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run_validate_pdpd.py
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import numpy as np
import paddle
import paddle.nn as nn
from process_data_pdpd import data_norm
from basic_model_pdpd import Dynamicor
from run_train_pdpd import read_data, Net_multi, Net_single, inference
import visual_data
import matplotlib.pyplot as plt
import os
import argparse
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('--points_name', default="60+8", type=str)
parser.add_argument('--Layer_depth', default=5, 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('--Net_pattern', default='single', type=str, help="single or multi networks")
parser.add_argument('--epochs_adam', default=400000, type=int)
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=15, 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()
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, )
vald_path = os.path.join('work', work_name, 'validation')
isCreated = os.path.exists(vald_path)
if not isCreated:
os.makedirs(vald_path)
times, nodes, field = read_data()
Dyn_model = Dynamicor(device, nodes[:, (0, 1), :])
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)
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)
Visual = visual_data.matplotlib_vision(vald_path, field_name=('p', 'u', 'v'), input_name=('x', 'y'))
Visual.font['size'] = 20
start_epoch, log_loss = Net_model.loadmodel(os.path.join(work_path, 'latest_model.pth'))
####################################### plot loss #################################################################################
try:
print("plot training loss")
plt.figure(1, figsize=(15, 10))
plt.clf()
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, -1], 'unsupervised loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 0], 'conversation loss')
plt.savefig(os.path.join(vald_path, 'loss_eqs_data.jpg'), dpi=300)
plt.figure(2, figsize=(15, 10))
plt.clf()
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 1], 'inlet boundary loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 2], 'outlet boundary loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 3], 'cylinder boundary loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 4], 'measurement loss')
Visual.plot_loss(np.arange(len(log_loss)), np.array(log_loss)[:, 5], 'initial boundary loss')
plt.savefig(os.path.join(vald_path, 'loss_boundary.jpg'), dpi=300)
except:
pass
####################################### plot several fields #################################################################################
print("plot several true and predicted fields")
inds = np.concatenate((np.zeros((1,), dtype=np.int32), np.linspace(0, 100, 11, dtype=np.int32)))
input_visual_p = paddle.to_tensor(input_visual[inds], dtype='float32', place=device)
field_visual_p = inference(input_visual_p, Net_model, opts)
field_visual_t = field_visual[inds]
field_visual_p = field_visual_p.cpu().numpy()
input_visual_p = input_visual_p.cpu().numpy()
ori_input = input_norm.back(input_visual_p[:, 0, 0, :])
for t in range(len(inds)):
plt.figure(3, figsize=(20, 10))
plt.clf()
Visual.plot_fields_ms(field_visual_t[t], field_visual_p[t], input_visual_p[0, :, :, :2],
cmin_max=[[-4, -4], [10, 4]], field_name=['p', 'u', 'v'])
# plt.suptitle('$t$ = ' + str(ori_input[t, 0]) + ' T', )
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(vald_path, 'loca_' + str(inds[t]) + '.jpg'))
plt.figure(4, figsize=(15, 12))
plt.clf()
Visual.plot_fields_ms(field_visual_t[t], field_visual_p[t], input_visual_p[0, :, :, :2])
plt.subplots_adjust(wspace=0.2, hspace=0.3)
plt.savefig(os.path.join(vald_path, 'full_' + str(inds[t]) + '.jpg'))
####################################### plot continous fields #################################################################################
input_visual_p = paddle.to_tensor(input_visual[::5], dtype='float32', place=device)
field_visual_p = inference(input_visual_p, Net_model, opts)
field_visual_t = field_visual[::5]
field_visual_p = field_visual_p.cpu().numpy()
input_visual_p = input_visual_p.cpu().numpy()
ori_input = input_norm.back(input_visual_p[:, 0, 0, :])
# plot continous fields at points
ind_xs = np.random.choice(np.arange(0, Nx), 4)
ind_ys = np.random.choice(np.arange(0, Ny), 4)
plt.figure(6, figsize=(15, 10))
plt.clf()
lims = [[-1, 1], [-0.5, 1.5], [-1, 1]]
tits = ['p', 'u', 'v']
for j in range(3):
plt.subplot(2, 2, j+1)
plt.ylim(lims[j])
plt.title(tits[j])
for i, (ind_x, ind_y) in enumerate(zip(ind_xs, ind_ys)):
plt.plot(ori_input[:, -1], field_visual_t[:, ind_x, ind_y, j])
plt.scatter(ori_input[:, -1], field_visual_p[:, ind_x, ind_y, j])
plt.legend(['original', 'predicted'])
plt.savefig(os.path.join(vald_path, 'contous_fields_point.jpg'))
####################################### plot dynamic coefficient ############################################################################
print("plot dynamic coefficient")
ori_forces = Dyn_model(paddle.to_tensor(field_visual_t[:, :-1], place=device))
pre_forces = Dyn_model(paddle.to_tensor(field_visual_p[:, :-1], place=device))
ori_forces = ori_forces.cpu().numpy()
pre_forces = pre_forces.cpu().numpy()
plt.figure(6, figsize=(15, 10))
plt.clf()
plt.plot(ori_input[:, -1], ori_forces[:, 0], 'r-')
plt.plot(ori_input[:, -1], pre_forces[:, 0], 'ro')
plt.plot(ori_input[:, -1], ori_forces[:, 1], 'b-')
plt.plot(ori_input[:, -1], pre_forces[:, 1], 'bo')
plt.ylim([-0.5, 1.0])
plt.legend(['original lift', 'predicted lift', 'original drag', 'predicted drag'])
plt.savefig(os.path.join(vald_path, 'forces.jpg'))
####################################### L2 error ##############################################################################
print("plot fields L2 error ")
err = field_visual_p - field_visual_t
err_L2 = np.linalg.norm(err, axis=(1, 2))/np.linalg.norm(field_visual_t[:, :, :, (0, 1, 1)], axis=(1, 2))
plt.figure(10, figsize=(15, 10))
plt.clf()
for i in range(3):
plt.plot(ori_input[:, -1], err_L2[:, i], '-', linewidth=2.0)
plt.legend(['p', 'u', 'v'])
plt.xlabel('Time t/s')
plt.ylabel('Relative $L_2$ error')
plt.grid()
plt.savefig(os.path.join(vald_path, 'L2.jpg'))
####################################### plot fields gif #################################################################################
# plot continous fields
print("plot several true and predicted fields gif")
fig = plt.figure(100, figsize=(15, 10))
Visual.plot_fields_am(np.concatenate([field_visual_t, field_visual_t[(0,),]], axis=0),
np.concatenate([field_visual_p, field_visual_p[(0,),]], axis=0),
np.concatenate([input_visual_p, input_visual_p[(0,),]], axis=0)[:, :, :, :2],
0, fig, cmin_max=[[-4, -4], [10, 4]],)