domino crash model arch and example

examples/structural_mechanics/crash_domino/requirements.txt

@@ -0,0 +1,6 @@
+torchinfo
+warp-lang
+tensorboard
+cuml
+einops
+tensorstore

examples/structural_mechanics/crash_domino/src/compute_statistics.py

@@ -0,0 +1,162 @@
+# 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.
+
+"""
+Compute and save scaling factors for DoMINO datasets.
+
+This script computes mean, standard deviation, minimum, and maximum values
+for all field variables in a DoMINO dataset. The computed statistics are
+saved in a structured format that can be easily loaded and used for
+normalization during training and inference.
+
+The script uses the same configuration system as the training script,
+ensuring consistency in dataset handling and processing parameters.
+"""
+
+import os
+import time
+from pathlib import Path
+
+import hydra
+import torch
+from omegaconf import DictConfig, OmegaConf
+
+from physicsnemo.distributed import DistributedManager
+from physicsnemo.launch.logging import PythonLogger, RankZeroLoggingWrapper
+
+from physicsnemo.datapipes.cae.domino_crash_datapipe import compute_scaling_factors
+from utils import ScalingFactors
+
+
+@hydra.main(version_base="1.3", config_path="conf", config_name="config")
+def main(cfg: DictConfig) -> None:
+    """
+    Main function to compute and save scaling factors.
+
+    Args:
+        cfg: Hydra configuration object containing all parameters
+    """
+    ################################
+    # Initialize distributed manager
+    ################################
+    DistributedManager.initialize()
+    dist = DistributedManager()
+
+    ################################
+    # Initialize logger
+    ################################
+    logger = PythonLogger("ComputeStatistics")
+    logger = RankZeroLoggingWrapper(logger, dist)
+
+    logger.info("Starting scaling factors computation")
+    logger.info(f"Config summary:\n{OmegaConf.to_yaml(cfg, sort_keys=True)}")
+
+    ################################
+    # Create output directory
+    ################################
+    output_dir = os.path.dirname(cfg.data.scaling_factors)
+    os.makedirs(output_dir, exist_ok=True)
+
+    if dist.world_size > 1:
+        torch.distributed.barrier()
+
+    ################################
+    # Check if scaling exists
+    ################################
+    pickle_path = output_dir + "/scaling_factors.pkl"
+
+    try:
+        scaling_factors = ScalingFactors.load(pickle_path)
+        logger.info(f"Scaling factors loaded from: {pickle_path}")
+    except FileNotFoundError:
+        logger.info(f"Scaling factors not found at: {pickle_path}; recomputing.")
+        scaling_factors = None
+
+    ################################
+    # Compute scaling factors
+    ################################
+    if scaling_factors is None:
+        logger.info("Computing scaling factors from dataset...")
+        start_time = time.perf_counter()
+
+        target_keys = [
+            "surface_fields",
+            "stl_centers",
+            "surface_mesh_centers",
+        ]
+
+        mean, std, min_val, max_val = compute_scaling_factors(
+            cfg=cfg,
+            input_path=cfg.data.input_dir,
+            target_keys=target_keys,
+            max_samples=cfg.data.max_samples_for_statistics,
+        )
+        mean = {k: m.cpu().numpy() for k, m in mean.items()}
+        std = {k: s.cpu().numpy() for k, s in std.items()}
+        min_val = {k: m.cpu().numpy() for k, m in min_val.items()}
+        max_val = {k: m.cpu().numpy() for k, m in max_val.items()}
+
+        compute_time = time.perf_counter() - start_time
+        logger.info(
+            f"Scaling factors computation completed in {compute_time:.2f} seconds"
+        )
+
+        ################################
+        # Create structured data object
+        ################################
+        dataset_info = {
+            "input_path": cfg.data.input_dir,
+            "model_type": cfg.model.model_type,
+            "normalization": cfg.model.normalization,
+            "compute_time": compute_time,
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
+            "config_name": cfg.project.name,
+        }
+
+        scaling_factors = ScalingFactors(
+            mean=mean,
+            std=std,
+            min_val=min_val,
+            max_val=max_val,
+            field_keys=target_keys,
+        )
+
+        ################################
+        # Save scaling factors
+        ################################
+        if dist.rank == 0:
+            # Save as structured pickle file
+            pickle_path = output_dir + "/scaling_factors.pkl"
+            scaling_factors.save(pickle_path)
+            logger.info(f"Scaling factors saved to: {pickle_path}")
+
+            # Save summary report
+            summary_path = output_dir + "/scaling_factors_summary.txt"
+            with open(summary_path, "w") as f:
+                f.write(scaling_factors.summary())
+            logger.info(f"Summary report saved to: {summary_path}")
+
+        ################################
+        # Display summary
+        ################################
+        logger.info("Scaling factors computation summary:")
+        logger.info(f"Field keys processed: {scaling_factors.field_keys}")
+
+        logger.info("Scaling factors computation completed successfully!")
+
+
+if __name__ == "__main__":
+    main()

examples/structural_mechanics/crash_domino/src/conf/cached.yaml

@@ -0,0 +1,35 @@
+# 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.
+
+defaults:
+  - config
+  - _self_
+
+exp_tag: cached
+
+data: # Input directory for training and validation data
+  input_dir: /lustre/cached/drivaer_aws/drivaer_data_full/train/
+  input_dir_val: /lustre/cached/drivaer_aws/drivaer_data_full/val/
+data_processor:
+  use_cache: true
+
+train: # Training configurable parameters
+  dataloader:
+    num_workers: 12
+
+val: # Validation configurable parameters
+  dataloader:
+    num_workers: 6