def main(_):
start = time.clock()
if args.dataset == 'MSRA':
di = MSRA15Importer(args.data_root, cacheDir='./cache/MSRA/',refineNet=None)
Seq_all = []
MID = args.test_sub
for seq in range(9):
if seq == MID:
Seq_train_ = di.loadSequence('P{}'.format(seq), rng=rng, shuffle=False, docom=False, cube=(175, 175, 175))
else:
Seq_train_ = di.loadSequence('P{}'.format(seq), rng=rng, shuffle=False, docom=False, cube=None)
Seq_all.append(Seq_train_)
Seq_test_raw = Seq_all.pop(MID)
Seq_test = Seq_test_raw.data
Seq_train = [seq_data for seq_ in Seq_all for seq_data in seq_.data]
train_num = len(Seq_train)
print ('loaded over with %d train samples'%train_num)
imgs = np.asarray([d.dpt.copy() for d in Seq_train], 'float32')
gt3Dcrops = np.asarray([d.gt3Dcrop for d in Seq_train], dtype='float32')
M = np.asarray([d.T for d in Seq_train], dtype='float32')
com2D = np.asarray([d.com2D for d in Seq_train], 'float32')
cube = np.asarray([d.cube for d in Seq_train], 'float32')
# uv_crop = np.asarray([d.gtcrop for d in Seq_train], dtype='float32')[:, :, 0:-1]
del Seq_train
train_stream = MultiDataStream([imgs, gt3Dcrops, M, com2D, cube])
else:
raise ValueError('error dataset %s'%args.dataset)
test_num=len(Seq_test)
print ('loaded over with %d test samples'%test_num)
test_gt3Dcrops = np.asarray([d.gt3Dcrop for d in Seq_test], dtype='float32')
test_M = np.asarray([d.T for d in Seq_test], dtype='float32')
# test_com2D = np.asarray([d.com2D for d in Seq_test], 'float32')
# test_uv_crop = np.asarray([d.gtcrop for d in Seq_test], dtype='float32')[:, :, 0:-1]
test_uv = np.asarray([d.gtorig for d in Seq_test], 'float32')[:, :, 0:-1]
test_com3D = np.asarray([d.com3D for d in Seq_test], 'float32')
test_cube = np.asarray([d.cube for d in Seq_test], 'float32')
test_imgs = np.asarray([d.dpt.copy() for d in Seq_test], 'float32')
test_data=np.ones_like(test_imgs)
for it in range(test_num):
test_data[it]=norm_dm(test_imgs[it], test_com3D[it], test_cube[it])
del Seq_test
test_stream = MultiDataStream([test_data, test_gt3Dcrops, test_M, test_com3D, test_uv, test_cube])
clip_index = np.int(np.floor(test_num/args.batch_size)) * args.batch_size
extra_test_data = [test_data[clip_index:], test_gt3Dcrops[clip_index:], test_M[clip_index:],
test_com3D[clip_index:], test_uv[clip_index:], test_cube[clip_index:]]
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
tf.set_random_seed(1)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = Model(sess, args)
model.train(args, train_stream, test_stream) if args.phase == 'train' \
else model.test(args, test_stream, extra_test_data=None)
end = time.clock()
print ('running time: %f s'%(end-start))
def getPCL(self, dpt, T):
"""
Get pointcloud from frame
:param dpt: depth image
:param T: 2D transformation of crop
"""
return MSRA15Importer.depthToPCL(dpt, T)
def __init__(self, imgSeqs=None, basepath=None, localCache=True):
"""
constructor
"""
super(MSRA15Dataset, self).__init__(imgSeqs, localCache)
if basepath is None:
basepath = '../../data/MSRA15/'
self.lmi = MSRA15Importer(basepath)
from util.handdetector import HandDetector
import numpy as np
from netlib.basemodel import basenet2
from data.transformations import transformPoints2D
import argparse
parser = argparse.ArgumentParser(description='set test subject')
parser.add_argument('--test-sub', type=int, default=None)
args = parser.parse_args()
rng = np.random.RandomState(23455)
import tensorflow as tf
train_root = '/content/cvpr15_MSRAHandGestureDB'
shuffle = False
di = MSRA15Importer(train_root, cacheDir='../../cache/MSRA', refineNet=None)
Seq_all = []
MID = args.test_sub
for seq in range(9):
shuffle = True
if seq == MID:
shuffle = False
Seq_train_ = di.loadSequence('P{}'.format(seq),
rng=rng,
shuffle=shuffle,
docom=True,
cube=(175, 175, 175))
else:
Seq_train_ = di.loadSequence('P{}'.format(seq),
rng=rng,
from data.dataset import MSRA15Dataset
from util.handpose_evaluation import MSRAHandposeEvaluation
from util.helpers import shuffle_many_inplace
if __name__ == '__main__':
eval_prefix = 'MSRA15_COM_AUGMENT'
if not os.path.exists('./eval/'+eval_prefix+'/'):
os.makedirs('./eval/'+eval_prefix+'/')
rng = numpy.random.RandomState(23455)
print("create data")
aug_modes = ['com', 'rot', 'none'] # 'sc',
di = MSRA15Importer('../data/MSRA15/')
Seq0_1 = di.loadSequence('P0', shuffle=True, rng=rng, docom=False)
Seq0_1 = Seq0_1._replace(name='P0_gt')
Seq0_2 = di.loadSequence('P0', shuffle=True, rng=rng, docom=True)
Seq0_2 = Seq0_2._replace(name='P0_com')
Seq1_1 = di.loadSequence('P1', shuffle=True, rng=rng, docom=False)
Seq1_1 = Seq1_1._replace(name='P1_gt')
Seq1_2 = di.loadSequence('P1', shuffle=True, rng=rng, docom=True)
Seq1_2 = Seq1_2._replace(name='P1_com')
Seq2_1 = di.loadSequence('P2', shuffle=True, rng=rng, docom=False)
Seq2_1 = Seq2_1._replace(name='P2_gt')
Seq2_2 = di.loadSequence('P2', shuffle=True, rng=rng, docom=True)
Seq2_2 = Seq2_2._replace(name='P2_com')
Seq3_1 = di.loadSequence('P3', shuffle=True, rng=rng, docom=False)
Seq3_1 = Seq3_1._replace(name='P3_gt')
Seq3_2 = di.loadSequence('P3', shuffle=True, rng=rng, docom=True)
print('Start frame index can be specified: -s <start_idx> or enter now:')
start_idx = input().lower()
if len(start_idx.strip()) == 0:
start_idx = 0
else:
start_idx = int(start_idx)
else:
print('Start frame index is {}'.format(start_idx))
rng = numpy.random.RandomState(23455)
# subset to label
subset_idxs = []
if person == 'P0':
di = MSRA15Importer('/home/boonyew/Documents/semi-auto-anno-master/semi-auto-anno/data/msra/', useCache=False)
Seq2 = di.loadSequence(person, shuffle=False)
hc = MSRAHandConstraints([Seq2.name])
hpe = MSRAHandposeEvaluation([j.gt3Dorig for j in Seq2.data], [j.gt3Dorig for j in Seq2.data])
for idx, seq in enumerate(Seq2.data):
ed = {'vis': [], 'pb': {'pb': [], 'pbp': []}}
Seq2.data[idx] = seq._replace(gtorig=numpy.zeros_like(seq.gtorig), extraData=ed)
# common subset for all
subset_idxs = [16, 21, 26, 29, 45, 49, 52, 54, 58, 104, 108, 114, 138, 144, 148, 170, 175, 178, 210, 214, 217, 231, 237, 249, 252, 259, 264, 283, 287, 296, 307, 345, 370, 381, 384, 386, 405, 412, 423, 429, 436, 458, 465, 469, 490, 498, 505, 526, 530, 533, 537, 546, 553, 576, 607, 612, 624, 631, 657, 667, 669, 673, 685, 697, 704, 735, 742, 751, 765, 781, 784, 789, 793, 801, 805, 816, 820, 827, 830, 874, 886, 888, 893, 896, 899, 911, 923, 934, 962, 969, 983, 1023, 1027, 1029, 1034, 1046, 1054, 1057, 1070, 1075, 1085, 1093, 1098, 1110, 1114, 1134, 1138, 1146, 1173, 1181, 1184, 1188, 1191, 1194, 1208, 1213, 1221, 1224, 1228, 1241, 1248, 1251, 1255, 1262, 1267, 1274, 1286, 1295, 1308, 1312, 1335, 1341, 1349, 1353, 1383, 1386, 1389, 1410, 1414, 1422, 1432, 1449, 1452, 1455, 1465, 1473, 1477, 1489, 1504, 1523, 1532, 1542, 1550, 1552, 1571, 1580, 1586, 1591, 1609, 1613, 1617, 1628, 1632, 1644, 1653, 1656, 1688, 1694, 1695, 1698, 1709, 1713, 1725, 1745, 1752, 1756, 1762, 1772, 1778, 1795, 1812, 1814, 1817, 1830, 1833, 1848, 1853, 1858, 1864, 1869, 1873, 1887, 1892, 1897, 1904, 1927, 1930, 1934, 1937, 1965, 1973, 1978, 1991, 2017, 2028, 2033, 2048, 2055, 2058, 2067, 2074, 2094, 2131, 2137, 2146, 2150, 2166, 2170, 2177, 2185, 2191, 2196, 2203, 2208, 2213, 2222, 2255, 2269, 2273, 2288, 2291, 2298, 2305, 2325, 2331, 2334, 2339, 2343, 2347, 2351, 2372, 2380, 2390, 2394, 2416, 2428, 2434, 2462, 2468, 2484, 2497, 2504, 2509, 2511, 2515, 2529, 2543, 2566, 2572, 2584, 2590, 2609, 2617, 2627, 2631, 2644, 2651, 2654, 2661, 2685, 2687, 2693, 2702, 2737, 2749, 2754, 2763, 2775, 2778, 2790, 2792, 2808, 2813, 2816, 2820, 2829, 2835, 2852, 2856, 2872, 2891, 2898, 2905, 2911, 2942, 2945, 2949, 2952, 2989, 3011, 3015, 3031, 3034, 3037]
else:
raise NotImplementedError("")
replace_off = 0
replace_file = None # './params_tracking.npz'
#from util.cameradevice import CreativeCameraDevice, FileDevice
__author__ = "Markus Oberweger <*****@*****.**>"
__copyright__ = "Copyright 2015, ICG, Graz University of Technology, Austria"
__credits__ = ["Markus Oberweger"]
__license__ = "GPL"
__version__ = "1.0"
__maintainer__ = "Markus Oberweger"
__email__ = "*****@*****.**"
__status__ = "Development"
if __name__ == '__main__':
rng = numpy.random.RandomState(23455)
di = MSRA15Importer('/content/drive/My Drive/KNOWLEDGE ENGINEERING/KE Semester 4/Core Course/CA2 (Matthew)/cvpr15_MSRAHandGestureDB/')
Seq2 = di.loadSequence('P0')
testSeqs = [Seq2]
# di = ICVLImporter('../data/ICVL/')
# Seq2 = di.loadSequence('test_seq_1')
# testSeqs = [Seq2]
#di = NYUImporter('../data/NYU/')
#Seq2 = di.loadSequence('test_1')
#testSeqs = [Seq2]
# load trained network
poseNetParams = ResNetParams(type=1, nChan=1, wIn=128, hIn=128, batchSize=1, numJoints=14, nDims=3)
poseNetParams.loadFile = "/content/deep-prior-pp/src/eval/MSRA_network_prior_0.pkl"
comrefNetParams = ScaleNetParams(type=1, nChan=1, wIn=128, hIn=128, batchSize=1, resizeFactor=2, numJoints=1, nDims=3)
import sys
from data.importers import MSRA15Importer
from data.dataset import MSRA15Dataset
# from util.handpose_evaluation import MSRAHandposeEvaluation
from sklearn.decomposition import PCA
if __name__ == '__main__':
rng = np.random.RandomState(23455)
print("create data")
aug_modes = ['com', 'rot', 'none'] # 'sc',
comref = None # "./eval/MSRA15_COM_AUGMENT/net_MSRA15_COM_AUGMENT.pkl"
docom = False
di = MSRA15Importer('../data/MSRA15/', refineNet=comref)
seqs = []
# seqs.append(di.loadSequence('P0', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P1', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P2', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P3', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P4', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P5', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P6', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P7', shuffle=True, rng=rng, docom=docom))
# seqs.append(di.loadSequence('P8', shuffle=True, rng=rng, docom=docom))
seqs.append(
di.loadSequence('Retrain',
shuffle=True,
rng=rng,
docom=docom,
def main(argv):
rng = np.random.RandomState(23455)
print("create data")
# aug_modes = ['com', 'rot', 'none'] # 'sc',
comref = None # "./eval/MSRA15_COM_AUGMENT/net_MSRA15_COM_AUGMENT.pkl"
docom = False
di = MSRA15Importer('../data/MSRA15/', refineNet=comref)
# testSeqs = [di.loadSequence('Retrain', shuffle=True, rng=rng, docom=docom, cube=(200,200,200))]
testSeqs = [di.loadSequence('P0', shuffle=True, rng=rng, docom=docom)]
testDataSet = MSRA15Dataset(testSeqs)
test_data, test_gt3D = testDataSet.imgStackDepthOnly(testSeqs[0].name)
test_data_cube = np.asarray([testSeqs[0].config['cube']] * test_data.shape[0])
test_data = np.transpose(test_data, axes=[0, 2, 3, 1])
test_label = np.reshape(test_gt3D, [-1, test_gt3D.shape[1] * 3])
model = get_model()
model.build_loss(weight_decay=FLAGS.weight_decay,
lr=FLAGS.init_lr,
lr_decay_rate=FLAGS.lr_decay_rate,
lr_decay_step=FLAGS.lr_decay_step)
epoch_batch_num = test_data.shape[0] // FLAGS.batch_size
model_path_suffix = '{}_{}_stage{}'.format(FLAGS.model_name, FLAGS.data_set, FLAGS.stages)
# 测试模型参数加载路径
model_weights_path = os.path.join(FLAGS.cacheDir,
FLAGS.weightDir,
model_path_suffix,
'{}-{}'.format(FLAGS.model_name, FLAGS.test_iters))
eval_result_dir = os.path.join(FLAGS.evalDir,
model_path_suffix,
'test_iter{}'.format(FLAGS.test_iters))
os.makedirs(eval_result_dir, exist_ok=True)
joint_errors = []
total_errors = []
losses = []
with tf.Session() as sess:
saver = tf.train.Saver()
# print(model_weights_path)
saver.restore(sess, model_weights_path)
print('load model from: {}'.format(model_weights_path))
joint_infer = tf.reshape(model.model_output, shape=[-1, model.model_output.shape[1].value//3, 3])* (
tf.reshape(model.cube_holder, [-1, 1, 3]) / 2.)
joint_gt = tf.reshape(model.label_holder, [-1, model.label_holder.shape[1].value // 3, 3]) * (
tf.reshape(model.cube_holder, [-1, 1, 3]) / 2.)
# batch中每个关节点的平均误差
# joint_error_op = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square(joint_infer - joint_gt), axis=-1)), axis=0)
joint_error_op = tf.sqrt(tf.reduce_sum(tf.square(joint_infer - joint_gt), axis=-1))
for i in range(epoch_batch_num):
batch_x, batch_y, batch_cube = test_data[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size],\
test_label[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size], \
test_data_cube[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size],
ops = get_test_ops(model)
ops.append(joint_error_op)
loss, stage_error, joint_error = sess.run(ops,
feed_dict={model.input_images: batch_x,
model.label_holder: batch_y,
model.cube_holder: batch_cube})
print('step {}, total loss {}, final stage error {} mm\n'.format(i, loss, stage_error))
losses.append(loss)
total_errors.append(stage_error)
joint_errors.append(joint_error)
print('average loss {}, average final stage error {} mm \n'.format(np.mean(losses), np.mean(total_errors)))
# 保存评测误差
pickle.dump(joint_errors, open('{}/joint_errors.pkl'.format(eval_result_dir), 'wb'))
import matplotlib
matplotlib.use('Agg') # 图像输出到文件
draw_PCK(joint_errors, eval_result_dir)
draw_joint_error(joint_errors, eval_result_dir)
def main(argv):
rng = np.random.RandomState(23455)
print("create data")
aug_modes = ['com', 'rot', 'none'] # 'sc',
comref = None # "./eval/MSRA15_COM_AUGMENT/net_MSRA15_COM_AUGMENT.pkl"
docom = False
di = MSRA15Importer('../data/MSRA15/', refineNet=comref)
seqs = []
seqs.append(di.loadSequence('P0', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P1', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P2', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P3', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P4', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P5', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P6', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P7', shuffle=True, rng=rng, docom=docom))
seqs.append(di.loadSequence('P8', shuffle=True, rng=rng, docom=docom))
testSeqs = [seqs[0]]
trainSeqs = [seq for seq in seqs if seq not in testSeqs]
print("training: {}".format(' '.join([s.name for s in trainSeqs])))
print("testing: {}".format(' '.join([s.name for s in testSeqs])))
# create training data
trainDataSet = MSRA15Dataset(trainSeqs, localCache=False)
nSamp = np.sum([len(s.data) for s in trainSeqs])
d1, g1 = trainDataSet.imgStackDepthOnly(trainSeqs[0].name) # 在这里进行归一化处理
# 存储所有数据（[-1,1]）
train_data = np.ones((nSamp, d1.shape[1], d1.shape[2], d1.shape[3]), dtype='float32')
# 存储所有的标签([-1,1])
train_gt3D = np.ones((nSamp, g1.shape[1], g1.shape[2]), dtype='float32')
# 存储所有的立体限制框
train_data_cube = np.ones((nSamp, 3), dtype='float32')
# 存储所有的中心点
train_data_com = np.ones((nSamp, 3), dtype='float32')
# 存储所有的3D点
train_gt3Dcrop = np.ones_like(train_gt3D)
del d1, g1
gc.collect()
gc.collect()
gc.collect()
oldIdx = 0
for seq in trainSeqs:
d, g = trainDataSet.imgStackDepthOnly(seq.name)
train_data[oldIdx:oldIdx + d.shape[0]] = d
train_gt3D[oldIdx:oldIdx + d.shape[0]] = g
train_data_cube[oldIdx:oldIdx + d.shape[0]] = np.asarray([seq.config['cube']] * d.shape[0])
train_data_com[oldIdx:oldIdx + d.shape[0]] = np.asarray([da.com for da in seq.data])
train_gt3Dcrop[oldIdx:oldIdx + d.shape[0]] = np.asarray([da.gt3Dcrop for da in seq.data])
oldIdx += d.shape[0]
del d, g
gc.collect()
gc.collect()
gc.collect()
mb = (train_data.nbytes) / (1024 * 1024)
print("data size: {}Mb".format(mb))
testDataSet = MSRA15Dataset(testSeqs)
test_data, test_gt3D = testDataSet.imgStackDepthOnly(testSeqs[0].name)
test_data_cube = np.asarray([testSeqs[0].config['cube']] * test_data.shape[0])
# 矩阵转置
train_data = np.transpose(train_data, axes=[0, 2, 3, 1])
test_data = np.transpose(test_data, axes=[0, 2, 3, 1])
train_label = np.reshape(train_gt3D, [-1, train_gt3D.shape[1]*3])
test_label = np.reshape(test_gt3D, [-1, test_gt3D.shape[1]*3])
print(train_data.shape)
# 模型路径的后缀
model_path_suffix = '{}_{}_stage{}'.format(FLAGS.model_name, FLAGS.data_set, FLAGS.stages)
# 模型保存路径
model_save_dir = os.path.join(FLAGS.cacheDir,
FLAGS.weightDir,
model_path_suffix)
# 训练和测试日志保存路径
train_log_save_dir = os.path.join(FLAGS.cacheDir,
FLAGS.logDir,
model_path_suffix,
'train')
test_log_save_dir = os.path.join(FLAGS.cacheDir,
FLAGS.logDir,
model_path_suffix,
'test')
os.makedirs(model_save_dir,exist_ok=True)
os.makedirs(train_log_save_dir,exist_ok=True)
os.makedirs(test_log_save_dir,exist_ok=True)
# 构建模型
model = getModel()
model.build_loss(weight_decay=FLAGS.weight_decay,
lr=FLAGS.init_lr,
lr_decay_rate=FLAGS.lr_decay_rate,
lr_decay_step=FLAGS.lr_decay_step)
train_data_generator = Data_Generator(FLAGS.batch_size, images=train_data,
labels=train_label, cubes=train_data_cube)
test_data_generator = Data_Generator(FLAGS.batch_size, images=test_data,
labels=test_label, cubes=test_data_cube)
print("=====Model Build=====")
merged_summary = tf.summary.merge_all()
t1 = time.time()
with tf.Session() as sess:
# 创建 tensorboard
train_writer = tf.summary.FileWriter(train_log_save_dir, sess.graph)
test_writer = tf.summary.FileWriter(test_log_save_dir, sess.graph)
# 创建 model saver
saver = tf.train.Saver(max_to_keep=None)
# 初始化 all vars
init_op = tf.global_variables_initializer()
sess.run(init_op)
# 加载模型
if FLAGS.pretrained_model != '':
saver.restore(sess, os.path.join(model_save_dir, FLAGS.pretrained_model))
print('load model from {}'.format(os.path.join(model_save_dir, FLAGS.pretrained_model)))
# 检查权值
for variable in tf.trainable_variables():
with tf.variable_scope('', reuse=True):
var = tf.get_variable(variable.name.split(':0')[0])
print(variable.name, np.mean(sess.run(var)))
global_step = 0
while True:
# 获取一个batch数据
batch_x, batch_y, batch_cube = train_data_generator.next()
train_ops = get_train_ops(model)
# Forward and update weights
# stage_losses_np, total_loss_np, _, current_lr, \
# global_step, stage_errores_np, summaries = sess.run([model.stage_loss,
# model.total_loss,
# model.train_op,
# model.cur_lr,
# model.global_step,
# model.stage_error,
# merged_summary],
# feed_dict={model.input_images: batch_x,
# model.label_holder: batch_y,
# model.cube_holder: batch_cube})
train_ops.append(merged_summary)
train_step_results = sess.run(train_ops,
feed_dict={model.input_images: batch_x,
model.label_holder: batch_y,
model.cube_holder: batch_cube})
summaries = train_step_results[-1]
global_step = train_step_results[-2]
train_writer.add_summary(summaries, global_step)
# 打印训练中间过程
if (global_step) % FLAGS.verbose_iters == 0:
# Show training info
print_current_training_stats(global_step, train_step_results, time.time() - t1)
# 验证一下
if (global_step) % FLAGS.validation_iters == 0:
test_losses = []
test_errors = []
for _ in range(20):
batch_x, batch_y, batch_cube = test_data_generator.next()
eval_ops = get_eval_ops(model)
# test_batch_loss, test_batch_error, \
# summaries = sess.run([model.stage_loss[-1], model.stage_error[-1]
# , merged_summary],
# feed_dict={model.input_images: batch_x,
# model.label_holder: batch_y,
# model.cube_holder: batch_cube})
eval_ops.append(merged_summary)
test_batch_loss, test_batch_error, \
summaries = sess.run(eval_ops,
feed_dict={model.input_images: batch_x,
model.label_holder: batch_y,
model.cube_holder: batch_cube})
test_losses.append(test_batch_loss)
test_errors.append(test_batch_error)
test_mean_loss = np.mean(test_losses)
test_mean_error = np.mean(test_errors)
print('\n Validation loss:{} Validation error:{}mm\n'.format(test_mean_loss, test_mean_error))
test_writer.add_summary(summaries, global_step)
# 保存模型
if (global_step) % FLAGS.model_save_iters == 0:
saver.save(sess=sess, global_step=global_step,
save_path=os.path.join(model_save_dir, FLAGS.model_name))
print('\nModel checkpoint saved...\n')
if global_step == FLAGS.training_iters:
break
print('Training done.')
