【SCU】【PPSCI Export&Infer No.10】 cfdgcn

.pre-commit-config.yaml

@@ -53,4 +53,4 @@ repos:
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
 
 exclude: |
-  ^jointContribution/
+  ^jointContribution/

docs/zh/examples/cfdgcn.md

@@ -2,6 +2,20 @@
 
 <a href="https://aistudio.baidu.com/projectdetail/7127446" class="md-button md-button--primary" style>AI Studio快速体验</a>
 
+=== "模型导出命令"
+
+    ``` sh
+    python cfdgcn.py mode=export
+    ```
+
+
+=== "模型推理命令"
+
+    ``` sh
+    python cfdgcn.py mode=infer
+    ```
+
+
 === "模型训练命令"
 
     ``` sh
@@ -23,7 +37,7 @@
     # generalization experiments
     mpirun -np $((BATCH_SIZE+1)) python cfdgcn.py \
       TRAIN.batch_size=$((BATCH_SIZE)) \
-      TRAIN_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_train" \  
+      TRAIN_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_train" \
       TRAIN_MESH_GRAPH_PATH="./data/NACA0012_machsplit_noshock/mesh_fine. su2" \
       EVAL_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_test" \
       EVAL_MESH_GRAPH_PATH="./data/NACA0012_machsplit_noshock/mesh_fine.su2" \

examples/cfdgcn/cfdgcn.py

@@ -157,6 +157,90 @@ def train(cfg: DictConfig):
             )
 
 
+def export(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.CFDGCN(
+        **cfg.MODEL,
+        process_sim=process_sim,
+        fine_marker_dict=fine_marker_dict,
+        su2_module=su2paddle.SU2Module,
+    )
+
+    solver = ppsci.solver.Solver(
+        model, pretrained_model_path=cfg.EXPORT.pretrained_model_path
+    )
+
+    # export model
+    from paddle.static import InputSpec
+
+    input_spec = [
+        {key: InputSpec([None, 1], "float32", name=key) for key in model.input_keys},
+    ]
+
+    solver.export(input_spec, cfg.INFER.export_path)
+
+
+def inference(cfg: DictConfig):
+
+    # 初始化预测器
+    from deploy.python_infer import pinn_predictor
+
+    predictor = pinn_predictor.PINNPredictor(cfg)
+
+    # 设置 dataloader 配置
+    infer_dataloader_cfg = {
+        "dataset": {
+            "name": "MeshAirfoilDataset",
+            "input_keys": ("input",),
+            "label_keys": ("label",),
+            "data_dir": cfg.INFER_DATA_DIR,
+            "mesh_graph_path": cfg.INFER_MESH_GRAPH_PATH,
+            "transpose_edges": True,
+        },
+        "batch_size": cfg.INFER.batch_size,
+        "sampler": {
+            "name": "BatchSampler",
+            "drop_last": False,
+            "shuffle": False,
+        },
+    }
+
+    # 初始化数据集和 dataloader
+    infer_dataset = ppsci.data.MeshAirfoilDataset(
+        input_keys=infer_dataloader_cfg["dataset"]["input_keys"],
+        label_keys=infer_dataloader_cfg["dataset"]["label_keys"],
+        data_dir=infer_dataloader_cfg["dataset"]["data_dir"],
+        mesh_graph_path=infer_dataloader_cfg["dataset"]["mesh_graph_path"],
+        transpose_edges=infer_dataloader_cfg["dataset"]["transpose_edges"],
+    )
+    infer_dataloader = ppsci.dataloader.DataLoader(
+        infer_dataset,
+        batch_size=infer_dataloader_cfg["batch_size"],
+        shuffle=infer_dataloader_cfg["sampler"]["shuffle"],
+        drop_last=infer_dataloader_cfg["sampler"]["drop_last"],
+        num_workers=infer_dataloader_cfg.get("num_workers", 1),
+    )
+
+    # 进行推理并可视化结果
+    with predictor.no_grad_context_manager(True):
+        for index, (input_dict, label_dict, _) in enumerate(infer_dataloader):
+            # 获取真实值
+            truefield = label_dict["label"].y
+            # 模型预测
+            output_dict = predictor.predict(input_dict, cfg.INFER.batch_size)
+            prefield = output_dict["pred"]
+
+            # 可视化结果
+            utils.log_images(
+                input_dict["input"].pos,
+                prefield,
+                truefield,
+                infer_dataset.elems_list,
+                index,
+                "cylinder",
+            )
+
+
 def evaluate(cfg: DictConfig):
     # set dataloader config
     train_dataloader_cfg = {
@@ -257,8 +341,14 @@ def main(cfg: DictConfig):
         train(cfg)
     elif cfg.mode == "eval":
         evaluate(cfg)
+    elif cfg.mode == "export":
+        export(cfg)
+    elif cfg.mode == "infer":
+        inference(cfg)
     else:
-        raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+        raise ValueError(
+            f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"
+        )
 
 
 if __name__ == "__main__":

examples/cfdgcn/conf/cfdgcn.yaml

@@ -62,3 +62,17 @@ EVAL:
   batch_size: 1
   pretrained_model_path: null
   eval_with_no_grad: true
+
+# inference settings
+INFER:
+  pretrained_model_path: "https://paddle-org.bj.bcebos.com/paddlescience/models/cfdgcn/cfdgcn_pretrained.pdparams"
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: true
+  min_subgraph_size: 5
+  gpu_mem: 2000
+  gpu_id: 0
+  max_batch_size: 512
+  num_cpu_threads: 4
+  batch_size: 512