【Hackathon 8th No.11】DrivAerNet++ 论文复现

docs/zh/api/arch.md

@@ -38,6 +38,7 @@
         - LNO
         - TGCN
         - RegDGCNN
+        - RegPointNet
         - IFMMLP
       show_root_heading: true
       heading_level: 3

docs/zh/api/data/dataset.md

@@ -33,5 +33,6 @@
         - CGCNNDataset
         - PEMSDataset
         - DrivAerNetDataset
+        - DrivAerNetPlusPlusDataset
         - IFMMoeDataset
       show_root_heading: true

examples/drivaernetplusplus/conf/drivaernetplusplus.yaml

@@ -0,0 +1,77 @@
+defaults:
+  - ppsci_default
+  - TRAIN: train_default
+  - TRAIN/ema: ema_default
+  - TRAIN/swa: swa_default
+  - EVAL: eval_default
+  - INFER: infer_default
+  - hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
+  - _self_
+
+hydra:
+  run:
+    dir: outputs_drivaernetplusplus/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  job:
+    name: ${mode}
+    chdir: false
+  callbacks:
+    init_callback:
+      _target_: ppsci.utils.callbacks.InitCallback
+  sweep:
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: eval
+seed: 1
+output_dir: ${hydra:run.dir}
+log_freq: 100
+
+# model settings
+MODEL:
+  input_keys: ["vertices"]
+  output_keys: ["cd_value"]
+  weight_keys: ["weight_keys"]
+  dropout: 0.0
+  emb_dims: 1024
+  channels: [6, 64, 128, 256, 512, 1024]
+  linear_sizes: [128, 64, 32, 16]
+  k: 40
+  output_channels: 1
+
+# training settings
+TRAIN:
+  iters_per_epoch: 5399
+  epochs: 200
+  num_points: 100000
+  num_workers: 32
+  eval_during_train: True
+  train_ids_file: "train_design_ids.txt"
+  eval_ids_file: "val_design_ids.txt"
+  batch_size: 32
+  scheduler:
+    mode: "min"
+    patience: 20
+    factor: 0.1
+    verbose: True
+
+# evaluation settings
+EVAL:
+  num_points: 100000
+  batch_size: 1
+  pretrained_model_path: "https://dataset.bj.bcebos.com/PaddleScience/DNNFluid-Car/DrivAer%2B%2B/DragPrediction_DrivAerNet_PointNet_r2_batchsize16_200epochs_100kpoints_tsne_NeurIPS_best_model.pdparams"
+  eval_with_no_grad: True
+  ids_file: "test_design_ids.txt"
+  num_workers: 8
+
+# optimizer settings
+optimizer:
+  weight_decay: 0.0001
+  lr: 0.001
+  optimizer: 'adam'
+
+ARGS:
+  # dataset settings
+  dataset_path: 'data/DrivAerNetPlusPlus_Processed_Point_Clouds_100k_paddle'
+  aero_coeff: 'data/DrivAerNetPlusPlus_Drag_8k.csv'
+  subset_dir: 'data/subset_dir'

examples/drivaernetplusplus/drivaernetplusplus.py

@@ -0,0 +1,200 @@
+# Copyright (c) 2024 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.
+
+
+import warnings
+from functools import partial
+
+import hydra
+import paddle
+from omegaconf import DictConfig
+
+import ppsci
+
+
+def train(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.RegPointNet(
+        input_keys=cfg.MODEL.input_keys,
+        label_keys=cfg.MODEL.output_keys,
+        weight_keys=cfg.MODEL.weight_keys,
+        args=cfg.MODEL,
+    )
+
+    train_dataloader_cfg = {
+        "dataset": {
+            "name": "DrivAerNetPlusPlusDataset",
+            "root_dir": cfg.ARGS.dataset_path,
+            "input_keys": cfg.MODEL.input_keys,
+            "label_keys": cfg.MODEL.output_keys,
+            "weight_keys": cfg.MODEL.weight_keys,
+            "subset_dir": cfg.ARGS.subset_dir,
+            "ids_file": cfg.TRAIN.train_ids_file,
+            "csv_file": cfg.ARGS.aero_coeff,
+            "num_points": cfg.TRAIN.num_points,
+        },
+        "batch_size": cfg.TRAIN.batch_size,
+        "num_workers": cfg.TRAIN.num_workers,
+    }
+
+    drivaernetplusplus_constraint = ppsci.constraint.SupervisedConstraint(
+        train_dataloader_cfg,
+        ppsci.loss.MSELoss("mean"),
+        name="DrivAerNetplusplus_constraint",
+    )
+
+    constraint = {drivaernetplusplus_constraint.name: drivaernetplusplus_constraint}
+
+    valid_dataloader_cfg = {
+        "dataset": {
+            "name": "DrivAerNetPlusPlusDataset",
+            "root_dir": cfg.ARGS.dataset_path,
+            "input_keys": cfg.MODEL.input_keys,
+            "label_keys": cfg.MODEL.output_keys,
+            "weight_keys": cfg.MODEL.weight_keys,
+            "subset_dir": cfg.ARGS.subset_dir,
+            "ids_file": cfg.TRAIN.eval_ids_file,
+            "csv_file": cfg.ARGS.aero_coeff,
+            "num_points": cfg.TRAIN.num_points,
+        },
+        "batch_size": cfg.TRAIN.batch_size,
+        "num_workers": cfg.TRAIN.num_workers,
+    }
+
+    drivaernetplusplus_valid = ppsci.validate.SupervisedValidator(
+        valid_dataloader_cfg,
+        loss=ppsci.loss.MSELoss("mean"),
+        metric={"MSE": ppsci.metric.MSE()},
+        name="DrivAerNetplusplus_valid",
+    )
+
+    validator = {drivaernetplusplus_valid.name: drivaernetplusplus_valid}
+
+    # set optimizer
+    lr_scheduler = ppsci.optimizer.lr_scheduler.ReduceOnPlateau(
+        epochs=cfg.TRAIN.epochs,
+        iters_per_epoch=(
+            cfg.TRAIN.iters_per_epoch
+            // (paddle.distributed.get_world_size() * cfg.TRAIN.batch_size)
+            + 1
+        ),
+        learning_rate=cfg.optimizer.lr,
+        mode=cfg.TRAIN.scheduler.mode,
+        patience=cfg.TRAIN.scheduler.patience,
+        factor=cfg.TRAIN.scheduler.factor,
+        verbose=cfg.TRAIN.scheduler.verbose,
+    )()
+
+    optimizer = (
+        ppsci.optimizer.Adam(lr_scheduler, weight_decay=cfg.optimizer.weight_decay)(
+            model
+        )
+        if cfg.optimizer.optimizer == "adam"
+        else ppsci.optimizer.SGD(lr_scheduler, weight_decay=cfg.optimizer.weight_decay)(
+            model
+        )
+    )
+
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model=model,
+        iters_per_epoch=(
+            cfg.TRAIN.iters_per_epoch
+            // (paddle.distributed.get_world_size() * cfg.TRAIN.batch_size)
+            + 1
+        ),
+        constraint=constraint,
+        output_dir=cfg.output_dir,
+        optimizer=optimizer,
+        lr_scheduler=lr_scheduler,
+        epochs=cfg.TRAIN.epochs,
+        validator=validator,
+        eval_during_train=cfg.TRAIN.eval_during_train,
+        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+    )
+
+    lr_scheduler.step = partial(lr_scheduler.step, metrics=solver.cur_metric)
+    solver.lr_scheduler = lr_scheduler
+
+    # train model
+    solver.train()
+
+    solver.eval()
+
+
+def evaluate(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.RegPointNet(
+        input_keys=cfg.MODEL.input_keys,
+        label_keys=cfg.MODEL.output_keys,
+        weight_keys=cfg.MODEL.weight_keys,
+        args=cfg.MODEL,
+    )
+
+    valid_dataloader_cfg = {
+        "dataset": {
+            "name": "DrivAerNetPlusPlusDataset",
+            "root_dir": cfg.ARGS.dataset_path,
+            "input_keys": cfg.MODEL.input_keys,
+            "label_keys": cfg.MODEL.output_keys,
+            "weight_keys": cfg.MODEL.weight_keys,
+            "subset_dir": cfg.ARGS.subset_dir,
+            "ids_file": cfg.EVAL.ids_file,
+            "csv_file": cfg.ARGS.aero_coeff,
+            "num_points": cfg.EVAL.num_points,
+        },
+        "batch_size": cfg.EVAL.batch_size,
+        "num_workers": cfg.EVAL.num_workers,
+    }
+
+    drivaernetplusplus_valid = ppsci.validate.SupervisedValidator(
+        valid_dataloader_cfg,
+        loss=ppsci.loss.MSELoss("mean"),
+        metric={
+            "MSE": ppsci.metric.MSE(),
+            "MAE": ppsci.metric.MAE(),
+            "Max AE": ppsci.metric.MaxAE(),
+            "R²": ppsci.metric.R2Score(),
+        },
+        name="DrivAerNetPlusPlus_valid",
+    )
+
+    validator = {drivaernetplusplus_valid.name: drivaernetplusplus_valid}
+
+    solver = ppsci.solver.Solver(
+        model=model,
+        validator=validator,
+        pretrained_model_path=cfg.EVAL.pretrained_model_path,
+        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
+    )
+
+    # evaluate model
+    solver.eval()
+
+
+@hydra.main(
+    version_base=None, config_path="./conf", config_name="drivaernetplusplus.yaml"
+)
+def main(cfg: DictConfig):
+    warnings.filterwarnings("ignore")
+    if cfg.mode == "train":
+        train(cfg)
+    elif cfg.mode == "eval":
+        evaluate(cfg)
+    else:
+        raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+
+
+if __name__ == "__main__":
+    main()