【PPSCI Export&Infer No.23】viv

docs/zh/examples/viv.md

@@ -14,6 +14,18 @@
     python viv.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/viv/viv_pretrained.pdparams
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python viv.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    python viv.py mode=infer
+    ```
+
 | 预训练模型  | 指标 |
 |:--| :--|
 | [viv_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/viv_pretrained.pdparams)<br>[viv_pretrained.pdeqn](https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/viv_pretrained.pdeqn) | 'eta': 1.1416150300647132e-06<br>'f': 4.635014192899689e-06 |

examples/fsi/conf/viv.yaml

@@ -11,6 +11,8 @@ hydra:
           - TRAIN.checkpoint_path
           - TRAIN.pretrained_model_path
           - EVAL.pretrained_model_path
+          - INFER.pretrained_model_path
+          - INFER.export_path
           - mode
           - output_dir
           - log_freq
@@ -60,3 +62,23 @@ TRAIN:
 EVAL:
   pretrained_model_path: null
   batch_size: 32
+
+# inference settings
+INFER:
+  pretrained_model_path: "https://paddle-org.bj.bcebos.com/paddlescience/models/viv/viv_pretrained.pdparams"
+  export_path: ./inference/viv
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  input_keys: ${MODEL.input_keys}
+  output_keys: ["eta", "f"]
+  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

examples/fsi/viv.py

@@ -200,14 +200,88 @@ def evaluate(cfg: DictConfig):
     solver.visualize()
 
 
+def export(cfg: DictConfig):
+    from paddle import nn
+    from paddle.static import InputSpec
+
+    # set model
+    model = ppsci.arch.MLP(**cfg.MODEL)
+    # initialize equation
+    equation = {"VIV": ppsci.equation.Vibration(2, -4, 0)}
+    # initialize solver
+    solver = ppsci.solver.Solver(
+        model,
+        equation=equation,
+        pretrained_model_path=cfg.INFER.pretrained_model_path,
+    )
+    # Convert equation to func
+    f_func = ppsci.lambdify(
+        solver.equation["VIV"].equations["f"],
+        solver.model,
+        list(solver.equation["VIV"].learnable_parameters),
+    )
+
+    class Wrapped_Model(nn.Layer):
+        def __init__(self, model, func):
+            super().__init__()
+            self.model = model
+            self.func = func
+
+        def forward(self, x):
+            model_out = self.model(x)
+            func_out = self.func(x)
+            return {**model_out, "f": func_out}
+
+    solver.model = Wrapped_Model(model, f_func)
+    # export models
+    input_spec = [
+        {key: InputSpec([None, 1], "float32", name=key) for key in model.input_keys},
+    ]
+    solver.export(input_spec, cfg.INFER.export_path, skip_prune_program=True)
+
+
+def inference(cfg: DictConfig):
+    from deploy.python_infer import pinn_predictor
+
+    # set model predictor
+    predictor = pinn_predictor.PINNPredictor(cfg)
+
+    infer_mat = ppsci.utils.reader.load_mat_file(
+        cfg.VIV_DATA_PATH,
+        ("t_f", "eta_gt", "f_gt"),
+        alias_dict={"eta_gt": "eta", "f_gt": "f"},
+    )
+
+    input_dict = {key: infer_mat[key] for key in cfg.INFER.input_keys}
+
+    output_dict = predictor.predict(input_dict, cfg.INFER.batch_size)
+
+    # mapping data to cfg.INFER.output_keys
+    output_dict = {
+        store_key: output_dict[infer_key]
+        for store_key, infer_key in zip(cfg.INFER.output_keys, output_dict.keys())
+    }
+    infer_mat.update(output_dict)
+
+    ppsci.visualize.plot.save_plot_from_1d_dict(
+        "./viv_pred", infer_mat, ("t_f",), ("eta", "eta_gt", "f", "f_gt")
+    )
+
+
 @hydra.main(version_base=None, config_path="./conf", config_name="viv.yaml")
 def main(cfg: DictConfig):
     if cfg.mode == "train":
         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__":

ppsci/utils/symbolic.py

@@ -490,7 +490,7 @@ class ComposedNode(nn.Layer):
     def __init__(self, callable_nodes: List[Node]):
         super().__init__()
         assert len(callable_nodes)
-        self.callable_nodes = callable_nodes
+        self.callable_nodes = nn.LayerList(callable_nodes)
 
     def forward(self, data_dict: DATA_DICT) -> paddle.Tensor:
         # call all callable_nodes in order