From 162a21c17ce6b20d8778326152391cd99c2b897b Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Sun, 23 Apr 2023 09:14:05 +0000 Subject: [PATCH 01/31] fix random and create demo --- ppsci/arch/mlp.py | 5 ++- ppsci/constraint/interior_constraint.py | 11 +++--- ppsci/data/dataset/__init__.py | 2 + ppsci/data/dataset/array_dataset.py | 25 +++++++++++++ ppsci/equation/pde/__init__.py | 2 + ppsci/geometry/pointcloud.py | 49 ++++++++++++++++--------- 6 files changed, 70 insertions(+), 24 deletions(-) diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index 695fb8261..b394f4c26 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -102,6 +102,9 @@ def forward_tensor(self, x): def forward(self, x): if self._input_transform is not None: + import copy + + x_old = copy.copy(x) x = self._input_transform(x) y = self.concat_to_tensor(x, self.input_keys, axis=-1) @@ -109,5 +112,5 @@ def forward(self, x): y = self.split_to_dict(y, self.output_keys, axis=-1) if self._output_transform is not None: - y = self._output_transform(y) + y = self._output_transform(y, x_old) return y diff --git a/ppsci/constraint/interior_constraint.py b/ppsci/constraint/interior_constraint.py index f470100d0..2059a70ea 100644 --- a/ppsci/constraint/interior_constraint.py +++ b/ppsci/constraint/interior_constraint.py @@ -16,6 +16,7 @@ from typing import Any from typing import Callable from typing import Dict +from typing import Optional from typing import Union import numpy as np @@ -43,12 +44,12 @@ class InteriorConstraint(base.Constraint): loss (loss.LossBase): Loss functor. random (Literal["pseudo", "LHS"], optional): Random method for sampling data in geometry. Defaults to "pseudo". - criteria (Callable, optional): Criteria for refining specified boundaries. + criteria (Optional[Callable]): Criteria for refining specified boundaries. Defaults to None. evenly (bool, optional): Whether to use evenly distribution sampling. Defaults to False. - weight_dict (Dict[str, Callable], optional): Define the weight of each - constraint variable. Defaults to None. + weight_dict (Optional[Dict[str, Union[Callable, float]]]): Define the + weight of each constraint variable. Defaults to None. name (str, optional): Name of constraint object. Defaults to "EQ". """ @@ -60,9 +61,9 @@ def __init__( dataloader_cfg: Dict[str, Any], loss: loss.LossBase, random: Literal["pseudo", "LHS"] = "pseudo", - criteria: Callable = None, + criteria: Optional[Callable] = None, evenly: bool = False, - weight_dict: Dict[str, Callable] = None, + weight_dict: Optional[Dict[str, Union[Callable, float]]] = None, name: str = "EQ", ): self.label_expr = label_expr diff --git a/ppsci/data/dataset/__init__.py b/ppsci/data/dataset/__init__.py index a7ddee77e..a1243e6ca 100644 --- a/ppsci/data/dataset/__init__.py +++ b/ppsci/data/dataset/__init__.py @@ -14,6 +14,7 @@ import copy from ppsci.data.dataset.array_dataset import IterableNamedArrayDataset +from ppsci.data.dataset.array_dataset import MiniBatchDataset from ppsci.data.dataset.array_dataset import NamedArrayDataset from ppsci.data.dataset.trphysx_dataset import CylinderDataset from ppsci.data.dataset.trphysx_dataset import LorenzDataset @@ -24,6 +25,7 @@ __all__ = [ "IterableNamedArrayDataset", "NamedArrayDataset", + "MiniBatchDataset", "CylinderDataset", "LorenzDataset", "RosslerDataset", diff --git a/ppsci/data/dataset/array_dataset.py b/ppsci/data/dataset/array_dataset.py index 21a3ef312..d3acd9e10 100644 --- a/ppsci/data/dataset/array_dataset.py +++ b/ppsci/data/dataset/array_dataset.py @@ -93,3 +93,28 @@ def __iter__(self): def __len__(self): return 1 + + +class MiniBatchDataset(io.Dataset): + def __init__(self, input, label, weight): + super().__init__() + self.input = input + self.label = label + self.num_samples = self.check_input(input) + + def check_input(self, input): + len_input = set() + for _, value in input.items(): + len_input.add(len(value)) + if len(len_input) is not 1: + raise AttributeError("Input dimension mismatch") + else: + return list(len_input)[0] + + def __getitem__(self, idx): + input_item = {key: value[idx] for key, value in self.input.items()} + label_item = {key: value[idx] for key, value in self.label.items()} + return (input_item, label_item) + + def __len__(self): + return self.num_samples diff --git a/ppsci/equation/pde/__init__.py b/ppsci/equation/pde/__init__.py index bc86342f7..35425a4b9 100644 --- a/ppsci/equation/pde/__init__.py +++ b/ppsci/equation/pde/__init__.py @@ -15,6 +15,7 @@ from ppsci.equation.pde.base import PDE from ppsci.equation.pde.laplace import Laplace from ppsci.equation.pde.navier_stokes import NavierStokes +from ppsci.equation.pde.navier_stokes_ansatz_uvp import NavierStokesAnsatz from ppsci.equation.pde.normal_dot_vec import NormalDotVec __all__ = [ @@ -22,4 +23,5 @@ "Laplace", "NavierStokes", "NormalDotVec", + "NavierStokesAnsatz", ] diff --git a/ppsci/geometry/pointcloud.py b/ppsci/geometry/pointcloud.py index 0453def98..20c5533df 100644 --- a/ppsci/geometry/pointcloud.py +++ b/ppsci/geometry/pointcloud.py @@ -12,6 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Dict +from typing import Union + import numpy as np from ppsci.geometry import geometry @@ -22,40 +25,46 @@ class PointCloud(geometry.Geometry): """Class for point cloud geometry, i.e. a set of points from given file or array. Args: - interior_path (str): File which store interior points of a point cloud. + coord_dict (str): File which store interior points of a point cloud. boundary_path (str): File which store boundary points of a point cloud. boundary_normal_path (str): File which store boundary normals of a point cloud. - coord_keys (List[str]): List of coordinate keys, such as ["x", "y"]. + data_key (List[str]): List of coordinate keys, such as ["x", "y"]. alias_dict (List[str]): Alias name for coord key, such as {"X:0": "x", "X:1": "y"}. """ def __init__( self, - interior_path, - coord_keys, + coord_dict: Union[ + str, Dict + ], #  1. 改造PointCloud，在支持从文件读入数据的情况下，同时支持直接使用给定内存中的数据 + extra_data: Dict, # 2. 支持额外信息点 + data_key, boundary_path=None, boundary_normal_path=None, alias_dict=None, ): # Interior points from CSV file - if interior_path.endswith(".csv"): + if str(coord_dict).endswith(".csv"): # read data - data_dict = misc.load_csv_file(interior_path, coord_keys) + data_dict = misc.load_csv_file(coord_dict, data_key) + elif isinstance(coord_dict, dict): + data_dict = coord_dict + data_dict.update(extra_data) - # convert to numpy array - self.interior = [] - for key in coord_keys: - self.interior.append(data_dict[key]) - self.interior = np.concatenate(self.interior, -1) + # convert to numpy array + self.interior = [] + for key in data_key: + self.interior.append(data_dict[key]) + self.interior = np.concatenate(self.interior, -1) # Boundary points from CSV file if boundary_path is not None: # read data - data_dict = misc.load_csv_file(boundary_path, coord_keys) + data_dict = misc.load_csv_file(boundary_path, data_key) # convert to numpy array self.boundary = {} - for key in coord_keys: + for key in data_key: self.boundary.append(data_dict[key]) self.boundary = np.concatenate(self.boundary, -1) else: @@ -64,25 +73,25 @@ def __init__( # Normal of boundary points from CSV file if boundary_normal_path is not None: # read data - data_dict = misc.load_csv_file(boundary_normal_path, coord_keys) + data_dict = misc.load_csv_file(boundary_normal_path, data_key) # convert to numpy array self.normal = {} - for key in coord_keys: + for key in data_key: self.normal.append(data_dict[key]) self.normal = np.concatenate(self.normal, -1) else: self.normal = None self.input_keys = [] - for key in coord_keys: - if key in alias_dict: + for key in data_key: + if alias_dict is not None and key in alias_dict: self.input_keys.append(alias_dict[key]) else: self.input_keys.append(key) super().__init__( - len(coord_keys), + len(data_key), (np.amin(self.interior, axis=0), np.amax(self.interior, axis=0)), np.inf, ) @@ -158,6 +167,10 @@ def random_points(self, n, random="pseudo"): np.random.choice(len(self.interior), size=n, replace=False) ] + def uniform_points(self, n: int, boundary=True): + """Compute the equispaced points in the geometry.""" + return self.interior + def union(self, rhs): raise NotImplementedError( "Union operation for PointCloud is not supported yet." From ca255c65d16e670f036ca94ee0d594797dabc75b Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Thu, 27 Apr 2023 03:47:42 +0000 Subject: [PATCH 02/31] Reproduce Fig. 2.(a) successfully --- examples/pipe/pipe_post.py | 139 ++++++++++++++++++++ examples/pipe/poiseuille_flow_FCNN.py | 105 ++++++++++++--- examples/pipe/poiseuille_flow_analytical.py | 36 ----- ppsci/arch/activation.py | 20 ++- ppsci/solver/printer.py | 2 +- ppsci/solver/solver.py | 2 +- ppsci/solver/train.py | 62 ++++++++- 7 files changed, 306 insertions(+), 60 deletions(-) create mode 100644 examples/pipe/pipe_post.py delete mode 100644 examples/pipe/poiseuille_flow_analytical.py diff --git a/examples/pipe/pipe_post.py b/examples/pipe/pipe_post.py new file mode 100644 index 000000000..455ce3f01 --- /dev/null +++ b/examples/pipe/pipe_post.py @@ -0,0 +1,139 @@ +import os +import pdb + +import matplotlib.pyplot as plt # For plotting +import numpy as np +import pandas as pd +import seaborn as sns +from scipy import stats + +os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/pipe") +data1 = np.load("train_nu.npz") +# data1 = np.load('pipe_test_1dnu.npz') +nu = data1["nu_1d"] +# nu = np.sort(nu) +print("nu is", nu) +############################ +# profile viscosity + +# ss +data = np.load("pred_poiseuille_para_0425.npz") +mesh = data["mesh"] +print("shape of mesh is", mesh.shape) +u = data["u"] +v = data["v"] +p = data["p"] +ut = data["ut"] +uMaxP = data["uMaxP"] +uMaxA = data["uMaxA"] +print("shape of uMaxA", uMaxA.shape) +Ny, Nx, Np = u.shape +print("mesh shape = ", mesh.shape) +print("u shape", u.shape) +idxP = 28 + +# plt.figure() +# plt.contourf(mesh[0,:,:, idxP], mesh[1,:,:,idxP], u[:, :, idxP]) +# plt.axis('equal') +# plt.colorbar() +# plt.savefig('pipe1.png') + +# idxP = np.array([0,28,49]) +idxP = [3] +plot_x = 0.8 +plot_y = 0.07 +fontsize = 16 + +# y = np.linspace(-0.05,0.05,50) +# ii = 0 +# for idxPi in idxP: +# plt.figure() +# for i in range(Nx): +# pP, = plt.plot(y,u[:, i, idxPi]) +# pT, = plt.plot(y,ut[:, i, idxPi], 'r--') +# ii = ii+1 +# plt.close('all') +# plt.legend([pP, pT], ['NN Surrogate,nu = 2.1e-4', 'Truth'],fontsize = fontsize) +# print ('max u = ', np.max(u[:, :, idxP])) +# print ('max ut = ', np.max(ut[:, :, idxP])) +# plt.savefig('pipe2.png') + +# plt.text(0, 0.1, r'$\delta$', +# {'color': 'k', 'fontsize': 24, 'ha': 'center', 'va': 'center', +#'bbox': dict(boxstyle="round", fc="w", ec="k", pad=0.2)}) +d = 0.1 +# plot spanwise u profile along y, looping from nu_small to nu_large +# u = data['u'] +idx_X = int(round(Nx / 2)) +y = np.linspace(-0.05, 0.05, 50) +can = [3, 6, 14, 49] +# for idxP in range(len(nu)): +xtext = [0, 0.5, 1] +ytext = [0.45, 0.28, 0.1, 0.01] +plt.figure(1) +Re = [] +plt.figure(1) +plt.clf() +for idxP in range(len(can)): + # plt.figure(1) + # plt.clf() + print(f"idxP = {idxP}") + ax1 = plt.subplot(111) + (pT,) = plt.plot( + y, ut[:, idx_X, can[idxP]], color="darkblue", linestyle="-", lw=3.0, alpha=1.0 + ) + (pP,) = plt.plot( + y, + u[:, idx_X, can[idxP]], + color="red", + linestyle="--", + dashes=(5, 5), + lw=2.0, + alpha=1.0, + ) + tmpRe = np.max(u[:, idx_X, can[idxP]]) * d / nu[can[idxP]] + Re.append(tmpRe) + # print("Re is",Re) + nu_current = float("{0:.5f}".format(nu[can[idxP]])) + # plt.title(r'$\nu = $' + str(nu_current)) + plt.text( + -0.012, + ytext[idxP], + r"$\nu = $" + str(nu_current), + {"color": "k", "fontsize": 16}, + ) + + +# plt.legend([pT, pP], ['Analytical', 'NN surrogate'], fontsize = 16,loc = 10) +plt.ylabel(r"$u(y)$", fontsize=16) +plt.xlabel(r"$y$", fontsize=16) +ax1.tick_params(axis="x", labelsize=16) +ax1.tick_params(axis="y", labelsize=16) +ax1.set_xlim([-0.05, 0.05]) +ax1.set_ylim([0.0, 0.62]) +figureName = "pipe_uProfiles_nuIdx_.png" +plt.savefig(figureName, bbox_inches="tight") +exit() + +print("Re is", Re) +np.savez("test_Re", Re=Re) +plt.figure(2) +plt.clf() +ax1 = plt.subplot(111) +sns.kdeplot(uMaxA[0, :], shade=True, label="Analytical", linestyle="-", linewidth=3) +sns.kdeplot( + uMaxP[0, :], + shade=False, + label="DNN", + linestyle="--", + linewidth=3.5, + color="darkred", +) +plt.legend(prop={"size": 16}) +plt.xlabel(r"$u_c$", fontsize=16) +plt.ylabel(r"PDF", fontsize=16) +ax1.tick_params(axis="x", labelsize=16) +ax1.tick_params(axis="y", labelsize=16) +figureName = "pipe_unformUQ.png" +plt.savefig(figureName, bbox_inches="tight") +plt.show() diff --git a/examples/pipe/poiseuille_flow_FCNN.py b/examples/pipe/poiseuille_flow_FCNN.py index 1ca430162..3081bc4ba 100644 --- a/examples/pipe/poiseuille_flow_FCNN.py +++ b/examples/pipe/poiseuille_flow_FCNN.py @@ -124,13 +124,14 @@ def momentum_z_compute_func(out): os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/pipe") # set output directory - output_dir = "./output_pipe" - initial_bias_u = np.load("./data/init_net_params/initial_bias_u.npz") - initial_bias_v = np.load("./data/init_net_params/initial_bias_v.npz") - initial_bias_p = np.load("./data/init_net_params/initial_bias_p.npz") - initial_weight_u = np.load("./data/init_net_params/initial_weight_u.npz") - initial_weight_v = np.load("./data/init_net_params/initial_weight_v.npz") - initial_weight_p = np.load("./data/init_net_params/initial_weight_p.npz") + output_dir = "./output_pipe_1" + dir = "/workspace/wangguan/LabelFree-DNN-Surrogate/net_params" + initial_bias_u = np.load(dir + f"/bias_u_epoch_1.npz") + initial_bias_v = np.load(dir + f"/bias_v_epoch_1.npz") + initial_bias_p = np.load(dir + f"/bias_p_epoch_1.npz") + initial_weight_u = np.load(dir + f"/weight_u_epoch_1.npz") + initial_weight_v = np.load(dir + f"/weight_v_epoch_1.npz") + initial_weight_p = np.load(dir + f"/weight_p_epoch_1.npz") from paddle.fluid import core @@ -182,10 +183,13 @@ def momentum_z_compute_func(out): data_1d_nu = np.linspace(nuStart, nuEnd, N_p, endpoint=True) print("train_nu is", data_1d_nu) - + np.savez("train_nu", nu_1d=data_1d_nu) data_2d_xy_before = np.array(np.meshgrid(data_1d_x, data_1d_y, data_1d_nu)) data_2d_xy_before_reshape = data_2d_xy_before.reshape(3, -1) data_2d_xy = data_2d_xy_before_reshape.T + import copy + + data_2d_xy_old = copy.deepcopy(data_2d_xy) np.random.shuffle(data_2d_xy) input_x = data_2d_xy[:, 0].reshape(data_2d_xy.shape[0], 1).astype("float32") @@ -266,7 +270,6 @@ def output_transform(out, input): def input_transform(input): if periodicBC == True: x, y = input["x"], input["y"] - print(x.stop_gradient, y.stop_gradient) nu = input["nu"] b = 2 * np.pi / (X_OUT - X_IN) c = np.pi * (X_IN + X_OUT) / (X_IN - X_OUT) @@ -328,18 +331,80 @@ def input_transform(input): optimizer, epochs=EPOCHS, iters_per_epoch=ITERS_PER_EPOCH, - eval_during_train=True, + eval_during_train=False, + save_freq=10, log_freq=LOG_FREQ, - eval_freq=EVAL_FREQ, equation=equation, - visualizer=visualizer, + checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/pipe/output_pipe/checkpoints/epoch_3000", ) - # train model - solver.train() - - # evaluate after finished training - solver.eval() - - # visualize prediction after finished training - solver.visualize() + def predict( + input_dict, + solver, + ): + for key, val in input_dict.items(): + input_dict[key] = paddle.to_tensor(val, dtype="float32") + evaluator = ppsci.utils.expression.ExpressionSolver( + input_dict.keys(), ["u", "v", "p"], solver.model + ) + output_expr_dict = { + "u": lambda d: d["u"], + "v": lambda d: d["v"], + "p": lambda d: d["p"], + } + for output_key, output_expr in output_expr_dict.items(): + evaluator.add_target_expr(output_expr, output_key) + output_dict = evaluator(input_dict) + return output_dict + + data_2d_xy = data_2d_xy_old + input_dict = { + "x": data_2d_xy[:, 0:1], + "y": data_2d_xy[:, 1:2], + "nu": data_2d_xy[:, 2:3], + } + output_dict = predict(input_dict, solver) + + # analytical solution + N_pTest = 500 + uSolaM = np.zeros([N_y, N_x, N_p]) + dP = P_IN - P_OUT + for i in range(N_p): + uy = (R**2 - data_1d_y**2) * dP / (2 * L * data_1d_nu[i] * RHO) + uSolaM[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) + uMax_a = np.zeros([1, N_pTest]) + + data_1d_nuDist = np.random.normal(nuMean, 0.2 * nuMean, N_pTest) + data_2d_xy_before_test = np.array( + np.meshgrid((X_IN - X_OUT) / 2.0, 0, data_1d_nuDist) + ) + data_2d_xy_before_test_reshape = data_2d_xy_before_test.reshape(3, -1) + data_2d_xy_test = data_2d_xy_before_test_reshape.T + data_2d_xy_test = data_2d_xy_before_test_reshape.T + + u_pred_2d_xy = output_dict["u"].numpy() + v_pred_2d_xy = output_dict["v"].numpy() + p_pred_2d_xy = output_dict["p"].numpy() + u_pred_2d_xy_mesh = u_pred_2d_xy.reshape(N_y, N_x, N_p) + v_pred_2d_xy_mesh = v_pred_2d_xy.reshape(N_y, N_x, N_p) + p_pred_2d_xy_mesh = p_pred_2d_xy.reshape(N_y, N_x, N_p) + + input_dict_test = { + "x": data_2d_xy_test[:, 0:1], + "y": data_2d_xy_test[:, 1:2], + "nu": data_2d_xy_test[:, 2:3], + } + output_dict_test = predict(input_dict_test, solver) + + uMax_pred = output_dict_test["u"].numpy() + + np.savez( + "pred_poiseuille_para_0425", + mesh=data_2d_xy_before, + u=u_pred_2d_xy_mesh, + v=v_pred_2d_xy_mesh, + p=p_pred_2d_xy_mesh, + ut=uSolaM, + uMaxP=uMax_pred, + uMaxA=uMax_a, + ) diff --git a/examples/pipe/poiseuille_flow_analytical.py b/examples/pipe/poiseuille_flow_analytical.py deleted file mode 100644 index 6af77f57a..000000000 --- a/examples/pipe/poiseuille_flow_analytical.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy as np - -if __name__ == "__main__": - R = 0.05 # radius of pipe - RHO = 1 # density - L = 1.0 # length of pipe - nuMean = 0.001 # average viscosity - nuStd = 0.9 - dP = 0.1 - - X_IN = 0 # inlet position of pipe - X_OUT = X_IN + L # outlet position of pipe - - yStart = -R - yEnd = yStart + 2 * R - - nuStart = nuMean - nuMean * nuStd # 0.0001 - nuEnd = nuMean + nuMean * nuStd # 0.1 - - N_x = 10 - N_y = 50 - N_p = 50 - N_pTest = 500 - uSolaM = np.zeros([N_y, N_x, N_p]) - data_1d_x = np.linspace(X_IN, X_OUT, N_x, endpoint=True) - data_1d_y = np.linspace(yStart, yEnd, N_y, endpoint=True) - data_1d_nu = np.linspace(nuStart, nuEnd, N_p, endpoint=True) - data_1d_nuDist = np.random.normal(nuMean, 0.2 * nuMean, N_pTest) - - for i in range(N_p): - uy = (R**2 - data_1d_y**2) * dP / (2 * L * data_1d_nu[i] * RHO) - uSolaM[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) - - uMax_a = np.zeros([1, N_pTest]) - for i in range(N_pTest): - uMax_a[0, i] = (R**2) * dP / (2 * L * data_1d_nuDist[i] * RHO) diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 2e287e024..ea49b4293 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -18,6 +18,24 @@ import paddle.nn as nn import paddle.nn.functional as F + +class Swish(nn.Layer): + def __init__(self, beta: float = 1.0): + super().__init__() + self.beta = paddle.create_parameter( + shape=[1], + dtype="float32", + default_initializer=paddle.nn.initializer.Constant(beta), + ) + + def forward(self, x): + return x * F.sigmoid(x * self.beta) + + +# def swish(x): +# beta = +# return x * F.sigmoid(x * beta) + act_func_dict = { "elu": F.elu, "relu": F.relu, @@ -26,7 +44,7 @@ "silu": F.silu, "sin": paddle.sin, "cos": paddle.cos, - "swish": F.silu, + "swish": Swish(1.0), "tanh": F.tanh, "identity": nn.Identity(), } diff --git a/ppsci/solver/printer.py b/ppsci/solver/printer.py index 5bd383908..d8a8a9d9f 100644 --- a/ppsci/solver/printer.py +++ b/ppsci/solver/printer.py @@ -43,7 +43,7 @@ def log_train_info(trainer, batch_size, epoch_id, iter_id): metric_msg = ", ".join( [ - f"{key}: {trainer.train_output_info[key].avg:.5f}" + f"{key}: {trainer.train_output_info[key].val:.9f}" for key in trainer.train_output_info ] ) diff --git a/ppsci/solver/solver.py b/ppsci/solver/solver.py index 7fc4b4859..a9d174ea5 100644 --- a/ppsci/solver/solver.py +++ b/ppsci/solver/solver.py @@ -303,7 +303,7 @@ def train(self): metric_msg = ", ".join( [self.train_output_info[key].avg_info for key in self.train_output_info] ) - logger.info(f"[Train][Epoch {epoch_id}/{self.epochs}][Avg] {metric_msg}") + # logger.info(f"[Train][Epoch {epoch_id}/{self.epochs}][Avg] {metric_msg}") self.train_output_info.clear() cur_metric = float("inf") diff --git a/ppsci/solver/train.py b/ppsci/solver/train.py index a18b10898..8123d01dc 100644 --- a/ppsci/solver/train.py +++ b/ppsci/solver/train.py @@ -80,7 +80,6 @@ def train_epoch_func(solver, epoch_id, log_freq): if solver.update_freq > 1: total_loss = total_loss / solver.update_freq loss_dict["loss"] = float(total_loss) - # backward if solver.use_amp: total_loss_scaled = solver.scaler.scale(total_loss) @@ -92,6 +91,65 @@ def train_epoch_func(solver, epoch_id, log_freq): solver.lr_scheduler.step() else: total_loss.backward() + # import numpy as np + # dir = "/workspace/wangguan/LabelFree-DNN-Surrogate/net_params" + # bias_u = np.load(dir + f"/bias_u_epoch_{iter_id}.npz") + # bias_v = np.load(dir + f"/bias_v_epoch_{iter_id}.npz") + # bias_p = np.load(dir + f"/bias_p_epoch_{iter_id}.npz") + # weight_u = np.load(dir + f"/weight_u_epoch_{iter_id}.npz") + # weight_v = np.load(dir + f"/weight_v_epoch_{iter_id}.npz") + # weight_p = np.load(dir + f"/weight_p_epoch_{iter_id}.npz") + # weight_grad_u = np.load(dir + f"/weight_grad_u_epoch_{iter_id}.npz") + # weight_grad_v = np.load(dir + f"/weight_grad_v_epoch_{iter_id}.npz") + # weight_grad_p = np.load(dir + f"/weight_grad_p_epoch_{iter_id}.npz") + # # u + # print("w0_u is close:", np.allclose(solver.model.model_list[0].linears[0].weight.numpy(), weight_u[f"w_0"])) + # print("w1_u is close:", np.allclose(solver.model.model_list[0].linears[1].weight.numpy(), weight_u[f"w_1"])) + # print("w2_u is close:", np.allclose(solver.model.model_list[0].linears[2].weight.numpy(), weight_u[f"w_2"])) + # print("w3_u is close:", np.allclose(solver.model.model_list[0].last_fc.weight.numpy(), weight_u[f"w_3"])) + + # print("b0_u is close:", np.allclose(solver.model.model_list[0].linears[0].bias.numpy(), bias_u[f"b_0"])) + # print("b1_u is close:", np.allclose(solver.model.model_list[0].linears[1].bias.numpy(), bias_u[f"b_1"])) + # print("b2_u is close:", np.allclose(solver.model.model_list[0].linears[2].bias.numpy(), bias_u[f"b_2"])) + # print("b3_u is close:", np.allclose(solver.model.model_list[0].last_fc.bias.numpy(), bias_u[f"b_3"])) + + # print("w0_u grad is close:", np.allclose(solver.model.model_list[0].linears[0].weight.grad.numpy(), weight_grad_u[f"w_0"])) + # print("w1_u grad is close:", np.allclose(solver.model.model_list[0].linears[1].weight.grad.numpy(), weight_grad_u[f"w_1"])) + # print("w2_u grad is close:", np.allclose(solver.model.model_list[0].linears[2].weight.grad.numpy(), weight_grad_u[f"w_2"])) + # print("w3_u grad is close:", np.allclose(solver.model.model_list[0].last_fc.weight.grad.numpy(), weight_grad_u[f"w_3"])) + + # #v + # print("w0_v is close:", np.allclose(solver.model.model_list[1].linears[0].weight.numpy(), weight_v[f"w_0"])) + # print("w1_v is close:", np.allclose(solver.model.model_list[1].linears[1].weight.numpy(), weight_v[f"w_1"])) + # print("w2_v is close:", np.allclose(solver.model.model_list[1].linears[2].weight.numpy(), weight_v[f"w_2"])) + # print("w3_v is close:", np.allclose(solver.model.model_list[1].last_fc.weight.numpy(), weight_v[f"w_3"])) + + # print("b0_v is close:", np.allclose(solver.model.model_list[1].linears[0].bias.numpy(), bias_v[f"b_0"])) + # print("b1_v is close:", np.allclose(solver.model.model_list[1].linears[1].bias.numpy(), bias_v[f"b_1"])) + # print("b2_v is close:", np.allclose(solver.model.model_list[1].linears[2].bias.numpy(), bias_v[f"b_2"])) + # print("b3_v is close:", np.allclose(solver.model.model_list[1].last_fc.bias.numpy(), bias_v[f"b_3"])) + + # print("w0_v grad is close:", np.allclose(solver.model.model_list[1].linears[0].weight.grad.numpy(), weight_grad_v[f"w_0"])) + # print("w1_v grad is close:", np.allclose(solver.model.model_list[1].linears[1].weight.grad.numpy(), weight_grad_v[f"w_1"])) + # print("w2_v grad is close:", np.allclose(solver.model.model_list[1].linears[2].weight.grad.numpy(), weight_grad_v[f"w_2"])) + # print("w3_v grad is close:", np.allclose(solver.model.model_list[1].last_fc.weight.grad.numpy(), weight_grad_v[f"w_3"])) + + # #p + # print("w0_p is close:", np.allclose(solver.model.model_list[2].linears[0].weight.numpy(), weight_p[f"w_0"])) + # print("w1_p is close:", np.allclose(solver.model.model_list[2].linears[1].weight.numpy(), weight_p[f"w_1"])) + # print("w2_p is close:", np.allclose(solver.model.model_list[2].linears[2].weight.numpy(), weight_p[f"w_2"])) + # print("w3_p is close:", np.allclose(solver.model.model_list[2].last_fc.weight.numpy(), weight_p[f"w_3"])) + + # print("b0_p is close:", np.allclose(solver.model.model_list[2].linears[0].bias.numpy(), bias_p[f"b_0"])) + # print("b1_p is close:", np.allclose(solver.model.model_list[2].linears[1].bias.numpy(), bias_p[f"b_1"])) + # print("b2_p is close:", np.allclose(solver.model.model_list[2].linears[2].bias.numpy(), bias_p[f"b_2"])) + # print("b3_p is close:", np.allclose(solver.model.model_list[2].last_fc.bias.numpy(), bias_p[f"b_3"])) + + # print("w0_p grad is close:", np.allclose(solver.model.model_list[2].linears[0].weight.grad.numpy(), weight_grad_p[f"w_0"])) + # print("w1_p grad is close:", np.allclose(solver.model.model_list[2].linears[1].weight.grad.numpy(), weight_grad_p[f"w_1"])) + # print("w2_p grad is close:", np.allclose(solver.model.model_list[2].linears[2].weight.grad.numpy(), weight_grad_p[f"w_2"])) + # print("w3_p grad is close:", np.allclose(solver.model.model_list[2].last_fc.weight.grad.numpy(), weight_grad_p[f"w_3"])) + if iter_id % solver.update_freq == 0: solver.optimizer.step() solver.optimizer.clear_grad() @@ -109,6 +167,8 @@ def train_epoch_func(solver, epoch_id, log_freq): if iter_id == 1 or iter_id % log_freq == 0: printer.log_train_info(solver, total_batch_size, epoch_id, iter_id) + # real_loss = float(loss_dict["loss"]) + # print(f"{(epoch_id - 1) * 195 + iter_id} step: loss is {real_loss}") batch_tic = time.perf_counter() From cfc840708043774ca178d5b147315dcb2bb2eded Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Thu, 27 Apr 2023 09:15:51 +0000 Subject: [PATCH 03/31] reproduce fig1 (a) and (b) successfully --- examples/pipe/data/compare/dvdy_epoch_1.npz | Bin 774 -> 0 bytes examples/pipe/data/compare/v_epoch_1.npz | Bin 768 -> 0 bytes examples/pipe/data/compare/y_epoch_1.npz | Bin 1280 -> 0 bytes examples/pipe/data/compare_1/dvdy_epoch_1.npz | Bin 774 -> 0 bytes examples/pipe/data/compare_1/v_epoch_1.npz | Bin 768 -> 0 bytes examples/pipe/data/compare_1/y_epoch_1.npz | Bin 1280 -> 0 bytes examples/pipe/data/compare_2/dvdy_epoch_1.npz | Bin 774 -> 0 bytes examples/pipe/data/compare_2/v_epoch_1.npz | Bin 768 -> 0 bytes examples/pipe/data/compare_2/y_epoch_1.npz | Bin 1280 -> 0 bytes examples/pipe/data/compare_3/dvdy_epoch_1.npz | Bin 774 -> 0 bytes examples/pipe/data/compare_3/v_epoch_1.npz | Bin 25856 -> 0 bytes examples/pipe/data/compare_3/y_epoch_1.npz | Bin 1280 -> 0 bytes examples/pipe/data/compare_4/dvdy_epoch_1.npz | Bin 774 -> 0 bytes examples/pipe/data/compare_4/v_epoch_1.npz | Bin 768 -> 0 bytes examples/pipe/data/compare_4/y_epoch_1.npz | Bin 1280 -> 0 bytes examples/pipe/data/input/input_x_y_nu.npz | Bin 300726 -> 0 bytes .../bias_p_epoch_1.npz} | Bin .../bias_u_epoch_1.npz} | Bin .../bias_v_epoch_1.npz} | Bin .../weight_p_epoch_1.npz} | Bin .../weight_u_epoch_1.npz} | Bin .../weight_v_epoch_1.npz} | Bin .../pipe/output_pipe/checkpoints/latest.pdopt | Bin 138802 -> 139262 bytes .../output_pipe/checkpoints/latest.pdparams | Bin 66915 -> 67170 bytes .../output_pipe/checkpoints/latest.pdstates | Bin 44 -> 45 bytes examples/pipe/pipe_post.py | 11 +++---- examples/pipe/poiseuille_flow_FCNN.py | 27 ++++++++++-------- 27 files changed, 21 insertions(+), 17 deletions(-) delete mode 100644 examples/pipe/data/compare/dvdy_epoch_1.npz delete mode 100644 examples/pipe/data/compare/v_epoch_1.npz delete mode 100644 examples/pipe/data/compare/y_epoch_1.npz delete mode 100644 examples/pipe/data/compare_1/dvdy_epoch_1.npz delete mode 100644 examples/pipe/data/compare_1/v_epoch_1.npz delete mode 100644 examples/pipe/data/compare_1/y_epoch_1.npz delete mode 100644 examples/pipe/data/compare_2/dvdy_epoch_1.npz delete mode 100644 examples/pipe/data/compare_2/v_epoch_1.npz delete mode 100644 examples/pipe/data/compare_2/y_epoch_1.npz delete mode 100644 examples/pipe/data/compare_3/dvdy_epoch_1.npz delete mode 100644 examples/pipe/data/compare_3/v_epoch_1.npz delete mode 100644 examples/pipe/data/compare_3/y_epoch_1.npz delete mode 100644 examples/pipe/data/compare_4/dvdy_epoch_1.npz delete mode 100644 examples/pipe/data/compare_4/v_epoch_1.npz delete mode 100644 examples/pipe/data/compare_4/y_epoch_1.npz delete mode 100644 examples/pipe/data/input/input_x_y_nu.npz rename examples/pipe/data/{init_net_params/initial_bias_p.npz => net_params/bias_p_epoch_1.npz} (100%) rename examples/pipe/data/{init_net_params/initial_bias_u.npz => net_params/bias_u_epoch_1.npz} (100%) rename examples/pipe/data/{init_net_params/initial_bias_v.npz => net_params/bias_v_epoch_1.npz} (100%) rename examples/pipe/data/{init_net_params/initial_weight_p.npz => net_params/weight_p_epoch_1.npz} (100%) rename examples/pipe/data/{init_net_params/initial_weight_u.npz => net_params/weight_u_epoch_1.npz} (100%) rename examples/pipe/data/{init_net_params/initial_weight_v.npz => net_params/weight_v_epoch_1.npz} (100%) diff --git a/examples/pipe/data/compare/dvdy_epoch_1.npz b/examples/pipe/data/compare/dvdy_epoch_1.npz deleted file mode 100644 index d920edea6cc2b60f686175d74e9132ab04dd968e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 774 zcmbVKZAepL6u$Fgn5C4?@JG;u^s0@-lE}L6v}h|D$?St!f5vPRMcZU6VM*p@1Qmlu 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similarity index 100% rename from examples/pipe/data/init_net_params/initial_bias_u.npz rename to examples/pipe/data/net_params/bias_u_epoch_1.npz diff --git a/examples/pipe/data/init_net_params/initial_bias_v.npz b/examples/pipe/data/net_params/bias_v_epoch_1.npz similarity index 100% rename from examples/pipe/data/init_net_params/initial_bias_v.npz rename to examples/pipe/data/net_params/bias_v_epoch_1.npz diff --git a/examples/pipe/data/init_net_params/initial_weight_p.npz b/examples/pipe/data/net_params/weight_p_epoch_1.npz similarity index 100% rename from examples/pipe/data/init_net_params/initial_weight_p.npz rename to examples/pipe/data/net_params/weight_p_epoch_1.npz diff --git a/examples/pipe/data/init_net_params/initial_weight_u.npz b/examples/pipe/data/net_params/weight_u_epoch_1.npz similarity index 100% rename from examples/pipe/data/init_net_params/initial_weight_u.npz rename to examples/pipe/data/net_params/weight_u_epoch_1.npz diff --git 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16 XcmdPVWoclUsx*;Bh1GWlcc~r#AOi#n delta 15 WcmdPZVQFBQsyLBFg~eN`R1W|euLETO diff --git a/examples/pipe/pipe_post.py b/examples/pipe/pipe_post.py index 455ce3f01..fe0a73c01 100644 --- a/examples/pipe/pipe_post.py +++ b/examples/pipe/pipe_post.py @@ -113,21 +113,22 @@ ax1.set_ylim([0.0, 0.62]) figureName = "pipe_uProfiles_nuIdx_.png" plt.savefig(figureName, bbox_inches="tight") -exit() print("Re is", Re) np.savez("test_Re", Re=Re) plt.figure(2) plt.clf() ax1 = plt.subplot(111) -sns.kdeplot(uMaxA[0, :], shade=True, label="Analytical", linestyle="-", linewidth=3) sns.kdeplot( - uMaxP[0, :], - shade=False, + uMaxA, fill=True, color="black", label="Analytical", linestyle="-", linewidth=3 +) +sns.kdeplot( + uMaxP, + fill=False, + color="red", label="DNN", linestyle="--", linewidth=3.5, - color="darkred", ) plt.legend(prop={"size": 16}) plt.xlabel(r"$u_c$", fontsize=16) diff --git a/examples/pipe/poiseuille_flow_FCNN.py b/examples/pipe/poiseuille_flow_FCNN.py index 3081bc4ba..66e6e0395 100644 --- a/examples/pipe/poiseuille_flow_FCNN.py +++ b/examples/pipe/poiseuille_flow_FCNN.py @@ -12,8 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import os.path as osp - import numpy as np import paddle @@ -125,7 +123,7 @@ def momentum_z_compute_func(out): os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/pipe") # set output directory output_dir = "./output_pipe_1" - dir = "/workspace/wangguan/LabelFree-DNN-Surrogate/net_params" + dir = "./data/net_params" initial_bias_u = np.load(dir + f"/bias_u_epoch_1.npz") initial_bias_v = np.load(dir + f"/bias_v_epoch_1.npz") initial_bias_p = np.load(dir + f"/bias_p_epoch_1.npz") @@ -365,22 +363,13 @@ def predict( } output_dict = predict(input_dict, solver) - # analytical solution N_pTest = 500 - uSolaM = np.zeros([N_y, N_x, N_p]) - dP = P_IN - P_OUT - for i in range(N_p): - uy = (R**2 - data_1d_y**2) * dP / (2 * L * data_1d_nu[i] * RHO) - uSolaM[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) - uMax_a = np.zeros([1, N_pTest]) - data_1d_nuDist = np.random.normal(nuMean, 0.2 * nuMean, N_pTest) data_2d_xy_before_test = np.array( np.meshgrid((X_IN - X_OUT) / 2.0, 0, data_1d_nuDist) ) data_2d_xy_before_test_reshape = data_2d_xy_before_test.reshape(3, -1) data_2d_xy_test = data_2d_xy_before_test_reshape.T - data_2d_xy_test = data_2d_xy_before_test_reshape.T u_pred_2d_xy = output_dict["u"].numpy() v_pred_2d_xy = output_dict["v"].numpy() @@ -398,6 +387,18 @@ def predict( uMax_pred = output_dict_test["u"].numpy() + # analytical solution + uSolaM = np.zeros([N_y, N_x, N_p]) + dP = P_IN - P_OUT + + for i in range(N_p): + uy = (R**2 - data_1d_y**2) * dP / (2 * L * data_1d_nu[i] * RHO) + uSolaM[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) + uMax_a = np.zeros([N_pTest, 1]) + for i in range(N_pTest): + uMax_a[i] = (R**2) * dP / (2 * L * data_1d_nuDist[i] * RHO) + + # save result for post process np.savez( "pred_poiseuille_para_0425", mesh=data_2d_xy_before, @@ -408,3 +409,5 @@ def predict( uMaxP=uMax_pred, uMaxA=uMax_a, ) + + # data_1d_nuDist = np.random.normal(2e-4, 1.9e-3, 20) From 774d825678ac51426ea437077d13fc97e4d38f0d Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 28 Apr 2023 07:40:48 +0000 Subject: [PATCH 04/31] reproduce aneurysm case in paper 3.2 --- .../data/net2_params/bias_epoch_0.npz | Bin 0 -> 1218 bytes .../data/net2_params/weight_epoch_0.npz | Bin 0 -> 4494 bytes .../data/net3_params/bias_epoch_0.npz | Bin 0 -> 1218 bytes .../data/net3_params/weight_epoch_0.npz | Bin 0 -> 4494 bytes .../data/net4_params/bias_epoch_0.npz | Bin 0 -> 1218 bytes .../data/net4_params/weight_epoch_0.npz | Bin 0 -> 4494 bytes examples/aneurysm/main.py | 342 ++++++++++++++++++ 7 files changed, 342 insertions(+) create mode 100644 examples/aneurysm/data/net2_params/bias_epoch_0.npz create mode 100644 examples/aneurysm/data/net2_params/weight_epoch_0.npz create mode 100644 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elif next(iter(out.keys())) == "v": + v = out["v"] + # The no-slip condition of velocity on the wall + new_out["v"] = (self.h**2 - y**2) * v + elif next(iter(out.keys())) == "p": + p = out["p"] + # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] + new_out["p"] = ( + (X_IN - x) * 0 + + (P_IN - P_OUT) * (X_OUT - x) / L + + 0 * y + + (X_IN - x) * (X_OUT - x) * p + ) + else: + raise NotImplementedError(f"{out.keys()} are outputs to be implemented") + + return new_out + + +shared_transform = Output_transform() +model_2.register_output_transform(shared_transform) +model_3.register_output_transform(shared_transform) +model_4.register_output_transform(shared_transform) +model = ppsci.arch.ModelList([model_2, model_3, model_4]) + + +equation = {"NavierStokes": NavierStokes(RHO, 2, False)} + +pde_constraint = ppsci.constraint.InteriorConstraint( + equation["NavierStokes"].equations, + {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, + geom=interior_geom, + dataloader_cfg={ + "dataset": "NamedArrayDataset", + "num_workers": 1, + "batch_size": BATCH_SIZE, + "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), + "sampler": { + "name": "BatchSampler", + "shuffle": False, + "drop_last": False, + }, + }, + loss=ppsci.loss.MSELoss("mean"), + evenly=True, + weight_dict={"u": 1, "v": 1, "p": 1}, + name="EQ", +) + +# initialize solver +solver = ppsci.solver.Solver( + model, + {pde_constraint.name: pde_constraint}, + output_dir, + optimizer2, + epochs=EPOCHS, + iters_per_epoch=int(x.shape[0] / BATCH_SIZE), + eval_during_train=False, + save_freq=10, + log_freq=1, + equation=equation, + # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/pipe/output_pipe/checkpoints/epoch_3000", +) +solver.train() From 4b6c28553cf4869584279567de25924c8dd0a71b Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Thu, 4 May 2023 03:33:08 +0000 Subject: [PATCH 05/31] Aneurysm converged --- .../{main.py => aneurysm_flow_FCNN.py} | 14 +- ...cale0005to002_eval0to002mean001_3sigma.npz | Bin 0 -> 4244 bytes .../aneurysm/data/net2_params/bias_0_0.npz | Bin 0 -> 1218 bytes .../data/net2_params/bias_grad_0_0.npz | Bin 0 -> 1218 bytes .../data/net2_params/bias_grad_0_1.npz | Bin 0 -> 1218 bytes .../aneurysm/data/net2_params/weight_0_0.npz | Bin 0 -> 4494 bytes .../data/net2_params/weight_grad_0_0.npz | Bin 0 -> 4494 bytes .../data/net2_params/weight_grad_0_1.npz | Bin 0 -> 4494 bytes .../aneurysm/data/net3_params/bias_0_0.npz | Bin 0 -> 1218 bytes .../data/net3_params/bias_grad_0_0.npz | Bin 0 -> 1218 bytes .../data/net3_params/bias_grad_0_1.npz | Bin 0 -> 1218 bytes .../aneurysm/data/net3_params/weight_0_0.npz | Bin 0 -> 4494 bytes .../data/net3_params/weight_grad_0_0.npz | Bin 0 -> 4494 bytes .../data/net3_params/weight_grad_0_1.npz | Bin 0 -> 4494 bytes .../aneurysm/data/net4_params/bias_0_0.npz | Bin 0 -> 1218 bytes .../data/net4_params/bias_grad_0_0.npz | Bin 0 -> 1218 bytes .../data/net4_params/bias_grad_0_1.npz | Bin 0 -> 1218 bytes .../aneurysm/data/net4_params/weight_0_0.npz | Bin 0 -> 4494 bytes .../data/net4_params/weight_grad_0_0.npz | Bin 0 -> 4494 bytes .../data/net4_params/weight_grad_0_1.npz | Bin 0 -> 4494 bytes examples/aneurysm/plot_geoaneurysm.py | 141 ++++++++++++++++++ examples/aneurysm/single_test.py | 56 +++++++ examples/pipe/poiseuille_flow_FCNN.py | 6 +- ppsci/arch/activation.py | 15 +- ppsci/solver/train.py | 21 +-- 25 files changed, 225 insertions(+), 28 deletions(-) rename examples/aneurysm/{main.py => aneurysm_flow_FCNN.py} (96%) create mode 100644 examples/aneurysm/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz create mode 100644 examples/aneurysm/data/net2_params/bias_0_0.npz create mode 100644 examples/aneurysm/data/net2_params/bias_grad_0_0.npz create mode 100644 examples/aneurysm/data/net2_params/bias_grad_0_1.npz create mode 100644 examples/aneurysm/data/net2_params/weight_0_0.npz create mode 100644 examples/aneurysm/data/net2_params/weight_grad_0_0.npz create mode 100644 examples/aneurysm/data/net2_params/weight_grad_0_1.npz create mode 100644 examples/aneurysm/data/net3_params/bias_0_0.npz create mode 100644 examples/aneurysm/data/net3_params/bias_grad_0_0.npz create mode 100644 examples/aneurysm/data/net3_params/bias_grad_0_1.npz create mode 100644 examples/aneurysm/data/net3_params/weight_0_0.npz create mode 100644 examples/aneurysm/data/net3_params/weight_grad_0_0.npz create mode 100644 examples/aneurysm/data/net3_params/weight_grad_0_1.npz create mode 100644 examples/aneurysm/data/net4_params/bias_0_0.npz create mode 100644 examples/aneurysm/data/net4_params/bias_grad_0_0.npz create mode 100644 examples/aneurysm/data/net4_params/bias_grad_0_1.npz create mode 100644 examples/aneurysm/data/net4_params/weight_0_0.npz create mode 100644 examples/aneurysm/data/net4_params/weight_grad_0_0.npz create mode 100644 examples/aneurysm/data/net4_params/weight_grad_0_1.npz create mode 100644 examples/aneurysm/plot_geoaneurysm.py create mode 100644 examples/aneurysm/single_test.py diff --git a/examples/aneurysm/main.py b/examples/aneurysm/aneurysm_flow_FCNN.py similarity index 96% rename from examples/aneurysm/main.py rename to examples/aneurysm/aneurysm_flow_FCNN.py index d185bdde0..14b26e9b3 100644 --- a/examples/aneurysm/main.py +++ b/examples/aneurysm/aneurysm_flow_FCNN.py @@ -116,7 +116,7 @@ def momentum_z_compute_func(out): os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") -output_dir = "./output_0428" +output_dir = "./output_0504" ppsci.utils.misc.set_random_seed(42) # initialize logger @@ -244,15 +244,6 @@ def momentum_z_compute_func(out): np.load(f"data/net4_params/weight_epoch_0.npz"), np.load(f"data/net4_params/bias_epoch_0.npz"), ) -optimizer2 = ppsci.optimizer.Adam(LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15)( - [model_2] -) -optimizer3 = ppsci.optimizer.Adam(LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15)( - [model_3] -) -optimizer4 = ppsci.optimizer.Adam(LEARNING_RATE, 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as plt +import numpy as np +import single_test + +scale_test = np.load("aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")["scale"] +os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") +caseCount = [1.0, 151.0, 486.0] + +W_ctl = np.zeros([len(scale_test), 1]) +W_ctl_Ml = np.zeros([len(scale_test), 1]) + +# aneurysm +plot_x = 0.8 +plot_y = 0.06 +fontsize = 14 +axis_limit = [0, 1, -0.15, 0.15] +path = "Cases/" +for caseIdx in caseCount: + ## geo_case + scale = scale_test[int(caseIdx - 1)] + + nu = 1e-3 + + dP = 0.1 + mu = 0.5 + sigma = 0.1 + epochs = 500 + ## Aneurysm + ## + token = "aneurysm0005to002" + token1 = "aneurysm0to002" + + ###################################################################### + # BELOW SHOULD REAMIN UNCHANGED!!!! + ###################################################################### + + print("path is", path + str(caseIdx)) + + Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") + Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") + x = Data_CFD["x"] + y = Data_CFD["y"] + U_CFD = Data_CFD["U"] + U = Data_NN["U"] + + device = "cpu" + u, v, p = single_test.det_test( + x, y, nu, dP, mu, sigma, scale, epochs, path, device, caseIdx + ) + print("shape of u", u.shape) + print("shape of v", v.shape) + print("shape of p", p.shape) + w = np.zeros_like(u) + U = np.concatenate([u, v, w], axis=1) + + # Contour Comparison + + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "uContour_test.png", + bbox_inches="tight", + ) + + print( + "path is", + "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "uContour_test.png", + ) + + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "vContour_test.png", + bbox_inches="tight", + ) + + plt.close("all") + # plt.show() + + Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") + unique_x = Data_CFD_wss["x"] + wall_shear_mag_up = Data_CFD_wss["wss"] + Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") + NNwall_shear_mag_up = Data_NN_wss["wss"] + # show plot + + plt.figure() + + plt.plot( + unique_x, + wall_shear_mag_up, + label="CFD", + color="darkblue", + linestyle="-", + lw=3.0, + alpha=1.0, + ) + plt.plot( + unique_x, + NNwall_shear_mag_up, + label="DNN", + color="red", + linestyle="--", + dashes=(5, 5), + lw=2.0, + alpha=1.0, + ) + plt.xlabel(r"x", fontsize=16) + plt.ylabel(r"$\tau_{c}$", fontsize=16) + plt.legend(prop={"size": 16}) + plt.savefig( + "plot/" + str(int(caseIdx)) + "nu" + str(nu) + "wallShear_test.png", + bbox_inches="tight", + ) + plt.close("all") + + ## show center wall shear + # CFD + W_ctl[int(caseIdx - 1)] = wall_shear_mag_up[int(len(wall_shear_mag_up) / 2)] + # NN + W_ctl_Ml[int(caseIdx - 1)] = NNwall_shear_mag_up[int(len(NNwall_shear_mag_up) / 2)] diff --git a/examples/aneurysm/single_test.py b/examples/aneurysm/single_test.py new file mode 100644 index 000000000..dbf9d393a --- /dev/null +++ b/examples/aneurysm/single_test.py @@ -0,0 +1,56 @@ +from math import pi +from math import sqrt + +import numpy as np +import torch + + +def det_test(x, y, nu, dP, mu, sigma, scale, epochs, path1, device, caseIdx): + + R = ( + scale + * 1 + / sqrt(2 * np.pi * sigma**2) + * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) + ) + rInlet = 0.05 + yUp = rInlet - R + xt = torch.FloatTensor(x).to(device) + yt = torch.FloatTensor(y).to(device) + xt = xt.view(len(xt), -1) + yt = yt.view(len(yt), -1) + scalet = scale * torch.ones_like(xt) + Rt = torch.FloatTensor(yUp).to(device) + Rt = Rt.view(len(Rt), -1) + xt.requires_grad = True + yt.requires_grad = True + scalet.requires_grad = True + net_in = torch.cat((xt, yt, scalet), 1) + u_t = net2(net_in) + v_t = net3(net_in) + P_t = net4(net_in) + u_hard = u_t * (Rt**2 - yt**2) + v_hard = (Rt**2 - yt**2) * v_t + L = 1 + xStart = 0 + xEnd = L + P_hard = ( + (xStart - xt) * 0 + + dP * (xEnd - xt) / L + + 0 * yt + + (xStart - xt) * (xEnd - xt) * P_t + ) + u_hard = u_hard.cpu().data.numpy() + v_hard = v_hard.cpu().data.numpy() + P_hard = P_hard.cpu().data.numpy() + + np.savez( + path1 + str(int(caseIdx)) + "ML_WallStress_uvp", + x_center=x, + y_center=y, + u_center=u_hard, + v_center=v_hard, + p_center=P_hard, + ) + + return u_hard, v_hard, P_hard diff --git a/examples/pipe/poiseuille_flow_FCNN.py b/examples/pipe/poiseuille_flow_FCNN.py index 66e6e0395..5de6ce9b6 100644 --- a/examples/pipe/poiseuille_flow_FCNN.py +++ b/examples/pipe/poiseuille_flow_FCNN.py @@ -207,7 +207,7 @@ def momentum_z_compute_func(out): ["u"], LAYER_NUMBER, HIDDEN_SIZE, - "swish", + "swish_beta", False, False, initial_weight_u, @@ -219,7 +219,7 @@ def momentum_z_compute_func(out): ["v"], LAYER_NUMBER, HIDDEN_SIZE, - "swish", + "swish_beta", False, False, initial_weight_v, @@ -231,7 +231,7 @@ def momentum_z_compute_func(out): ["p"], LAYER_NUMBER, HIDDEN_SIZE, - "swish", + "swish_beta", False, False, initial_weight_p, diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index ea49b4293..d0c9ab1d5 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -19,7 +19,7 @@ import paddle.nn.functional as F -class Swish(nn.Layer): +class Swish_beta(nn.Layer): def __init__(self, beta: float = 1.0): super().__init__() self.beta = paddle.create_parameter( @@ -32,9 +32,13 @@ def forward(self, x): return x * F.sigmoid(x * self.beta) -# def swish(x): -# beta = -# return x * F.sigmoid(x * beta) +class Swish(nn.Layer): + def __init__(self): + super().__init__() + + def forward(self, x): + return x * F.sigmoid(x) + act_func_dict = { "elu": F.elu, @@ -44,7 +48,8 @@ def forward(self, x): "silu": F.silu, "sin": paddle.sin, "cos": paddle.cos, - "swish": Swish(1.0), + "swish_beta": Swish_beta(1.0), + "swish": Swish(), "tanh": F.tanh, "identity": nn.Identity(), } diff --git a/ppsci/solver/train.py b/ppsci/solver/train.py index 8123d01dc..d20cada0c 100644 --- a/ppsci/solver/train.py +++ b/ppsci/solver/train.py @@ -92,16 +92,17 @@ def train_epoch_func(solver, epoch_id, log_freq): else: total_loss.backward() # import numpy as np - # dir = "/workspace/wangguan/LabelFree-DNN-Surrogate/net_params" - # bias_u = np.load(dir + f"/bias_u_epoch_{iter_id}.npz") - # bias_v = np.load(dir + f"/bias_v_epoch_{iter_id}.npz") - # bias_p = np.load(dir + f"/bias_p_epoch_{iter_id}.npz") - # weight_u = np.load(dir + f"/weight_u_epoch_{iter_id}.npz") - # weight_v = np.load(dir + f"/weight_v_epoch_{iter_id}.npz") - # weight_p = np.load(dir + f"/weight_p_epoch_{iter_id}.npz") - # weight_grad_u = np.load(dir + f"/weight_grad_u_epoch_{iter_id}.npz") - # weight_grad_v = np.load(dir + f"/weight_grad_v_epoch_{iter_id}.npz") - # weight_grad_p = np.load(dir + f"/weight_grad_p_epoch_{iter_id}.npz") + # # import os + # bias_u = np.load("./data/net2_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") + # bias_v = np.load("./data/net3_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") + # bias_p = np.load("./data/net4_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") + # weight_u = np.load("./data/net2_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") + # weight_v = np.load("./data/net3_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") + # weight_p = np.load("./data/net4_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") + + # weight_grad_u = np.load("./data/net2_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") + # weight_grad_v = np.load("./data/net3_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") + # weight_grad_p = np.load("./data/net4_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") # # u # print("w0_u is close:", np.allclose(solver.model.model_list[0].linears[0].weight.numpy(), weight_u[f"w_0"])) # print("w1_u is close:", np.allclose(solver.model.model_list[0].linears[1].weight.numpy(), weight_u[f"w_1"])) From e8b53e2279e69c1c11e6de04a44cb0129362477d Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 5 May 2023 09:26:53 +0000 Subject: [PATCH 06/31] clean pipe flow code --- examples/aneurysm/aneurysm_flow_FCNN.py | 205 +++++++++++++-- examples/aneurysm/single_test.py | 23 +- examples/pipe/bug.py | 65 ----- examples/pipe/bug_1.py | 96 ------- .../pipe/output_pipe/checkpoints/latest.pdeqn | Bin 96 -> 0 bytes .../pipe/output_pipe/checkpoints/latest.pdopt | Bin 139262 -> 0 bytes .../output_pipe/checkpoints/latest.pdparams | Bin 67170 -> 0 bytes .../output_pipe/checkpoints/latest.pdstates | Bin 45 -> 0 bytes examples/pipe/pipe_post.py | 140 ---------- examples/pipe/poiseuille_flow_FCNN.py | 244 ++++++++++-------- 10 files changed, 330 insertions(+), 443 deletions(-) delete mode 100644 examples/pipe/bug.py delete mode 100644 examples/pipe/bug_1.py delete mode 100644 examples/pipe/output_pipe/checkpoints/latest.pdeqn delete mode 100644 examples/pipe/output_pipe/checkpoints/latest.pdopt delete mode 100644 examples/pipe/output_pipe/checkpoints/latest.pdparams delete mode 100644 examples/pipe/output_pipe/checkpoints/latest.pdstates delete mode 100644 examples/pipe/pipe_post.py diff --git a/examples/aneurysm/aneurysm_flow_FCNN.py b/examples/aneurysm/aneurysm_flow_FCNN.py index 14b26e9b3..c75fe7ac3 100644 --- a/examples/aneurysm/aneurysm_flow_FCNN.py +++ b/examples/aneurysm/aneurysm_flow_FCNN.py @@ -11,7 +11,7 @@ from ppsci.autodiff import jacobian -def ThreeD_mesh(x_1d, x_2d, tmp_1d, sigma, mu): +def ThreeD_mesh(x_2d, tmp_1d): tmp_3d = np.expand_dims(np.tile(tmp_1d, len(x_2d)), 1).astype("float") x = [] for x0 in x_2d: @@ -116,7 +116,7 @@ def momentum_z_compute_func(out): os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") -output_dir = "./output_0504" +output_dir = "./output_0504_debug" ppsci.utils.misc.set_random_seed(42) # initialize logger @@ -124,31 +124,25 @@ def momentum_z_compute_func(out): core.set_prim_eager_enabled(True) EPOCHS = 500 - L = 1 X_IN = 0 X_OUT = X_IN + L P_OUT = 0 # pressure at the outlet of pipe P_IN = 0.1 # pressure at the inlet of pipe rInlet = 0.05 - nPt = 100 unique_x = np.linspace(X_IN, X_OUT, nPt) mu = 0.5 * (X_OUT - X_IN) - N_y = 20 x_2d = np.tile(unique_x, N_y) x_2d = np.reshape(x_2d, (len(x_2d), 1)) - nu = 1e-3 - sigma = 0.1 - scaleStart = -0.02 scaleEnd = 0 Ng = 50 scale_1d = np.linspace(scaleStart, scaleEnd, Ng, endpoint=True) -x, scale = ThreeD_mesh(unique_x, x_2d, scale_1d, sigma, mu) +x, scale = ThreeD_mesh(x_2d, scale_1d) # axisymetric boundary R = ( @@ -161,7 +155,6 @@ def momentum_z_compute_func(out): # Generate stenosis yUp = (rInlet - R) * np.ones_like(x) yDown = (-rInlet + R) * np.ones_like(x) - idx = np.where(scale == scaleStart) plt.figure() plt.scatter(x[idx], yUp[idx]) @@ -174,25 +167,14 @@ def momentum_z_compute_func(out): for x0 in unique_x: index = np.where(x[:, 0] == x0)[0] Rsec = max(yUp[index]) - # print('shape of index',index.shape) tmpy = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) - # print('shape of tmpy',tmpy.shape) y[index] = tmpy -print("shape of x", x.shape) -print("shape of y", y.shape) -print("shape of sacle", scale.shape) -g = 9.8 RHO = 1 - BATCH_SIZE = 50 LEARNING_RATE = 1e-3 LAYER_NUMBER = 4 - 1 - -path = "Cases/" - -h_nD = 30 HIDDEN_SIZE = 20 index = [i for i in range(x.shape[0])] @@ -245,8 +227,6 @@ def momentum_z_compute_func(out): np.load(f"data/net4_params/bias_epoch_0.npz"), ) -h = None - class Output_transform: def __init__(self) -> None: @@ -331,6 +311,181 @@ def __call__(self, out, input): save_freq=10, log_freq=1, equation=equation, - # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/pipe/output_pipe/checkpoints/epoch_3000", + checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_280", ) -solver.train() + +# solver.train() +def predict( + input_dict, + solver, +): + for key, val in input_dict.items(): + input_dict[key] = paddle.to_tensor(val, dtype="float32") + evaluator = ppsci.utils.expression.ExpressionSolver( + input_dict.keys(), ["u", "v", "p"], solver.model + ) + output_expr_dict = { + "u": lambda d: d["u"], + "v": lambda d: d["v"], + "p": lambda d: d["p"], + } + for output_key, output_expr in output_expr_dict.items(): + evaluator.add_target_expr(output_expr, output_key) + output_dict = evaluator(input_dict) + return output_dict + + +def single_test(x, y, scale, caseIdx): + xt = paddle.to_tensor(x, dtype="float32") + yt = paddle.to_tensor(y, dtype="float32") + scalet = scale * paddle.ones_like(xt) + net_in = {"x": xt, "y": yt, "scale": scalet} + output_dict = predict(net_in, solver) + np.savez( + "./" + str(int(caseIdx)) + "ML_WallStress_uvp", + x_center=x, + y_center=y, + u_center=output_dict["u"], + v_center=output_dict["v"], + p_center=output_dict["p"], + ) + return output_dict + + +scale_test = np.load("aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")["scale"] +os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") +caseCount = [1.0, 151.0, 486.0] +W_ctl = np.zeros([len(scale_test), 1]) +W_ctl_Ml = np.zeros([len(scale_test), 1]) +plot_x = 0.8 +plot_y = 0.06 +fontsize = 14 +axis_limit = [0, 1, -0.15, 0.15] +path = "./data/cases/" +for caseIdx in caseCount: + scale = scale_test[int(caseIdx - 1)] + Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") + Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") + x = Data_CFD["x"] + y = Data_CFD["y"] + U_CFD = Data_CFD["U"] + U = Data_NN["U"] + n = len(x) + output_dict = single_test( + x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, caseIdx + ) + u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] + w = np.zeros_like(u) + U = np.concatenate([u, v, w], axis=1) + + # velocity U + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_uContour_test.png", + bbox_inches="tight", + ) + + # velocity V + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_vContour_test.png", + bbox_inches="tight", + ) + plt.close("all") + + # wall shear stress + Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") + unique_x = Data_CFD_wss["x"] + wall_shear_mag_up = Data_CFD_wss["wss"] + Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") + NNwall_shear_mag_up = Data_NN_wss["wss"] + + plt.figure() + plt.plot( + unique_x, + wall_shear_mag_up, + label="CFD", + color="darkblue", + linestyle="-", + lw=3.0, + alpha=1.0, + ) + plt.plot( + unique_x, + NNwall_shear_mag_up, + label="DNN", + color="red", + linestyle="--", + dashes=(5, 5), + lw=2.0, + alpha=1.0, + ) + plt.xlabel(r"x", fontsize=16) + plt.ylabel(r"$\tau_{c}$", fontsize=16) + plt.legend(prop={"size": 16}) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_wallshear_test.png", + bbox_inches="tight", + ) + plt.close("all") + + # x_centerline = np.linspace(X_IN, X_OUT, 100, endpoint=True).reshape(100, 1) + # y_centerline = np.zeros((100, 1)) + # output_dict_centerline = single_test( + # x_centerline, y_centerline, np.ones((100, 1)) * scale, caseIdx + # ) + # y_cfd = Data_CFD["y"] + # index = np.where(abs(y_cfd) < 0.001002) + # x_cfd = Data_CFD["x"][index] + # p_cfd = Data_CFD["P"][index] + # index = np.argsort(x_cfd) + # x_cfd = x_cfd[index] + # y_cfd = y_cfd[index] + # p_cfd = p_cfd[index] + # plt.figure() + # plt.plot( + # x_cfd, + # p_cfd, + # label="CFD", + # color="darkblue", + # linestyle="-", + # lw=3.0, + # alpha=1.0, + # ) + # plt.plot( + # x_centerline, + # output_dict_centerline['p'], + # label="DNN", + # color="red", + # linestyle="--", + # lw=3.0, + # alpha=1.0, + # ) + # plt.xlabel(r"x", fontsize=16) + # plt.ylabel(r"$p_{c}$", fontsize=16) + # plt.legend(prop={"size": 16}) + # plt.savefig( + # "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_center_pressure.png", + # bbox_inches="tight", + # ) + # plt.close("all") diff --git a/examples/aneurysm/single_test.py b/examples/aneurysm/single_test.py index dbf9d393a..24bdf9c29 100644 --- a/examples/aneurysm/single_test.py +++ b/examples/aneurysm/single_test.py @@ -1,26 +1,27 @@ +import math from math import pi -from math import sqrt import numpy as np +import paddle import torch -def det_test(x, y, nu, dP, mu, sigma, scale, epochs, path1, device, caseIdx): - +def det_test(x, y, dP, mu, sigma, scale, path1, caseIdx): R = ( scale * 1 - / sqrt(2 * np.pi * sigma**2) + / math.sqrt(2 * np.pi * sigma**2) * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) ) rInlet = 0.05 yUp = rInlet - R - xt = torch.FloatTensor(x).to(device) - yt = torch.FloatTensor(y).to(device) + xt = paddle.to_tensor(xt, dtype="float32") + yt = paddle.to_tensor(xt, dtype="float32") + scalet = paddle.to_tensor(xt, dtype="float32") xt = xt.view(len(xt), -1) yt = yt.view(len(yt), -1) - scalet = scale * torch.ones_like(xt) - Rt = torch.FloatTensor(yUp).to(device) + scalet = scale * paddle.ones_like(xt) + Rt = paddle.to_tensor(yUp, dtype="float32") Rt = Rt.view(len(Rt), -1) xt.requires_grad = True yt.requires_grad = True @@ -40,9 +41,9 @@ def det_test(x, y, nu, dP, mu, sigma, scale, epochs, path1, device, caseIdx): + 0 * yt + (xStart - xt) * (xEnd - xt) * P_t ) - u_hard = u_hard.cpu().data.numpy() - v_hard = v_hard.cpu().data.numpy() - P_hard = P_hard.cpu().data.numpy() + u_hard = u_hard.numpy() + v_hard = v_hard.numpy() + P_hard = P_hard.numpy() np.savez( path1 + str(int(caseIdx)) + "ML_WallStress_uvp", diff --git a/examples/pipe/bug.py b/examples/pipe/bug.py deleted file mode 100644 index eaf611edf..000000000 --- a/examples/pipe/bug.py +++ /dev/null @@ -1,65 +0,0 @@ -import os -from typing import Dict -from typing import Tuple - -import numpy as np -import paddle - -from ppsci.autodiff import jacobian - -if __name__ == "__main__": - paddle.seed(42) - np.random.seed(42) - os.chdir( - "/workspace/wangguan/PaddleScience_Surrogate/examples/pipe" - ) # working folder - - open_bug = True - if open_bug is True: - from paddle.fluid import core - - core.set_prim_eager_enabled(True) - else: - pass - x = paddle.to_tensor( - np.array([6, 6, 6]).reshape(3, 1), dtype="float32", stop_gradient=False - ) - y = paddle.to_tensor( - np.array([6, 6, 6]).reshape(3, 1), dtype="float32", stop_gradient=False - ) - nu = paddle.to_tensor( - np.array([6, 6, 6]).reshape(3, 1), dtype="float32", stop_gradient=False - ) - input_dict = {"x": x, "x": x, "y": y, "nu": nu} - - weight = np.array([1, 2, 3, 4]).reshape(4, 1) - bias = np.array([6, 6, 6]).reshape(3, 1) - - w_para = paddle.nn.initializer.Assign(weight) - b_para = paddle.nn.initializer.Assign(bias) - - last_fc = paddle.nn.Linear( - 4, - 1, - weight_attr=paddle.ParamAttr(initializer=w_para), - bias_attr=paddle.ParamAttr(initializer=b_para), - ) - - def concat_to_tensor(data_dict, keys, axis=-1): - data = [data_dict[key] for key in keys] - return paddle.concat(data, axis) - - def split_to_dict(data_tensor, keys, axis=-1): - data = paddle.split(data_tensor, len(keys), axis=axis) - return {key: data[i] for i, key in enumerate(keys)} - - def forward_tensor(last_fc, _x): - _y = _x - _y = last_fc(_y) - return _y - - output = concat_to_tensor(input_dict, ["x", "x", "y", "nu"], axis=-1) - output = forward_tensor(last_fc, output) - output = split_to_dict(output, ["v"], axis=-1) - _v = output["v"] - d_vdy_paddle = jacobian(_v, y) diff --git a/examples/pipe/bug_1.py b/examples/pipe/bug_1.py deleted file mode 100644 index 149bdc5a9..000000000 --- a/examples/pipe/bug_1.py +++ /dev/null @@ -1,96 +0,0 @@ -import os - -import numpy as np -import paddle - -import ppsci -from ppsci.autodiff import jacobian - - -class My_PDE(ppsci.equation.pde.base.PDE): - """Class for navier-stokes equation. [nu] as self-variable - - Args: - rho (float): Density. - dim (int): Dimension of equation. - time (bool): Whether the euqation is time-dependent. - """ - - def __init__(self): - super().__init__() - - def eq_1(out): - y = out["y"] - v = out["v"] - dvdy_paddle = jacobian(v, y) - return dvdy_paddle - - self.add_equation("eq_1", eq_1) - - -if __name__ == "__main__": - ppsci.utils.misc.set_random_seed(42) - from paddle.fluid import core - - core.set_prim_eager_enabled(True) - os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/pipe") - ppsci.utils.logger.init_logger("ppsci", f"./output_pipe/train.log", "info") - - input = np.load("./data/input/input_x_y_nu.npz") - x = paddle.to_tensor(input["x"], dtype="float32", stop_gradient=False) - y = paddle.to_tensor(input["y"], dtype="float32", stop_gradient=False) - nu = paddle.to_tensor(input["nu"], dtype="float32", stop_gradient=False) - - v_dict = np.load("./data/compare_4/v_epoch_1.npz") - y_dict = np.load("./data/compare_4/y_epoch_1.npz") - dvdy_dict = np.load("./data/compare_4/dvdy_epoch_1.npz") - - initial_bias_v = np.load("./data/init_net_params/initial_bias_v.npz") - initial_weight_v = np.load("./data/init_net_params/initial_weight_v.npz") - model = ppsci.arch.MLP( - ["x", "x", "y", "nu"], - ["v"], - 0, - 50, - "identity", - False, - False, - initial_weight_v, - initial_bias_v, - ) - - eq = My_PDE() - interior_geom = ppsci.geometry.PointCloud( - coord_dict={"x": x, "y": y}, extra_data={"nu": nu}, data_key=["x", "y", "nu"] - ) - pde_constraint = ppsci.constraint.InteriorConstraint( - eq.equations, - {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, - geom=interior_geom, - dataloader_cfg={ - "dataset": "NamedArrayDataset", - "num_workers": 1, - "batch_size": 128, - "iters_per_epoch": 10, - "sampler": { - "name": "BatchSampler", - "shuffle": False, - "drop_last": False, - }, - }, - loss=ppsci.loss.MSELoss("mean"), - evenly=True, - weight_dict={"u": 1, "v": 1, "p": 1}, - name="EQ", - ) - - # initialize solver - solver = ppsci.solver.Solver( - model, - {pde_constraint.name: pde_constraint}, - ppsci.optimizer.Adam(5e-3)([model]), - equation=eq, - ) - - # train model - solver.train() diff --git a/examples/pipe/output_pipe/checkpoints/latest.pdeqn b/examples/pipe/output_pipe/checkpoints/latest.pdeqn deleted file mode 100644 index a7f5b73983f0e5e79b0fb234db7a0b869fe6f967..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 96 zcmZo*nHtIf0ku;!dU*U2%Q91of=lwVQ;Vnca3|;IYBxsYR(NE}6+C rQzlQ*44Tp-9b8gWnp{!}6b;D_NGwXsO)W_+@&i&14pVBUlf8K-H*XB}X6RksjCauXpv}QMO}&}yG_~OHppZy<)ArEKbWr%Vo&Fm`LpMfd zr%m(@-MndgaM;e!@J&0jv$E49{xdNCOS9U+1c6t-+22^=OD5(+b4N}p4fl3`TuwS zAF=>W$*gj^Ayqx&)iJ)NV@cJiE+3|0D59!!k8jnbTkES_Mqk0!ViD$e`hC8&bP>OA zLtOP9G>79_O1N^FZB-MJG^>wUDpf~J*joM8xQMTwY+k)aO0ueCVOiB+aSU=*%I5#N zZ&Y=&C60OZje$#q-6Ca{8L|V9!UTwW=p+a-xr&HZ3-m$fE`72w0DQ=g_(xL(ap#vI 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add main --- examples/aneurysm/aneurysm_flow_FCNN.py | 683 +++++++++++------------- 1 file changed, 320 insertions(+), 363 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow_FCNN.py b/examples/aneurysm/aneurysm_flow_FCNN.py index c75fe7ac3..170997d60 100644 --- a/examples/aneurysm/aneurysm_flow_FCNN.py +++ b/examples/aneurysm/aneurysm_flow_FCNN.py @@ -115,377 +115,334 @@ def momentum_z_compute_func(out): self.add_equation("momentum_z", momentum_z_compute_func) -os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") -output_dir = "./output_0504_debug" -ppsci.utils.misc.set_random_seed(42) - -# initialize logger -ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") -core.set_prim_eager_enabled(True) - -EPOCHS = 500 -L = 1 -X_IN = 0 -X_OUT = X_IN + L -P_OUT = 0 # pressure at the outlet of pipe -P_IN = 0.1 # pressure at the inlet of pipe -rInlet = 0.05 -nPt = 100 -unique_x = np.linspace(X_IN, X_OUT, nPt) -mu = 0.5 * (X_OUT - X_IN) -N_y = 20 -x_2d = np.tile(unique_x, N_y) -x_2d = np.reshape(x_2d, (len(x_2d), 1)) -nu = 1e-3 -sigma = 0.1 -scaleStart = -0.02 -scaleEnd = 0 -Ng = 50 -scale_1d = np.linspace(scaleStart, scaleEnd, Ng, endpoint=True) -x, scale = ThreeD_mesh(x_2d, scale_1d) - -# axisymetric boundary -R = ( - scale - * 1 - / math.sqrt(2 * np.pi * sigma**2) - * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) -) - -# Generate stenosis -yUp = (rInlet - R) * np.ones_like(x) -yDown = (-rInlet + R) * np.ones_like(x) -idx = np.where(scale == scaleStart) -plt.figure() -plt.scatter(x[idx], yUp[idx]) -plt.scatter(x[idx], yDown[idx]) -plt.axis("equal") -plt.show() -plt.savefig("idealized_stenotid_vessel", bbox_inches="tight") - -y = np.zeros([len(x), 1]) -for x0 in unique_x: - index = np.where(x[:, 0] == x0)[0] - Rsec = max(yUp[index]) - tmpy = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) - y[index] = tmpy - - -RHO = 1 -BATCH_SIZE = 50 -LEARNING_RATE = 1e-3 -LAYER_NUMBER = 4 - 1 -HIDDEN_SIZE = 20 - -index = [i for i in range(x.shape[0])] -res = list(zip(x, y, scale)) -np.random.shuffle(res) -x, y, scale = zip(*res) -x = np.array(x).astype(float) -y = np.array(y).astype(float) -scale = np.array(scale).astype(float) - -interior_geom = ppsci.geometry.PointCloud( - coord_dict={"x": x, "y": y}, - extra_data={"scale": scale}, - data_key=["x", "y", "scale"], -) - -model_2 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["u"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net2_params/weight_epoch_0.npz"), - np.load(f"data/net2_params/bias_epoch_0.npz"), -) - -model_3 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["v"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net3_params/weight_epoch_0.npz"), - np.load(f"data/net3_params/bias_epoch_0.npz"), -) - -model_4 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["p"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net4_params/weight_epoch_0.npz"), - np.load(f"data/net4_params/bias_epoch_0.npz"), -) - - -class Output_transform: - def __init__(self) -> None: - pass - - def __call__(self, out, input): - new_out = {} - x, y, scale = input["x"], input["y"], input["scale"] - # axisymetric boundary - if next(iter(out.keys())) == "u": - R = ( - scale - * 1 - / np.sqrt(2 * np.pi * sigma**2) - * paddle.exp(-((x - mu) ** 2) / (2 * sigma**2)) - ) - self.h = rInlet - R - u = out["u"] - # The no-slip condition of velocity on the wall - new_out["u"] = u * (self.h**2 - y**2) - elif next(iter(out.keys())) == "v": - v = out["v"] - # The no-slip condition of velocity on the wall - new_out["v"] = (self.h**2 - y**2) * v - elif next(iter(out.keys())) == "p": - p = out["p"] - # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] - new_out["p"] = ( - (X_IN - x) * 0 - + (P_IN - P_OUT) * (X_OUT - x) / L - + 0 * y - + (X_IN - x) * (X_OUT - x) * p - ) - else: - raise NotImplementedError(f"{out.keys()} are outputs to be implemented") - - return new_out - - -shared_transform = Output_transform() -model_2.register_output_transform(shared_transform) -model_3.register_output_transform(shared_transform) -model_4.register_output_transform(shared_transform) -model = ppsci.arch.ModelList([model_2, model_3, model_4]) - -optimizer2 = ppsci.optimizer.Adam( - LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 -)([model]) - -equation = {"NavierStokes": NavierStokes(RHO, 2, False)} - -pde_constraint = ppsci.constraint.InteriorConstraint( - equation["NavierStokes"].equations, - {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, - geom=interior_geom, - dataloader_cfg={ - "dataset": "NamedArrayDataset", - "num_workers": 1, - "batch_size": BATCH_SIZE, - "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), - "sampler": { - "name": "BatchSampler", - "shuffle": False, - "drop_last": False, - }, - }, - loss=ppsci.loss.MSELoss("mean"), - evenly=True, - weight_dict={"u": 1, "v": 1, "p": 1}, - name="EQ", -) - -# initialize solver -solver = ppsci.solver.Solver( - model, - {pde_constraint.name: pde_constraint}, - output_dir, - optimizer2, - epochs=EPOCHS, - iters_per_epoch=int(x.shape[0] / BATCH_SIZE), - eval_during_train=False, - save_freq=10, - log_freq=1, - equation=equation, - checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_280", -) - -# solver.train() -def predict( - input_dict, - solver, -): - for key, val in input_dict.items(): - input_dict[key] = paddle.to_tensor(val, dtype="float32") - evaluator = ppsci.utils.expression.ExpressionSolver( - input_dict.keys(), ["u", "v", "p"], solver.model - ) - output_expr_dict = { - "u": lambda d: d["u"], - "v": lambda d: d["v"], - "p": lambda d: d["p"], - } - for output_key, output_expr in output_expr_dict.items(): - evaluator.add_target_expr(output_expr, output_key) - output_dict = evaluator(input_dict) - return output_dict - - -def single_test(x, y, scale, caseIdx): - xt = paddle.to_tensor(x, dtype="float32") - yt = paddle.to_tensor(y, dtype="float32") - scalet = scale * paddle.ones_like(xt) - net_in = {"x": xt, "y": yt, "scale": scalet} - output_dict = predict(net_in, solver) - np.savez( - "./" + str(int(caseIdx)) + "ML_WallStress_uvp", - x_center=x, - y_center=y, - u_center=output_dict["u"], - v_center=output_dict["v"], - p_center=output_dict["p"], +if __name__ == "__main__": + os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") + output_dir = "./output_0504_debug" + ppsci.utils.misc.set_random_seed(42) + + # initialize logger + ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") + core.set_prim_eager_enabled(True) + + EPOCHS = 500 + L = 1 + X_IN = 0 + X_OUT = X_IN + L + P_OUT = 0 # pressure at the outlet of pipe + P_IN = 0.1 # pressure at the inlet of pipe + rInlet = 0.05 + nPt = 100 + unique_x = np.linspace(X_IN, X_OUT, nPt) + mu = 0.5 * (X_OUT - X_IN) + N_y = 20 + x_2d = np.tile(unique_x, N_y) + x_2d = np.reshape(x_2d, (len(x_2d), 1)) + nu = 1e-3 + sigma = 0.1 + scaleStart = -0.02 + scaleEnd = 0 + Ng = 50 + scale_1d = np.linspace(scaleStart, scaleEnd, Ng, endpoint=True) + x, scale = ThreeD_mesh(x_2d, scale_1d) + + # axisymetric boundary + R = ( + scale + * 1 + / math.sqrt(2 * np.pi * sigma**2) + * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) ) - return output_dict - - -scale_test = np.load("aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")["scale"] -os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") -caseCount = [1.0, 151.0, 486.0] -W_ctl = np.zeros([len(scale_test), 1]) -W_ctl_Ml = np.zeros([len(scale_test), 1]) -plot_x = 0.8 -plot_y = 0.06 -fontsize = 14 -axis_limit = [0, 1, -0.15, 0.15] -path = "./data/cases/" -for caseIdx in caseCount: - scale = scale_test[int(caseIdx - 1)] - Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") - Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") - x = Data_CFD["x"] - y = Data_CFD["y"] - U_CFD = Data_CFD["U"] - U = Data_NN["U"] - n = len(x) - output_dict = single_test( - x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, caseIdx - ) - u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] - w = np.zeros_like(u) - U = np.concatenate([u, v, w], axis=1) - # velocity U + # Generate stenosis + yUp = (rInlet - R) * np.ones_like(x) + yDown = (-rInlet + R) * np.ones_like(x) + idx = np.where(scale == scaleStart) plt.figure() - plt.subplot(212) - plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.colorbar() - plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) - plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.savefig( - "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_uContour_test.png", - bbox_inches="tight", + plt.scatter(x[idx], yUp[idx]) + plt.scatter(x[idx], yDown[idx]) + plt.axis("equal") + plt.show() + plt.savefig("idealized_stenotid_vessel", bbox_inches="tight") + + y = np.zeros([len(x), 1]) + for x0 in unique_x: + index = np.where(x[:, 0] == x0)[0] + Rsec = max(yUp[index]) + tmpy = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) + y[index] = tmpy + + RHO = 1 + BATCH_SIZE = 50 + LEARNING_RATE = 1e-3 + LAYER_NUMBER = 4 - 1 + HIDDEN_SIZE = 20 + + index = [i for i in range(x.shape[0])] + res = list(zip(x, y, scale)) + np.random.shuffle(res) + x, y, scale = zip(*res) + x = np.array(x).astype(float) + y = np.array(y).astype(float) + scale = np.array(scale).astype(float) + + interior_geom = ppsci.geometry.PointCloud( + coord_dict={"x": x, "y": y}, + extra_data={"scale": scale}, + data_key=["x", "y", "scale"], ) - # velocity V - plt.figure() - plt.subplot(212) - plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.colorbar() - plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) - plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.savefig( - "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_vContour_test.png", - bbox_inches="tight", + model_2 = ppsci.arch.MLP( + ["x", "y", "scale"], + ["u"], + LAYER_NUMBER, + HIDDEN_SIZE, + "swish", + False, + False, + np.load(f"data/net2_params/weight_epoch_0.npz"), + np.load(f"data/net2_params/bias_epoch_0.npz"), ) - plt.close("all") - # wall shear stress - Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") - unique_x = Data_CFD_wss["x"] - wall_shear_mag_up = Data_CFD_wss["wss"] - Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") - NNwall_shear_mag_up = Data_NN_wss["wss"] + model_3 = ppsci.arch.MLP( + ["x", "y", "scale"], + ["v"], + LAYER_NUMBER, + HIDDEN_SIZE, + "swish", + False, + False, + np.load(f"data/net3_params/weight_epoch_0.npz"), + np.load(f"data/net3_params/bias_epoch_0.npz"), + ) - plt.figure() - plt.plot( - unique_x, - wall_shear_mag_up, - label="CFD", - color="darkblue", - linestyle="-", - lw=3.0, - alpha=1.0, + model_4 = ppsci.arch.MLP( + ["x", "y", "scale"], + ["p"], + LAYER_NUMBER, + HIDDEN_SIZE, + "swish", + False, + False, + np.load(f"data/net4_params/weight_epoch_0.npz"), + np.load(f"data/net4_params/bias_epoch_0.npz"), ) - plt.plot( - unique_x, - NNwall_shear_mag_up, - label="DNN", - color="red", - linestyle="--", - dashes=(5, 5), - lw=2.0, - alpha=1.0, + + class Output_transform: + def __init__(self) -> None: + pass + + def __call__(self, out, input): + new_out = {} + x, y, scale = input["x"], input["y"], input["scale"] + # axisymetric boundary + if next(iter(out.keys())) == "u": + R = ( + scale + * 1 + / np.sqrt(2 * np.pi * sigma**2) + * paddle.exp(-((x - mu) ** 2) / (2 * sigma**2)) + ) + self.h = rInlet - R + u = out["u"] + # The no-slip condition of velocity on the wall + new_out["u"] = u * (self.h**2 - y**2) + elif next(iter(out.keys())) == "v": + v = out["v"] + # The no-slip condition of velocity on the wall + new_out["v"] = (self.h**2 - y**2) * v + elif next(iter(out.keys())) == "p": + p = out["p"] + # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] + new_out["p"] = ( + (X_IN - x) * 0 + + (P_IN - P_OUT) * (X_OUT - x) / L + + 0 * y + + (X_IN - x) * (X_OUT - x) * p + ) + else: + raise NotImplementedError(f"{out.keys()} are outputs to be implemented") + + return new_out + + shared_transform = Output_transform() + model_2.register_output_transform(shared_transform) + model_3.register_output_transform(shared_transform) + model_4.register_output_transform(shared_transform) + model = ppsci.arch.ModelList([model_2, model_3, model_4]) + + optimizer2 = ppsci.optimizer.Adam( + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 + )([model]) + + equation = {"NavierStokes": NavierStokes(RHO, 2, False)} + + pde_constraint = ppsci.constraint.InteriorConstraint( + equation["NavierStokes"].equations, + {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, + geom=interior_geom, + dataloader_cfg={ + "dataset": "NamedArrayDataset", + "num_workers": 1, + "batch_size": BATCH_SIZE, + "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), + "sampler": { + "name": "BatchSampler", + "shuffle": False, + "drop_last": False, + }, + }, + loss=ppsci.loss.MSELoss("mean"), + evenly=True, + weight_dict={"u": 1, "v": 1, "p": 1}, + name="EQ", ) - plt.xlabel(r"x", fontsize=16) - plt.ylabel(r"$\tau_{c}$", fontsize=16) - plt.legend(prop={"size": 16}) - plt.savefig( - "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_wallshear_test.png", - bbox_inches="tight", + + # initialize solver + solver = ppsci.solver.Solver( + model, + {pde_constraint.name: pde_constraint}, + output_dir, + optimizer2, + epochs=EPOCHS, + iters_per_epoch=int(x.shape[0] / BATCH_SIZE), + eval_during_train=False, + save_freq=10, + log_freq=1, + equation=equation, + checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_280", ) - plt.close("all") - - # x_centerline = np.linspace(X_IN, X_OUT, 100, endpoint=True).reshape(100, 1) - # y_centerline = np.zeros((100, 1)) - # output_dict_centerline = single_test( - # x_centerline, y_centerline, np.ones((100, 1)) * scale, caseIdx - # ) - # y_cfd = Data_CFD["y"] - # index = np.where(abs(y_cfd) < 0.001002) - # x_cfd = Data_CFD["x"][index] - # p_cfd = Data_CFD["P"][index] - # index = np.argsort(x_cfd) - # x_cfd = x_cfd[index] - # y_cfd = y_cfd[index] - # p_cfd = p_cfd[index] - # plt.figure() - # plt.plot( - # x_cfd, - # p_cfd, - # label="CFD", - # color="darkblue", - # linestyle="-", - # lw=3.0, - # alpha=1.0, - # ) - # plt.plot( - # x_centerline, - # output_dict_centerline['p'], - # label="DNN", - # color="red", - # linestyle="--", - # lw=3.0, - # alpha=1.0, - # ) - # plt.xlabel(r"x", fontsize=16) - # plt.ylabel(r"$p_{c}$", fontsize=16) - # plt.legend(prop={"size": 16}) - # plt.savefig( - # "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_center_pressure.png", - # bbox_inches="tight", - # ) - # plt.close("all") + + # solver.train() + def predict( + input_dict, + solver, + ): + for key, val in input_dict.items(): + input_dict[key] = paddle.to_tensor(val, dtype="float32") + evaluator = ppsci.utils.expression.ExpressionSolver( + input_dict.keys(), ["u", "v", "p"], solver.model + ) + output_expr_dict = { + "u": lambda d: d["u"], + "v": lambda d: d["v"], + "p": lambda d: d["p"], + } + for output_key, output_expr in output_expr_dict.items(): + evaluator.add_target_expr(output_expr, output_key) + output_dict = evaluator(input_dict) + return output_dict + + def single_test(x, y, scale, caseIdx): + xt = paddle.to_tensor(x, dtype="float32") + yt = paddle.to_tensor(y, dtype="float32") + scalet = scale * paddle.ones_like(xt) + net_in = {"x": xt, "y": yt, "scale": scalet} + output_dict = predict(net_in, solver) + np.savez( + "./" + str(int(caseIdx)) + "ML_WallStress_uvp", + x_center=x, + y_center=y, + u_center=output_dict["u"], + v_center=output_dict["v"], + p_center=output_dict["p"], + ) + return output_dict + + scale_test = np.load("aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ + "scale" + ] + os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") + caseCount = [1.0, 151.0, 486.0] + W_ctl = np.zeros([len(scale_test), 1]) + W_ctl_Ml = np.zeros([len(scale_test), 1]) + plot_x = 0.8 + plot_y = 0.06 + fontsize = 14 + axis_limit = [0, 1, -0.15, 0.15] + path = "./data/cases/" + for caseIdx in caseCount: + scale = scale_test[int(caseIdx - 1)] + Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") + Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") + x = Data_CFD["x"] + y = Data_CFD["y"] + U_CFD = Data_CFD["U"] + U = Data_NN["U"] + n = len(x) + output_dict = single_test( + x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, caseIdx + ) + u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] + w = np.zeros_like(u) + U = np.concatenate([u, v, w], axis=1) + + # velocity U + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_uContour_test.png", + bbox_inches="tight", + ) + + # velocity V + plt.figure() + plt.subplot(212) + plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.colorbar() + plt.subplot(211) + plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.colorbar() + plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.axis(axis_limit) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_vContour_test.png", + bbox_inches="tight", + ) + plt.close("all") + + # wall shear stress + Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") + unique_x = Data_CFD_wss["x"] + wall_shear_mag_up = Data_CFD_wss["wss"] + Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") + NNwall_shear_mag_up = Data_NN_wss["wss"] + + plt.figure() + plt.plot( + unique_x, + wall_shear_mag_up, + label="CFD", + color="darkblue", + linestyle="-", + lw=3.0, + alpha=1.0, + ) + plt.plot( + unique_x, + NNwall_shear_mag_up, + label="DNN", + color="red", + linestyle="--", + dashes=(5, 5), + lw=2.0, + alpha=1.0, + ) + plt.xlabel(r"x", fontsize=16) + plt.ylabel(r"$\tau_{c}$", fontsize=16) + plt.legend(prop={"size": 16}) + plt.savefig( + "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_wallshear_test.png", + bbox_inches="tight", + ) + plt.close("all") From 200bde2a4623f9ec1282ee8b4d5cfb3411af5c91 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Tue, 16 May 2023 09:41:44 +0000 Subject: [PATCH 08/31] clean code --- examples/aneurysm/aneurysm_flow_FCNN.py | 152 +++++++++--------- ...cale0005to002_eval0to002mean001_3sigma.npz | Bin 4244 -> 0 bytes examples/aneurysm/plot_geoaneurysm.py | 141 ---------------- examples/aneurysm/single_test.py | 57 ------- 4 files changed, 76 insertions(+), 274 deletions(-) delete mode 100644 examples/aneurysm/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz delete mode 100644 examples/aneurysm/plot_geoaneurysm.py delete mode 100644 examples/aneurysm/single_test.py diff --git a/examples/aneurysm/aneurysm_flow_FCNN.py b/examples/aneurysm/aneurysm_flow_FCNN.py index 170997d60..86700fcaf 100644 --- a/examples/aneurysm/aneurysm_flow_FCNN.py +++ b/examples/aneurysm/aneurysm_flow_FCNN.py @@ -55,8 +55,8 @@ def momentum_x_compute_func(out): momentum_x = ( u * jacobian(u, x) + v * jacobian(u, y) - - nu / rho * hessian(u, x) - - nu / rho * hessian(u, y) + - NU / rho * hessian(u, x) + - NU / rho * hessian(u, y) + 1 / rho * jacobian(p, x) ) if self.time: @@ -66,7 +66,7 @@ def momentum_x_compute_func(out): z = out["z"] w = out["w"] momentum_x += w * jacobian(u, z) - momentum_x -= nu / rho * hessian(u, z) + momentum_x -= NU / rho * hessian(u, z) return momentum_x self.add_equation("momentum_x", momentum_x_compute_func) @@ -77,8 +77,8 @@ def momentum_y_compute_func(out): momentum_y = ( u * jacobian(v, x) + v * jacobian(v, y) - - nu / rho * hessian(v, x) - - nu / rho * hessian(v, y) + - NU / rho * hessian(v, x) + - NU / rho * hessian(v, y) + 1 / rho * jacobian(p, y) ) if self.time: @@ -88,7 +88,7 @@ def momentum_y_compute_func(out): z = out["z"] w = out["w"] momentum_y += w * jacobian(v, z) - momentum_y -= nu / rho * hessian(v, z) + momentum_y -= NU / rho * hessian(v, z) return momentum_y self.add_equation("momentum_y", momentum_y_compute_func) @@ -102,9 +102,9 @@ def momentum_z_compute_func(out): u * jacobian(w, x) + v * jacobian(w, y) + w * jacobian(w, z) - - nu / rho * hessian(w, x) - - nu / rho * hessian(w, y) - - nu / rho * hessian(w, z) + - NU / rho * hessian(w, x) + - NU / rho * hessian(w, y) + - NU / rho * hessian(w, z) + 1 / rho * jacobian(p, z) ) if self.time: @@ -115,68 +115,94 @@ def momentum_z_compute_func(out): self.add_equation("momentum_z", momentum_z_compute_func) +def predict( + input_dict, + solver, +): + for key, val in input_dict.items(): + input_dict[key] = paddle.to_tensor(val, dtype="float32") + evaluator = ppsci.utils.expression.ExpressionSolver( + input_dict.keys(), ["u", "v", "p"], solver.model + ) + output_expr_dict = { + "u": lambda d: d["u"], + "v": lambda d: d["v"], + "p": lambda d: d["p"], + } + for output_key, output_expr in output_expr_dict.items(): + evaluator.add_target_expr(output_expr, output_key) + output_dict = evaluator(input_dict) + return output_dict + + if __name__ == "__main__": os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") - output_dir = "./output_0504_debug" + # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) + # set output directory + output_dir = "./output_0504_debug" + # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") core.set_prim_eager_enabled(True) + # Hyper parameters EPOCHS = 500 + BATCH_SIZE = 50 + LEARNING_RATE = 1e-3 + LAYER_NUMBER = 4 - 1 + HIDDEN_SIZE = 20 + + # Physic properties + P_OUT = 0 # pressure at the outlet of pipe + P_IN = 0.1 # pressure at the inlet of pipe + NU = 1e-3 + RHO = 1 + + # Geometry L = 1 X_IN = 0 X_OUT = X_IN + L - P_OUT = 0 # pressure at the outlet of pipe - P_IN = 0.1 # pressure at the inlet of pipe - rInlet = 0.05 - nPt = 100 - unique_x = np.linspace(X_IN, X_OUT, nPt) + R_INLET = 0.05 + unique_x = np.linspace(X_IN, X_OUT, 100) mu = 0.5 * (X_OUT - X_IN) N_y = 20 x_2d = np.tile(unique_x, N_y) x_2d = np.reshape(x_2d, (len(x_2d), 1)) - nu = 1e-3 - sigma = 0.1 - scaleStart = -0.02 - scaleEnd = 0 - Ng = 50 - scale_1d = np.linspace(scaleStart, scaleEnd, Ng, endpoint=True) + SIGMA = 0.1 + SCALE_START = -0.02 + SCALE_END = 0 + scale_1d = np.linspace(SCALE_START, SCALE_END, 50, endpoint=True) x, scale = ThreeD_mesh(x_2d, scale_1d) - # axisymetric boundary + # Axisymetric boundary R = ( scale * 1 - / math.sqrt(2 * np.pi * sigma**2) - * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) + / math.sqrt(2 * np.pi * SIGMA**2) + * np.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) # Generate stenosis - yUp = (rInlet - R) * np.ones_like(x) - yDown = (-rInlet + R) * np.ones_like(x) - idx = np.where(scale == scaleStart) + y_up = (R_INLET - R) * np.ones_like(x) + y_down = (-R_INLET + R) * np.ones_like(x) + idx = np.where(scale == SCALE_START) plt.figure() - plt.scatter(x[idx], yUp[idx]) - plt.scatter(x[idx], yDown[idx]) + plt.scatter(x[idx], y_up[idx]) + plt.scatter(x[idx], y_down[idx]) plt.axis("equal") plt.show() - plt.savefig("idealized_stenotid_vessel", bbox_inches="tight") + plt.savefig("./plot/idealized_stenotid_vessel", bbox_inches="tight") + # Points and shuffle(for alignment) y = np.zeros([len(x), 1]) for x0 in unique_x: index = np.where(x[:, 0] == x0)[0] - Rsec = max(yUp[index]) + Rsec = max(y_up[index]) tmpy = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) y[index] = tmpy - RHO = 1 - BATCH_SIZE = 50 - LEARNING_RATE = 1e-3 - LAYER_NUMBER = 4 - 1 - HIDDEN_SIZE = 20 - index = [i for i in range(x.shape[0])] res = list(zip(x, y, scale)) np.random.shuffle(res) @@ -239,10 +265,10 @@ def __call__(self, out, input): R = ( scale * 1 - / np.sqrt(2 * np.pi * sigma**2) - * paddle.exp(-((x - mu) ** 2) / (2 * sigma**2)) + / np.sqrt(2 * np.pi * SIGMA**2) + * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) - self.h = rInlet - R + self.h = R_INLET - R u = out["u"] # The no-slip condition of velocity on the wall new_out["u"] = u * (self.h**2 - y**2) @@ -270,7 +296,7 @@ def __call__(self, out, input): model_4.register_output_transform(shared_transform) model = ppsci.arch.ModelList([model_2, model_3, model_4]) - optimizer2 = ppsci.optimizer.Adam( + optimizer = ppsci.optimizer.Adam( LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 )([model]) @@ -302,53 +328,27 @@ def __call__(self, out, input): model, {pde_constraint.name: pde_constraint}, output_dir, - optimizer2, + optimizer, epochs=EPOCHS, iters_per_epoch=int(x.shape[0] / BATCH_SIZE), eval_during_train=False, save_freq=10, log_freq=1, equation=equation, - checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_280", + checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_500", ) # solver.train() - def predict( - input_dict, - solver, - ): - for key, val in input_dict.items(): - input_dict[key] = paddle.to_tensor(val, dtype="float32") - evaluator = ppsci.utils.expression.ExpressionSolver( - input_dict.keys(), ["u", "v", "p"], solver.model - ) - output_expr_dict = { - "u": lambda d: d["u"], - "v": lambda d: d["v"], - "p": lambda d: d["p"], - } - for output_key, output_expr in output_expr_dict.items(): - evaluator.add_target_expr(output_expr, output_key) - output_dict = evaluator(input_dict) - return output_dict - def single_test(x, y, scale, caseIdx): + def single_test(x, y, scale, solver): xt = paddle.to_tensor(x, dtype="float32") yt = paddle.to_tensor(y, dtype="float32") scalet = scale * paddle.ones_like(xt) net_in = {"x": xt, "y": yt, "scale": scalet} output_dict = predict(net_in, solver) - np.savez( - "./" + str(int(caseIdx)) + "ML_WallStress_uvp", - x_center=x, - y_center=y, - u_center=output_dict["u"], - v_center=output_dict["v"], - p_center=output_dict["p"], - ) return output_dict - scale_test = np.load("aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ + scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ "scale" ] os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") @@ -370,13 +370,13 @@ def single_test(x, y, scale, caseIdx): U = Data_NN["U"] n = len(x) output_dict = single_test( - x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, caseIdx + x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, solver ) u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] w = np.zeros_like(u) U = np.concatenate([u, v, w], axis=1) - # velocity U + # Streamwise velocity component u plt.figure() plt.subplot(212) plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) @@ -393,7 +393,7 @@ def single_test(x, y, scale, caseIdx): bbox_inches="tight", ) - # velocity V + # Spanwise velocity component v plt.figure() plt.subplot(212) plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) @@ -411,7 +411,7 @@ def single_test(x, y, scale, caseIdx): ) plt.close("all") - # wall shear stress + # Centerline wall shear profile tau_c Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") unique_x = Data_CFD_wss["x"] wall_shear_mag_up = Data_CFD_wss["wss"] @@ -442,7 +442,7 @@ def single_test(x, y, scale, caseIdx): plt.ylabel(r"$\tau_{c}$", fontsize=16) plt.legend(prop={"size": 16}) plt.savefig( - "plot/" + str(int(caseIdx)) + "_nu_" + str(nu) + "_wallshear_test.png", + "plot/" + str(int(caseIdx)) + "_nu_" + str(NU) + "_wallshear_test.png", bbox_inches="tight", ) plt.close("all") diff --git a/examples/aneurysm/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz b/examples/aneurysm/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz deleted file mode 100644 index f84b6c2acf463ac4f7fb12fb6283c808f633f990..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4244 zcmbW*X*kv0y9aPeqB5j3sE`IKV-zBVOp)PF9!;c-JM+BR?0MeU#w3X%6%t9&@HG^n zl4NL7PfwCcB$5%P{j_2PE6pDVFc?7zoav5-{1E>(Fk 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token = "aneurysm0005to002" - token1 = "aneurysm0to002" - - ###################################################################### - # BELOW SHOULD REAMIN UNCHANGED!!!! - ###################################################################### - - print("path is", path + str(caseIdx)) - - Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") - Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") - x = Data_CFD["x"] - y = Data_CFD["y"] - U_CFD = Data_CFD["U"] - U = Data_NN["U"] - - device = "cpu" - u, v, p = single_test.det_test( - x, y, nu, dP, mu, sigma, scale, epochs, path, device, caseIdx - ) - print("shape of u", u.shape) - print("shape of v", v.shape) - print("shape of p", p.shape) - w = np.zeros_like(u) - U = np.concatenate([u, v, w], axis=1) - - # Contour Comparison - - plt.figure() - plt.subplot(212) - plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.colorbar() - plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) - plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.savefig( - "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "uContour_test.png", - bbox_inches="tight", - ) - - print( - "path is", - "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "uContour_test.png", - ) - - plt.figure() - plt.subplot(212) - plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.colorbar() - plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) - plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) - plt.axis(axis_limit) - plt.savefig( - "plot/" + str(int(caseIdx)) + "scale" + str(scale) + "vContour_test.png", - bbox_inches="tight", - ) - - plt.close("all") - # plt.show() - - Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") - unique_x = Data_CFD_wss["x"] - wall_shear_mag_up = Data_CFD_wss["wss"] - Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") - NNwall_shear_mag_up = Data_NN_wss["wss"] - # show plot - - plt.figure() - - plt.plot( - unique_x, - wall_shear_mag_up, - label="CFD", - color="darkblue", - linestyle="-", - lw=3.0, - alpha=1.0, - ) - plt.plot( - unique_x, - NNwall_shear_mag_up, - label="DNN", - color="red", - linestyle="--", - dashes=(5, 5), - lw=2.0, - alpha=1.0, - ) - plt.xlabel(r"x", fontsize=16) - plt.ylabel(r"$\tau_{c}$", fontsize=16) - plt.legend(prop={"size": 16}) - plt.savefig( - "plot/" + str(int(caseIdx)) + "nu" + str(nu) + "wallShear_test.png", - bbox_inches="tight", - ) - plt.close("all") - - ## show center wall shear - # CFD - W_ctl[int(caseIdx - 1)] = wall_shear_mag_up[int(len(wall_shear_mag_up) / 2)] - # NN - W_ctl_Ml[int(caseIdx - 1)] = NNwall_shear_mag_up[int(len(NNwall_shear_mag_up) / 2)] diff --git a/examples/aneurysm/single_test.py b/examples/aneurysm/single_test.py deleted file mode 100644 index 24bdf9c29..000000000 --- a/examples/aneurysm/single_test.py +++ /dev/null @@ -1,57 +0,0 @@ -import math -from math import pi - -import numpy as np -import paddle -import torch - - -def det_test(x, y, dP, mu, sigma, scale, path1, caseIdx): - R = ( - scale - * 1 - / math.sqrt(2 * np.pi * sigma**2) - * np.exp(-((x - mu) ** 2) / (2 * sigma**2)) - ) - rInlet = 0.05 - yUp = rInlet - R - xt = paddle.to_tensor(xt, dtype="float32") - yt = paddle.to_tensor(xt, dtype="float32") - scalet = paddle.to_tensor(xt, dtype="float32") - xt = xt.view(len(xt), -1) - yt = yt.view(len(yt), -1) - scalet = scale * paddle.ones_like(xt) - Rt = paddle.to_tensor(yUp, dtype="float32") - Rt = Rt.view(len(Rt), -1) - xt.requires_grad = True - yt.requires_grad = True - scalet.requires_grad = True - net_in = torch.cat((xt, yt, scalet), 1) - u_t = net2(net_in) - v_t = net3(net_in) - P_t = net4(net_in) - u_hard = u_t * (Rt**2 - yt**2) - v_hard = (Rt**2 - yt**2) * v_t - L = 1 - xStart = 0 - xEnd = L - P_hard = ( - (xStart - xt) * 0 - + dP * (xEnd - xt) / L - + 0 * yt - + (xStart - xt) * (xEnd - xt) * P_t - ) - u_hard = u_hard.numpy() - v_hard = v_hard.numpy() - P_hard = P_hard.numpy() - - np.savez( - path1 + str(int(caseIdx)) + "ML_WallStress_uvp", - x_center=x, - y_center=y, - u_center=u_hard, - v_center=v_hard, - p_center=P_hard, - ) - - return u_hard, v_hard, P_hard From 7d957c9c12e862c2cc47f63284acb5e0a3166e2c Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Wed, 24 May 2023 05:00:31 +0000 Subject: [PATCH 09/31] using xvaier instead tf net initial params --- ...aneurysm_flow_FCNN.py => aneurysm_flow.py} | 141 +----------------- ...euille_flow_FCNN.py => poiseuille_flow.py} | 51 +++---- 2 files changed, 34 insertions(+), 158 deletions(-) rename examples/aneurysm/{aneurysm_flow_FCNN.py => aneurysm_flow.py} (68%) rename examples/pipe/{poiseuille_flow_FCNN.py => poiseuille_flow.py} (91%) diff --git a/examples/aneurysm/aneurysm_flow_FCNN.py b/examples/aneurysm/aneurysm_flow.py similarity index 68% rename from examples/aneurysm/aneurysm_flow_FCNN.py rename to examples/aneurysm/aneurysm_flow.py index 86700fcaf..1c5d526d2 100644 --- a/examples/aneurysm/aneurysm_flow_FCNN.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -1,5 +1,4 @@ import math -import os import matplotlib.pyplot as plt import numpy as np @@ -7,8 +6,6 @@ from paddle.fluid import core import ppsci -from ppsci.autodiff import hessian -from ppsci.autodiff import jacobian def ThreeD_mesh(x_2d, tmp_1d): @@ -21,100 +18,6 @@ def ThreeD_mesh(x_2d, tmp_1d): return x, tmp_3d -class NavierStokes(ppsci.equation.pde.base.PDE): - """Class for navier-stokes equation. - - Args: - rho (float): Density. - dim (int): Dimension of equation. - time (bool): Whether the euqation is time-dependent. - """ - - def __init__(self, rho: float, dim: int, time: bool): - super().__init__() - self.rho = rho - self.dim = dim - self.time = time - - def continuity_compute_func(out): - x, y = out["x"], out["y"] - u, v = out["u"], out["v"] - continuity = jacobian(u, x) + jacobian(v, y) - - if self.dim == 3: - z = out["z"] - w = out["w"] - continuity += jacobian(w, z) - return continuity - - self.add_equation("continuity", continuity_compute_func) - - def momentum_x_compute_func(out): - x, y = out["x"], out["y"] - u, v, p = out["u"], out["v"], out["p"] - momentum_x = ( - u * jacobian(u, x) - + v * jacobian(u, y) - - NU / rho * hessian(u, x) - - NU / rho * hessian(u, y) - + 1 / rho * jacobian(p, x) - ) - if self.time: - t = out["t"] - momentum_x += jacobian(u, t) - if self.dim == 3: - z = out["z"] - w = out["w"] - momentum_x += w * jacobian(u, z) - momentum_x -= NU / rho * hessian(u, z) - return momentum_x - - self.add_equation("momentum_x", momentum_x_compute_func) - - def momentum_y_compute_func(out): - x, y = out["x"], out["y"] - u, v, p = out["u"], out["v"], out["p"] - momentum_y = ( - u * jacobian(v, x) - + v * jacobian(v, y) - - NU / rho * hessian(v, x) - - NU / rho * hessian(v, y) - + 1 / rho * jacobian(p, y) - ) - if self.time: - t = out["t"] - momentum_y += jacobian(v, t) - if self.dim == 3: - z = out["z"] - w = out["w"] - momentum_y += w * jacobian(v, z) - momentum_y -= NU / rho * hessian(v, z) - return momentum_y - - self.add_equation("momentum_y", momentum_y_compute_func) - - if self.dim == 3: - - def momentum_z_compute_func(out): - x, y = out["x"], out["y"] - u, v, w, p = out["u"], out["v"], out["w"], out["p"] - momentum_z = ( - u * jacobian(w, x) - + v * jacobian(w, y) - + w * jacobian(w, z) - - NU / rho * hessian(w, x) - - NU / rho * hessian(w, y) - - NU / rho * hessian(w, z) - + 1 / rho * jacobian(p, z) - ) - if self.time: - t = out["t"] - momentum_z += jacobian(w, t) - return momentum_z - - self.add_equation("momentum_z", momentum_z_compute_func) - - def predict( input_dict, solver, @@ -136,12 +39,11 @@ def predict( if __name__ == "__main__": - os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) # set output directory - output_dir = "./output_0504_debug" + output_dir = "./output_aneurysm_debug" # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") @@ -212,45 +114,20 @@ def predict( scale = np.array(scale).astype(float) interior_geom = ppsci.geometry.PointCloud( - coord_dict={"x": x, "y": y}, - extra_data={"scale": scale}, - data_key=["x", "y", "scale"], + interior={"x": x, "y": y, "scale": scale}, + coord_keys=["x", "y", "scale"], ) model_2 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["u"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net2_params/weight_epoch_0.npz"), - np.load(f"data/net2_params/bias_epoch_0.npz"), + ["x", "y", "scale"], ["u"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False ) model_3 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["v"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net3_params/weight_epoch_0.npz"), - np.load(f"data/net3_params/bias_epoch_0.npz"), + ["x", "y", "scale"], ["v"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False ) model_4 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["p"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - np.load(f"data/net4_params/weight_epoch_0.npz"), - np.load(f"data/net4_params/bias_epoch_0.npz"), + ["x", "y", "scale"], ["p"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False ) class Output_transform: @@ -300,7 +177,7 @@ def __call__(self, out, input): LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 )([model]) - equation = {"NavierStokes": NavierStokes(RHO, 2, False)} + equation = {"NavierStokes": ppsci.equation.NavierStokes(NU, RHO, 2, False)} pde_constraint = ppsci.constraint.InteriorConstraint( equation["NavierStokes"].equations, @@ -335,10 +212,9 @@ def __call__(self, out, input): save_freq=10, log_freq=1, equation=equation, - checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504/checkpoints/epoch_500", ) - # solver.train() + solver.train() def single_test(x, y, scale, solver): xt = paddle.to_tensor(x, dtype="float32") @@ -351,7 +227,6 @@ def single_test(x, y, scale, solver): scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ "scale" ] - os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") caseCount = [1.0, 151.0, 486.0] W_ctl = np.zeros([len(scale_test), 1]) W_ctl_Ml = np.zeros([len(scale_test), 1]) diff --git a/examples/pipe/poiseuille_flow_FCNN.py b/examples/pipe/poiseuille_flow.py similarity index 91% rename from examples/pipe/poiseuille_flow_FCNN.py rename to examples/pipe/poiseuille_flow.py index df220c0d7..332d8a0f4 100644 --- a/examples/pipe/poiseuille_flow_FCNN.py +++ b/examples/pipe/poiseuille_flow.py @@ -145,10 +145,8 @@ def predict( # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) - os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/pipe") # set output directory - output_dir = "./output" - dir = "./data/net_params" + output_dir = "./output_poiseuille_flow" core.set_prim_eager_enabled(True) @@ -202,9 +200,8 @@ def predict( interior_data = {"x": input_x, "y": input_y, "nu": input_nu} interior_geom = ppsci.geometry.PointCloud( - coord_dict={"x": input_x, "y": input_y}, - extra_data={"nu": input_nu}, - data_key=["x", "y", "nu"], + interior={"x": input_x, "y": input_y, "nu": input_nu}, + coord_keys=["x", "y", "nu"], ) # set model @@ -216,8 +213,6 @@ def predict( "swish_beta", False, False, - np.load(dir + f"/weight_u_epoch_1.npz"), - np.load(dir + f"/bias_u_epoch_1.npz"), ) model_v = ppsci.arch.MLP( @@ -228,8 +223,6 @@ def predict( "swish_beta", False, False, - np.load(dir + f"/weight_v_epoch_1.npz"), - np.load(dir + f"/bias_v_epoch_1.npz"), ) model_p = ppsci.arch.MLP( @@ -240,8 +233,8 @@ def predict( "swish_beta", False, False, - np.load(dir + f"/weight_p_epoch_1.npz"), - np.load(dir + f"/bias_p_epoch_1.npz"), + # np.load(dir + f"/weight_p_epoch_1.npz"), + # np.load(dir + f"/bias_p_epoch_1.npz"), ) def output_transform(out, input): @@ -335,9 +328,10 @@ def input_transform(input): save_freq=10, log_freq=LOG_FREQ, equation=equation, - checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/pipe/output/checkpoints/epoch_3000", ) + solver.train() + # Cross-section velocity profiles of 4 different viscosity sample # Predicted result input_dict = { @@ -351,12 +345,12 @@ def input_transform(input): p_pred = output_dict["p"].numpy().reshape(N_y, N_x, N_p) # Analytical result, y = data_1d_y - uSolaM = np.zeros([N_y, N_x, N_p]) + u_analytical = np.zeros([N_y, N_x, N_p]) dP = P_IN - P_OUT for i in range(N_p): uy = (R**2 - data_1d_y**2) * dP / (2 * L * data_1d_nu[i] * RHO) - uSolaM[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) + u_analytical[:, :, i] = np.tile(uy.reshape([N_y, 1]), N_x) fontsize = 16 idx_X = int(round(N_x / 2)) # pipe velocity section at L/2 @@ -370,7 +364,7 @@ def input_transform(input): ax1 = plt.subplot(111) plt.plot( data_1d_y, - uSolaM[:, idx_X, nu_index[idxP]], + u_analytical[:, idx_X, nu_index[idxP]], color="darkblue", linestyle="-", lw=3.0, @@ -403,10 +397,12 @@ def input_transform(input): # Distribution of center velocity # Predicted result - N_pTest = 500 - data_1d_nuDist = np.random.normal(NU_MEAN, 0.2 * NU_MEAN, N_pTest) + num_test = 500 + data_1d_nu_distribution = np.random.normal(NU_MEAN, 0.2 * NU_MEAN, num_test) data_2d_xy_test = ( - np.array(np.meshgrid((X_IN - X_OUT) / 2.0, 0, data_1d_nuDist)).reshape(3, -1).T + np.array(np.meshgrid((X_IN - X_OUT) / 2.0, 0, data_1d_nu_distribution)) + .reshape(3, -1) + .T ) input_dict_test = { @@ -415,22 +411,27 @@ def input_transform(input): "nu": data_2d_xy_test[:, 2:3], } output_dict_test = predict(input_dict_test, solver) - uMax_pred = output_dict_test["u"].numpy() + u_max_pred = output_dict_test["u"].numpy() # Analytical result, y = 0 - uMax_a = np.zeros([N_pTest, 1]) - for i in range(N_pTest): - uMax_a[i] = (R**2) * dP / (2 * L * data_1d_nuDist[i] * RHO) + u_max_a = np.zeros([num_test, 1]) + for i in range(num_test): + u_max_a[i] = (R**2) * dP / (2 * L * data_1d_nu_distribution[i] * RHO) # Plot plt.figure(2) plt.clf() ax1 = plt.subplot(111) sns.kdeplot( - uMax_a, fill=True, color="black", label="Analytical", linestyle="-", linewidth=3 + u_max_a, + fill=True, + color="black", + label="Analytical", + linestyle="-", + linewidth=3, ) sns.kdeplot( - uMax_pred, + u_max_pred, fill=False, color="red", label="DNN", From 683adc787b3dfb36206f331175086043ad7fe4d5 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Wed, 31 May 2023 03:02:37 +0000 Subject: [PATCH 10/31] muli opt:w --- examples/aneurysm/aneurysm_flow.py | 191 +++++++++++++----- .../aneurysm/data/net2_params/bias_0_0.npz | Bin 1218 -> 0 bytes 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return new_x, new_scale def predict( @@ -41,20 +39,21 @@ def predict( if __name__ == "__main__": # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) + import os + os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") # set output directory - output_dir = "./output_aneurysm_debug" - + OUTPUT_DIR = "./output_0531" + PLOT_DIR = osp.join(OUTPUT_DIR, "visu") + os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger - ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") + ppsci.utils.logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") core.set_prim_eager_enabled(True) # Hyper parameters - EPOCHS = 500 + EPOCHS = 1 BATCH_SIZE = 50 LEARNING_RATE = 1e-3 - LAYER_NUMBER = 4 - 1 - HIDDEN_SIZE = 20 # Physic properties P_OUT = 0 # pressure at the outlet of pipe @@ -70,13 +69,20 @@ def predict( unique_x = np.linspace(X_IN, X_OUT, 100) mu = 0.5 * (X_OUT - X_IN) N_y = 20 - x_2d = np.tile(unique_x, N_y) - x_2d = np.reshape(x_2d, (len(x_2d), 1)) + x_inital = np.linspace(X_IN, X_OUT, 100, dtype=paddle.get_default_dtype()).reshape( + 100, 1 + ) + mu = 0.5 * (X_OUT - X_IN) + x_20_copy = np.tile(x_inital, (20, 1)) # duplicate 20 times of x for dataloader SIGMA = 0.1 SCALE_START = -0.02 SCALE_END = 0 - scale_1d = np.linspace(SCALE_START, SCALE_END, 50, endpoint=True) - x, scale = ThreeD_mesh(x_2d, scale_1d) + scale_initial = np.linspace( + SCALE_START, SCALE_END, 50, endpoint=True, dtype=paddle.get_default_dtype() + ).reshape(50, 1) + x, scale = generate_scale_for_all_x( + x_20_copy, scale_initial + ) # generate 50 scale factors for each x (mesh grid) # Axisymetric boundary R = ( @@ -86,49 +92,128 @@ def predict( * np.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) - # Generate stenosis + # Visualize stenosis(scale == 0.2) y_up = (R_INLET - R) * np.ones_like(x) y_down = (-R_INLET + R) * np.ones_like(x) - idx = np.where(scale == SCALE_START) + idx = np.where(scale == 0) # plot vessel which scale is 0.2 by finding its indexs plt.figure() plt.scatter(x[idx], y_up[idx]) plt.scatter(x[idx], y_down[idx]) plt.axis("equal") plt.show() - plt.savefig("./plot/idealized_stenotid_vessel", bbox_inches="tight") + plt.savefig(osp.join(PLOT_DIR, "idealized_stenotid_vessel"), bbox_inches="tight") # Points and shuffle(for alignment) y = np.zeros([len(x), 1]) - for x0 in unique_x: + for x0 in x_inital: index = np.where(x[:, 0] == x0)[0] Rsec = max(y_up[index]) - tmpy = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) - y[index] = tmpy - - index = [i for i in range(x.shape[0])] - res = list(zip(x, y, scale)) - np.random.shuffle(res) - x, y, scale = zip(*res) - x = np.array(x).astype(float) - y = np.array(y).astype(float) - scale = np.array(scale).astype(float) + # y is linear to scale, so we place linespace to get 1000 x, it coressponds to vessels + y[index] = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) + idx = np.where(scale == 0) # plot vessel which scale is 0.2 by finding its indexs + plt.figure() + plt.scatter(x[idx], y[idx]) + plt.axis("equal") + plt.show() + plt.savefig(osp.join(PLOT_DIR, "one_scale_sample"), bbox_inches="tight") + + # index = [i for i in range(x.shape[0])] + # res = list(zip(x, y, scale)) + # np.random.shuffle(res) + # x, y, scale = zip(*res) + # x = np.array(x).astype(float) + # y = np.array(y).astype(float) + # scale = np.array(scale).astype(float) + + np.savez("x_0526_pd", x=x) + np.savez("y_0526_pd", y=y) + np.savez("scale_0526_pd", scale=scale) interior_geom = ppsci.geometry.PointCloud( interior={"x": x, "y": y, "scale": scale}, coord_keys=["x", "y", "scale"], ) + def _calculate_fan_in_and_fan_out(tensor): + if tensor.ndim < 2: + raise ValueError( + f"tensor.ndim should be no less than 2, but got {tensor.ndim}." + ) + num_input_fmaps = tensor.shape[1] + num_output_fmaps = tensor.shape[0] + receptive_field_size = 1 + if tensor.ndim > 2: + receptive_field_size = np.prod(tensor.shape[2:]) + fan_in = num_input_fmaps * receptive_field_size + fan_out = num_output_fmaps * receptive_field_size + return fan_in, fan_out + + def init_func(m): + if misc.typename(m) == "Linear": + ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) + + model_1 = ppsci.arch.MLP( + ["x", "y", "scale"], + ["u"], + 3, + 20, + "swish", + False, + False, + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net2_params/weight_0_0.npz" + ), + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net2_params/bias_0_0.npz" + ), + ) + model_2 = ppsci.arch.MLP( - ["x", "y", "scale"], ["u"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False + ["x", "y", "scale"], + ["v"], + 3, + 20, + "swish", + False, + False, + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net3_params/weight_0_0.npz" + ), + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net3_params/bias_0_0.npz" + ), ) model_3 = ppsci.arch.MLP( - ["x", "y", "scale"], ["v"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False + ["x", "y", "scale"], + ["p"], + 3, + 20, + "swish", + False, + False, + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net4_params/weight_0_0.npz" + ), + np.load( + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net4_params/bias_0_0.npz" + ), ) + # model_1.apply(init_func) + # model_2.apply(init_func) + # model_3.apply(init_func) - model_4 = ppsci.arch.MLP( - ["x", "y", "scale"], ["p"], LAYER_NUMBER, HIDDEN_SIZE, "swish", False, False - ) + # print(f"layer 1 mean : {np.mean(model_1.linears[0].weight.numpy())}") + # print(f"layer 1 var : {np.var(model_1.linears[0].weight.numpy())}") + + # print(f"layer 2 mean : {np.mean(model_1.linears[1].weight.numpy())}") + # print(f"layer 2 var : {np.var(model_1.linears[1].weight.numpy())}") + + # print(f"layer 3 mean : {np.mean(model_1.linears[2].weight.numpy())}") + # print(f"layer 3 var : {np.var(model_1.linears[2].weight.numpy())}") + + # print(f"layer 4 mean : {np.mean(model_1.last_fc.weight.numpy())}") + # print(f"layer 4 var : {np.var(model_1.last_fc.weight.numpy())}") class Output_transform: def __init__(self) -> None: @@ -168,14 +253,21 @@ def __call__(self, out, input): return new_out shared_transform = Output_transform() + model_1.register_output_transform(shared_transform) model_2.register_output_transform(shared_transform) model_3.register_output_transform(shared_transform) - model_4.register_output_transform(shared_transform) - model = ppsci.arch.ModelList([model_2, model_3, model_4]) + model = ppsci.arch.ModelList([model_1, model_2, model_3]) - optimizer = ppsci.optimizer.Adam( + optimizer_1 = ppsci.optimizer.Adam( + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 + )([model_1]) + optimizer_2 = ppsci.optimizer.Adam( + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 + )([model_2]) + optimizer_3 = ppsci.optimizer.Adam( LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 - )([model]) + )([model_3]) + optimizer = ppsci.optimizer.OptimizerList((optimizer_1, optimizer_2, optimizer_3)) equation = {"NavierStokes": ppsci.equation.NavierStokes(NU, RHO, 2, False)} @@ -204,7 +296,7 @@ def __call__(self, out, input): solver = ppsci.solver.Solver( model, {pde_constraint.name: pde_constraint}, - output_dir, + OUTPUT_DIR, optimizer, epochs=EPOCHS, iters_per_epoch=int(x.shape[0] / BATCH_SIZE), @@ -212,6 +304,7 @@ def __call__(self, out, input): save_freq=10, log_freq=1, equation=equation, + # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504_debug/checkpoints/epoch_300" ) solver.train() @@ -221,7 +314,7 @@ def single_test(x, y, scale, solver): yt = paddle.to_tensor(y, dtype="float32") scalet = scale * paddle.ones_like(xt) net_in = {"x": xt, "y": yt, "scale": scalet} - output_dict = predict(net_in, solver) + output_dict = solver.predict(net_in, 10000) return output_dict scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ @@ -264,7 +357,7 @@ def single_test(x, y, scale, solver): plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) plt.savefig( - "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_uContour_test.png", + osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_uContour_test.png"), bbox_inches="tight", ) @@ -281,21 +374,21 @@ def single_test(x, y, scale, solver): plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) plt.savefig( - "plot/" + str(int(caseIdx)) + "_scale_" + str(scale) + "_vContour_test.png", + osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_vContour_test.png"), bbox_inches="tight", ) plt.close("all") # Centerline wall shear profile tau_c Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") - unique_x = Data_CFD_wss["x"] + x_inital = Data_CFD_wss["x"] wall_shear_mag_up = Data_CFD_wss["wss"] Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") NNwall_shear_mag_up = Data_NN_wss["wss"] plt.figure() plt.plot( - unique_x, + x_inital, wall_shear_mag_up, label="CFD", color="darkblue", @@ -304,7 +397,7 @@ def single_test(x, y, scale, solver): alpha=1.0, ) plt.plot( - unique_x, + x_inital, NNwall_shear_mag_up, label="DNN", color="red", @@ -317,7 +410,7 @@ def single_test(x, y, scale, solver): plt.ylabel(r"$\tau_{c}$", fontsize=16) plt.legend(prop={"size": 16}) plt.savefig( - "plot/" + str(int(caseIdx)) + "_nu_" + str(NU) + "_wallshear_test.png", + osp.join(PLOT_DIR, f"{int(caseIdx)}_nu__{scale}_wallshear_test.png"), bbox_inches="tight", ) plt.close("all") diff --git a/examples/aneurysm/data/net2_params/bias_0_0.npz b/examples/aneurysm/data/net2_params/bias_0_0.npz deleted file mode 100644 index 795af420c7965d6b32b3b2457bdc4b807f349e2f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1218 zcmWIWW@Zs#fB;2?K)<}(7l0fPW@iv#NQyVm%PXj4WDo!gfs}!yKwz?8sBb_dBSRTO 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zzkL$c$We)`bgD09UdRKBc|SqQ+J8asDP?>)KZ9mhYe2wfO`H{?K?T-%aPreMe0j{B zS>B4k@tgrx*lE$R8WS31D$UMcxC*Z0RH&k&l#42hqvZjCe6G3`*RVVdp1k(On(e}# z#7q%w8V7L&uD976@k~q~l8)(5?NH;*_kVzVE0mQpz50nnKVJVy{}p|_satdGXm$1bs#L3g2{Rs)BqjKz~b$xvS5ejHr5 z3A>ISra3FNqoZF5?fbMEWomM1^MQRRQa@Ieb;F%9r!FSeIg+ws(lE|80&_N*;^JFb zxX^wzW_*{>A3IC2wDSNy_bkN&ZO#;XL5I0SALL(tiDdRS`jK4iUTQnDmy*_Y^4$7U zY(nr>+HlPP)yfEr9Q)Xs_Jz1&^bYJfWdSdqH^AKXWKw=0Bm4pV`m0I(e`%-x8S@|S z%l{nz|I0r8XVKr26#rWEKuAW-nDJ+>;y;W2PHq0RsL1?B(Vq>> import ppsci + >>> model1 = ppsci.arch.MLP(("x",), ("u",), 5, 20) + >>> opt1 = ppsci.optimizer.Adam(1e-3)((model1,)) + >>> model2 = ppsci.arch.MLP(("y",), ("v",), 5, 20) + >>> opt2 = ppsci.optimizer.Adam(1e-3)((model2,)) + >>> opt = ppsci.optimizer.OptimizerList((opt1, opt2)) + """ + + def __init__(self, optimizer_list: Tuple[optim.Optimizer, ...]): + super().__init__() + self._opt_list = optimizer_list + if "LBFGS" in set(misc.typename(opt) for opt in optimizer_list): + raise ValueError(f"LBFGS is not supported in OptimizerList yet.") + + def step(self): + for opt in self._opt_list: + opt.step() + + def clear_grad(self): + for opt in self._opt_list: + opt.clear_grad() + + def get_lr(self) -> float: + """Return learning rate of first optimizer""" + return self._opt_list[0].get_lr() + + def set_state_dict(self, state_dicts: List[Dict[str, "paddle.Tensor"]]): + for i, opt in enumerate(self._opt_list): + opt.set_state_dict(state_dicts[i]) + + def state_dict(self) -> List[Dict[str, "paddle.Tensor"]]: + state_dicts = [opt.state_dict() for opt in self._opt_list] + return state_dicts + + def __len__(self) -> int: + return len(self._opt_list) + + def __getitem__(self, idx): + return self._opt_list[idx] + + def __setitem__(self, idx, opt): + raise NotImplementedError(f"Can not modify any item in OptimizerList.") diff --git a/ppsci/solver/train.py b/ppsci/solver/train.py index 0279fbf94..e97292e22 100644 --- a/ppsci/solver/train.py +++ b/ppsci/solver/train.py @@ -83,17 +83,18 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): total_loss.backward() # import numpy as np # # import os - # bias_u = np.load("./data/net2_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") - # bias_v = np.load("./data/net3_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") - # bias_p = np.load("./data/net4_params" + f"/bias_{epoch_id - 1}_{iter_id - 1}.npz") - # weight_u = np.load("./data/net2_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") - # weight_v = np.load("./data/net3_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") - # weight_p = np.load("./data/net4_params" + f"/weight_{epoch_id - 1}_{iter_id - 1}.npz") - - # weight_grad_u = np.load("./data/net2_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") - # weight_grad_v = np.load("./data/net3_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") - # weight_grad_p = np.load("./data/net4_params" + f"/weight_grad_{epoch_id - 1}_{iter_id - 1}.npz") + # path = "/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526" + # bias_u = np.load(path + "/net2_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") + # bias_v = np.load(path + "/net3_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") + # bias_p = np.load(path + "/net4_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") + # weight_u = np.load(path + "/net2_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") + # weight_v = np.load(path + "/net3_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") + # weight_p = np.load(path + "/net4_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") + # weight_grad_u = np.load(path + "/net2_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") + # weight_grad_v = np.load(path + "/net3_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") + # weight_grad_p = np.load(path + "/net4_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") # # u + # print("----------- print [u] net parameters -----------") # print("w0_u is close:", np.allclose(solver.model.model_list[0].linears[0].weight.numpy(), weight_u[f"w_0"])) # print("w1_u is close:", np.allclose(solver.model.model_list[0].linears[1].weight.numpy(), weight_u[f"w_1"])) # print("w2_u is close:", np.allclose(solver.model.model_list[0].linears[2].weight.numpy(), weight_u[f"w_2"])) @@ -110,6 +111,7 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): # print("w3_u grad is close:", np.allclose(solver.model.model_list[0].last_fc.weight.grad.numpy(), weight_grad_u[f"w_3"])) # #v + # print("----------- print [v] net parameters -----------") # print("w0_v is close:", np.allclose(solver.model.model_list[1].linears[0].weight.numpy(), weight_v[f"w_0"])) # print("w1_v is close:", np.allclose(solver.model.model_list[1].linears[1].weight.numpy(), weight_v[f"w_1"])) # print("w2_v is close:", np.allclose(solver.model.model_list[1].linears[2].weight.numpy(), weight_v[f"w_2"])) @@ -126,6 +128,7 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): # print("w3_v grad is close:", np.allclose(solver.model.model_list[1].last_fc.weight.grad.numpy(), weight_grad_v[f"w_3"])) # #p + # print("----------- print [p] net parameters -----------") # print("w0_p is close:", np.allclose(solver.model.model_list[2].linears[0].weight.numpy(), weight_p[f"w_0"])) # print("w1_p is close:", np.allclose(solver.model.model_list[2].linears[1].weight.numpy(), weight_p[f"w_1"])) # print("w2_p is close:", np.allclose(solver.model.model_list[2].linears[2].weight.numpy(), weight_p[f"w_2"])) diff --git a/ppsci/utils/initializer.py b/ppsci/utils/initializer.py index ebb58e0fe..ec07032b1 100644 --- a/ppsci/utils/initializer.py +++ b/ppsci/utils/initializer.py @@ -427,6 +427,14 @@ def kaiming_normal_( fan = _calculate_correct_fan(tensor, mode, reverse) gain = _calculate_gain(nonlinearity, a) std = gain / math.sqrt(fan) + # print("------------------------------------------") + # print(f"layer 1 mean : {np.mean(tensor.numpy())}") + # print(f"layer 1 var : {np.var(tensor.numpy())}") + # print("\n") + # tmp = _no_grad_normal_(tensor, 0, std) + # print(f"layer 1 mean : {np.mean(tmp.numpy())}") + # print(f"layer 1 var : {np.var(tmp.numpy())}") + # print("\n") return _no_grad_normal_(tensor, 0, std) From 9aa1279496c1ace86f1244df188dbc9d7194b776 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Wed, 31 May 2023 12:23:06 +0000 Subject: [PATCH 11/31] need shuffle --- examples/aneurysm/aneurysm_flow.py | 37 +++++++++++++++--------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index fb20c0415..0dd763c5e 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -51,7 +51,7 @@ def predict( core.set_prim_eager_enabled(True) # Hyper parameters - EPOCHS = 1 + EPOCHS = 500 BATCH_SIZE = 50 LEARNING_RATE = 1e-3 @@ -100,28 +100,27 @@ def predict( plt.scatter(x[idx], y_up[idx]) plt.scatter(x[idx], y_down[idx]) plt.axis("equal") - plt.show() plt.savefig(osp.join(PLOT_DIR, "idealized_stenotid_vessel"), bbox_inches="tight") # Points and shuffle(for alignment) - y = np.zeros([len(x), 1]) + y = np.zeros([len(x), 1], dtype=paddle.get_default_dtype()) for x0 in x_inital: index = np.where(x[:, 0] == x0)[0] - Rsec = max(y_up[index]) # y is linear to scale, so we place linespace to get 1000 x, it coressponds to vessels - y[index] = np.linspace(-Rsec, Rsec, len(index)).reshape(len(index), -1) + y[index] = np.linspace(-max(y_up[index]), max(y_up[index]), len(index)).reshape( + len(index), -1 + ) idx = np.where(scale == 0) # plot vessel which scale is 0.2 by finding its indexs plt.figure() plt.scatter(x[idx], y[idx]) plt.axis("equal") - plt.show() plt.savefig(osp.join(PLOT_DIR, "one_scale_sample"), bbox_inches="tight") # index = [i for i in range(x.shape[0])] # res = list(zip(x, y, scale)) # np.random.shuffle(res) - # x, y, scale = zip(*res) + # x, y, scale = zip(*res)fastype # x = np.array(x).astype(float) # y = np.array(y).astype(float) # scale = np.array(scale).astype(float) @@ -161,10 +160,10 @@ def init_func(m): False, False, np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net2_params/weight_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net2_params/weight_0_0.npz" ), np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net2_params/bias_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net2_params/bias_0_0.npz" ), ) @@ -177,10 +176,10 @@ def init_func(m): False, False, np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net3_params/weight_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net3_params/weight_0_0.npz" ), np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net3_params/bias_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net3_params/bias_0_0.npz" ), ) @@ -193,10 +192,10 @@ def init_func(m): False, False, np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net4_params/weight_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net4_params/weight_0_0.npz" ), np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526/net4_params/bias_0_0.npz" + f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net4_params/bias_0_0.npz" ), ) # model_1.apply(init_func) @@ -282,7 +281,7 @@ def __call__(self, out, input): "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), "sampler": { "name": "BatchSampler", - "shuffle": False, + "shuffle": True, "drop_last": False, }, }, @@ -302,12 +301,12 @@ def __call__(self, out, input): iters_per_epoch=int(x.shape[0] / BATCH_SIZE), eval_during_train=False, save_freq=10, - log_freq=1, + log_freq=100, equation=equation, - # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0504_debug/checkpoints/epoch_300" + checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0531/checkpoints/epoch_30", ) - solver.train() + # solver.train() def single_test(x, y, scale, solver): xt = paddle.to_tensor(x, dtype="float32") @@ -331,11 +330,11 @@ def single_test(x, y, scale, solver): for caseIdx in caseCount: scale = scale_test[int(caseIdx - 1)] Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") - Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") + # Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") x = Data_CFD["x"] y = Data_CFD["y"] U_CFD = Data_CFD["U"] - U = Data_NN["U"] + # U = Data_NN["U"] n = len(x) output_dict = single_test( x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, solver From 2cb23a3fc6e7800e2040b366433fdaa173e331cb Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Thu, 1 Jun 2023 06:24:03 +0000 Subject: [PATCH 12/31] refine by review --- examples/aneurysm/aneurysm_flow.py | 138 ++++++++++++----------------- 1 file changed, 56 insertions(+), 82 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 0dd763c5e..225ebee6b 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -43,7 +43,7 @@ def predict( os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") # set output directory - OUTPUT_DIR = "./output_0531" + OUTPUT_DIR = "./output_0601" PLOT_DIR = osp.join(OUTPUT_DIR, "visu") os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger @@ -151,56 +151,14 @@ def init_func(m): if misc.typename(m) == "Linear": ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) - model_1 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["u"], - 3, - 20, - "swish", - False, - False, - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net2_params/weight_0_0.npz" - ), - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net2_params/bias_0_0.npz" - ), - ) + model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u"), 3, 20, "swish") - model_2 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["v"], - 3, - 20, - "swish", - False, - False, - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net3_params/weight_0_0.npz" - ), - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net3_params/bias_0_0.npz" - ), - ) + model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v"), 3, 20, "swish") - model_3 = ppsci.arch.MLP( - ["x", "y", "scale"], - ["p"], - 3, - 20, - "swish", - False, - False, - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net4_params/weight_0_0.npz" - ), - np.load( - f"/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/net_params/net4_params/bias_0_0.npz" - ), - ) - # model_1.apply(init_func) - # model_2.apply(init_func) - # model_3.apply(init_func) + model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p"), 3, 20, "swish") + model_1.apply(init_func) + model_2.apply(init_func) + model_3.apply(init_func) # print(f"layer 1 mean : {np.mean(model_1.linears[0].weight.numpy())}") # print(f"layer 1 var : {np.var(model_1.linears[0].weight.numpy())}") @@ -247,7 +205,7 @@ def __call__(self, out, input): + (X_IN - x) * (X_OUT - x) * p ) else: - raise NotImplementedError(f"{out.keys()} are outputs to be implemented") + ValueError(f"{next(iter(out.keys()))} is not a valid key.") return new_out @@ -255,17 +213,17 @@ def __call__(self, out, input): model_1.register_output_transform(shared_transform) model_2.register_output_transform(shared_transform) model_3.register_output_transform(shared_transform) - model = ppsci.arch.ModelList([model_1, model_2, model_3]) + model = ppsci.arch.ModelList((model_1, model_2, model_3)) optimizer_1 = ppsci.optimizer.Adam( - LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 - )([model_1]) + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15 + )((model_1,)) optimizer_2 = ppsci.optimizer.Adam( - LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 - )([model_2]) + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15 + )((model_2,)) optimizer_3 = ppsci.optimizer.Adam( - LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=10**-15 - )([model_3]) + LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15 + )((model_3,)) optimizer = ppsci.optimizer.OptimizerList((optimizer_1, optimizer_2, optimizer_3)) equation = {"NavierStokes": ppsci.equation.NavierStokes(NU, RHO, 2, False)} @@ -287,14 +245,13 @@ def __call__(self, out, input): }, loss=ppsci.loss.MSELoss("mean"), evenly=True, - weight_dict={"u": 1, "v": 1, "p": 1}, name="EQ", ) - + constrain_dict = {pde_constraint.name: pde_constraint} # initialize solver solver = ppsci.solver.Solver( model, - {pde_constraint.name: pde_constraint}, + constrain_dict, OUTPUT_DIR, optimizer, epochs=EPOCHS, @@ -303,14 +260,14 @@ def __call__(self, out, input): save_freq=10, log_freq=100, equation=equation, - checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0531/checkpoints/epoch_30", + # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0601/checkpoints/epoch_460", ) - # solver.train() + solver.train() def single_test(x, y, scale, solver): - xt = paddle.to_tensor(x, dtype="float32") - yt = paddle.to_tensor(y, dtype="float32") + xt = paddle.to_tensor(x) + yt = paddle.to_tensor(y) scalet = scale * paddle.ones_like(xt) net_in = {"x": xt, "y": yt, "scale": scalet} output_dict = solver.predict(net_in, 10000) @@ -327,31 +284,48 @@ def single_test(x, y, scale, solver): fontsize = 14 axis_limit = [0, 1, -0.15, 0.15] path = "./data/cases/" + dp = 0.1 for caseIdx in caseCount: scale = scale_test[int(caseIdx - 1)] - Data_CFD = np.load(path + str(caseIdx) + "CFD_contour.npz") - # Data_NN = np.load(path + str(caseIdx) + "NN_contour.npz") - x = Data_CFD["x"] - y = Data_CFD["y"] - U_CFD = Data_CFD["U"] - # U = Data_NN["U"] + data_CFD = np.load(osp.join(path, str(caseIdx) + "CFD_contour.npz")) + x = data_CFD["x"].astype(paddle.get_default_dtype()) + y = data_CFD["y"].astype(paddle.get_default_dtype()) + u_cfd = data_CFD["U"].astype(paddle.get_default_dtype()) + # p_cfd = data_CFD["P"].astype(paddle.get_default_dtype()) # missing data + n = len(x) output_dict = single_test( - x.reshape(n, 1), y.reshape(n, 1), np.ones((n, 1)) * scale, solver + x.reshape(n, 1), + y.reshape(n, 1), + np.ones((n, 1), dtype=paddle.get_default_dtype()) * scale, + solver, ) u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] w = np.zeros_like(u) - U = np.concatenate([u, v, w], axis=1) + u_vec = np.concatenate([u, v, w], axis=1) + print(f"shape of vec : {(u_vec[:, 0] - u_cfd[:, 0]).shape}") + error_u = np.linalg.norm(u_vec[:, 0] - u_cfd[:, 0], ord=2) / ( + dp * len(u_vec[:, 0]) + ) + error_v = np.linalg.norm(u_vec[:, 1] - u_cfd[:, 1], ord=2) / ( + dp * len(u_vec[:, 0]) + ) + # error_p = np.linalg.norm(p - p_cfd) / (dp * dp) + + print(f"Table 1 : Aneurysm - Geometry error u : {error_u: .3e}") + print(f"Table 1 : Aneurysm - Geometry error v : {error_v: .3e}") + # print(f"Table 1 : Aneurysm - Geometry error p : {error_p}") + exit() # Streamwise velocity component u plt.figure() plt.subplot(212) - plt.scatter(x, y, c=U[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.scatter(x, y, c=u_vec[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0])) plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) plt.colorbar() plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 0], vmin=min(U_CFD[:, 0]), vmax=max(U_CFD[:, 0])) + plt.scatter(x, y, c=u_cfd[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0])) plt.colorbar() plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) @@ -363,12 +337,12 @@ def single_test(x, y, scale, solver): # Spanwise velocity component v plt.figure() plt.subplot(212) - plt.scatter(x, y, c=U[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.scatter(x, y, c=u_vec[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1])) plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) plt.colorbar() plt.subplot(211) - plt.scatter(x, y, c=U_CFD[:, 1], vmin=min(U_CFD[:, 1]), vmax=max(U_CFD[:, 1])) + plt.scatter(x, y, c=u_cfd[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1])) plt.colorbar() plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) plt.axis(axis_limit) @@ -379,11 +353,11 @@ def single_test(x, y, scale, solver): plt.close("all") # Centerline wall shear profile tau_c - Data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") - x_inital = Data_CFD_wss["x"] - wall_shear_mag_up = Data_CFD_wss["wss"] - Data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") - NNwall_shear_mag_up = Data_NN_wss["wss"] + data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") + x_inital = data_CFD_wss["x"] + wall_shear_mag_up = data_CFD_wss["wss"] + data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") + nn_wall_shear_mag_up = data_NN_wss["wss"] plt.figure() plt.plot( @@ -397,7 +371,7 @@ def single_test(x, y, scale, solver): ) plt.plot( x_inital, - NNwall_shear_mag_up, + nn_wall_shear_mag_up, label="DNN", color="red", linestyle="--", From 5fc66e360565aaeced9c501874bcd8022e41f725 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 07:35:57 +0000 Subject: [PATCH 13/31] add wss formula --- examples/aneurysm/aneurysm_flow.py | 149 ++++++++++++----------------- 1 file changed, 60 insertions(+), 89 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 225ebee6b..0a6e2c8a9 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -1,4 +1,5 @@ import math +import os import os.path as osp import matplotlib.pyplot as plt @@ -9,39 +10,10 @@ import ppsci from ppsci.utils import misc - -def generate_scale_for_all_x(x, scale): - new_scale = np.tile(scale, (len(x), 1)) - new_x = np.array([np.tile(val, len(scale)) for val in x]).reshape(len(new_scale), 1) - return new_x, new_scale - - -def predict( - input_dict, - solver, -): - for key, val in input_dict.items(): - input_dict[key] = paddle.to_tensor(val, dtype="float32") - evaluator = ppsci.utils.expression.ExpressionSolver( - input_dict.keys(), ["u", "v", "p"], solver.model - ) - output_expr_dict = { - "u": lambda d: d["u"], - "v": lambda d: d["v"], - "p": lambda d: d["p"], - } - for output_key, output_expr in output_expr_dict.items(): - evaluator.add_target_expr(output_expr, output_key) - output_dict = evaluator(input_dict) - return output_dict - - if __name__ == "__main__": # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) - import os - os.chdir("/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm") # set output directory OUTPUT_DIR = "./output_0601" PLOT_DIR = osp.join(OUTPUT_DIR, "visu") @@ -68,11 +40,10 @@ def predict( R_INLET = 0.05 unique_x = np.linspace(X_IN, X_OUT, 100) mu = 0.5 * (X_OUT - X_IN) - N_y = 20 + N_Y = 20 x_inital = np.linspace(X_IN, X_OUT, 100, dtype=paddle.get_default_dtype()).reshape( 100, 1 ) - mu = 0.5 * (X_OUT - X_IN) x_20_copy = np.tile(x_inital, (20, 1)) # duplicate 20 times of x for dataloader SIGMA = 0.1 SCALE_START = -0.02 @@ -80,9 +51,10 @@ def predict( scale_initial = np.linspace( SCALE_START, SCALE_END, 50, endpoint=True, dtype=paddle.get_default_dtype() ).reshape(50, 1) - x, scale = generate_scale_for_all_x( - x_20_copy, scale_initial - ) # generate 50 scale factors for each x (mesh grid) + scale = np.tile(scale_initial, (len(x_20_copy), 1)) + x = np.array([np.tile(val, len(scale_initial)) for val in x_20_copy]).reshape( + len(scale), 1 + ) # Axisymetric boundary R = ( @@ -117,14 +89,6 @@ def predict( plt.axis("equal") plt.savefig(osp.join(PLOT_DIR, "one_scale_sample"), bbox_inches="tight") - # index = [i for i in range(x.shape[0])] - # res = list(zip(x, y, scale)) - # np.random.shuffle(res) - # x, y, scale = zip(*res)fastype - # x = np.array(x).astype(float) - # y = np.array(y).astype(float) - # scale = np.array(scale).astype(float) - np.savez("x_0526_pd", x=x) np.savez("y_0526_pd", y=y) np.savez("scale_0526_pd", scale=scale) @@ -133,28 +97,12 @@ def predict( coord_keys=["x", "y", "scale"], ) - def _calculate_fan_in_and_fan_out(tensor): - if tensor.ndim < 2: - raise ValueError( - f"tensor.ndim should be no less than 2, but got {tensor.ndim}." - ) - num_input_fmaps = tensor.shape[1] - num_output_fmaps = tensor.shape[0] - receptive_field_size = 1 - if tensor.ndim > 2: - receptive_field_size = np.prod(tensor.shape[2:]) - fan_in = num_input_fmaps * receptive_field_size - fan_out = num_output_fmaps * receptive_field_size - return fan_in, fan_out - def init_func(m): if misc.typename(m) == "Linear": ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u"), 3, 20, "swish") - model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v"), 3, 20, "swish") - model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p"), 3, 20, "swish") model_1.apply(init_func) model_2.apply(init_func) @@ -239,7 +187,7 @@ def __call__(self, out, input): "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), "sampler": { "name": "BatchSampler", - "shuffle": True, + "shuffle": False, "drop_last": False, }, }, @@ -258,9 +206,8 @@ def __call__(self, out, input): iters_per_epoch=int(x.shape[0] / BATCH_SIZE), eval_during_train=False, save_freq=10, - log_freq=100, + log_freq=1, equation=equation, - # checkpoint_path="/workspace/wangguan/PaddleScience_Surrogate/examples/aneurysm/output_0601/checkpoints/epoch_460", ) solver.train() @@ -270,22 +217,29 @@ def single_test(x, y, scale, solver): yt = paddle.to_tensor(y) scalet = scale * paddle.ones_like(xt) net_in = {"x": xt, "y": yt, "scale": scalet} - output_dict = solver.predict(net_in, 10000) + output_dict = solver.predict(net_in, 100) return output_dict scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ "scale" ] - caseCount = [1.0, 151.0, 486.0] - W_ctl = np.zeros([len(scale_test), 1]) - W_ctl_Ml = np.zeros([len(scale_test), 1]) - plot_x = 0.8 - plot_y = 0.06 - fontsize = 14 + CASE_COUNT = [1.0, 151.0, 486.0] + w_ctl = np.zeros([len(scale_test), 1]) + w_ctl_Ml = np.zeros([len(scale_test), 1]) + PLOT_X = 0.8 + PLOT_Y = 0.06 + FONTSIZE = 14 axis_limit = [0, 1, -0.15, 0.15] path = "./data/cases/" - dp = 0.1 - for caseIdx in caseCount: + D_P = 0.1 + error_u = [] + error_v = [] + N_CL = 200 # number of sampling points in centerline (confused about centerline, but the paper did not explain) + x_centerline = np.linspace( + X_IN, X_OUT, N_CL, dtype=paddle.get_default_dtype() + ).reshape(N_CL, 1) + y_centerline = np.zeros_like(x_centerline, dtype=paddle.get_default_dtype()) + for caseIdx in CASE_COUNT: scale = scale_test[int(caseIdx - 1)] data_CFD = np.load(osp.join(path, str(caseIdx) + "CFD_contour.npz")) x = data_CFD["x"].astype(paddle.get_default_dtype()) @@ -303,31 +257,25 @@ def single_test(x, y, scale, solver): u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] w = np.zeros_like(u) u_vec = np.concatenate([u, v, w], axis=1) - print(f"shape of vec : {(u_vec[:, 0] - u_cfd[:, 0]).shape}") - error_u = np.linalg.norm(u_vec[:, 0] - u_cfd[:, 0], ord=2) / ( - dp * len(u_vec[:, 0]) + error_u.append( + np.linalg.norm(u_vec[:, 0] - u_cfd[:, 0]) / (D_P * len(u_vec[:, 0])) ) - error_v = np.linalg.norm(u_vec[:, 1] - u_cfd[:, 1], ord=2) / ( - dp * len(u_vec[:, 0]) + error_v.append( + np.linalg.norm(u_vec[:, 1] - u_cfd[:, 1]) / (D_P * len(u_vec[:, 0])) ) - # error_p = np.linalg.norm(p - p_cfd) / (dp * dp) - - print(f"Table 1 : Aneurysm - Geometry error u : {error_u: .3e}") - print(f"Table 1 : Aneurysm - Geometry error v : {error_v: .3e}") - # print(f"Table 1 : Aneurysm - Geometry error p : {error_p}") - exit() + # error_p = np.linalg.norm(p - p_cfd) / (D_P * D_P) # Streamwise velocity component u plt.figure() plt.subplot(212) plt.scatter(x, y, c=u_vec[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.text(PLOT_X, PLOT_Y, r"DNN", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.colorbar() plt.subplot(211) plt.scatter(x, y, c=u_cfd[:, 0], vmin=min(u_cfd[:, 0]), vmax=max(u_cfd[:, 0])) plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.savefig( osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_uContour_test.png"), @@ -338,13 +286,13 @@ def single_test(x, y, scale, solver): plt.figure() plt.subplot(212) plt.scatter(x, y, c=u_vec[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1])) - plt.text(plot_x, plot_y, r"DNN", {"color": "b", "fontsize": fontsize}) + plt.text(PLOT_X, PLOT_Y, r"DNN", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.colorbar() plt.subplot(211) plt.scatter(x, y, c=u_cfd[:, 1], vmin=min(u_cfd[:, 1]), vmax=max(u_cfd[:, 1])) plt.colorbar() - plt.text(plot_x, plot_y, r"CFD", {"color": "b", "fontsize": fontsize}) + plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.savefig( osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_vContour_test.png"), @@ -352,12 +300,32 @@ def single_test(x, y, scale, solver): ) plt.close("all") - # Centerline wall shear profile tau_c + # Centerline wall shear profile tau_c (downside) data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") x_inital = data_CFD_wss["x"] wall_shear_mag_up = data_CFD_wss["wss"] - data_NN_wss = np.load(path + str(caseIdx) + "NN_wss.npz") - nn_wall_shear_mag_up = data_NN_wss["wss"] + + D_H = 0.001 # The spanwise distance is approximately the height of the wall + r_cl = ( + scale + * 1 + / np.sqrt(2 * np.pi * SIGMA**2) + * np.exp(-((x_centerline - mu) ** 2) / (2 * SIGMA**2)) + ) + y_wall = (-R_INLET + D_H) * np.ones_like( + x_centerline, dtype=paddle.get_default_dtype() + ) + r_cl + output_dict_wss = single_test( + x_centerline, y_wall, np.ones((N_CL, 1)) * scale, solver + ) + v_cl_total = np.zeros_like( + x_centerline, dtype=paddle.get_default_dtype() + ) # assuming normal velocity along the wall is zero + u_cl = output_dict_wss["u"].numpy() + v_cl = output_dict_wss["v"].numpy() + for i in range(N_CL): + v_cl_total[i] = np.sqrt(u_cl[i] ** 2 + v_cl[i] ** 2) + tau_c = NU * v_cl_total / D_H plt.figure() plt.plot( @@ -371,7 +339,7 @@ def single_test(x, y, scale, solver): ) plt.plot( x_inital, - nn_wall_shear_mag_up, + tau_c, label="DNN", color="red", linestyle="--", @@ -387,3 +355,6 @@ def single_test(x, y, scale, solver): bbox_inches="tight", ) plt.close("all") + print(f"epochs = {EPOCHS}") + print(f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}") + print(f"Table 1 : Aneurysm - Geometry error v : {sum(error_v) / len(error_v): .3e}") From 50d6936f4903c5089db2fca064210d03e1160074 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 08:08:53 +0000 Subject: [PATCH 14/31] review fix --- examples/aneurysm/aneurysm_flow.py | 12 +- examples/pipe/poiseuille_flow.py | 8 +- ppsci/arch/activation.py | 11 +- ppsci/arch/mlp.py | 33 +---- ppsci/data/dataset/__init__.py | 2 - ppsci/data/dataset/array_dataset.py | 25 ---- ppsci/solver/printer.py | 2 +- ppsci/solver/solver.py | 8 +- ppsci/solver/train.py | 201 ++++++++++++---------------- ppsci/utils/initializer.py | 8 -- 10 files changed, 112 insertions(+), 198 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 0a6e2c8a9..1153a6688 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -101,9 +101,15 @@ def init_func(m): if misc.typename(m) == "Linear": ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) - model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u"), 3, 20, "swish") - model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v"), 3, 20, "swish") - model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p"), 3, 20, "swish") + model_1 = ppsci.arch.MLP( + ("x", "y", "scale"), ("u"), 3, 20, "silu" + ) # swish without learnable variable + model_2 = ppsci.arch.MLP( + ("x", "y", "scale"), ("v"), 3, 20, "silu" + ) # swish without learnable variable + model_3 = ppsci.arch.MLP( + ("x", "y", "scale"), ("p"), 3, 20, "silu" + ) # swish without learnable variable model_1.apply(init_func) model_2.apply(init_func) model_3.apply(init_func) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 332d8a0f4..ae3b04b85 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -210,7 +210,7 @@ def predict( ["u"], LAYER_NUMBER, HIDDEN_SIZE, - "swish_beta", + "swish", False, False, ) @@ -220,7 +220,7 @@ def predict( ["v"], LAYER_NUMBER, HIDDEN_SIZE, - "swish_beta", + "swish", False, False, ) @@ -230,11 +230,9 @@ def predict( ["p"], LAYER_NUMBER, HIDDEN_SIZE, - "swish_beta", + "swish", False, False, - # np.load(dir + f"/weight_p_epoch_1.npz"), - # np.load(dir + f"/bias_p_epoch_1.npz"), ) def output_transform(out, input): diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index ef259c818..9ca442872 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -19,20 +19,19 @@ from paddle import nn -class Swish_beta(nn.Layer): +class Swish(nn.Layer): def __init__(self, beta: float = 1.0): super().__init__() self.beta = paddle.create_parameter( - shape=[1], - dtype="float32", + shape=[0], default_initializer=paddle.nn.initializer.Constant(beta), ) def forward(self, x): - return x * F.sigmoid(x * self.beta) + return x * F.sigmoid(self.beta * x) -class Swish(nn.Layer): +class Silu(nn.Layer): def __init__(self): super().__init__() @@ -46,7 +45,7 @@ def forward(self, x): "selu": F.selu, "gelu": F.gelu, "sigmoid": F.sigmoid, - "silu": F.silu, + "silu": Silu, "sin": paddle.sin, "cos": paddle.cos, "swish_beta": Swish_beta(1.0), diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index c543cd648..fae52b51c 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -16,7 +16,6 @@ from typing import Tuple from typing import Union -import paddle from paddle import nn from ppsci.arch import activation as act_mod @@ -51,9 +50,6 @@ def __init__( activation: str = "tanh", skip_connection: bool = False, weight_norm: bool = False, - weight_init=None, - bias_init=None, - net_special_name: str = None, input_dim: Optional[int] = None, ): super().__init__() @@ -79,35 +75,14 @@ def __init__( # initialize FC layer(s) cur_size = len(self.input_keys) if input_dim is None else input_dim - for i, _size in enumerate(hidden_size): - if weight_init is not None: - w_para = paddle.nn.initializer.Assign(weight_init[f"w_{i}"]) - b_para = paddle.nn.initializer.Assign(bias_init[f"b_{i}"]) - self.linears.append( - nn.Linear( - cur_size, - _size, - weight_attr=paddle.ParamAttr(initializer=w_para), - bias_attr=paddle.ParamAttr(initializer=b_para), - ) - ) - else: - self.linears.append(nn.Linear(cur_size, _size)) + for _size in hidden_size: + self.linears.append(nn.Linear(cur_size, _size)) if weight_norm: self.linears[-1] = nn.utils.weight_norm(self.linears[-1], dim=1) cur_size = _size self.linears = nn.LayerList(self.linears) - if weight_init is not None: - w_para = paddle.nn.initializer.Assign(weight_init[f"w_{num_layers}"]) - b_para = paddle.nn.initializer.Assign(bias_init[f"b_{num_layers}"]) - self.last_fc = nn.Linear( - cur_size, - len(self.output_keys), - weight_attr=paddle.ParamAttr(initializer=w_para), - bias_attr=paddle.ParamAttr(initializer=b_para), - ) - else: - self.last_fc = nn.Linear(cur_size, len(self.output_keys)) + + self.last_fc = nn.Linear(cur_size, len(self.output_keys)) # initialize activation function self.act = act_mod.get_activation(activation) diff --git a/ppsci/data/dataset/__init__.py b/ppsci/data/dataset/__init__.py index 0e890fb34..3d4d7f533 100644 --- a/ppsci/data/dataset/__init__.py +++ b/ppsci/data/dataset/__init__.py @@ -15,7 +15,6 @@ import copy from ppsci.data.dataset.array_dataset import IterableNamedArrayDataset -from ppsci.data.dataset.array_dataset import MiniBatchDataset from ppsci.data.dataset.array_dataset import NamedArrayDataset from ppsci.data.dataset.csv_dataset import CSVDataset from ppsci.data.dataset.csv_dataset import IterableCSVDataset @@ -33,7 +32,6 @@ __all__ = [ "IterableNamedArrayDataset", "NamedArrayDataset", - "MiniBatchDataset", "CSVDataset", "IterableCSVDataset", "ERA5Dataset", diff --git a/ppsci/data/dataset/array_dataset.py b/ppsci/data/dataset/array_dataset.py index c22bb2068..427265256 100644 --- a/ppsci/data/dataset/array_dataset.py +++ b/ppsci/data/dataset/array_dataset.py @@ -108,28 +108,3 @@ def __iter__(self): def __len__(self): return 1 - - -class MiniBatchDataset(io.Dataset): - def __init__(self, input, label, weight): - super().__init__() - self.input = input - self.label = label - self.num_samples = self.check_input(input) - - def check_input(self, input): - len_input = set() - for _, value in input.items(): - len_input.add(len(value)) - if len(len_input) is not 1: - raise AttributeError("Input dimension mismatch") - else: - return list(len_input)[0] - - def __getitem__(self, idx): - input_item = {key: value[idx] for key, value in self.input.items()} - label_item = {key: value[idx] for key, value in self.label.items()} - return (input_item, label_item) - - def __len__(self): - return self.num_samples diff --git a/ppsci/solver/printer.py b/ppsci/solver/printer.py index d8a8a9d9f..5bd383908 100644 --- a/ppsci/solver/printer.py +++ b/ppsci/solver/printer.py @@ -43,7 +43,7 @@ def log_train_info(trainer, batch_size, epoch_id, iter_id): metric_msg = ", ".join( [ - f"{key}: {trainer.train_output_info[key].val:.9f}" + f"{key}: {trainer.train_output_info[key].avg:.5f}" for key in trainer.train_output_info ] ) diff --git a/ppsci/solver/solver.py b/ppsci/solver/solver.py index c4683a6eb..31496524e 100644 --- a/ppsci/solver/solver.py +++ b/ppsci/solver/solver.py @@ -343,14 +343,15 @@ def train(self): """Training""" self.global_step = self.best_metric["epoch"] * self.iters_per_epoch + 1 + LOSS = [] for epoch_id in range(self.best_metric["epoch"] + 1, self.epochs + 1): - self.train_epoch_func(self, epoch_id, self.log_freq) + self.train_epoch_func(self, epoch_id, self.log_freq, LOSS) # log training summation at end of a epoch metric_msg = ", ".join( [self.train_output_info[key].avg_info for key in self.train_output_info] ) - # logger.info(f"[Train][Epoch {epoch_id}/{self.epochs}][Avg] {metric_msg}") + logger.info(f"[Train][Epoch {epoch_id}/{self.epochs}][Avg] {metric_msg}") self.train_output_info.clear() cur_metric = float("inf") @@ -409,6 +410,9 @@ def train(self): self.equation, ) + import numpy as np + + np.savetxt("Loss_track_pipe_para.csv", np.array(LOSS)) # close VisualDL if self.vdl_writer is not None: self.vdl_writer.close() diff --git a/ppsci/solver/train.py b/ppsci/solver/train.py index e97292e22..16deae148 100644 --- a/ppsci/solver/train.py +++ b/ppsci/solver/train.py @@ -13,14 +13,19 @@ # limitations under the License. import time +from typing import TYPE_CHECKING + +from paddle.distributed.fleet.utils import hybrid_parallel_util as hpu from ppsci.solver import printer -from ppsci.utils import expression from ppsci.utils import misc from ppsci.utils import profiler +if TYPE_CHECKING: + from ppsci import solver + -def train_epoch_func(solver, epoch_id: int, log_freq: int): +def train_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int): """Train program for one epoch Args: @@ -38,9 +43,12 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): reader_cost = 0 batch_cost = 0 reader_tic = time.perf_counter() + + input_dicts = [] + label_dicts = [] + weight_dicts = [] for _, _constraint in solver.constraint.items(): input_dict, label_dict, weight_dict = next(_constraint.data_iter) - # profile code below # profiler.add_profiler_step(solver.cfg["profiler_options"]) if iter_id == 5: @@ -48,105 +56,58 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): for key in solver.train_time_info: solver.train_time_info[key].reset() reader_cost += time.perf_counter() - reader_tic - total_batch_size += next(iter(input_dict.values())).shape[0] - for v in input_dict.values(): v.stop_gradient = False - evaluator = expression.ExpressionSolver( - _constraint.input_keys, _constraint.output_keys, solver.model - ) - for output_name, output_formula in _constraint.output_expr.items(): - if output_name in label_dict: - evaluator.add_target_expr(output_formula, output_name) - - # forward for every constraint - with solver.autocast_context_manager(): - output_dict = evaluator(input_dict) - constraint_loss = _constraint.loss(output_dict, label_dict, weight_dict) - total_loss += constraint_loss - - loss_dict[_constraint.name] = float(constraint_loss) + # gather each constraint's input, label, weight to a list + input_dicts.append(input_dict) + label_dicts.append(label_dict) + weight_dicts.append(weight_dict) + total_batch_size += next(iter(input_dict.values())).shape[0] reader_tic = time.perf_counter() - if solver.update_freq > 1: - total_loss = total_loss / solver.update_freq - loss_dict["loss"] = float(total_loss) - # backward - if solver.use_amp: - total_loss_scaled = solver.scaler.scale(total_loss) - total_loss_scaled.backward() - if iter_id % solver.update_freq == 0: + with solver.no_sync_context_manager(solver.world_size > 1, solver.model): + # forward for every constraint, including model and equation expression + with solver.autocast_context_manager(solver.use_amp, solver.amp_level): + constraint_losses = solver.forward_helper.train_forward( + [ + _constraint.output_expr + for _constraint in solver.constraint.values() + ], + input_dicts, + solver.model, + solver.constraint, + label_dicts, + weight_dicts, + ) + # accumulate all losses + for i, _constraint in enumerate(solver.constraint.values()): + total_loss += constraint_losses[i] + loss_dict[_constraint.name] += ( + float(constraint_losses[i]) / solver.update_freq + ) + if solver.update_freq > 1: + total_loss = total_loss / solver.update_freq + loss_dict["loss"] = float(total_loss) + + # backward + if solver.use_amp: + total_loss_scaled = solver.scaler.scale(total_loss) + total_loss_scaled.backward() + else: + total_loss.backward() + + # update parameters + if iter_id % solver.update_freq == 0 or iter_id == solver.iters_per_epoch: + if solver.world_size > 1: + # fuse + allreduce manually before optimization if use DDP + no_sync + # details in https://github.com/PaddlePaddle/Paddle/issues/48898#issuecomment-1343838622 + hpu.fused_allreduce_gradients(list(solver.model.parameters()), None) + if solver.use_amp: solver.scaler.minimize(solver.optimizer, total_loss_scaled) - solver.optimizer.clear_grad() - else: - total_loss.backward() - # import numpy as np - # # import os - # path = "/workspace/wangguan/LabelFree-DNN-Surrogate/ParametricAneurysm/Cases_0526" - # bias_u = np.load(path + "/net2_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") - # bias_v = np.load(path + "/net3_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") - # bias_p = np.load(path + "/net4_params" + f"/bias_{epoch_id - 1}_{iter_id}.npz") - # weight_u = np.load(path + "/net2_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") - # weight_v = np.load(path + "/net3_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") - # weight_p = np.load(path + "/net4_params" + f"/weight_{epoch_id - 1}_{iter_id}.npz") - # weight_grad_u = np.load(path + "/net2_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") - # weight_grad_v = np.load(path + "/net3_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") - # weight_grad_p = np.load(path + "/net4_params" + f"/weight_grad_{epoch_id - 1}_{iter_id}.npz") - # # u - # print("----------- print [u] net parameters -----------") - # print("w0_u is close:", np.allclose(solver.model.model_list[0].linears[0].weight.numpy(), weight_u[f"w_0"])) - # print("w1_u is close:", np.allclose(solver.model.model_list[0].linears[1].weight.numpy(), weight_u[f"w_1"])) - # print("w2_u is close:", np.allclose(solver.model.model_list[0].linears[2].weight.numpy(), weight_u[f"w_2"])) - # print("w3_u is close:", np.allclose(solver.model.model_list[0].last_fc.weight.numpy(), weight_u[f"w_3"])) - - # print("b0_u is close:", np.allclose(solver.model.model_list[0].linears[0].bias.numpy(), bias_u[f"b_0"])) - # print("b1_u is close:", np.allclose(solver.model.model_list[0].linears[1].bias.numpy(), bias_u[f"b_1"])) - # print("b2_u is close:", np.allclose(solver.model.model_list[0].linears[2].bias.numpy(), bias_u[f"b_2"])) - # print("b3_u is close:", np.allclose(solver.model.model_list[0].last_fc.bias.numpy(), bias_u[f"b_3"])) - - # print("w0_u grad is close:", np.allclose(solver.model.model_list[0].linears[0].weight.grad.numpy(), weight_grad_u[f"w_0"])) - # print("w1_u grad is close:", np.allclose(solver.model.model_list[0].linears[1].weight.grad.numpy(), weight_grad_u[f"w_1"])) - # print("w2_u grad is close:", np.allclose(solver.model.model_list[0].linears[2].weight.grad.numpy(), weight_grad_u[f"w_2"])) - # print("w3_u grad is close:", np.allclose(solver.model.model_list[0].last_fc.weight.grad.numpy(), weight_grad_u[f"w_3"])) - - # #v - # print("----------- print [v] net parameters -----------") - # print("w0_v is close:", np.allclose(solver.model.model_list[1].linears[0].weight.numpy(), weight_v[f"w_0"])) - # print("w1_v is close:", np.allclose(solver.model.model_list[1].linears[1].weight.numpy(), weight_v[f"w_1"])) - # print("w2_v is close:", np.allclose(solver.model.model_list[1].linears[2].weight.numpy(), weight_v[f"w_2"])) - # print("w3_v is close:", np.allclose(solver.model.model_list[1].last_fc.weight.numpy(), weight_v[f"w_3"])) - - # print("b0_v is close:", np.allclose(solver.model.model_list[1].linears[0].bias.numpy(), bias_v[f"b_0"])) - # print("b1_v is close:", np.allclose(solver.model.model_list[1].linears[1].bias.numpy(), bias_v[f"b_1"])) - # print("b2_v is close:", np.allclose(solver.model.model_list[1].linears[2].bias.numpy(), bias_v[f"b_2"])) - # print("b3_v is close:", np.allclose(solver.model.model_list[1].last_fc.bias.numpy(), bias_v[f"b_3"])) - - # print("w0_v grad is close:", np.allclose(solver.model.model_list[1].linears[0].weight.grad.numpy(), weight_grad_v[f"w_0"])) - # print("w1_v grad is close:", np.allclose(solver.model.model_list[1].linears[1].weight.grad.numpy(), weight_grad_v[f"w_1"])) - # print("w2_v grad is close:", np.allclose(solver.model.model_list[1].linears[2].weight.grad.numpy(), weight_grad_v[f"w_2"])) - # print("w3_v grad is close:", np.allclose(solver.model.model_list[1].last_fc.weight.grad.numpy(), weight_grad_v[f"w_3"])) - - # #p - # print("----------- print [p] net parameters -----------") - # print("w0_p is close:", np.allclose(solver.model.model_list[2].linears[0].weight.numpy(), weight_p[f"w_0"])) - # print("w1_p is close:", np.allclose(solver.model.model_list[2].linears[1].weight.numpy(), weight_p[f"w_1"])) - # print("w2_p is close:", np.allclose(solver.model.model_list[2].linears[2].weight.numpy(), weight_p[f"w_2"])) - # print("w3_p is close:", np.allclose(solver.model.model_list[2].last_fc.weight.numpy(), weight_p[f"w_3"])) - - # print("b0_p is close:", np.allclose(solver.model.model_list[2].linears[0].bias.numpy(), bias_p[f"b_0"])) - # print("b1_p is close:", np.allclose(solver.model.model_list[2].linears[1].bias.numpy(), bias_p[f"b_1"])) - # print("b2_p is close:", np.allclose(solver.model.model_list[2].linears[2].bias.numpy(), bias_p[f"b_2"])) - # print("b3_p is close:", np.allclose(solver.model.model_list[2].last_fc.bias.numpy(), bias_p[f"b_3"])) - - # print("w0_p grad is close:", np.allclose(solver.model.model_list[2].linears[0].weight.grad.numpy(), weight_grad_p[f"w_0"])) - # print("w1_p grad is close:", np.allclose(solver.model.model_list[2].linears[1].weight.grad.numpy(), weight_grad_p[f"w_1"])) - # print("w2_p grad is close:", np.allclose(solver.model.model_list[2].linears[2].weight.grad.numpy(), weight_grad_p[f"w_2"])) - # print("w3_p grad is close:", np.allclose(solver.model.model_list[2].last_fc.weight.grad.numpy(), weight_grad_p[f"w_3"])) - - if iter_id % solver.update_freq == 0: + else: solver.optimizer.step() - solver.optimizer.clear_grad() + solver.optimizer.clear_grad() # update learning rate by step if solver.lr_scheduler is not None and not solver.lr_scheduler.by_epoch: @@ -162,12 +123,10 @@ def train_epoch_func(solver, epoch_id: int, log_freq: int): if iter_id == 1 or iter_id % log_freq == 0: printer.log_train_info(solver, total_batch_size, epoch_id, iter_id) - # real_loss = float(loss_dict["loss"]) - # print(f"{(epoch_id - 1) * 195 + iter_id} step: loss is {real_loss}") batch_tic = time.perf_counter() -def train_LBFGS_epoch_func(solver, epoch_id: int, log_freq: int): +def train_LBFGS_epoch_func(solver: "solver.Solver", epoch_id: int, log_freq: int): """Train function for one epoch with L-BFGS optimizer. Args: @@ -208,30 +167,38 @@ def closure(): Tensor: Computed loss. """ total_loss = 0 - for i, _constraint in enumerate(solver.constraint.values()): - evaluator = expression.ExpressionSolver( - _constraint.input_keys, _constraint.output_keys, solver.model - ) - for output_name, output_formula in _constraint.output_expr.items(): - if output_name in label_dict: - evaluator.add_target_expr(output_formula, output_name) - - # forward for every batched data dict - with solver.autocast_context_manager(): - output_dict_i = evaluator(input_dicts[i]) - constraint_loss = _constraint.loss( - output_dict_i, label_dicts[i], weight_dicts[i] + with solver.no_sync_context_manager(solver.world_size > 1, solver.model): + with solver.autocast_context_manager(solver.use_amp, solver.amp_level): + # forward for every constraint, including model and equation expression + constraint_losses = solver.forward_helper.train_forward( + [ + _constraint.output_expr + for _constraint in solver.constraint.values() + ], + input_dicts, + solver.model, + solver.constraint, + label_dicts, + weight_dicts, ) - total_loss += constraint_loss + # accumulate all losses + for i, _constraint in enumerate(solver.constraint.values()): + total_loss += constraint_losses[i] + loss_dict[_constraint.name] = float(constraint_losses[i]) + loss_dict["loss"] = float(total_loss) - loss_dict[_constraint.name] = float(constraint_loss) + # backward + solver.optimizer.clear_grad() + total_loss.backward() - solver.optimizer.clear_grad() - total_loss.backward() - loss_dict["loss"] = float(total_loss) + if solver.world_size > 1: + # fuse + allreduce manually before optimization if use DDP model + # details in https://github.com/PaddlePaddle/Paddle/issues/48898#issuecomment-1343838622 + hpu.fused_allreduce_gradients(list(solver.model.parameters()), None) return total_loss + # update parameters solver.optimizer.step(closure) # update learning rate by step @@ -245,7 +212,7 @@ def closure(): solver.train_time_info["reader_cost"].update(reader_cost) solver.train_time_info["batch_cost"].update(batch_cost) printer.update_train_loss(solver, loss_dict, total_batch_size) - if iter_id == 1 and iter_id % log_freq == 0: + if iter_id == 1 or iter_id % log_freq == 0: printer.log_train_info(solver, total_batch_size, epoch_id, iter_id) batch_tic = time.perf_counter() diff --git a/ppsci/utils/initializer.py b/ppsci/utils/initializer.py index ec07032b1..ebb58e0fe 100644 --- a/ppsci/utils/initializer.py +++ b/ppsci/utils/initializer.py @@ -427,14 +427,6 @@ def kaiming_normal_( fan = _calculate_correct_fan(tensor, mode, reverse) gain = _calculate_gain(nonlinearity, a) std = gain / math.sqrt(fan) - # print("------------------------------------------") - # print(f"layer 1 mean : {np.mean(tensor.numpy())}") - # print(f"layer 1 var : {np.var(tensor.numpy())}") - # print("\n") - # tmp = _no_grad_normal_(tensor, 0, std) - # print(f"layer 1 mean : {np.mean(tmp.numpy())}") - # print(f"layer 1 var : {np.var(tmp.numpy())}") - # print("\n") return _no_grad_normal_(tensor, 0, std) From 67f064a80823135c5863f29b153bd48503cec9f2 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 08:11:06 +0000 Subject: [PATCH 15/31] delete useless comment --- ppsci/solver/solver.py | 275 +++++++++++++++++++++++++++-------------- 1 file changed, 184 insertions(+), 91 deletions(-) diff --git a/ppsci/solver/solver.py b/ppsci/solver/solver.py index 31496524e..03db5c21a 100644 --- a/ppsci/solver/solver.py +++ b/ppsci/solver/solver.py @@ -15,10 +15,11 @@ from __future__ import annotations import contextlib -import copy +import itertools import os import sys from typing import Any +from typing import Callable from typing import Dict from typing import Optional from typing import Union @@ -37,6 +38,7 @@ import ppsci from ppsci.utils import config +from ppsci.utils import expression from ppsci.utils import logger from ppsci.utils import misc from ppsci.utils import save_load @@ -48,7 +50,7 @@ class Solver: Args: model (nn.Layer): Model. constraint (Optional[Dict[str, ppsci.constraint.Constraint]]): Constraint(s) applied on model. Defaults to None. - output_dir (str, optional): Output directory. Defaults to "./output/". + output_dir (Optional[str]): Output directory. Defaults to "./output/". optimizer (Optional[optimizer.Optimizer]): Optimizer object. Defaults to None. lr_scheduler (Optional[optimizer.lr.LRScheduler]): Learning rate scheduler. Defaults to None. epochs (int, optional): Training epoch(s). Defaults to 5. @@ -73,6 +75,7 @@ class Solver: compute_metric_by_batch (bool, optional): Whether calculate metrics after each batch during evaluate. Defaults to False. eval_with_no_grad (bool, optional): Whether set `stop_gradient=True` for every Tensor if no differentiation involved during computation, generally for save GPU memory and accelerate computing. Defaults to False. + to_static (bool, optional): Whether enable to_static for forward pass. Defaults to False. Examples: >>> import ppsci @@ -97,14 +100,14 @@ class Solver: ... "./output", ... opt, ... None, - ... ) + ... ) # doctest: +SKIP """ def __init__( self, model: nn.Layer, constraint: Optional[Dict[str, ppsci.constraint.Constraint]] = None, - output_dir: str = "./output/", + output_dir: Optional[str] = "./output/", optimizer: Optional[optim.Optimizer] = None, lr_scheduler: Optional[optim.lr.LRScheduler] = None, epochs: int = 5, @@ -128,6 +131,7 @@ def __init__( checkpoint_path: Optional[str] = None, compute_metric_by_batch: bool = False, eval_with_no_grad: bool = False, + to_static: bool = False, ): # set model self.model = model @@ -200,6 +204,16 @@ def __init__( # whether calculate metrics after each batch during evaluate self.compute_metric_by_batch = compute_metric_by_batch + if validator is not None: + for metric in itertools.chain( + *[_v.metric.values() for _v in self.validator.values()] + ): + if metric.keep_batch ^ compute_metric_by_batch: + raise ValueError( + f"{misc.typename(metric)}.keep_batch should be " + f"{compute_metric_by_batch} when compute_metric_by_batch=" + f"{compute_metric_by_batch}." + ) # whether set `stop_gradient=True` for every Tensor if no differentiation involved during computation self.eval_with_no_grad = eval_with_no_grad @@ -216,6 +230,10 @@ def __init__( if isinstance(loaded_metric, dict): self.best_metric.update(loaded_metric) + # init logger without FileHandler if not initialized before + if logger._logger is None: + logger.init_logger("ppsci", None) + # choosing an appropriate training function for different optimizers if isinstance(self.optimizer, optim.LBFGS): self.train_epoch_func = ppsci.solver.train.train_LBFGS_epoch_func @@ -227,7 +245,9 @@ def __init__( # decorate model(s) and optimizer(s) for AMP if self.use_amp: - self.model = amp.decorate(self.model, self.optimizer, self.amp_level) + self.model, self.optimizer = amp.decorate( + self.model, self.optimizer, self.amp_level + ) # wrap model and optimizer to parallel object self.rank = dist.get_rank() @@ -238,6 +258,11 @@ def __init__( self.model = fleet.distributed_model(self.model) if self.optimizer is not None: self.optimizer = fleet.distributed_optimizer(self.optimizer) + logger.warning( + f"Detected world_size({self.world_size}) > 1, it is recommended to " + "scale up the learning rate and reduce the epochs or " + "iters_per_epoch according to the world_size both linearly." + ) self.global_step = 0 @@ -247,8 +272,13 @@ def __init__( if version.Version(paddle.__version__) != version.Version("0.0.0") else f"develop({paddle.version.commit[:7]})" ) - if logger._logger is not None: - logger.info(f"Using paddlepaddle {paddle_version} on device {self.device}") + logger.info(f"Using paddlepaddle {paddle_version} on device {self.device}") + + self.forward_helper = expression.ExpressionSolver() + + # whether enable static for forward pass, default to Fals + jit.enable_to_static(to_static) + logger.info(f"Set to_static={to_static} for forward computation.") @staticmethod def from_config(cfg: Dict[str, Any]) -> Solver: @@ -340,12 +370,11 @@ def from_config(cfg: Dict[str, Any]) -> Solver: ) def train(self): - """Training""" + """Training.""" self.global_step = self.best_metric["epoch"] * self.iters_per_epoch + 1 - LOSS = [] for epoch_id in range(self.best_metric["epoch"] + 1, self.epochs + 1): - self.train_epoch_func(self, epoch_id, self.log_freq, LOSS) + self.train_epoch_func(self, epoch_id, self.log_freq) # log training summation at end of a epoch metric_msg = ", ".join( @@ -380,6 +409,7 @@ def train(self): ) logger.scaler("eval_metric", cur_metric, epoch_id, self.vdl_writer) + # visualize after evaluation if self.visualizer is not None: self.visualize(epoch_id) @@ -399,7 +429,7 @@ def train(self): self.equation, ) - # always save the latest model for convenient resume training + # save the latest model for convenient resume training save_load.save_checkpoint( self.model, self.optimizer, @@ -410,19 +440,20 @@ def train(self): self.equation, ) - import numpy as np - - np.savetxt("Loss_track_pipe_para.csv", np.array(LOSS)) # close VisualDL if self.vdl_writer is not None: self.vdl_writer.close() - def eval(self, epoch_id: int = 0): - """Evaluation""" - train_state = self.model.training - if train_state: - self.model.eval() + @misc.run_on_eval_mode + def eval(self, epoch_id: int = 0) -> float: + """Evaluation. + Args: + epoch_id (int, optional): Epoch id. Defaults to 0. + + Returns: + float: The value of the evaluation, used to judge the quality of the model. + """ # set eval func self.eval_func = ppsci.solver.eval.eval_func @@ -433,123 +464,157 @@ def eval(self, epoch_id: int = 0): logger.info(f"[Eval][Epoch {epoch_id}][Avg] {metric_msg}") self.eval_output_info.clear() - if train_state: - self.model.train() return result + @misc.run_on_eval_mode def visualize(self, epoch_id: int = 0): - """Visualization""" - train_state = self.model.training - if train_state: - self.model.eval() + """Visualization. - # init train func + Args: + epoch_id (int, optional): Epoch id. Defaults to 0. + """ + # set visualize func self.visu_func = ppsci.solver.visu.visualize_func self.visu_func(self, epoch_id) logger.info(f"[Visualize][Epoch {epoch_id}] Finished visualization") - if train_state: - self.model.train() - - @paddle.no_grad() + @misc.run_on_eval_mode def predict( self, input_dict: Dict[str, Union[np.ndarray, paddle.Tensor]], + expr_dict: Optional[Dict[str, Callable]] = None, batch_size: int = 64, + no_grad: bool = True, ) -> Dict[str, paddle.Tensor]: - """Pure prediction using model.forward(...), support single device prediction yet. + """Pure prediction using model.forward(...) and expression(optional, if given). Args: input_dict (Dict[str, Union[np.ndarray, paddle.Tensor]]): Input data in dict. + expr_dict (Optional[Dict[str, Callable]]): Expression dict, which guide to + compute equation variable with callable function. Defaults to None. batch_size (int, optional): Predicting by batch size. Defaults to 64. - + no_grad (bool): Whether set stop_gradient=True for entire prediction, mainly + for memory-efficiency. Defaults to True. Returns: Dict[str, paddle.Tensor]: Prediction in dict. """ - train_state = self.model.training - if train_state: - self.model.eval() - - if self.world_size > 1: - raise NotImplementedError( - "Solver.predict only support single device yet, " - f"but got {self.world_size} devices." - ) - num_samples = len(next(iter(input_dict.values()))) - batch_num = (num_samples + (batch_size - 1)) // batch_size + num_pad = (self.world_size - num_samples % self.world_size) % self.world_size + # pad with last element if `num_samples` is not divisible by `world_size` + # ensuring every device get same number of data. + if num_pad > 0: + for k, v in input_dict.items(): + repeat_times = (num_pad, *(1 for _ in range(v.ndim - 1))) + input_dict[k] = paddle.concat( + ( + v, + paddle.tile(v[num_samples - 1 : num_samples], repeat_times), + ), + ) + + num_samples_pad = num_samples + num_pad + local_num_samples_pad = num_samples_pad // self.world_size + local_input_dict = ( + {k: v[self.rank :: self.world_size] for k, v in input_dict.items()} + if self.world_size > 1 + else input_dict + ) + local_batch_num = (local_num_samples_pad + (batch_size - 1)) // batch_size pred_dict = misc.Prettydefaultdict(list) - for batch_id in range(batch_num): - batch_input_dict = {} - st = batch_id * batch_size - ed = min(num_samples, (batch_id + 1) * batch_size) - - # prepare batch input dict - for key in input_dict: - if not paddle.is_tensor(input_dict[key]): - batch_input_dict[key] = paddle.to_tensor( - input_dict[key][st:ed], paddle.get_default_dtype() + with self.no_grad_context_manager(no_grad), self.no_sync_context_manager( + self.world_size > 1, self.model + ): + for batch_id in range(local_batch_num): + batch_input_dict = {} + st = batch_id * batch_size + ed = min(local_num_samples_pad, (batch_id + 1) * batch_size) + + # prepare batch input dict + for key in local_input_dict: + if not paddle.is_tensor(local_input_dict[key]): + batch_input_dict[key] = paddle.to_tensor( + local_input_dict[key][st:ed], paddle.get_default_dtype() + ) + else: + batch_input_dict[key] = local_input_dict[key][st:ed] + batch_input_dict[key].stop_gradient = no_grad + + # forward + with self.autocast_context_manager(self.use_amp, self.amp_level): + batch_output_dict = self.forward_helper.visu_forward( + expr_dict, batch_input_dict, self.model ) - else: - batch_input_dict[key] = input_dict[key][st:ed] - batch_input_dict[key].stop_gradient = False - # forward - with self.autocast_context_manager(): - batch_output_dict = self.model(batch_input_dict) - - # collect batch data - for key, batch_output in batch_output_dict.items(): - pred_dict[key].append(batch_output) - - pred_dict = {key: paddle.concat(value) for key, value in pred_dict.items()} + # collect batch data + for key, batch_output in batch_output_dict.items(): + pred_dict[key].append(batch_output.detach()) + + # concatenate local predictions + pred_dict = {key: paddle.concat(value) for key, value in pred_dict.items()} + + if self.world_size > 1: + # gather global predictions from all devices if world_size > 1 + pred_dict = { + key: misc.all_gather(value) for key, value in pred_dict.items() + } + + # rearange predictions as the same order of input_dict according to inverse + # permutation, then discard predictions of padding data at the end + perm = np.arange(num_samples_pad, dtype="int64") + perm = np.concatenate( + [perm[rank :: self.world_size] for rank in range(self.world_size)], + axis=0, + ) + perm_inv = np.empty_like(perm) + perm_inv[perm] = np.arange(num_samples_pad, dtype="int64") + perm_inv = paddle.to_tensor(perm_inv) + pred_dict = { + key: value[perm_inv][:num_samples] + for key, value in pred_dict.items() + } - if train_state: - self.model.train() return pred_dict + @misc.run_on_eval_mode def export(self): - """Export to inference model""" - pretrained_path = self.cfg["Global"]["pretrained_model"] - if pretrained_path is not None: - save_load.load_pretrain(self.model, pretrained_path, self.equation) + """Export to inference model.""" + raise NotImplementedError("model export is not supported yet.") - self.model.eval() + def autocast_context_manager( + self, enable: bool, level: Literal["O0", "O1", "O2"] = "O1" + ) -> contextlib.AbstractContextManager: + """Smart autocast context manager for Auto Mix Precision. - input_spec = copy.deepcopy(self.cfg["Export"]["input_shape"]) - config.replace_shape_with_inputspec_(input_spec) - static_model = jit.to_static(self.model, input_spec=input_spec) - - export_dir = self.cfg["Global"]["save_inference_dir"] - save_path = os.path.join(export_dir, "inference") - jit.save(static_model, save_path) - logger.info(f"The inference model has been exported to {export_dir}") - - def autocast_context_manager(self) -> contextlib.AbstractContextManager: - """Autocast context manager for Auto Mix Precision. + Args: + enable (bool): Enable autocast. + level (Literal["O0", "O1", "O2"]): Autocast level. Returns: - Union[contextlib.AbstractContextManager]: Context manager. + contextlib.AbstractContextManager: Smart autocast context manager. """ - if self.use_amp: - ctx_manager = amp.auto_cast(level=self.amp_level) + if enable: + ctx_manager = amp.auto_cast(level=level) else: ctx_manager = ( contextlib.nullcontext() if sys.version_info >= (3, 7) else contextlib.suppress() ) - return ctx_manager - def no_grad_context_manager(self) -> contextlib.AbstractContextManager: - """No grad manager. + def no_grad_context_manager( + self, enable: bool + ) -> contextlib.AbstractContextManager: + """Smart no_grad context manager. + + Args: + enable (bool): Enable no_grad. Returns: - Union[contextlib.AbstractContextManager]: Context manager. + contextlib.AbstractContextManager: Smart no_grad context manager. """ - if self.eval_with_no_grad: + if enable: ctx_manager = paddle.no_grad() else: ctx_manager = ( @@ -557,5 +622,33 @@ def no_grad_context_manager(self) -> contextlib.AbstractContextManager: if sys.version_info >= (3, 7) else contextlib.suppress() ) + return ctx_manager + + def no_sync_context_manager( + self, + enable: bool, + ddp_model: paddle.DataParallel, + ) -> contextlib.AbstractContextManager: + """Smart no_sync context manager for given model. + NOTE: Only `paddle.DataParallel` object has `no_sync` interface. + + Args: + enable (bool): Enable no_sync. + Returns: + contextlib.AbstractContextManager: Smart no_sync context manager. + """ + if enable: + if not isinstance(ddp_model, paddle.DataParallel): + raise TypeError( + "no_sync interface is only for model with type paddle.DataParallel, " + f"but got type {misc.typename(ddp_model)}" + ) + ctx_manager = ddp_model.no_sync() + else: + ctx_manager = ( + contextlib.nullcontext() + if sys.version_info >= (3, 7) + else contextlib.suppress() + ) return ctx_manager From 204c5e574c3b8cacc2808a4afaf2142d19a40457 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 10:05:48 +0000 Subject: [PATCH 16/31] reformat pipe flow --- examples/pipe/poiseuille_flow.py | 144 ++++++++-------------------- ppsci/arch/activation.py | 3 +- ppsci/data/__init__.py | 2 + ppsci/equation/pde/navier_stokes.py | 3 + ppsci/optimizer/optimizer.py | 3 - 5 files changed, 49 insertions(+), 106 deletions(-) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index ae3b04b85..7d0ea9dd9 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -121,32 +121,12 @@ def momentum_z_compute_func(out): self.add_equation("momentum_z", momentum_z_compute_func) -def predict( - input_dict, - solver, -): - for key, val in input_dict.items(): - input_dict[key] = paddle.to_tensor(val, dtype="float32") - evaluator = ppsci.utils.expression.ExpressionSolver( - input_dict.keys(), ["u", "v", "p"], solver.model - ) - output_expr_dict = { - "u": lambda d: d["u"], - "v": lambda d: d["v"], - "p": lambda d: d["p"], - } - for output_key, output_expr in output_expr_dict.items(): - evaluator.add_target_expr(output_expr, output_key) - output_dict = evaluator(input_dict) - return output_dict - - if __name__ == "__main__": # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) # set output directory - output_dir = "./output_poiseuille_flow" + output_dir = "./output_0602" core.set_prim_eager_enabled(True) @@ -170,9 +150,6 @@ def predict( EPOCHS = 3000 # 5000 ITERS_PER_EPOCH = int((N_x * N_y * N_p) / BATCH_SIZE) - HIDDEN_SIZE = 50 - LAYER_NUMBER = 4 - 1 # last fc - LOG_FREQ = 1 EVAL_FREQ = 100 # display step VISU_FREQ = 100 # visulize step @@ -184,9 +161,15 @@ def predict( NU_END = NU_MEAN + NU_MEAN * NU_STD # 0.1 ## prepare data with (?, 2) - data_1d_x = np.linspace(X_IN, X_OUT, N_x, endpoint=True) - data_1d_y = np.linspace(Y_START, Y_END, N_y, endpoint=True) - data_1d_nu = np.linspace(NU_START, NU_END, N_p, endpoint=True) + data_1d_x = np.linspace( + X_IN, X_OUT, N_x, endpoint=True, dtype=paddle.get_default_dtype() + ) + data_1d_y = np.linspace( + Y_START, Y_END, N_y, endpoint=True, dtype=paddle.get_default_dtype() + ) + data_1d_nu = np.linspace( + NU_START, NU_END, N_p, endpoint=True, dtype=paddle.get_default_dtype() + ) data_2d_xy = ( np.array(np.meshgrid(data_1d_x, data_1d_y, data_1d_nu)).reshape(3, -1).T @@ -194,72 +177,22 @@ def predict( data_2d_xy_old = copy.deepcopy(data_2d_xy) np.random.shuffle(data_2d_xy) - input_x = data_2d_xy[:, 0].reshape(data_2d_xy.shape[0], 1).astype("float32") - input_y = data_2d_xy[:, 1].reshape(data_2d_xy.shape[0], 1).astype("float32") - input_nu = data_2d_xy[:, 2].reshape(data_2d_xy.shape[0], 1).astype("float32") + input_x = data_2d_xy[:, 0].reshape(data_2d_xy.shape[0], 1) + input_y = data_2d_xy[:, 1].reshape(data_2d_xy.shape[0], 1) + input_nu = data_2d_xy[:, 2].reshape(data_2d_xy.shape[0], 1) interior_data = {"x": input_x, "y": input_y, "nu": input_nu} interior_geom = ppsci.geometry.PointCloud( interior={"x": input_x, "y": input_y, "nu": input_nu}, - coord_keys=["x", "y", "nu"], + coord_keys=("x", "y", "nu"), ) # set model - model_u = ppsci.arch.MLP( - ["sin(x)", "cos(x)", "y", "nu"], - ["u"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - ) - - model_v = ppsci.arch.MLP( - ["sin(x)", "cos(x)", "y", "nu"], - ["v"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - ) - - model_p = ppsci.arch.MLP( - ["sin(x)", "cos(x)", "y", "nu"], - ["p"], - LAYER_NUMBER, - HIDDEN_SIZE, - "swish", - False, - False, - ) + model_u = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("u"), 3, 50, "swish") - def output_transform(out, input): - new_out = {} - x, y = input["x"], input["y"] - - if next(iter(out.keys())) == "u": - u = out["u"] - # The no-slip condition of velocity on the wall - new_out["u"] = u * (R**2 - y**2) - elif next(iter(out.keys())) == "v": - v = out["v"] - # The no-slip condition of velocity on the wall - new_out["v"] = (R**2 - y**2) * v - elif next(iter(out.keys())) == "p": - p = out["p"] - # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] - new_out["p"] = ( - (X_IN - x) * 0 - + (P_IN - P_OUT) * (X_OUT - x) / L - + 0 * y - + (X_IN - x) * (X_OUT - x) * p - ) - else: - raise NotImplementedError(f"{out.keys()} are outputs to be implemented") + model_v = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("v"), 3, 50, "swish") - return new_out + model_p = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("p"), 3, 50, "swish") def input_transform(input): x, y = input["x"], input["y"] @@ -273,16 +206,33 @@ def input_transform(input): model_u.register_input_transform(input_transform) model_v.register_input_transform(input_transform) model_p.register_input_transform(input_transform) - model_u.register_output_transform(output_transform) - model_v.register_output_transform(output_transform) - model_p.register_output_transform(output_transform) - model = ppsci.arch.ModelList([model_u, model_v, model_p]) + model_u.register_output_transform( + lambda out, input: {"u": out["u"] * (R**2 - input["y"] ** 2)} + ) + model_v.register_output_transform( + lambda out, input: {"v": (R**2 - input["y"] ** 2) * out["v"]} + ) + model_p.register_output_transform( + lambda out, input: { + "p": ( + (X_IN - input["x"]) * 0 + + (P_IN - P_OUT) * (X_OUT - input["x"]) / L + + 0 * input["y"] + + (X_IN - input["x"]) * (X_OUT - input["x"]) * out["p"] + ) + } + ) + model = ppsci.arch.ModelList((model_u, model_v, model_p)) # set optimizer optimizer = ppsci.optimizer.Adam(LEARNING_RATE)([model]) # set euqation - equation = {"NavierStokes": NavierStokes(RHO, 2, False)} + equation = { + "NavierStokes": ppsci.equation.NavierStokes( + nu=lambda out: out["nu"], rho=RHO, dim=2, time=False + ) + } pde_constraint = ppsci.constraint.InteriorConstraint( equation["NavierStokes"].equations, @@ -301,19 +251,9 @@ def input_transform(input): }, loss=ppsci.loss.MSELoss("mean"), evenly=True, - weight_dict={"u": 1, "v": 1, "p": 1}, name="EQ", ) - visualizer = { - "visulzie_u": ppsci.visualize.VisualizerVtu( - interior_data, - {"u": lambda d: d["u"], "v": lambda d: d["v"], "p": lambda d: d["p"]}, - VISU_FREQ, - "result_u", - ) - } - # initialize solver solver = ppsci.solver.Solver( model, @@ -324,7 +264,7 @@ def input_transform(input): iters_per_epoch=ITERS_PER_EPOCH, eval_during_train=False, save_freq=10, - log_freq=LOG_FREQ, + log_freq=1, equation=equation, ) @@ -337,7 +277,7 @@ def input_transform(input): "y": data_2d_xy_old[:, 1:2], "nu": data_2d_xy_old[:, 2:3], } - output_dict = predict(input_dict, solver) + output_dict = solver.predict(input_dict) u_pred = output_dict["u"].numpy().reshape(N_y, N_x, N_p) v_pred = output_dict["v"].numpy().reshape(N_y, N_x, N_p) p_pred = output_dict["p"].numpy().reshape(N_y, N_x, N_p) @@ -408,7 +348,7 @@ def input_transform(input): "y": data_2d_xy_test[:, 1:2], "nu": data_2d_xy_test[:, 2:3], } - output_dict_test = predict(input_dict_test, solver) + output_dict_test = solver.predict(input_dict_test) u_max_pred = output_dict_test["u"].numpy() # Analytical result, y = 0 diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 19c62b765..86dcdbb83 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -23,7 +23,8 @@ class Swish(nn.Layer): def __init__(self, beta: float = 1.0): super().__init__() self.beta = paddle.create_parameter( - shape=[0], + shape=[1], + dtype=paddle.get_default_dtype(), default_initializer=paddle.nn.initializer.Constant(beta), ) diff --git a/ppsci/data/__init__.py b/ppsci/data/__init__.py index f39a663ff..fcdc6a838 100644 --- a/ppsci/data/__init__.py +++ b/ppsci/data/__init__.py @@ -78,6 +78,8 @@ def build_dataloader(_dataset, cfg): ) sampler_cfg["batch_size"] = cfg["batch_size"] + if sampler_cfg["shuffle"] is False: + logger.warning(f"Data picked by sampler is not shuffled") sampler = getattr(io, sampler_cls)(_dataset, **sampler_cfg) # build collate_fn if specified diff --git a/ppsci/equation/pde/navier_stokes.py b/ppsci/equation/pde/navier_stokes.py index d2021af2c..70cc3e86b 100644 --- a/ppsci/equation/pde/navier_stokes.py +++ b/ppsci/equation/pde/navier_stokes.py @@ -51,6 +51,7 @@ def continuity_compute_func(out): self.add_equation("continuity", continuity_compute_func) def momentum_x_compute_func(out): + nu = self.nu(out) if callable(self.nu) else self.nu x, y = out["x"], out["y"] u, v, p = out["u"], out["v"], out["p"] momentum_x = ( @@ -73,6 +74,7 @@ def momentum_x_compute_func(out): self.add_equation("momentum_x", momentum_x_compute_func) def momentum_y_compute_func(out): + nu = self.nu(out) if callable(self.nu) else self.nu x, y = out["x"], out["y"] u, v, p = out["u"], out["v"], out["p"] momentum_y = ( @@ -97,6 +99,7 @@ def momentum_y_compute_func(out): if self.dim == 3: def momentum_z_compute_func(out): + nu = self.nu(out) if callable(self.nu) else self.nu x, y, z = out["x"], out["y"], out["z"] u, v, w, p = out["u"], out["v"], out["w"], out["p"] momentum_z = ( diff --git a/ppsci/optimizer/optimizer.py b/ppsci/optimizer/optimizer.py index 2b68fc8fc..54679d265 100644 --- a/ppsci/optimizer/optimizer.py +++ b/ppsci/optimizer/optimizer.py @@ -19,9 +19,6 @@ from typing import Tuple from typing import Union -if TYPE_CHECKING: - import paddle - from paddle import nn from paddle import optimizer as optim from paddle import regularizer From 82e2e5ddb4645b678e2e811c165087509cea8896 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 10:08:09 +0000 Subject: [PATCH 17/31] merge new NS eqn which thinks of nu as variable --- examples/pipe/poiseuille_flow.py | 95 -------------------------------- 1 file changed, 95 deletions(-) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 7d0ea9dd9..926f02742 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -26,101 +26,6 @@ from ppsci.autodiff import jacobian from ppsci.equation.pde import base - -class NavierStokes(base.PDE): - """Class for navier-stokes equation. [nu] as self-variable - - Args: - rho (float): Density. - dim (int): Dimension of equation. - time (bool): Whether the euqation is time-dependent. - """ - - def __init__(self, rho: float, dim: int, time: bool): - super().__init__() - self.rho = rho - self.dim = dim - self.time = time - - def continuity_compute_func(out): - x, y, nu = out["x"], out["y"], out["nu"] - u, v = out["u"], out["v"] - continuity = jacobian(u, x) + jacobian(v, y) - - if self.dim == 3: - z = out["z"] - w = out["w"] - continuity += jacobian(w, z) - return continuity - - self.add_equation("continuity", continuity_compute_func) - - def momentum_x_compute_func(out): - x, y, nu = out["x"], out["y"], out["nu"] - u, v, p = out["u"], out["v"], out["p"] - momentum_x = ( - u * jacobian(u, x) - + v * jacobian(u, y) - - nu / rho * hessian(u, x) - - nu / rho * hessian(u, y) - + 1 / rho * jacobian(p, x) - ) - if self.time: - t = out["t"] - momentum_x += jacobian(u, t) - if self.dim == 3: - z = out["z"] - w = out["w"] - momentum_x += w * jacobian(u, z) - momentum_x -= nu / rho * hessian(u, z) - return momentum_x - - self.add_equation("momentum_x", momentum_x_compute_func) - - def momentum_y_compute_func(out): - x, y, nu = out["x"], out["y"], out["nu"] - u, v, p = out["u"], out["v"], out["p"] - momentum_y = ( - u * jacobian(v, x) - + v * jacobian(v, y) - - nu / rho * hessian(v, x) - - nu / rho * hessian(v, y) - + 1 / rho * jacobian(p, y) - ) - if self.time: - t = out["t"] - momentum_y += jacobian(v, t) - if self.dim == 3: - z = out["z"] - w = out["w"] - momentum_y += w * jacobian(v, z) - momentum_y -= nu / rho * hessian(v, z) - return momentum_y - - self.add_equation("momentum_y", momentum_y_compute_func) - - if self.dim == 3: - - def momentum_z_compute_func(out): - x, y, nu = out["x"], out["y"], out["nu"] - u, v, w, p = out["u"], out["v"], out["w"], out["p"] - momentum_z = ( - u * jacobian(w, x) - + v * jacobian(w, y) - + w * jacobian(w, z) - - nu / rho * hessian(w, x) - - nu / rho * hessian(w, y) - - nu / rho * hessian(w, z) - + 1 / rho * jacobian(p, z) - ) - if self.time: - t = out["t"] - momentum_z += jacobian(w, t) - return momentum_z - - self.add_equation("momentum_z", momentum_z_compute_func) - - if __name__ == "__main__": # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) From 7bef78e65c8d0955f0e520bb2533af4be82bf2f4 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 10:48:32 +0000 Subject: [PATCH 18/31] reformat transform --- examples/aneurysm/aneurysm_flow.py | 87 ++++++++++++++---------------- examples/pipe/poiseuille_flow.py | 67 ++++++++++++----------- ppsci/arch/mlp.py | 2 +- 3 files changed, 77 insertions(+), 79 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 1153a6688..d329074dc 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -15,12 +15,12 @@ ppsci.utils.misc.set_random_seed(42) # set output directory - OUTPUT_DIR = "./output_0601" + OUTPUT_DIR = "./output_aneurysm_flow" PLOT_DIR = osp.join(OUTPUT_DIR, "visu") os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") - core.set_prim_eager_enabled(True) + core.set_prim_eager_enabled(False) # Hyper parameters EPOCHS = 500 @@ -114,59 +114,50 @@ def init_func(m): model_2.apply(init_func) model_3.apply(init_func) - # print(f"layer 1 mean : {np.mean(model_1.linears[0].weight.numpy())}") - # print(f"layer 1 var : {np.var(model_1.linears[0].weight.numpy())}") - - # print(f"layer 2 mean : {np.mean(model_1.linears[1].weight.numpy())}") - # print(f"layer 2 var : {np.var(model_1.linears[1].weight.numpy())}") - - # print(f"layer 3 mean : {np.mean(model_1.linears[2].weight.numpy())}") - # print(f"layer 3 var : {np.var(model_1.linears[2].weight.numpy())}") - - # print(f"layer 4 mean : {np.mean(model_1.last_fc.weight.numpy())}") - # print(f"layer 4 var : {np.var(model_1.last_fc.weight.numpy())}") - - class Output_transform: + class Transform: def __init__(self) -> None: pass - def __call__(self, out, input): - new_out = {} - x, y, scale = input["x"], input["y"], input["scale"] - # axisymetric boundary - if next(iter(out.keys())) == "u": - R = ( - scale - * 1 - / np.sqrt(2 * np.pi * SIGMA**2) - * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) - ) - self.h = R_INLET - R - u = out["u"] - # The no-slip condition of velocity on the wall - new_out["u"] = u * (self.h**2 - y**2) - elif next(iter(out.keys())) == "v": - v = out["v"] - # The no-slip condition of velocity on the wall - new_out["v"] = (self.h**2 - y**2) * v - elif next(iter(out.keys())) == "p": - p = out["p"] - # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] - new_out["p"] = ( + def input_trans(self, input): + self.input = input + + def output_trans_u(self, out): + x, scale = self.input["x"], self.input["scale"] + R = ( + scale + * 1 + / np.sqrt(2 * np.pi * SIGMA**2) + * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) + ) + self.h = R_INLET - R + u = out["u"] + # The no-slip condition of velocity on the wall + return {"u": u * (self.h**2 - y**2)} + + def output_trans_v(self, out): + y = self.input["y"] + v = out["v"] + # The no-slip condition of velocity on the wall + return {"v": (self.h**2 - y**2) * v} + + def output_trans_p(self, out): + x = self.input["x"] + p = out["p"] + # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] + return { + "p": ( (X_IN - x) * 0 + (P_IN - P_OUT) * (X_OUT - x) / L - + 0 * y + (X_IN - x) * (X_OUT - x) * p ) - else: - ValueError(f"{next(iter(out.keys()))} is not a valid key.") - - return new_out - - shared_transform = Output_transform() - model_1.register_output_transform(shared_transform) - model_2.register_output_transform(shared_transform) - model_3.register_output_transform(shared_transform) + } + + model_1.register_input_transform(Transform.input_trans) + model_2.register_input_transform(Transform.input_trans) + model_3.register_input_transform(Transform.input_trans) + model_1.register_output_transform(Transform.output_trans_u) + model_2.register_output_transform(Transform.output_trans_v) + model_3.register_output_transform(Transform.output_trans_p) model = ppsci.arch.ModelList((model_1, model_2, model_3)) optimizer_1 = ppsci.optimizer.Adam( diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 926f02742..eaa617ee2 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -31,9 +31,9 @@ ppsci.utils.misc.set_random_seed(42) # set output directory - output_dir = "./output_0602" + output_dir = "./output_poiseuille_flow" - core.set_prim_eager_enabled(True) + core.set_prim_eager_enabled(False) # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") @@ -99,34 +99,41 @@ model_p = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("p"), 3, 50, "swish") - def input_transform(input): - x, y = input["x"], input["y"] - nu = input["nu"] - b = 2 * np.pi / (X_OUT - X_IN) - c = np.pi * (X_IN + X_OUT) / (X_IN - X_OUT) - sin_x = X_IN * paddle.sin(b * x + c) - cos_x = X_IN * paddle.cos(b * x + c) - return {"sin(x)": sin_x, "cos(x)": cos_x, "y": y, "nu": nu} - - model_u.register_input_transform(input_transform) - model_v.register_input_transform(input_transform) - model_p.register_input_transform(input_transform) - model_u.register_output_transform( - lambda out, input: {"u": out["u"] * (R**2 - input["y"] ** 2)} - ) - model_v.register_output_transform( - lambda out, input: {"v": (R**2 - input["y"] ** 2) * out["v"]} - ) - model_p.register_output_transform( - lambda out, input: { - "p": ( - (X_IN - input["x"]) * 0 - + (P_IN - P_OUT) * (X_OUT - input["x"]) / L - + 0 * input["y"] - + (X_IN - input["x"]) * (X_OUT - input["x"]) * out["p"] - ) - } - ) + class Transform: + def __init__(self) -> None: + pass + + def input_trans(self, input): + self.input = input + x, y = input["x"], input["y"] + nu = input["nu"] + b = 2 * np.pi / (X_OUT - X_IN) + c = np.pi * (X_IN + X_OUT) / (X_IN - X_OUT) + sin_x = X_IN * paddle.sin(b * x + c) + cos_x = X_IN * paddle.cos(b * x + c) + return {"sin(x)": sin_x, "cos(x)": cos_x, "y": y, "nu": nu} + + def output_trans_u(self, out): + return {"u": out["u"] * (R**2 - self.input["y"] ** 2)} + + def output_trans_v(self, out): + return {"v": (R**2 - self.input["y"] ** 2) * out["v"]} + + def output_trans_p(self, out): + return { + "p": ( + (X_IN - self.input["x"]) * 0 + + (P_IN - P_OUT) * (X_OUT - self.input["x"]) / L + + (X_IN - self.input["x"]) * (X_OUT - self.input["x"]) * out["p"] + ) + } + + model_u.register_input_transform(Transform.input_trans) + model_v.register_input_transform(Transform.input_trans) + model_p.register_input_transform(Transform.input_trans) + model_u.register_output_transform(Transform.output_trans_u) + model_v.register_output_transform(Transform.output_trans_v) + model_p.register_output_transform(Transform.output_trans_p) model = ppsci.arch.ModelList((model_u, model_v, model_p)) # set optimizer diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index ef48ebf6f..3613a8aeb 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -151,5 +151,5 @@ def forward(self, x): y = self.split_to_dict(y, self.output_keys, axis=1) if self._output_transform is not None: - y = self._output_transform(y, x) + y = self._output_transform(y) return y From 334c2a9432f6917bc080f3e1f3cfb147399d7097 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 10:59:18 +0000 Subject: [PATCH 19/31] recover mlp code --- examples/pipe/poiseuille_flow.py | 2 +- ppsci/arch/mlp.py | 8 +++----- ppsci/data/__init__.py | 2 -- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index eaa617ee2..1b5598679 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -137,7 +137,7 @@ def output_trans_p(self, out): model = ppsci.arch.ModelList((model_u, model_v, model_p)) # set optimizer - optimizer = ppsci.optimizer.Adam(LEARNING_RATE)([model]) + optimizer = ppsci.optimizer.Adam(LEARNING_RATE)((model,)) # set euqation equation = { diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index 3613a8aeb..890e325fa 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -143,12 +143,10 @@ def forward_tensor(self, x): def forward(self, x): if self._input_transform is not None: - _x = self._input_transform(x) - else: - _x = x - y = self.concat_to_tensor(_x, self.input_keys, axis=1) + x = self._input_transform(x) + y = self.concat_to_tensor(x, self.input_keys, axis=-1) y = self.forward_tensor(y) - y = self.split_to_dict(y, self.output_keys, axis=1) + y = self.split_to_dict(y, self.output_keys, axis=-1) if self._output_transform is not None: y = self._output_transform(y) diff --git a/ppsci/data/__init__.py b/ppsci/data/__init__.py index fcdc6a838..f39a663ff 100644 --- a/ppsci/data/__init__.py +++ b/ppsci/data/__init__.py @@ -78,8 +78,6 @@ def build_dataloader(_dataset, cfg): ) sampler_cfg["batch_size"] = cfg["batch_size"] - if sampler_cfg["shuffle"] is False: - logger.warning(f"Data picked by sampler is not shuffled") sampler = getattr(io, sampler_cls)(_dataset, **sampler_cfg) # build collate_fn if specified From b80657affca9e231a2dd64996796069dde30469d Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Fri, 2 Jun 2023 11:01:10 +0000 Subject: [PATCH 20/31] delete blank line --- ppsci/arch/mlp.py | 1 + 1 file changed, 1 insertion(+) diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index 890e325fa..ebd36bb2b 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -144,6 +144,7 @@ def forward_tensor(self, x): def forward(self, x): if self._input_transform is not None: x = self._input_transform(x) + y = self.concat_to_tensor(x, self.input_keys, axis=-1) y = self.forward_tensor(y) y = self.split_to_dict(y, self.output_keys, axis=-1) From ec6f9fd3ffa58fc2bbc9481ec337cd1eb71e8fed Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 03:17:11 +0000 Subject: [PATCH 21/31] fix by reviews --- examples/aneurysm/aneurysm_flow.py | 110 +++++++++++++++------------- examples/pipe/poiseuille_flow.py | 2 - ppsci/arch/activation.py | 5 +- ppsci/equation/pde/navier_stokes.py | 7 +- 4 files changed, 69 insertions(+), 55 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index d329074dc..ae78d6d04 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -1,3 +1,17 @@ +# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. + +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at + +# http://www.apache.org/licenses/LICENSE-2.0 + +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import math import os import os.path as osp @@ -5,12 +19,13 @@ import matplotlib.pyplot as plt import numpy as np import paddle -from paddle.fluid import core import ppsci +from ppsci.utils import config from ppsci.utils import misc if __name__ == "__main__": + args = config.parse_args() # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) @@ -20,12 +35,8 @@ os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") - core.set_prim_eager_enabled(False) # Hyper parameters - EPOCHS = 500 - BATCH_SIZE = 50 - LEARNING_RATE = 1e-3 # Physic properties P_OUT = 0 # pressure at the outlet of pipe @@ -59,7 +70,6 @@ # Axisymetric boundary R = ( scale - * 1 / math.sqrt(2 * np.pi * SIGMA**2) * np.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) @@ -89,27 +99,18 @@ plt.axis("equal") plt.savefig(osp.join(PLOT_DIR, "one_scale_sample"), bbox_inches="tight") - np.savez("x_0526_pd", x=x) - np.savez("y_0526_pd", y=y) - np.savez("scale_0526_pd", scale=scale) interior_geom = ppsci.geometry.PointCloud( interior={"x": x, "y": y, "scale": scale}, - coord_keys=["x", "y", "scale"], + coord_keys=("x", "y", "scale"), ) def init_func(m): if misc.typename(m) == "Linear": ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) - model_1 = ppsci.arch.MLP( - ("x", "y", "scale"), ("u"), 3, 20, "silu" - ) # swish without learnable variable - model_2 = ppsci.arch.MLP( - ("x", "y", "scale"), ("v"), 3, 20, "silu" - ) # swish without learnable variable - model_3 = ppsci.arch.MLP( - ("x", "y", "scale"), ("p"), 3, 20, "silu" - ) # swish without learnable variable + model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u"), 3, 20, "silu") + model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v"), 3, 20, "silu") + model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p"), 3, 20, "silu") model_1.apply(init_func) model_2.apply(init_func) model_3.apply(init_func) @@ -125,7 +126,6 @@ def output_trans_u(self, out): x, scale = self.input["x"], self.input["scale"] R = ( scale - * 1 / np.sqrt(2 * np.pi * SIGMA**2) * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) @@ -160,6 +160,8 @@ def output_trans_p(self, out): model_3.register_output_transform(Transform.output_trans_p) model = ppsci.arch.ModelList((model_1, model_2, model_3)) + LEARNING_RATE = 1e-3 + optimizer_1 = ppsci.optimizer.Adam( LEARNING_RATE, beta1=0.9, beta2=0.99, epsilon=1e-15 )((model_1,)) @@ -173,6 +175,8 @@ def output_trans_p(self, out): equation = {"NavierStokes": ppsci.equation.NavierStokes(NU, RHO, 2, False)} + BATCH_SIZE = 50 + pde_constraint = ppsci.constraint.InteriorConstraint( equation["NavierStokes"].equations, {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, @@ -184,7 +188,7 @@ def output_trans_p(self, out): "iters_per_epoch": int(x.shape[0] / BATCH_SIZE), "sampler": { "name": "BatchSampler", - "shuffle": False, + "shuffle": True, "drop_last": False, }, }, @@ -192,18 +196,20 @@ def output_trans_p(self, out): evenly=True, name="EQ", ) - constrain_dict = {pde_constraint.name: pde_constraint} + constraint = {pde_constraint.name: pde_constraint} + + EPOCHS = 500 if not args.epochs else args.epochs + # initialize solver solver = ppsci.solver.Solver( model, - constrain_dict, + constraint, OUTPUT_DIR, optimizer, epochs=EPOCHS, iters_per_epoch=int(x.shape[0] / BATCH_SIZE), - eval_during_train=False, save_freq=10, - log_freq=1, + log_freq=10, equation=equation, ) @@ -213,16 +219,14 @@ def single_test(x, y, scale, solver): xt = paddle.to_tensor(x) yt = paddle.to_tensor(y) scalet = scale * paddle.ones_like(xt) - net_in = {"x": xt, "y": yt, "scale": scalet} - output_dict = solver.predict(net_in, 100) + input_dict = {"x": xt, "y": yt, "scale": scalet} + output_dict = solver.predict(input_dict, batch_size=100) return output_dict scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ "scale" ] - CASE_COUNT = [1.0, 151.0, 486.0] - w_ctl = np.zeros([len(scale_test), 1]) - w_ctl_Ml = np.zeros([len(scale_test), 1]) + CASE_SELECTED = [1, 151, 486] PLOT_X = 0.8 PLOT_Y = 0.06 FONTSIZE = 14 @@ -235,10 +239,10 @@ def single_test(x, y, scale, solver): x_centerline = np.linspace( X_IN, X_OUT, N_CL, dtype=paddle.get_default_dtype() ).reshape(N_CL, 1) - y_centerline = np.zeros_like(x_centerline, dtype=paddle.get_default_dtype()) - for caseIdx in CASE_COUNT: - scale = scale_test[int(caseIdx - 1)] - data_CFD = np.load(osp.join(path, str(caseIdx) + "CFD_contour.npz")) + y_centerline = np.zeros_like(x_centerline) + for case_id in CASE_SELECTED: + scale = scale_test[case_id - 1] + data_CFD = np.load(osp.join(path, f"{case_id}CFD_contour.npz")) x = data_CFD["x"].astype(paddle.get_default_dtype()) y = data_CFD["y"].astype(paddle.get_default_dtype()) u_cfd = data_CFD["U"].astype(paddle.get_default_dtype()) @@ -248,10 +252,14 @@ def single_test(x, y, scale, solver): output_dict = single_test( x.reshape(n, 1), y.reshape(n, 1), - np.ones((n, 1), dtype=paddle.get_default_dtype()) * scale, + np.full((n, 1), scale, dtype=paddle.get_default_dtype()), solver, ) - u, v, p = output_dict["u"], output_dict["v"], output_dict["p"] + u, v, p = ( + output_dict["u"].numpy(), + output_dict["v"].numpy(), + output_dict["p"].numpy(), + ) w = np.zeros_like(u) u_vec = np.concatenate([u, v, w], axis=1) error_u.append( @@ -275,7 +283,7 @@ def single_test(x, y, scale, solver): plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.savefig( - osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_uContour_test.png"), + osp.join(PLOT_DIR, f"{case_id}_scale_{scale}_uContour_test.png"), bbox_inches="tight", ) @@ -292,31 +300,31 @@ def single_test(x, y, scale, solver): plt.text(PLOT_X, PLOT_Y, r"CFD", {"color": "b", "fontsize": FONTSIZE}) plt.axis(axis_limit) plt.savefig( - osp.join(PLOT_DIR, f"{int(caseIdx)}_scale_{scale}_vContour_test.png"), + osp.join(PLOT_DIR, f"{case_id}_scale_{scale}_vContour_test.png"), bbox_inches="tight", ) plt.close("all") # Centerline wall shear profile tau_c (downside) - data_CFD_wss = np.load(path + str(caseIdx) + "CFD_wss.npz") + data_CFD_wss = np.load(osp.join(path, f"{case_id}CFD_wss.npz")) x_inital = data_CFD_wss["x"] wall_shear_mag_up = data_CFD_wss["wss"] D_H = 0.001 # The spanwise distance is approximately the height of the wall r_cl = ( scale - * 1 / np.sqrt(2 * np.pi * SIGMA**2) * np.exp(-((x_centerline - mu) ** 2) / (2 * SIGMA**2)) ) - y_wall = (-R_INLET + D_H) * np.ones_like( - x_centerline, dtype=paddle.get_default_dtype() - ) + r_cl + y_wall = (-R_INLET + D_H) * np.ones_like(x_centerline) + r_cl output_dict_wss = single_test( - x_centerline, y_wall, np.ones((N_CL, 1)) * scale, solver + x_centerline, + y_wall, + np.full((N_CL, 1), scale, dtype=paddle.get_default_dtype()), + solver, ) v_cl_total = np.zeros_like( - x_centerline, dtype=paddle.get_default_dtype() + x_centerline ) # assuming normal velocity along the wall is zero u_cl = output_dict_wss["u"].numpy() v_cl = output_dict_wss["v"].numpy() @@ -348,10 +356,14 @@ def single_test(x, y, scale, solver): plt.ylabel(r"$\tau_{c}$", fontsize=16) plt.legend(prop={"size": 16}) plt.savefig( - osp.join(PLOT_DIR, f"{int(caseIdx)}_nu__{scale}_wallshear_test.png"), + osp.join(PLOT_DIR, f"{case_id}_nu__{scale}_wallshear_test.png"), bbox_inches="tight", ) plt.close("all") - print(f"epochs = {EPOCHS}") - print(f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}") - print(f"Table 1 : Aneurysm - Geometry error v : {sum(error_v) / len(error_v): .3e}") + ppsci.utils.logger(f"epochs = {EPOCHS}") + ppsci.utils.logger( + f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}" + ) + ppsci.utils.logger( + f"Table 1 : Aneurysm - Geometry error v : {sum(error_v) / len(error_v): .3e}" + ) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 1b5598679..b5b1144bb 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -94,9 +94,7 @@ # set model model_u = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("u"), 3, 50, "swish") - model_v = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("v"), 3, 50, "swish") - model_p = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("p"), 3, 50, "swish") class Transform: diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 86dcdbb83..73dc74f9f 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -22,11 +22,12 @@ class Swish(nn.Layer): def __init__(self, beta: float = 1.0): super().__init__() - self.beta = paddle.create_parameter( + self.beta = self.create_parameter( shape=[1], dtype=paddle.get_default_dtype(), default_initializer=paddle.nn.initializer.Constant(beta), ) + self.add_parameter("beta", beta) def forward(self, x): return x * F.sigmoid(self.beta * x) @@ -43,10 +44,10 @@ def silu(x): "selu": F.selu, "gelu": F.gelu, "sigmoid": F.sigmoid, + "silu": silu, "sin": paddle.sin, "cos": paddle.cos, "swish": Swish(), - "silu": silu, "tanh": F.tanh, "identity": nn.Identity(), } diff --git a/ppsci/equation/pde/navier_stokes.py b/ppsci/equation/pde/navier_stokes.py index 70cc3e86b..b2553bb40 100644 --- a/ppsci/equation/pde/navier_stokes.py +++ b/ppsci/equation/pde/navier_stokes.py @@ -12,6 +12,9 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Callable +from typing import Union + from ppsci.autodiff import hessian from ppsci.autodiff import jacobian from ppsci.equation.pde import base @@ -21,7 +24,7 @@ class NavierStokes(base.PDE): """Class for navier-stokes equation. Args: - nu (float): Dynamic viscosity. + nu (Union[float, Callable]): Dynamic viscosity. rho (float): Density. dim (int): Dimension of equation. time (bool): Whether the euqation is time-dependent. @@ -31,7 +34,7 @@ class NavierStokes(base.PDE): >>> pde = ppsci.equation.NavierStokes(0.1, 1.0, 3, False) """ - def __init__(self, nu: float, rho: float, dim: int, time: bool): + def __init__(self, nu: Union[float, Callable], rho: float, dim: int, time: bool): super().__init__() self.nu = nu self.rho = rho From 31aeea8e24de8eeecef71baf887334c6820a7379 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 03:27:26 +0000 Subject: [PATCH 22/31] fix epochs --- examples/pipe/poiseuille_flow.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index b5b1144bb..def1ec70e 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -19,22 +19,18 @@ import numpy as np import paddle import seaborn as sns -from paddle.fluid import core import ppsci -from ppsci.autodiff import hessian -from ppsci.autodiff import jacobian -from ppsci.equation.pde import base +from ppsci.utils import config if __name__ == "__main__": + args = config.parse_args() # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) # set output directory output_dir = "./output_poiseuille_flow" - core.set_prim_eager_enabled(False) - # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") @@ -52,7 +48,7 @@ LEARNING_RATE = 5e-3 BATCH_SIZE = 128 - EPOCHS = 3000 # 5000 + EPOCHS = 3000 if not args.epochs else args.epochs # 5000 ITERS_PER_EPOCH = int((N_x * N_y * N_p) / BATCH_SIZE) EVAL_FREQ = 100 # display step From 669a0c5ec8ff420294814880840d3ce5a4f7e834 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 03:33:07 +0000 Subject: [PATCH 23/31] delete useless lines --- examples/aneurysm/aneurysm_flow.py | 2 -- examples/pipe/poiseuille_flow.py | 15 +++++++-------- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index ae78d6d04..f761ce4b5 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -36,8 +36,6 @@ # initialize logger ppsci.utils.logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") - # Hyper parameters - # Physic properties P_OUT = 0 # pressure at the outlet of pipe P_IN = 0.1 # pressure at the inlet of pipe diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index def1ec70e..5eb520454 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -46,14 +46,6 @@ N_y = 50 N_p = 50 - LEARNING_RATE = 5e-3 - BATCH_SIZE = 128 - EPOCHS = 3000 if not args.epochs else args.epochs # 5000 - ITERS_PER_EPOCH = int((N_x * N_y * N_p) / BATCH_SIZE) - - EVAL_FREQ = 100 # display step - VISU_FREQ = 100 # visulize step - X_IN = 0 X_OUT = X_IN + L Y_START = -R @@ -130,6 +122,8 @@ def output_trans_p(self, out): model_p.register_output_transform(Transform.output_trans_p) model = ppsci.arch.ModelList((model_u, model_v, model_p)) + LEARNING_RATE = 5e-3 + # set optimizer optimizer = ppsci.optimizer.Adam(LEARNING_RATE)((model,)) @@ -140,6 +134,9 @@ def output_trans_p(self, out): ) } + BATCH_SIZE = 128 + ITERS_PER_EPOCH = int((N_x * N_y * N_p) / BATCH_SIZE) + pde_constraint = ppsci.constraint.InteriorConstraint( equation["NavierStokes"].equations, {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, @@ -160,6 +157,8 @@ def output_trans_p(self, out): name="EQ", ) + EPOCHS = 3000 if not args.epochs else args.epochs # 5000 + # initialize solver solver = ppsci.solver.Solver( model, From 2af8a3a88d2526c7a77984ac9db9ce6d7e5d19eb Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 06:34:12 +0000 Subject: [PATCH 24/31] bug fix --- examples/aneurysm/aneurysm_flow.py | 27 +++++++++++++++------------ examples/pipe/poiseuille_flow.py | 18 ++++++++++-------- ppsci/arch/activation.py | 2 +- 3 files changed, 26 insertions(+), 21 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index f761ce4b5..932610320 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -22,6 +22,7 @@ import ppsci from ppsci.utils import config +from ppsci.utils import logger from ppsci.utils import misc if __name__ == "__main__": @@ -34,7 +35,7 @@ PLOT_DIR = osp.join(OUTPUT_DIR, "visu") os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger - ppsci.utils.logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") + logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") # Physic properties P_OUT = 0 # pressure at the outlet of pipe @@ -119,9 +120,10 @@ def __init__(self) -> None: def input_trans(self, input): self.input = input + return input def output_trans_u(self, out): - x, scale = self.input["x"], self.input["scale"] + x, y, scale = self.input["x"], self.input["y"], self.input["scale"] R = ( scale / np.sqrt(2 * np.pi * SIGMA**2) @@ -150,12 +152,13 @@ def output_trans_p(self, out): ) } - model_1.register_input_transform(Transform.input_trans) - model_2.register_input_transform(Transform.input_trans) - model_3.register_input_transform(Transform.input_trans) - model_1.register_output_transform(Transform.output_trans_u) - model_2.register_output_transform(Transform.output_trans_v) - model_3.register_output_transform(Transform.output_trans_p) + trm = Transform() + model_1.register_input_transform(trm.input_trans) + model_2.register_input_transform(trm.input_trans) + model_3.register_input_transform(trm.input_trans) + model_1.register_output_transform(trm.output_trans_u) + model_2.register_output_transform(trm.output_trans_v) + model_3.register_output_transform(trm.output_trans_p) model = ppsci.arch.ModelList((model_1, model_2, model_3)) LEARNING_RATE = 1e-3 @@ -211,7 +214,7 @@ def output_trans_p(self, out): equation=equation, ) - solver.train() + # solver.train() def single_test(x, y, scale, solver): xt = paddle.to_tensor(x) @@ -358,10 +361,10 @@ def single_test(x, y, scale, solver): bbox_inches="tight", ) plt.close("all") - ppsci.utils.logger(f"epochs = {EPOCHS}") - ppsci.utils.logger( + logger.info(f"epochs = {EPOCHS}") + logger.info( f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}" ) - ppsci.utils.logger( + logger.info( f"Table 1 : Aneurysm - Geometry error v : {sum(error_v) / len(error_v): .3e}" ) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 5eb520454..5a6ea1411 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -22,6 +22,7 @@ import ppsci from ppsci.utils import config +from ppsci.utils import logger if __name__ == "__main__": args = config.parse_args() @@ -32,7 +33,7 @@ output_dir = "./output_poiseuille_flow" # initialize logger - ppsci.utils.logger.init_logger("ppsci", f"{output_dir}/train.log", "info") + logger.init_logger("ppsci", f"{output_dir}/train.log", "info") NU_MEAN = 0.001 NU_STD = 0.9 @@ -114,12 +115,13 @@ def output_trans_p(self, out): ) } - model_u.register_input_transform(Transform.input_trans) - model_v.register_input_transform(Transform.input_trans) - model_p.register_input_transform(Transform.input_trans) - model_u.register_output_transform(Transform.output_trans_u) - model_v.register_output_transform(Transform.output_trans_v) - model_p.register_output_transform(Transform.output_trans_p) + trm = Transform() + model_u.register_input_transform(trm.input_trans) + model_v.register_input_transform(trm.input_trans) + model_p.register_input_transform(trm.input_trans) + model_u.register_output_transform(trm.output_trans_u) + model_v.register_output_transform(trm.output_trans_v) + model_p.register_output_transform(trm.output_trans_p) model = ppsci.arch.ModelList((model_u, model_v, model_p)) LEARNING_RATE = 5e-3 @@ -173,7 +175,7 @@ def output_trans_p(self, out): equation=equation, ) - solver.train() + # solver.train() # Cross-section velocity profiles of 4 different viscosity sample # Predicted result diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 73dc74f9f..b010a49d1 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -27,7 +27,7 @@ def __init__(self, beta: float = 1.0): dtype=paddle.get_default_dtype(), default_initializer=paddle.nn.initializer.Constant(beta), ) - self.add_parameter("beta", beta) + self.add_parameter("beta", self.beta) def forward(self, x): return x * F.sigmoid(self.beta * x) From f821d82927287baaae48b32d7049a02da28c51ac Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 06:41:21 +0000 Subject: [PATCH 25/31] forget to uncomment train --- examples/aneurysm/aneurysm_flow.py | 2 +- examples/pipe/poiseuille_flow.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 932610320..0fdfcb862 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -214,7 +214,7 @@ def output_trans_p(self, out): equation=equation, ) - # solver.train() + solver.train() def single_test(x, y, scale, solver): xt = paddle.to_tensor(x) diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 5a6ea1411..81515c27c 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -175,7 +175,7 @@ def output_trans_p(self, out): equation=equation, ) - # solver.train() + solver.train() # Cross-section velocity profiles of 4 different viscosity sample # Predicted result From 1f30ad4325bf1e69ac8321cc9071ae46cca19953 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 12:30:09 +0000 Subject: [PATCH 26/31] fix multi-learnable bug --- examples/aneurysm/aneurysm_flow.py | 3 ++ ppsci/arch/activation.py | 78 ++++++++++++++++++++++++++---- ppsci/arch/mlp.py | 18 +++---- 3 files changed, 79 insertions(+), 20 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 0fdfcb862..0ae949ab5 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -27,6 +27,8 @@ if __name__ == "__main__": args = config.parse_args() + paddle.fluid.core.set_prim_eager_enabled(True) + # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) @@ -34,6 +36,7 @@ OUTPUT_DIR = "./output_aneurysm_flow" PLOT_DIR = osp.join(OUTPUT_DIR, "visu") os.makedirs(PLOT_DIR, exist_ok=True) + # initialize logger logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index b010a49d1..4fcd63962 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -20,6 +20,15 @@ class Swish(nn.Layer): + r""" + Swish Activation. + + .. math:: + + Swish(x)= \aplpha x \cdot \frac{1}{1 + e^{-x}} + + """ + def __init__(self, beta: float = 1.0): super().__init__() self.beta = self.create_parameter( @@ -38,17 +47,68 @@ def silu(x): return x * F.sigmoid(x) +class SILU(nn.Layer): + r""" + SILU Activation. + + .. math:: + + SILU(x)= x \cdot \frac{1}{1 + e^{-x}} + + """ + + def __init__(self): + super().__init__() + + def forward(self, x): + return silu(x) + + +class Cos(nn.Layer): + r""" + Cos Activation. + + .. math:: + + Cos(x)= cos(x) + + """ + + def __init__(self): + super().__init__() + + def forward(self, x): + return paddle.cos(x) + + +class Sin(nn.Layer): + r""" + Sin Activation. + + .. math:: + + Sin(x)= sin(x) + + """ + + def __init__(self): + super().__init__() + + def forward(self, x): + return paddle.sin(x) + + act_func_dict = { - "elu": F.elu, - "relu": F.relu, - "selu": F.selu, - "gelu": F.gelu, - "sigmoid": F.sigmoid, - "silu": silu, - "sin": paddle.sin, - "cos": paddle.cos, + "elu": nn.ELU(), + "relu": nn.ReLU(), + "selu": nn.SELU(), + "gelu": nn.GELU(), + "sigmoid": nn.Sigmoid(), + "silu": SILU(), + "sin": Sin(), + "cos": Cos(), "swish": Swish(), - "tanh": F.tanh, + "tanh": nn.Tanh(), "identity": nn.Identity(), } diff --git a/ppsci/arch/mlp.py b/ppsci/arch/mlp.py index ebd36bb2b..b9daa67df 100644 --- a/ppsci/arch/mlp.py +++ b/ppsci/arch/mlp.py @@ -29,16 +29,10 @@ def __init__(self, in_features: int, out_features: int, bias: bool = True) -> No super().__init__() self.in_features = in_features self.out_features = out_features - self.weight_v = self.create_parameter( - (in_features, out_features), dtype=paddle.get_default_dtype() - ) - self.weight_g = self.create_parameter( - (out_features,), dtype=paddle.get_default_dtype() - ) + self.weight_v = self.create_parameter((in_features, out_features)) + self.weight_g = self.create_parameter((out_features,)) if bias: - self.bias = self.create_parameter( - (out_features,), dtype=paddle.get_default_dtype() - ) + self.bias = self.create_parameter((out_features,)) else: self.bias = None self._init_weights() @@ -120,7 +114,9 @@ def __init__( self.last_fc = nn.Linear(cur_size, len(self.output_keys)) # initialize activation function - self.act = act_mod.get_activation(activation) + self.act = nn.LayerList( + [act_mod.get_activation(activation) for _ in range(len(hidden_size))] + ) self.skip_connection = skip_connection @@ -135,7 +131,7 @@ def forward_tensor(self, x): y = y + skip else: skip = y - y = self.act(y) + y = self.act[i](y) y = self.last_fc(y) From 169759d5ad3ad5c532c3f28e3418147d9ab946dd Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Mon, 5 Jun 2023 13:19:52 +0000 Subject: [PATCH 27/31] fix by reviews --- examples/aneurysm/aneurysm_flow.py | 62 +++++++++++++------------- examples/pipe/poiseuille_flow.py | 70 +++++++++++++++--------------- ppsci/arch/activation.py | 54 +---------------------- 3 files changed, 68 insertions(+), 118 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 0ae949ab5..db5ed9a2e 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -34,8 +34,6 @@ # set output directory OUTPUT_DIR = "./output_aneurysm_flow" - PLOT_DIR = osp.join(OUTPUT_DIR, "visu") - os.makedirs(PLOT_DIR, exist_ok=True) # initialize logger logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") @@ -51,16 +49,18 @@ X_IN = 0 X_OUT = X_IN + L R_INLET = 0.05 - unique_x = np.linspace(X_IN, X_OUT, 100) mu = 0.5 * (X_OUT - X_IN) N_Y = 20 + x_inital = np.linspace(X_IN, X_OUT, 100, dtype=paddle.get_default_dtype()).reshape( 100, 1 ) x_20_copy = np.tile(x_inital, (20, 1)) # duplicate 20 times of x for dataloader + SIGMA = 0.1 SCALE_START = -0.02 SCALE_END = 0 + scale_initial = np.linspace( SCALE_START, SCALE_END, 50, endpoint=True, dtype=paddle.get_default_dtype() ).reshape(50, 1) @@ -70,15 +70,17 @@ ) # Axisymetric boundary - R = ( + r_func = ( scale / math.sqrt(2 * np.pi * SIGMA**2) * np.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) # Visualize stenosis(scale == 0.2) - y_up = (R_INLET - R) * np.ones_like(x) - y_down = (-R_INLET + R) * np.ones_like(x) + PLOT_DIR = osp.join(OUTPUT_DIR, "visu") + os.makedirs(PLOT_DIR, exist_ok=True) + y_up = (R_INLET - r_func) * np.ones_like(x) + y_down = (-R_INLET + r_func) * np.ones_like(x) idx = np.where(scale == 0) # plot vessel which scale is 0.2 by finding its indexs plt.figure() plt.scatter(x[idx], y_up[idx]) @@ -91,9 +93,12 @@ for x0 in x_inital: index = np.where(x[:, 0] == x0)[0] # y is linear to scale, so we place linespace to get 1000 x, it coressponds to vessels - y[index] = np.linspace(-max(y_up[index]), max(y_up[index]), len(index)).reshape( - len(index), -1 - ) + y[index] = np.linspace( + -max(y_up[index]), + max(y_up[index]), + len(index), + dtype=paddle.get_default_dtype(), + ).reshape(len(index), -1) idx = np.where(scale == 0) # plot vessel which scale is 0.2 by finding its indexs plt.figure() @@ -105,14 +110,15 @@ interior={"x": x, "y": y, "scale": scale}, coord_keys=("x", "y", "scale"), ) + geom = {"interior": interior_geom} def init_func(m): if misc.typename(m) == "Linear": ppsci.utils.initializer.kaiming_normal_(m.weight, reverse=True) - model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u"), 3, 20, "silu") - model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v"), 3, 20, "silu") - model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p"), 3, 20, "silu") + model_1 = ppsci.arch.MLP(("x", "y", "scale"), ("u",), 3, 20, "silu") + model_2 = ppsci.arch.MLP(("x", "y", "scale"), ("v",), 3, 20, "silu") + model_3 = ppsci.arch.MLP(("x", "y", "scale"), ("p",), 3, 20, "silu") model_1.apply(init_func) model_2.apply(init_func) model_3.apply(init_func) @@ -127,12 +133,12 @@ def input_trans(self, input): def output_trans_u(self, out): x, y, scale = self.input["x"], self.input["y"], self.input["scale"] - R = ( + r_func = ( scale / np.sqrt(2 * np.pi * SIGMA**2) * paddle.exp(-((x - mu) ** 2) / (2 * SIGMA**2)) ) - self.h = R_INLET - R + self.h = R_INLET - r_func u = out["u"] # The no-slip condition of velocity on the wall return {"u": u * (self.h**2 - y**2)} @@ -148,20 +154,16 @@ def output_trans_p(self, out): p = out["p"] # The pressure inlet [p_in = 0.1] and outlet [p_out = 0] return { - "p": ( - (X_IN - x) * 0 - + (P_IN - P_OUT) * (X_OUT - x) / L - + (X_IN - x) * (X_OUT - x) * p - ) + "p": ((P_IN - P_OUT) * (X_OUT - x) / L + (X_IN - x) * (X_OUT - x) * p) } - trm = Transform() - model_1.register_input_transform(trm.input_trans) - model_2.register_input_transform(trm.input_trans) - model_3.register_input_transform(trm.input_trans) - model_1.register_output_transform(trm.output_trans_u) - model_2.register_output_transform(trm.output_trans_v) - model_3.register_output_transform(trm.output_trans_p) + transform = Transform() + model_1.register_input_transform(transform.input_trans) + model_2.register_input_transform(transform.input_trans) + model_3.register_input_transform(transform.input_trans) + model_1.register_output_transform(transform.output_trans_u) + model_2.register_output_transform(transform.output_trans_v) + model_3.register_output_transform(transform.output_trans_p) model = ppsci.arch.ModelList((model_1, model_2, model_3)) LEARNING_RATE = 1e-3 @@ -184,7 +186,7 @@ def output_trans_p(self, out): pde_constraint = ppsci.constraint.InteriorConstraint( equation["NavierStokes"].equations, {"continuity": 0, "momentum_x": 0, "momentum_y": 0}, - geom=interior_geom, + geom=geom["interior"], dataloader_cfg={ "dataset": "NamedArrayDataset", "num_workers": 1, @@ -213,7 +215,6 @@ def output_trans_p(self, out): epochs=EPOCHS, iters_per_epoch=int(x.shape[0] / BATCH_SIZE), save_freq=10, - log_freq=10, equation=equation, ) @@ -222,7 +223,7 @@ def output_trans_p(self, out): def single_test(x, y, scale, solver): xt = paddle.to_tensor(x) yt = paddle.to_tensor(y) - scalet = scale * paddle.ones_like(xt) + scalet = paddle.full_like(xt, scale) input_dict = {"x": xt, "y": yt, "scale": scalet} output_dict = solver.predict(input_dict, batch_size=100) return output_dict @@ -332,8 +333,7 @@ def single_test(x, y, scale, solver): ) # assuming normal velocity along the wall is zero u_cl = output_dict_wss["u"].numpy() v_cl = output_dict_wss["v"].numpy() - for i in range(N_CL): - v_cl_total[i] = np.sqrt(u_cl[i] ** 2 + v_cl[i] ** 2) + v_cl_total = np.sqrt(u_cl**2 + v_cl**2) tau_c = NU * v_cl_total / D_H plt.figure() diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 81515c27c..d574995bd 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -14,6 +14,7 @@ import copy import os +import os.path as osp import matplotlib.pyplot as plt import numpy as np @@ -21,19 +22,24 @@ import seaborn as sns import ppsci +from ppsci.utils import checker from ppsci.utils import config from ppsci.utils import logger +if not checker.dynamic_import_to_globals("seaborn"): + raise ModuleNotFoundError(f"Please install seaborn through pip first.") + + if __name__ == "__main__": args = config.parse_args() # set random seed for reproducibility ppsci.utils.misc.set_random_seed(42) # set output directory - output_dir = "./output_poiseuille_flow" + OUTPUT_DIR = "./output_poiseuille_flow" # initialize logger - logger.init_logger("ppsci", f"{output_dir}/train.log", "info") + logger.init_logger("ppsci", f"{OUTPUT_DIR}/train.log", "info") NU_MEAN = 0.001 NU_STD = 0.9 @@ -68,12 +74,12 @@ data_2d_xy = ( np.array(np.meshgrid(data_1d_x, data_1d_y, data_1d_nu)).reshape(3, -1).T ) - data_2d_xy_old = copy.deepcopy(data_2d_xy) - np.random.shuffle(data_2d_xy) + data_2d_xy_shuffle = copy.deepcopy(data_2d_xy) + np.random.shuffle(data_2d_xy_shuffle) - input_x = data_2d_xy[:, 0].reshape(data_2d_xy.shape[0], 1) - input_y = data_2d_xy[:, 1].reshape(data_2d_xy.shape[0], 1) - input_nu = data_2d_xy[:, 2].reshape(data_2d_xy.shape[0], 1) + input_x = data_2d_xy_shuffle[:, 0].reshape(data_2d_xy_shuffle.shape[0], 1) + input_y = data_2d_xy_shuffle[:, 1].reshape(data_2d_xy_shuffle.shape[0], 1) + input_nu = data_2d_xy_shuffle[:, 2].reshape(data_2d_xy_shuffle.shape[0], 1) interior_data = {"x": input_x, "y": input_y, "nu": input_nu} interior_geom = ppsci.geometry.PointCloud( @@ -82,14 +88,11 @@ ) # set model - model_u = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("u"), 3, 50, "swish") - model_v = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("v"), 3, 50, "swish") - model_p = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("p"), 3, 50, "swish") + model_u = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("u",), 3, 50, "swish") + model_v = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("v",), 3, 50, "swish") + model_p = ppsci.arch.MLP(("sin(x)", "cos(x)", "y", "nu"), ("p",), 3, 50, "swish") class Transform: - def __init__(self) -> None: - pass - def input_trans(self, input): self.input = input x, y = input["x"], input["y"] @@ -109,25 +112,22 @@ def output_trans_v(self, out): def output_trans_p(self, out): return { "p": ( - (X_IN - self.input["x"]) * 0 - + (P_IN - P_OUT) * (X_OUT - self.input["x"]) / L + (P_IN - P_OUT) * (X_OUT - self.input["x"]) / L + (X_IN - self.input["x"]) * (X_OUT - self.input["x"]) * out["p"] ) } - trm = Transform() - model_u.register_input_transform(trm.input_trans) - model_v.register_input_transform(trm.input_trans) - model_p.register_input_transform(trm.input_trans) - model_u.register_output_transform(trm.output_trans_u) - model_v.register_output_transform(trm.output_trans_v) - model_p.register_output_transform(trm.output_trans_p) + transform = Transform() + model_u.register_input_transform(transform.input_trans) + model_v.register_input_transform(transform.input_trans) + model_p.register_input_transform(transform.input_trans) + model_u.register_output_transform(transform.output_trans_u) + model_v.register_output_transform(transform.output_trans_v) + model_p.register_output_transform(transform.output_trans_p) model = ppsci.arch.ModelList((model_u, model_v, model_p)) - LEARNING_RATE = 5e-3 - # set optimizer - optimizer = ppsci.optimizer.Adam(LEARNING_RATE)((model,)) + optimizer = ppsci.optimizer.Adam(5e-3)((model,)) # set euqation equation = { @@ -159,19 +159,18 @@ def output_trans_p(self, out): name="EQ", ) - EPOCHS = 3000 if not args.epochs else args.epochs # 5000 + EPOCHS = 3000 if not args.epochs else args.epochs # initialize solver solver = ppsci.solver.Solver( model, {pde_constraint.name: pde_constraint}, - output_dir, + OUTPUT_DIR, optimizer, epochs=EPOCHS, iters_per_epoch=ITERS_PER_EPOCH, eval_during_train=False, save_freq=10, - log_freq=1, equation=equation, ) @@ -180,9 +179,9 @@ def output_trans_p(self, out): # Cross-section velocity profiles of 4 different viscosity sample # Predicted result input_dict = { - "x": data_2d_xy_old[:, 0:1], - "y": data_2d_xy_old[:, 1:2], - "nu": data_2d_xy_old[:, 2:3], + "x": data_2d_xy[:, 0:1], + "y": data_2d_xy[:, 1:2], + "nu": data_2d_xy[:, 2:3], } output_dict = solver.predict(input_dict) u_pred = output_dict["u"].numpy().reshape(N_y, N_x, N_p) @@ -203,6 +202,8 @@ def output_trans_p(self, out): ytext = [0.45, 0.28, 0.1, 0.01] # Plot + PLOT_DIR = osp.join(OUTPUT_DIR, "visu") + os.makedirs(PLOT_DIR, exist_ok=True) plt.figure(1) plt.clf() for idxP in range(len(nu_index)): @@ -224,11 +225,10 @@ def output_trans_p(self, out): lw=2.0, alpha=1.0, ) - nu_current = float("{0:.5f}".format(data_1d_nu[nu_index[idxP]])) plt.text( -0.012, ytext[idxP], - r"$\nu = $" + str(nu_current), + rf"$\nu = $ {data_1d_nu[nu_index[idxP]]}", {"color": "k", "fontsize": fontsize}, ) @@ -238,7 +238,7 @@ def output_trans_p(self, out): ax1.tick_params(axis="y", labelsize=fontsize) ax1.set_xlim([-0.05, 0.05]) ax1.set_ylim([0.0, 0.62]) - plt.savefig("pipe_uProfiles.png", bbox_inches="tight") + plt.savefig(osp.join(PLOT_DIR, "pipe_uProfiles.png"), bbox_inches="tight") # Distribution of center velocity # Predicted result @@ -288,4 +288,4 @@ def output_trans_p(self, out): plt.ylabel(r"PDF", fontsize=fontsize) ax1.tick_params(axis="x", labelsize=fontsize) ax1.tick_params(axis="y", labelsize=fontsize) - plt.savefig("pipe_unformUQ.png", bbox_inches="tight") + plt.savefig(osp.join(PLOT_DIR, "pipe_unformUQ.png"), bbox_inches="tight") diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 4fcd63962..71080923e 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -20,20 +20,10 @@ class Swish(nn.Layer): - r""" - Swish Activation. - - .. math:: - - Swish(x)= \aplpha x \cdot \frac{1}{1 + e^{-x}} - - """ - def __init__(self, beta: float = 1.0): super().__init__() self.beta = self.create_parameter( - shape=[1], - dtype=paddle.get_default_dtype(), + shape=[], default_initializer=paddle.nn.initializer.Constant(beta), ) self.add_parameter("beta", self.beta) @@ -42,38 +32,7 @@ def forward(self, x): return x * F.sigmoid(self.beta * x) -def silu(x): - """numeric stable silu""" - return x * F.sigmoid(x) - - -class SILU(nn.Layer): - r""" - SILU Activation. - - .. math:: - - SILU(x)= x \cdot \frac{1}{1 + e^{-x}} - - """ - - def __init__(self): - super().__init__() - - def forward(self, x): - return silu(x) - - class Cos(nn.Layer): - r""" - Cos Activation. - - .. math:: - - Cos(x)= cos(x) - - """ - def __init__(self): super().__init__() @@ -82,15 +41,6 @@ def forward(self, x): class Sin(nn.Layer): - r""" - Sin Activation. - - .. math:: - - Sin(x)= sin(x) - - """ - def __init__(self): super().__init__() @@ -104,7 +54,7 @@ def forward(self, x): "selu": nn.SELU(), "gelu": nn.GELU(), "sigmoid": nn.Sigmoid(), - "silu": SILU(), + "silu": nn.Silu(), "sin": Sin(), "cos": Cos(), "swish": Swish(), From 620e38ced66f57373e0d04583e719d5888f9b28e Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Tue, 6 Jun 2023 04:03:06 +0000 Subject: [PATCH 28/31] fix by reviews --- examples/aneurysm/aneurysm_flow.py | 21 +++++++++++---------- ppsci/arch/activation.py | 17 +++++++++++++++-- 2 files changed, 26 insertions(+), 12 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index db5ed9a2e..367467a61 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -220,13 +220,15 @@ def output_trans_p(self, out): solver.train() - def single_test(x, y, scale, solver): + def model_predict( + x: np.ndarray, y: np.ndarray, scale: np.ndarray, solver: ppsci.solver.Solver + ): xt = paddle.to_tensor(x) yt = paddle.to_tensor(y) scalet = paddle.full_like(xt, scale) input_dict = {"x": xt, "y": yt, "scale": scalet} output_dict = solver.predict(input_dict, batch_size=100) - return output_dict + return {k: v.numpy() for k, v in output_dict.items()} scale_test = np.load("./data/aneurysm_scale0005to002_eval0to002mean001_3sigma.npz")[ "scale" @@ -254,16 +256,16 @@ def single_test(x, y, scale, solver): # p_cfd = data_CFD["P"].astype(paddle.get_default_dtype()) # missing data n = len(x) - output_dict = single_test( + output_dict = model_predict( x.reshape(n, 1), y.reshape(n, 1), np.full((n, 1), scale, dtype=paddle.get_default_dtype()), solver, ) u, v, p = ( - output_dict["u"].numpy(), - output_dict["v"].numpy(), - output_dict["p"].numpy(), + output_dict["u"], + output_dict["v"], + output_dict["p"], ) w = np.zeros_like(u) u_vec = np.concatenate([u, v, w], axis=1) @@ -322,7 +324,7 @@ def single_test(x, y, scale, solver): * np.exp(-((x_centerline - mu) ** 2) / (2 * SIGMA**2)) ) y_wall = (-R_INLET + D_H) * np.ones_like(x_centerline) + r_cl - output_dict_wss = single_test( + output_dict_wss = model_predict( x_centerline, y_wall, np.full((N_CL, 1), scale, dtype=paddle.get_default_dtype()), @@ -331,8 +333,8 @@ def single_test(x, y, scale, solver): v_cl_total = np.zeros_like( x_centerline ) # assuming normal velocity along the wall is zero - u_cl = output_dict_wss["u"].numpy() - v_cl = output_dict_wss["v"].numpy() + u_cl = output_dict_wss["u"] + v_cl = output_dict_wss["v"] v_cl_total = np.sqrt(u_cl**2 + v_cl**2) tau_c = NU * v_cl_total / D_H @@ -364,7 +366,6 @@ def single_test(x, y, scale, solver): bbox_inches="tight", ) plt.close("all") - logger.info(f"epochs = {EPOCHS}") logger.info( f"Table 1 : Aneurysm - Geometry error u : {sum(error_u) / len(error_u): .3e}" ) diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 71080923e..37b669c93 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -26,7 +26,6 @@ def __init__(self, beta: float = 1.0): shape=[], default_initializer=paddle.nn.initializer.Constant(beta), ) - self.add_parameter("beta", self.beta) def forward(self, x): return x * F.sigmoid(self.beta * x) @@ -48,13 +47,27 @@ def forward(self, x): return paddle.sin(x) +class SILU(nn.Layer): + r""" + SILU Activation. + .. math:: + SILU(x)= x \cdot \frac{1}{1 + e^{-x}} + """ + + def __init__(self): + super().__init__() + + def forward(self, x): + return x * F.sigmoid(x) + + act_func_dict = { "elu": nn.ELU(), "relu": nn.ReLU(), "selu": nn.SELU(), "gelu": nn.GELU(), "sigmoid": nn.Sigmoid(), - "silu": nn.Silu(), + "silu": SILU(), "sin": Sin(), "cos": Cos(), "swish": Swish(), From 76797fb765c053467029107d87a0f66d45ef78ae Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Tue, 6 Jun 2023 04:05:53 +0000 Subject: [PATCH 29/31] clean activation --- ppsci/arch/activation.py | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/ppsci/arch/activation.py b/ppsci/arch/activation.py index 37b669c93..fa2a2e943 100644 --- a/ppsci/arch/activation.py +++ b/ppsci/arch/activation.py @@ -47,13 +47,7 @@ def forward(self, x): return paddle.sin(x) -class SILU(nn.Layer): - r""" - SILU Activation. - .. math:: - SILU(x)= x \cdot \frac{1}{1 + e^{-x}} - """ - +class Silu(nn.Layer): def __init__(self): super().__init__() @@ -67,7 +61,7 @@ def forward(self, x): "selu": nn.SELU(), "gelu": nn.GELU(), "sigmoid": nn.Sigmoid(), - "silu": SILU(), + "silu": Silu(), "sin": Sin(), "cos": Cos(), "swish": Swish(), From 4d933f39d32680e6383b1e9b56f0090a8d3b2608 Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Tue, 6 Jun 2023 06:39:52 +0000 Subject: [PATCH 30/31] add reference --- examples/aneurysm/aneurysm_flow.py | 5 +++++ examples/pipe/poiseuille_flow.py | 4 ++++ 2 files changed, 9 insertions(+) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 367467a61..561012c05 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -12,6 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. + +""" +Reference: https://github.com/Jianxun-Wang/LabelFree-DNN-Surrogate +""" + import math import os import os.path as osp diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index d574995bd..2b5b66d79 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -12,6 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +""" +Reference: https://github.com/Jianxun-Wang/LabelFree-DNN-Surrogate +""" + import copy import os import os.path as osp From e974416665311926c66081134b9a5cb170cee73b Mon Sep 17 00:00:00 2001 From: wangguan <772359200@qq.com> Date: Wed, 7 Jun 2023 11:32:58 +0000 Subject: [PATCH 31/31] fix by reviews --- examples/aneurysm/aneurysm_flow.py | 3 +-- examples/pipe/poiseuille_flow.py | 4 +--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/examples/aneurysm/aneurysm_flow.py b/examples/aneurysm/aneurysm_flow.py index 561012c05..6207e712a 100644 --- a/examples/aneurysm/aneurysm_flow.py +++ b/examples/aneurysm/aneurysm_flow.py @@ -12,7 +12,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - """ Reference: https://github.com/Jianxun-Wang/LabelFree-DNN-Surrogate """ @@ -209,7 +208,7 @@ def output_trans_p(self, out): ) constraint = {pde_constraint.name: pde_constraint} - EPOCHS = 500 if not args.epochs else args.epochs + EPOCHS = 400 if not args.epochs else args.epochs # initialize solver solver = ppsci.solver.Solver( diff --git a/examples/pipe/poiseuille_flow.py b/examples/pipe/poiseuille_flow.py index 2b5b66d79..b2c7ed24e 100644 --- a/examples/pipe/poiseuille_flow.py +++ b/examples/pipe/poiseuille_flow.py @@ -263,9 +263,7 @@ def output_trans_p(self, out): u_max_pred = output_dict_test["u"].numpy() # Analytical result, y = 0 - u_max_a = np.zeros([num_test, 1]) - for i in range(num_test): - u_max_a[i] = (R**2) * dP / (2 * L * data_1d_nu_distribution[i] * RHO) + u_max_a = (R**2) * dP / (2 * L * data_1d_nu_distribution * RHO) # Plot plt.figure(2)