4, 'eg2m_in_channels': 4, 'em2g_in_channels': 4}\n", "2023-12-05 06:21:28,127 - utils.py[line:165] - INFO: {'name': 'era5', 'root_dir': './dataset_tp', 'feature_dims': 69, 'pressure_level_num': 13, 'data_sink': False, 'batch_size': 1, 't_in': 1, 't_out_train': 1, 't_out_valid': 20, 't_out_test': 20, 'train_interval': 1, 'valid_interval': 1, 'test_interval': 1, 'pred_lead_time': 6, 'data_frequency': 6, 'train_period': [2018, 2018], 'valid_period': [2021, 2021], 'test_period': [2022, 2022], 'patch': False, 'rollout_steps': 1, 'num_workers': 1, 'mesh_level': 5, 'grid_resolution': 0.5, 'tp': True, 'tp_dir': './dataset_tp/tp_log_data', 'h_size': 360, 'w_size': 720}\n", "2023-12-05 06:21:28,127 - utils.py[line:165] - INFO: {'name': 'era5', 'root_dir': './dataset_tp', 'feature_dims': 69, 'pressure_level_num': 13, 'data_sink': False, 'batch_size': 1, 't_in': 1, 't_out_train': 1, 't_out_valid': 20, 't_out_test': 20, 'train_interval': 1, 'valid_interval': 1, 'test_interval': 1, 'pred_lead_time': 6, 'data_frequency': 6, 'train_period': [2018, 2018], 'valid_period': [2021, 2021], 'test_period': [2022, 2022], 'patch': False, 'rollout_steps': 1, 'num_workers': 1, 'mesh_level': 5, 'grid_resolution': 0.5, 'tp': True, 'tp_dir': './dataset_tp/tp_log_data', 'h_size': 360, 'w_size': 720}\n", "2023-12-05 06:21:28,129 - utils.py[line:165] - INFO: {'name': 'adamw', 'initial_lr': 0.000125, 'finetune_lr': 3e-07, 'finetune_epochs': 1, 'warmup_epochs': 1, 'weight_decay': 0.1, 'gamma': 0.5, 'epochs': 100}\n", "2023-12-05 06:21:28,129 - utils.py[line:165] - INFO: {'name': 'adamw', 'initial_lr': 0.000125, 'finetune_lr': 3e-07, 'finetune_epochs': 1, 'warmup_epochs': 1, 'weight_decay': 0.1, 'gamma': 0.5, 'epochs': 100}\n", "2023-12-05 06:21:28,131 - utils.py[line:165] - INFO: {'summary_dir': 'GraphCastTp_latent_dims_512_processing_steps_10_recompute_True_vm_in_channels_3_em_in_channels_4_eg2m_in_channels_4_em2g_in_channels_4_adamw_oop', 'eval_interval': 10, 'save_checkpoint_steps': 5, 'keep_checkpoint_max': 10, 'save_rmse_acc': False, 'plt_key_info': True, 'key_info_timestep': [6, 72, 120], 'ckpt_path': '', 'backbone_ckpt_path': './dataset_tp/ckpt/GraphCast-device0-1_2008.ckpt'}\n", "2023-12-05 06:21:28,131 - utils.py[line:165] - INFO: {'summary_dir': 'GraphCastTp_latent_dims_512_processing_steps_10_recompute_True_vm_in_channels_3_em_in_channels_4_eg2m_in_channels_4_em2g_in_channels_4_adamw_oop', 'eval_interval': 10, 'save_checkpoint_steps': 5, 'keep_checkpoint_max': 10, 'save_rmse_acc': False, 'plt_key_info': True, 'key_info_timestep': [6, 72, 120], 'ckpt_path': '', 'backbone_ckpt_path': './dataset_tp/ckpt/GraphCast-device0-1_2008.ckpt'}\n", "2023-12-05 06:21:28,132 - utils.py[line:165] - INFO: {'name': 'oop', 'distribute': False, 'mixed_precision': True, 'amp_level': 'O2', 'load_ckpt': False}\n", "2023-12-05 06:21:28,132 - utils.py[line:165] - INFO: {'name': 'oop', 'distribute': False, 'mixed_precision': True, 'amp_level': 'O2', 'load_ckpt': False}\n" ] } ], "source": [ "model = init_tp_model(config, run_mode='train')\n", "logger = get_logger(config)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 损失函数\n", "\n", "LP Loss, 考虑相对误差损失。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "sj_std, wj, ai = get_coe(config)\n", "data_params = config.get('data')\n", "loss_fn = LossNet(ai, wj, sj_std, data_params.get('feature_dims'), data_params['tp'])\n", "loss_cell = CustomWithLossCell(backbone=model, loss_fn=loss_fn, data_params=data_params)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "trainer = GraphCastTrainerTp(config, model, loss_cell, logger)\n", "trainer.train()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## 模型评估和可视化\n", "\n", "训练完成后我们使用第20个ckpt进行推理。" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 259200, 69)\n" ] } ], "source": [ "inference_module = InferenceModuleTp(model, config, logger)\n", "test_dataset_generator = Era5DataTp(data_params=data_params, run_mode='test')\n", "test_dataset = Dataset(test_dataset_generator, distribute=False,\n", " num_workers=data_params.get('num_workers'), shuffle=False)\n", "test_dataset = test_dataset.create_dataset(data_params.get('batch_size'))\n", "data = next(test_dataset.create_dict_iterator())\n", "inputs = data['inputs']\n", "labels = data['labels']\n", "print(inputs.shape)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 20, 360, 720) (1, 20, 360, 720)\n" ] } ], "source": [ "def unlog_trans(x, eps=1e-5):\n", " \"\"\"Inverse transformation of log(TP / epsilon + 1)\"\"\"\n", " return eps * (ops.exp(x) - 1)\n", "\n", "pred = inference_module.forecast(inputs)\n", "labels = unlog_trans(labels).asnumpy()\n", "pred = unlog_trans(pred).asnumpy()\n", "print(labels.shape, pred.shape)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def plt_comparison(pred, label, root_dir='./'):\n", " plt.subplot(1, 2, 1)\n", " plt.imshow(label, cmap='jet')\n", " plt.title('Truth')\n", " plt.xticks(np.arange(0, 721, 180), np.arange(-180, 181, 90))\n", " plt.xlabel('longitude')\n", " plt.yticks(np.arange(0, 361, 180), np.arange(-90, 91, 90))\n", " plt.ylabel('latitude')\n", " plt.subplot(1, 2, 2)\n", " plt.imshow(pred, cmap='jet')\n", " plt.title('pred')\n", " plt.xticks(np.arange(0, 721, 180), np.arange(-180, 181, 90))\n", " plt.xlabel('longitude')\n", " plt.yticks(np.arange(0, 361, 180), np.arange(-90, 91, 90))\n", " plt.savefig(f\"{root_dir}/tp_comparison.png\", dpi=150)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def trans_colorbar(data):\n", " ori = [0., 1., 10., 100]\n", " new_v = [0., 50., 75, 100.]\n", " trans = []\n", " for i in range(1, len(ori)):\n", " x = np.where((data > ori[i-1]) & (data <= ori[i]), (data - ori[i-1]) * (new_v[i] - new_v[i-1]) / (ori[i] - ori[i-1]) + new_v[i-1], 1.)\n", " trans.append(x)\n", " res = 1.\n", " for x in trans:\n", " res *= x\n", " return res\n", "\n", "plot_pred = trans_colorbar(pred[0, 1] * 1000)\n", "plot_labels = trans_colorbar(labels[0, 1] * 1000)\n", "plt_comparison(plot_pred, plot_labels) # 左图是真实的降水分布图,右图是模型预测的结果。" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.16 ('ci_mindearth_zcs')", "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": "3.7.16" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "c7ead8612f1385c63cf2d90a430010e67c9b8b5349767eabffab14772abf5010" } } }, "nbformat": 4, "nbformat_minor": 2 }