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train_rall_L12.py
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train_rall_L12.py
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from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
import os
import sys
import time
import numpy as np
from torch.utils.tensorboard import SummaryWriter
from loader.data_gener_gs import dataGener_train
from loss.offset_loss_exp import offset_loss
from network.patch_net_new import patch_net
from network.siamese_triunet_new import siamese_minus
# multiple radar scans as one real batch
img_batch = 343 # constant to dataset
# img_batch = 1331 # constant to dataset
# img_batch = 729 # constant to dataset
iter_train = 1000 # how many poses for training
num_epoch = 201
l_rate = 0.001
decay = 0.98
w_decay = 0.00001
# params
pose_num = 8865
# select on certain poses from zero
start_id = 0
end_id = 7580
# for RSL comparison
d_xyt = np.array([[-6, -4, -2, 0, 2, 4, 6],
[-6, -4, -2, 0, 2, 4, 6],
[-6, -4, -2, 0, 2, 4, 6],])
r_xyt = np.array([[-6, 6],
[-6, 6],
[-6, 6]])
# d_xyt = np.array([[-4, -3, -2, -1, 0, 1, 2, 3, 4],
# [-4, -3, -2, -1, 0, 1, 2, 3, 4],
# [-4, -3, -2, -1, 0, 1, 2, 3, 4],])
# r_xyt = np.array([[-4, 4],
# [-4, 4],
# [-4, 4]])
# d_xyt = np.array([[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5],
# [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5],
# [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5],])
# r_xyt = np.array([[-5, 5],
# [-5, 5],
# [-5, 5]])
img_res = 0.25
xySizes = np.array([-800, 1200, -500, 1500])
radar_size = 512
# radar_size = 256
# 183
pose_txt = 'data/Data/ablation_study/25_512/seq01/pose_xy_01.txt'
map_file = 'data/Data/ablation_study/25_512/oxford_laser_map.png'
radar_dir = 'data/Data/ablation_study/25_512/seq01/radar_scans/'
# pose_txt = 'data/Data/ablation_study/25_256/seq01/pose_xy_01.txt'
# map_file = 'data/Data/ablation_study/25_256/oxford_laser_map.png'
# radar_dir = 'data/Data/ablation_study/25_256/seq01/radar_scans/'
# 251
# pose_txt = '/home/yinhuan/Data/183/seqes/seq01/pose_xy_01.txt'
# map_file = '/home/yinhuan/Data/183/seqes/laser_map.png'
# radar_dir = '/home/yinhuan/Data/183/seqes/seq01/radar_scans/'
# 57
# pose_txt = '/home/yinhuan/data/seqes/seq01/pose_xy_01.txt'
# map_file = '/home/yinhuan/data/seqes/laser_map.png'
# radar_dir = '/home/yinhuan/data/seqes/seq01/radar_scans/'
P_portion = 7
# P_portion = 9
# P_portion = 11
K_portion = 16
# K_portion = 8
alpha = 5
# beta = 5
beta = 1
# date = '06.29_MCL_025_512'
date = '07.10_MCL_25_512_777_r7'
# pre_sia_model_path = 'data/radar_lite/models/07.05_pre_siamese_25_512/siamese_model_10.pth'
pre_sia_model_path = 'data/radar_lite/models/07.10_MCL_25_512_777_r6/siamese_model_200.pth'
pre_patch_model_path = 'data/radar_lite/models/07.10_MCL_25_512_777_r6/patch_model_200.pth'
if __name__ == "__main__":
# device = 'cpu'
# device = 'cuda:2'
device = torch.cuda.current_device()
data_gener = dataGener_train(pose_txt, map_file, radar_dir, pose_num, d_xyt, r_xyt,\
img_res, xySizes, radar_size, img_batch, P_portion, start_id, end_id, device)
# make folders
if not os.path.exists('data/radar_lite/models/' + date):
os.mkdir('data/radar_lite/models/' + date)
if not os.path.exists('data/radar_lite/log/' + date):
os.mkdir('data/radar_lite/log/' + date)
# if not os.path.exists('/home/yinhuan/Data/183/radar_lite/models/' + date):
# os.mkdir('/home/yinhuan/Data/183/radar_lite/models/' + date)
# if not os.path.exists('/home/yinhuan/Data/183/radar_lite/log/' + date):
# os.mkdir('/home/yinhuan/Data/183/radar_lite/log/' + date)
# if not os.path.exists('/home/yinhuan/radar_lite/models/' + date):
# os.mkdir('/home/yinhuan/radar_lite/models/' + date)
# if not os.path.exists('/home/yinhuan/radar_lite/log/' + date):
# os.mkdir('/home/yinhuan/radar_lite/log/' + date)
print('Build Model')
# load pre-trained siamese model
# CAN try multiple GPU
siamese_model = siamese_minus().to(device)
siamese_model.load_state_dict(torch.load(pre_sia_model_path))
siamese_model.train()
# create patch model
patch_model = patch_net(P_portion, K_portion).to(device)
patch_model.load_state_dict(torch.load(pre_patch_model_path))
patch_model.train()
# loss
offset_loss = offset_loss(P_portion, alpha, beta, d_xyt, device).to(device)
# optimizer
optimizer = torch.optim.Adam(list(siamese_model.parameters()) + list(patch_model.parameters()), lr=l_rate, weight_decay=w_decay)
# board
writer = SummaryWriter('data/radar_lite/log/' + date)
# writer = SummaryWriter('/home/yinhuan/Data/183/radar_lite/log/' + date)
# writer = SummaryWriter('/home/yinhuan/radar_lite/log/' + date)
for epoch in range(num_epoch):
print('epoch ' + str(epoch))
# save all
torch.save(siamese_model.state_dict(), \
'data/radar_lite/models/' + date + '/%s_model_%d.pth' % ('siamese', epoch))
torch.save(patch_model.state_dict(), \
'data/radar_lite/models/' + date + '/%s_model_%d.pth' % ('patch', epoch))
# torch.save(siamese_model.state_dict(), \
# '/home/yinhuan/Data/183/radar_lite/models/' + date + '/%s_model_%d.pth' % ('siamese', epoch))
# torch.save(patch_model.state_dict(), \
# '/home/yinhuan/Data/183/radar_lite/models/' + date + '/%s_model_%d.pth' % ('patch', epoch))
# torch.save(siamese_model.state_dict(), \
# '/home/yinhuan/radar_lite/models/' + date + '/%s_model_%d.pth' % ('siamese', epoch))
# torch.save(patch_model.state_dict(), \
# '/home/yinhuan/radar_lite/models/' + date + '/%s_model_%d.pth' % ('patch', epoch))
for iter_ in range(iter_train):
t0 = time.time()
scan_, map_, gt_xyt = data_gener.get_random_data(device)
scan_pre, scan_mask, scan_feature, map_feature = siamese_model(scan_, map_)
map_scan_diff = data_gener.grid_sample_sub(scan_feature, map_feature, device)
avg_vector = patch_model(map_scan_diff)
loss, est_xyt = offset_loss(gt_xyt, avg_vector)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# heavy print
print('[%s %d: %d/%d] %s %f %s %f %s %s %s %s'\
%('Patch Training',epoch,iter_, iter_train-1,'L-r:',l_rate,'Loss:',loss.data,\
'GT:',str(gt_xyt.detach().cpu().numpy()),'EST:',str(est_xyt.detach().cpu().numpy())))
# add loss and error to board
writer.add_scalar(date+'/Loss', loss, epoch*iter_train + iter_)
writer.add_scalar(date+'/MSE_x', torch.pow(est_xyt[0]-gt_xyt[0],2), epoch*iter_train + iter_)
writer.add_scalar(date+'/MSE_y', torch.pow(est_xyt[1]-gt_xyt[1],2), epoch*iter_train + iter_)
writer.add_scalar(date+'/MSE_t', torch.pow(est_xyt[2]-gt_xyt[2],2), epoch*iter_train + iter_)
print('time: ', time.time() - t0)
# learning rate reduction
if epoch % 1 == 0:
l_rate*=decay
optimizer = torch.optim.Adam(list(siamese_model.parameters()) + list(patch_model.parameters()), lr=l_rate, weight_decay=w_decay)
print('Finished')