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main.py
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'''
Single-GPU training code
'''
import random
import argparse
import math
from datetime import datetime
from tensorflow.python.client import timeline
import numpy as np
import tensorflow as tf
import socket
import importlib
import os
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
# dataset using projection
import kitti_dataset
import pickle
import time
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default='train', help='train/test mode')
parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
parser.add_argument('--model', default='pwclo_model', help='Model name [default: pwclo_model]')
parser.add_argument('--data_root', default='../', help='Dataset directory')
parser.add_argument('--checkpoint_path', default = None, help='Path to the saved checkpoint')
parser.add_argument('--log_dir', default='log', help='Log dir [default: log_train]')
parser.add_argument('--result_dir', default='result', help='result dir [default: result]')
parser.add_argument('--train_list', nargs='+', type=int, default=range(7), help=' List of sequences for training [default: range(7)]')
parser.add_argument('--val_list', nargs='+', type=int, default=range(11), help=' List of sequences for validation [default: range(7, 11)]')
parser.add_argument('--test_list', nargs='+', type=int, default=range(11), help='List of sequences for testing [default: range(11)]')
parser.add_argument('--num_points', type=int, default=150000, help='Point Number total [default: 150000]')
parser.add_argument('--num_H_input', type=int, default=64, help='Point Number H [default: 64]')
parser.add_argument('--num_W_input', type=int, default=1800, help='Point Number W [default: 1800]')
parser.add_argument('--max_epoch', type=int, default=1000, help='Epoch to run [default: 1000]')
parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 16]')
parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]')
parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]')
parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]')
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')##########decay############3
parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]')
FLAGS = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(FLAGS.gpu)
MODE = FLAGS.mode
EPOCH_CNT = 0
BATCH_SIZE = FLAGS.batch_size
NUM_POINTS = FLAGS.num_points
H_input = FLAGS.num_H_input
W_input = FLAGS.num_W_input
DATA = FLAGS.data_root
CHECKPOINT_PATH = FLAGS.checkpoint_path
RESULT_PATH = FLAGS.result_dir
TRAIN_LIST = FLAGS.train_list
VAL_LIST = FLAGS.val_list
TEST_LIST = FLAGS.test_list
MAX_EPOCH = FLAGS.max_epoch
BASE_LEARNING_RATE = FLAGS.learning_rate
GPU_INDEX = FLAGS.gpu
MOMENTUM = FLAGS.momentum
OPTIMIZER = FLAGS.optimizer
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
MODEL = importlib.import_module(FLAGS.model) # import network module
MODEL_FILE = os.path.join(BASE_DIR, FLAGS.model+'.py')
UTIL_FILE = os.path.join(BASE_DIR, 'utils/pointnet_util.py')
LOG_DIR = FLAGS.log_dir + datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
os.system('cp %s %s' % (UTIL_FILE, LOG_DIR)) ###SAVE THE UTIL FILE
os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def
os.system('cp %s %s' % (__file__, LOG_DIR)) # bkp of train procedure
os.system('cp %s %s' % ('kitti_dataset.py', LOG_DIR)) # bkp of dataset file
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(FLAGS)+'\n')
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
TRAIN_DATASET = kitti_dataset.OdometryDataset(DATA, NUM_POINTS = NUM_POINTS, H_input = H_input, W_input = W_input)
TEST_DATASET = kitti_dataset.OdometryDataset(DATA, NUM_POINTS = NUM_POINTS, H_input = H_input, W_input = W_input)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def main(mode = 'train'):
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
pointclouds, T_gt, T_trans, T_trans_inv = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINTS) # B N 3
is_training = tf.placeholder(tf.bool, shape=())
# Note the global_step=batch parameter to minimize.
# That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains.
batch = tf.Variable(0)
w_x = tf.Variable(0.0, trainable = True, name = 'w_x')
w_q = tf.Variable(-2.5, trainable = True, name = 'w_q')
bn_decay = get_bn_decay(batch)
tf.summary.scalar('bn_decay', bn_decay)
print("--- Get model and loss")
# Get model and loss
l0_q, l0_t, l1_q, l1_t, l2_q, l2_t, l3_q, l3_t, pc1_output, q_gt, t_gt = MODEL.get_model( pointclouds, H_input, W_input, T_gt, T_trans, T_trans_inv, is_training, bn_decay=bn_decay)
loss = MODEL.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)
tf.summary.scalar('loss', loss)
print("--- Get training operator")
# Get training operator
learning_rate = get_learning_rate(batch)
tf.summary.scalar('learning_rate', learning_rate)
if OPTIMIZER == 'momentum':
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM)
elif OPTIMIZER == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=batch)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create a session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
config.log_device_placement = False
sess = tf.Session(config=config)
# Add summary writers
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph)
# Init variables
if CHECKPOINT_PATH != None:
model_path = CHECKPOINT_PATH
saver.restore(sess, model_path)
log_string ("model restored")
else:
init = tf.global_variables_initializer()
sess.run(init)
log_string ("Initialize model")
ops = {'pointclouds_pl': pointclouds,
'pred_q': l0_q,
'pred_t': l0_t,
'is_training_pl': is_training,
'T_gt': T_gt,
'T_trans': T_trans,
'T_trans_inv': T_trans_inv,
'pc1': pc1_output,
'loss': loss,
'train_op': train_op,
'merged': merged,
'step': batch,
}
if mode == 'train':
for epoch in range(0, MAX_EPOCH):
log_string('**** EPOCH %03d ****' % (epoch))
sys.stdout.flush()
train_one_epoch(sess, ops, train_writer, train_list = TRAIN_LIST)
if epoch % 20 == 0 and epoch <= 100:
cur_eval_error = eval_one_epoch(sess, ops, test_list = VAL_LIST)
if cur_eval_error < min_eval_error:
min_eval_error = cur_eval_error
save_dir = os.path.join(LOG_DIR, 'epoch_' + str(epoch) + '_best_model_dir')
os.mkdir(save_dir)
save_path = saver.save(sess, os.path.join(save_dir, str(cur_eval_error)+"_t_error_model.ckpt"))
log_string("Model saved in file: %s" % save_path)
if epoch % 2 == 0 and epoch > 100:
cur_eval_error = eval_one_epoch(sess, ops, test_list = VAL_LIST)
if cur_eval_error < min_eval_error:
min_eval_error = cur_eval_error
save_dir = os.path.join(LOG_DIR, 'epoch_' + str(epoch) + '_best_model_dir')
os.mkdir(save_dir)
save_path = saver.save(sess, os.path.join(save_dir, str(cur_eval_error)+"_t_error_model.ckpt"))
log_string("Model saved in file: %s" % save_path)
elif mode == 'test':
if CHECKPOINT_PATH != None:
eval_one_epoch(sess, ops, test_list = TEST_LIST)
log_string("Finished! Please check the result directory! ")
else:
log_string('Please verify the checkpoint for testing !!!')
def DataAugmentation():
anglex = np.clip(0.01 * np.random.randn(), -0.02, 0.02).astype(np.float32) * np.pi / 4.0
angley = np.clip(0.01 * np.random.randn(), -0.02, 0.02).astype(np.float32) * np.pi / 4.0
anglez = np.clip(0.05 * np.random.randn(), -0.1, 0.1).astype(np.float32) * np.pi / 4.0
cosx = np.cos(anglex)
cosy = np.cos(angley)
cosz = np.cos(anglez)
sinx = np.sin(anglex)
siny = np.sin(angley)
sinz = np.sin(anglez)
Rx = np.array([[1, 0, 0],
[0, cosx, -sinx],
[0, sinx, cosx]])
Ry = np.array([[cosy, 0, siny],
[0, 1, 0],
[-siny, 0, cosy]])
Rz = np.array([[cosz, -sinz, 0],
[sinz, cosz, 0],
[0, 0, 1]])
scale = np.diag(np.random.uniform(1.00, 1.00, 3).astype(np.float32))
R_trans = Rx.dot(Ry).dot(Rz).dot(scale.T)
# R_trans = Rx.dot(Ry).dot(Rz)
xx = np.clip(0.5 * np.random.randn(), -1.0, 1.0).astype(np.float32)
yy = np.clip(0.1 * np.random.randn(), -0.2, 0.2).astype(np.float32)
zz = np.clip(0.05 * np.random.randn(), -0.15, 0.15).astype(np.float32)
add_xyz = np.array([[xx], [yy], [zz]])
T_trans = np.concatenate([R_trans, add_xyz], axis=-1)
filler = np.array([0.0, 0.0, 0.0, 1.0])
filler = np.expand_dims(filler, axis=0) ##1*4
T_trans = np.concatenate([T_trans, filler], axis=0) # 4*4
return T_trans
def get_batch(dataset, idxs, start_idx, end_idx, training = 0):
bsize = end_idx-start_idx
batch_data = np.zeros((bsize, NUM_POINTS*2, 6))
batch_T_gt = np.zeros((bsize, 4, 4))
batch_T_trans = np.zeros((bsize, 4, 4))
batch_T_trans_inv = np.zeros((bsize, 4, 4))
if training == 0: #### training
batch_T_trans = np.tile(np.expand_dims(np.eye(4), axis = 0), [bsize, 1, 1]) # b 4 4
batch_T_trans_inv = batch_T_trans
for i in range(bsize):
pc1, pc2, n1, n2, T_gt = dataset[idxs[i+start_idx]] ########################################
batch_data[i, :NUM_POINTS, :3] = pc1
batch_data[i, NUM_POINTS:, :3] = pc2
batch_T_gt[i, :, :] = T_gt
else:
for i in range(bsize):
pc1, pc2, n1, n2, T_gt = dataset[idxs[i+start_idx]] ########################################
T_trans = DataAugmentation()
T_trans_inv = np.linalg.inv(T_trans)
batch_data[i, :NUM_POINTS, :3] = pc1
batch_data[i, NUM_POINTS:, :3] = pc2
batch_T_gt[i, :, :] = T_gt
batch_T_trans[i, :, :] = T_trans
batch_T_trans_inv[i, :, :] = T_trans_inv
return batch_data, batch_T_gt, batch_T_trans, batch_T_trans_inv
def train_one_epoch(sess, ops, train_writer):
global EPOCH_CNT
""" ops: dict mapping from string to tf ops """
is_training = True
train_idxs = np.arange(23201)
np.random.shuffle(train_idxs)
num_batches = len(train_idxs)// BATCH_SIZE
log_string(str(datetime.now()))
loss_sum = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = (batch_idx+1) * BATCH_SIZE
batch_data, T_gt, T_trans, T_trans_inv = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx, training = 1)
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['T_gt']: T_gt,
ops['T_trans']: T_trans,
ops['T_trans_inv']: T_trans_inv,
ops['is_training_pl']: is_training,}
# # timeline
# options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# run_metadata = tf.RunMetadata()
start_time = time.time()
summary, step, _, loss_val, pc1 = sess.run([ops['merged'], ops['step'],
ops['train_op'], ops['loss'], ops['pc1']], feed_dict = feed_dict)
end_time = time.time()
# np.savetxt('aft_warp.txt', pc1[0, :, :], fmt='%.08f')
print('run_time_batch: ' + str(BATCH_SIZE), end_time - start_time )
train_writer.add_summary(summary, step)
loss_sum += loss_val
EPOCH_CNT += 1
# # save the json
# fetched_timeline = timeline.Timeline(run_metadata.step_stats)
# chrome_trace = fetched_timeline.generate_chrome_trace_format()
# with open('timeline_pre_tf_77.json', 'w') as f:
# f.write(chrome_trace)
# if (batch_idx+1)%10 == 0:
# print(' -- %03d / %03d --' % (batch_idx+1, num_batches))
# print('mean loss: %f' % (loss_sum / 10))
# loss_sum = 0
def quat2mat(q):
''' Calculate rotation matrix corresponding to quaternion
https://afni.nimh.nih.gov/pub/dist/src/pkundu/meica.libs/nibabel/quaternions.py
Parameters
----------
q : 4 element array-like
Returns
-------
M : (3,3) array
Rotation matrix corresponding to input quaternion *q*
Notes
-----
Rotation matrix applies to column vectors, and is applied to the
left of coordinate vectors. The algorithm here allows non-unit
quaternions.
References
'''
w, x, y, z = q
Nq = w*w + x*x + y*y + z*z
if Nq < 1e-8:
return np.eye(3)
s = 2.0/Nq
X = x*s
Y = y*s
Z = z*s
wX = w*X; wY = w*Y; wZ = w*Z
xX = x*X; xY = x*Y; xZ = x*Z
yY = y*Y; yZ = y*Z; zZ = z*Z
return np.array(
[[ 1.0-(yY+zZ), xY-wZ, xZ+wY ],
[ xY+wZ, 1.0-(xX+zZ), yZ-wX ],
[ xZ-wY, yZ+wX, 1.0-(xX+yY) ]])
def read_calib_file(path): # changed
# taken from https://github.com/hunse/kitti
float_chars = set("0123456789.e+- ")
data = {}
with open(path, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
value = value.strip()
data[key] = value
if float_chars.issuperset(value):
# try to cast to float array
try:
data[key] = np.array(list(map(float, value.split(' '))))
except ValueError:
# casting error: data[key] already eq. value, so pass
pass
return data
def eval_one_epoch(sess, ops, test_list = range(11, 22)):
""" ops: dict mapping from string to tf ops """
global EPOCH_CNT
is_training = False
num_eval = 0
total_t_error = 0
eval_result = 0
for ii in test_list:
s = [0, 4541, 5642, 10303, 11104, 11375, 14136, 15237, 16338, 20409, 22000, 23201, \
24122, 25183, 28464, 29095, 30996, 32727, 33218, 35019, 40000, 40831]
e = [4541, 5642, 10303, 11104, 11375, 14136, 15237, 16338, 20409, 22000, 23201, \
24122, 25183, 28464, 29095, 30996, 32727, 33218, 35019, 40000, 40831, 43552]
Tr_path = os.path.join(DATA, str(ii).zfill(2), 'calib.txt')
Tr_data = read_calib_file(Tr_path)
Tr_data = Tr_data['Tr']
Tr = Tr_data.reshape(3,4)
Tr = np.vstack((Tr, np.array([0, 0, 0, 1.0])))
Tr_inv = np.linalg.inv(Tr)###
start = s[ii]
end = e[ii]
test_idxs = np.arange(start, end)
num_batches = (end-start+BATCH_SIZE-1) // BATCH_SIZE
log_string(str(datetime.now()))
log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT))
batch_data = np.zeros((BATCH_SIZE, NUM_POINTS*2, 3))
q_gt = np.zeros([BATCH_SIZE, 4 ])
t_gt = np.zeros([BATCH_SIZE, 3, 1])
tmp = 0
for batch_idx in range(num_batches):
start_idx = batch_idx * BATCH_SIZE
end_idx = min(end-start, (batch_idx+1) * BATCH_SIZE)
cur_batch_size = end_idx-start_idx
cur_batch_data, cur_T_gt, cur_T_trans, cur_T_trans_inv = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx, training = 0)
if cur_batch_size == BATCH_SIZE:
batch_data = cur_batch_data
T_gt = cur_T_gt
T_trans = cur_T_trans
T_trans_inv = cur_T_trans_inv
else:
batch_data[0:cur_batch_size] = cur_batch_data
T_gt[0:cur_batch_size] = cur_T_gt
T_trans[0:cur_batch_size] = cur_T_trans
T_trans_inv[0:cur_batch_size] = cur_T_trans_inv
# ---------------------------------------------------------------------
# ---- INFERENCE BELOW ----
feed_dict = {ops['pointclouds_pl']: batch_data,
ops['T_gt']: T_gt,
ops['T_trans']: T_trans,
ops['T_trans_inv']: T_trans_inv,
ops['is_training_pl']: is_training}
start_time = time.time()
pred_q, pred_t, pc1 = sess.run([ops['pred_q'], ops['pred_t'], ops['pc1']], feed_dict=feed_dict)
end_time = time.time()
print('run_time_batch: ' + str(BATCH_SIZE), end_time - start_time )
# ---- INFERENCE ABOVE ----
for n0 in range(cur_batch_size):
if BATCH_SIZE != 1:
q_one_batch = pred_q[n0:n0+1, :]
t_one_batch = pred_t[n0:n0+1, :]
else:
q_one_batch = pred_q
t_one_batch = pred_t
qq = np.reshape(q_one_batch, [4])
tt = np.reshape(t_one_batch, [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(Tr, TT)
TT = np.matmul(TT, Tr_inv)
if tmp == 0:
T_final = TT ### 4 4
T = T_final[ :3, : ]#### 3 4
T = T.reshape(1,1,12)
tmp += 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)
T = T.reshape(-1, 12)
fname_txt = os.path.join(LOG_DIR, str(ii).zfill(2) + '_pred.txt')
result_dir = "data/" + FLAGS.log_dir
if not os.path.exists(result_dir):
os.makedirs(result_dir)
np.savetxt(fname_txt, T, fmt='%.08f')
os.system('cp %s %s' % (fname_txt, result_dir)) ### SAVE THE txt FILE
result_f = os.popen("python ./kitti_evaluation.py --result_dir " + result_dir + " --eva_seqs " + str(ii).zfill(2) + "_pred", "r")
for line in result_f.readlines():
log_string(line)
if ('seq' in line):
cur_t_error = float(line.strip().split(' ')[-3])
total_t_error += cur_t_error
num_eval += 1
eval_result = total_t_error/num_eval
return eval_result
if __name__ == "__main__":
log_string('pid: %s'%(str(os.getpid())))
main(MODE)
LOG_FOUT.close()