【PPSCI Export&Infer No.15-16】 (#875)

* fix doc bugs

* fix codestyle bugs

* 【PPSCI Export&Infer No.15-16】

* fix codestyle bug for PPSCI Export&Infer No.15-16】

* fix codestyle bugs for 【PPSCI Export&Infer No.15-16】

* fix codestyle bugs for 【PPSCI Export&Infer No.15-16】

* fix codestyle bugs for 【PPSCI Export&Infer No.15-16】

* fix bugs for 【PPSCI Export&Infer No.15-16】

* fix codestyle bugs

Author: wufei2

docs/zh/examples/ldc2d_steady.md

@@ -14,6 +14,18 @@
     python ldc2d_steady_Re10.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_steady_Re10/ldc2d_steady_Re10_pretrained.pdparams
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python ldc2d_steady_Re10.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    python ldc2d_steady_Re10.py mode=infer
+    ```
+
 | 预训练模型  | 指标 |
 |:--| :--|
 | [ldc2d_steady_Re10_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_steady_Re10/ldc2d_steady_Re10_pretrained.pdparams) | loss(Residual): 365.36164<br>MSE.momentum_x(Residual): 0.01435<br>MSE.continuity(Residual): 0.04072<br>MSE.momentum_y(Residual): 0.02471 |

docs/zh/examples/ldc2d_unsteady.md

@@ -14,6 +14,18 @@
     python ldc2d_unsteady_Re10.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_unsteady_Re10/ldc2d_unsteady_Re10_pretrained.pdparams
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python ldc2d_unsteady_Re10.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    python ldc2d_unsteady_Re10.py mode=infer
+    ```
+
 | 预训练模型  | 指标 |
 |:--| :--|
 | [ldc2d_unsteady_Re10_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_unsteady_Re10/ldc2d_unsteady_Re10_pretrained.pdparams) | loss(Residual): 155652.67530<br>MSE.momentum_x(Residual): 6.78030<br>MSE.continuity(Residual): 0.16590<br>MSE.momentum_y(Residual): 12.05981 |

examples/ldc/conf/ldc2d_steady_Re10.yaml

@@ -22,6 +22,7 @@ hydra:
 mode: train # running mode: train/eval
 seed: 42
 output_dir: ${hydra:run.dir}
+log_freq: 20
 
 # set working condition
 NU: 0.01
@@ -55,3 +56,21 @@ EVAL:
   pretrained_model_path: null
   batch_size:
     residual_validator: 8192
+
+# inference settings
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_steady_Re10/ldc2d_steady_Re10_pretrained.pdparams
+  export_path: ./inference/ldc2d_steady_Re10
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${INFER.export_path}.onnx
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: true
+  min_subgraph_size: 5
+  gpu_mem: 2000
+  gpu_id: 0
+  max_batch_size: 8192
+  num_cpu_threads: 10
+  batch_size: 8192

examples/ldc/conf/ldc2d_unsteady_Re10.yaml

@@ -22,6 +22,7 @@ hydra:
 mode: train # running mode: train/eval
 seed: 42
 output_dir: ${hydra:run.dir}
+log_freq: 20
 
 # set working condition
 NU: 0.01
@@ -56,3 +57,21 @@ EVAL:
   pretrained_model_path: null
   batch_size:
     residual_validator: 8192
+
+# inference settings
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/ldc2d_unsteady_Re10/ldc2d_unsteady_Re10_pretrained.pdparams
+  export_path: ./inference/ldc2d_unsteady_Re10
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdiparams_path: ${INFER.export_path}.pdiparams
+  onnx_path: ${INFER.export_path}.onnx
+  device: gpu
+  engine: native
+  precision: fp32
+  ir_optim: true
+  min_subgraph_size: 5
+  gpu_mem: 2000
+  gpu_id: 0
+  max_batch_size: 8192
+  num_cpu_threads: 10
+  batch_size: 8192

examples/ldc/ldc2d_steady_Re10.py

@@ -234,6 +234,57 @@ def evaluate(cfg: DictConfig):
     solver.visualize()
 
 
+def export(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.MLP(**cfg.MODEL)
+
+    # 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)
+
+    # set geometry
+    geom = {"rect": ppsci.geometry.Rectangle((-0.05, -0.05), (0.05, 0.05))}
+    # manually collate input data for inference
+    NPOINT_PDE = 99**2
+    NPOINT_TOP = 101
+    NPOINT_BOTTOM = 101
+    NPOINT_LEFT = 99
+    NPOINT_RIGHT = 99
+    NPOINT_BC = NPOINT_TOP + NPOINT_BOTTOM + NPOINT_LEFT + NPOINT_RIGHT
+    input_dict = geom["rect"].sample_interior(NPOINT_PDE + NPOINT_BC, evenly=True)
+    output_dict = predictor.predict(
+        {key: input_dict[key] for key in cfg.MODEL.input_keys}, 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.MODEL.output_keys, output_dict.keys())
+    }
+
+    ppsci.visualize.save_vtu_from_dict(
+        "./ldc2d_steady_Re10.vtu",
+        {**input_dict, **output_dict},
+        input_dict.keys(),
+        cfg.MODEL.output_keys,
+    )
+
+
 @hydra.main(
     version_base=None, config_path="./conf", config_name="ldc2d_steady_Re10.yaml"
 )
@@ -242,8 +293,14 @@ 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/ldc/ldc2d_unsteady_Re10.py

@@ -308,6 +308,85 @@ def evaluate(cfg: DictConfig):
     solver.visualize()
 
 
+def export(cfg: DictConfig):
+    # set model
+    model = ppsci.arch.MLP(**cfg.MODEL)
+
+    # 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)
+
+    # set timestamps(including initial t0)
+    timestamps = np.linspace(0.0, 1.5, cfg.NTIME_ALL, endpoint=True)
+    # set time-geometry
+    geom = {
+        "time_rect": ppsci.geometry.TimeXGeometry(
+            ppsci.geometry.TimeDomain(0.0, 1.5, timestamps=timestamps),
+            ppsci.geometry.Rectangle((-0.05, -0.05), (0.05, 0.05)),
+        )
+    }
+    # manually collate input data for inference
+    NPOINT_PDE = 99**2
+    NPOINT_TOP = 101
+    NPOINT_DOWN = 101
+    NPOINT_LEFT = 99
+    NPOINT_RIGHT = 99
+    NPOINT_IC = 99**2
+    NTIME_PDE = cfg.NTIME_ALL - 1
+    NPOINT_BC = NPOINT_TOP + NPOINT_DOWN + NPOINT_LEFT + NPOINT_RIGHT
+    input_dict = geom["time_rect"].sample_initial_interior(
+        (NPOINT_IC + NPOINT_BC), evenly=True
+    )
+    input_pde_dict = geom["time_rect"].sample_interior(
+        (NPOINT_PDE + NPOINT_BC) * NTIME_PDE, evenly=True
+    )
+    # (interior+boundary) x all timestamps
+    for t in range(NTIME_PDE):
+        for key in geom["time_rect"].dim_keys:
+            input_dict[key] = np.concatenate(
+                (
+                    input_dict[key],
+                    input_pde_dict[key][
+                        t
+                        * (NPOINT_PDE + NPOINT_BC) : (t + 1)
+                        * (NPOINT_PDE + NPOINT_BC)
+                    ],
+                )
+            )
+    output_dict = predictor.predict(
+        {key: input_dict[key] for key in cfg.MODEL.input_keys}, 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.MODEL.output_keys, output_dict.keys())
+    }
+
+    ppsci.visualize.save_vtu_from_dict(
+        "./ldc2d_unsteady_Re10_pred.vtu",
+        {**input_dict, **output_dict},
+        input_dict.keys(),
+        cfg.MODEL.output_keys,
+        cfg.NTIME_ALL,
+    )
+
+
 @hydra.main(
     version_base=None, config_path="./conf", config_name="ldc2d_unsteady_Re10.yaml"
 )
@@ -316,8 +395,14 @@ 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__":