【SCU】【PPSCI Export&Infer No.32】phygeonet

docs/zh/examples/phygeonet.md

@@ -53,6 +53,40 @@
 
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    # heat_equation
+    python heat_equation.py mode=export
+
+    # heat_equation_bc
+    python heat_equation_with_bc.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    # heat_equation
+    # linux
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation.npz -P ./data/
+
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation.npz --create-dirs -o ./data/heat_equation.npz
+
+    python heat_equation.py mode=infer
+
+    # heat_equation_bc
+    # linux
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation_bc.npz -P ./data/
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation_bc_test.npz -P ./data/
+
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation_bc.npz --create-dirs -o ./data/heat_equation.npz
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/PhyGeoNet/heat_equation_bc_test.npz --create-dirs -o ./data/heat_equation.npz
+
+    python heat_equation_with_bc.py mode=infer
+    ```
+
 | 模型 | mRes | ev |
 | :-- | :-- | :-- |
 | [heat_equation_pretrain.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/PhyGeoNet/heat_equation_pretrain.pdparams)  | 0.815 |0.095|

examples/phygeonet/conf/heat_equation.yaml

@@ -51,3 +51,21 @@ TRAIN:
 EVAL:
   pretrained_model_path: null
   eval_with_no_grad: true
+
+# inference settings
+INFER:
+  pretrained_model_path: 'https://paddle-org.bj.bcebos.com/paddlescience/models/PhyGeoNet/heat_equation_pretrain.pdparams'
+  export_path: ./inference/heat_equation
+  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: 20
+  gpu_id: 0
+  max_batch_size: 256
+  num_cpu_threads: 10
+  batch_size: 256

examples/phygeonet/conf/heat_equation_with_bc.yaml

@@ -24,6 +24,7 @@ hydra:
 
 # general settings
 mode: train # running mode: train/eval
+log_freq: 50
 seed: 66
 data_dir: ./data/heat_equation_bc.npz
 test_data_dir: ./data/heat_equation_bc_test.npz
@@ -53,3 +54,21 @@ TRAIN:
 EVAL:
   pretrained_model_path: null
   eval_with_no_grad: true
+
+# inference settings
+INFER:
+  pretrained_model_path: 'https://paddle-org.bj.bcebos.com/paddlescience/models/PhyGeoNet/heat_equation_bc_pretrain.pdparams'
+  export_path: ./inference/heat_equation_bc
+  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: 20
+  gpu_id: 0
+  max_batch_size: 256
+  num_cpu_threads: 10
+  batch_size: 256

examples/phygeonet/heat_equation.py

@@ -1,3 +1,4 @@
+import os.path as osp
 from typing import Dict
 
 import hydra
@@ -153,14 +154,102 @@ def evaluate(cfg: DictConfig):
     plt.close(fig)
 
 
+def export(cfg: DictConfig):
+    model = ppsci.arch.USCNN(**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, 2, 19, 84], "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)
+    data = np.load(cfg.data_dir)
+    coords = data["coords"]
+    ofv_sb = data["OFV_sb"]
+
+    ## create model
+    pad_singleside = cfg.MODEL.pad_singleside
+    input_spec = {"coords": coords}
+
+    output_v = predictor.predict(input_spec, cfg.INFER.batch_size)
+    # mapping data to cfg.INFER.output_keys
+    output_v = {
+        store_key: output_v[infer_key]
+        for store_key, infer_key in zip(cfg.MODEL.output_keys, output_v.keys())
+    }
+
+    output_v = output_v["output_v"]
+
+    output_v[0, 0, -pad_singleside:, pad_singleside:-pad_singleside] = 0
+    output_v[0, 0, :pad_singleside, pad_singleside:-pad_singleside] = 1
+    output_v[0, 0, pad_singleside:-pad_singleside, -pad_singleside:] = 1
+    output_v[0, 0, pad_singleside:-pad_singleside, 0:pad_singleside] = 1
+    output_v[0, 0, 0, 0] = 0.5 * (output_v[0, 0, 0, 1] + output_v[0, 0, 1, 0])
+    output_v[0, 0, 0, -1] = 0.5 * (output_v[0, 0, 0, -2] + output_v[0, 0, 1, -1])
+
+    ev = paddle.sqrt(
+        paddle.mean((ofv_sb - output_v[0, 0]) ** 2) / paddle.mean(ofv_sb**2)
+    ).item()
+    logger.info(f"ev: {ev}")
+
+    fig = plt.figure()
+    ax = plt.subplot(1, 2, 1)
+    utils.visualize(
+        ax,
+        coords[0, 0, 1:-1, 1:-1],
+        coords[0, 1, 1:-1, 1:-1],
+        output_v[0, 0, 1:-1, 1:-1],
+        "horizontal",
+        [0, 1],
+    )
+    utils.set_axis_label(ax, "p")
+    ax.set_title("CNN " + r"$T$")
+    ax.set_aspect("equal")
+    ax = plt.subplot(1, 2, 2)
+    utils.visualize(
+        ax,
+        coords[0, 0, 1:-1, 1:-1],
+        coords[0, 1, 1:-1, 1:-1],
+        ofv_sb[1:-1, 1:-1],
+        "horizontal",
+        [0, 1],
+    )
+    utils.set_axis_label(ax, "p")
+    ax.set_aspect("equal")
+    ax.set_title("FV " + r"$T$")
+    fig.tight_layout(pad=1)
+    fig.savefig(osp.join(cfg.output_dir, "result.png"), bbox_inches="tight")
+    plt.close(fig)
+
+
 @hydra.main(version_base=None, config_path="./conf", config_name="heat_equation.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__":

examples/phygeonet/heat_equation_with_bc.py

@@ -188,6 +188,119 @@ def evaluate(cfg: DictConfig):
         plt.close(fig1)
 
 
+def export(cfg: DictConfig):
+    model = ppsci.arch.USCNN(**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, 19, 84], "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)
+    pad_singleside = cfg.MODEL.pad_singleside
+
+    data = np.load(cfg.test_data_dir)
+    paras = data["paras"]
+    truths = data["truths"]
+    coords = data["coords"]
+
+    paras = paras.reshape([paras.shape[0], 1, paras.shape[1], paras.shape[2]])
+    input_spec = {"coords": paras}
+    output_v = predictor.predict(input_spec, cfg.INFER.batch_size)
+    # mapping data to cfg.INFER.output_keys
+    output_v = {
+        store_key: output_v[infer_key]
+        for store_key, infer_key in zip(cfg.MODEL.output_keys, output_v.keys())
+    }
+    output_v = output_v["output_v"]
+    num_sample = output_v.shape[0]
+    for j in range(num_sample):
+        # Impose BC
+        output_v[j, 0, -pad_singleside:, pad_singleside:-pad_singleside] = output_v[
+            j, 0, 1:2, pad_singleside:-pad_singleside
+        ]
+        output_v[j, 0, :pad_singleside, pad_singleside:-pad_singleside] = output_v[
+            j, 0, -2:-1, pad_singleside:-pad_singleside
+        ]
+        output_v[j, 0, :, -pad_singleside:] = 0
+        output_v[j, 0, :, 0:pad_singleside] = paras[j, 0, 0, 0]
+
+    error = paddle.sqrt(
+        paddle.mean((truths - output_v) ** 2) / paddle.mean(truths**2)
+    ).item()
+    logger.info(f"The average error: {error / num_sample}")
+
+    output_vs = output_v
+    PARALIST = [1, 2, 3, 4, 5, 6, 7]
+    for i in range(len(PARALIST)):
+        truth = truths[i]
+        coord = coords[i]
+        output_v = output_vs[i]
+        truth = truth.reshape(1, 1, truth.shape[0], truth.shape[1])
+        coord = coord.reshape(1, 2, coord.shape[2], coord.shape[3])
+        fig1 = plt.figure()
+        xylabelsize = 20
+        xytickssize = 20
+        titlesize = 20
+        ax = plt.subplot(1, 2, 1)
+        _, cbar = utils.visualize(
+            ax,
+            coord[0, 0, :, :],
+            coord[0, 1, :, :],
+            output_v[0, :, :],
+            "horizontal",
+            [0, max(PARALIST)],
+        )
+        ax.set_aspect("equal")
+        utils.set_axis_label(ax, "p")
+        ax.set_title("PhyGeoNet " + r"$T$", fontsize=titlesize)
+        ax.set_xlabel(xlabel=r"$x$", fontsize=xylabelsize)
+        ax.set_ylabel(ylabel=r"$y$", fontsize=xylabelsize)
+        ax.set_xticks([-1, 0, 1])
+        ax.set_yticks([-1, 0, 1])
+        ax.tick_params(axis="x", labelsize=xytickssize)
+        ax.tick_params(axis="y", labelsize=xytickssize)
+        cbar.set_ticks([0, 1, 2, 3, 4, 5, 6, 7])
+        cbar.ax.tick_params(labelsize=xytickssize)
+        ax = plt.subplot(1, 2, 2)
+        _, cbar = utils.visualize(
+            ax,
+            coord[0, 0, :, :],
+            coord[0, 1, :, :],
+            truth[0, 0, :, :],
+            "horizontal",
+            [0, max(PARALIST)],
+        )
+        ax.set_aspect("equal")
+        utils.set_axis_label(ax, "p")
+        ax.set_title("FV " + r"$T$", fontsize=titlesize)
+        ax.set_xlabel(xlabel=r"$x$", fontsize=xylabelsize)
+        ax.set_ylabel(ylabel=r"$y$", fontsize=xylabelsize)
+        ax.set_xticks([-1, 0, 1])
+        ax.set_yticks([-1, 0, 1])
+        ax.tick_params(axis="x", labelsize=xytickssize)
+        ax.tick_params(axis="y", labelsize=xytickssize)
+        cbar.set_ticks([0, 1, 2, 3, 4, 5, 6, 7])
+        cbar.ax.tick_params(labelsize=xytickssize)
+        fig1.tight_layout(pad=1)
+        fig1.savefig(osp.join(cfg.output_dir, f"Para{i}T.png"), bbox_inches="tight")
+        plt.close(fig1)
+
+
 @hydra.main(
     version_base=None, config_path="./conf", config_name="heat_equation_with_bc.yaml"
 )
@@ -196,8 +309,14 @@ 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__":