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exp_steady_design.py
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import os
import time
import torch
import torch.nn as nn
from exp.exp_basic import Exp_Basic
from models.model_factory import get_model
from data_provider.data_factory import get_data
from utils.loss import L2Loss, DerivLoss
import matplotlib.pyplot as plt
from utils.visual import visual
from utils.drag_coefficient import cal_coefficient
import numpy as np
import scipy as sc
class Exp_Steady_Design(Exp_Basic):
def __init__(self, args):
super(Exp_Steady_Design, self).__init__(args)
def vali(self):
myloss = nn.MSELoss(reduction='none')
self.model.eval()
rel_err = 0.0
index = 0
with torch.no_grad():
for pos, fx, y, surf, geo, obj_file in self.test_loader:
x, fx, y, geo = pos.cuda(), fx.cuda(), y.cuda(), geo.cuda()
if self.args.fun_dim == 0:
fx = None
out = self.model(x.unsqueeze(0), fx.unsqueeze(0), geo=geo)[0]
loss_press = myloss(out[surf, -1], y[surf, -1]).mean(dim=0)
loss_velo_var = myloss(out[:, :-1], y[:, :-1]).mean(dim=0)
loss_velo = loss_velo_var.mean()
loss = loss_velo + 0.5 * loss_press
rel_err += loss.item()
index += 1
rel_err /= float(index)
return rel_err
def train(self):
if self.args.optimizer == 'AdamW':
optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.args.lr, weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'Adam':
optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.lr, weight_decay=self.args.weight_decay)
else:
raise ValueError('Optimizer only AdamW or Adam')
if self.args.scheduler == 'OneCycleLR':
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=self.args.lr, epochs=self.args.epochs,
steps_per_epoch=len(self.train_loader),
pct_start=self.args.pct_start)
elif self.args.scheduler == 'CosineAnnealingLR':
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.args.epochs)
elif self.args.scheduler == 'StepLR':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=self.args.step_size, gamma=self.args.gamma)
myloss = nn.MSELoss(reduction='none')
for ep in range(self.args.epochs):
self.model.train()
train_loss = 0
index = 0
for pos, fx, y, surf, geo in self.train_loader:
x, fx, y, geo = pos.cuda(), fx.cuda(), y.cuda(), geo.cuda()
if self.args.fun_dim == 0:
fx = None
out = self.model(x.unsqueeze(0), fx.unsqueeze(0), geo=geo)[0]
loss_press = myloss(out[surf, -1], y[surf, -1]).mean(dim=0)
loss_velo_var = myloss(out[:, :-1], y[:, :-1]).mean(dim=0)
loss_velo = loss_velo_var.mean()
loss = loss_velo + 0.5 * loss_press
train_loss += loss.item()
index += 1
optimizer.zero_grad()
loss.backward()
if self.args.max_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm)
optimizer.step()
if self.args.scheduler == 'OneCycleLR':
scheduler.step()
if self.args.scheduler == 'CosineAnnealingLR' or self.args.scheduler == 'StepLR':
scheduler.step()
train_loss = train_loss / float(index)
print("Epoch {} Train loss : {:.5f}".format(ep, train_loss))
rel_err = self.vali()
print("rel_err:{}".format(rel_err))
if ep % 100 == 0:
if not os.path.exists('./checkpoints'):
os.makedirs('./checkpoints')
print('save models')
torch.save(self.model.state_dict(), os.path.join('./checkpoints', self.args.save_name + '.pt'))
if not os.path.exists('./checkpoints'):
os.makedirs('./checkpoints')
print('final save models')
torch.save(self.model.state_dict(), os.path.join('./checkpoints', self.args.save_name + '.pt'))
def test(self):
self.model.load_state_dict(torch.load("./checkpoints/" + self.args.save_name + ".pt"))
self.model.eval()
if not os.path.exists('./results/' + self.args.save_name + '/'):
os.makedirs('./results/' + self.args.save_name + '/')
criterion_func = nn.MSELoss(reduction='none')
l2errs_press = []
l2errs_velo = []
mses_press = []
mses_velo_var = []
times = []
gt_coef_list = []
pred_coef_list = []
coef_error = 0
index = 0
with torch.no_grad():
for pos, fx, y, surf, geo, obj_file in self.test_loader:
x, fx, y, geo = pos.cuda(), fx.cuda(), y.cuda(), geo.cuda()
if self.args.fun_dim == 0:
fx = None
tic = time.time()
out = self.model(x.unsqueeze(0), fx.unsqueeze(0), geo=geo)[0]
toc = time.time()
if self.test_loader.coef_norm is not None:
mean = torch.tensor(self.test_loader.coef_norm[2]).cuda()
std = torch.tensor(self.test_loader.coef_norm[3]).cuda()
pred_press = out[surf, -1] * std[-1] + mean[-1]
gt_press = y[surf, -1] * std[-1] + mean[-1]
pred_velo = out[~surf, :-1] * std[:-1] + mean[:-1]
gt_velo = y[~surf, :-1] * std[:-1] + mean[:-1]
pred_coef = cal_coefficient(obj_file.split('/')[1], pred_press[:, None].detach().cpu().numpy(),
pred_velo.detach().cpu().numpy())
gt_coef = cal_coefficient(obj_file.split('/')[1], gt_press[:, None].detach().cpu().numpy(),
gt_velo.detach().cpu().numpy())
gt_coef_list.append(gt_coef)
pred_coef_list.append(pred_coef)
coef_error += (abs(pred_coef - gt_coef) / gt_coef)
l2err_press = torch.norm(pred_press - gt_press) / torch.norm(gt_press)
l2err_velo = torch.norm(pred_velo - gt_velo) / torch.norm(gt_velo)
mse_press = criterion_func(out[surf, -1], y[surf, -1]).mean(dim=0)
mse_velo_var = criterion_func(out[~surf, :-1], y[~surf, :-1]).mean(dim=0)
l2errs_press.append(l2err_press.cpu().numpy())
l2errs_velo.append(l2err_velo.cpu().numpy())
mses_press.append(mse_press.cpu().numpy())
mses_velo_var.append(mse_velo_var.cpu().numpy())
times.append(toc - tic)
index += 1
gt_coef_list = np.array(gt_coef_list)
pred_coef_list = np.array(pred_coef_list)
spear = sc.stats.spearmanr(gt_coef_list, pred_coef_list)[0]
print("rho_d: ", spear)
print("c_d: ", coef_error / index)
l2err_press = np.mean(l2errs_press)
l2err_velo = np.mean(l2errs_velo)
rmse_press = np.sqrt(np.mean(mses_press))
rmse_velo_var = np.sqrt(np.mean(mses_velo_var, axis=0))
if self.test_loader.coef_norm is not None:
rmse_press *= self.test_loader.coef_norm[3][-1]
rmse_velo_var *= self.test_loader.coef_norm[3][:-1]
print('relative l2 error press:', l2err_press)
print('relative l2 error velo:', l2err_velo)
print('press:', rmse_press)
print('velo:', rmse_velo_var, np.sqrt(np.mean(np.square(rmse_velo_var))))
print('time:', np.mean(times))