【PPSCI Export&Infer No.25】bracket

docs/zh/examples/bracket.md

@@ -26,6 +26,24 @@
     python bracket.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/bracket/bracket_pretrained.pdparams
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python bracket.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    # linux
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/bracket/bracket_dataset.tar
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/bracket/bracket_dataset.tar --output bracket_dataset.tar
+    # unzip it
+    tar -xvf bracket_dataset.tar
+    python bracket.py mode=infer
+    ```
+
 | 预训练模型  | 指标 |
 |:--| :--|
 | [bracket_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/bracket/bracket_pretrained.pdparams) | loss(commercial_ref_u_v_w_sigmas): 32.28704<br>MSE.u(commercial_ref_u_v_w_sigmas): 0.00005<br>MSE.v(commercial_ref_u_v_w_sigmas): 0.00000<br>MSE.w(commercial_ref_u_v_w_sigmas): 0.00734<br>MSE.sigma_xx(commercial_ref_u_v_w_sigmas): 27.64751<br>MSE.sigma_yy(commercial_ref_u_v_w_sigmas): 1.23101<br>MSE.sigma_zz(commercial_ref_u_v_w_sigmas): 0.89106<br>MSE.sigma_xy(commercial_ref_u_v_w_sigmas): 0.84370<br>MSE.sigma_xz(commercial_ref_u_v_w_sigmas): 1.42126<br>MSE.sigma_yz(commercial_ref_u_v_w_sigmas): 0.24510 |

examples/bracket/bracket.py

@@ -11,6 +11,65 @@
 import ppsci
 
 
+def export(cfg: DictConfig):
+    # set model
+    disp_net = ppsci.arch.MLP(**cfg.MODEL.disp_net)
+    stress_net = ppsci.arch.MLP(**cfg.MODEL.stress_net)
+    # wrap to a model_list
+    model = ppsci.arch.ModelList((disp_net, stress_net))
+
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model,
+        pretrained_model_path=cfg.INFER.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):
+    from deploy.python_infer import pinn_predictor
+
+    predictor = pinn_predictor.PINNPredictor(cfg)
+    ref_xyzu = ppsci.utils.reader.load_csv_file(
+        cfg.DEFORMATION_X_PATH,
+        ("x", "y", "z", "u"),
+        {
+            "x": "X Location (m)",
+            "y": "Y Location (m)",
+            "z": "Z Location (m)",
+            "u": "Directional Deformation (m)",
+        },
+        "\t",
+    )
+    input_dict = {
+        "x": ref_xyzu["x"],
+        "y": ref_xyzu["y"],
+        "z": ref_xyzu["z"],
+    }
+    output_dict = predictor.predict(input_dict, cfg.INFER.batch_size)
+
+    # mapping data to cfg.INFER.output_keys
+    output_keys = cfg.MODEL.disp_net.output_keys + cfg.MODEL.stress_net.output_keys
+    output_dict = {
+        store_key: output_dict[infer_key]
+        for store_key, infer_key in zip(output_keys, output_dict.keys())
+    }
+
+    ppsci.visualize.save_vtu_from_dict(
+        "./bracket_pred",
+        {**input_dict, **output_dict},
+        input_dict.keys(),
+        output_keys,
+    )
+
+
 def train(cfg: DictConfig):
     # set model
     disp_net = ppsci.arch.MLP(**cfg.MODEL.disp_net)
@@ -520,6 +579,10 @@ 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}'")
 

examples/bracket/conf/bracket.yaml

@@ -102,3 +102,21 @@ EVAL:
   eval_with_no_grad: true
   batch_size:
     sup_validator: 128
+
+# inference settings
+INFER:
+  pretrained_model_path: "https://paddle-org.bj.bcebos.com/paddlescience/models/bracket/bracket_pretrained.pdparams"
+  export_path: ./inference/bracket
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  device: gpu
+  engine: native
+  precision: fp32
+  onnx_path: ${INFER.export_path}.onnx
+  ir_optim: true
+  min_subgraph_size: 10
+  gpu_mem: 4000
+  gpu_id: 0
+  max_batch_size: 64
+  num_cpu_threads: 4
+  batch_size: 16