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run_train_pdpd.py
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run_train_pdpd.py
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import h5py
import numpy as np
import paddle
import paddle.nn as nn
from process_data_pdpd import data_norm, data_sampler
from basic_model_pdpd import gradients, DeepModel_single, DeepModel_multi
import visual_data
import matplotlib.pyplot as plt
import time
import os
import argparse
import sys
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
def get_args():
parser = argparse.ArgumentParser('PINNs for naiver-stokes cylinder with Karman Vortex', add_help=False)
parser.add_argument('-f', type=str, default="external parameters")
parser.add_argument('--Layer_depth', default=6, type=int, help="Number of Layers depth")
parser.add_argument('--Layer_width', default=64, type=int, help="Number of Layers width")
parser.add_argument('--in_norm', default=True, type=bool, help="input feature normalization")
parser.add_argument('--out_norm', default=True, type=bool, help="output fields normalization")
parser.add_argument('--activation', default=nn.Tanh(), help="activation function")
parser.add_argument('--points_name', default="24+6+4", type=str, help="distribution of supervised points")
parser.add_argument('--Net_pattern', default='single', type=str, help="single or multi networks")
parser.add_argument('--epochs_adam', default=400000, type=int, help='total training epoch')
parser.add_argument('--save_freq', default=5000, type=int, help="frequency to save model and image")
parser.add_argument('--print_freq', default=1000, type=int, help="frequency to print loss")
parser.add_argument('--device', default=0, type=int, help="gpu id")
parser.add_argument('--work_name', default='', type=str, help="work path to save files")
parser.add_argument('--Nx_EQs', default=30000, type=int, help="xy sampling in for equation loss")
parser.add_argument('--Nt_EQs', default=2, type=int, help="time sampling in for equation loss")
parser.add_argument('--Nt_BCs', default=120, type=int, help="time sampling in for boundary loss")
return parser.parse_args()
def read_data():
data = h5py.File('./data/cyl_Re250.mat', 'r')
nodes = np.array(data['grids_']).squeeze().transpose((3, 2, 1, 0)) # [Nx, Ny, Nf]
field = np.array(data['fields_']).squeeze().transpose((3, 2, 1, 0)) # [Nt, Nx, Ny, Nf]
times = np.array(data['dynamics_']).squeeze().transpose((1, 0))[3::4, (0,)] # (800, 3) -> (200, 1)
nodes = nodes[0]
times = times - times[0, 0]
return times[:120], nodes[:, :, 1:], field[:120, :, :, :] # Nx / 2
def BCS_ICS(nodes, points):
BCS = []
ICS = []
Num_Nodes = nodes.shape[0] * nodes.shape[1]
Index = np.arange(Num_Nodes).reshape((nodes.shape[0], nodes.shape[1]))
BCS.append(np.concatenate((Index[:93, -1], Index[284:, -1]), axis=0)) #in
BCS.append(Index[93:284, -1]) #out
BCS.append(Index[:, 0]) #wall
######监督测点生成
if points == "24+6+4":
point = np.concatenate((Index[175:220:15, 0:64:8].reshape(-1, 1),
Index[378:360:-15, 0:16:8].reshape(-1, 1),
Index[14, 0:16:8].reshape(-1, 1)), axis=0)[:, 0]
BCS.append(np.concatenate((Index[::96, 1], point), axis=0)) # 24+6+4 尾迹+前缘+圆周
elif points == "30+4":
point = np.concatenate((Index[175:220:15, 0:80:8].reshape(-1, 1)), axis=0)
BCS.append(np.concatenate((Index[::96, 1], point), axis=0)) # 30+4
elif points == "48+12+8":
point = np.concatenate((Index[175:220:15, 0:64:4].reshape(-1, 1),
Index[378:360:-15, 0:32:8].reshape(-1, 1),
Index[14, 0:32:8].reshape(-1, 1)), axis=0)[:, 0]
BCS.append(np.concatenate((Index[::48, 1], point), axis=0)) # 48+12+8 尾迹+前缘+圆周
elif points == "60+8":
point = np.concatenate((Index[175:220:15, 0:80:4].reshape(-1, 1)), axis=0)
BCS.append(np.concatenate((Index[::48, 1], point), axis=0)) # 60+8
elif points == "96+24+16":
point = np.concatenate((Index[175:220:15, 0:64:2].reshape(-1, 1),
Index[378:360:-15, 0:32:4].reshape(-1, 1),
Index[14, 0:32:4].reshape(-1, 1)), axis=0)[:, 0]
BCS.append(np.concatenate((Index[::24, 1], point), axis=0)) # 96+24+16
elif points == "120+16":
point = np.concatenate((Index[175:220:15, 0:80:2].reshape(-1, 1)), axis=0)
BCS.append(np.concatenate((Index[::24, 1], point), axis=0)) # 120+16
elif points == "192+48+32":
point = np.concatenate((Index[175:220:15, 0:128:2].reshape(-1, 1),
Index[378:360:-15, 0:64:4].reshape(-1, 1),
Index[14, 0:64:4].reshape(-1, 1)), axis=0)[:, 0]
BCS.append(np.concatenate((Index[::12, 1], point), axis=0)) # 192+48+32
elif points == "240+32":
point = np.concatenate((Index[175:220:15, 0:160:2].reshape(-1, 1)), axis=0)
BCS.append(np.concatenate((Index[::12, 1], point), axis=0)) # 240+32
elif points == "13":
BCS.append(Index[::30, 1]) # 13
elif points == "12+4":
point = np.concatenate((Index[175:220:30, 0:48:8].reshape(-1, 1)), axis=0)
BCS.append(np.concatenate((Index[::96, 1], point), axis=0)) # 12+4
else:
BCS.append(Index[::60, 1]) # 6
ICS.append(Index.reshape(-1))
INN = np.setdiff1d(ICS[0], np.concatenate(BCS[:-1], axis=0)) #其余点
return INN, BCS, ICS
class Net_single(DeepModel_single):
def __init__(self, planes, data_norm, active):
super(Net_single, self).__init__(planes, data_norm, active)
self.Re = 250.
def equation(self, inn_var, out_var):
# a = grad(psi.sum(), in_var, create_graph=True, retain_graph=True)[0]
p, u, v = out_var[:, 0:1], out_var[:, 1:2], out_var[:, 2:3]
duda = gradients(u, inn_var)
dudx, dudy, dudt = duda[:, 0:1], duda[:, 1:2], duda[:, 2:3]
dvda = gradients(v, inn_var)
dvdx, dvdy, dvdt = dvda[:, 0:1], dvda[:, 1:2], dvda[:, 2:3]
d2udx2 = gradients(dudx, inn_var)[:, 0:1]
d2udy2 = gradients(dudy, inn_var)[:, 1:2]
d2vdx2 = gradients(dvdx, inn_var)[:, 0:1]
d2vdy2 = gradients(dvdy, inn_var)[:, 1:2]
dpda = gradients(p, inn_var)
dpdx, dpdy = dpda[:, 0:1], dpda[:, 1:2]
eq1 = dudt + (u * dudx + v * dudy) + dpdx - 1 / self.Re * (d2udx2 + d2udy2)
eq2 = dvdt + (u * dvdx + v * dvdy) + dpdy - 1 / self.Re * (d2vdx2 + d2vdy2)
eq3 = dudx + dvdy
eqs = paddle.concat((eq1, eq2, eq3), axis=1)
return eqs
class Net_multi(DeepModel_multi):
def __init__(self, planes, data_norm, active):
super(Net_multi, self).__init__(planes, data_norm, active)
self.Re = 250.
def equation(self, inn_var, out_var):
# a = grad(psi.sum(), in_var, create_graph=True, retain_graph=True)[0]
p, u, v = out_var[:, 0:1], out_var[:, 1:2], out_var[:, 2:3]
duda = gradients(u, inn_var)
dudx, dudy, dudt = duda[:, 0:1], duda[:, 1:2], duda[:, 2:3]
dvda = gradients(v, inn_var)
dvdx, dvdy, dvdt = dvda[:, 0:1], dvda[:, 1:2], dvda[:, 2:3]
d2udx2 = gradients(dudx, inn_var)[:, 0:1]
d2udy2 = gradients(dudy, inn_var)[:, 1:2]
d2vdx2 = gradients(dvdx, inn_var)[:, 0:1]
d2vdy2 = gradients(dvdy, inn_var)[:, 1:2]
dpda = gradients(p, inn_var)
dpdx, dpdy = dpda[:, 0:1], dpda[:, 1:2]
eq1 = dudt + (u * dudx + v * dudy) + dpdx - 1 / self.Re * (d2udx2 + d2udy2)
eq2 = dvdt + (u * dvdx + v * dvdy) + dpdy - 1 / self.Re * (d2vdx2 + d2vdy2)
eq3 = dudx + dvdy
eqs = paddle.concat((eq1, eq2, eq3), axis=1)
return eqs
def train(inn_var, BCs, ICs, out_true, model, Loss, optimizer, scheduler, log_loss, opts):
inn = BCs[0].sampling(Nx=opts.Nx_EQs, Nt=opts.Nt_EQs) #随机抽取守恒损失计算点
BC_in = BCs[1].sampling(Nx='all', Nt=opts.Nt_BCs); ind_BC_in = BC_in.shape[0] #入口
BC_out = BCs[2].sampling(Nx='all', Nt=opts.Nt_BCs); ind_BC_out = BC_out.shape[0] + ind_BC_in #出口
BC_wall = BCs[3].sampling(Nx='all', Nt=opts.Nt_BCs); ind_BC_wall = BC_wall.shape[0] + ind_BC_out #圆柱
BC_meas = BCs[4].sampling(Nx='all', Nt=opts.Nt_BCs); ind_BC_meas = BC_meas.shape[0] + ind_BC_wall
IC_0 = ICs[0].sampling(Nx='all') #初始场
inn_BCs = paddle.concat((inn_var[BC_in], inn_var[BC_out], inn_var[BC_wall], inn_var[BC_meas], inn_var[IC_0]), axis=0)
out_BCs = paddle.concat((out_true[BC_in], out_true[BC_out], out_true[BC_wall], out_true[BC_meas], out_true[IC_0]), axis=0)
inn_EQs = inn_var[inn]
out_EQs = out_true[inn]
optimizer.clear_grad()
inn_EQs.stop_gradient = False
out_EQs_ = model(inn_EQs, in_norm=opts.in_norm, out_norm=opts.out_norm)
res_EQs = model.equation(inn_EQs, out_EQs_)
inn_BCs.stop_gradient = True
out_BCs_ = model(inn_BCs, in_norm=opts.in_norm, out_norm=opts.out_norm)
bcs_loss_1 = Loss(out_BCs_[:ind_BC_in, 1:], out_BCs[:ind_BC_in, 1:]) #进口速度
bcs_loss_2 = Loss(out_BCs_[ind_BC_in:ind_BC_out, 0], out_BCs[ind_BC_in:ind_BC_out, 0]) #出口压力
bcs_loss_3 = Loss(out_BCs_[ind_BC_out:ind_BC_wall, 1:], out_BCs[ind_BC_out:ind_BC_wall, 1:]) #壁面速度
bcs_loss_4 = Loss(out_BCs_[ind_BC_wall:ind_BC_meas, :], out_BCs[ind_BC_wall:ind_BC_meas, :]) #监督测点
ics_loss_0 = Loss(out_BCs_[ind_BC_meas:, :], out_BCs[ind_BC_meas:, :]) #初始条件损失
eqs_loss = (res_EQs**2).mean() #方程损失
loss_batch = bcs_loss_1 + bcs_loss_2 + bcs_loss_3 + bcs_loss_4 + ics_loss_0 + eqs_loss
loss_batch.backward()
data_loss = Loss(out_EQs_, out_EQs) #全部点的data loss 没有用来训练
log_loss.append([eqs_loss.item(), bcs_loss_1.item(), bcs_loss_2.item(), bcs_loss_3.item(), bcs_loss_4.item(),
ics_loss_0.item(), data_loss.item()])
# return loss_batch
# optimizer.step(closure)
optimizer.step()
scheduler.step()
def inference(inn_var, model, opts):
with paddle.no_grad():
out_pred = model(inn_var, in_norm=opts.in_norm, out_norm=opts.out_norm)
return out_pred
if __name__ == '__main__':
opts = get_args()
print(opts)
if paddle.fluid.is_compiled_with_cuda():
paddle.set_device("gpu:" + str(opts.device)) # 指定第一块gpu
device = "gpu:" + str(opts.device)
else:
paddle.set_device('cpu')
device = 'cpu'
points_name = opts.points_name
work_name = 'NS-cylinder-2d-t_pdpd_' + points_name + '-' + opts.work_name
work_path = os.path.join('work', work_name)
tran_path = os.path.join('work', work_name, 'train')
isCreated = os.path.exists(tran_path)
if not isCreated:
os.makedirs(tran_path)
# 将控制台的结果输出到a.log文件，可以改成a.txt
sys.stdout = visual_data.Logger(os.path.join(work_path, 'train.log'), sys.stdout)
times, nodes, field = read_data()
INN, BCS, ICS = BCS_ICS(nodes, points_name)
Nt, Nx, Ny, Nf = field.shape[0], field.shape[1], field.shape[2], field.shape[3]
times = np.tile(times[:, None, None, :], (1, Nx, Ny, 1))
nodes = np.tile(nodes[None, :, :, :], (Nt, 1, 1, 1))
times = times.reshape(-1, 1)
nodes = nodes.reshape(-1, 2)
field = field.reshape(-1, Nf)
input = np.concatenate((nodes, times), axis=-1)
input_norm = data_norm(input, method='mean-std')
field_norm = data_norm(field, method='mean-std')
input_visual = input.reshape((Nt, Nx, Ny, 3))
add_input = input_visual[:, 0, :, :].reshape((Nt, -1, Ny, 3))
input_visual = np.concatenate((input_visual, add_input), axis=1)
field_visual = field.reshape((Nt, Nx, Ny, Nf))
add_field = field_visual[:, 0, :, :].reshape((Nt, -1, Ny, Nf))
field_visual = np.concatenate((field_visual, add_field), axis=1)
# Training Data
input = paddle.to_tensor(input, dtype='float32')
field = paddle.to_tensor(field, dtype='float32')
NumNodes = Nx * Ny
BC_in = data_sampler(BCS[0], NumNodes, time=Nt)
BC_out = data_sampler(BCS[1], NumNodes, time=Nt)
BC_cyl = data_sampler(BCS[2], NumNodes, time=Nt)
BC_meas = data_sampler(BCS[3], NumNodes, time=Nt)
IC_cyl = data_sampler(ICS[0], NumNodes, time=0)
IN_cyl = data_sampler(INN, NumNodes, time=Nt)
BCs = [IN_cyl, BC_in, BC_out, BC_cyl, BC_meas]
ICs = [IC_cyl,]
L1Loss = nn.L1Loss()
HBLoss = nn.SmoothL1Loss()
L2Loss = nn.MSELoss()
planes = [3,] + [opts.Layer_width] * opts.Layer_depth + [3,]
if opts.Net_pattern == "single":
Net_model = Net_single(planes=planes, data_norm=(input_norm, field_norm), active=opts.activation).to(device)
elif opts.Net_pattern == "multi":
Net_model = Net_multi(planes=planes, data_norm=(input_norm, field_norm), active=opts.activation).to(device)
Boundary_epoch1 = [opts.epochs_adam*8/10, opts.epochs_adam*9/10]
Boundary_epoch2 = [opts.epochs_adam*11/10, opts.epochs_adam*12/10]
Scheduler1 = paddle.optimizer.lr.MultiStepDecay(learning_rate=0.001, milestones=Boundary_epoch1, gamma=0.1)
Optimizer1 = paddle.optimizer.Adam(parameters=Net_model.parameters(), learning_rate=Scheduler1, beta1=0.8,
beta2=0.9)
# Optimizer2 = paddle.incubate.optimizer.functional.minimize_lbfgs(parameters=Net_model.parameters(), learning_rate=.1, max_iter=100)
# Scheduler2 = paddle.optimizer.lr.MultiStepDecay(Optimizer2, milestones=Boundary_epoch2, gamma=0.1)
Visual = visual_data.matplotlib_vision('/', field_name=('p', 'u', 'v'), input_name=('x', 'y'))
Visual.font['size'] = 20
star_time = time.time()
start_epoch=0
log_loss=[]
"""load a pre-trained model"""
start_epoch, log_loss = Net_model.loadmodel(os.path.join(work_path, 'latest_model.pth'))
for i in range(start_epoch):
# update the learning rate for start_epoch times
Scheduler1.step()
# Training
for iter in range(start_epoch, opts.epochs_adam):
if iter < opts.epochs_adam:
train(input, BCs, ICs, field, Net_model, L2Loss, Optimizer1, Scheduler1, log_loss, opts)
learning_rate = Optimizer1.get_lr()
if iter > 0 and iter % opts.print_freq == 0:
print('iter: {:6d}/{:6d}, lr: {:.1e}, cost: {:.2f}, dat_loss: {:.2e} \n'
'eqs_loss: {:.2e}, BCS_loss_in: {:.2e}, BCS_loss_out: {:.2e}, '
'BCS_loss_wall: {:.2e}, BCS_loss_meas: {:.2e}, ICS_loss_0: {:.2e}'.
format(iter, opts.epochs_adam, learning_rate, time.time() - star_time, log_loss[-1][-1],
log_loss[-1][0], log_loss[-1][1], log_loss[-1][2],
log_loss[-1][3], log_loss[-1][4], log_loss[-1][5]))
plt.figure(1, figsize=(20, 15))
plt.clf()
plt.subplot(2, 1, 1)
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, -1], 'dat_loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 0], 'eqs_loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 5], 'ICS_loss_0')
plt.subplot(2, 1, 2)
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 1], 'BCS_loss_in')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 2], 'BCS_loss_out')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 3], 'BCS_loss_wall')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 4], 'BCS_loss_meas')
plt.savefig(os.path.join(tran_path, 'log_loss.svg'))
star_time = time.time()
if iter > 0 and iter % opts.save_freq == 0:
input_visual_p = paddle.to_tensor(input_visual[:100:10], dtype='float32', place='gpu:0')
field_visual_p = inference(input_visual_p, Net_model)
field_visual_t = field_visual[:100:10]
field_visual_p = field_visual_p.cpu().numpy()
for t in range(field_visual_p.shape[0]):
plt.figure(2, figsize=(20, 10))
plt.clf()
Visual.plot_fields_ms(field_visual_t[t], field_visual_p[t], input_visual_p[0, :, :, :2].numpy(),
cmin_max=[[-4, -4], [10, 4]], field_name=['p', 'u', 'v'])
plt.subplots_adjust(wspace=0.2, hspace=0.3) # left=0.05, bottom=0.05, right=0.95, top=0.95
plt.savefig(os.path.join(tran_path, 'loca_' + str(t) + '.jpg'))
plt.figure(3, figsize=(30, 20))
plt.clf()
Visual.plot_fields_ms(field_visual_t[t], field_visual_p[t], input_visual_p[0, :, :, :2].numpy())
plt.subplots_adjust(wspace=0.2, hspace=0.3)
plt.savefig(os.path.join(tran_path, 'full_' + str(t) + '.jpg'))
paddle.save({'epoch': iter, 'model': Net_model.state_dict(), 'log_loss': log_loss},
os.path.join(work_path, 'latest_model.pth'))