-
Notifications
You must be signed in to change notification settings - Fork 165
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
de89025
commit 38659ca
Showing
9 changed files
with
337 additions
and
51 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -16,4 +16,5 @@ | |
| - LorenzDataset | ||
| - RosslerDataset | ||
| - VtuDataset | ||
| - MeshAirfoilDataset | ||
| show_root_heading: false | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,170 @@ | ||
| # Copyright (c) 2023 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. | ||
|
|
||
| from paddle.nn import functional as F | ||
|
|
||
| import ppsci | ||
| from ppsci.utils import config | ||
| from ppsci.utils import logger | ||
|
|
||
|
|
||
| def train_mse_func(output_dict, label_dict, *args): | ||
| return F.mse_loss(output_dict["pred"], label_dict["label"].y) | ||
|
|
||
|
|
||
| def eval_rmse_func(output_dict, label_dict, *args): | ||
| mse_losses = [ | ||
| F.mse_loss(pred, label.y) | ||
| for (pred, label) in zip(output_dict["pred"], label_dict["label"]) | ||
| ] | ||
| return {"RMSE": (sum(mse_losses) / len(mse_losses)) ** 0.5} | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| args = config.parse_args() | ||
| # set random seed for reproducibility | ||
| ppsci.utils.misc.set_random_seed(42) | ||
| # set output directory | ||
| OUTPUT_DIR = "./output_AMGNet" if not args.output_dir else args.output_dir | ||
| # initialize logger | ||
| logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") | ||
|
|
||
| # set model | ||
| # model for airfoil | ||
| model = ppsci.arch.AMGNet( | ||
| 5, | ||
| 3, | ||
| 128, | ||
| num_layers=2, | ||
| message_passing_aggregator="sum", | ||
| message_passing_steps=6, | ||
| speed="norm", | ||
| ) | ||
| # # model for cylinder | ||
| # model = ppsci.arch.AMGNet( | ||
| # 5, | ||
| # 3, | ||
| # 128, | ||
| # num_layers=2, | ||
| # message_passing_aggregator="sum", | ||
| # message_passing_steps=6, | ||
| # speed="norm", | ||
| # ) | ||
|
|
||
| # set dataloader config | ||
| ITERS_PER_EPOCH = 42 | ||
| train_dataloader_cfg = { | ||
| "dataset": { | ||
| "name": "MeshAirfoilDataset", | ||
| "input_keys": ("input",), | ||
| "label_keys": ("label",), | ||
| "data_root": "./data/NACA0012_interpolate/outputs_train", | ||
| "mesh_graph_path": "./data/NACA0012_interpolate/mesh_fine.su2", | ||
| }, | ||
| "batch_size": 4, | ||
| "sampler": { | ||
| "name": "BatchSampler", | ||
| "drop_last": False, | ||
| "shuffle": True, | ||
| }, | ||
| "num_workers": 1, | ||
| } | ||
|
|
||
| # set constraint | ||
| sup_constraint = ppsci.constraint.SupervisedConstraint( | ||
| train_dataloader_cfg, | ||
| output_expr={"pred": lambda out: out["pred"]}, | ||
| loss=ppsci.loss.FunctionalLoss(train_mse_func), | ||
| name="Sup", | ||
| ) | ||
| # wrap constraints together | ||
| constraint = { | ||
| sup_constraint.name: sup_constraint, | ||
| } | ||
|
|
||
| # set training hyper-parameters | ||
| EPOCHS = 500 if not args.epochs else args.epochs | ||
|
|
||
| # set optimizer | ||
| optimizer = ppsci.optimizer.Adam(5e-4)(model) | ||
|
|
||
| # set validator | ||
| eval_dataloader_cfg = { | ||
| "dataset": { | ||
| "name": "MeshAirfoilDataset", | ||
| "input_keys": ("input",), | ||
| "label_keys": ("label",), | ||
| "data_root": "./data/NACA0012_interpolate/outputs_test", | ||
| "mesh_graph_path": "./data/NACA0012_interpolate/mesh_fine.su2", | ||
| }, | ||
| "batch_size": 1, | ||
| "sampler": { | ||
| "name": "BatchSampler", | ||
| "drop_last": False, | ||
| "shuffle": False, | ||
| }, | ||
| } | ||
| rmse_validator = ppsci.validate.SupervisedValidator( | ||
| eval_dataloader_cfg, | ||
| loss=ppsci.loss.FunctionalLoss(train_mse_func), | ||
| output_expr={"pred": lambda out: out["pred"]}, | ||
| metric={"RMSE": ppsci.metric.FunctionalMetric(eval_rmse_func)}, | ||
| name="RMSE_validator", | ||
| ) | ||
| validator = {rmse_validator.name: rmse_validator} | ||
|
|
||
| # initialize solver | ||
| solver = ppsci.solver.Solver( | ||
| model, | ||
| constraint, | ||
| OUTPUT_DIR, | ||
| optimizer, | ||
| None, | ||
| EPOCHS, | ||
| ITERS_PER_EPOCH, | ||
| eval_during_train=True, | ||
| eval_freq=50, | ||
| validator=validator, | ||
| eval_with_no_grad=True, | ||
| # pretrained_model_path="./output_AMGNet/checkpoints/latest" | ||
| ) | ||
| # train model | ||
| solver.train() | ||
| # solver.eval() | ||
| # with solver.no_grad_context_manager(True): | ||
| # sum_loss = 0 | ||
| # for index, batch in enumerate(loader): | ||
| # truefield = batch[0].y | ||
| # prefield = model(batch) | ||
| # # print(f"{index }prefield.mean() = {prefield.shape} {prefield.mean().item():.10f}") | ||
| # # log_images( | ||
| # # batch[0].pos, | ||
| # # prefield, | ||
| # # truefield, | ||
| # # trainer.data.elems_list, | ||
| # # "test", | ||
| # # index, | ||
| # # flag=my_type, | ||
| # # ) | ||
| # mes_loss = criterion(prefield, truefield) | ||
| # # print(f">>> mes_loss = {mes_loss.item():.10f}") | ||
| # sum_loss += mes_loss.item() | ||
| # print(index) | ||
| # # exit() | ||
| # avg_loss = sum_loss / (len(loader)) | ||
| # avg_loss = np.sqrt(avg_loss) | ||
| # root_logger.info(" trajectory_loss") | ||
| # root_logger.info(" " + str(avg_loss)) | ||
| # print("trajectory_loss=", avg_loss) | ||
| # print("============finish============") |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.