From 5291939746b646aba0372d913c7f39120dd073e9 Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Fri, 27 Oct 2023 13:42:16 +0800
Subject: [PATCH 1/7] fix

---
 docs/zh/examples/aneurysm.md         |  65 +++---
 examples/aneurysm/aneurysm.py        | 318 ++++++++++++++++++++++-----
 examples/aneurysm/conf/aneurysm.yaml |  83 +++++++
 3 files changed, 387 insertions(+), 79 deletions(-)
 create mode 100644 examples/aneurysm/conf/aneurysm.yaml

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index ac3cc2e7e..b3c8e9f9c 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -2,6 +2,11 @@
 
 <!-- <a href="TODO" class="md-button md-button--primary" style>AI Studio快速体验</a> -->
 
+=== "模型训练命令"
+    ``` sh
+    python aneurysm.py
+    ```
+
 ## 1. 背景简介
 
 深度学习方法可以用于处理血管瘤问题，其中包括基于物理信息的深度学习方法。这种方法可以用于脑血管瘤的压力建模，以预测和评估血管瘤破裂的风险。
@@ -32,9 +37,9 @@ $$
 
 上式中 $f$ 即为 MLP 模型本身，用 PaddleScience 代码表示如下
 
-``` py linenums="26"
+``` py linenums="23"
 --8<--
-examples/aneurysm/aneurysm.py:26:29
+examples/aneurysm/aneurysm.py:23
 --8<--
 ```
 
@@ -46,9 +51,9 @@ examples/aneurysm/aneurysm.py:26:29
 
 血管瘤模型涉及到 2 个方程，一是流体 N-S 方程，二是流量计算方程，因此使用 PaddleScience 内置的 `NavierStokes` 和 `NormalDotVec` 即可。
 
-``` py linenums="31"
+``` py linenums="25"
 --8<--
-examples/aneurysm/aneurysm.py:31:37
+examples/aneurysm/aneurysm.py:25:30
 --8<--
 ```
 
@@ -77,17 +82,17 @@ tar -xvf aneurysm_dataset.tar
 
 然后通过 PaddleScience 内置的 STL 几何类 `Mesh` 来读取、解析这些几何文件，并且通过布尔运算，组合出各个计算域，代码如下：
 
-``` py linenums="39"
+``` py linenums="30"
 --8<--
-examples/aneurysm/aneurysm.py:39:44
+examples/aneurysm/aneurysm.py:30:35
 --8<--
 ```
 
 在此之后可以对几何域进行缩放和平移，以缩放输入数据的坐标范围，促进模型训练收敛。
 
-``` py linenums="46"
+``` py linenums="37"
 --8<--
-examples/aneurysm/aneurysm.py:46:62
+examples/aneurysm/aneurysm.py:37:53
 --8<--
 ```
 
@@ -95,9 +100,9 @@ examples/aneurysm/aneurysm.py:46:62
 
 本案例共涉及到 6 个约束，在具体约束构建之前，可以先构建数据读取配置，以便后续构建多个约束时复用该配置。
 
-``` py linenums="64"
+``` py linenums="55"
 --8<--
-examples/aneurysm/aneurysm.py:64:75
+examples/aneurysm/aneurysm.py:55:65
 --8<--
 ```
 
@@ -105,9 +110,9 @@ examples/aneurysm/aneurysm.py:64:75
 
 以作用在内部点上的 `InteriorConstraint` 为例，代码如下：
 
-``` py linenums="125"
+``` py linenums="115"
 --8<--
-examples/aneurysm/aneurysm.py:125:132
+examples/aneurysm/aneurysm.py:115:122
 --8<--
 ```
 
@@ -128,17 +133,17 @@ examples/aneurysm/aneurysm.py:125:132
 接着需要对**血管入口、出口、血管壁**这三个表面施加约束，包括入口速度约束、出口压力约束、血管壁无滑移约束。
 在 `bc_inlet` 约束中，入口处的流速满足从中心点开始向周围呈二次抛物线衰减，此处使用抛物线函数表示速度随着远离圆心而衰减，再将其作为 `BoundaryConstraint` 的第二个参数(字典)的 value。
 
-``` py linenums="77"
+``` py linenums="67"
 --8<--
-examples/aneurysm/aneurysm.py:77:108
+examples/aneurysm/aneurysm.py:67:98
 --8<--
 ```
 
 血管出口、血管壁约束的构建方法类似，如下所示：
 
-``` py linenums="109"
+``` py linenums="99"
 --8<--
-examples/aneurysm/aneurysm.py:109:124
+examples/aneurysm/aneurysm.py:99:114
 --8<--
 ```
 
@@ -146,9 +151,9 @@ examples/aneurysm/aneurysm.py:109:124
 
 对于血管入口下方的一段区域和出口区域（面），需额外施加流入和流出的流量约束，由于流量计算涉及到具体面积，因此需要使用离散积分的方式进行计算，这些过程已经内置在了 `IntegralConstraint` 这一约束条件中。如下所示：
 
-``` py linenums="133"
+``` py linenums="123"
 --8<--
-examples/aneurysm/aneurysm.py:133:160
+examples/aneurysm/aneurysm.py:123:150
 --8<--
 ```
 
@@ -164,9 +169,9 @@ $$
 
 在微分方程约束、边界约束、初值约束构建完毕之后，以刚才的命名为关键字，封装到一个字典中，方便后续访问。
 
-``` py linenums="161"
+``` py linenums="151"
 --8<--
-examples/aneurysm/aneurysm.py:161:169
+examples/aneurysm/aneurysm.py:151:159
 --8<--
 ```
 
@@ -174,9 +179,9 @@ examples/aneurysm/aneurysm.py:161:169
 
 接下来需要指定训练轮数和学习率，此处按实验经验，使用 1500 轮训练轮数。
 
-``` py linenums="171"
+``` py linenums="161"
 --8<--
-examples/aneurysm/aneurysm.py:171:172
+examples/aneurysm/aneurysm.py:161:162
 --8<--
 ```
 
@@ -184,9 +189,9 @@ examples/aneurysm/aneurysm.py:171:172
 
 训练过程会调用优化器来更新模型参数，此处选择较为常用的 `Adam` 优化器，并配合使用机器学习中常用的 OneCycle 学习率调整策略。
 
-``` py linenums="174"
+``` py linenums="164"
 --8<--
-examples/aneurysm/aneurysm.py:174:183
+examples/aneurysm/aneurysm.py:164:173
 --8<--
 ```
 
@@ -194,9 +199,9 @@ examples/aneurysm/aneurysm.py:174:183
 
 在训练过程中通常会按一定轮数间隔，用验证集（测试集）评估当前模型的训练情况，因此使用 `ppsci.validate.GeometryValidator` 构建评估器。
 
-``` py linenums="185"
+``` py linenums="175"
 --8<--
-examples/aneurysm/aneurysm.py:185:234
+examples/aneurysm/aneurysm.py:175:224
 --8<--
 ```
 
@@ -206,9 +211,9 @@ examples/aneurysm/aneurysm.py:185:234
 
 本文中的输出数据是一个区域内的三维点集，因此只需要将评估的输出数据保存成 **vtu格式** 文件，最后用可视化软件打开查看即可。代码如下：
 
-``` py linenums="236"
+``` py linenums="226"
 --8<--
-examples/aneurysm/aneurysm.py:236:250
+examples/aneurysm/aneurysm.py:226:240
 --8<--
 ```
 
@@ -216,9 +221,9 @@ examples/aneurysm/aneurysm.py:236:250
 
 完成上述设置之后，只需要将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`，然后启动训练、评估、可视化。
 
-``` py linenums="251"
+``` py linenums="241"
 --8<--
-examples/aneurysm/aneurysm.py:251:278
+examples/aneurysm/aneurysm.py:241:267
 --8<--
 ```
 
diff --git a/examples/aneurysm/aneurysm.py b/examples/aneurysm/aneurysm.py
index 2d34ae0dc..3ced9f432 100644
--- a/examples/aneurysm/aneurysm.py
+++ b/examples/aneurysm/aneurysm.py
@@ -4,35 +4,29 @@
 pretrained model download link: https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams
 """
 
+from os import path as osp
+
+import hydra
 import numpy as np
+from omegaconf import DictConfig
 
 import ppsci
-from ppsci.utils import config
 from ppsci.utils import logger
 from ppsci.utils import reader
 
-if __name__ == "__main__":
-    args = config.parse_args()
+
+def train(cfg: DictConfig):
     # set random seed for reproducibility
-    SEED = 2023
-    ppsci.utils.misc.set_random_seed(SEED)
-    # set output directory
-    OUTPUT_DIR = (
-        f"./output_aneurysm_seed{SEED}" if not args.output_dir else args.output_dir
-    )
+    ppsci.utils.misc.set_random_seed(cfg.seed)
     # initialize logger
-    logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info")
+    logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
 
     # set model
-    model = ppsci.arch.MLP(
-        ("x", "y", "z"), ("u", "v", "w", "p"), 6, 512, "silu", weight_norm=True
-    )
+    model = ppsci.arch.MLP(**cfg.MODEL.model)
 
     # set equation
-    NU = 0.025
-    SCALE = 0.4
     equation = {
-        "NavierStokes": ppsci.equation.NavierStokes(NU * SCALE, 1.0, 3, False),
+        "NavierStokes": ppsci.equation.NavierStokes(cfg.NU * cfg.SCALE, 1.0, 3, False),
         "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
     }
 
@@ -62,10 +56,9 @@
     }
 
     # set dataloader config
-    ITERS_PER_EPOCH = 1000
     train_dataloader_cfg = {
         "dataset": "NamedArrayDataset",
-        "iters_per_epoch": ITERS_PER_EPOCH,
+        "iters_per_epoch": cfg.TRAIN.iters_per_epoch,
         "sampler": {
             "name": "BatchSampler",
             "drop_last": True,
@@ -102,7 +95,7 @@ def inlet_w_ref_func(_in):
         {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
         {"u": inlet_u_ref_func, "v": inlet_v_ref_func, "w": inlet_w_ref_func},
         geom["inlet_geo"],
-        {**train_dataloader_cfg, "batch_size": 1100},
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_inlet},
         ppsci.loss.MSELoss("sum"),
         name="inlet",
     )
@@ -110,7 +103,7 @@ def inlet_w_ref_func(_in):
         {"p": lambda d: d["p"]},
         {"p": 0},
         geom["outlet_geo"],
-        {**train_dataloader_cfg, "batch_size": 650},
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_outlet},
         ppsci.loss.MSELoss("sum"),
         name="outlet",
     )
@@ -118,7 +111,7 @@ def inlet_w_ref_func(_in):
         {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
         {"u": 0, "v": 0, "w": 0},
         geom["noslip_geo"],
-        {**train_dataloader_cfg, "batch_size": 5200},
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_noslip},
         ppsci.loss.MSELoss("sum"),
         name="no_slip",
     )
@@ -126,7 +119,7 @@ def inlet_w_ref_func(_in):
         equation["NavierStokes"].equations,
         {"continuity": 0, "momentum_x": 0, "momentum_y": 0, "momentum_z": 0},
         geom["interior_geo"],
-        {**train_dataloader_cfg, "batch_size": 6000},
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.pde_constraint},
         ppsci.loss.MSELoss("sum"),
         name="interior",
     )
@@ -136,12 +129,12 @@ def inlet_w_ref_func(_in):
         geom["outlet_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": 100,
-            "batch_size": 1,
-            "integral_batch_size": 310,
+            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_outlet,
+            "batch_size": cfg.TRAIN.batch_size.igc_outlet,
+            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_outlet,
         },
         ppsci.loss.IntegralLoss("sum"),
-        weight_dict={"normal_dot_vec": 0.1},
+        weight_dict=cfg.TRAIN.weight.igc_outlet,
         name="igc_outlet",
     )
     igc_integral = ppsci.constraint.IntegralConstraint(
@@ -150,12 +143,12 @@ def inlet_w_ref_func(_in):
         geom["integral_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": 100,
-            "batch_size": 1,
-            "integral_batch_size": 310,
+            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_integral,
+            "batch_size": cfg.TRAIN.batch_size.igc_integral,
+            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_integral,
         },
         ppsci.loss.IntegralLoss("sum"),
-        weight_dict={"normal_dot_vec": 0.1},
+        weight_dict=cfg.TRAIN.weight.igc_integral,
         name="igc_integral",
     )
     # wrap constraints together
@@ -169,15 +162,15 @@ def inlet_w_ref_func(_in):
     }
 
     # set training hyper-parameters
-    EPOCHS = 1500 if not args.epochs else args.epochs
+    # EPOCHS = 1500 if not args.epochs else args.epochs
 
     # set optimizer
     lr_scheduler = ppsci.optimizer.lr_scheduler.ExponentialDecay(
-        EPOCHS,
-        ITERS_PER_EPOCH,
-        0.001,
-        0.95,
-        EPOCHS * ITERS_PER_EPOCH // 100,
+        cfg.TRAIN.epochs,
+        cfg.TRAIN.iters_per_epoch,
+        cfg.TRAIN.learning_rate,
+        cfg.TRAIN.gamma,
+        cfg.TRAIN.epochs * cfg.TRAIN.iters_per_epoch // 100,
         by_epoch=False,
     )()
     optimizer = ppsci.optimizer.Adam(lr_scheduler)(model)
@@ -220,7 +213,7 @@ def inlet_w_ref_func(_in):
         "num_workers": 1,
     }
     sup_validator = ppsci.validate.SupervisedValidator(
-        {**eval_dataloader_cfg, "batch_size": 4096},
+        {**eval_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.sup_validator},
         ppsci.loss.MSELoss("mean"),
         {
             "p": lambda out: out["p"],
@@ -243,7 +236,7 @@ def inlet_w_ref_func(_in):
                 "v": lambda out: out["v"],
                 "w": lambda out: out["w"],
             },
-            batch_size=4096,
+            batch_size=cfg.TRAIN.batch_size.visualizer,
             prefix="result_u_v_w_p",
         ),
     }
@@ -252,16 +245,16 @@ def inlet_w_ref_func(_in):
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        OUTPUT_DIR,
+        cfg.output_dir,
         optimizer,
         lr_scheduler,
-        EPOCHS,
-        ITERS_PER_EPOCH,
-        save_freq=20,
-        eval_during_train=True,
-        log_freq=20,
-        eval_freq=20,
-        seed=SEED,
+        cfg.TRAIN.epochs,
+        cfg.TRAIN.iters_per_epoch,
+        save_freq=cfg.TRAIN.save_freq,
+        eval_during_train=cfg.TRAIN.eval_during_train,
+        log_freq=cfg.TRAIN.log_freq,
+        eval_freq=cfg.TRAIN.eval_freq,
+        seed=cfg.seed,
         equation=equation,
         geom=geom,
         validator=validator,
@@ -276,19 +269,246 @@ def inlet_w_ref_func(_in):
     # visualize prediction after finished training
     solver.visualize()
 
+
+def evaluate(cfg: DictConfig):
+    # set random seed for reproducibility
+    ppsci.utils.misc.set_random_seed(cfg.seed)
+    # initialize logger
+    logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
+
+    # set model
+    model = ppsci.arch.MLP(**cfg.MODEL.model)
+
+    # set equation
+    equation = {
+        "NavierStokes": ppsci.equation.NavierStokes(cfg.NU * cfg.SCALE, 1.0, 3, False),
+        "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
+    }
+
+    # set geometry
+    inlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_inlet.stl")
+    outlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_outlet.stl")
+    noslip_geo = ppsci.geometry.Mesh("./stl/aneurysm_noslip.stl")
+    integral_geo = ppsci.geometry.Mesh("./stl/aneurysm_integral.stl")
+    interior_geo = ppsci.geometry.Mesh("./stl/aneurysm_closed.stl")
+
+    # inlet velocity profile
+    CENTER = (-18.40381048596882, -50.285383353981196, 12.848136936899031)
+    SCALE = 0.4
+
+    # normalize meshes
+    inlet_geo = inlet_geo.translate(-np.array(CENTER)).scale(SCALE)
+    outlet_geo = outlet_geo.translate(-np.array(CENTER)).scale(SCALE)
+    noslip_geo = noslip_geo.translate(-np.array(CENTER)).scale(SCALE)
+    integral_geo = integral_geo.translate(-np.array(CENTER)).scale(SCALE)
+    interior_geo = interior_geo.translate(-np.array(CENTER)).scale(SCALE)
+    geom = {
+        "inlet_geo": inlet_geo,
+        "outlet_geo": outlet_geo,
+        "noslip_geo": noslip_geo,
+        "integral_geo": integral_geo,
+        "interior_geo": interior_geo,
+    }
+
+    # set dataloader config
+    train_dataloader_cfg = {
+        "dataset": "NamedArrayDataset",
+        "iters_per_epoch": cfg.TRAIN.iters_per_epoch,
+        "sampler": {
+            "name": "BatchSampler",
+            "drop_last": True,
+            "shuffle": True,
+        },
+        "num_workers": 1,
+    }
+
+    # set constraint
+    INLET_NORMAL = (0.8526, -0.428, 0.299)
+    INLET_AREA = 21.1284 * (SCALE**2)
+    INLET_CENTER = (-4.24298030045776, 4.082857101816247, -4.637790193399717)
+    INLET_RADIUS = np.sqrt(INLET_AREA / np.pi)
+    INLET_VEL = 1.5
+
+    def _compute_parabola(_in):
+        centered_x = _in["x"] - INLET_CENTER[0]
+        centered_y = _in["y"] - INLET_CENTER[1]
+        centered_z = _in["z"] - INLET_CENTER[2]
+        distance = np.sqrt(centered_x**2 + centered_y**2 + centered_z**2)
+        parabola = INLET_VEL * np.maximum((1 - (distance / INLET_RADIUS) ** 2), 0)
+        return parabola
+
+    def inlet_u_ref_func(_in):
+        return INLET_NORMAL[0] * _compute_parabola(_in)
+
+    def inlet_v_ref_func(_in):
+        return INLET_NORMAL[1] * _compute_parabola(_in)
+
+    def inlet_w_ref_func(_in):
+        return INLET_NORMAL[2] * _compute_parabola(_in)
+
+    bc_inlet = ppsci.constraint.BoundaryConstraint(
+        {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
+        {"u": inlet_u_ref_func, "v": inlet_v_ref_func, "w": inlet_w_ref_func},
+        geom["inlet_geo"],
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_inlet},
+        ppsci.loss.MSELoss("sum"),
+        name="inlet",
+    )
+    bc_outlet = ppsci.constraint.BoundaryConstraint(
+        {"p": lambda d: d["p"]},
+        {"p": 0},
+        geom["outlet_geo"],
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_outlet},
+        ppsci.loss.MSELoss("sum"),
+        name="outlet",
+    )
+    bc_noslip = ppsci.constraint.BoundaryConstraint(
+        {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
+        {"u": 0, "v": 0, "w": 0},
+        geom["noslip_geo"],
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_noslip},
+        ppsci.loss.MSELoss("sum"),
+        name="no_slip",
+    )
+    pde_constraint = ppsci.constraint.InteriorConstraint(
+        equation["NavierStokes"].equations,
+        {"continuity": 0, "momentum_x": 0, "momentum_y": 0, "momentum_z": 0},
+        geom["interior_geo"],
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.pde_constraint},
+        ppsci.loss.MSELoss("sum"),
+        name="interior",
+    )
+    igc_outlet = ppsci.constraint.IntegralConstraint(
+        equation["NormalDotVec"].equations,
+        {"normal_dot_vec": 2.54},
+        geom["outlet_geo"],
+        {
+            **train_dataloader_cfg,
+            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_outlet,
+            "batch_size": cfg.TRAIN.batch_size.igc_outlet,
+            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_outlet,
+        },
+        ppsci.loss.IntegralLoss("sum"),
+        weight_dict=cfg.TRAIN.weight.igc_outlet,
+        name="igc_outlet",
+    )
+    igc_integral = ppsci.constraint.IntegralConstraint(
+        equation["NormalDotVec"].equations,
+        {"normal_dot_vec": -2.54},
+        geom["integral_geo"],
+        {
+            **train_dataloader_cfg,
+            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_integral,
+            "batch_size": cfg.TRAIN.batch_size.igc_integral,
+            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_integral,
+        },
+        ppsci.loss.IntegralLoss("sum"),
+        weight_dict=cfg.TRAIN.weight.igc_integral,
+        name="igc_integral",
+    )
+    # wrap constraints together
+    constraint = {
+        bc_inlet.name: bc_inlet,
+        bc_outlet.name: bc_outlet,
+        bc_noslip.name: bc_noslip,
+        pde_constraint.name: pde_constraint,
+        igc_outlet.name: igc_outlet,
+        igc_integral.name: igc_integral,
+    }
+
+    # set validator
+    eval_data_dict = reader.load_csv_file(
+        "./data/aneurysm_parabolicInlet_sol0.csv",
+        ("x", "y", "z", "u", "v", "w", "p"),
+        {
+            "x": "Points:0",
+            "y": "Points:1",
+            "z": "Points:2",
+            "u": "U:0",
+            "v": "U:1",
+            "w": "U:2",
+            "p": "p",
+        },
+    )
+    input_dict = {
+        "x": (eval_data_dict["x"] - CENTER[0]) * SCALE,
+        "y": (eval_data_dict["y"] - CENTER[1]) * SCALE,
+        "z": (eval_data_dict["z"] - CENTER[2]) * SCALE,
+    }
+    if "area" in input_dict.keys():
+        input_dict["area"] *= SCALE ** (equation["NavierStokes"].dim)
+
+    label_dict = {
+        "p": eval_data_dict["p"],
+        "u": eval_data_dict["u"],
+        "v": eval_data_dict["v"],
+        "w": eval_data_dict["w"],
+    }
+    eval_dataloader_cfg = {
+        "dataset": {
+            "name": "NamedArrayDataset",
+            "input": input_dict,
+            "label": label_dict,
+        },
+        "sampler": {"name": "BatchSampler"},
+        "num_workers": 1,
+    }
+    sup_validator = ppsci.validate.SupervisedValidator(
+        {**eval_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.sup_validator},
+        ppsci.loss.MSELoss("mean"),
+        {
+            "p": lambda out: out["p"],
+            "u": lambda out: out["u"],
+            "v": lambda out: out["v"],
+            "w": lambda out: out["w"],
+        },
+        metric={"MSE": ppsci.metric.MSE()},
+        name="ref_u_v_w_p",
+    )
+    validator = {sup_validator.name: sup_validator}
+
+    # set visualizer(optional)
+    visualizer = {
+        "visulzie_u_v_w_p": ppsci.visualize.VisualizerVtu(
+            input_dict,
+            {
+                "p": lambda out: out["p"],
+                "u": lambda out: out["u"],
+                "v": lambda out: out["v"],
+                "w": lambda out: out["w"],
+            },
+            batch_size=cfg.TRAIN.batch_size.visualizer,
+            prefix="result_u_v_w_p",
+        ),
+    }
+
     # directly evaluate pretrained model(optional)
-    logger.init_logger("ppsci", f"{OUTPUT_DIR}/eval.log", "info")
+    logger.init_logger("ppsci", osp.join(cfg.output_dir, "eval.log"), "info")
     solver = ppsci.solver.Solver(
         model,
         constraint,
-        OUTPUT_DIR,
+        cfg.output_dir,
         equation=equation,
         geom=geom,
         validator=validator,
         visualizer=visualizer,
-        pretrained_model_path=f"{OUTPUT_DIR}/checkpoints/latest",
+        pretrained_model_path=cfg.EVAL.pretrained_model_path,
         eval_with_no_grad=True,
     )
     solver.eval()
     # visualize prediction for pretrained model(optional)
     solver.visualize()
+
+
+@hydra.main(version_base=None, config_path="./conf", config_name="aneurysm.yaml")
+def main(cfg: DictConfig):
+    if cfg.mode == "train":
+        train(cfg)
+    elif cfg.mode == "eval":
+        evaluate(cfg)
+    else:
+        raise ValueError(f"cfg.mode should in ['train', 'eval'], but got '{cfg.mode}'")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/aneurysm/conf/aneurysm.yaml b/examples/aneurysm/conf/aneurysm.yaml
new file mode 100644
index 000000000..0e5184b32
--- /dev/null
+++ b/examples/aneurysm/conf/aneurysm.yaml
@@ -0,0 +1,83 @@
+hydra:
+  run:
+    # dynamic output directory according to running time and override name
+    dir: output_aneurysm/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+  job:
+    name: ${mode} # name of logfile
+    chdir: false # keep current working direcotry unchanged
+    config:
+      override_dirname:
+        exclude_keys:
+          - TRAIN.checkpoint_path
+          - TRAIN.pretrained_model_path
+          - EVAL.pretrained_model_path
+          - mode
+          - output_dir
+  sweep:
+    # output directory for multirun
+    dir: ${hydra.run.dir}
+    subdir: ./
+
+# general settings
+mode: train # running mode: train/eval
+seed: 2023
+output_dir: ${hydra:run.dir}
+
+# set working condition
+NU: 0.0025
+SCALE: 0.4
+
+# model settings
+MODEL:
+  model:
+    input_keys: ["x", "y", "z"]
+    output_keys: ["u", "v", "p"]
+    num_layers: 9
+    hidden_size: 50
+    activation: "tanh"
+
+# training settings
+TRAIN:
+  epochs: 1500
+  iters_per_epoch: 1000
+  eval_during_train: true
+  eval_freq: 200
+  learning_rate: 0.001
+  gamma: 0.95
+  save_freq: 20
+  eval_during_train: true
+  log_freq: 20
+  eval_freq: 20
+  batch_size:
+    bc_inlet: 1100
+    bc_outlet: 650
+    bc_noslip: 5200
+    pde_constraint: 6000
+    igc_outlet: 1
+    igc_integral: 1
+    sup_validator: 4096
+    visualizer: 4096
+  iters_per_epoch:
+    igc_outlet: 100
+    igc_integral:100
+  integral_batch_size:
+    igc_outlet: 310
+    igc_integral: 310
+
+  lr_scheduler:
+    epochs: ${TRAIN.epochs}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: ${TRAIN.learning_rate}
+    gamma: ${TRAIN.gamma}
+    decay_steps: 1500*1000 // 100
+  weight:
+    igc_outlet: {"normal_dot_vec": 0.1}
+    igc_integral: {"normal_dot_vec": 0.1}
+  pretrained_model_path: null
+  checkpoint_path: null
+
+# evaluation settings
+EVAL:
+  pretrained_model_path: null
+  batch_size:
+    residual_validator: 8192

From 631ecf99248fa98a8c82bbaa59084bafa2503a92 Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Mon, 30 Oct 2023 11:20:38 +0800
Subject: [PATCH 2/7] fix

---
 docs/zh/examples/aneurysm.md         |   7 ++
 examples/aneurysm/aneurysm.py        | 138 ++++-----------------------
 examples/aneurysm/conf/aneurysm.yaml |  15 ++-
 3 files changed, 34 insertions(+), 126 deletions(-)

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index b3c8e9f9c..63c96696c 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -3,10 +3,17 @@
 <!-- <a href="TODO" class="md-button md-button--primary" style>AI Studio快速体验</a> -->
 
 === "模型训练命令"
+
     ``` sh
     python aneurysm.py
     ```
 
+=== "模型评估命令"
+
+    ``` sh
+    python aneurysm.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams
+    ```
+
 ## 1. 背景简介
 
 深度学习方法可以用于处理血管瘤问题，其中包括基于物理信息的深度学习方法。这种方法可以用于脑血管瘤的压力建模，以预测和评估血管瘤破裂的风险。
diff --git a/examples/aneurysm/aneurysm.py b/examples/aneurysm/aneurysm.py
index 3ced9f432..f42724b68 100644
--- a/examples/aneurysm/aneurysm.py
+++ b/examples/aneurysm/aneurysm.py
@@ -26,16 +26,18 @@ def train(cfg: DictConfig):
 
     # set equation
     equation = {
-        "NavierStokes": ppsci.equation.NavierStokes(cfg.NU * cfg.SCALE, 1.0, 3, False),
+        "NavierStokes": ppsci.equation.NavierStokes(
+            cfg.NU * cfg.SCALE, cfg.RHO, 3, False
+        ),
         "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
     }
 
     # set geometry
-    inlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_inlet.stl")
-    outlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_outlet.stl")
-    noslip_geo = ppsci.geometry.Mesh("./stl/aneurysm_noslip.stl")
-    integral_geo = ppsci.geometry.Mesh("./stl/aneurysm_integral.stl")
-    interior_geo = ppsci.geometry.Mesh("./stl/aneurysm_closed.stl")
+    inlet_geo = ppsci.geometry.Mesh(cfg.INLET_PATH)
+    outlet_geo = ppsci.geometry.Mesh(cfg.OUTLET_PATH)
+    noslip_geo = ppsci.geometry.Mesh(cfg.NOSLIP_PATH)
+    integral_geo = ppsci.geometry.Mesh(cfg.INTEGRAL_PATH)
+    interior_geo = ppsci.geometry.Mesh(cfg.CLOSED_PATH)
 
     # inlet velocity profile
     CENTER = (-18.40381048596882, -50.285383353981196, 12.848136936899031)
@@ -161,9 +163,6 @@ def inlet_w_ref_func(_in):
         igc_integral.name: igc_integral,
     }
 
-    # set training hyper-parameters
-    # EPOCHS = 1500 if not args.epochs else args.epochs
-
     # set optimizer
     lr_scheduler = ppsci.optimizer.lr_scheduler.ExponentialDecay(
         cfg.TRAIN.epochs,
@@ -213,7 +212,7 @@ def inlet_w_ref_func(_in):
         "num_workers": 1,
     }
     sup_validator = ppsci.validate.SupervisedValidator(
-        {**eval_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.sup_validator},
+        {**eval_dataloader_cfg, "batch_size": cfg.EVAL.batch_size.sup_validator},
         ppsci.loss.MSELoss("mean"),
         {
             "p": lambda out: out["p"],
@@ -236,7 +235,7 @@ def inlet_w_ref_func(_in):
                 "v": lambda out: out["v"],
                 "w": lambda out: out["w"],
             },
-            batch_size=cfg.TRAIN.batch_size.visualizer,
+            batch_size=cfg.EVAL.batch_size.visualizer,
             prefix="result_u_v_w_p",
         ),
     }
@@ -259,6 +258,7 @@ def inlet_w_ref_func(_in):
         geom=geom,
         validator=validator,
         visualizer=visualizer,
+        checkpoint_path=cfg.TRAIN.checkpoint_path,
         eval_with_no_grad=True,
     )
     # train model
@@ -274,14 +274,16 @@ def evaluate(cfg: DictConfig):
     # set random seed for reproducibility
     ppsci.utils.misc.set_random_seed(cfg.seed)
     # initialize logger
-    logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
+    logger.init_logger("ppsci", osp.join(cfg.output_dir, "eval.log"), "info")
 
     # set model
     model = ppsci.arch.MLP(**cfg.MODEL.model)
 
     # set equation
     equation = {
-        "NavierStokes": ppsci.equation.NavierStokes(cfg.NU * cfg.SCALE, 1.0, 3, False),
+        "NavierStokes": ppsci.equation.NavierStokes(
+            cfg.NU * cfg.SCALE, cfg.RHO, 3, False
+        ),
         "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
     }
 
@@ -310,112 +312,6 @@ def evaluate(cfg: DictConfig):
         "interior_geo": interior_geo,
     }
 
-    # set dataloader config
-    train_dataloader_cfg = {
-        "dataset": "NamedArrayDataset",
-        "iters_per_epoch": cfg.TRAIN.iters_per_epoch,
-        "sampler": {
-            "name": "BatchSampler",
-            "drop_last": True,
-            "shuffle": True,
-        },
-        "num_workers": 1,
-    }
-
-    # set constraint
-    INLET_NORMAL = (0.8526, -0.428, 0.299)
-    INLET_AREA = 21.1284 * (SCALE**2)
-    INLET_CENTER = (-4.24298030045776, 4.082857101816247, -4.637790193399717)
-    INLET_RADIUS = np.sqrt(INLET_AREA / np.pi)
-    INLET_VEL = 1.5
-
-    def _compute_parabola(_in):
-        centered_x = _in["x"] - INLET_CENTER[0]
-        centered_y = _in["y"] - INLET_CENTER[1]
-        centered_z = _in["z"] - INLET_CENTER[2]
-        distance = np.sqrt(centered_x**2 + centered_y**2 + centered_z**2)
-        parabola = INLET_VEL * np.maximum((1 - (distance / INLET_RADIUS) ** 2), 0)
-        return parabola
-
-    def inlet_u_ref_func(_in):
-        return INLET_NORMAL[0] * _compute_parabola(_in)
-
-    def inlet_v_ref_func(_in):
-        return INLET_NORMAL[1] * _compute_parabola(_in)
-
-    def inlet_w_ref_func(_in):
-        return INLET_NORMAL[2] * _compute_parabola(_in)
-
-    bc_inlet = ppsci.constraint.BoundaryConstraint(
-        {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
-        {"u": inlet_u_ref_func, "v": inlet_v_ref_func, "w": inlet_w_ref_func},
-        geom["inlet_geo"],
-        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_inlet},
-        ppsci.loss.MSELoss("sum"),
-        name="inlet",
-    )
-    bc_outlet = ppsci.constraint.BoundaryConstraint(
-        {"p": lambda d: d["p"]},
-        {"p": 0},
-        geom["outlet_geo"],
-        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_outlet},
-        ppsci.loss.MSELoss("sum"),
-        name="outlet",
-    )
-    bc_noslip = ppsci.constraint.BoundaryConstraint(
-        {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
-        {"u": 0, "v": 0, "w": 0},
-        geom["noslip_geo"],
-        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.bc_noslip},
-        ppsci.loss.MSELoss("sum"),
-        name="no_slip",
-    )
-    pde_constraint = ppsci.constraint.InteriorConstraint(
-        equation["NavierStokes"].equations,
-        {"continuity": 0, "momentum_x": 0, "momentum_y": 0, "momentum_z": 0},
-        geom["interior_geo"],
-        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.pde_constraint},
-        ppsci.loss.MSELoss("sum"),
-        name="interior",
-    )
-    igc_outlet = ppsci.constraint.IntegralConstraint(
-        equation["NormalDotVec"].equations,
-        {"normal_dot_vec": 2.54},
-        geom["outlet_geo"],
-        {
-            **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_outlet,
-            "batch_size": cfg.TRAIN.batch_size.igc_outlet,
-            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_outlet,
-        },
-        ppsci.loss.IntegralLoss("sum"),
-        weight_dict=cfg.TRAIN.weight.igc_outlet,
-        name="igc_outlet",
-    )
-    igc_integral = ppsci.constraint.IntegralConstraint(
-        equation["NormalDotVec"].equations,
-        {"normal_dot_vec": -2.54},
-        geom["integral_geo"],
-        {
-            **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_integral,
-            "batch_size": cfg.TRAIN.batch_size.igc_integral,
-            "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_integral,
-        },
-        ppsci.loss.IntegralLoss("sum"),
-        weight_dict=cfg.TRAIN.weight.igc_integral,
-        name="igc_integral",
-    )
-    # wrap constraints together
-    constraint = {
-        bc_inlet.name: bc_inlet,
-        bc_outlet.name: bc_outlet,
-        bc_noslip.name: bc_noslip,
-        pde_constraint.name: pde_constraint,
-        igc_outlet.name: igc_outlet,
-        igc_integral.name: igc_integral,
-    }
-
     # set validator
     eval_data_dict = reader.load_csv_file(
         "./data/aneurysm_parabolicInlet_sol0.csv",
@@ -483,11 +379,9 @@ def inlet_w_ref_func(_in):
     }
 
     # directly evaluate pretrained model(optional)
-    logger.init_logger("ppsci", osp.join(cfg.output_dir, "eval.log"), "info")
     solver = ppsci.solver.Solver(
         model,
-        constraint,
-        cfg.output_dir,
+        output_dir=cfg.output_dir,
         equation=equation,
         geom=geom,
         validator=validator,
diff --git a/examples/aneurysm/conf/aneurysm.yaml b/examples/aneurysm/conf/aneurysm.yaml
index 0e5184b32..a98c4221d 100644
--- a/examples/aneurysm/conf/aneurysm.yaml
+++ b/examples/aneurysm/conf/aneurysm.yaml
@@ -26,6 +26,14 @@ output_dir: ${hydra:run.dir}
 # set working condition
 NU: 0.0025
 SCALE: 0.4
+RHO: 1.0
+
+# set geometry file path
+INLET_PATH: ./stl/aneurysm_inlet.stl
+OUTLET_PATH: ./stl/aneurysm_outlet.stl
+NOSLIP_PATH: ./stl/aneurysm_noslip.stl
+INTEGRAL_PATH: ./stl/aneurysm_integral.stl
+CLOSED_PATH: ./stl/aneurysm_closed.stl
 
 # model settings
 MODEL:
@@ -55,15 +63,12 @@ TRAIN:
     pde_constraint: 6000
     igc_outlet: 1
     igc_integral: 1
-    sup_validator: 4096
-    visualizer: 4096
   iters_per_epoch:
     igc_outlet: 100
-    igc_integral:100
+    igc_integral: 100
   integral_batch_size:
     igc_outlet: 310
     igc_integral: 310
-
   lr_scheduler:
     epochs: ${TRAIN.epochs}
     iters_per_epoch: ${TRAIN.iters_per_epoch}
@@ -81,3 +86,5 @@ EVAL:
   pretrained_model_path: null
   batch_size:
     residual_validator: 8192
+    sup_validator: 4096
+    visualizer: 4096

From a9d756787be8dcc69df4caf85c3da2b9347ca64b Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Mon, 30 Oct 2023 16:41:50 +0800
Subject: [PATCH 3/7] fix

---
 docs/zh/examples/aneurysm.md         | 12 ++++++++++
 examples/aneurysm/aneurysm.py        | 36 ++++++----------------------
 examples/aneurysm/conf/aneurysm.yaml | 15 ++++--------
 3 files changed, 24 insertions(+), 39 deletions(-)

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index 63c96696c..fcba4caf9 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -5,12 +5,24 @@
 === "模型训练命令"
 
     ``` sh
+    # linux
+    wget https://paddle-org.bj.bcebos.com/paddlescience/datasets/aneurysm/aneurysm_dataset.tar
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/aneurysm/aneurysm_dataset.tar --output aneurysm_dataset.tar
+    # unzip it
+    tar -xvf aneurysm_dataset.tar
     python aneurysm.py
     ```
 
 === "模型评估命令"
 
     ``` sh
+    # linux
+    wget https://paddle-org.bj.bcebos.com/paddlescience/datasets/aneurysm/aneurysm_dataset.tar
+    # windows
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/aneurysm/aneurysm_dataset.tar --output aneurysm_dataset.tar
+    # unzip it
+    tar -xvf aneurysm_dataset.tar
     python aneurysm.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams
     ```
 
diff --git a/examples/aneurysm/aneurysm.py b/examples/aneurysm/aneurysm.py
index f42724b68..7cc2d03cb 100644
--- a/examples/aneurysm/aneurysm.py
+++ b/examples/aneurysm/aneurysm.py
@@ -131,7 +131,7 @@ def inlet_w_ref_func(_in):
         geom["outlet_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_outlet,
+            "iters_per_epoch": cfg.TRAIN.iters_igc_outlet,
             "batch_size": cfg.TRAIN.batch_size.igc_outlet,
             "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_outlet,
         },
@@ -145,7 +145,7 @@ def inlet_w_ref_func(_in):
         geom["integral_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_per_epoch.igc_integral,
+            "iters_per_epoch": cfg.TRAIN.iters_igc_integral,
             "batch_size": cfg.TRAIN.batch_size.igc_integral,
             "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_integral,
         },
@@ -235,7 +235,7 @@ def inlet_w_ref_func(_in):
                 "v": lambda out: out["v"],
                 "w": lambda out: out["w"],
             },
-            batch_size=cfg.EVAL.batch_size.visualizer,
+            batch_size=cfg.EVAL.batch_size.sup_validator,
             prefix="result_u_v_w_p",
         ),
     }
@@ -259,7 +259,7 @@ def inlet_w_ref_func(_in):
         validator=validator,
         visualizer=visualizer,
         checkpoint_path=cfg.TRAIN.checkpoint_path,
-        eval_with_no_grad=True,
+        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
     )
     # train model
     solver.train()
@@ -287,31 +287,10 @@ def evaluate(cfg: DictConfig):
         "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
     }
 
-    # set geometry
-    inlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_inlet.stl")
-    outlet_geo = ppsci.geometry.Mesh("./stl/aneurysm_outlet.stl")
-    noslip_geo = ppsci.geometry.Mesh("./stl/aneurysm_noslip.stl")
-    integral_geo = ppsci.geometry.Mesh("./stl/aneurysm_integral.stl")
-    interior_geo = ppsci.geometry.Mesh("./stl/aneurysm_closed.stl")
-
     # inlet velocity profile
     CENTER = (-18.40381048596882, -50.285383353981196, 12.848136936899031)
     SCALE = 0.4
 
-    # normalize meshes
-    inlet_geo = inlet_geo.translate(-np.array(CENTER)).scale(SCALE)
-    outlet_geo = outlet_geo.translate(-np.array(CENTER)).scale(SCALE)
-    noslip_geo = noslip_geo.translate(-np.array(CENTER)).scale(SCALE)
-    integral_geo = integral_geo.translate(-np.array(CENTER)).scale(SCALE)
-    interior_geo = interior_geo.translate(-np.array(CENTER)).scale(SCALE)
-    geom = {
-        "inlet_geo": inlet_geo,
-        "outlet_geo": outlet_geo,
-        "noslip_geo": noslip_geo,
-        "integral_geo": integral_geo,
-        "interior_geo": interior_geo,
-    }
-
     # set validator
     eval_data_dict = reader.load_csv_file(
         "./data/aneurysm_parabolicInlet_sol0.csv",
@@ -350,7 +329,7 @@ def evaluate(cfg: DictConfig):
         "num_workers": 1,
     }
     sup_validator = ppsci.validate.SupervisedValidator(
-        {**eval_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.sup_validator},
+        {**eval_dataloader_cfg, "batch_size": cfg.EVAL.batch_size.sup_validator},
         ppsci.loss.MSELoss("mean"),
         {
             "p": lambda out: out["p"],
@@ -373,7 +352,7 @@ def evaluate(cfg: DictConfig):
                 "v": lambda out: out["v"],
                 "w": lambda out: out["w"],
             },
-            batch_size=cfg.TRAIN.batch_size.visualizer,
+            batch_size=cfg.EVAL.batch_size.sup_validator,
             prefix="result_u_v_w_p",
         ),
     }
@@ -383,11 +362,10 @@ def evaluate(cfg: DictConfig):
         model,
         output_dir=cfg.output_dir,
         equation=equation,
-        geom=geom,
         validator=validator,
         visualizer=visualizer,
         pretrained_model_path=cfg.EVAL.pretrained_model_path,
-        eval_with_no_grad=True,
+        eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
     )
     solver.eval()
     # visualize prediction for pretrained model(optional)
diff --git a/examples/aneurysm/conf/aneurysm.yaml b/examples/aneurysm/conf/aneurysm.yaml
index a98c4221d..f1b8eb7eb 100644
--- a/examples/aneurysm/conf/aneurysm.yaml
+++ b/examples/aneurysm/conf/aneurysm.yaml
@@ -49,13 +49,10 @@ TRAIN:
   epochs: 1500
   iters_per_epoch: 1000
   eval_during_train: true
-  eval_freq: 200
+  eval_freq: 20
   learning_rate: 0.001
-  gamma: 0.95
   save_freq: 20
-  eval_during_train: true
   log_freq: 20
-  eval_freq: 20
   batch_size:
     bc_inlet: 1100
     bc_outlet: 650
@@ -63,9 +60,8 @@ TRAIN:
     pde_constraint: 6000
     igc_outlet: 1
     igc_integral: 1
-  iters_per_epoch:
-    igc_outlet: 100
-    igc_integral: 100
+  iters_igc_outlet: 100
+  iters_igc_integral: 100
   integral_batch_size:
     igc_outlet: 310
     igc_integral: 310
@@ -73,8 +69,7 @@ TRAIN:
     epochs: ${TRAIN.epochs}
     iters_per_epoch: ${TRAIN.iters_per_epoch}
     learning_rate: ${TRAIN.learning_rate}
-    gamma: ${TRAIN.gamma}
-    decay_steps: 1500*1000 // 100
+    gamma: 0.95
   weight:
     igc_outlet: {"normal_dot_vec": 0.1}
     igc_integral: {"normal_dot_vec": 0.1}
@@ -84,7 +79,7 @@ TRAIN:
 # evaluation settings
 EVAL:
   pretrained_model_path: null
+  eval_with_no_grad: true
   batch_size:
     residual_validator: 8192
     sup_validator: 4096
-    visualizer: 4096

From c5045f846830b21c250ffcca5a01da9d1b6f23cb Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Tue, 31 Oct 2023 11:20:54 +0800
Subject: [PATCH 4/7] CI


From 8cc161f46070195cd5c2f1dacac5e7877322ff04 Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Tue, 31 Oct 2023 11:46:10 +0800
Subject: [PATCH 5/7] fix

---
 docs/zh/examples/aneurysm.md         |  56 ++++++-------
 examples/aneurysm/aneurysm.py        | 120 +++++++++++----------------
 examples/aneurysm/conf/aneurysm.yaml |  66 +++++++++------
 3 files changed, 117 insertions(+), 125 deletions(-)

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index fcba4caf9..da91358f3 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -56,9 +56,9 @@ $$
 
 上式中 $f$ 即为 MLP 模型本身，用 PaddleScience 代码表示如下
 
-``` py linenums="23"
+``` py linenums="25"
 --8<--
-examples/aneurysm/aneurysm.py:23
+examples/aneurysm/aneurysm.py:25
 --8<--
 ```
 
@@ -70,9 +70,9 @@ examples/aneurysm/aneurysm.py:23
 
 血管瘤模型涉及到 2 个方程，一是流体 N-S 方程，二是流量计算方程，因此使用 PaddleScience 内置的 `NavierStokes` 和 `NormalDotVec` 即可。
 
-``` py linenums="25"
+``` py linenums="27"
 --8<--
-examples/aneurysm/aneurysm.py:25:30
+examples/aneurysm/aneurysm.py:27:33
 --8<--
 ```
 
@@ -101,17 +101,17 @@ tar -xvf aneurysm_dataset.tar
 
 然后通过 PaddleScience 内置的 STL 几何类 `Mesh` 来读取、解析这些几何文件，并且通过布尔运算，组合出各个计算域，代码如下：
 
-``` py linenums="30"
+``` py linenums="35"
 --8<--
-examples/aneurysm/aneurysm.py:30:35
+examples/aneurysm/aneurysm.py:35:40
 --8<--
 ```
 
 在此之后可以对几何域进行缩放和平移，以缩放输入数据的坐标范围，促进模型训练收敛。
 
-``` py linenums="37"
+``` py linenums="42"
 --8<--
-examples/aneurysm/aneurysm.py:37:53
+examples/aneurysm/aneurysm.py:42:54
 --8<--
 ```
 
@@ -119,9 +119,9 @@ examples/aneurysm/aneurysm.py:37:53
 
 本案例共涉及到 6 个约束，在具体约束构建之前，可以先构建数据读取配置，以便后续构建多个约束时复用该配置。
 
-``` py linenums="55"
+``` py linenums="56"
 --8<--
-examples/aneurysm/aneurysm.py:55:65
+examples/aneurysm/aneurysm.py:56:66
 --8<--
 ```
 
@@ -129,9 +129,9 @@ examples/aneurysm/aneurysm.py:55:65
 
 以作用在内部点上的 `InteriorConstraint` 为例，代码如下：
 
-``` py linenums="115"
+``` py linenums="113"
 --8<--
-examples/aneurysm/aneurysm.py:115:122
+examples/aneurysm/aneurysm.py:113:120
 --8<--
 ```
 
@@ -152,17 +152,17 @@ examples/aneurysm/aneurysm.py:115:122
 接着需要对**血管入口、出口、血管壁**这三个表面施加约束，包括入口速度约束、出口压力约束、血管壁无滑移约束。
 在 `bc_inlet` 约束中，入口处的流速满足从中心点开始向周围呈二次抛物线衰减，此处使用抛物线函数表示速度随着远离圆心而衰减，再将其作为 `BoundaryConstraint` 的第二个参数(字典)的 value。
 
-``` py linenums="67"
+``` py linenums="68"
 --8<--
-examples/aneurysm/aneurysm.py:67:98
+examples/aneurysm/aneurysm.py:68:96
 --8<--
 ```
 
 血管出口、血管壁约束的构建方法类似，如下所示：
 
-``` py linenums="99"
+``` py linenums="97"
 --8<--
-examples/aneurysm/aneurysm.py:99:114
+examples/aneurysm/aneurysm.py:97:112
 --8<--
 ```
 
@@ -170,9 +170,9 @@ examples/aneurysm/aneurysm.py:99:114
 
 对于血管入口下方的一段区域和出口区域（面），需额外施加流入和流出的流量约束，由于流量计算涉及到具体面积，因此需要使用离散积分的方式进行计算，这些过程已经内置在了 `IntegralConstraint` 这一约束条件中。如下所示：
 
-``` py linenums="123"
+``` py linenums="121"
 --8<--
-examples/aneurysm/aneurysm.py:123:150
+examples/aneurysm/aneurysm.py:123:148
 --8<--
 ```
 
@@ -188,9 +188,9 @@ $$
 
 在微分方程约束、边界约束、初值约束构建完毕之后，以刚才的命名为关键字，封装到一个字典中，方便后续访问。
 
-``` py linenums="151"
+``` py linenums="149"
 --8<--
-examples/aneurysm/aneurysm.py:151:159
+examples/aneurysm/aneurysm.py:149:157
 --8<--
 ```
 
@@ -198,9 +198,9 @@ examples/aneurysm/aneurysm.py:151:159
 
 接下来需要指定训练轮数和学习率，此处按实验经验，使用 1500 轮训练轮数。
 
-``` py linenums="161"
+``` py linenums="61"
 --8<--
-examples/aneurysm/aneurysm.py:161:162
+examples/aneurysm/conf/aneurysm.yaml:61:61
 --8<--
 ```
 
@@ -208,9 +208,9 @@ examples/aneurysm/aneurysm.py:161:162
 
 训练过程会调用优化器来更新模型参数，此处选择较为常用的 `Adam` 优化器，并配合使用机器学习中常用的 OneCycle 学习率调整策略。
 
-``` py linenums="164"
+``` py linenums="166"
 --8<--
-examples/aneurysm/aneurysm.py:164:173
+examples/aneurysm/aneurysm.py:166:214
 --8<--
 ```
 
@@ -218,9 +218,9 @@ examples/aneurysm/aneurysm.py:164:173
 
 在训练过程中通常会按一定轮数间隔，用验证集（测试集）评估当前模型的训练情况，因此使用 `ppsci.validate.GeometryValidator` 构建评估器。
 
-``` py linenums="175"
+``` py linenums="216"
 --8<--
-examples/aneurysm/aneurysm.py:175:224
+examples/aneurysm/aneurysm.py:216:229
 --8<--
 ```
 
@@ -230,9 +230,9 @@ examples/aneurysm/aneurysm.py:175:224
 
 本文中的输出数据是一个区域内的三维点集，因此只需要将评估的输出数据保存成 **vtu格式** 文件，最后用可视化软件打开查看即可。代码如下：
 
-``` py linenums="226"
+``` py linenums="231"
 --8<--
-examples/aneurysm/aneurysm.py:226:240
+examples/aneurysm/aneurysm.py:231:259
 --8<--
 ```
 
diff --git a/examples/aneurysm/aneurysm.py b/examples/aneurysm/aneurysm.py
index 7cc2d03cb..93e11cef2 100644
--- a/examples/aneurysm/aneurysm.py
+++ b/examples/aneurysm/aneurysm.py
@@ -22,33 +22,29 @@ def train(cfg: DictConfig):
     logger.init_logger("ppsci", osp.join(cfg.output_dir, "train.log"), "info")
 
     # set model
-    model = ppsci.arch.MLP(**cfg.MODEL.model)
+    model = ppsci.arch.MLP(**cfg.MODEL)
 
     # set equation
     equation = {
         "NavierStokes": ppsci.equation.NavierStokes(
-            cfg.NU * cfg.SCALE, cfg.RHO, 3, False
+            cfg.NU * cfg.SCALE, cfg.RHO, cfg.DIM, False
         ),
         "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
     }
 
     # set geometry
-    inlet_geo = ppsci.geometry.Mesh(cfg.INLET_PATH)
-    outlet_geo = ppsci.geometry.Mesh(cfg.OUTLET_PATH)
-    noslip_geo = ppsci.geometry.Mesh(cfg.NOSLIP_PATH)
-    integral_geo = ppsci.geometry.Mesh(cfg.INTEGRAL_PATH)
-    interior_geo = ppsci.geometry.Mesh(cfg.CLOSED_PATH)
-
-    # inlet velocity profile
-    CENTER = (-18.40381048596882, -50.285383353981196, 12.848136936899031)
-    SCALE = 0.4
+    inlet_geo = ppsci.geometry.Mesh(cfg.INLET_STL_PATH)
+    outlet_geo = ppsci.geometry.Mesh(cfg.OUTLET_STL_PATH)
+    noslip_geo = ppsci.geometry.Mesh(cfg.NOSLIP_STL_PATH)
+    integral_geo = ppsci.geometry.Mesh(cfg.INTEGRAL_STL_PATH)
+    interior_geo = ppsci.geometry.Mesh(cfg.INTERIOR_STL_PATH)
 
     # normalize meshes
-    inlet_geo = inlet_geo.translate(-np.array(CENTER)).scale(SCALE)
-    outlet_geo = outlet_geo.translate(-np.array(CENTER)).scale(SCALE)
-    noslip_geo = noslip_geo.translate(-np.array(CENTER)).scale(SCALE)
-    integral_geo = integral_geo.translate(-np.array(CENTER)).scale(SCALE)
-    interior_geo = interior_geo.translate(-np.array(CENTER)).scale(SCALE)
+    inlet_geo = inlet_geo.translate(-np.array(cfg.CENTER)).scale(cfg.SCALE)
+    outlet_geo = outlet_geo.translate(-np.array(cfg.CENTER)).scale(cfg.SCALE)
+    noslip_geo = noslip_geo.translate(-np.array(cfg.CENTER)).scale(cfg.SCALE)
+    integral_geo = integral_geo.translate(-np.array(cfg.CENTER)).scale(cfg.SCALE)
+    interior_geo = interior_geo.translate(-np.array(cfg.CENTER)).scale(cfg.SCALE)
     geom = {
         "inlet_geo": inlet_geo,
         "outlet_geo": outlet_geo,
@@ -70,28 +66,25 @@ def train(cfg: DictConfig):
     }
 
     # set constraint
-    INLET_NORMAL = (0.8526, -0.428, 0.299)
-    INLET_AREA = 21.1284 * (SCALE**2)
-    INLET_CENTER = (-4.24298030045776, 4.082857101816247, -4.637790193399717)
+    INLET_AREA = 21.1284 * (cfg.SCALE**2)
     INLET_RADIUS = np.sqrt(INLET_AREA / np.pi)
-    INLET_VEL = 1.5
 
     def _compute_parabola(_in):
-        centered_x = _in["x"] - INLET_CENTER[0]
-        centered_y = _in["y"] - INLET_CENTER[1]
-        centered_z = _in["z"] - INLET_CENTER[2]
+        centered_x = _in["x"] - cfg.INLET_CENTER[0]
+        centered_y = _in["y"] - cfg.INLET_CENTER[1]
+        centered_z = _in["z"] - cfg.INLET_CENTER[2]
         distance = np.sqrt(centered_x**2 + centered_y**2 + centered_z**2)
-        parabola = INLET_VEL * np.maximum((1 - (distance / INLET_RADIUS) ** 2), 0)
+        parabola = cfg.INLET_VEL * np.maximum((1 - (distance / INLET_RADIUS) ** 2), 0)
         return parabola
 
     def inlet_u_ref_func(_in):
-        return INLET_NORMAL[0] * _compute_parabola(_in)
+        return cfg.INLET_NORMAL[0] * _compute_parabola(_in)
 
     def inlet_v_ref_func(_in):
-        return INLET_NORMAL[1] * _compute_parabola(_in)
+        return cfg.INLET_NORMAL[1] * _compute_parabola(_in)
 
     def inlet_w_ref_func(_in):
-        return INLET_NORMAL[2] * _compute_parabola(_in)
+        return cfg.INLET_NORMAL[2] * _compute_parabola(_in)
 
     bc_inlet = ppsci.constraint.BoundaryConstraint(
         {"u": lambda d: d["u"], "v": lambda d: d["v"], "w": lambda d: d["w"]},
@@ -117,11 +110,11 @@ def inlet_w_ref_func(_in):
         ppsci.loss.MSELoss("sum"),
         name="no_slip",
     )
-    pde_constraint = ppsci.constraint.InteriorConstraint(
+    pde = ppsci.constraint.InteriorConstraint(
         equation["NavierStokes"].equations,
         {"continuity": 0, "momentum_x": 0, "momentum_y": 0, "momentum_z": 0},
         geom["interior_geo"],
-        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.pde_constraint},
+        {**train_dataloader_cfg, "batch_size": cfg.TRAIN.batch_size.pde},
         ppsci.loss.MSELoss("sum"),
         name="interior",
     )
@@ -131,7 +124,7 @@ def inlet_w_ref_func(_in):
         geom["outlet_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_igc_outlet,
+            "iters_per_epoch": cfg.TRAIN.iters_integral.igc_outlet,
             "batch_size": cfg.TRAIN.batch_size.igc_outlet,
             "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_outlet,
         },
@@ -145,7 +138,7 @@ def inlet_w_ref_func(_in):
         geom["integral_geo"],
         {
             **train_dataloader_cfg,
-            "iters_per_epoch": cfg.TRAIN.iters_igc_integral,
+            "iters_per_epoch": cfg.TRAIN.iters_integral.igc_integral,
             "batch_size": cfg.TRAIN.batch_size.igc_integral,
             "integral_batch_size": cfg.TRAIN.integral_batch_size.igc_integral,
         },
@@ -158,25 +151,20 @@ def inlet_w_ref_func(_in):
         bc_inlet.name: bc_inlet,
         bc_outlet.name: bc_outlet,
         bc_noslip.name: bc_noslip,
-        pde_constraint.name: pde_constraint,
+        pde.name: pde,
         igc_outlet.name: igc_outlet,
         igc_integral.name: igc_integral,
     }
 
     # set optimizer
     lr_scheduler = ppsci.optimizer.lr_scheduler.ExponentialDecay(
-        cfg.TRAIN.epochs,
-        cfg.TRAIN.iters_per_epoch,
-        cfg.TRAIN.learning_rate,
-        cfg.TRAIN.gamma,
-        cfg.TRAIN.epochs * cfg.TRAIN.iters_per_epoch // 100,
-        by_epoch=False,
+        **cfg.TRAIN.lr_scheduler
     )()
     optimizer = ppsci.optimizer.Adam(lr_scheduler)(model)
 
     # set validator
     eval_data_dict = reader.load_csv_file(
-        "./data/aneurysm_parabolicInlet_sol0.csv",
+        cfg.EVAL_CSV_PATH,
         ("x", "y", "z", "u", "v", "w", "p"),
         {
             "x": "Points:0",
@@ -189,12 +177,12 @@ def inlet_w_ref_func(_in):
         },
     )
     input_dict = {
-        "x": (eval_data_dict["x"] - CENTER[0]) * SCALE,
-        "y": (eval_data_dict["y"] - CENTER[1]) * SCALE,
-        "z": (eval_data_dict["z"] - CENTER[2]) * SCALE,
+        "x": (eval_data_dict["x"] - cfg.CENTER[0]) * cfg.SCALE,
+        "y": (eval_data_dict["y"] - cfg.CENTER[1]) * cfg.SCALE,
+        "z": (eval_data_dict["z"] - cfg.CENTER[2]) * cfg.SCALE,
     }
     if "area" in input_dict.keys():
-        input_dict["area"] *= SCALE ** (equation["NavierStokes"].dim)
+        input_dict["area"] *= cfg.SCALE ** (equation["NavierStokes"].dim)
 
     label_dict = {
         "p": eval_data_dict["p"],
@@ -227,7 +215,7 @@ def inlet_w_ref_func(_in):
 
     # set visualizer(optional)
     visualizer = {
-        "visulzie_u_v_w_p": ppsci.visualize.VisualizerVtu(
+        "visualize_u_v_w_p": ppsci.visualize.VisualizerVtu(
             input_dict,
             {
                 "p": lambda out: out["p"],
@@ -250,20 +238,20 @@ def inlet_w_ref_func(_in):
         cfg.TRAIN.epochs,
         cfg.TRAIN.iters_per_epoch,
         save_freq=cfg.TRAIN.save_freq,
-        eval_during_train=cfg.TRAIN.eval_during_train,
-        log_freq=cfg.TRAIN.log_freq,
+        log_freq=cfg.log_freq,
+        eval_during_train=True,
         eval_freq=cfg.TRAIN.eval_freq,
         seed=cfg.seed,
         equation=equation,
         geom=geom,
         validator=validator,
         visualizer=visualizer,
+        pretrained_model_path=cfg.TRAIN.pretrained_model_path,
         checkpoint_path=cfg.TRAIN.checkpoint_path,
         eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
     )
     # train model
     solver.train()
-
     # evaluate after finished training
     solver.eval()
     # visualize prediction after finished training
@@ -277,23 +265,11 @@ def evaluate(cfg: DictConfig):
     logger.init_logger("ppsci", osp.join(cfg.output_dir, "eval.log"), "info")
 
     # set model
-    model = ppsci.arch.MLP(**cfg.MODEL.model)
-
-    # set equation
-    equation = {
-        "NavierStokes": ppsci.equation.NavierStokes(
-            cfg.NU * cfg.SCALE, cfg.RHO, 3, False
-        ),
-        "NormalDotVec": ppsci.equation.NormalDotVec(("u", "v", "w")),
-    }
-
-    # inlet velocity profile
-    CENTER = (-18.40381048596882, -50.285383353981196, 12.848136936899031)
-    SCALE = 0.4
+    model = ppsci.arch.MLP(**cfg.MODEL)
 
     # set validator
     eval_data_dict = reader.load_csv_file(
-        "./data/aneurysm_parabolicInlet_sol0.csv",
+        cfg.EVAL_CSV_PATH,
         ("x", "y", "z", "u", "v", "w", "p"),
         {
             "x": "Points:0",
@@ -306,12 +282,12 @@ def evaluate(cfg: DictConfig):
         },
     )
     input_dict = {
-        "x": (eval_data_dict["x"] - CENTER[0]) * SCALE,
-        "y": (eval_data_dict["y"] - CENTER[1]) * SCALE,
-        "z": (eval_data_dict["z"] - CENTER[2]) * SCALE,
+        "x": (eval_data_dict["x"] - cfg.CENTER[0]) * cfg.SCALE,
+        "y": (eval_data_dict["y"] - cfg.CENTER[1]) * cfg.SCALE,
+        "z": (eval_data_dict["z"] - cfg.CENTER[2]) * cfg.SCALE,
     }
     if "area" in input_dict.keys():
-        input_dict["area"] *= SCALE ** (equation["NavierStokes"].dim)
+        input_dict["area"] *= cfg.SCALE**cfg.DIM
 
     label_dict = {
         "p": eval_data_dict["p"],
@@ -342,9 +318,9 @@ def evaluate(cfg: DictConfig):
     )
     validator = {sup_validator.name: sup_validator}
 
-    # set visualizer(optional)
+    # set visualizer
     visualizer = {
-        "visulzie_u_v_w_p": ppsci.visualize.VisualizerVtu(
+        "visualize_u_v_w_p": ppsci.visualize.VisualizerVtu(
             input_dict,
             {
                 "p": lambda out: out["p"],
@@ -357,18 +333,22 @@ def evaluate(cfg: DictConfig):
         ),
     }
 
-    # directly evaluate pretrained model(optional)
+    # initialize solver
     solver = ppsci.solver.Solver(
         model,
         output_dir=cfg.output_dir,
-        equation=equation,
+        epochs=cfg.TRAIN.epochs,
+        iters_per_epoch=cfg.TRAIN.iters_per_epoch,
+        log_freq=cfg.log_freq,
+        seed=cfg.seed,
         validator=validator,
         visualizer=visualizer,
         pretrained_model_path=cfg.EVAL.pretrained_model_path,
         eval_with_no_grad=cfg.EVAL.eval_with_no_grad,
     )
+    # evaluate
     solver.eval()
-    # visualize prediction for pretrained model(optional)
+    # visualize prediction
     solver.visualize()
 
 
diff --git a/examples/aneurysm/conf/aneurysm.yaml b/examples/aneurysm/conf/aneurysm.yaml
index f1b8eb7eb..a3562262d 100644
--- a/examples/aneurysm/conf/aneurysm.yaml
+++ b/examples/aneurysm/conf/aneurysm.yaml
@@ -1,10 +1,10 @@
 hydra:
   run:
     # dynamic output directory according to running time and override name
-    dir: output_aneurysm/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
+    dir: outputs_aneurysm/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
   job:
     name: ${mode} # name of logfile
-    chdir: false # keep current working direcotry unchanged
+    chdir: false # keep current working direcotry unchaned
     config:
       override_dirname:
         exclude_keys:
@@ -13,6 +13,7 @@ hydra:
           - EVAL.pretrained_model_path
           - mode
           - output_dir
+          - log_freq
   sweep:
     # output directory for multirun
     dir: ${hydra.run.dir}
@@ -22,54 +23,66 @@ hydra:
 mode: train # running mode: train/eval
 seed: 2023
 output_dir: ${hydra:run.dir}
+log_freq: 20
 
 # set working condition
-NU: 0.0025
+NU: 0.025
 SCALE: 0.4
 RHO: 1.0
+DIM: 3
 
 # set geometry file path
-INLET_PATH: ./stl/aneurysm_inlet.stl
-OUTLET_PATH: ./stl/aneurysm_outlet.stl
-NOSLIP_PATH: ./stl/aneurysm_noslip.stl
-INTEGRAL_PATH: ./stl/aneurysm_integral.stl
-CLOSED_PATH: ./stl/aneurysm_closed.stl
+INLET_STL_PATH: "./stl/aneurysm_inlet.stl"
+OUTLET_STL_PATH: "./stl/aneurysm_outlet.stl"
+NOSLIP_STL_PATH: "./stl/aneurysm_noslip.stl"
+INTEGRAL_STL_PATH: "./stl/aneurysm_integral.stl"
+INTERIOR_STL_PATH: "./stl/aneurysm_closed.stl"
+
+# inlet velocity profile
+CENTER: [-18.40381048596882, -50.285383353981196, 12.848136936899031]
+INLET_NORMAL: [0.8526, -0.428, 0.299]
+INLET_CENTER: [-4.24298030045776, 4.082857101816247, -4.637790193399717]
+INLET_VEL: 1.5
+
+# set evaluate data path
+EVAL_CSV_PATH: "./data/aneurysm_parabolicInlet_sol0.csv"
 
 # model settings
 MODEL:
-  model:
-    input_keys: ["x", "y", "z"]
-    output_keys: ["u", "v", "p"]
-    num_layers: 9
-    hidden_size: 50
-    activation: "tanh"
+  input_keys: ["x", "y", "z"]
+  output_keys: ["u", "v", "w", "p"]
+  num_layers: 6
+  hidden_size: 512
+  activation: "silu"
+  weight_norm: true
 
 # training settings
 TRAIN:
   epochs: 1500
   iters_per_epoch: 1000
+  iters_integral:
+    igc_outlet: 100
+    igc_integral: 100
+  save_freq: 20
   eval_during_train: true
   eval_freq: 20
-  learning_rate: 0.001
-  save_freq: 20
-  log_freq: 20
+  lr_scheduler:
+    epochs: ${TRAIN.epochs}
+    iters_per_epoch: ${TRAIN.iters_per_epoch}
+    learning_rate: 0.001
+    gamma: 0.95
+    decay_steps: 15000
+    by_epoch: false
   batch_size:
     bc_inlet: 1100
     bc_outlet: 650
     bc_noslip: 5200
-    pde_constraint: 6000
+    pde: 6000
     igc_outlet: 1
     igc_integral: 1
-  iters_igc_outlet: 100
-  iters_igc_integral: 100
   integral_batch_size:
     igc_outlet: 310
     igc_integral: 310
-  lr_scheduler:
-    epochs: ${TRAIN.epochs}
-    iters_per_epoch: ${TRAIN.iters_per_epoch}
-    learning_rate: ${TRAIN.learning_rate}
-    gamma: 0.95
   weight:
     igc_outlet: {"normal_dot_vec": 0.1}
     igc_integral: {"normal_dot_vec": 0.1}
@@ -81,5 +94,4 @@ EVAL:
   pretrained_model_path: null
   eval_with_no_grad: true
   batch_size:
-    residual_validator: 8192
-    sup_validator: 4096
+    sup_validator: 4096
\ No newline at end of file

From 5280d2ec91fb596bc7c2f780b4f6d1b7478f5dc0 Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Tue, 31 Oct 2023 12:21:59 +0800
Subject: [PATCH 6/7] add result

---
 docs/zh/examples/aneurysm.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index da91358f3..883c29c5e 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -25,6 +25,9 @@
     tar -xvf aneurysm_dataset.tar
     python aneurysm.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams
     ```
+| 预训练模型  | 指标 |
+|:--| :--|
+| [aneurysm_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams) | loss(ref_u_v_w_p): 0.01488<br>MSE.p(ref_u_v_w_p): 0.01412<br>MSE.u(ref_u_v_w_p): 0.00021<br>MSE.v(ref_u_v_w_p): 0.00024<br>MSE.w(ref_u_v_w_p): 0.00032 |
 
 ## 1. 背景简介
 

From de9f6e042d058c8a29a4dfcef826780a5adacbbc Mon Sep 17 00:00:00 2001
From: enkilee <luhputu0815@gmail.com>
Date: Wed, 1 Nov 2023 13:25:40 +0800
Subject: [PATCH 7/7] fix

---
 docs/zh/examples/aneurysm.md | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/docs/zh/examples/aneurysm.md b/docs/zh/examples/aneurysm.md
index 883c29c5e..ed73444ac 100644
--- a/docs/zh/examples/aneurysm.md
+++ b/docs/zh/examples/aneurysm.md
@@ -25,6 +25,7 @@
     tar -xvf aneurysm_dataset.tar
     python aneurysm.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams
     ```
+
 | 预训练模型  | 指标 |
 |:--| :--|
 | [aneurysm_pretrained.pdparams](https://paddle-org.bj.bcebos.com/paddlescience/models/aneurysm/aneurysm_pretrained.pdparams) | loss(ref_u_v_w_p): 0.01488<br>MSE.p(ref_u_v_w_p): 0.01412<br>MSE.u(ref_u_v_w_p): 0.00021<br>MSE.v(ref_u_v_w_p): 0.00024<br>MSE.w(ref_u_v_w_p): 0.00032 |
@@ -59,9 +60,9 @@ $$
 
 上式中 $f$ 即为 MLP 模型本身，用 PaddleScience 代码表示如下
 
-``` py linenums="25"
+``` py linenums="24"
 --8<--
-examples/aneurysm/aneurysm.py:25
+examples/aneurysm/aneurysm.py:24:25
 --8<--
 ```
 
@@ -175,7 +176,7 @@ examples/aneurysm/aneurysm.py:97:112
 
 ``` py linenums="121"
 --8<--
-examples/aneurysm/aneurysm.py:123:148
+examples/aneurysm/aneurysm.py:121:148
 --8<--
 ```
 
@@ -201,9 +202,9 @@ examples/aneurysm/aneurysm.py:149:157
 
 接下来需要指定训练轮数和学习率，此处按实验经验，使用 1500 轮训练轮数。
 
-``` py linenums="61"
+``` py linenums="59"
 --8<--
-examples/aneurysm/conf/aneurysm.yaml:61:61
+examples/aneurysm/conf/aneurysm.yaml:59:75
 --8<--
 ```
 
@@ -211,9 +212,9 @@ examples/aneurysm/conf/aneurysm.yaml:61:61
 
 训练过程会调用优化器来更新模型参数，此处选择较为常用的 `Adam` 优化器，并配合使用机器学习中常用的 OneCycle 学习率调整策略。
 
-``` py linenums="166"
+``` py linenums="159"
 --8<--
-examples/aneurysm/aneurysm.py:166:214
+examples/aneurysm/aneurysm.py:159:163
 --8<--
 ```
 
@@ -221,9 +222,9 @@ examples/aneurysm/aneurysm.py:166:214
 
 在训练过程中通常会按一定轮数间隔，用验证集（测试集）评估当前模型的训练情况，因此使用 `ppsci.validate.GeometryValidator` 构建评估器。
 
-``` py linenums="216"
+``` py linenums="165"
 --8<--
-examples/aneurysm/aneurysm.py:216:229
+examples/aneurysm/aneurysm.py:165:241
 --8<--
 ```
 
@@ -233,9 +234,9 @@ examples/aneurysm/aneurysm.py:216:229
 
 本文中的输出数据是一个区域内的三维点集，因此只需要将评估的输出数据保存成 **vtu格式** 文件，最后用可视化软件打开查看即可。代码如下：
 
-``` py linenums="231"
+``` py linenums="216"
 --8<--
-examples/aneurysm/aneurysm.py:231:259
+examples/aneurysm/aneurysm.py:216:229
 --8<--
 ```
 
@@ -243,9 +244,9 @@ examples/aneurysm/aneurysm.py:231:259
 
 完成上述设置之后，只需要将上述实例化的对象按顺序传递给 `ppsci.solver.Solver`，然后启动训练、评估、可视化。
 
-``` py linenums="241"
+``` py linenums="231"
 --8<--
-examples/aneurysm/aneurysm.py:241:267
+examples/aneurysm/aneurysm.py:231:258
 --8<--
 ```