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hybrid_network_train.py
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hybrid_network_train.py
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import torch
import pyrtklib as prl
import rtk_util as util
import json
import sys
import numpy as np
import pandas as pd
import pymap3d as p3d
from model import HybridShareNet
from torch.nn import HuberLoss,MSELoss
import matplotlib.pyplot as plt
from tqdm import tqdm
import os
DEVICE = 'cuda'
try:
config = sys.argv[1]
except:
config = "config/hybrid_share/p40_klt3_train.json"
with open(config) as f:
conf = json.load(f)
mode = conf['mode']
if mode not in ['train','predict']:
raise RuntimeError("%s is not a valid option"%mode)
os.makedirs(conf['model'],exist_ok=True)
result = config.split("/")[-1].split(".json")[0]
result_path = "result/hybrid_share/"+result
os.makedirs(result_path,exist_ok=True)
obs,nav,sta = util.read_obs(conf['obs'],conf['eph'])
prl.sortobs(obs)
obss = util.split_obs(obs)
tmp = []
if conf.get("gt",None):
gt = pd.read_csv(conf['gt'],skiprows = 30, header = None,sep =' +', skipfooter = 4, error_bad_lines=False, engine='python')
gt[0] = gt[0]+18 # leap seconds
gts = []
# filter and normalize
gather_data = []
for o in obss:
t = o.data[0].time
t = t.time+t.sec
if t > conf['start_time'] and (conf['end_time'] == -1 and 1 or t < conf['end_time']):
tmp.append(o)
if conf.get("gt",None):
gt_row = gt.loc[(gt[0]-t).abs().argmin()]
gts.append([gt_row[3]+gt_row[4]/60+gt_row[5]/3600,gt_row[6]+gt_row[7]/60+gt_row[8]/3600,gt_row[9]])
ret = util.get_ls_pnt_pos(o,nav)
if not ret['status']:
continue
rs = ret['data']['eph']
dts = ret['data']['dts']
sats = ret['data']['sats']
exclude = ret['data']['exclude']
prs = ret['data']['prs']
resd = np.array(ret['data']['residual'])
SNR = np.array(ret['data']['SNR'])
azel = np.delete(np.array(ret['data']['azel']).reshape((-1,2)),exclude,axis=0)
gather_data.append(np.hstack([SNR.reshape(-1,1),azel[:,1:],resd]))
norm_data = np.vstack(gather_data)
imean = norm_data.mean(axis=0)
istd = norm_data.std(axis=0)
print(f"preprocess done, mean:{imean}, std:{istd}")
net = HybridShareNet(torch.tensor(imean,dtype=torch.float32),torch.tensor(istd,dtype=torch.float32))
net.double()
net = net.to(DEVICE)
obss = tmp
pos_errs = []
opt = torch.optim.Adam(net.parameters(),lr = 0.01)
epoch = conf.get('epoch',500)
batch = conf.get('batch',128)
lossFn = MSELoss(reduction='sum')
vis_loss = []
for k in range(epoch):
loss = 0
with tqdm(range(len(obss)),desc=f"Epoch {k+1}") as t:
for i in t:
o = obss[i]
if conf.get("gt",None):
gt_row = gts[i]
ret = util.get_ls_pnt_pos(o,nav)
if not ret['status']:
continue
pos_err_src = p3d.ecef2enu(*ret['pos'][:3],gt_row[0],gt_row[1],gt_row[2])
rs = ret['data']['eph']
dts = ret['data']['dts']
sats = ret['data']['sats']
exclude = ret['data']['exclude']
prs = ret['data']['prs']
resd = np.array(ret['data']['residual'])
SNR = np.array(ret['data']['SNR'])
azel = np.delete(np.array(ret['data']['azel']).reshape((-1,2)),exclude,axis=0)
in_data = torch.tensor(np.hstack([SNR.reshape(-1,1),azel[:,1:],resd]),dtype=torch.float32).to(DEVICE)
predict= net(in_data)
weight = predict[0]
bias = predict[1]
#print(predict_weight)
select_sats = list(np.delete(np.array(sats),exclude))
ret = util.get_ls_pnt_pos_torch(o,nav,torch.diag(weight),bias.reshape(-1,1),p_init=ret['pos'])
gt_ecef = p3d.geodetic2ecef(*gt_row)
enu = p3d.ecef2enu(*ret['pos'][:3],gt_row[0],gt_row[1],gt_row[2])
epoch_loss = torch.norm(torch.hstack(enu[:3]))
#epoch_loss = lossFn(ret['pos'][:3],torch.tensor(gt_ecef).to(DEVICE))
loss += epoch_loss
t.set_postfix({'epoch loss':epoch_loss.item()})
#torch.norm(ret['pos'][:3]-torch.tensor(gt_ecef).to(DEVICE))
# pos_err_pre = p3d.ecef2enu(*ret['pos'][:3],gt_row[0],gt_row[1],gt_row[2])
# pos_errs.append([np.linalg.norm(pos_err_src[:2]),np.linalg.norm(pos_err_pre[:2])])
opt.zero_grad()
loss.backward()
opt.step()
print(loss.item()/len(obss))
vis_loss.append(loss.item())
if k == 100:
torch.save(net.state_dict(),conf['model']+"/hybrid_share_100.pth")
vis_loss_300 = np.array(vis_loss)
plt.plot(vis_loss)
plt.savefig(result_path+"/loss_100.png")
np.savetxt(result_path+"/loss_100.csv",vis_loss_300.reshape(-1,1))
torch.save(net.state_dict(),conf['model']+f"/hybrid_share_3d.pth")
vis_loss = np.array(vis_loss)
plt.plot(vis_loss)
plt.savefig(result_path+f"/loss_{epoch}.png")
np.savetxt(result_path+f"/loss_{epoch}.csv",vis_loss.reshape(-1,1))