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train_dylo_tracking_odom.py
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train_dylo_tracking_odom.py
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# -*- coding:UTF-8 -*-
import os
import sys
import torch
import datetime
import torch.utils.data
import numpy as np
import time
from tqdm import tqdm
from configs import odometry_tracking_args
from tools.excel_tools import SaveExcel
from tools.euler_tools import quat2mat
from tools.logger_tools import log_print, creat_logger
from kitti_pytorch import tracking_dataset
from dylo_model import dylo_model, get_loss
from utils1.collate_functions import collate_pair_wo_label
args = odometry_tracking_args()
'''CREATE DIR'''
base_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(base_dir)
experiment_dir = os.path.join(base_dir, 'experiment')
if not os.path.exists(experiment_dir): os.makedirs(experiment_dir)
if not args.task_name:
file_dir = os.path.join(experiment_dir, '{}_ODOM_KITTI_{}'.format(args.model_name, str(
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))))
else:
file_dir = os.path.join(experiment_dir, args.task_name)
if not os.path.exists(file_dir): os.makedirs(file_dir)
eval_dir = os.path.join(file_dir, 'eval')
if not os.path.exists(eval_dir): os.makedirs(eval_dir)
log_dir = os.path.join(file_dir, 'logs')
if not os.path.exists(log_dir): os.makedirs(log_dir)
checkpoints_dir = os.path.join(file_dir, 'checkpoints/odom')
if not os.path.exists(checkpoints_dir): os.makedirs(checkpoints_dir)
'''LOG'''
def main():
global args
train_dir_list = [4,7,8,9,15,18,19]#[0, 2, 3, 4, 5, 6, 7, 9, 10]
test_dir_list = [4,7,8,9,15,18,19]#[7, 8, 9, 10]
logger = creat_logger(log_dir, args.model_name)
logger.info('----------------------------------------TRAINING----------------------------------')
logger.info('PARAMETER ...')
logger.info(args)
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
model = dylo_model(args, args.batch_size, args.H_input, args.W_input, args.is_training)
# train set
train_dataset = tracking_dataset(
is_training = 1,
num_point=args.num_points,
data_dir_list=train_dir_list,
config=args
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=collate_pair_wo_label,
pin_memory=True,
drop_last=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)
if args.multi_gpu is not None:
device_ids = [int(x) for x in args.multi_gpu.split(',')]
torch.backends.cudnn.benchmark = True
model = torch.nn.DataParallel(model, device_ids=device_ids)
model.cuda(device_ids[0])
log_print(logger, 'multi gpu are:' + str(args.multi_gpu))
else:
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(args.gpu)
model.cuda()
log_print(logger, 'just one gpu is:' + str(args.gpu))
if args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate,
momentum=args.momentum)
elif args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate, betas=(0.9, 0.999),
eps=1e-08, weight_decay=args.weight_decay)
optimizer.param_groups[0]['initial_lr'] = args.learning_rate
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_stepsize,
gamma=args.lr_gamma, last_epoch=-1)
if args.ckpt is not None:
checkpoint = torch.load(args.ckpt)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['opt_state_dict'])
scheduler.load_state_dict(checkpoint['scheduler'])
init_epoch = checkpoint['epoch']
log_print(logger, 'load model {}'.format(args.ckpt))
else:
init_epoch = 0
log_print(logger, 'Training from scratch')
# eval once before training
if args.eval_before == 1:
eval_pose(model, test_dir_list, init_epoch)
# excel_eval.update(eval_dir)
for epoch in range(init_epoch + 1, args.max_epoch):
total_loss = 0
total_seen = 0
optimizer.zero_grad()
for i, data in tqdm(enumerate(train_loader, 0), total=len(train_loader), smoothing=0.9):
torch.cuda.synchronize()
start_train_one_batch = time.time()
pos2, pos1, sample_id, T_gt, T_trans, T_trans_inv, Tr = data
# print(type(sample_id))
# sample_id = sample_id.cpu().detach().numpy()
# print(type(pos2[0]))
torch.cuda.synchronize()
#print('load_data_time: ', time.time() - start_train_one_batch)
pos2 = [b.cuda() for b in pos2]
pos1 = [b.cuda() for b in pos1]
T_trans = T_trans.cuda().to(torch.float32)
T_trans_inv = T_trans_inv.cuda().to(torch.float32)
T_gt = T_gt.cuda().to(torch.float32)
model = model.train()
# visual1 = imback2.cpu().detach().numpy()
# np.save('visual/img_90{}'.format(sample_id), visual1)
torch.cuda.synchronize()
#print('load_data_time + model_trans_time: ', time.time() - start_train_one_batch)
l0_q, l0_t, l1_q, l1_t, l2_q, l2_t, l3_q, l3_t, pc1_ouput, q_gt, t_gt, w_x, w_q = model(pos2, pos1, T_gt, T_trans, T_trans_inv)
loss = get_loss(l0_q, l0_t, l1_q, l1_t, l2_q, l2_t, l3_q, l3_t, q_gt, t_gt, w_x, w_q)
# visual2 = xyz1.cpu().detach().numpy()
# np.save('visual/pos_proj_90{}'.format(sample_id), visual2)
torch.cuda.synchronize()
#print('load_data_time + model_trans_time + forward ', time.time() - start_train_one_batch)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.cuda.synchronize()
#print('load_data_time + model_trans_time + forward + back_ward ', time.time() - start_train_one_batch)
if args.multi_gpu is not None:
total_loss += loss.mean().cpu().data * args.batch_size
else:
total_loss += loss.cpu().data * args.batch_size
total_seen += args.batch_size
scheduler.step()
lr = max(optimizer.param_groups[0]['lr'], args.learning_rate_clip)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
train_loss = total_loss / total_seen
log_print(logger,'EPOCH {} train mean loss: {:04f}'.format(epoch, float(train_loss)))
if epoch % 5 == 0:
save_path = os.path.join(checkpoints_dir,
'{}_{:03d}_{:04f}.pth.tar'.format(model.__class__.__name__, epoch, float(train_loss)))
torch.save({
'model_state_dict': model.module.state_dict() if args.multi_gpu else model.state_dict(),
'opt_state_dict': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'epoch': epoch
}, save_path)
log_print(logger, 'Save {}...'.format(model.__class__.__name__))
eval_pose(model, test_dir_list, epoch)
# excel_eval.update(eval_dir)
def eval_pose(model, test_list, epoch):
for item in test_list:
test_dataset = tracking_dataset(
is_training = 0,
num_point = args.num_points,
data_dir_list = [item],
config = args
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.eval_batch_size,
shuffle=False,
num_workers=args.workers,
collate_fn=collate_pair_wo_label,
pin_memory=True,
worker_init_fn=lambda x: np.random.seed((torch.initial_seed()) % (2 ** 32))
)
line = 0
total_time = 0
for batch_id, data in tqdm(enumerate(test_loader), total=len(test_loader), smoothing=0.9):
torch.cuda.synchronize()
start_prepare = time.time()
pos2, pos1, sample_id, T_gt, T_trans, T_trans_inv, Tr = data
torch.cuda.synchronize()
#print('data_prepare_time: ', time.time() - start_prepare)
pos2 = [b.cuda() for b in pos2]
pos1 = [b.cuda() for b in pos1]
T_trans = T_trans.cuda().to(torch.float32)
T_trans_inv = T_trans_inv.cuda().to(torch.float32)
T_gt = T_gt.cuda().to(torch.float32)
model = model.eval()
with torch.no_grad():
torch.cuda.synchronize()
start_time = time.time()
l0_q, l0_t, l1_q, l1_t, l2_q, l2_t, l3_q, l3_t, pc1_ouput, q_gt, t_gt, w_x, w_q = model(pos2, pos1, T_gt, T_trans, T_trans_inv)
torch.cuda.synchronize()
#print('eval_one_time: ', time.time() - start_time)
torch.cuda.synchronize()
total_time += (time.time() - start_time)
pc1_sample_2048 = pc1_ouput.cpu()
l0_q = l0_q.cpu()
l0_t = l0_t.cpu()
pc1 = pc1_sample_2048.numpy()
pred_q = l0_q.numpy()
pred_t = l0_t.numpy()
# deal with a batch_size
for n0 in range(pc1.shape[0]):
cur_Tr = Tr[n0, :, :]
qq = pred_q[n0:n0 + 1, :]
qq = qq.reshape(4)
tt = pred_t[n0:n0 + 1, :]
tt = tt.reshape(3, 1)
RR = quat2mat(qq)
filler = np.array([0.0, 0.0, 0.0, 1.0])
filler = np.expand_dims(filler, axis=0) ##1*4
TT = np.concatenate([np.concatenate([RR, tt], axis=-1), filler], axis=0)
TT = np.matmul(cur_Tr, TT)
TT = np.matmul(TT, np.linalg.inv(cur_Tr))
if line == 0:
T_final = TT
T = T_final[:3, :]
T = T.reshape(1, 1, 12)
line += 1
else:
T_final = np.matmul(T_final, TT)
T_current = T_final[:3, :]
T_current = T_current.reshape(1, 1, 12)
T = np.append(T, T_current, axis=0)
avg_time = total_time / 4541
#print('avg_time: ', avg_time)
T = T.reshape(-1, 12)
fname_file = os.path.join(log_dir, str(item).zfill(2) + '_pred.npy')
fname_txt = os.path.join(log_dir, str(item).zfill(2) + '_pred.txt')
data_dir = os.path.join(eval_dir, 'odometry_' + str(item).zfill(2))
if not os.path.exists(data_dir):
os.makedirs(data_dir)
np.save(fname_file, T)
np.savetxt(fname_txt, T)
os.system('cp %s %s' % (fname_file, data_dir)) ###SAVE THE txt FILE
os.system('python evaluation.py --result_dir ' + data_dir + ' --eva_seqs ' + str(item).zfill(
2) + '_pred' + ' --epoch ' + str(epoch))
return 0
if __name__ == '__main__':
main()