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| - LorenzDataset | ||
| - RosslerDataset | ||
| - VtuDataset | ||
| - MeshAirfoilDataset | ||
| show_root_heading: false | ||
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| # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
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| # 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 | ||
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| # http://www.apache.org/licenses/LICENSE-2.0 | ||
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| # 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. | ||
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| from paddle.nn import functional as F | ||
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| import ppsci | ||
| from ppsci.utils import config | ||
| from ppsci.utils import logger | ||
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| def train_mse_func(output_dict, label_dict, *args): | ||
| return F.mse_loss(output_dict["pred"], label_dict["label"].y) | ||
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| 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} | ||
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| 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") | ||
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| # 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", | ||
| # ) | ||
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| # 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, | ||
| } | ||
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| # 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, | ||
| } | ||
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| # set training hyper-parameters | ||
| EPOCHS = 500 if not args.epochs else args.epochs | ||
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| # set optimizer | ||
| optimizer = ppsci.optimizer.Adam(5e-4)(model) | ||
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| # 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} | ||
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| # 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============") |
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