【PPSCI Export&Infer No.22】VP_NSFNet4 (#864)

smallpoxscattered · web-flow · commit 0717f068e3ed · 2024-04-24T10:25:17.000+08:00
* [SCI Export&amp;Infer No.24] biharmonic2d

* P[PSCI Export&amp;Infer No.724] biharmonic2d fix

* add export&amp;infer nsfnet4

* add export&amp;infer nsfnet4
diff --git a/docs/zh/examples/nsfnet4.md b/docs/zh/examples/nsfnet4.md
@@ -29,6 +29,25 @@
 
     ```
 
+=== "模型导出命令"
+
+    ``` sh
+    python VP_NSFNet4.py mode=export
+    ```
+
+=== "模型推理命令"
+
+    ``` sh
+    # VP_NSFNet4
+    # linux
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/NSFNet/NSF4_data.zip -P ./data/
+    unzip ./data/NSF4_data.zip
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/NSFNet/NSF4_data.zip --output ./data/NSF4_data.zip
+    # unzip ./data/NSF4_data.zip
+    python VP_NSFNet4.py mode=infer
+    ```
+
 ## 1. 背景简介
 
  最近几年, 深度学习在很多领域取得了非凡的成就, 尤其是计算机视觉和自然语言处理方面, 而受启发于深度学习的快速发展, 基于深度学习强大的函数逼近能力, 神经网络在科学计算领域也取得了成功, 现阶段的研究主要分为两大类, 一类是将物理信息以及物理限制加入损失函数来对神经网络进行训练,  其代表有 PINN 以及 Deep Ritz Net, 另一类是通过数据驱动的深度神经网络算子, 其代表有 FNO 以及 DeepONet。这些方法都在科学实践中获得了广泛应用, 比如天气预测, 量子化学, 生物工程, 以及计算流体等领域。而为充分探索PINN对流体方程的求解能力, 本次复现[论文](https://arxiv.org/abs/2003.06496)作者设计了NSFNets, 并且先后使用具有解析解或数值解的二维、三维纳韦斯托克方程以及使用DNS方法进行高精度求解的数据集作为参考,  进行正问题求解训练。论文实验表明PINN对不可压纳韦斯托克方程具有优秀的数值求解能力,  本项目主要目标是使用PaddleScience复现论文所实现的高精度求解纳韦斯托克方程的代码。
diff --git a/examples/nsfnet/VP_NSFNet4.py b/examples/nsfnet/VP_NSFNet4.py
@@ -389,7 +389,7 @@ def evaluate(cfg: DictConfig):
     t_plot = paddle.to_tensor((t[-1]) * np.ones(x_plot.shape), paddle.float32)
     sol = model({"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot})
     fig, ax = plt.subplots(1, 4, figsize=(16, 4))
-    cmap = plt.cm.get_cmap("jet")
+    cmap = matplotlib.colormaps.get_cmap("jet")
 
     ax[0].contourf(grid_x, grid_y, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
     ax[0].set_title("u prediction")
@@ -422,7 +422,167 @@ def evaluate(cfg: DictConfig):
     t_plot = paddle.to_tensor((t[-1]) * np.ones(x_plot.shape), paddle.float32)
     sol = model({"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot})
     fig, ax = plt.subplots(1, 4, figsize=(16, 4))
-    cmap = plt.cm.get_cmap("jet")
+    cmap = matplotlib.colormaps.get_cmap("jet")
+
+    ax[0].contourf(grid_y, grid_z, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[0].set_title("u prediction")
+    ax[1].contourf(grid_y, grid_z, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[1].set_title("v prediction")
+    ax[2].contourf(grid_y, grid_z, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[2].set_title("w prediction")
+    ax[3].contourf(grid_y, grid_z, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[3].set_title("p prediction")
+    norm = matplotlib.colors.Normalize(
+        vmin=sol["u"].min(), vmax=sol["u"].max()
+    )  # set maximum and minimum
+    im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
+    ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    plt.savefig(osp.join(cfg.output_dir, "x=0 plane"))
+
+
+def export(cfg: DictConfig):
+    from paddle.static import InputSpec
+
+    # set models
+    model = ppsci.arch.MLP(**cfg.MODEL)
+
+    # load pretrained model
+    solver = ppsci.solver.Solver(
+        model=model, pretrained_model_path=cfg.INFER.pretrained_model_path
+    )
+
+    # 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)
+
+
+def inference(cfg: DictConfig):
+    from deploy.python_infer import pinn_predictor
+
+    # set model predictor
+    predictor = pinn_predictor.PINNPredictor(cfg)
+
+    # infer Data
+    test_x = np.load(osp.join(cfg.data_dir, "test43_l.npy")).astype(np.float32)
+    test_v = np.load(osp.join(cfg.data_dir, "test43_vp.npy")).astype(np.float32)
+    t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065]).astype(
+        np.float32
+    )
+    t_star = np.tile(t.reshape(5, 1), (1, 3000)).reshape(-1, 1)
+    x_star = np.tile(test_x[:, 0:1], (5, 1)).reshape(-1, 1)
+    y_star = np.tile(test_x[:, 1:2], (5, 1)).reshape(-1, 1)
+    z_star = np.tile(test_x[:, 2:3], (5, 1)).reshape(-1, 1)
+    u_star = test_v[:, 0:1]
+    v_star = test_v[:, 1:2]
+    w_star = test_v[:, 2:3]
+    p_star = test_v[:, 3:4]
+
+    pred = predictor.predict(
+        {
+            "x": x_star,
+            "y": y_star,
+            "z": z_star,
+            "t": t_star,
+        },
+        cfg.INFER.batch_size,
+    )
+
+    pred = {
+        store_key: pred[infer_key]
+        for store_key, infer_key in zip(cfg.INFER.output_keys, pred.keys())
+    }
+
+    u_pred = pred["u"].reshape((5, -1))
+    v_pred = pred["v"].reshape((5, -1))
+    w_pred = pred["w"].reshape((5, -1))
+    p_pred = pred["p"].reshape((5, -1))
+    u_star = u_star.reshape((5, -1))
+    v_star = v_star.reshape((5, -1))
+    w_star = w_star.reshape((5, -1))
+    p_star = p_star.reshape((5, -1))
+
+    # NS equation can figure out pressure drop, need background pressure p_star.mean()
+    p_pred = p_pred - p_pred.mean() + p_star.mean()
+
+    u_error = np.linalg.norm(u_pred - u_star, axis=1) / np.linalg.norm(u_star, axis=1)
+    v_error = np.linalg.norm(v_pred - v_star, axis=1) / np.linalg.norm(v_star, axis=1)
+    w_error = np.linalg.norm(w_pred - w_star, axis=1) / np.linalg.norm(w_star, axis=1)
+    p_error = np.linalg.norm(p_pred - p_star, axis=1) / np.linalg.norm(w_star, axis=1)
+    t = np.array([0.0065, 4 * 0.0065, 7 * 0.0065, 10 * 0.0065, 13 * 0.0065])
+    plt.plot(t, np.array(u_error))
+    plt.plot(t, np.array(v_error))
+    plt.plot(t, np.array(w_error))
+    plt.plot(t, np.array(p_error))
+    plt.legend(["u_error", "v_error", "w_error", "p_error"])
+    plt.xlabel("t")
+    plt.ylabel("Relative l2 Error")
+    plt.title("Relative l2 Error, on test dataset")
+    plt.savefig(osp.join(cfg.output_dir, "error.jpg"))
+
+    grid_x, grid_y = np.mgrid[
+        x_star.min() : x_star.max() : 100j, y_star.min() : y_star.max() : 100j
+    ].astype(np.float32)
+    x_plot = grid_x.reshape(-1, 1)
+    y_plot = grid_y.reshape(-1, 1)
+    z_plot = (z_star.min() * np.ones(y_plot.shape)).astype(np.float32)
+    t_plot = ((t[-1]) * np.ones(x_plot.shape)).astype(np.float32)
+    sol = predictor.predict(
+        {"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot}, cfg.INFER.batch_size
+    )
+    sol = {
+        store_key: sol[infer_key]
+        for store_key, infer_key in zip(cfg.INFER.output_keys, sol.keys())
+    }
+    fig, ax = plt.subplots(1, 4, figsize=(16, 4))
+    cmap = matplotlib.colormaps.get_cmap("jet")
+
+    ax[0].contourf(grid_x, grid_y, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[0].set_title("u prediction")
+    ax[1].contourf(grid_x, grid_y, sol["v"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[1].set_title("v prediction")
+    ax[2].contourf(grid_x, grid_y, sol["w"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[2].set_title("w prediction")
+    ax[3].contourf(grid_x, grid_y, sol["p"].reshape(grid_x.shape), levels=50, cmap=cmap)
+    ax[3].set_title("p prediction")
+    norm = matplotlib.colors.Normalize(
+        vmin=sol["u"].min(), vmax=sol["u"].max()
+    )  # set maximum and minimum
+    im = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
+    ax13 = fig.add_axes([0.125, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.325, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.525, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    ax13 = fig.add_axes([0.725, 0.0, 0.175, 0.02])
+    plt.colorbar(im, cax=ax13, orientation="horizontal")
+    plt.savefig(osp.join(cfg.output_dir, "z=0 plane"))
+
+    grid_y, grid_z = np.mgrid[
+        y_star.min() : y_star.max() : 100j, z_star.min() : z_star.max() : 100j
+    ].astype(np.float32)
+    z_plot = grid_z.reshape(-1, 1)
+    y_plot = grid_y.reshape(-1, 1)
+    x_plot = (x_star.min() * np.ones(y_plot.shape)).astype(np.float32)
+    t_plot = ((t[-1]) * np.ones(x_plot.shape)).astype(np.float32)
+    sol = predictor.predict(
+        {"x": x_plot, "y": y_plot, "z": z_plot, "t": t_plot}, cfg.INFER.batch_size
+    )
+    sol = {
+        store_key: sol[infer_key]
+        for store_key, infer_key in zip(cfg.INFER.output_keys, sol.keys())
+    }
+    fig, ax = plt.subplots(1, 4, figsize=(16, 4))
+    cmap = matplotlib.colormaps.get_cmap("jet")
 
     ax[0].contourf(grid_y, grid_z, sol["u"].reshape(grid_x.shape), levels=50, cmap=cmap)
     ax[0].set_title("u prediction")
@@ -453,9 +613,13 @@ 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(
-            osp.join("cfg.mode should in ['train', 'eval'], but got", cfg.mode)
+            f"cfg.mode should in ['train', 'eval', 'export', 'infer'], but got '{cfg.mode}'"
         )
 
 
diff --git a/examples/nsfnet/conf/VP_NSFNet4.yaml b/examples/nsfnet/conf/VP_NSFNet4.yaml
@@ -23,6 +23,7 @@ hydra:
 seed: 1234
 output_dir: ${hydra:run.dir}
 data_dir: ./data/
+log_freq: 20
 MODEL:
   input_keys: ["x", "y","z","t"]
   output_keys: ["u", "v", "w","p"]
@@ -52,3 +53,21 @@ EVAL:
   pretrained_model_path: null
   eval_with_no_grad: true
 
+
+INFER:
+  pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/nsfnet/nsfnet4.pdparams
+  export_path: ./inference/VP_NSFNet4
+  pdmodel_path: ${INFER.export_path}.pdmodel
+  pdpiparams_path: ${INFER.export_path}.pdiparams
+  output_keys: ['p', 'u', 'v', 'w']
+  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
