feat(domino): support domino for training and test

Author: xiaoyewww

examples/domino/README.md

@@ -0,0 +1,43 @@
+# DoMINO: Decomposable Multi-scale Iterative Neural Operator for External Aerodynamics
+
+DoMINO代码复现。
+
+## 安装依赖
+
+```shell
+# 安装PaddlePaddle
+
+cd /path/PaddleScience
+pip install -e .
+
+cd /path/PaddleScience/examples/domino
+pip install -r requirements.txt
+```
+
+## 数据集下载与处理
+
+1. 参考[DoMINO](https://github.com/NVIDIA/modulus/tree/main/examples/cfd/external_aerodynamics/domino#training-the-domino-model)数据下载和处理方式，执行`download_aws_dataset.sh`和`process_data.py`，获取数据。
+
+2. 本仓库训练数据为`process_data.py`的后处理数据。
+
+## 训练
+
+1. 修改`conf/config.yaml`路径, 原始配置文件参考[DoMINO](https://github.com/NVIDIA/modulus/blob/main/examples/cfd/external_aerodynamics/domino/src/conf/config.yaml)。
+
+2. 训练
+
+```shell
+cd /path/PaddleScience/examples/domino
+python train.py
+```
+
+## 推理
+
+1. 修改`conf/config.yaml`路径, 原始配置文件参考[DoMINO](https://github.com/NVIDIA/modulus/blob/main/examples/cfd/external_aerodynamics/domino/src/conf/config.yaml)。
+
+2. 推理
+
+```shell
+cd /path/PaddleScience/examples/domino
+python test.py
+```

examples/domino/conf/config.yaml

@@ -17,6 +17,7 @@
 project: # Project name
   name: AWS_Dataset
 
+seed: 42
 exp_tag: 1 # Experiment tag
 # Main output directory.
 output: outputs/${project.name}/${exp_tag}
@@ -27,8 +28,8 @@ hydra: # Hydra config
   output_subdir: hydra  # Default is .hydra which causes files not being uploaded in W&B.
 
 data: # Input directory for training and validation data
-  input_dir: /lustre/rranade/modulus_dev/data/volume_data/
-  input_dir_val: /lustre/rranade/modulus_dev/data/volume_data_val/
+  input_dir: /home/aistudio/modulus/examples/cfd/external_aerodynamics/domino/outputs/volume_data/
+  input_dir_val: /home/aistudio/modulus/examples/cfd/external_aerodynamics/domino/outputs/volume_data/
   bounding_box: # Bounding box dimensions for computational domain
     min: [-3.5, -2.25 , -0.32]
     max: [8.5 , 2.25  , 3.00]
@@ -55,12 +56,12 @@ variables:
 model:
   model_type: combined # train which model? surface, volume, combined
   loss_function: "mse" # mse or rmse
-  interp_res: [128, 64, 48] # resolution of latent space
+  interp_res: [64, 32, 24] # resolution of latent space
   use_sdf_in_basis_func: true # SDF in basis function network
   positional_encoding: false # calculate positional encoding?
-  volume_points_sample: 8192 # Number of points to sample in volume per epoch
-  surface_points_sample: 8192 # Number of points to sample on surface per epoch
-  geom_points_sample: 200_000 # Number of points to sample on STL per epoch
+  volume_points_sample: 1024 # Number of points to sample in volume per epoch
+  surface_points_sample: 1024 # Number of points to sample on surface per epoch
+  geom_points_sample: 2_000 # Number of points to sample on STL per epoch
   surface_neighbors: true # Pre-compute surface neighborhood from input data
   num_surface_neighbors: 7 # How many neighbors?
   use_surface_normals: true # Use surface normals and surface areas for surface computation?
@@ -95,11 +96,10 @@ model:
     scaling_params: [30.0, 1.226] # [inlet_velocity, air_density]
 
 train: # Training configurable parameters
-  epochs: 500
+  epochs: 50
   checkpoint_interval: 1
   dataloader:
     batch_size: 1
-    pin_memory: true
   sampler:
     shuffle: true
     drop_last: false
@@ -108,15 +108,14 @@ train: # Training configurable parameters
 val: # Validation configurable parameters
   dataloader:
     batch_size: 1
-    pin_memory: true
   sampler:
     shuffle: true
     drop_last: false
 
 eval: # Testing configurable parameters
-  test_path: /lustre/rranade/benchmarking/drivaer_aws_surface_test_new/
-  save_path: /lustre/rranade/domino/mesh_predictions_surf_final1/
-  checkpoint_name: DoMINO.0.50.pt
+  test_path: /home/aistudio/modulus/examples/cfd/external_aerodynamics/domino/drivaer_data_full_new
+  save_path: /home/aistudio/modulus/examples/cfd/external_aerodynamics/domino/outputs/mesh_predictions_surf_final1/
+  checkpoint_name: /home/aistudio/xiaoyewww/PaddleScience/examples/domino/outputs/AWS_Dataset/1/models/DoMINO.0.30.pdparams
 
 data_processor: # Data processor configurable parameters
   kind: drivaer_aws # must be either drivesim or drivaer_aws

examples/domino/modulus/__init__.py

@@ -0,0 +1,17 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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 .domino_datapipe import DoMINODataPipe  # noqa: F401