├── ConvLSTMCell.py
├── README.md
├── c3d_clstm.py
├── inputs.py
├── tensorlayer-rnnlayer.py
├── testing_isogr.py
├── training_isogr_depth.py
├── training_isogr_rgb.py
├── training_skig_depth.py
├── training_skig_rgb.py
└── trte_splits
    ├── IsoGD_Image
        ├── train_depth_list.txt
        ├── train_rgb_list.txt
        ├── valid_depth_list.txt
        └── valid_rgb_list.txt
    └── SKIG
        ├── testing_depth_list1.txt
        ├── testing_depth_list2.txt
        ├── testing_depth_list3.txt
        ├── testing_rgb_list1.txt
        ├── testing_rgb_list2.txt
        ├── testing_rgb_list3.txt
        ├── training_depth_list1.txt
        ├── training_depth_list2.txt
        ├── training_depth_list3.txt
        ├── training_rgb_list1.txt
        ├── training_rgb_list2.txt
        └── training_rgb_list3.txt


/ConvLSTMCell.py:
--------------------------------------------------------------------------------
  1 | import tensorflow as tf
  2 | 
  3 | from tensorflow.python.ops import array_ops
  4 | from tensorflow.python.ops import variable_scope as vs
  5 | from tensorflow.python.ops.math_ops import sigmoid
  6 | from tensorflow.python.ops.math_ops import tanh
  7 | from tensorflow.python.platform import tf_logging as logging
  8 | from tensorflow.python.ops import rnn_cell
  9 | from tensorflow.python.ops import init_ops
 10 | from tensorflow.python.ops.rnn_cell import LSTMStateTuple
 11 | 
 12 | # Future : Replace it with tensorflow.python.util.nest
 13 | import collections
 14 | import six
 15 | def _is_sequence(seq):
 16 |   return (isinstance(seq, collections.Sequence)
 17 |           and not isinstance(seq, six.string_types))
 18 | 
 19 | class ConvLSTMCell(rnn_cell.RNNCell):
 20 |   """ Convolutional LSTM network cell (ConvLSTM).
 21 |   The implementation is based on http://arxiv.org/abs/1506.04214. 
 22 |    and BasicLSTMCell in TensorFlow. 
 23 |    https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/rnn_cell.py
 24 |    
 25 |    Future : Peephole connection will be added as the full LSTMCell
 26 |             implementation of TensorFlow.
 27 |   """
 28 |   def __init__(self, num_units, input_size=None,
 29 |                use_peepholes=False, cell_clip=None,
 30 |                initializer=None, num_proj=None, proj_clip=None,
 31 |                num_unit_shards=1, num_proj_shards=1,
 32 |                forget_bias=1.0, state_is_tuple=False,
 33 |                activation=tanh):
 34 | 
 35 | #    if not state_is_tuple:
 36 | #      logging.warn(
 37 | #          "%s: Using a concatenated state is slower and will soon be "
 38 | #          "deprecated.  Use state_is_tuple=True." % self)
 39 |     if input_size is not None:
 40 |       logging.warn("%s: The input_size parameter is deprecated." % self)
 41 | 
 42 |     #self._use_peepholes = use_peepholes
 43 |     #self._cell_clip = cell_clip
 44 |     #self._initializer = initializer
 45 |     #self._num_proj = num_proj
 46 |     #self._num_unit_shards = num_unit_shards
 47 |     #self._num_proj_shards = num_proj_shards
 48 | 
 49 |     self._num_units = num_units
 50 |     self._forget_bias = forget_bias
 51 |     self._state_is_tuple = state_is_tuple
 52 |     self._activation = activation
 53 | 
 54 |   @property
 55 |   def state_size(self):
 56 |     return (LSTMStateTuple(self._num_units, self._num_units)
 57 |             if self._state_is_tuple else 2 * self._num_units)
 58 | 
 59 |   @property
 60 |   def output_size(self):
 61 |     return self._num_units
 62 | 
 63 |   def zero_state(self, batch_size, height, width):
 64 |     return tf.zeros([batch_size, height, width, self._num_units*2])
 65 | 
 66 |   def __call__(self, inputs, state, k_size=3, scope=None):
 67 |     """Convolutional Long short-term memory cell (ConvLSTM)."""
 68 |     with vs.variable_scope(scope or type(self).__name__): # "ConvLSTMCell"
 69 |       if self._state_is_tuple:
 70 |         c, h = state
 71 |       else:
 72 |         c, h = array_ops.split(3, 2, state)
 73 | 
 74 |       # batch_size * height * width * channel
 75 |       concat = _conv([inputs, h], 4 * self._num_units, k_size, True)
 76 | 
 77 |       # i = input_gate, j = new_input, f = forget_gate, o = output_gate
 78 |       i, j, f, o = array_ops.split(3, 4, concat)
 79 | 
 80 |       new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) *
 81 |                self._activation(j))
 82 |       new_h = self._activation(new_c) * sigmoid(o)
 83 | 
 84 |       if self._state_is_tuple:
 85 |         new_state = LSTMStateTuple(new_c, new_h)
 86 |       else:
 87 |         new_state = array_ops.concat(3, [new_c, new_h])
 88 |       return new_h, new_state
 89 |       
 90 | def _conv(args, output_size, k_size, bias=True, bias_start=0.0, scope=None):
 91 |   if args is None or (_is_sequence(args) and not args):
 92 |     raise ValueError("`args` must be specified")
 93 |   if not _is_sequence(args):
 94 |     args = [args]
 95 | 
 96 |   # Calculate the total size of arguments on dimension 3.
 97 |   # (batch_size x height x width x arg_size)
 98 |   total_arg_size = 0
 99 |   shapes = [a.get_shape().as_list() for a in args]
100 |   height = shapes[0][1]
101 |   width  = shapes[0][2]
102 |   for shape in shapes:
103 |     if len(shape) != 4:
104 |       raise ValueError("Conv is expecting 3D arguments: %s" % str(shapes))
105 |     if not shape[3]:
106 |       raise ValueError("Conv expects shape[3] of arguments: %s" % str(shapes))
107 |     if shape[1] == height and shape[2] == width:
108 |       total_arg_size += shape[3]
109 |     else :
110 |       raise ValueError("Inconsistent height and width size in arguments: %s" % str(shapes))
111 |   
112 |   with vs.variable_scope(scope or "Conv"):
113 |     kernel = vs.get_variable("Kernel", [k_size, k_size, total_arg_size, output_size])
114 |     
115 |     if len(args) == 1:
116 |       res = tf.nn.conv2d(args[0], kernel, [1, 1, 1, 1], padding='SAME')
117 |     else:
118 |       res = tf.nn.conv2d(array_ops.concat(3, args), kernel, [1, 1, 1, 1], padding='SAME')
119 | 
120 |     if not bias: return res
121 |     bias_term = vs.get_variable( "Bias", [output_size],
122 |       initializer=init_ops.constant_initializer(bias_start))
123 |   return res + bias_term
124 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Conv3D_CLSTM
 2 | 
 3 | ## The code of the new architecture "Res3D+ConvLSTM+MobileNet" implemented by TF-1.2 has been released in https://github.com/GuangmingZhu/AttentionConvLSTM, this repository is not in support now!
 4 | 
 5 | ## Prerequisites
 6 | 
 7 | 1) Tensorflow-0.11 <br/>
 8 | 2) Tensorlayer (commit ba30379f1b86f930d6e86e1c8db49cbd2d9aa314) <br/> 
 9 |    git clone https://github.com/zsdonghao/tensorlayer.git <br/>
10 |    git checkout ba30379f1b86f930d6e86e1c8db49cbd2d9aa314 <br/>
11 |    #### The original Tensorlayer does not support the convolutional LSTM, so the class RNNLayer in the tensorlayer/layers.py needs to be modified according to the tensorlayer-rnnlayer.py. <br/>
12 |    
13 | ## Get the trained models
14 | The trained models used in the paper can be obtained on the link: https://pan.baidu.com/s/1o8kDT9K Password: efm2. <br/>
15 | 
16 | ## How to use the code
17 | Use training_*.py to train the networks. Please replace the paths in the codes with your paths first. <br/>
18 | Use testing_*.py to validate the networks. Please replace the paths in the codes with your paths first. <br/>
19 | 
20 | ## Citation
21 | Please cite the following paper if you feel this repository useful. <br/>
22 | ```
23 | @article{Zhu2017MultimodalGR,
24 |   title={Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM},
25 |   author={Guangming Zhu and Liang Zhang and Peiyi Shen and Juan Song},
26 |   journal={IEEE Access},
27 |   year={2017},
28 |   volume={5},
29 |   pages={4517-4524}
30 | }
31 | ```
32 | ## Contact
33 | For any question, please contact
34 | ```
35 |   gmzhu@xidian.edu.cn
36 | ```
37 | 


--------------------------------------------------------------------------------
/c3d_clstm.py:
--------------------------------------------------------------------------------
  1 | import io
  2 | import sys
  3 | import numpy as np
  4 | import tensorflow as tf
  5 | slim = tf.contrib.slim
  6 | import tensorlayer as tl
  7 | from tensorflow.python.framework import ops
  8 | import ConvLSTMCell as clstm
  9 | 
 10 | def c3d_clstm(inputs, num_classes, reuse, is_training):
 11 |   """Builds the Conv3D-ConvLSTM Networks."""
 12 |   with tf.device('/gpu:0'):
 13 |     with tf.variable_scope('Conv3D_ConvLSTM', reuse=reuse):
 14 |       tl.layers.set_name_reuse(reuse)
 15 |       if inputs.get_shape().ndims!=5:
 16 |         raise Exception("The input dimension of 3DCNN must be rank 5")
 17 |       network_input = tl.layers.InputLayer(inputs, name='input_layer')      #Input Layer
 18 |       # 3DCNN-BN Layer 1
 19 |       conv3d_1 = tl.layers.Conv3dLayer(network_input,
 20 |                                         act=tf.identity,
 21 |                                         shape=[3,3,3,3,64],
 22 |                                         strides=[1,1,1,1,1],
 23 |                                         padding='SAME',
 24 |                                         name='Conv3d_1')
 25 |       conv3d_1 = tl.layers.BatchNormLayer(layer=conv3d_1, 
 26 |                                         act=tf.nn.relu,
 27 |                                         is_train=is_training,
 28 |                                         name='BatchNorm_1')
 29 |       pool3d_1 = tl.layers.PoolLayer(conv3d_1,
 30 |                                         ksize=[1,1,2,2,1],
 31 |                                         strides=[1,1,2,2,1],
 32 |                                         padding='SAME',
 33 |                                         pool = tf.nn.max_pool3d,
 34 |                                         name='Pool3D_1')
 35 |       # 3DCNN-BN Layer 2
 36 |       conv3d_2_3x3 = tl.layers.Conv3dLayer(pool3d_1, 
 37 |                                         act=tf.identity, 
 38 |                                         shape=[3,3,3,64,128], 
 39 |                                         strides=[1,1,1,1,1],
 40 |                                         padding='SAME',
 41 |                                         name='Conv3d_2_3x3')
 42 |       conv3d_2_3x3 = tl.layers.BatchNormLayer(layer=conv3d_2_3x3, 
 43 |                                         act=tf.nn.relu,
 44 |                                         is_train=is_training, 
 45 |                                         name='BatchNorm_2_3x3')
 46 |       pool3d_2 = tl.layers.PoolLayer(conv3d_2_3x3,
 47 |                                         ksize=[1,2,2,2,1],
 48 |                                         strides=[1,2,2,2,1],
 49 |                                         padding='SAME',
 50 |                                         pool = tf.nn.max_pool3d,
 51 |                                         name='Pool3D_2')
 52 |       # 3DCNN-BN Layer 3
 53 |       conv3d_3a_3x3 = tl.layers.Conv3dLayer(pool3d_2, 
 54 |                                         act=tf.identity, 
 55 |                                         shape=[3,3,3,128,256],
 56 |                                         strides=[1,1,1,1,1],
 57 |                                         padding='SAME',
 58 |                                         name='Conv3d_3a_3x3')
 59 |       conv3d_3b_3x3 = tl.layers.Conv3dLayer(conv3d_3a_3x3, 
 60 |                                         act=tf.identity, 
 61 |                                         shape=[3,3,3,256,256],
 62 |                                         strides=[1,1,1,1,1],
 63 |                                         padding='SAME',
 64 |                                         name='Conv3d_3b_3x3')
 65 |       conv3d_3_3x3 = tl.layers.BatchNormLayer(layer=conv3d_3b_3x3, 
 66 |                                         act=tf.nn.relu,
 67 |                                         is_train=is_training, 
 68 |                                         name='BatchNorm_3_3x3')
 69 | #      pool3d_3 = tl.layers.PoolLayer(conv3d_3_3x3,
 70 | #                                        ksize=[1,2,2,2,1],
 71 | #                                        strides=[1,2,2,2,1],
 72 | #                                        padding='SAME',
 73 | #                                        pool = tf.nn.max_pool3d,
 74 | #                                        name='Pool3D_3')
 75 |       # ConvLstm Layer
 76 |       shape3d = conv3d_3_3x3.outputs.get_shape().as_list()
 77 |       num_steps = shape3d[1]
 78 |       convlstm1 = tl.layers.RNNLayer(conv3d_3_3x3,
 79 |                                         cell_fn=clstm.ConvLSTMCell,
 80 |                                         cell_init_args={'state_is_tuple':False},
 81 |                                         n_hidden=256,
 82 |                                         initializer=tf.random_uniform_initializer(-0.1, 0.1),
 83 |                                         n_steps=num_steps,
 84 |                                         return_last=False,
 85 |                                         return_seq_2d=False,
 86 |                                         name='clstm_layer_1')
 87 |       convlstm2 = tl.layers.RNNLayer(convlstm1,
 88 |                                         cell_fn=clstm.ConvLSTMCell,
 89 |                                         cell_init_args={'state_is_tuple':False},
 90 |                                         n_hidden=384,
 91 |                                         initializer=tf.random_uniform_initializer(-0.1, 0.1),
 92 |                                         n_steps=num_steps,
 93 |                                         return_last=True,
 94 |                                         return_seq_2d=False,
 95 |                                         name='clstm_layer_2')
 96 |       # SPP Layer 1
 97 |       spp_bin_1 = tl.layers.PoolLayer(convlstm2,
 98 |                                         ksize=[1,28,28,1],
 99 |                                         strides=[1,28,28,1],
100 |                                         padding='SAME',
101 |                                         pool = tf.nn.max_pool,
102 |                                         name='SPP_1')
103 |       spp_bin_1 = tl.layers.FlattenLayer(spp_bin_1, 
104 |                                         name='Flatten_SPP_1')
105 |       spp_bin_2 = tl.layers.PoolLayer(convlstm2,
106 |                                         ksize=[1,14,14,1],
107 |                                         strides=[1,14,14,1],
108 |                                         padding='SAME',
109 |                                         pool = tf.nn.max_pool,
110 |                                         name='SPP_2')
111 |       spp_bin_2 = tl.layers.FlattenLayer(spp_bin_2, 
112 |                                         name='Flatten_SPP_2')
113 |       spp_bin_4 = tl.layers.PoolLayer(convlstm2,
114 |                                         ksize=[1,7,7,1],
115 |                                         strides=[1,7,7,1],
116 |                                         padding='SAME',
117 |                                         pool = tf.nn.max_pool,
118 |                                         name='SPP_4')
119 |       spp_bin_4 = tl.layers.FlattenLayer(spp_bin_4, 
120 |                                         name='Flatten_SPP_4')
121 |       spp_bin_7 = tl.layers.PoolLayer(convlstm2,
122 |                                         ksize=[1,4,4,1],
123 |                                         strides=[1,4,4,1],
124 |                                         padding='SAME',
125 |                                         pool = tf.nn.max_pool,
126 |                                         name='SPP_8')
127 |       spp_bin_7 = tl.layers.FlattenLayer(spp_bin_7, 
128 |                                         name='Flatten_SPP_7')
129 |       concat_spp = tl.layers.ConcatLayer(layer=[spp_bin_1,
130 |                                               spp_bin_2,
131 |                                               spp_bin_4,
132 |                                               spp_bin_7],
133 |                                         concat_dim=1,
134 |                                         name='Concat_SPP')
135 |       # FC Layer 1
136 |       classes = tl.layers.DropconnectDenseLayer(concat_spp, 
137 |                                         keep=0.5,
138 |                                         n_units=num_classes,
139 |                                         act=tf.identity,
140 |                                         name='Classes')
141 |     return classes
142 | 
143 | 


--------------------------------------------------------------------------------
/inputs.py:
--------------------------------------------------------------------------------
  1 | import io
  2 | import os
  3 | import sys
  4 | import math
  5 | import random
  6 | import numpy as np
  7 | from scipy.misc import imread, imresize
  8 | 
  9 | 
 10 | def load_video_list(path):
 11 |   assert os.path.exists(path)
 12 |   f = open(path, 'r')
 13 |   f_lines = f.readlines()
 14 |   f.close()
 15 |   video_data = {} 
 16 |   video_label = []
 17 |   for idx, line in enumerate(f_lines):
 18 |     video_key = '%06d' % idx
 19 |     video_data[video_key] = {} 
 20 |     videopath  = line.split(' ')[0]
 21 |     framecnt   = int(line.split(' ')[1])
 22 |     videolabel = int(line.split(' ')[2])
 23 |     video_data[video_key]['videopath'] = videopath
 24 |     video_data[video_key]['framecnt'] = framecnt
 25 |     video_label.append(videolabel)
 26 |   return video_data,video_label
 27 | 
 28 | def prepare_isogr_rgb_data(image_info):
 29 |   video_path = image_info[0]
 30 |   video_frame_cnt = image_info[1]
 31 |   output_frame_cnt = image_info[2]
 32 |   is_training = image_info[3]
 33 |   assert os.path.exists(video_path)
 34 |   rand_frames = np.zeros(output_frame_cnt)
 35 |   div = float(video_frame_cnt)/float(output_frame_cnt)
 36 |   scale = math.floor(div)
 37 |   if is_training:
 38 |     if scale == 0:
 39 |       rand_frames[0:video_frame_cnt] = np.arange(0, video_frame_cnt)
 40 |       rand_frames[video_frame_cnt::] = video_frame_cnt-1
 41 |     elif scale == 1:
 42 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
 43 |     else:
 44 |       rand_frames[::] = div*np.arange(0, output_frame_cnt) + \
 45 |                         float(scale)/2*(np.random.random(size=output_frame_cnt)-0.5)
 46 |   else:
 47 |     if scale == 0:
 48 |       rand_frames[0:video_frame_cnt] = np.arange(0, video_frame_cnt)
 49 |       rand_frames[video_frame_cnt::] = video_frame_cnt-1
 50 |     else:
 51 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
 52 |   rand_frames[0] = max(rand_frames[0], 0)
 53 |   rand_frames[output_frame_cnt-1] = min(rand_frames[output_frame_cnt-1], video_frame_cnt-1)
 54 |   rand_frames = np.floor(rand_frames)+1
 55 | 
 56 |   average_values = [112,112,112]
 57 |   processed_images = np.empty((output_frame_cnt, 112, 112, 3), dtype=np.float32)
 58 |   crop_random = random.random()
 59 |   for idx in xrange(0, output_frame_cnt):
 60 |     image_file = '%s%06d.jpg' %(video_path, rand_frames[idx])
 61 |     assert os.path.exists(image_file)
 62 |     image = imread(image_file)
 63 |     image_h, image_w, image_c = np.shape(image)
 64 |     square_sz = min(image_h, image_w)
 65 |     if is_training:
 66 |       crop_h = int((image_h - square_sz)*crop_random)
 67 |       crop_w = int((image_w - square_sz)*crop_random)
 68 |     else:
 69 |       crop_h = int((image_h - square_sz)/2)
 70 |       crop_w = int((image_w - square_sz)/2)
 71 |     image_crop = image[crop_h:crop_h+square_sz,crop_w:crop_w+square_sz,::]
 72 |     processed_images[idx] = imresize(image_crop, (112,112)) - average_values
 73 |   return processed_images
 74 | 
 75 | def prepare_isogr_depth_data(image_info):
 76 |   video_path = image_info[0]
 77 |   video_frame_cnt = image_info[1]
 78 |   output_frame_cnt = image_info[2]
 79 |   is_training = image_info[3]
 80 |   assert os.path.exists(video_path)
 81 |   rand_frames = np.zeros(output_frame_cnt)
 82 |   div = float(video_frame_cnt)/float(output_frame_cnt)
 83 |   scale = math.floor(div)
 84 |   if is_training:
 85 |     if scale == 0:
 86 |       rand_frames[0:video_frame_cnt] = np.arange(0, video_frame_cnt)
 87 |       rand_frames[video_frame_cnt::] = video_frame_cnt-1
 88 |     elif scale == 1:
 89 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
 90 |     else:
 91 |       rand_frames[::] = div*np.arange(0, output_frame_cnt) + \
 92 |                         float(scale)/2*(np.random.random(size=output_frame_cnt)-0.5)
 93 |   else:
 94 |     if scale == 0:
 95 |       rand_frames[0:video_frame_cnt] = np.arange(0, video_frame_cnt)
 96 |       rand_frames[video_frame_cnt::] = video_frame_cnt-1
 97 |     else:
 98 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
 99 |   rand_frames[0] = max(rand_frames[0], 0)
100 |   rand_frames[output_frame_cnt-1] = min(rand_frames[output_frame_cnt-1], video_frame_cnt-1)
101 |   rand_frames = np.floor(rand_frames)+1
102 | 
103 |   average_values = [127,127,127] 
104 |   processed_images = np.empty((output_frame_cnt, 112, 112, 3), dtype=np.float32)
105 |   crop_random = random.random()
106 |   for idx in xrange(0, output_frame_cnt):
107 |     image_file = '%s%06d.jpg' %(video_path, rand_frames[idx])
108 |     assert os.path.exists(image_file)
109 |     image = imread(image_file)
110 |     image_h, image_w, image_c = np.shape(image)
111 |     square_sz = min(image_h, image_w)
112 |     if is_training:
113 |       crop_h = int((image_h - square_sz)*crop_random)
114 |       crop_w = int((image_w - square_sz)*crop_random)
115 |     else:
116 |       crop_h = int((image_h - square_sz)/2)
117 |       crop_w = int((image_w - square_sz)/2)
118 |     image_crop = image[crop_h:crop_h+square_sz,crop_w:crop_w+square_sz,::]
119 |     processed_images[idx] = imresize(image_crop, (112,112)) - average_values
120 |   return processed_images
121 | 
122 | def prepare_skig_rgb_data(image_info):
123 |   video_path = image_info[0]
124 |   video_frame_cnt = image_info[1]
125 |   output_frame_cnt = image_info[2]
126 |   is_training = image_info[3]
127 |   assert os.path.exists(video_path)
128 |   rand_frames = np.zeros(output_frame_cnt)
129 |   div = float(video_frame_cnt)/float(output_frame_cnt)
130 |   scale = math.floor(div)
131 |   if is_training:
132 |     if scale == 0:
133 |       rand_frames[0:video_frame_cnt] = np.arange(1, video_frame_cnt+1)
134 |       rand_frames[video_frame_cnt::] = video_frame_cnt
135 |     elif scale == 1:
136 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
137 |     else:
138 |       rand_frames[::] = div*np.arange(0, output_frame_cnt) + \
139 |                         float(scale)/2*(np.random.random(size=output_frame_cnt)-0.5)
140 |   else:
141 |     if scale == 0:
142 |       rand_frames[0:video_frame_cnt] = np.arange(1, video_frame_cnt+1)
143 |       rand_frames[video_frame_cnt::] = video_frame_cnt
144 |     else:
145 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
146 |   rand_frames[0] = max(rand_frames[0], 1)
147 |   rand_frames[output_frame_cnt-1] = min(rand_frames[output_frame_cnt-1], video_frame_cnt)
148 |   rand_frames = np.floor(rand_frames)
149 | 
150 |   average_values = [132,112,96]
151 |   processed_images = np.empty((output_frame_cnt, 112, 112, 3), dtype=np.float32)
152 |   crop_random = random.random()
153 |   for idx in xrange(0,output_frame_cnt):
154 |     image_file = '%s/%04d.jpg' %(video_path, rand_frames[idx])
155 |     image = imread(image_file)
156 |     image_h, image_w, image_c = np.shape(image)
157 |     square_sz = min(image_h, image_w)
158 |     if is_training:
159 |       crop_h = int((image_h - square_sz)*crop_random)
160 |       crop_w = int((image_w - square_sz)*crop_random)
161 |     else:
162 |       crop_h = int((image_h - square_sz)/2)
163 |       crop_w = int((image_w - square_sz)/2)
164 |     image_crop = image[crop_h:crop_h+square_sz,crop_w:crop_w+square_sz,::]
165 |     processed_images[idx] = imresize(image_crop, (112,112)) - average_values
166 |   return processed_images
167 | 
168 | def prepare_skig_depth_data(image_info):
169 |   video_path = image_info[0]
170 |   video_frame_cnt = image_info[1]
171 |   output_frame_cnt = image_info[2]
172 |   is_training = image_info[3]
173 |   assert os.path.exists(video_path)
174 |   rand_frames = np.zeros(output_frame_cnt)
175 |   div = float(video_frame_cnt)/float(output_frame_cnt)
176 |   scale = math.floor(div)
177 |   if is_training:
178 |     if scale == 0:
179 |       rand_frames[0:video_frame_cnt] = np.arange(1, video_frame_cnt+1)
180 |       rand_frames[video_frame_cnt::] = video_frame_cnt
181 |     elif scale == 1:
182 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
183 |     else:
184 |       rand_frames[::] = div*np.arange(0, output_frame_cnt) + \
185 |                         float(scale)/2*(np.random.random(size=output_frame_cnt)-0.5)
186 |   else:
187 |     if scale == 0:
188 |       rand_frames[0:video_frame_cnt] = np.arange(1, video_frame_cnt+1)
189 |       rand_frames[video_frame_cnt::] = video_frame_cnt
190 |     else:
191 |       rand_frames[::] = div*np.arange(0, output_frame_cnt)
192 |   rand_frames[0] = max(rand_frames[0], 1)
193 |   rand_frames[output_frame_cnt-1] = min(rand_frames[output_frame_cnt-1], video_frame_cnt)
194 |   rand_frames = np.floor(rand_frames)
195 | 
196 |   average_values = [237,237,237]
197 |   processed_images = np.empty((output_frame_cnt, 112, 112, 3), dtype=np.float32)
198 |   crop_random = random.random()
199 |   for idx in xrange(0,output_frame_cnt):
200 |     image_file = '%s/%04d.jpg' %(video_path, rand_frames[idx])
201 |     image = imread(image_file)
202 |     image_h, image_w, image_c = np.shape(image)
203 |     square_sz = min(image_h, image_w)
204 |     if is_training:
205 |       crop_h = int((image_h - square_sz)*crop_random)
206 |       crop_w = int((image_w - square_sz)*crop_random)
207 |     else:
208 |       crop_h = int((image_h - square_sz)/2)
209 |       crop_w = int((image_w - square_sz)/2)
210 |     image_crop = image[crop_h:crop_h+square_sz,crop_w:crop_w+square_sz,::]
211 |     processed_images[idx] = imresize(image_crop, (112,112)) - average_values
212 |   return processed_images
213 | 


--------------------------------------------------------------------------------
/tensorlayer-rnnlayer.py:
--------------------------------------------------------------------------------
  1 | ## Recurrent layer
  2 | class RNNLayer(Layer):
  3 |     """
  4 |     The :class:`RNNLayer` class is a RNN layer, you can implement vanilla RNN,
  5 |     """
  6 |     def __init__(
  7 |         self,
  8 |         layer = None,
  9 |         cell_fn = tf.nn.rnn_cell.BasicRNNCell,
 10 |         cell_init_args = {},
 11 |         n_hidden = 100,
 12 |         initializer = tf.random_uniform_initializer(-0.1, 0.1),
 13 |         n_steps = 5,
 14 |         initial_state = None,
 15 |         return_last = False,
 16 |         # is_reshape = True,
 17 |         return_seq_2d = False,
 18 |         name = 'rnn_layer',
 19 |     ):
 20 |         Layer.__init__(self, name=name)
 21 |         self.inputs = layer.outputs
 22 | 
 23 |         print("  tensorlayer:Instantiate RNNLayer %s: n_hidden:%d, n_steps:%d, in_dim:%d %s, cell_fn:%s " % (self.name, n_hidden,
 24 |             n_steps, self.inputs.get_shape().ndims, self.inputs.get_shape(), cell_fn.__name__))
 25 |         # You can get the dimension by .get_shape() or ._shape, and check the
 26 |         # dimension by .with_rank() as follow.
 27 |         # self.inputs.get_shape().with_rank(2)
 28 |         # self.inputs.get_shape().with_rank(3)
 29 | 
 30 |         # Input dimension should be rank 3 [batch_size, n_steps(max), n_features]
 31 |         try:
 32 |             #self.inputs.get_shape().with_rank(3)
 33 |             self.inputs.get_shape().with_rank_at_least(3) #Guangming Zhu
 34 |         except:
 35 |             raise Exception("RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]")
 36 | 
 37 | 
 38 |         # is_reshape : boolean (deprecate)
 39 |         #     Reshape the inputs to 3 dimension tensor.\n
 40 |         #     If input is［batch_size, n_steps, n_features], we do not need to reshape it.\n
 41 |         #     If input is [batch_size * n_steps, n_features], we need to reshape it.
 42 |         # if is_reshape:
 43 |         #     self.inputs = tf.reshape(self.inputs, shape=[-1, n_steps, int(self.inputs._shape[-1])])
 44 | 
 45 |         fixed_batch_size = self.inputs.get_shape().with_rank_at_least(1)[0]
 46 | 
 47 |         if fixed_batch_size.value:
 48 |             batch_size = fixed_batch_size.value
 49 |             print("     RNN batch_size (concurrent processes): %d" % batch_size)
 50 |         else:
 51 |             from tensorflow.python.ops import array_ops
 52 |             batch_size = array_ops.shape(self.inputs)[0]
 53 |             print("     non specified batch_size, uses a tensor instead.")
 54 |         self.batch_size = batch_size
 55 |         height = self.inputs.get_shape().as_list()[2] #Guangming Zhu
 56 |         width = self.inputs.get_shape().as_list()[3] #Guangming Zhu
 57 | 
 58 |         # Simplified version of tensorflow.models.rnn.rnn.py's rnn().
 59 |         # This builds an unrolled LSTM for tutorial purposes only.
 60 |         # In general, use the rnn() or state_saving_rnn() from rnn.py.
 61 |         #
 62 |         # The alternative version of the code below is:
 63 |         #
 64 |         # from tensorflow.models.rnn import rnn
 65 |         # inputs = [tf.squeeze(input_, [1])
 66 |         #           for input_ in tf.split(1, num_steps, inputs)]
 67 |         # outputs, state = rnn.rnn(cell, inputs, initial_state=self._initial_state)
 68 |         outputs = []
 69 |         self.cell = cell = cell_fn(num_units=n_hidden, **cell_init_args)
 70 |         if initial_state is None:
 71 |             if self.inputs.get_shape().ndims==3: #Guangming Zhu
 72 |                 self.initial_state = cell.zero_state(batch_size, dtype=tf.float32)  # 1.2.3
 73 |             elif self.inputs.get_shape().ndims==5: #Guangming Zhu
 74 |                 self.initial_state = cell.zero_state(batch_size, height, width)   #Guangming Zhu
 75 |         state = self.initial_state
 76 |         # with tf.variable_scope("model", reuse=None, initializer=initializer):
 77 |         with tf.variable_scope(name, initializer=initializer) as vs:
 78 |             for time_step in range(n_steps):
 79 |                 if time_step > 0: tf.get_variable_scope().reuse_variables()
 80 |                 (cell_output, state) = cell(self.inputs[:, time_step, :], state)
 81 |                 outputs.append(cell_output)
 82 | 
 83 |             # Retrieve just the RNN variables.
 84 |             # rnn_variables = [v for v in tf.all_variables() if v.name.startswith(vs.name)]
 85 |             rnn_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=vs.name)
 86 | 
 87 |         print("     n_params : %d" % (len(rnn_variables)))
 88 | 
 89 |         if return_last:
 90 |             # 2D Tensor [batch_size, n_hidden]
 91 |             self.outputs = outputs[-1]
 92 |         else:
 93 |             if return_seq_2d:
 94 |                 # PTB tutorial: stack dense layer after that, or compute the cost from the output
 95 |                 # 2D Tensor [n_example, n_hidden]
 96 |                 if self.inputs.get_shape().ndims==3: #Guangming Zhu
 97 |                     self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_hidden])
 98 |                 elif self.inputs.get_shape().ndims==5: #Guangming Zhu
 99 |                     self.outputs = tf.reshape(tf.concat(1, outputs), [-1, height, width, n_hidden]) #Guangming Zhu
100 |             else:
101 |                 # <akara>: stack more RNN layer after that
102 |                 # 3D Tensor [n_example/n_steps, n_steps, n_hidden]
103 |                 if self.inputs.get_shape().ndims==3: #Guangming Zhu
104 |                     self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_steps, n_hidden])
105 |                 elif self.inputs.get_shape().ndims==5: #Guangming Zhu
106 |                     self.outputs = tf.reshape(tf.concat(1, outputs), [-1, n_steps, height, width, n_hidden]) #Guangming Zhu
107 | 
108 |         self.final_state = state
109 | 
110 |         self.all_layers = list(layer.all_layers)
111 |         self.all_params = list(layer.all_params)
112 |         self.all_drop = dict(layer.all_drop)
113 |         # print(type(self.outputs))
114 |         self.all_layers.extend( [self.outputs] )
115 |         self.all_params.extend( rnn_variables )
116 | 


--------------------------------------------------------------------------------
/testing_isogr.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  3 | import io
  4 | import sys
  5 | import numpy as np
  6 | import tensorflow as tf
  7 | slim = tf.contrib.slim
  8 | import tensorlayer as tl
  9 | import inputs as data
 10 | import c3d_clstm as net 
 11 | import time
 12 | from datetime import datetime
 13 | import threading
 14 | 
 15 | seq_len = 32
 16 | batch_size = 12
 17 | 
 18 | num_classes = 249
 19 | dataset_name = 'isogr'
 20 | model_prefix='/raid/gmzhu/tensorflow/c3d_clstm_net2'
 21 | 
 22 | curtime = '%s' % datetime.now()
 23 | d = curtime.split(' ')[0]
 24 | t = curtime.split(' ')[1]
 25 | strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2], 
 26 |                             t.split(':')[0],t.split(':')[1],t.split(':')[2])
 27 | 
 28 | x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
 29 | y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
 30 |   
 31 | sess = tf.InteractiveSession()
 32 | 
 33 | networks = net.c3d_clstm(x, num_classes, False, False)
 34 | network_pred = tf.nn.softmax(networks.outputs)
 35 | network_y_op = tf.argmax(tf.nn.softmax(networks.outputs),1)
 36 | network_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(network_y_op, tf.int32), y), tf.float32))
 37 |   
 38 | sess.run(tf.initialize_all_variables())
 39 | 
 40 | # RGB
 41 | testing_datalist = '/ssd/dataset/IsoGD_Image/valid_rgb_list.txt'
 42 | X_test,y_test = data.load_video_list(testing_datalist)
 43 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
 44 | y_test  = np.asarray(y_test, dtype=np.int32)
 45 | rgb_prediction = np.zeros((len(y_test),num_classes), dtype=np.float32)
 46 | load_params = tl.files.load_npz(name='%s/train_results_paper/isogr_rgb_model_strategy_3.npz'%(model_prefix))
 47 | tl.files.assign_params(sess, load_params, networks)
 48 | #networks.print_params(True)
 49 | average_accuracy = 0.0
 50 | test_iterations = 0
 51 | print '%s: rgb testing' % datetime.now()
 52 | for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
 53 |                                                    y_test, 
 54 |                                                    batch_size, 
 55 |                                                    shuffle=False):
 56 |   # Read data for each batch      
 57 |   image_path = []
 58 |   image_fcnt = []
 59 |   image_olen = []
 60 |   is_training = []
 61 |   for data_a in range(batch_size):
 62 |     X_index_a = X_indices[data_a]
 63 |     key_str = '%06d' % X_index_a
 64 |     image_path.append(X_test[key_str]['videopath'])
 65 |     image_fcnt.append(X_test[key_str]['framecnt'])
 66 |     image_olen.append(seq_len)
 67 |     is_training.append(False) # Testing
 68 |     image_info = zip(image_path,image_fcnt,image_olen,is_training)
 69 |   X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
 70 |                                       data.prepare_isogr_rgb_data)
 71 |   feed_dict = {x: X_data_t, y: y_label_t}
 72 |   dp_dict = tl.utils.dict_to_one(networks.all_drop)
 73 |   feed_dict.update(dp_dict)
 74 |   predict_value,accu_value = sess.run([network_pred, network_accu], feed_dict=feed_dict)
 75 |   rgb_prediction[test_iterations*batch_size:(test_iterations+1)*batch_size,:]=predict_value
 76 |   average_accuracy = average_accuracy + accu_value
 77 |   test_iterations = test_iterations + 1
 78 | average_accuracy = average_accuracy / test_iterations
 79 | format_str = ('%s: rgb average_accuracy = %.6f')
 80 | print (format_str % (datetime.now(), average_accuracy))
 81 | 
 82 | # Depth
 83 | testing_datalist = '/ssd/dataset/IsoGD_Image/valid_depth_list.txt'
 84 | X_test,y_test = data.load_video_list(testing_datalist)
 85 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
 86 | y_test  = np.asarray(y_test, dtype=np.int32)
 87 | depth_prediction = np.zeros((len(y_test),num_classes), dtype=np.float32)
 88 | load_params = tl.files.load_npz(name='%s/train_results_paper/isogr_depth_model_strategy_3.npz'%(model_prefix))
 89 | tl.files.assign_params(sess, load_params, networks)
 90 | #networks.print_params(True)
 91 | average_accuracy = 0.0
 92 | test_iterations = 0
 93 | print '%s: depth testing' % datetime.now()
 94 | for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
 95 |                                                    y_test, 
 96 |                                                    batch_size, 
 97 |                                                    shuffle=False):
 98 |   # Read data for each batch      
 99 |   image_path = []
100 |   image_fcnt = []
101 |   image_olen = []
102 |   is_training = []
103 |   for data_a in range(batch_size):
104 |     X_index_a = X_indices[data_a]
105 |     key_str = '%06d' % X_index_a
106 |     image_path.append(X_test[key_str]['videopath'])
107 |     image_fcnt.append(X_test[key_str]['framecnt'])
108 |     image_olen.append(seq_len)
109 |     is_training.append(False) # Testing
110 |     image_info = zip(image_path,image_fcnt,image_olen,is_training)
111 |   X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
112 |                                       data.prepare_isogr_depth_data)
113 |   feed_dict = {x: X_data_t, y: y_label_t}
114 |   dp_dict = tl.utils.dict_to_one(networks.all_drop)
115 |   feed_dict.update(dp_dict)
116 |   predict_value,accu_value = sess.run([network_pred, network_accu], feed_dict=feed_dict)
117 |   depth_prediction[test_iterations*batch_size:(test_iterations+1)*batch_size,:]=predict_value
118 |   average_accuracy = average_accuracy + accu_value
119 |   test_iterations = test_iterations + 1
120 | average_accuracy = average_accuracy / test_iterations
121 | format_str = ('%s: depth average_accuracy = %.6f')
122 | print (format_str % (datetime.now(), average_accuracy))
123 | 
124 | fusion_prediction = rgb_prediction + depth_prediction
125 | prediction_values = tf.argmax(fusion_prediction, 1)
126 | final_accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.cast(prediction_values, tf.int32), y_test), tf.float32))
127 | print final_accuracy.eval()
128 | 
129 | # In the end, close TensorFlow session.
130 | sess.close()
131 | 


--------------------------------------------------------------------------------
/training_isogr_depth.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.environ['CUDA_VISIBLE_DEVICES'] = '1'
  3 | import io
  4 | import sys
  5 | import numpy as np
  6 | import tensorflow as tf
  7 | slim = tf.contrib.slim
  8 | import tensorlayer as tl
  9 | import inputs as data
 10 | import c3d_clstm as net 
 11 | import time
 12 | from datetime import datetime
 13 | import threading
 14 | import cStringIO
 15 | 
 16 | seq_len = 32
 17 | batch_size = 13
 18 | n_epoch = 1000
 19 | learning_rate = 0.1
 20 | decay_steps = 15000
 21 | decay_rate  = 0.1
 22 | weight_decay= 0.004
 23 | print_freq = 20
 24 | queue_num = 5
 25 | start_step = 0
 26 | 
 27 | num_classes = 249
 28 | dataset_name = 'isogr_depth'
 29 | training_datalist = '/ssd/dataset/IsoGD_Image/train_depth_list.txt'
 30 | testing_datalist = '/ssd/dataset/IsoGD_Image/valid_depth_list.txt'
 31 | 
 32 | curtime = '%s' % datetime.now()
 33 | d = curtime.split(' ')[0]
 34 | t = curtime.split(' ')[1]
 35 | strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2], 
 36 |                             t.split(':')[0],t.split(':')[1],t.split(':')[2])
 37 | 
 38 | saved_stdout = sys.stdout
 39 | mem_log = cStringIO.StringIO()
 40 | sys.stdout = mem_log
 41 | logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
 42 | log = open(logfile, 'w')
 43 | 
 44 | sess = tf.InteractiveSession()
 45 | x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
 46 | y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
 47 |   
 48 | networks = net.c3d_clstm(x, num_classes, False, True)
 49 | networks_y = networks.outputs
 50 | networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
 51 | networks_cost = tl.cost.cross_entropy(networks_y, y)
 52 | correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
 53 | networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 54 |     
 55 | predictons = net.c3d_clstm(x, num_classes, True, False)
 56 | predicton_y_op = tf.argmax(tf.nn.softmax(predictons.outputs),1)
 57 | predicton_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predicton_y_op, tf.int32), y), tf.float32))
 58 |   
 59 | l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
 60 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
 61 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
 62 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[14]) + \
 63 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[20]) + \
 64 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[22]) + \
 65 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[24]) 
 66 | cost = networks_cost + l2_cost 
 67 |   
 68 | # Decay the learning rate exponentially based on the number of steps.
 69 | #global_step = tf.Variable(start_step*2, trainable=False)
 70 | #lr = tf.train.exponential_decay(learning_rate,
 71 | #                                global_step,
 72 | #                                decay_steps,
 73 | #                                decay_rate,
 74 | #                                staircase=True)
 75 | #lr2 = tf.train.exponential_decay(learning_rate*10,
 76 | #                                global_step,
 77 | #                                decay_steps,
 78 | #                                decay_rate,
 79 | #                                staircase=True)
 80 | #var_list1 = networks.all_params[:24] 
 81 | #var_list2 = networks.all_params[24:]
 82 | #opt1 = tf.train.GradientDescentOptimizer(lr)
 83 | #opt2 = tf.train.GradientDescentOptimizer(lr2)
 84 | #grads = tf.gradients(cost, var_list1 + var_list2)
 85 | #grads1 = grads[:len(var_list1)]
 86 | #grads2 = grads[len(var_list1):]
 87 | #train_op1 = opt1.apply_gradients(zip(grads1, var_list1), global_step=global_step)
 88 | #train_op2 = opt2.apply_gradients(zip(grads2, var_list2), global_step=global_step)
 89 | #train_op = tf.group(train_op1, train_op2)
 90 | global_step = tf.Variable(start_step, trainable=False)
 91 | lr = tf.train.exponential_decay(learning_rate,
 92 |                                 global_step,
 93 |                                 decay_steps,
 94 |                                 decay_rate,
 95 |                                 staircase=True)
 96 | train_params = networks.all_params
 97 | train_op = tf.train.GradientDescentOptimizer(lr).minimize(cost, 
 98 |                                        var_list=train_params,
 99 |                                        global_step=global_step)
100 | 
101 | sess.run(tf.initialize_all_variables())
102 | if start_step>0:
103 |   load_params = tl.files.load_npz(name='%s_model_iter_%d.npz'%(dataset_name, start_step))
104 |   tl.files.assign_params(sess, load_params, networks)
105 | networks.print_params(True)
106 |   
107 | # Data Reading
108 | X_train,y_train = data.load_video_list(training_datalist)
109 | X_tridx = np.asarray(np.arange(0, len(y_train)), dtype=np.int32)
110 | y_train = np.asarray(y_train, dtype=np.int32)
111 | X_test,y_test = data.load_video_list(testing_datalist)
112 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
113 | y_test  = np.asarray(y_test, dtype=np.int32)
114 |   
115 | X_data_a  = np.empty((batch_size*queue_num, seq_len, 112, 112, 3),float)      
116 | y_label_a = np.empty((batch_size*queue_num,),int)
117 | 
118 | full_flg  = np.zeros((queue_num, 1))
119 | rdwr_lock = threading.Lock()
120 | 
121 | def training_data_read():
122 |   wr_pos = 0
123 |   for i in range(n_epoch):
124 |     for X_indices, y_labels in tl.iterate.minibatches(X_tridx,
125 |                                                       y_train, 
126 |                                                       batch_size, 
127 |                                                       shuffle=True):
128 |       # 1. Waiting
129 |       while True:
130 |         rdwr_lock.acquire()
131 |         if full_flg[wr_pos] == 1:
132 |           rdwr_lock.release()
133 |           time.sleep(1)
134 |           continue
135 |         rdwr_lock.release()
136 |         break
137 |       # 2. Reading data
138 |       image_path = []
139 |       image_fcnt = []
140 |       image_olen = []
141 |       is_training = []
142 |       for data_a in range(batch_size):
143 |         X_index_a = X_indices[data_a]
144 |         key_str = '%06d' % X_index_a
145 |         image_path.append(X_train[key_str]['videopath'])
146 |         image_fcnt.append(X_train[key_str]['framecnt'])
147 |         image_olen.append(seq_len)
148 |         is_training.append(True) # Training
149 |       image_info = zip(image_path,image_fcnt,image_olen,is_training)
150 |       X_data_a[wr_pos*batch_size:(wr_pos+1)*batch_size,:,:,:,:] = \
151 |                   tl.prepro.threading_data([_ for _ in image_info], 
152 |                                            data.prepare_isogr_depth_data)
153 |       y_label_a[wr_pos*batch_size:(wr_pos+1)*batch_size] = y_labels
154 |       # 3. Update flags
155 |       rdwr_lock.acquire()
156 |       full_flg[wr_pos] = 1
157 |       rdwr_lock.release()
158 |       wr_pos = (wr_pos+1)%queue_num      
159 |   
160 | wr_thread = threading.Thread(target=training_data_read)
161 | wr_thread.start()
162 | 
163 | # Output the saved logs to stdout and the opened log file
164 | sys.stdout = saved_stdout
165 | mem_log.seek(0)
166 | print mem_log.read()
167 | mem_log.seek(0)
168 | log.writelines(['%s' % mem_log.read()])
169 | log.flush()
170 | mem_log.close()
171 | 
172 | step = start_step
173 | rd_pos = 0
174 | for epoch in range(n_epoch):
175 |   # Train Stage
176 |   for _,_ in tl.iterate.minibatches(X_tridx, 
177 |                                     y_train, 
178 |                                     batch_size, 
179 |                                     shuffle=True):
180 |     # 1. Read data for each batch      
181 |     while True:
182 |       rdwr_lock.acquire()
183 |       if full_flg[rd_pos] == 0:
184 |         rdwr_lock.release()
185 |         time.sleep(1)
186 |         continue
187 |       rdwr_lock.release()
188 |       break
189 |     # 2. Training
190 |     feed_dict = {x: X_data_a[rd_pos*batch_size:(rd_pos+1)*batch_size,:,:,:,:], 
191 |                  y: y_label_a[rd_pos*batch_size:(rd_pos+1)*batch_size]}
192 |     feed_dict.update(networks.all_drop)
193 |     start_time = time.time()
194 |     _,loss_value,lr_value,acc = sess.run([train_op,cost,lr,networks_accu], feed_dict=feed_dict)
195 |     duration = time.time() - start_time
196 |     # 3. Update flags
197 |     rdwr_lock.acquire()
198 |     full_flg[rd_pos] = 0
199 |     rdwr_lock.release()
200 |     rd_pos = (rd_pos+1)%queue_num
201 |     # 4. Statistics
202 |     if step%print_freq == 0:
203 |       average_acc = acc
204 |       total_loss = loss_value
205 |       training_time = duration
206 |     else:
207 |       average_acc += acc
208 |       total_loss = total_loss + loss_value
209 |       training_time = training_time + duration
210 |     if (step+1)%print_freq == 0:
211 |       training_bps = batch_size*print_freq / training_time
212 |       average_loss = total_loss / print_freq
213 |       average_acc = average_acc / print_freq
214 |       format_str = ('%s: iter = %d, lr=%f, average_loss = %.2f average_acc = %.6f (training: %.1f batches/sec)')
215 |       print (format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps))
216 |       log.writelines([format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps), 
217 |                       '\n'])
218 |       log.flush()
219 |     step = step + 1
220 | 
221 |   tl.files.save_npz(networks.all_params, 
222 |                     name='%s_model_iter_%d.npz'%(dataset_name, step), 
223 |                     sess=sess)
224 |   print("Model saved in file: %s_model_iter_%d.npz" %(dataset_name, step))
225 | 
226 |   # Test Stage
227 |   average_accuracy = 0.0
228 |   test_iterations = 0;
229 |   for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
230 |                                                      y_test, 
231 |                                                      batch_size, 
232 |                                                      shuffle=True):
233 |     # Read data for each batch      
234 |     image_path = []
235 |     image_fcnt = []
236 |     image_olen = []
237 |     is_training = []
238 |     for data_a in range(batch_size):
239 |       X_index_a = X_indices[data_a]
240 |       key_str = '%06d' % X_index_a
241 |       image_path.append(X_test[key_str]['videopath'])
242 |       image_fcnt.append(X_test[key_str]['framecnt'])
243 |       image_olen.append(seq_len)
244 |       is_training.append(False) # Testing
245 |     image_info = zip(image_path,image_fcnt,image_olen,is_training)
246 |     X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
247 |                                         data.prepare_isogr_depth_data)
248 |     feed_dict = {x: X_data_t, y: y_label_t}
249 |     dp_dict = tl.utils.dict_to_one(predictons.all_drop)
250 |     feed_dict.update(dp_dict)
251 |     _,accu_value = sess.run([predicton_y_op, predicton_accu], feed_dict=feed_dict)
252 |     average_accuracy = average_accuracy + accu_value
253 |     test_iterations = test_iterations + 1
254 |   average_accuracy = average_accuracy / test_iterations
255 |   format_str = ('%s: epoch = %d, average_accuracy = %.6f')
256 |   print (format_str % (datetime.now(), epoch, average_accuracy))
257 |   log.writelines([format_str % (datetime.now(), epoch, average_accuracy), '\n'])
258 |   log.flush()
259 | 
260 | # In the end, close TensorFlow session.
261 | log.close()
262 | sess.close()
263 | 


--------------------------------------------------------------------------------
/training_isogr_rgb.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.environ['CUDA_VISIBLE_DEVICES'] = '2'
  3 | import io
  4 | import sys
  5 | import numpy as np
  6 | import tensorflow as tf
  7 | slim = tf.contrib.slim
  8 | import tensorlayer as tl
  9 | import inputs as data
 10 | import c3d_clstm as net 
 11 | import time
 12 | from datetime import datetime
 13 | import threading
 14 | import cStringIO
 15 | 
 16 | seq_len = 32
 17 | batch_size = 13
 18 | n_epoch = 1000
 19 | learning_rate = 0.1
 20 | decay_steps = 15000
 21 | decay_rate  = 0.1
 22 | weight_decay= 0.004
 23 | print_freq = 20
 24 | queue_num = 5
 25 | start_step = 0
 26 | 
 27 | num_classes = 249
 28 | dataset_name = 'isogr_rgb'
 29 | training_datalist = '/ssd/dataset/IsoGD_Image/train_rgb_list.txt'
 30 | testing_datalist = '/ssd/dataset/IsoGD_Image/valid_rgb_list.txt'
 31 | 
 32 | curtime = '%s' % datetime.now()
 33 | d = curtime.split(' ')[0]
 34 | t = curtime.split(' ')[1]
 35 | strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2], 
 36 |                             t.split(':')[0],t.split(':')[1],t.split(':')[2])
 37 | 
 38 | saved_stdout = sys.stdout
 39 | mem_log = cStringIO.StringIO()
 40 | sys.stdout = mem_log
 41 | logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
 42 | log = open(logfile, 'w')
 43 | 
 44 | sess = tf.InteractiveSession()
 45 | x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
 46 | y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
 47 |   
 48 | networks = net.c3d_clstm(x, num_classes, False, True)
 49 | networks_y = networks.outputs
 50 | networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
 51 | networks_cost = tl.cost.cross_entropy(networks_y, y)
 52 | correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
 53 | networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 54 |     
 55 | predictons = net.c3d_clstm(x, num_classes, True, False)
 56 | predicton_y_op = tf.argmax(tf.nn.softmax(predictons.outputs),1)
 57 | predicton_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predicton_y_op, tf.int32), y), tf.float32))
 58 |   
 59 | l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
 60 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
 61 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
 62 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[14]) + \
 63 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[20]) + \
 64 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[22]) + \
 65 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[24]) 
 66 | cost = networks_cost + l2_cost 
 67 |   
 68 | # Decay the learning rate exponentially based on the number of steps.
 69 | #global_step = tf.Variable(start_step*2, trainable=False)
 70 | #lr = tf.train.exponential_decay(learning_rate,
 71 | #                                global_step,
 72 | #                                decay_steps,
 73 | #                                decay_rate,
 74 | #                                staircase=True)
 75 | #lr2 = tf.train.exponential_decay(learning_rate*10,
 76 | #                                global_step,
 77 | #                                decay_steps,
 78 | #                                decay_rate,
 79 | #                                staircase=True)
 80 | #var_list1 = networks.all_params[:24] 
 81 | #var_list2 = networks.all_params[24:]
 82 | #opt1 = tf.train.GradientDescentOptimizer(lr)
 83 | #opt2 = tf.train.GradientDescentOptimizer(lr2)
 84 | #grads = tf.gradients(cost, var_list1 + var_list2)
 85 | #grads1 = grads[:len(var_list1)]
 86 | #grads2 = grads[len(var_list1):]
 87 | #train_op1 = opt1.apply_gradients(zip(grads1, var_list1), global_step=global_step)
 88 | #train_op2 = opt2.apply_gradients(zip(grads2, var_list2), global_step=global_step)
 89 | #train_op = tf.group(train_op1, train_op2)
 90 | global_step = tf.Variable(start_step, trainable=False)
 91 | lr = tf.train.exponential_decay(learning_rate,
 92 |                                 global_step,
 93 |                                 decay_steps,
 94 |                                 decay_rate,
 95 |                                 staircase=True)
 96 | train_params = networks.all_params
 97 | train_op = tf.train.GradientDescentOptimizer(lr).minimize(cost, 
 98 |                                        var_list=train_params,
 99 |                                        global_step=global_step)
100 | 
101 | sess.run(tf.initialize_all_variables())
102 | if start_step>0:
103 |   load_params = tl.files.load_npz(name='%s_model_iter_%d.npz'%(dataset_name, start_step))
104 |   tl.files.assign_params(sess, load_params, networks)
105 | networks.print_params(True)
106 |   
107 | # Data Reading
108 | X_train,y_train = data.load_video_list(training_datalist)
109 | X_tridx = np.asarray(np.arange(0, len(y_train)), dtype=np.int32)
110 | y_train = np.asarray(y_train, dtype=np.int32)
111 | X_test,y_test = data.load_video_list(testing_datalist)
112 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
113 | y_test  = np.asarray(y_test, dtype=np.int32)
114 |   
115 | X_data_a  = np.empty((batch_size*queue_num, seq_len, 112, 112, 3),float)      
116 | y_label_a = np.empty((batch_size*queue_num,),int)
117 | 
118 | full_flg  = np.zeros((queue_num, 1))
119 | rdwr_lock = threading.Lock()
120 | 
121 | def training_data_read():
122 |   wr_pos = 0
123 |   for i in range(n_epoch):
124 |     for X_indices, y_labels in tl.iterate.minibatches(X_tridx,
125 |                                                       y_train, 
126 |                                                       batch_size, 
127 |                                                       shuffle=True):
128 |       # 1. Waiting
129 |       while True:
130 |         rdwr_lock.acquire()
131 |         if full_flg[wr_pos] == 1:
132 |           rdwr_lock.release()
133 |           time.sleep(1)
134 |           continue
135 |         rdwr_lock.release()
136 |         break
137 |       # 2. Reading data
138 |       image_path = []
139 |       image_fcnt = []
140 |       image_olen = []
141 |       is_training = []
142 |       for data_a in range(batch_size):
143 |         X_index_a = X_indices[data_a]
144 |         key_str = '%06d' % X_index_a
145 |         image_path.append(X_train[key_str]['videopath'])
146 |         image_fcnt.append(X_train[key_str]['framecnt'])
147 |         image_olen.append(seq_len)
148 |         is_training.append(True) # Training
149 |       image_info = zip(image_path,image_fcnt,image_olen,is_training)
150 |       X_data_a[wr_pos*batch_size:(wr_pos+1)*batch_size,:,:,:,:] = \
151 |                   tl.prepro.threading_data([_ for _ in image_info], 
152 |                                            data.prepare_isogr_rgb_data)
153 |       y_label_a[wr_pos*batch_size:(wr_pos+1)*batch_size] = y_labels
154 |       # 3. Update flags
155 |       rdwr_lock.acquire()
156 |       full_flg[wr_pos] = 1
157 |       rdwr_lock.release()
158 |       wr_pos = (wr_pos+1)%queue_num      
159 |   
160 | wr_thread = threading.Thread(target=training_data_read)
161 | wr_thread.start()
162 | 
163 | # Output the saved logs to stdout and the opened log file
164 | sys.stdout = saved_stdout
165 | mem_log.seek(0)
166 | print mem_log.read()
167 | mem_log.seek(0)
168 | log.writelines(['%s' % mem_log.read()])
169 | log.flush()
170 | mem_log.close()
171 | 
172 | step = start_step
173 | rd_pos = 0
174 | for epoch in range(n_epoch):
175 |   # Train Stage
176 |   for _,_ in tl.iterate.minibatches(X_tridx, 
177 |                                     y_train, 
178 |                                     batch_size, 
179 |                                     shuffle=True):
180 |     # 1. Read data for each batch      
181 |     while True:
182 |       rdwr_lock.acquire()
183 |       if full_flg[rd_pos] == 0:
184 |         rdwr_lock.release()
185 |         time.sleep(1)
186 |         continue
187 |       rdwr_lock.release()
188 |       break
189 |     # 2. Training
190 |     feed_dict = {x: X_data_a[rd_pos*batch_size:(rd_pos+1)*batch_size,:,:,:,:], 
191 |                  y: y_label_a[rd_pos*batch_size:(rd_pos+1)*batch_size]}
192 |     feed_dict.update(networks.all_drop)
193 |     start_time = time.time()
194 |     _,loss_value,lr_value,acc = sess.run([train_op,cost,lr,networks_accu], feed_dict=feed_dict)
195 |     duration = time.time() - start_time
196 |     # 3. Update flags
197 |     rdwr_lock.acquire()
198 |     full_flg[rd_pos] = 0
199 |     rdwr_lock.release()
200 |     rd_pos = (rd_pos+1)%queue_num
201 |     # 4. Statistics
202 |     if step%print_freq == 0:
203 |       average_acc = acc
204 |       total_loss = loss_value
205 |       training_time = duration
206 |     else:
207 |       average_acc += acc
208 |       total_loss = total_loss + loss_value
209 |       training_time = training_time + duration
210 |     if (step+1)%print_freq == 0:
211 |       training_bps = batch_size*print_freq / training_time
212 |       average_loss = total_loss / print_freq
213 |       average_acc = average_acc / print_freq
214 |       format_str = ('%s: iter = %d, lr=%f, average_loss = %.2f average_acc = %.6f (training: %.1f batches/sec)')
215 |       print (format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps))
216 |       log.writelines([format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps), 
217 |                       '\n'])
218 |       log.flush()
219 |     step = step + 1
220 | 
221 |   tl.files.save_npz(networks.all_params, 
222 |                     name='%s_model_iter_%d.npz'%(dataset_name, step), 
223 |                     sess=sess)
224 |   print("Model saved in file: %s_model_iter_%d.npz" %(dataset_name, step))
225 | 
226 |   # Test Stage
227 |   average_accuracy = 0.0
228 |   test_iterations = 0;
229 |   for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
230 |                                                      y_test, 
231 |                                                      batch_size, 
232 |                                                      shuffle=True):
233 |     # Read data for each batch      
234 |     image_path = []
235 |     image_fcnt = []
236 |     image_olen = []
237 |     is_training = []
238 |     for data_a in range(batch_size):
239 |       X_index_a = X_indices[data_a]
240 |       key_str = '%06d' % X_index_a
241 |       image_path.append(X_test[key_str]['videopath'])
242 |       image_fcnt.append(X_test[key_str]['framecnt'])
243 |       image_olen.append(seq_len)
244 |       is_training.append(False) # Testing
245 |     image_info = zip(image_path,image_fcnt,image_olen,is_training)
246 |     X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
247 |                                         data.prepare_isogr_rgb_data)
248 |     feed_dict = {x: X_data_t, y: y_label_t}
249 |     dp_dict = tl.utils.dict_to_one(predictons.all_drop)
250 |     feed_dict.update(dp_dict)
251 |     _,accu_value = sess.run([predicton_y_op, predicton_accu], feed_dict=feed_dict)
252 |     average_accuracy = average_accuracy + accu_value
253 |     test_iterations = test_iterations + 1
254 |   average_accuracy = average_accuracy / test_iterations
255 |   format_str = ('%s: epoch = %d, average_accuracy = %.6f')
256 |   print (format_str % (datetime.now(), epoch, average_accuracy))
257 |   log.writelines([format_str % (datetime.now(), epoch, average_accuracy), '\n'])
258 |   log.flush()
259 | 
260 | # In the end, close TensorFlow session.
261 | log.close()
262 | sess.close()
263 | 


--------------------------------------------------------------------------------
/training_skig_depth.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.environ['CUDA_VISIBLE_DEVICES'] = '2'
  3 | import io
  4 | import sys
  5 | import numpy as np
  6 | import tensorflow as tf
  7 | slim = tf.contrib.slim
  8 | import tensorlayer as tl
  9 | import inputs as data
 10 | import c3d_clstm as net 
 11 | import time
 12 | from datetime import datetime
 13 | import threading
 14 | import cStringIO
 15 | 
 16 | seq_len = 32
 17 | batch_size = 13
 18 | n_epoch = 165
 19 | learning_rate = 0.01
 20 | decay_steps = 10000
 21 | decay_rate  = 0.1
 22 | weight_decay= 0.00004
 23 | print_freq = 20
 24 | queue_num = 5
 25 | start_step = 0
 26 | 
 27 | num_classes = 10
 28 | dataset_name = 'skig_depth_s1'
 29 | training_datalist = '/ssd/gmzhu/SKIG/trte_splits/training_depth_list1.txt'
 30 | testing_datalist = '/ssd/gmzhu/SKIG/trte_splits/testing_depth_list1.txt'
 31 | model_prefix='/raid/gmzhu/tensorflow/c3d_clstm_net2'
 32 | 
 33 | curtime = '%s' % datetime.now()
 34 | d = curtime.split(' ')[0]
 35 | t = curtime.split(' ')[1]
 36 | strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2], 
 37 |                             t.split(':')[0],t.split(':')[1],t.split(':')[2])
 38 | 
 39 | saved_stdout = sys.stdout
 40 | mem_log = cStringIO.StringIO()
 41 | sys.stdout = mem_log
 42 | logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
 43 | log = open(logfile, 'w')
 44 | 
 45 | sess = tf.InteractiveSession()
 46 | x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
 47 | y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
 48 |   
 49 | networks = net.c3d_clstm(x, num_classes, False, True)
 50 | networks_y = networks.outputs
 51 | networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
 52 | networks_cost = tl.cost.cross_entropy(networks_y, y)
 53 | correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
 54 | networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 55 |     
 56 | predictons = net.c3d_clstm(x, num_classes, True, False)
 57 | predicton_y_op = tf.argmax(tf.nn.softmax(predictons.outputs),1)
 58 | predicton_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predicton_y_op, tf.int32), y), tf.float32))
 59 |   
 60 | l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
 61 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
 62 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
 63 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[14]) + \
 64 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[20]) + \
 65 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[22]) + \
 66 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[24]) 
 67 | cost = networks_cost + l2_cost 
 68 |   
 69 | # Decay the learning rate exponentially based on the number of steps.
 70 | global_step = tf.Variable(start_step*2, trainable=False)
 71 | lr1 = tf.train.exponential_decay(learning_rate,
 72 |                                 global_step,
 73 |                                 decay_steps,
 74 |                                 decay_rate,
 75 |                                 staircase=True)
 76 | lr2 = tf.train.exponential_decay(learning_rate*10,
 77 |                                 global_step,
 78 |                                 decay_steps,
 79 |                                 decay_rate,
 80 |                                 staircase=True)
 81 | var_list1 = networks.all_params[:24] 
 82 | var_list2 = networks.all_params[24:]
 83 | opt1 = tf.train.GradientDescentOptimizer(lr1)
 84 | opt2 = tf.train.GradientDescentOptimizer(lr2)
 85 | grads = tf.gradients(cost, var_list1 + var_list2)
 86 | grads1 = grads[:len(var_list1)]
 87 | grads2 = grads[len(var_list1):]
 88 | train_op1 = opt1.apply_gradients(zip(grads1, var_list1), global_step=global_step)
 89 | train_op2 = opt2.apply_gradients(zip(grads2, var_list2), global_step=global_step)
 90 | train_op = tf.group(train_op1, train_op2)
 91 | 
 92 | sess.run(tf.initialize_all_variables())
 93 | 
 94 | if start_step>0:
 95 |   load_params = tl.files.load_npz(name='%s_model_iter_%d.npz'%(dataset_name, start_step))
 96 |   tl.files.assign_params(sess, load_params, networks)
 97 | else:
 98 |   load_params = tl.files.load_npz(name='isogr_rgb_model_iter_60000.npz')
 99 |   tl.files.assign_params(sess, load_params[0:24], networks)
100 | networks.print_params(True)
101 |   
102 | # Data Reading
103 | X_train,y_train = data.load_video_list(training_datalist)
104 | X_tridx = np.asarray(np.arange(0, len(y_train)), dtype=np.int32)
105 | y_train = np.asarray(y_train, dtype=np.int32)
106 | X_test,y_test = data.load_video_list(testing_datalist)
107 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
108 | y_test  = np.asarray(y_test, dtype=np.int32)
109 |   
110 | X_data_a  = np.empty((batch_size*queue_num, seq_len, 112, 112, 3),float)      
111 | y_label_a = np.empty((batch_size*queue_num,),int)
112 | 
113 | full_flg  = np.zeros((queue_num, 1))
114 | rdwr_lock = threading.Lock()
115 | 
116 | def training_data_read():
117 |   wr_pos = 0
118 |   for i in range(n_epoch):
119 |     for X_indices, y_labels in tl.iterate.minibatches(X_tridx,
120 |                                                       y_train, 
121 |                                                       batch_size, 
122 |                                                       shuffle=True):
123 |       # 1. Waiting
124 |       while True:
125 |         rdwr_lock.acquire()
126 |         if full_flg[wr_pos] == 1:
127 |           rdwr_lock.release()
128 |           time.sleep(1)
129 |           continue
130 |         rdwr_lock.release()
131 |         break
132 |       # 2. Reading data
133 |       image_path = []
134 |       image_fcnt = []
135 |       image_olen = []
136 |       is_training = []
137 |       for data_a in range(batch_size):
138 |         X_index_a = X_indices[data_a]
139 |         key_str = '%06d' % X_index_a
140 |         image_path.append(X_train[key_str]['videopath'])
141 |         image_fcnt.append(X_train[key_str]['framecnt'])
142 |         image_olen.append(seq_len)
143 |         is_training.append(True) # Training
144 |       image_info = zip(image_path,image_fcnt,image_olen,is_training)
145 |       X_data_a[wr_pos*batch_size:(wr_pos+1)*batch_size,:,:,:,:] = \
146 |                   tl.prepro.threading_data([_ for _ in image_info], 
147 |                                            data.prepare_skig_depth_data)
148 |       y_label_a[wr_pos*batch_size:(wr_pos+1)*batch_size] = y_labels
149 |       # 3. Update flags
150 |       rdwr_lock.acquire()
151 |       full_flg[wr_pos] = 1
152 |       rdwr_lock.release()
153 |       wr_pos = (wr_pos+1)%queue_num      
154 |   
155 | wr_thread = threading.Thread(target=training_data_read)
156 | wr_thread.start()
157 | 
158 | # Output the saved logs to stdout and the opened log file
159 | sys.stdout = saved_stdout
160 | mem_log.seek(0)
161 | print mem_log.read()
162 | mem_log.seek(0)
163 | log.writelines(['%s' % mem_log.read()])
164 | log.flush()
165 | mem_log.close()
166 | 
167 | step = start_step
168 | rd_pos = 0
169 | for epoch in range(n_epoch):
170 |   # Train Stage
171 |   for _,_ in tl.iterate.minibatches(X_tridx, 
172 |                                     y_train, 
173 |                                     batch_size, 
174 |                                     shuffle=True):
175 |     # 1. Read data for each batch      
176 |     while True:
177 |       rdwr_lock.acquire()
178 |       if full_flg[rd_pos] == 0:
179 |         rdwr_lock.release()
180 |         time.sleep(1)
181 |         continue
182 |       rdwr_lock.release()
183 |       break
184 |     # 2. Training
185 |     feed_dict = {x: X_data_a[rd_pos*batch_size:(rd_pos+1)*batch_size,:,:,:,:], 
186 |                  y: y_label_a[rd_pos*batch_size:(rd_pos+1)*batch_size]}
187 |     feed_dict.update(networks.all_drop)
188 |     start_time = time.time()
189 |     _,loss_value,lr_value,acc = sess.run([train_op,cost,lr1,networks_accu], feed_dict=feed_dict)
190 |     duration = time.time() - start_time
191 |     # 3. Update flags
192 |     rdwr_lock.acquire()
193 |     full_flg[rd_pos] = 0
194 |     rdwr_lock.release()
195 |     rd_pos = (rd_pos+1)%queue_num
196 |     # 4. Statistics
197 |     if step%print_freq == 0:
198 |       average_acc = acc
199 |       total_loss = loss_value
200 |       training_time = duration
201 |     else:
202 |       average_acc += acc
203 |       total_loss = total_loss + loss_value
204 |       training_time = training_time + duration
205 |     if (step+1)%print_freq == 0:
206 |       training_bps = batch_size*print_freq / training_time
207 |       average_loss = total_loss / print_freq
208 |       average_acc = average_acc / print_freq
209 |       format_str = ('%s: iter = %d, lr=%f, average_loss = %.2f average_acc = %.6f (training: %.1f batches/sec)')
210 |       print (format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps))
211 |       log.writelines([format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps), 
212 |                       '\n'])
213 |       log.flush()
214 |     step = step + 1
215 |     if step%300 == 0:
216 |       tl.files.save_npz(networks.all_params, 
217 |                         name='%s_model_iter_%d.npz'%(dataset_name, step), 
218 |                         sess=sess)
219 |       print("Model saved in file: %s_model_iter_%d.npz" %(dataset_name, step))
220 |     
221 |       # Test Stage
222 |       average_accuracy = 0.0
223 |       test_iterations = 0;
224 |       for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
225 |                                                          y_test, 
226 |                                                          batch_size, 
227 |                                                          shuffle=True):
228 |         # Read data for each batch      
229 |         image_path = []
230 |         image_fcnt = []
231 |         image_olen = []
232 |         is_training = []
233 |         for data_a in range(batch_size):
234 |           X_index_a = X_indices[data_a]
235 |           key_str = '%06d' % X_index_a
236 |           image_path.append(X_test[key_str]['videopath'])
237 |           image_fcnt.append(X_test[key_str]['framecnt'])
238 |           image_olen.append(seq_len)
239 |           is_training.append(False) # Testing
240 |         image_info = zip(image_path,image_fcnt,image_olen,is_training)
241 |         X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
242 |                                             data.prepare_skig_depth_data)
243 |         feed_dict = {x: X_data_t, y: y_label_t}
244 |         dp_dict = tl.utils.dict_to_one(predictons.all_drop)
245 |         feed_dict.update(dp_dict)
246 |         _,accu_value = sess.run([predicton_y_op, predicton_accu], feed_dict=feed_dict)
247 |         average_accuracy = average_accuracy + accu_value
248 |         test_iterations = test_iterations + 1
249 |       average_accuracy = average_accuracy / test_iterations
250 |       format_str = ('%s: epoch = %d, average_accuracy = %.6f')
251 |       print (format_str % (datetime.now(), epoch, average_accuracy))
252 |       log.writelines([format_str % (datetime.now(), epoch, average_accuracy), '\n'])
253 |       log.flush()
254 | 
255 | # In the end, close TensorFlow session.
256 | log.close()
257 | sess.close()
258 | 


--------------------------------------------------------------------------------
/training_skig_rgb.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | os.environ['CUDA_VISIBLE_DEVICES'] = '2'
  3 | import io
  4 | import sys
  5 | import numpy as np
  6 | import tensorflow as tf
  7 | slim = tf.contrib.slim
  8 | import tensorlayer as tl
  9 | import inputs as data
 10 | import c3d_clstm as net 
 11 | import time
 12 | from datetime import datetime
 13 | import threading
 14 | import cStringIO
 15 | 
 16 | seq_len = 32
 17 | batch_size = 13
 18 | n_epoch = 165
 19 | learning_rate = 0.01
 20 | decay_steps = 10000
 21 | decay_rate  = 0.1
 22 | weight_decay= 0.00004
 23 | print_freq = 20
 24 | queue_num = 5
 25 | start_step = 0
 26 | 
 27 | num_classes = 10
 28 | dataset_name = 'skig_rgb_s1'
 29 | training_datalist = '/ssd/gmzhu/SKIG/trte_splits/training_rgb_list1.txt'
 30 | testing_datalist = '/ssd/gmzhu/SKIG/trte_splits/testing_rgb_list1.txt'
 31 | 
 32 | curtime = '%s' % datetime.now()
 33 | d = curtime.split(' ')[0]
 34 | t = curtime.split(' ')[1]
 35 | strtime = '%s%s%s-%s%s%s' %(d.split('-')[0],d.split('-')[1],d.split('-')[2], 
 36 |                             t.split(':')[0],t.split(':')[1],t.split(':')[2])
 37 | 
 38 | saved_stdout = sys.stdout
 39 | mem_log = cStringIO.StringIO()
 40 | sys.stdout = mem_log
 41 | logfile = './log/training_%s_%s.log' %(dataset_name, strtime)
 42 | log = open(logfile, 'w')
 43 | 
 44 | sess = tf.InteractiveSession()
 45 | x = tf.placeholder(tf.float32, [batch_size, seq_len, 112, 112, 3], name='x')
 46 | y = tf.placeholder(tf.int32, shape=[batch_size, ], name='y')
 47 |   
 48 | networks = net.c3d_clstm(x, num_classes, False, True)
 49 | networks_y = networks.outputs
 50 | networks_y_op = tf.argmax(tf.nn.softmax(networks_y), 1)
 51 | networks_cost = tl.cost.cross_entropy(networks_y, y)
 52 | correct_pred = tf.equal(tf.cast(networks_y_op, tf.int32), y)
 53 | networks_accu = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 54 |     
 55 | predictons = net.c3d_clstm(x, num_classes, True, False)
 56 | predicton_y_op = tf.argmax(tf.nn.softmax(predictons.outputs),1)
 57 | predicton_accu = tf.reduce_mean(tf.cast(tf.equal(tf.cast(predicton_y_op, tf.int32), y), tf.float32))
 58 |   
 59 | l2_cost = tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[0]) + \
 60 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[6]) + \
 61 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[12]) + \
 62 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[14]) + \
 63 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[20]) + \
 64 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[22]) + \
 65 |           tf.contrib.layers.l2_regularizer(weight_decay)(networks.all_params[24]) 
 66 | cost = networks_cost + l2_cost 
 67 |   
 68 | # Decay the learning rate exponentially based on the number of steps.
 69 | global_step = tf.Variable(start_step*2, trainable=False)
 70 | lr1 = tf.train.exponential_decay(learning_rate,
 71 |                                 global_step,
 72 |                                 decay_steps,
 73 |                                 decay_rate,
 74 |                                 staircase=True)
 75 | lr2 = tf.train.exponential_decay(learning_rate*10,
 76 |                                 global_step,
 77 |                                 decay_steps,
 78 |                                 decay_rate,
 79 |                                 staircase=True)
 80 | var_list1 = networks.all_params[:24] 
 81 | var_list2 = networks.all_params[24:]
 82 | opt1 = tf.train.GradientDescentOptimizer(lr1)
 83 | opt2 = tf.train.GradientDescentOptimizer(lr2)
 84 | grads = tf.gradients(cost, var_list1 + var_list2)
 85 | grads1 = grads[:len(var_list1)]
 86 | grads2 = grads[len(var_list1):]
 87 | train_op1 = opt1.apply_gradients(zip(grads1, var_list1), global_step=global_step)
 88 | train_op2 = opt2.apply_gradients(zip(grads2, var_list2), global_step=global_step)
 89 | train_op = tf.group(train_op1, train_op2)
 90 | 
 91 | sess.run(tf.initialize_all_variables())
 92 | 
 93 | if start_step>0:
 94 |   load_params = tl.files.load_npz(name='%s_model_iter_%d.npz'%(dataset_name, start_step))
 95 |   tl.files.assign_params(sess, load_params, networks)
 96 | else:
 97 |   load_params = tl.files.load_npz(name='isogr_rgb_model_iter_60000.npz')
 98 |   tl.files.assign_params(sess, load_params[0:24], networks)
 99 | networks.print_params(True)
100 |   
101 | # Data Reading
102 | X_train,y_train = data.load_video_list(training_datalist)
103 | X_tridx = np.asarray(np.arange(0, len(y_train)), dtype=np.int32)
104 | y_train = np.asarray(y_train, dtype=np.int32)
105 | X_test,y_test = data.load_video_list(testing_datalist)
106 | X_teidx = np.asarray(np.arange(0, len(y_test)), dtype=np.int32)
107 | y_test  = np.asarray(y_test, dtype=np.int32)
108 |   
109 | X_data_a  = np.empty((batch_size*queue_num, seq_len, 112, 112, 3),float)      
110 | y_label_a = np.empty((batch_size*queue_num,),int)
111 | 
112 | full_flg  = np.zeros((queue_num, 1))
113 | rdwr_lock = threading.Lock()
114 | 
115 | def training_data_read():
116 |   wr_pos = 0
117 |   for i in range(n_epoch):
118 |     for X_indices, y_labels in tl.iterate.minibatches(X_tridx,
119 |                                                       y_train, 
120 |                                                       batch_size, 
121 |                                                       shuffle=True):
122 |       # 1. Waiting
123 |       while True:
124 |         rdwr_lock.acquire()
125 |         if full_flg[wr_pos] == 1:
126 |           rdwr_lock.release()
127 |           time.sleep(1)
128 |           continue
129 |         rdwr_lock.release()
130 |         break
131 |       # 2. Reading data
132 |       image_path = []
133 |       image_fcnt = []
134 |       image_olen = []
135 |       is_training = []
136 |       for data_a in range(batch_size):
137 |         X_index_a = X_indices[data_a]
138 |         key_str = '%06d' % X_index_a
139 |         image_path.append(X_train[key_str]['videopath'])
140 |         image_fcnt.append(X_train[key_str]['framecnt'])
141 |         image_olen.append(seq_len)
142 |         is_training.append(True) # Training
143 |       image_info = zip(image_path,image_fcnt,image_olen,is_training)
144 |       X_data_a[wr_pos*batch_size:(wr_pos+1)*batch_size,:,:,:,:] = \
145 |                   tl.prepro.threading_data([_ for _ in image_info], 
146 |                                            data.prepare_skig_rgb_data)
147 |       y_label_a[wr_pos*batch_size:(wr_pos+1)*batch_size] = y_labels
148 |       # 3. Update flags
149 |       rdwr_lock.acquire()
150 |       full_flg[wr_pos] = 1
151 |       rdwr_lock.release()
152 |       wr_pos = (wr_pos+1)%queue_num      
153 |   
154 | wr_thread = threading.Thread(target=training_data_read)
155 | wr_thread.start()
156 | 
157 | # Output the saved logs to stdout and the opened log file
158 | sys.stdout = saved_stdout
159 | mem_log.seek(0)
160 | print mem_log.read()
161 | mem_log.seek(0)
162 | log.writelines(['%s' % mem_log.read()])
163 | log.flush()
164 | mem_log.close()
165 | 
166 | step = start_step
167 | rd_pos = 0
168 | for epoch in range(n_epoch):
169 |   # Train Stage
170 |   for _,_ in tl.iterate.minibatches(X_tridx, 
171 |                                     y_train, 
172 |                                     batch_size, 
173 |                                     shuffle=True):
174 |     # 1. Read data for each batch      
175 |     while True:
176 |       rdwr_lock.acquire()
177 |       if full_flg[rd_pos] == 0:
178 |         rdwr_lock.release()
179 |         time.sleep(1)
180 |         continue
181 |       rdwr_lock.release()
182 |       break
183 |     # 2. Training
184 |     feed_dict = {x: X_data_a[rd_pos*batch_size:(rd_pos+1)*batch_size,:,:,:,:], 
185 |                  y: y_label_a[rd_pos*batch_size:(rd_pos+1)*batch_size]}
186 |     feed_dict.update(networks.all_drop)
187 |     start_time = time.time()
188 |     _,loss_value,lr_value,acc = sess.run([train_op,cost,lr1,networks_accu], feed_dict=feed_dict)
189 |     duration = time.time() - start_time
190 |     # 3. Update flags
191 |     rdwr_lock.acquire()
192 |     full_flg[rd_pos] = 0
193 |     rdwr_lock.release()
194 |     rd_pos = (rd_pos+1)%queue_num
195 |     # 4. Statistics
196 |     if step%print_freq == 0:
197 |       average_acc = acc
198 |       total_loss = loss_value
199 |       training_time = duration
200 |     else:
201 |       average_acc += acc
202 |       total_loss = total_loss + loss_value
203 |       training_time = training_time + duration
204 |     if (step+1)%print_freq == 0:
205 |       training_bps = batch_size*print_freq / training_time
206 |       average_loss = total_loss / print_freq
207 |       average_acc = average_acc / print_freq
208 |       format_str = ('%s: iter = %d, lr=%f, average_loss = %.2f average_acc = %.6f (training: %.1f batches/sec)')
209 |       print (format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps))
210 |       log.writelines([format_str % (datetime.now(), step+1, lr_value, average_loss, average_acc, training_bps), 
211 |                       '\n'])
212 |       log.flush()
213 |     step = step + 1
214 |     if step%300 == 0:
215 |       tl.files.save_npz(networks.all_params, 
216 |                         name='%s_model_iter_%d.npz'%(dataset_name, step), 
217 |                         sess=sess)
218 |       print("Model saved in file: %s_model_iter_%d.npz" %(dataset_name, step))
219 |     
220 |       # Test Stage
221 |       average_accuracy = 0.0
222 |       test_iterations = 0;
223 |       for X_indices, y_label_t in tl.iterate.minibatches(X_teidx, 
224 |                                                          y_test, 
225 |                                                          batch_size, 
226 |                                                          shuffle=True):
227 |         # Read data for each batch      
228 |         image_path = []
229 |         image_fcnt = []
230 |         image_olen = []
231 |         is_training = []
232 |         for data_a in range(batch_size):
233 |           X_index_a = X_indices[data_a]
234 |           key_str = '%06d' % X_index_a
235 |           image_path.append(X_test[key_str]['videopath'])
236 |           image_fcnt.append(X_test[key_str]['framecnt'])
237 |           image_olen.append(seq_len)
238 |           is_training.append(False) # Testing
239 |         image_info = zip(image_path,image_fcnt,image_olen,is_training)
240 |         X_data_t = tl.prepro.threading_data([_ for _ in image_info], 
241 |                                             data.prepare_skig_rgb_data)
242 |         feed_dict = {x: X_data_t, y: y_label_t}
243 |         dp_dict = tl.utils.dict_to_one(predictons.all_drop)
244 |         feed_dict.update(dp_dict)
245 |         _,accu_value = sess.run([predicton_y_op, predicton_accu], feed_dict=feed_dict)
246 |         average_accuracy = average_accuracy + accu_value
247 |         test_iterations = test_iterations + 1
248 |       average_accuracy = average_accuracy / test_iterations
249 |       format_str = ('%s: epoch = %d, average_accuracy = %.6f')
250 |       print (format_str % (datetime.now(), epoch, average_accuracy))
251 |       log.writelines([format_str % (datetime.now(), epoch, average_accuracy), '\n'])
252 |       log.flush()
253 | 
254 | # In the end, close TensorFlow session.
255 | log.close()
256 | sess.close()
257 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_depth_list1.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/DepthImages/51110 197 0
  2 | /ssd/gmzhu/SKIG/DepthImages/51111 210 1
  3 | /ssd/gmzhu/SKIG/DepthImages/51112 136 2
  4 | /ssd/gmzhu/SKIG/DepthImages/51113 277 3
  5 | /ssd/gmzhu/SKIG/DepthImages/51114 150 4
  6 | /ssd/gmzhu/SKIG/DepthImages/51115 98 5
  7 | /ssd/gmzhu/SKIG/DepthImages/51116 87 6
  8 | /ssd/gmzhu/SKIG/DepthImages/51117 110 7
  9 | /ssd/gmzhu/SKIG/DepthImages/51118 145 8
 10 | /ssd/gmzhu/SKIG/DepthImages/51119 126 9
 11 | /ssd/gmzhu/SKIG/DepthImages/51120 168 0
 12 | /ssd/gmzhu/SKIG/DepthImages/51121 207 1
 13 | /ssd/gmzhu/SKIG/DepthImages/51122 146 2
 14 | /ssd/gmzhu/SKIG/DepthImages/51123 262 3
 15 | /ssd/gmzhu/SKIG/DepthImages/51124 149 4
 16 | /ssd/gmzhu/SKIG/DepthImages/51125 95 5
 17 | /ssd/gmzhu/SKIG/DepthImages/51126 89 6
 18 | /ssd/gmzhu/SKIG/DepthImages/51127 122 7
 19 | /ssd/gmzhu/SKIG/DepthImages/51128 130 8
 20 | /ssd/gmzhu/SKIG/DepthImages/51129 134 9
 21 | /ssd/gmzhu/SKIG/DepthImages/51130 186 0
 22 | /ssd/gmzhu/SKIG/DepthImages/51131 164 1
 23 | /ssd/gmzhu/SKIG/DepthImages/51132 139 2
 24 | /ssd/gmzhu/SKIG/DepthImages/51133 261 3
 25 | /ssd/gmzhu/SKIG/DepthImages/51134 172 4
 26 | /ssd/gmzhu/SKIG/DepthImages/51135 112 5
 27 | /ssd/gmzhu/SKIG/DepthImages/51136 96 6
 28 | /ssd/gmzhu/SKIG/DepthImages/51137 122 7
 29 | /ssd/gmzhu/SKIG/DepthImages/51138 139 8
 30 | /ssd/gmzhu/SKIG/DepthImages/51139 132 9
 31 | /ssd/gmzhu/SKIG/DepthImages/51210 144 0
 32 | /ssd/gmzhu/SKIG/DepthImages/51211 163 1
 33 | /ssd/gmzhu/SKIG/DepthImages/51212 169 2
 34 | /ssd/gmzhu/SKIG/DepthImages/51213 284 3
 35 | /ssd/gmzhu/SKIG/DepthImages/51214 131 4
 36 | /ssd/gmzhu/SKIG/DepthImages/51215 86 5
 37 | /ssd/gmzhu/SKIG/DepthImages/51216 70 6
 38 | /ssd/gmzhu/SKIG/DepthImages/51217 96 7
 39 | /ssd/gmzhu/SKIG/DepthImages/51218 121 8
 40 | /ssd/gmzhu/SKIG/DepthImages/51219 121 9
 41 | /ssd/gmzhu/SKIG/DepthImages/51220 138 0
 42 | /ssd/gmzhu/SKIG/DepthImages/51221 173 1
 43 | /ssd/gmzhu/SKIG/DepthImages/51222 153 2
 44 | /ssd/gmzhu/SKIG/DepthImages/51223 317 3
 45 | /ssd/gmzhu/SKIG/DepthImages/51224 165 4
 46 | /ssd/gmzhu/SKIG/DepthImages/51225 82 5
 47 | /ssd/gmzhu/SKIG/DepthImages/51226 79 6
 48 | /ssd/gmzhu/SKIG/DepthImages/51227 97 7
 49 | /ssd/gmzhu/SKIG/DepthImages/51228 137 8
 50 | /ssd/gmzhu/SKIG/DepthImages/51229 117 9
 51 | /ssd/gmzhu/SKIG/DepthImages/51230 142 0
 52 | /ssd/gmzhu/SKIG/DepthImages/51231 157 1
 53 | /ssd/gmzhu/SKIG/DepthImages/51232 142 2
 54 | /ssd/gmzhu/SKIG/DepthImages/51233 314 3
 55 | /ssd/gmzhu/SKIG/DepthImages/51234 166 4
 56 | /ssd/gmzhu/SKIG/DepthImages/51235 81 5
 57 | /ssd/gmzhu/SKIG/DepthImages/51236 92 6
 58 | /ssd/gmzhu/SKIG/DepthImages/51237 123 7
 59 | /ssd/gmzhu/SKIG/DepthImages/51238 117 8
 60 | /ssd/gmzhu/SKIG/DepthImages/51239 112 9
 61 | /ssd/gmzhu/SKIG/DepthImages/52110 162 0
 62 | /ssd/gmzhu/SKIG/DepthImages/52111 185 1
 63 | /ssd/gmzhu/SKIG/DepthImages/52112 154 2
 64 | /ssd/gmzhu/SKIG/DepthImages/52113 294 3
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162 | /ssd/gmzhu/SKIG/DepthImages/53221 128 1
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216 | /ssd/gmzhu/SKIG/DepthImages/61215 127 5
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220 | /ssd/gmzhu/SKIG/DepthImages/61219 110 9
221 | /ssd/gmzhu/SKIG/DepthImages/61220 339 0
222 | /ssd/gmzhu/SKIG/DepthImages/61221 297 1
223 | /ssd/gmzhu/SKIG/DepthImages/61222 167 2
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225 | /ssd/gmzhu/SKIG/DepthImages/61224 147 4
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228 | /ssd/gmzhu/SKIG/DepthImages/61227 112 7
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230 | /ssd/gmzhu/SKIG/DepthImages/61229 119 9
231 | /ssd/gmzhu/SKIG/DepthImages/61230 281 0
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238 | /ssd/gmzhu/SKIG/DepthImages/61237 109 7
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240 | /ssd/gmzhu/SKIG/DepthImages/61239 147 9
241 | /ssd/gmzhu/SKIG/DepthImages/62110 302 0
242 | /ssd/gmzhu/SKIG/DepthImages/62111 299 1
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245 | /ssd/gmzhu/SKIG/DepthImages/62114 167 4
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247 | /ssd/gmzhu/SKIG/DepthImages/62116 102 6
248 | /ssd/gmzhu/SKIG/DepthImages/62117 123 7
249 | /ssd/gmzhu/SKIG/DepthImages/62118 114 8
250 | /ssd/gmzhu/SKIG/DepthImages/62119 121 9
251 | /ssd/gmzhu/SKIG/DepthImages/62120 255 0
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261 | /ssd/gmzhu/SKIG/DepthImages/62130 265 0
262 | /ssd/gmzhu/SKIG/DepthImages/62131 311 1
263 | /ssd/gmzhu/SKIG/DepthImages/62132 173 2
264 | /ssd/gmzhu/SKIG/DepthImages/62133 350 3
265 | /ssd/gmzhu/SKIG/DepthImages/62134 172 4
266 | /ssd/gmzhu/SKIG/DepthImages/62135 125 5
267 | /ssd/gmzhu/SKIG/DepthImages/62136 123 6
268 | /ssd/gmzhu/SKIG/DepthImages/62137 123 7
269 | /ssd/gmzhu/SKIG/DepthImages/62138 115 8
270 | /ssd/gmzhu/SKIG/DepthImages/62139 140 9
271 | /ssd/gmzhu/SKIG/DepthImages/62210 255 0
272 | /ssd/gmzhu/SKIG/DepthImages/62211 264 1
273 | /ssd/gmzhu/SKIG/DepthImages/62212 162 2
274 | /ssd/gmzhu/SKIG/DepthImages/62213 330 3
275 | /ssd/gmzhu/SKIG/DepthImages/62214 156 4
276 | /ssd/gmzhu/SKIG/DepthImages/62215 116 5
277 | /ssd/gmzhu/SKIG/DepthImages/62216 107 6
278 | /ssd/gmzhu/SKIG/DepthImages/62217 121 7
279 | /ssd/gmzhu/SKIG/DepthImages/62218 142 8
280 | /ssd/gmzhu/SKIG/DepthImages/62219 152 9
281 | /ssd/gmzhu/SKIG/DepthImages/62220 331 0
282 | /ssd/gmzhu/SKIG/DepthImages/62221 262 1
283 | /ssd/gmzhu/SKIG/DepthImages/62222 186 2
284 | /ssd/gmzhu/SKIG/DepthImages/62223 333 3
285 | /ssd/gmzhu/SKIG/DepthImages/62224 180 4
286 | /ssd/gmzhu/SKIG/DepthImages/62225 110 5
287 | /ssd/gmzhu/SKIG/DepthImages/62226 96 6
288 | /ssd/gmzhu/SKIG/DepthImages/62227 141 7
289 | /ssd/gmzhu/SKIG/DepthImages/62228 141 8
290 | /ssd/gmzhu/SKIG/DepthImages/62229 125 9
291 | /ssd/gmzhu/SKIG/DepthImages/62230 350 0
292 | /ssd/gmzhu/SKIG/DepthImages/62231 265 1
293 | /ssd/gmzhu/SKIG/DepthImages/62232 170 2
294 | /ssd/gmzhu/SKIG/DepthImages/62233 337 3
295 | /ssd/gmzhu/SKIG/DepthImages/62234 164 4
296 | /ssd/gmzhu/SKIG/DepthImages/62235 111 5
297 | /ssd/gmzhu/SKIG/DepthImages/62236 106 6
298 | /ssd/gmzhu/SKIG/DepthImages/62237 122 7
299 | /ssd/gmzhu/SKIG/DepthImages/62238 148 8
300 | /ssd/gmzhu/SKIG/DepthImages/62239 138 9
301 | /ssd/gmzhu/SKIG/DepthImages/63110 328 0
302 | /ssd/gmzhu/SKIG/DepthImages/63111 261 1
303 | /ssd/gmzhu/SKIG/DepthImages/63112 180 2
304 | /ssd/gmzhu/SKIG/DepthImages/63113 328 3
305 | /ssd/gmzhu/SKIG/DepthImages/63114 185 4
306 | /ssd/gmzhu/SKIG/DepthImages/63115 132 5
307 | /ssd/gmzhu/SKIG/DepthImages/63116 124 6
308 | /ssd/gmzhu/SKIG/DepthImages/63117 100 7
309 | /ssd/gmzhu/SKIG/DepthImages/63118 120 8
310 | /ssd/gmzhu/SKIG/DepthImages/63119 120 9
311 | /ssd/gmzhu/SKIG/DepthImages/63120 306 0
312 | /ssd/gmzhu/SKIG/DepthImages/63121 283 1
313 | /ssd/gmzhu/SKIG/DepthImages/63122 168 2
314 | /ssd/gmzhu/SKIG/DepthImages/63123 322 3
315 | /ssd/gmzhu/SKIG/DepthImages/63124 184 4
316 | /ssd/gmzhu/SKIG/DepthImages/63125 115 5
317 | /ssd/gmzhu/SKIG/DepthImages/63126 119 6
318 | /ssd/gmzhu/SKIG/DepthImages/63127 109 7
319 | /ssd/gmzhu/SKIG/DepthImages/63128 129 8
320 | /ssd/gmzhu/SKIG/DepthImages/63129 150 9
321 | /ssd/gmzhu/SKIG/DepthImages/63130 297 0
322 | /ssd/gmzhu/SKIG/DepthImages/63131 281 1
323 | /ssd/gmzhu/SKIG/DepthImages/63132 173 2
324 | /ssd/gmzhu/SKIG/DepthImages/63133 348 3
325 | /ssd/gmzhu/SKIG/DepthImages/63134 189 4
326 | /ssd/gmzhu/SKIG/DepthImages/63135 123 5
327 | /ssd/gmzhu/SKIG/DepthImages/63136 116 6
328 | /ssd/gmzhu/SKIG/DepthImages/63137 105 7
329 | /ssd/gmzhu/SKIG/DepthImages/63138 150 8
330 | /ssd/gmzhu/SKIG/DepthImages/63139 146 9
331 | /ssd/gmzhu/SKIG/DepthImages/63210 308 0
332 | /ssd/gmzhu/SKIG/DepthImages/63211 244 1
333 | /ssd/gmzhu/SKIG/DepthImages/63212 158 2
334 | /ssd/gmzhu/SKIG/DepthImages/63213 317 3
335 | /ssd/gmzhu/SKIG/DepthImages/63214 147 4
336 | /ssd/gmzhu/SKIG/DepthImages/63215 120 5
337 | /ssd/gmzhu/SKIG/DepthImages/63216 114 6
338 | /ssd/gmzhu/SKIG/DepthImages/63217 106 7
339 | /ssd/gmzhu/SKIG/DepthImages/63218 117 8
340 | /ssd/gmzhu/SKIG/DepthImages/63219 129 9
341 | /ssd/gmzhu/SKIG/DepthImages/63220 284 0
342 | /ssd/gmzhu/SKIG/DepthImages/63221 236 1
343 | /ssd/gmzhu/SKIG/DepthImages/63222 160 2
344 | /ssd/gmzhu/SKIG/DepthImages/63223 329 3
345 | /ssd/gmzhu/SKIG/DepthImages/63224 165 4
346 | /ssd/gmzhu/SKIG/DepthImages/63225 125 5
347 | /ssd/gmzhu/SKIG/DepthImages/63226 122 6
348 | /ssd/gmzhu/SKIG/DepthImages/63227 116 7
349 | /ssd/gmzhu/SKIG/DepthImages/63228 128 8
350 | /ssd/gmzhu/SKIG/DepthImages/63229 134 9
351 | /ssd/gmzhu/SKIG/DepthImages/63230 270 0
352 | /ssd/gmzhu/SKIG/DepthImages/63231 275 1
353 | /ssd/gmzhu/SKIG/DepthImages/63232 159 2
354 | /ssd/gmzhu/SKIG/DepthImages/63233 330 3
355 | /ssd/gmzhu/SKIG/DepthImages/63234 141 4
356 | /ssd/gmzhu/SKIG/DepthImages/63235 117 5
357 | /ssd/gmzhu/SKIG/DepthImages/63236 107 6
358 | /ssd/gmzhu/SKIG/DepthImages/63237 110 7
359 | /ssd/gmzhu/SKIG/DepthImages/63238 151 8
360 | /ssd/gmzhu/SKIG/DepthImages/63239 130 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_depth_list2.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/DepthImages/31110 176 0
  2 | /ssd/gmzhu/SKIG/DepthImages/31111 142 1
  3 | /ssd/gmzhu/SKIG/DepthImages/31112 130 2
  4 | /ssd/gmzhu/SKIG/DepthImages/31113 256 3
  5 | /ssd/gmzhu/SKIG/DepthImages/31114 161 4
  6 | /ssd/gmzhu/SKIG/DepthImages/31115 96 5
  7 | /ssd/gmzhu/SKIG/DepthImages/31116 84 6
  8 | /ssd/gmzhu/SKIG/DepthImages/31117 102 7
  9 | /ssd/gmzhu/SKIG/DepthImages/31118 107 8
 10 | /ssd/gmzhu/SKIG/DepthImages/31119 120 9
 11 | /ssd/gmzhu/SKIG/DepthImages/31120 154 0
 12 | /ssd/gmzhu/SKIG/DepthImages/31121 157 1
 13 | /ssd/gmzhu/SKIG/DepthImages/31122 130 2
 14 | /ssd/gmzhu/SKIG/DepthImages/31123 270 3
 15 | /ssd/gmzhu/SKIG/DepthImages/31124 148 4
 16 | /ssd/gmzhu/SKIG/DepthImages/31125 102 5
 17 | /ssd/gmzhu/SKIG/DepthImages/31126 89 6
 18 | /ssd/gmzhu/SKIG/DepthImages/31127 65 7
 19 | /ssd/gmzhu/SKIG/DepthImages/31128 115 8
 20 | /ssd/gmzhu/SKIG/DepthImages/31129 123 9
 21 | /ssd/gmzhu/SKIG/DepthImages/31130 140 0
 22 | /ssd/gmzhu/SKIG/DepthImages/31131 152 1
 23 | /ssd/gmzhu/SKIG/DepthImages/31132 134 2
 24 | /ssd/gmzhu/SKIG/DepthImages/31133 291 3
 25 | /ssd/gmzhu/SKIG/DepthImages/31134 149 4
 26 | /ssd/gmzhu/SKIG/DepthImages/31135 105 5
 27 | /ssd/gmzhu/SKIG/DepthImages/31136 104 6
 28 | /ssd/gmzhu/SKIG/DepthImages/31137 94 7
 29 | /ssd/gmzhu/SKIG/DepthImages/31138 136 8
 30 | /ssd/gmzhu/SKIG/DepthImages/31139 119 9
 31 | /ssd/gmzhu/SKIG/DepthImages/31210 133 0
 32 | /ssd/gmzhu/SKIG/DepthImages/31211 146 1
 33 | /ssd/gmzhu/SKIG/DepthImages/31212 135 2
 34 | /ssd/gmzhu/SKIG/DepthImages/31213 234 3
 35 | /ssd/gmzhu/SKIG/DepthImages/31214 145 4
 36 | /ssd/gmzhu/SKIG/DepthImages/31215 75 5
 37 | /ssd/gmzhu/SKIG/DepthImages/31216 79 6
 38 | /ssd/gmzhu/SKIG/DepthImages/31217 96 7
 39 | /ssd/gmzhu/SKIG/DepthImages/31218 108 8
 40 | /ssd/gmzhu/SKIG/DepthImages/31219 103 9
 41 | /ssd/gmzhu/SKIG/DepthImages/31220 146 0
 42 | /ssd/gmzhu/SKIG/DepthImages/31221 140 1
 43 | /ssd/gmzhu/SKIG/DepthImages/31222 138 2
 44 | /ssd/gmzhu/SKIG/DepthImages/31223 242 3
 45 | /ssd/gmzhu/SKIG/DepthImages/31224 137 4
 46 | /ssd/gmzhu/SKIG/DepthImages/31225 95 5
 47 | /ssd/gmzhu/SKIG/DepthImages/31226 83 6
 48 | /ssd/gmzhu/SKIG/DepthImages/31227 102 7
 49 | /ssd/gmzhu/SKIG/DepthImages/31228 110 8
 50 | /ssd/gmzhu/SKIG/DepthImages/31229 111 9
 51 | /ssd/gmzhu/SKIG/DepthImages/31230 147 0
 52 | /ssd/gmzhu/SKIG/DepthImages/31231 152 1
 53 | /ssd/gmzhu/SKIG/DepthImages/31232 134 2
 54 | /ssd/gmzhu/SKIG/DepthImages/31233 261 3
 55 | /ssd/gmzhu/SKIG/DepthImages/31234 140 4
 56 | /ssd/gmzhu/SKIG/DepthImages/31235 104 5
 57 | /ssd/gmzhu/SKIG/DepthImages/31236 76 6
 58 | /ssd/gmzhu/SKIG/DepthImages/31237 84 7
 59 | /ssd/gmzhu/SKIG/DepthImages/31238 101 8
 60 | /ssd/gmzhu/SKIG/DepthImages/31239 93 9
 61 | /ssd/gmzhu/SKIG/DepthImages/32110 139 0
 62 | /ssd/gmzhu/SKIG/DepthImages/32111 145 1
 63 | /ssd/gmzhu/SKIG/DepthImages/32112 105 2
 64 | /ssd/gmzhu/SKIG/DepthImages/32113 230 3
 65 | /ssd/gmzhu/SKIG/DepthImages/32114 132 4
 66 | /ssd/gmzhu/SKIG/DepthImages/32115 77 5
 67 | /ssd/gmzhu/SKIG/DepthImages/32116 80 6
 68 | /ssd/gmzhu/SKIG/DepthImages/32117 84 7
 69 | /ssd/gmzhu/SKIG/DepthImages/32118 125 8
 70 | /ssd/gmzhu/SKIG/DepthImages/32119 105 9
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105 | /ssd/gmzhu/SKIG/DepthImages/32224 143 4
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107 | /ssd/gmzhu/SKIG/DepthImages/32226 97 6
108 | /ssd/gmzhu/SKIG/DepthImages/32227 104 7
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110 | /ssd/gmzhu/SKIG/DepthImages/32229 128 9
111 | /ssd/gmzhu/SKIG/DepthImages/32230 151 0
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114 | /ssd/gmzhu/SKIG/DepthImages/32233 269 3
115 | /ssd/gmzhu/SKIG/DepthImages/32234 149 4
116 | /ssd/gmzhu/SKIG/DepthImages/32235 96 5
117 | /ssd/gmzhu/SKIG/DepthImages/32236 95 6
118 | /ssd/gmzhu/SKIG/DepthImages/32237 101 7
119 | /ssd/gmzhu/SKIG/DepthImages/32238 120 8
120 | /ssd/gmzhu/SKIG/DepthImages/32239 112 9
121 | /ssd/gmzhu/SKIG/DepthImages/33110 208 0
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131 | /ssd/gmzhu/SKIG/DepthImages/33120 149 0
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148 | /ssd/gmzhu/SKIG/DepthImages/33137 95 7
149 | /ssd/gmzhu/SKIG/DepthImages/33138 155 8
150 | /ssd/gmzhu/SKIG/DepthImages/33139 106 9
151 | /ssd/gmzhu/SKIG/DepthImages/33210 181 0
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153 | /ssd/gmzhu/SKIG/DepthImages/33212 152 2
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155 | /ssd/gmzhu/SKIG/DepthImages/33214 112 4
156 | /ssd/gmzhu/SKIG/DepthImages/33215 92 5
157 | /ssd/gmzhu/SKIG/DepthImages/33216 102 6
158 | /ssd/gmzhu/SKIG/DepthImages/33217 68 7
159 | /ssd/gmzhu/SKIG/DepthImages/33218 113 8
160 | /ssd/gmzhu/SKIG/DepthImages/33219 113 9
161 | /ssd/gmzhu/SKIG/DepthImages/33220 168 0
162 | /ssd/gmzhu/SKIG/DepthImages/33221 158 1
163 | /ssd/gmzhu/SKIG/DepthImages/33222 143 2
164 | /ssd/gmzhu/SKIG/DepthImages/33223 250 3
165 | /ssd/gmzhu/SKIG/DepthImages/33224 105 4
166 | /ssd/gmzhu/SKIG/DepthImages/33225 96 5
167 | /ssd/gmzhu/SKIG/DepthImages/33226 91 6
168 | /ssd/gmzhu/SKIG/DepthImages/33227 102 7
169 | /ssd/gmzhu/SKIG/DepthImages/33228 116 8
170 | /ssd/gmzhu/SKIG/DepthImages/33229 99 9
171 | /ssd/gmzhu/SKIG/DepthImages/33230 153 0
172 | /ssd/gmzhu/SKIG/DepthImages/33231 160 1
173 | /ssd/gmzhu/SKIG/DepthImages/33232 125 2
174 | /ssd/gmzhu/SKIG/DepthImages/33233 288 3
175 | /ssd/gmzhu/SKIG/DepthImages/33234 147 4
176 | /ssd/gmzhu/SKIG/DepthImages/33235 105 5
177 | /ssd/gmzhu/SKIG/DepthImages/33236 102 6
178 | /ssd/gmzhu/SKIG/DepthImages/33237 88 7
179 | /ssd/gmzhu/SKIG/DepthImages/33238 112 8
180 | /ssd/gmzhu/SKIG/DepthImages/33239 104 9
181 | /ssd/gmzhu/SKIG/DepthImages/41110 193 0
182 | /ssd/gmzhu/SKIG/DepthImages/41111 203 1
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185 | /ssd/gmzhu/SKIG/DepthImages/41114 197 4
186 | /ssd/gmzhu/SKIG/DepthImages/41115 119 5
187 | /ssd/gmzhu/SKIG/DepthImages/41116 131 6
188 | /ssd/gmzhu/SKIG/DepthImages/41117 140 7
189 | /ssd/gmzhu/SKIG/DepthImages/41118 149 8
190 | /ssd/gmzhu/SKIG/DepthImages/41119 117 9
191 | /ssd/gmzhu/SKIG/DepthImages/41120 225 0
192 | /ssd/gmzhu/SKIG/DepthImages/41121 209 1
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194 | /ssd/gmzhu/SKIG/DepthImages/41123 331 3
195 | /ssd/gmzhu/SKIG/DepthImages/41124 201 4
196 | /ssd/gmzhu/SKIG/DepthImages/41125 120 5
197 | /ssd/gmzhu/SKIG/DepthImages/41126 135 6
198 | /ssd/gmzhu/SKIG/DepthImages/41127 103 7
199 | /ssd/gmzhu/SKIG/DepthImages/41128 151 8
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201 | /ssd/gmzhu/SKIG/DepthImages/41130 185 0
202 | /ssd/gmzhu/SKIG/DepthImages/41131 207 1
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205 | /ssd/gmzhu/SKIG/DepthImages/41134 192 4
206 | /ssd/gmzhu/SKIG/DepthImages/41135 117 5
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208 | /ssd/gmzhu/SKIG/DepthImages/41137 125 7
209 | /ssd/gmzhu/SKIG/DepthImages/41138 170 8
210 | /ssd/gmzhu/SKIG/DepthImages/41139 133 9
211 | /ssd/gmzhu/SKIG/DepthImages/41210 153 0
212 | /ssd/gmzhu/SKIG/DepthImages/41211 179 1
213 | /ssd/gmzhu/SKIG/DepthImages/41212 160 2
214 | /ssd/gmzhu/SKIG/DepthImages/41213 290 3
215 | /ssd/gmzhu/SKIG/DepthImages/41214 189 4
216 | /ssd/gmzhu/SKIG/DepthImages/41215 98 5
217 | /ssd/gmzhu/SKIG/DepthImages/41216 108 6
218 | /ssd/gmzhu/SKIG/DepthImages/41217 95 7
219 | /ssd/gmzhu/SKIG/DepthImages/41218 123 8
220 | /ssd/gmzhu/SKIG/DepthImages/41219 139 9
221 | /ssd/gmzhu/SKIG/DepthImages/41220 202 0
222 | /ssd/gmzhu/SKIG/DepthImages/41221 167 1
223 | /ssd/gmzhu/SKIG/DepthImages/41222 122 2
224 | /ssd/gmzhu/SKIG/DepthImages/41223 312 3
225 | /ssd/gmzhu/SKIG/DepthImages/41224 210 4
226 | /ssd/gmzhu/SKIG/DepthImages/41225 125 5
227 | /ssd/gmzhu/SKIG/DepthImages/41226 125 6
228 | /ssd/gmzhu/SKIG/DepthImages/41227 94 7
229 | /ssd/gmzhu/SKIG/DepthImages/41228 153 8
230 | /ssd/gmzhu/SKIG/DepthImages/41229 122 9
231 | /ssd/gmzhu/SKIG/DepthImages/41230 164 0
232 | /ssd/gmzhu/SKIG/DepthImages/41231 168 1
233 | /ssd/gmzhu/SKIG/DepthImages/41232 127 2
234 | /ssd/gmzhu/SKIG/DepthImages/41233 345 3
235 | /ssd/gmzhu/SKIG/DepthImages/41234 199 4
236 | /ssd/gmzhu/SKIG/DepthImages/41235 120 5
237 | /ssd/gmzhu/SKIG/DepthImages/41236 113 6
238 | /ssd/gmzhu/SKIG/DepthImages/41237 115 7
239 | /ssd/gmzhu/SKIG/DepthImages/41238 149 8
240 | /ssd/gmzhu/SKIG/DepthImages/41239 126 9
241 | /ssd/gmzhu/SKIG/DepthImages/42110 137 0
242 | /ssd/gmzhu/SKIG/DepthImages/42111 141 1
243 | /ssd/gmzhu/SKIG/DepthImages/42112 105 2
244 | /ssd/gmzhu/SKIG/DepthImages/42113 265 3
245 | /ssd/gmzhu/SKIG/DepthImages/42114 178 4
246 | /ssd/gmzhu/SKIG/DepthImages/42115 93 5
247 | /ssd/gmzhu/SKIG/DepthImages/42116 113 6
248 | /ssd/gmzhu/SKIG/DepthImages/42117 110 7
249 | /ssd/gmzhu/SKIG/DepthImages/42118 77 8
250 | /ssd/gmzhu/SKIG/DepthImages/42119 106 9
251 | /ssd/gmzhu/SKIG/DepthImages/42120 136 0
252 | /ssd/gmzhu/SKIG/DepthImages/42121 160 1
253 | /ssd/gmzhu/SKIG/DepthImages/42122 133 2
254 | /ssd/gmzhu/SKIG/DepthImages/42123 289 3
255 | /ssd/gmzhu/SKIG/DepthImages/42124 185 4
256 | /ssd/gmzhu/SKIG/DepthImages/42125 109 5
257 | /ssd/gmzhu/SKIG/DepthImages/42126 104 6
258 | /ssd/gmzhu/SKIG/DepthImages/42127 124 7
259 | /ssd/gmzhu/SKIG/DepthImages/42128 98 8
260 | /ssd/gmzhu/SKIG/DepthImages/42129 113 9
261 | /ssd/gmzhu/SKIG/DepthImages/42130 139 0
262 | /ssd/gmzhu/SKIG/DepthImages/42131 168 1
263 | /ssd/gmzhu/SKIG/DepthImages/42132 109 2
264 | /ssd/gmzhu/SKIG/DepthImages/42133 300 3
265 | /ssd/gmzhu/SKIG/DepthImages/42134 192 4
266 | /ssd/gmzhu/SKIG/DepthImages/42135 109 5
267 | /ssd/gmzhu/SKIG/DepthImages/42136 144 6
268 | /ssd/gmzhu/SKIG/DepthImages/42137 119 7
269 | /ssd/gmzhu/SKIG/DepthImages/42138 96 8
270 | /ssd/gmzhu/SKIG/DepthImages/42139 125 9
271 | /ssd/gmzhu/SKIG/DepthImages/42210 131 0
272 | /ssd/gmzhu/SKIG/DepthImages/42211 151 1
273 | /ssd/gmzhu/SKIG/DepthImages/42212 126 2
274 | /ssd/gmzhu/SKIG/DepthImages/42213 302 3
275 | /ssd/gmzhu/SKIG/DepthImages/42214 147 4
276 | /ssd/gmzhu/SKIG/DepthImages/42215 91 5
277 | /ssd/gmzhu/SKIG/DepthImages/42216 94 6
278 | /ssd/gmzhu/SKIG/DepthImages/42217 114 7
279 | /ssd/gmzhu/SKIG/DepthImages/42218 114 8
280 | /ssd/gmzhu/SKIG/DepthImages/42219 114 9
281 | /ssd/gmzhu/SKIG/DepthImages/42220 145 0
282 | /ssd/gmzhu/SKIG/DepthImages/42221 165 1
283 | /ssd/gmzhu/SKIG/DepthImages/42222 114 2
284 | /ssd/gmzhu/SKIG/DepthImages/42223 317 3
285 | /ssd/gmzhu/SKIG/DepthImages/42224 143 4
286 | /ssd/gmzhu/SKIG/DepthImages/42225 78 5
287 | /ssd/gmzhu/SKIG/DepthImages/42226 99 6
288 | /ssd/gmzhu/SKIG/DepthImages/42227 99 7
289 | /ssd/gmzhu/SKIG/DepthImages/42228 101 8
290 | /ssd/gmzhu/SKIG/DepthImages/42229 122 9
291 | /ssd/gmzhu/SKIG/DepthImages/42230 163 0
292 | /ssd/gmzhu/SKIG/DepthImages/42231 161 1
293 | /ssd/gmzhu/SKIG/DepthImages/42232 115 2
294 | /ssd/gmzhu/SKIG/DepthImages/42233 296 3
295 | /ssd/gmzhu/SKIG/DepthImages/42234 135 4
296 | /ssd/gmzhu/SKIG/DepthImages/42235 81 5
297 | /ssd/gmzhu/SKIG/DepthImages/42236 84 6
298 | /ssd/gmzhu/SKIG/DepthImages/42237 102 7
299 | /ssd/gmzhu/SKIG/DepthImages/42238 97 8
300 | /ssd/gmzhu/SKIG/DepthImages/42239 108 9
301 | /ssd/gmzhu/SKIG/DepthImages/43110 165 0
302 | /ssd/gmzhu/SKIG/DepthImages/43111 130 1
303 | /ssd/gmzhu/SKIG/DepthImages/43112 125 2
304 | /ssd/gmzhu/SKIG/DepthImages/43113 196 3
305 | /ssd/gmzhu/SKIG/DepthImages/43114 113 4
306 | /ssd/gmzhu/SKIG/DepthImages/43115 82 5
307 | /ssd/gmzhu/SKIG/DepthImages/43116 102 6
308 | /ssd/gmzhu/SKIG/DepthImages/43117 94 7
309 | /ssd/gmzhu/SKIG/DepthImages/43118 131 8
310 | /ssd/gmzhu/SKIG/DepthImages/43119 136 9
311 | /ssd/gmzhu/SKIG/DepthImages/43120 147 0
312 | /ssd/gmzhu/SKIG/DepthImages/43121 139 1
313 | /ssd/gmzhu/SKIG/DepthImages/43122 146 2
314 | /ssd/gmzhu/SKIG/DepthImages/43123 254 3
315 | /ssd/gmzhu/SKIG/DepthImages/43124 132 4
316 | /ssd/gmzhu/SKIG/DepthImages/43125 73 5
317 | /ssd/gmzhu/SKIG/DepthImages/43126 102 6
318 | /ssd/gmzhu/SKIG/DepthImages/43127 114 7
319 | /ssd/gmzhu/SKIG/DepthImages/43128 133 8
320 | /ssd/gmzhu/SKIG/DepthImages/43129 140 9
321 | /ssd/gmzhu/SKIG/DepthImages/43130 142 0
322 | /ssd/gmzhu/SKIG/DepthImages/43131 136 1
323 | /ssd/gmzhu/SKIG/DepthImages/43132 153 2
324 | /ssd/gmzhu/SKIG/DepthImages/43133 233 3
325 | /ssd/gmzhu/SKIG/DepthImages/43134 136 4
326 | /ssd/gmzhu/SKIG/DepthImages/43135 86 5
327 | /ssd/gmzhu/SKIG/DepthImages/43136 93 6
328 | /ssd/gmzhu/SKIG/DepthImages/43137 110 7
329 | /ssd/gmzhu/SKIG/DepthImages/43138 126 8
330 | /ssd/gmzhu/SKIG/DepthImages/43139 143 9
331 | /ssd/gmzhu/SKIG/DepthImages/43210 157 0
332 | /ssd/gmzhu/SKIG/DepthImages/43211 168 1
333 | /ssd/gmzhu/SKIG/DepthImages/43212 138 2
334 | /ssd/gmzhu/SKIG/DepthImages/43213 169 3
335 | /ssd/gmzhu/SKIG/DepthImages/43214 173 4
336 | /ssd/gmzhu/SKIG/DepthImages/43215 94 5
337 | /ssd/gmzhu/SKIG/DepthImages/43216 99 6
338 | /ssd/gmzhu/SKIG/DepthImages/43217 100 7
339 | /ssd/gmzhu/SKIG/DepthImages/43218 129 8
340 | /ssd/gmzhu/SKIG/DepthImages/43219 100 9
341 | /ssd/gmzhu/SKIG/DepthImages/43220 171 0
342 | /ssd/gmzhu/SKIG/DepthImages/43221 168 1
343 | /ssd/gmzhu/SKIG/DepthImages/43222 171 2
344 | /ssd/gmzhu/SKIG/DepthImages/43223 191 3
345 | /ssd/gmzhu/SKIG/DepthImages/43224 143 4
346 | /ssd/gmzhu/SKIG/DepthImages/43225 96 5
347 | /ssd/gmzhu/SKIG/DepthImages/43226 91 6
348 | /ssd/gmzhu/SKIG/DepthImages/43227 87 7
349 | /ssd/gmzhu/SKIG/DepthImages/43228 112 8
350 | /ssd/gmzhu/SKIG/DepthImages/43229 93 9
351 | /ssd/gmzhu/SKIG/DepthImages/43230 159 0
352 | /ssd/gmzhu/SKIG/DepthImages/43231 174 1
353 | /ssd/gmzhu/SKIG/DepthImages/43232 139 2
354 | /ssd/gmzhu/SKIG/DepthImages/43233 178 3
355 | /ssd/gmzhu/SKIG/DepthImages/43234 148 4
356 | /ssd/gmzhu/SKIG/DepthImages/43235 88 5
357 | /ssd/gmzhu/SKIG/DepthImages/43236 104 6
358 | /ssd/gmzhu/SKIG/DepthImages/43237 88 7
359 | /ssd/gmzhu/SKIG/DepthImages/43238 128 8
360 | /ssd/gmzhu/SKIG/DepthImages/43239 115 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_depth_list3.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/DepthImages/11110 159 0
  2 | /ssd/gmzhu/SKIG/DepthImages/11111 165 1
  3 | /ssd/gmzhu/SKIG/DepthImages/11112 115 2
  4 | /ssd/gmzhu/SKIG/DepthImages/11113 254 3
  5 | /ssd/gmzhu/SKIG/DepthImages/11114 119 4
  6 | /ssd/gmzhu/SKIG/DepthImages/11115 98 5
  7 | /ssd/gmzhu/SKIG/DepthImages/11116 83 6
  8 | /ssd/gmzhu/SKIG/DepthImages/11117 94 7
  9 | /ssd/gmzhu/SKIG/DepthImages/11118 127 8
 10 | /ssd/gmzhu/SKIG/DepthImages/11119 93 9
 11 | /ssd/gmzhu/SKIG/DepthImages/11120 158 0
 12 | /ssd/gmzhu/SKIG/DepthImages/11121 168 1
 13 | /ssd/gmzhu/SKIG/DepthImages/11122 132 2
 14 | /ssd/gmzhu/SKIG/DepthImages/11123 252 3
 15 | /ssd/gmzhu/SKIG/DepthImages/11124 126 4
 16 | /ssd/gmzhu/SKIG/DepthImages/11125 103 5
 17 | /ssd/gmzhu/SKIG/DepthImages/11126 94 6
 18 | /ssd/gmzhu/SKIG/DepthImages/11127 107 7
 19 | /ssd/gmzhu/SKIG/DepthImages/11128 122 8
 20 | /ssd/gmzhu/SKIG/DepthImages/11129 105 9
 21 | /ssd/gmzhu/SKIG/DepthImages/11130 179 0
 22 | /ssd/gmzhu/SKIG/DepthImages/11131 163 1
 23 | /ssd/gmzhu/SKIG/DepthImages/11132 124 2
 24 | /ssd/gmzhu/SKIG/DepthImages/11133 275 3
 25 | /ssd/gmzhu/SKIG/DepthImages/11134 133 4
 26 | /ssd/gmzhu/SKIG/DepthImages/11135 114 5
 27 | /ssd/gmzhu/SKIG/DepthImages/11136 81 6
 28 | /ssd/gmzhu/SKIG/DepthImages/11137 111 7
 29 | /ssd/gmzhu/SKIG/DepthImages/11138 121 8
 30 | /ssd/gmzhu/SKIG/DepthImages/11139 91 9
 31 | /ssd/gmzhu/SKIG/DepthImages/11210 154 0
 32 | /ssd/gmzhu/SKIG/DepthImages/11211 146 1
 33 | /ssd/gmzhu/SKIG/DepthImages/11212 110 2
 34 | /ssd/gmzhu/SKIG/DepthImages/11213 222 3
 35 | /ssd/gmzhu/SKIG/DepthImages/11214 110 4
 36 | /ssd/gmzhu/SKIG/DepthImages/11215 99 5
 37 | /ssd/gmzhu/SKIG/DepthImages/11216 79 6
 38 | /ssd/gmzhu/SKIG/DepthImages/11217 82 7
 39 | /ssd/gmzhu/SKIG/DepthImages/11218 120 8
 40 | /ssd/gmzhu/SKIG/DepthImages/11219 84 9
 41 | /ssd/gmzhu/SKIG/DepthImages/11220 149 0
 42 | /ssd/gmzhu/SKIG/DepthImages/11221 134 1
 43 | /ssd/gmzhu/SKIG/DepthImages/11222 110 2
 44 | /ssd/gmzhu/SKIG/DepthImages/11223 208 3
 45 | /ssd/gmzhu/SKIG/DepthImages/11224 115 4
 46 | /ssd/gmzhu/SKIG/DepthImages/11225 92 5
 47 | /ssd/gmzhu/SKIG/DepthImages/11226 82 6
 48 | /ssd/gmzhu/SKIG/DepthImages/11227 94 7
 49 | /ssd/gmzhu/SKIG/DepthImages/11228 113 8
 50 | /ssd/gmzhu/SKIG/DepthImages/11229 85 9
 51 | /ssd/gmzhu/SKIG/DepthImages/11230 167 0
 52 | /ssd/gmzhu/SKIG/DepthImages/11231 149 1
 53 | /ssd/gmzhu/SKIG/DepthImages/11232 118 2
 54 | /ssd/gmzhu/SKIG/DepthImages/11233 231 3
 55 | /ssd/gmzhu/SKIG/DepthImages/11234 127 4
 56 | /ssd/gmzhu/SKIG/DepthImages/11235 92 5
 57 | /ssd/gmzhu/SKIG/DepthImages/11236 96 6
 58 | /ssd/gmzhu/SKIG/DepthImages/11237 96 7
 59 | /ssd/gmzhu/SKIG/DepthImages/11238 122 8
 60 | /ssd/gmzhu/SKIG/DepthImages/11239 80 9
 61 | /ssd/gmzhu/SKIG/DepthImages/12110 160 0
 62 | /ssd/gmzhu/SKIG/DepthImages/12111 166 1
 63 | /ssd/gmzhu/SKIG/DepthImages/12112 126 2
 64 | /ssd/gmzhu/SKIG/DepthImages/12113 243 3
 65 | /ssd/gmzhu/SKIG/DepthImages/12114 111 4
 66 | /ssd/gmzhu/SKIG/DepthImages/12115 93 5
 67 | /ssd/gmzhu/SKIG/DepthImages/12116 72 6
 68 | /ssd/gmzhu/SKIG/DepthImages/12117 81 7
 69 | /ssd/gmzhu/SKIG/DepthImages/12118 121 8
 70 | /ssd/gmzhu/SKIG/DepthImages/12119 97 9
 71 | /ssd/gmzhu/SKIG/DepthImages/12120 150 0
 72 | /ssd/gmzhu/SKIG/DepthImages/12121 176 1
 73 | /ssd/gmzhu/SKIG/DepthImages/12122 126 2
 74 | /ssd/gmzhu/SKIG/DepthImages/12123 277 3
 75 | /ssd/gmzhu/SKIG/DepthImages/12124 139 4
 76 | /ssd/gmzhu/SKIG/DepthImages/12125 96 5
 77 | /ssd/gmzhu/SKIG/DepthImages/12126 79 6
 78 | /ssd/gmzhu/SKIG/DepthImages/12127 92 7
 79 | /ssd/gmzhu/SKIG/DepthImages/12128 114 8
 80 | /ssd/gmzhu/SKIG/DepthImages/12129 101 9
 81 | /ssd/gmzhu/SKIG/DepthImages/12130 161 0
 82 | /ssd/gmzhu/SKIG/DepthImages/12131 174 1
 83 | /ssd/gmzhu/SKIG/DepthImages/12132 126 2
 84 | /ssd/gmzhu/SKIG/DepthImages/12133 265 3
 85 | /ssd/gmzhu/SKIG/DepthImages/12134 118 4
 86 | /ssd/gmzhu/SKIG/DepthImages/12135 84 5
 87 | /ssd/gmzhu/SKIG/DepthImages/12136 76 6
 88 | /ssd/gmzhu/SKIG/DepthImages/12137 84 7
 89 | /ssd/gmzhu/SKIG/DepthImages/12138 114 8
 90 | /ssd/gmzhu/SKIG/DepthImages/12139 93 9
 91 | /ssd/gmzhu/SKIG/DepthImages/12210 149 0
 92 | /ssd/gmzhu/SKIG/DepthImages/12211 133 1
 93 | /ssd/gmzhu/SKIG/DepthImages/12212 132 2
 94 | /ssd/gmzhu/SKIG/DepthImages/12213 214 3
 95 | /ssd/gmzhu/SKIG/DepthImages/12214 144 4
 96 | /ssd/gmzhu/SKIG/DepthImages/12215 83 5
 97 | /ssd/gmzhu/SKIG/DepthImages/12216 82 6
 98 | /ssd/gmzhu/SKIG/DepthImages/12217 72 7
 99 | /ssd/gmzhu/SKIG/DepthImages/12218 106 8
100 | /ssd/gmzhu/SKIG/DepthImages/12219 81 9
101 | /ssd/gmzhu/SKIG/DepthImages/12220 156 0
102 | /ssd/gmzhu/SKIG/DepthImages/12221 153 1
103 | /ssd/gmzhu/SKIG/DepthImages/12222 143 2
104 | /ssd/gmzhu/SKIG/DepthImages/12223 250 3
105 | /ssd/gmzhu/SKIG/DepthImages/12224 126 4
106 | /ssd/gmzhu/SKIG/DepthImages/12225 90 5
107 | /ssd/gmzhu/SKIG/DepthImages/12226 89 6
108 | /ssd/gmzhu/SKIG/DepthImages/12227 63 7
109 | /ssd/gmzhu/SKIG/DepthImages/12228 101 8
110 | /ssd/gmzhu/SKIG/DepthImages/12229 82 9
111 | /ssd/gmzhu/SKIG/DepthImages/12230 153 0
112 | /ssd/gmzhu/SKIG/DepthImages/12231 138 1
113 | /ssd/gmzhu/SKIG/DepthImages/12232 142 2
114 | /ssd/gmzhu/SKIG/DepthImages/12233 236 3
115 | /ssd/gmzhu/SKIG/DepthImages/12234 123 4
116 | /ssd/gmzhu/SKIG/DepthImages/12235 91 5
117 | /ssd/gmzhu/SKIG/DepthImages/12236 83 6
118 | /ssd/gmzhu/SKIG/DepthImages/12237 69 7
119 | /ssd/gmzhu/SKIG/DepthImages/12238 104 8
120 | /ssd/gmzhu/SKIG/DepthImages/12239 84 9
121 | /ssd/gmzhu/SKIG/DepthImages/13110 167 0
122 | /ssd/gmzhu/SKIG/DepthImages/13111 168 1
123 | /ssd/gmzhu/SKIG/DepthImages/13112 120 2
124 | /ssd/gmzhu/SKIG/DepthImages/13113 258 3
125 | /ssd/gmzhu/SKIG/DepthImages/13114 119 4
126 | /ssd/gmzhu/SKIG/DepthImages/13115 105 5
127 | /ssd/gmzhu/SKIG/DepthImages/13116 79 6
128 | /ssd/gmzhu/SKIG/DepthImages/13117 82 7
129 | /ssd/gmzhu/SKIG/DepthImages/13118 116 8
130 | /ssd/gmzhu/SKIG/DepthImages/13119 91 9
131 | /ssd/gmzhu/SKIG/DepthImages/13120 182 0
132 | /ssd/gmzhu/SKIG/DepthImages/13121 172 1
133 | /ssd/gmzhu/SKIG/DepthImages/13122 117 2
134 | /ssd/gmzhu/SKIG/DepthImages/13123 248 3
135 | /ssd/gmzhu/SKIG/DepthImages/13124 138 4
136 | /ssd/gmzhu/SKIG/DepthImages/13125 107 5
137 | /ssd/gmzhu/SKIG/DepthImages/13126 89 6
138 | /ssd/gmzhu/SKIG/DepthImages/13127 86 7
139 | /ssd/gmzhu/SKIG/DepthImages/13128 114 8
140 | /ssd/gmzhu/SKIG/DepthImages/13129 105 9
141 | /ssd/gmzhu/SKIG/DepthImages/13130 172 0
142 | /ssd/gmzhu/SKIG/DepthImages/13131 163 1
143 | /ssd/gmzhu/SKIG/DepthImages/13132 130 2
144 | /ssd/gmzhu/SKIG/DepthImages/13133 269 3
145 | /ssd/gmzhu/SKIG/DepthImages/13134 140 4
146 | /ssd/gmzhu/SKIG/DepthImages/13135 101 5
147 | /ssd/gmzhu/SKIG/DepthImages/13136 92 6
148 | /ssd/gmzhu/SKIG/DepthImages/13137 88 7
149 | /ssd/gmzhu/SKIG/DepthImages/13138 127 8
150 | /ssd/gmzhu/SKIG/DepthImages/13139 95 9
151 | /ssd/gmzhu/SKIG/DepthImages/13210 154 0
152 | /ssd/gmzhu/SKIG/DepthImages/13211 146 1
153 | /ssd/gmzhu/SKIG/DepthImages/13212 135 2
154 | /ssd/gmzhu/SKIG/DepthImages/13213 230 3
155 | /ssd/gmzhu/SKIG/DepthImages/13214 114 4
156 | /ssd/gmzhu/SKIG/DepthImages/13215 85 5
157 | /ssd/gmzhu/SKIG/DepthImages/13216 86 6
158 | /ssd/gmzhu/SKIG/DepthImages/13217 69 7
159 | /ssd/gmzhu/SKIG/DepthImages/13218 113 8
160 | /ssd/gmzhu/SKIG/DepthImages/13219 85 9
161 | /ssd/gmzhu/SKIG/DepthImages/13220 156 0
162 | /ssd/gmzhu/SKIG/DepthImages/13221 156 1
163 | /ssd/gmzhu/SKIG/DepthImages/13222 126 2
164 | /ssd/gmzhu/SKIG/DepthImages/13223 239 3
165 | /ssd/gmzhu/SKIG/DepthImages/13224 124 4
166 | /ssd/gmzhu/SKIG/DepthImages/13225 91 5
167 | /ssd/gmzhu/SKIG/DepthImages/13226 91 6
168 | /ssd/gmzhu/SKIG/DepthImages/13227 71 7
169 | /ssd/gmzhu/SKIG/DepthImages/13228 121 8
170 | /ssd/gmzhu/SKIG/DepthImages/13229 92 9
171 | /ssd/gmzhu/SKIG/DepthImages/13230 170 0
172 | /ssd/gmzhu/SKIG/DepthImages/13231 159 1
173 | /ssd/gmzhu/SKIG/DepthImages/13232 124 2
174 | /ssd/gmzhu/SKIG/DepthImages/13233 250 3
175 | /ssd/gmzhu/SKIG/DepthImages/13234 118 4
176 | /ssd/gmzhu/SKIG/DepthImages/13235 91 5
177 | /ssd/gmzhu/SKIG/DepthImages/13236 85 6
178 | /ssd/gmzhu/SKIG/DepthImages/13237 70 7
179 | /ssd/gmzhu/SKIG/DepthImages/13238 121 8
180 | /ssd/gmzhu/SKIG/DepthImages/13239 89 9
181 | /ssd/gmzhu/SKIG/DepthImages/21110 164 0
182 | /ssd/gmzhu/SKIG/DepthImages/21111 170 1
183 | /ssd/gmzhu/SKIG/DepthImages/21112 130 2
184 | /ssd/gmzhu/SKIG/DepthImages/21113 221 3
185 | /ssd/gmzhu/SKIG/DepthImages/21114 146 4
186 | /ssd/gmzhu/SKIG/DepthImages/21115 120 5
187 | /ssd/gmzhu/SKIG/DepthImages/21116 94 6
188 | /ssd/gmzhu/SKIG/DepthImages/21117 90 7
189 | /ssd/gmzhu/SKIG/DepthImages/21118 115 8
190 | /ssd/gmzhu/SKIG/DepthImages/21119 99 9
191 | /ssd/gmzhu/SKIG/DepthImages/21120 190 0
192 | /ssd/gmzhu/SKIG/DepthImages/21121 167 1
193 | /ssd/gmzhu/SKIG/DepthImages/21122 138 2
194 | /ssd/gmzhu/SKIG/DepthImages/21123 242 3
195 | /ssd/gmzhu/SKIG/DepthImages/21124 128 4
196 | /ssd/gmzhu/SKIG/DepthImages/21125 123 5
197 | /ssd/gmzhu/SKIG/DepthImages/21126 86 6
198 | /ssd/gmzhu/SKIG/DepthImages/21127 101 7
199 | /ssd/gmzhu/SKIG/DepthImages/21128 115 8
200 | /ssd/gmzhu/SKIG/DepthImages/21129 112 9
201 | /ssd/gmzhu/SKIG/DepthImages/21130 192 0
202 | /ssd/gmzhu/SKIG/DepthImages/21131 181 1
203 | /ssd/gmzhu/SKIG/DepthImages/21132 138 2
204 | /ssd/gmzhu/SKIG/DepthImages/21133 279 3
205 | /ssd/gmzhu/SKIG/DepthImages/21134 128 4
206 | /ssd/gmzhu/SKIG/DepthImages/21135 112 5
207 | /ssd/gmzhu/SKIG/DepthImages/21136 96 6
208 | /ssd/gmzhu/SKIG/DepthImages/21137 96 7
209 | /ssd/gmzhu/SKIG/DepthImages/21138 125 8
210 | /ssd/gmzhu/SKIG/DepthImages/21139 107 9
211 | /ssd/gmzhu/SKIG/DepthImages/21210 184 0
212 | /ssd/gmzhu/SKIG/DepthImages/21211 181 1
213 | /ssd/gmzhu/SKIG/DepthImages/21212 124 2
214 | /ssd/gmzhu/SKIG/DepthImages/21213 243 3
215 | /ssd/gmzhu/SKIG/DepthImages/21214 115 4
216 | /ssd/gmzhu/SKIG/DepthImages/21215 100 5
217 | /ssd/gmzhu/SKIG/DepthImages/21216 99 6
218 | /ssd/gmzhu/SKIG/DepthImages/21217 86 7
219 | /ssd/gmzhu/SKIG/DepthImages/21218 121 8
220 | /ssd/gmzhu/SKIG/DepthImages/21219 95 9
221 | /ssd/gmzhu/SKIG/DepthImages/21220 170 0
222 | /ssd/gmzhu/SKIG/DepthImages/21221 169 1
223 | /ssd/gmzhu/SKIG/DepthImages/21222 123 2
224 | /ssd/gmzhu/SKIG/DepthImages/21223 243 3
225 | /ssd/gmzhu/SKIG/DepthImages/21224 122 4
226 | /ssd/gmzhu/SKIG/DepthImages/21225 101 5
227 | /ssd/gmzhu/SKIG/DepthImages/21226 99 6
228 | /ssd/gmzhu/SKIG/DepthImages/21227 93 7
229 | /ssd/gmzhu/SKIG/DepthImages/21228 138 8
230 | /ssd/gmzhu/SKIG/DepthImages/21229 92 9
231 | /ssd/gmzhu/SKIG/DepthImages/21230 170 0
232 | /ssd/gmzhu/SKIG/DepthImages/21231 168 1
233 | /ssd/gmzhu/SKIG/DepthImages/21232 131 2
234 | /ssd/gmzhu/SKIG/DepthImages/21233 242 3
235 | /ssd/gmzhu/SKIG/DepthImages/21234 139 4
236 | /ssd/gmzhu/SKIG/DepthImages/21235 111 5
237 | /ssd/gmzhu/SKIG/DepthImages/21236 100 6
238 | /ssd/gmzhu/SKIG/DepthImages/21237 93 7
239 | /ssd/gmzhu/SKIG/DepthImages/21238 127 8
240 | /ssd/gmzhu/SKIG/DepthImages/21239 102 9
241 | /ssd/gmzhu/SKIG/DepthImages/22110 175 0
242 | /ssd/gmzhu/SKIG/DepthImages/22111 149 1
243 | /ssd/gmzhu/SKIG/DepthImages/22112 127 2
244 | /ssd/gmzhu/SKIG/DepthImages/22113 213 3
245 | /ssd/gmzhu/SKIG/DepthImages/22114 135 4
246 | /ssd/gmzhu/SKIG/DepthImages/22115 112 5
247 | /ssd/gmzhu/SKIG/DepthImages/22116 92 6
248 | /ssd/gmzhu/SKIG/DepthImages/22117 94 7
249 | /ssd/gmzhu/SKIG/DepthImages/22118 111 8
250 | /ssd/gmzhu/SKIG/DepthImages/22119 95 9
251 | /ssd/gmzhu/SKIG/DepthImages/22120 180 0
252 | /ssd/gmzhu/SKIG/DepthImages/22121 163 1
253 | /ssd/gmzhu/SKIG/DepthImages/22122 122 2
254 | /ssd/gmzhu/SKIG/DepthImages/22123 289 3
255 | /ssd/gmzhu/SKIG/DepthImages/22124 138 4
256 | /ssd/gmzhu/SKIG/DepthImages/22125 105 5
257 | /ssd/gmzhu/SKIG/DepthImages/22126 100 6
258 | /ssd/gmzhu/SKIG/DepthImages/22127 115 7
259 | /ssd/gmzhu/SKIG/DepthImages/22128 124 8
260 | /ssd/gmzhu/SKIG/DepthImages/22129 101 9
261 | /ssd/gmzhu/SKIG/DepthImages/22130 164 0
262 | /ssd/gmzhu/SKIG/DepthImages/22131 179 1
263 | /ssd/gmzhu/SKIG/DepthImages/22132 117 2
264 | /ssd/gmzhu/SKIG/DepthImages/22133 263 3
265 | /ssd/gmzhu/SKIG/DepthImages/22134 133 4
266 | /ssd/gmzhu/SKIG/DepthImages/22135 114 5
267 | /ssd/gmzhu/SKIG/DepthImages/22136 104 6
268 | /ssd/gmzhu/SKIG/DepthImages/22137 100 7
269 | /ssd/gmzhu/SKIG/DepthImages/22138 125 8
270 | /ssd/gmzhu/SKIG/DepthImages/22139 104 9
271 | /ssd/gmzhu/SKIG/DepthImages/22210 171 0
272 | /ssd/gmzhu/SKIG/DepthImages/22211 179 1
273 | /ssd/gmzhu/SKIG/DepthImages/22212 120 2
274 | /ssd/gmzhu/SKIG/DepthImages/22213 258 3
275 | /ssd/gmzhu/SKIG/DepthImages/22214 143 4
276 | /ssd/gmzhu/SKIG/DepthImages/22215 95 5
277 | /ssd/gmzhu/SKIG/DepthImages/22216 93 6
278 | /ssd/gmzhu/SKIG/DepthImages/22217 89 7
279 | /ssd/gmzhu/SKIG/DepthImages/22218 119 8
280 | /ssd/gmzhu/SKIG/DepthImages/22219 96 9
281 | /ssd/gmzhu/SKIG/DepthImages/22220 173 0
282 | /ssd/gmzhu/SKIG/DepthImages/22221 161 1
283 | /ssd/gmzhu/SKIG/DepthImages/22222 130 2
284 | /ssd/gmzhu/SKIG/DepthImages/22223 258 3
285 | /ssd/gmzhu/SKIG/DepthImages/22224 134 4
286 | /ssd/gmzhu/SKIG/DepthImages/22225 105 5
287 | /ssd/gmzhu/SKIG/DepthImages/22226 99 6
288 | /ssd/gmzhu/SKIG/DepthImages/22227 100 7
289 | /ssd/gmzhu/SKIG/DepthImages/22228 122 8
290 | /ssd/gmzhu/SKIG/DepthImages/22229 97 9
291 | /ssd/gmzhu/SKIG/DepthImages/22230 178 0
292 | /ssd/gmzhu/SKIG/DepthImages/22231 178 1
293 | /ssd/gmzhu/SKIG/DepthImages/22232 136 2
294 | /ssd/gmzhu/SKIG/DepthImages/22233 254 3
295 | /ssd/gmzhu/SKIG/DepthImages/22234 143 4
296 | /ssd/gmzhu/SKIG/DepthImages/22235 104 5
297 | /ssd/gmzhu/SKIG/DepthImages/22236 97 6
298 | /ssd/gmzhu/SKIG/DepthImages/22237 105 7
299 | /ssd/gmzhu/SKIG/DepthImages/22238 124 8
300 | /ssd/gmzhu/SKIG/DepthImages/22239 102 9
301 | /ssd/gmzhu/SKIG/DepthImages/23110 174 0
302 | /ssd/gmzhu/SKIG/DepthImages/23111 168 1
303 | /ssd/gmzhu/SKIG/DepthImages/23112 107 2
304 | /ssd/gmzhu/SKIG/DepthImages/23113 245 3
305 | /ssd/gmzhu/SKIG/DepthImages/23114 122 4
306 | /ssd/gmzhu/SKIG/DepthImages/23115 89 5
307 | /ssd/gmzhu/SKIG/DepthImages/23116 87 6
308 | /ssd/gmzhu/SKIG/DepthImages/23117 81 7
309 | /ssd/gmzhu/SKIG/DepthImages/23118 113 8
310 | /ssd/gmzhu/SKIG/DepthImages/23119 96 9
311 | /ssd/gmzhu/SKIG/DepthImages/23120 187 0
312 | /ssd/gmzhu/SKIG/DepthImages/23121 189 1
313 | /ssd/gmzhu/SKIG/DepthImages/23122 129 2
314 | /ssd/gmzhu/SKIG/DepthImages/23123 244 3
315 | /ssd/gmzhu/SKIG/DepthImages/23124 115 4
316 | /ssd/gmzhu/SKIG/DepthImages/23125 104 5
317 | /ssd/gmzhu/SKIG/DepthImages/23126 95 6
318 | /ssd/gmzhu/SKIG/DepthImages/23127 85 7
319 | /ssd/gmzhu/SKIG/DepthImages/23128 116 8
320 | /ssd/gmzhu/SKIG/DepthImages/23129 98 9
321 | /ssd/gmzhu/SKIG/DepthImages/23130 175 0
322 | /ssd/gmzhu/SKIG/DepthImages/23131 182 1
323 | /ssd/gmzhu/SKIG/DepthImages/23132 104 2
324 | /ssd/gmzhu/SKIG/DepthImages/23133 233 3
325 | /ssd/gmzhu/SKIG/DepthImages/23134 125 4
326 | /ssd/gmzhu/SKIG/DepthImages/23135 104 5
327 | /ssd/gmzhu/SKIG/DepthImages/23136 96 6
328 | /ssd/gmzhu/SKIG/DepthImages/23137 91 7
329 | /ssd/gmzhu/SKIG/DepthImages/23138 111 8
330 | /ssd/gmzhu/SKIG/DepthImages/23139 92 9
331 | /ssd/gmzhu/SKIG/DepthImages/23210 175 0
332 | /ssd/gmzhu/SKIG/DepthImages/23211 157 1
333 | /ssd/gmzhu/SKIG/DepthImages/23212 117 2
334 | /ssd/gmzhu/SKIG/DepthImages/23213 239 3
335 | /ssd/gmzhu/SKIG/DepthImages/23214 116 4
336 | /ssd/gmzhu/SKIG/DepthImages/23215 94 5
337 | /ssd/gmzhu/SKIG/DepthImages/23216 91 6
338 | /ssd/gmzhu/SKIG/DepthImages/23217 78 7
339 | /ssd/gmzhu/SKIG/DepthImages/23218 107 8
340 | /ssd/gmzhu/SKIG/DepthImages/23219 85 9
341 | /ssd/gmzhu/SKIG/DepthImages/23220 162 0
342 | /ssd/gmzhu/SKIG/DepthImages/23221 162 1
343 | /ssd/gmzhu/SKIG/DepthImages/23222 113 2
344 | /ssd/gmzhu/SKIG/DepthImages/23223 219 3
345 | /ssd/gmzhu/SKIG/DepthImages/23224 119 4
346 | /ssd/gmzhu/SKIG/DepthImages/23225 93 5
347 | /ssd/gmzhu/SKIG/DepthImages/23226 87 6
348 | /ssd/gmzhu/SKIG/DepthImages/23227 80 7
349 | /ssd/gmzhu/SKIG/DepthImages/23228 100 8
350 | /ssd/gmzhu/SKIG/DepthImages/23229 87 9
351 | /ssd/gmzhu/SKIG/DepthImages/23230 159 0
352 | /ssd/gmzhu/SKIG/DepthImages/23231 162 1
353 | /ssd/gmzhu/SKIG/DepthImages/23232 120 2
354 | /ssd/gmzhu/SKIG/DepthImages/23233 212 3
355 | /ssd/gmzhu/SKIG/DepthImages/23234 121 4
356 | /ssd/gmzhu/SKIG/DepthImages/23235 108 5
357 | /ssd/gmzhu/SKIG/DepthImages/23236 92 6
358 | /ssd/gmzhu/SKIG/DepthImages/23237 87 7
359 | /ssd/gmzhu/SKIG/DepthImages/23238 113 8
360 | /ssd/gmzhu/SKIG/DepthImages/23239 88 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_rgb_list1.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/RGBImages/51110 197 0
  2 | /ssd/gmzhu/SKIG/RGBImages/51111 210 1
  3 | /ssd/gmzhu/SKIG/RGBImages/51112 136 2
  4 | /ssd/gmzhu/SKIG/RGBImages/51113 277 3
  5 | /ssd/gmzhu/SKIG/RGBImages/51114 150 4
  6 | /ssd/gmzhu/SKIG/RGBImages/51115 98 5
  7 | /ssd/gmzhu/SKIG/RGBImages/51116 87 6
  8 | /ssd/gmzhu/SKIG/RGBImages/51117 110 7
  9 | /ssd/gmzhu/SKIG/RGBImages/51118 145 8
 10 | /ssd/gmzhu/SKIG/RGBImages/51119 126 9
 11 | /ssd/gmzhu/SKIG/RGBImages/51120 168 0
 12 | /ssd/gmzhu/SKIG/RGBImages/51121 207 1
 13 | /ssd/gmzhu/SKIG/RGBImages/51122 146 2
 14 | /ssd/gmzhu/SKIG/RGBImages/51123 262 3
 15 | /ssd/gmzhu/SKIG/RGBImages/51124 149 4
 16 | /ssd/gmzhu/SKIG/RGBImages/51125 95 5
 17 | /ssd/gmzhu/SKIG/RGBImages/51126 89 6
 18 | /ssd/gmzhu/SKIG/RGBImages/51127 122 7
 19 | /ssd/gmzhu/SKIG/RGBImages/51128 130 8
 20 | /ssd/gmzhu/SKIG/RGBImages/51129 134 9
 21 | /ssd/gmzhu/SKIG/RGBImages/51130 186 0
 22 | /ssd/gmzhu/SKIG/RGBImages/51131 164 1
 23 | /ssd/gmzhu/SKIG/RGBImages/51132 139 2
 24 | /ssd/gmzhu/SKIG/RGBImages/51133 261 3
 25 | /ssd/gmzhu/SKIG/RGBImages/51134 172 4
 26 | /ssd/gmzhu/SKIG/RGBImages/51135 112 5
 27 | /ssd/gmzhu/SKIG/RGBImages/51136 96 6
 28 | /ssd/gmzhu/SKIG/RGBImages/51137 122 7
 29 | /ssd/gmzhu/SKIG/RGBImages/51138 139 8
 30 | /ssd/gmzhu/SKIG/RGBImages/51139 132 9
 31 | /ssd/gmzhu/SKIG/RGBImages/51210 144 0
 32 | /ssd/gmzhu/SKIG/RGBImages/51211 163 1
 33 | /ssd/gmzhu/SKIG/RGBImages/51212 169 2
 34 | /ssd/gmzhu/SKIG/RGBImages/51213 284 3
 35 | /ssd/gmzhu/SKIG/RGBImages/51214 131 4
 36 | /ssd/gmzhu/SKIG/RGBImages/51215 86 5
 37 | /ssd/gmzhu/SKIG/RGBImages/51216 70 6
 38 | /ssd/gmzhu/SKIG/RGBImages/51217 96 7
 39 | /ssd/gmzhu/SKIG/RGBImages/51218 121 8
 40 | /ssd/gmzhu/SKIG/RGBImages/51219 121 9
 41 | /ssd/gmzhu/SKIG/RGBImages/51220 138 0
 42 | /ssd/gmzhu/SKIG/RGBImages/51221 173 1
 43 | /ssd/gmzhu/SKIG/RGBImages/51222 153 2
 44 | /ssd/gmzhu/SKIG/RGBImages/51223 317 3
 45 | /ssd/gmzhu/SKIG/RGBImages/51224 165 4
 46 | /ssd/gmzhu/SKIG/RGBImages/51225 82 5
 47 | /ssd/gmzhu/SKIG/RGBImages/51226 79 6
 48 | /ssd/gmzhu/SKIG/RGBImages/51227 97 7
 49 | /ssd/gmzhu/SKIG/RGBImages/51228 137 8
 50 | /ssd/gmzhu/SKIG/RGBImages/51229 117 9
 51 | /ssd/gmzhu/SKIG/RGBImages/51230 142 0
 52 | /ssd/gmzhu/SKIG/RGBImages/51231 157 1
 53 | /ssd/gmzhu/SKIG/RGBImages/51232 142 2
 54 | /ssd/gmzhu/SKIG/RGBImages/51233 314 3
 55 | /ssd/gmzhu/SKIG/RGBImages/51234 166 4
 56 | /ssd/gmzhu/SKIG/RGBImages/51235 81 5
 57 | /ssd/gmzhu/SKIG/RGBImages/51236 92 6
 58 | /ssd/gmzhu/SKIG/RGBImages/51237 123 7
 59 | /ssd/gmzhu/SKIG/RGBImages/51238 117 8
 60 | /ssd/gmzhu/SKIG/RGBImages/51239 112 9
 61 | /ssd/gmzhu/SKIG/RGBImages/52110 162 0
 62 | /ssd/gmzhu/SKIG/RGBImages/52111 185 1
 63 | /ssd/gmzhu/SKIG/RGBImages/52112 154 2
 64 | /ssd/gmzhu/SKIG/RGBImages/52113 294 3
 65 | /ssd/gmzhu/SKIG/RGBImages/52114 153 4
 66 | /ssd/gmzhu/SKIG/RGBImages/52115 89 5
 67 | /ssd/gmzhu/SKIG/RGBImages/52116 95 6
 68 | /ssd/gmzhu/SKIG/RGBImages/52117 107 7
 69 | /ssd/gmzhu/SKIG/RGBImages/52118 131 8
 70 | /ssd/gmzhu/SKIG/RGBImages/52119 140 9
 71 | /ssd/gmzhu/SKIG/RGBImages/52120 144 0
 72 | /ssd/gmzhu/SKIG/RGBImages/52121 172 1
 73 | /ssd/gmzhu/SKIG/RGBImages/52122 140 2
 74 | /ssd/gmzhu/SKIG/RGBImages/52123 325 3
 75 | /ssd/gmzhu/SKIG/RGBImages/52124 172 4
 76 | /ssd/gmzhu/SKIG/RGBImages/52125 100 5
 77 | /ssd/gmzhu/SKIG/RGBImages/52126 80 6
 78 | /ssd/gmzhu/SKIG/RGBImages/52127 104 7
 79 | /ssd/gmzhu/SKIG/RGBImages/52128 128 8
 80 | /ssd/gmzhu/SKIG/RGBImages/52129 133 9
 81 | /ssd/gmzhu/SKIG/RGBImages/52130 162 0
 82 | /ssd/gmzhu/SKIG/RGBImages/52131 178 1
 83 | /ssd/gmzhu/SKIG/RGBImages/52132 156 2
 84 | /ssd/gmzhu/SKIG/RGBImages/52133 325 3
 85 | /ssd/gmzhu/SKIG/RGBImages/52134 150 4
 86 | /ssd/gmzhu/SKIG/RGBImages/52135 125 5
 87 | /ssd/gmzhu/SKIG/RGBImages/52136 90 6
 88 | /ssd/gmzhu/SKIG/RGBImages/52137 101 7
 89 | /ssd/gmzhu/SKIG/RGBImages/52138 147 8
 90 | /ssd/gmzhu/SKIG/RGBImages/52139 135 9
 91 | /ssd/gmzhu/SKIG/RGBImages/52210 152 0
 92 | /ssd/gmzhu/SKIG/RGBImages/52211 164 1
 93 | /ssd/gmzhu/SKIG/RGBImages/52212 165 2
 94 | /ssd/gmzhu/SKIG/RGBImages/52213 258 3
 95 | /ssd/gmzhu/SKIG/RGBImages/52214 130 4
 96 | /ssd/gmzhu/SKIG/RGBImages/52215 94 5
 97 | /ssd/gmzhu/SKIG/RGBImages/52216 92 6
 98 | /ssd/gmzhu/SKIG/RGBImages/52217 92 7
 99 | /ssd/gmzhu/SKIG/RGBImages/52218 120 8
100 | /ssd/gmzhu/SKIG/RGBImages/52219 120 9
101 | /ssd/gmzhu/SKIG/RGBImages/52220 143 0
102 | /ssd/gmzhu/SKIG/RGBImages/52221 159 1
103 | /ssd/gmzhu/SKIG/RGBImages/52222 158 2
104 | /ssd/gmzhu/SKIG/RGBImages/52223 258 3
105 | /ssd/gmzhu/SKIG/RGBImages/52224 152 4
106 | /ssd/gmzhu/SKIG/RGBImages/52225 100 5
107 | /ssd/gmzhu/SKIG/RGBImages/52226 92 6
108 | /ssd/gmzhu/SKIG/RGBImages/52227 88 7
109 | /ssd/gmzhu/SKIG/RGBImages/52228 111 8
110 | /ssd/gmzhu/SKIG/RGBImages/52229 113 9
111 | /ssd/gmzhu/SKIG/RGBImages/52230 135 0
112 | /ssd/gmzhu/SKIG/RGBImages/52231 149 1
113 | /ssd/gmzhu/SKIG/RGBImages/52232 152 2
114 | /ssd/gmzhu/SKIG/RGBImages/52233 278 3
115 | /ssd/gmzhu/SKIG/RGBImages/52234 135 4
116 | /ssd/gmzhu/SKIG/RGBImages/52235 97 5
117 | /ssd/gmzhu/SKIG/RGBImages/52236 90 6
118 | /ssd/gmzhu/SKIG/RGBImages/52237 93 7
119 | /ssd/gmzhu/SKIG/RGBImages/52238 114 8
120 | /ssd/gmzhu/SKIG/RGBImages/52239 127 9
121 | /ssd/gmzhu/SKIG/RGBImages/53110 160 0
122 | /ssd/gmzhu/SKIG/RGBImages/53111 145 1
123 | /ssd/gmzhu/SKIG/RGBImages/53112 150 2
124 | /ssd/gmzhu/SKIG/RGBImages/53113 269 3
125 | /ssd/gmzhu/SKIG/RGBImages/53114 131 4
126 | /ssd/gmzhu/SKIG/RGBImages/53115 88 5
127 | /ssd/gmzhu/SKIG/RGBImages/53116 87 6
128 | /ssd/gmzhu/SKIG/RGBImages/53117 94 7
129 | /ssd/gmzhu/SKIG/RGBImages/53118 108 8
130 | /ssd/gmzhu/SKIG/RGBImages/53119 106 9
131 | /ssd/gmzhu/SKIG/RGBImages/53120 152 0
132 | /ssd/gmzhu/SKIG/RGBImages/53121 158 1
133 | /ssd/gmzhu/SKIG/RGBImages/53122 128 2
134 | /ssd/gmzhu/SKIG/RGBImages/53123 249 3
135 | /ssd/gmzhu/SKIG/RGBImages/53124 142 4
136 | /ssd/gmzhu/SKIG/RGBImages/53125 94 5
137 | /ssd/gmzhu/SKIG/RGBImages/53126 72 6
138 | /ssd/gmzhu/SKIG/RGBImages/53127 108 7
139 | /ssd/gmzhu/SKIG/RGBImages/53128 123 8
140 | /ssd/gmzhu/SKIG/RGBImages/53129 114 9
141 | /ssd/gmzhu/SKIG/RGBImages/53130 177 0
142 | /ssd/gmzhu/SKIG/RGBImages/53131 181 1
143 | /ssd/gmzhu/SKIG/RGBImages/53132 145 2
144 | /ssd/gmzhu/SKIG/RGBImages/53133 276 3
145 | /ssd/gmzhu/SKIG/RGBImages/53134 147 4
146 | /ssd/gmzhu/SKIG/RGBImages/53135 90 5
147 | /ssd/gmzhu/SKIG/RGBImages/53136 80 6
148 | /ssd/gmzhu/SKIG/RGBImages/53137 85 7
149 | /ssd/gmzhu/SKIG/RGBImages/53138 114 8
150 | /ssd/gmzhu/SKIG/RGBImages/53139 121 9
151 | /ssd/gmzhu/SKIG/RGBImages/53210 135 0
152 | /ssd/gmzhu/SKIG/RGBImages/53211 135 1
153 | /ssd/gmzhu/SKIG/RGBImages/53212 125 2
154 | /ssd/gmzhu/SKIG/RGBImages/53213 240 3
155 | /ssd/gmzhu/SKIG/RGBImages/53214 134 4
156 | /ssd/gmzhu/SKIG/RGBImages/53215 100 5
157 | /ssd/gmzhu/SKIG/RGBImages/53216 86 6
158 | /ssd/gmzhu/SKIG/RGBImages/53217 82 7
159 | /ssd/gmzhu/SKIG/RGBImages/53218 110 8
160 | /ssd/gmzhu/SKIG/RGBImages/53219 124 9
161 | /ssd/gmzhu/SKIG/RGBImages/53220 127 0
162 | /ssd/gmzhu/SKIG/RGBImages/53221 128 1
163 | /ssd/gmzhu/SKIG/RGBImages/53222 136 2
164 | /ssd/gmzhu/SKIG/RGBImages/53223 254 3
165 | /ssd/gmzhu/SKIG/RGBImages/53224 159 4
166 | /ssd/gmzhu/SKIG/RGBImages/53225 101 5
167 | /ssd/gmzhu/SKIG/RGBImages/53226 76 6
168 | /ssd/gmzhu/SKIG/RGBImages/53227 102 7
169 | /ssd/gmzhu/SKIG/RGBImages/53228 108 8
170 | /ssd/gmzhu/SKIG/RGBImages/53229 122 9
171 | /ssd/gmzhu/SKIG/RGBImages/53230 126 0
172 | /ssd/gmzhu/SKIG/RGBImages/53231 137 1
173 | /ssd/gmzhu/SKIG/RGBImages/53232 136 2
174 | /ssd/gmzhu/SKIG/RGBImages/53233 252 3
175 | /ssd/gmzhu/SKIG/RGBImages/53234 136 4
176 | /ssd/gmzhu/SKIG/RGBImages/53235 83 5
177 | /ssd/gmzhu/SKIG/RGBImages/53236 93 6
178 | /ssd/gmzhu/SKIG/RGBImages/53237 85 7
179 | /ssd/gmzhu/SKIG/RGBImages/53238 105 8
180 | /ssd/gmzhu/SKIG/RGBImages/53239 107 9
181 | /ssd/gmzhu/SKIG/RGBImages/61110 268 0
182 | /ssd/gmzhu/SKIG/RGBImages/61111 270 1
183 | /ssd/gmzhu/SKIG/RGBImages/61112 186 2
184 | /ssd/gmzhu/SKIG/RGBImages/61113 303 3
185 | /ssd/gmzhu/SKIG/RGBImages/61114 145 4
186 | /ssd/gmzhu/SKIG/RGBImages/61115 111 5
187 | /ssd/gmzhu/SKIG/RGBImages/61116 85 6
188 | /ssd/gmzhu/SKIG/RGBImages/61117 95 7
189 | /ssd/gmzhu/SKIG/RGBImages/61118 139 8
190 | /ssd/gmzhu/SKIG/RGBImages/61119 113 9
191 | /ssd/gmzhu/SKIG/RGBImages/61120 301 0
192 | /ssd/gmzhu/SKIG/RGBImages/61121 318 1
193 | /ssd/gmzhu/SKIG/RGBImages/61122 191 2
194 | /ssd/gmzhu/SKIG/RGBImages/61123 361 3
195 | /ssd/gmzhu/SKIG/RGBImages/61124 160 4
196 | /ssd/gmzhu/SKIG/RGBImages/61125 127 5
197 | /ssd/gmzhu/SKIG/RGBImages/61126 96 6
198 | /ssd/gmzhu/SKIG/RGBImages/61127 91 7
199 | /ssd/gmzhu/SKIG/RGBImages/61128 147 8
200 | /ssd/gmzhu/SKIG/RGBImages/61129 123 9
201 | /ssd/gmzhu/SKIG/RGBImages/61130 321 0
202 | /ssd/gmzhu/SKIG/RGBImages/61131 291 1
203 | /ssd/gmzhu/SKIG/RGBImages/61132 198 2
204 | /ssd/gmzhu/SKIG/RGBImages/61133 405 3
205 | /ssd/gmzhu/SKIG/RGBImages/61134 145 4
206 | /ssd/gmzhu/SKIG/RGBImages/61135 124 5
207 | /ssd/gmzhu/SKIG/RGBImages/61136 110 6
208 | /ssd/gmzhu/SKIG/RGBImages/61137 99 7
209 | /ssd/gmzhu/SKIG/RGBImages/61138 141 8
210 | /ssd/gmzhu/SKIG/RGBImages/61139 118 9
211 | /ssd/gmzhu/SKIG/RGBImages/61210 270 0
212 | /ssd/gmzhu/SKIG/RGBImages/61211 276 1
213 | /ssd/gmzhu/SKIG/RGBImages/61212 159 2
214 | /ssd/gmzhu/SKIG/RGBImages/61213 295 3
215 | /ssd/gmzhu/SKIG/RGBImages/61214 148 4
216 | /ssd/gmzhu/SKIG/RGBImages/61215 127 5
217 | /ssd/gmzhu/SKIG/RGBImages/61216 117 6
218 | /ssd/gmzhu/SKIG/RGBImages/61217 100 7
219 | /ssd/gmzhu/SKIG/RGBImages/61218 132 8
220 | /ssd/gmzhu/SKIG/RGBImages/61219 110 9
221 | /ssd/gmzhu/SKIG/RGBImages/61220 339 0
222 | /ssd/gmzhu/SKIG/RGBImages/61221 297 1
223 | /ssd/gmzhu/SKIG/RGBImages/61222 167 2
224 | /ssd/gmzhu/SKIG/RGBImages/61223 309 3
225 | /ssd/gmzhu/SKIG/RGBImages/61224 147 4
226 | /ssd/gmzhu/SKIG/RGBImages/61225 125 5
227 | /ssd/gmzhu/SKIG/RGBImages/61226 108 6
228 | /ssd/gmzhu/SKIG/RGBImages/61227 112 7
229 | /ssd/gmzhu/SKIG/RGBImages/61228 133 8
230 | /ssd/gmzhu/SKIG/RGBImages/61229 119 9
231 | /ssd/gmzhu/SKIG/RGBImages/61230 281 0
232 | /ssd/gmzhu/SKIG/RGBImages/61231 278 1
233 | /ssd/gmzhu/SKIG/RGBImages/61232 145 2
234 | /ssd/gmzhu/SKIG/RGBImages/61233 314 3
235 | /ssd/gmzhu/SKIG/RGBImages/61234 147 4
236 | /ssd/gmzhu/SKIG/RGBImages/61235 110 5
237 | /ssd/gmzhu/SKIG/RGBImages/61236 101 6
238 | /ssd/gmzhu/SKIG/RGBImages/61237 109 7
239 | /ssd/gmzhu/SKIG/RGBImages/61238 129 8
240 | /ssd/gmzhu/SKIG/RGBImages/61239 147 9
241 | /ssd/gmzhu/SKIG/RGBImages/62110 302 0
242 | /ssd/gmzhu/SKIG/RGBImages/62111 299 1
243 | /ssd/gmzhu/SKIG/RGBImages/62112 163 2
244 | /ssd/gmzhu/SKIG/RGBImages/62113 314 3
245 | /ssd/gmzhu/SKIG/RGBImages/62114 167 4
246 | /ssd/gmzhu/SKIG/RGBImages/62115 117 5
247 | /ssd/gmzhu/SKIG/RGBImages/62116 102 6
248 | /ssd/gmzhu/SKIG/RGBImages/62117 123 7
249 | /ssd/gmzhu/SKIG/RGBImages/62118 114 8
250 | /ssd/gmzhu/SKIG/RGBImages/62119 121 9
251 | /ssd/gmzhu/SKIG/RGBImages/62120 255 0
252 | /ssd/gmzhu/SKIG/RGBImages/62121 263 1
253 | /ssd/gmzhu/SKIG/RGBImages/62122 177 2
254 | /ssd/gmzhu/SKIG/RGBImages/62123 321 3
255 | /ssd/gmzhu/SKIG/RGBImages/62124 164 4
256 | /ssd/gmzhu/SKIG/RGBImages/62125 105 5
257 | /ssd/gmzhu/SKIG/RGBImages/62126 95 6
258 | /ssd/gmzhu/SKIG/RGBImages/62127 138 7
259 | /ssd/gmzhu/SKIG/RGBImages/62128 117 8
260 | /ssd/gmzhu/SKIG/RGBImages/62129 147 9
261 | /ssd/gmzhu/SKIG/RGBImages/62130 265 0
262 | /ssd/gmzhu/SKIG/RGBImages/62131 311 1
263 | /ssd/gmzhu/SKIG/RGBImages/62132 173 2
264 | /ssd/gmzhu/SKIG/RGBImages/62133 350 3
265 | /ssd/gmzhu/SKIG/RGBImages/62134 172 4
266 | /ssd/gmzhu/SKIG/RGBImages/62135 125 5
267 | /ssd/gmzhu/SKIG/RGBImages/62136 123 6
268 | /ssd/gmzhu/SKIG/RGBImages/62137 123 7
269 | /ssd/gmzhu/SKIG/RGBImages/62138 115 8
270 | /ssd/gmzhu/SKIG/RGBImages/62139 140 9
271 | /ssd/gmzhu/SKIG/RGBImages/62210 255 0
272 | /ssd/gmzhu/SKIG/RGBImages/62211 264 1
273 | /ssd/gmzhu/SKIG/RGBImages/62212 162 2
274 | /ssd/gmzhu/SKIG/RGBImages/62213 330 3
275 | /ssd/gmzhu/SKIG/RGBImages/62214 156 4
276 | /ssd/gmzhu/SKIG/RGBImages/62215 116 5
277 | /ssd/gmzhu/SKIG/RGBImages/62216 107 6
278 | /ssd/gmzhu/SKIG/RGBImages/62217 121 7
279 | /ssd/gmzhu/SKIG/RGBImages/62218 142 8
280 | /ssd/gmzhu/SKIG/RGBImages/62219 152 9
281 | /ssd/gmzhu/SKIG/RGBImages/62220 331 0
282 | /ssd/gmzhu/SKIG/RGBImages/62221 262 1
283 | /ssd/gmzhu/SKIG/RGBImages/62222 186 2
284 | /ssd/gmzhu/SKIG/RGBImages/62223 333 3
285 | /ssd/gmzhu/SKIG/RGBImages/62224 180 4
286 | /ssd/gmzhu/SKIG/RGBImages/62225 110 5
287 | /ssd/gmzhu/SKIG/RGBImages/62226 96 6
288 | /ssd/gmzhu/SKIG/RGBImages/62227 141 7
289 | /ssd/gmzhu/SKIG/RGBImages/62228 141 8
290 | /ssd/gmzhu/SKIG/RGBImages/62229 125 9
291 | /ssd/gmzhu/SKIG/RGBImages/62230 350 0
292 | /ssd/gmzhu/SKIG/RGBImages/62231 265 1
293 | /ssd/gmzhu/SKIG/RGBImages/62232 170 2
294 | /ssd/gmzhu/SKIG/RGBImages/62233 337 3
295 | /ssd/gmzhu/SKIG/RGBImages/62234 164 4
296 | /ssd/gmzhu/SKIG/RGBImages/62235 111 5
297 | /ssd/gmzhu/SKIG/RGBImages/62236 106 6
298 | /ssd/gmzhu/SKIG/RGBImages/62237 122 7
299 | /ssd/gmzhu/SKIG/RGBImages/62238 148 8
300 | /ssd/gmzhu/SKIG/RGBImages/62239 138 9
301 | /ssd/gmzhu/SKIG/RGBImages/63110 328 0
302 | /ssd/gmzhu/SKIG/RGBImages/63111 261 1
303 | /ssd/gmzhu/SKIG/RGBImages/63112 180 2
304 | /ssd/gmzhu/SKIG/RGBImages/63113 328 3
305 | /ssd/gmzhu/SKIG/RGBImages/63114 185 4
306 | /ssd/gmzhu/SKIG/RGBImages/63115 132 5
307 | /ssd/gmzhu/SKIG/RGBImages/63116 124 6
308 | /ssd/gmzhu/SKIG/RGBImages/63117 100 7
309 | /ssd/gmzhu/SKIG/RGBImages/63118 120 8
310 | /ssd/gmzhu/SKIG/RGBImages/63119 120 9
311 | /ssd/gmzhu/SKIG/RGBImages/63120 306 0
312 | /ssd/gmzhu/SKIG/RGBImages/63121 283 1
313 | /ssd/gmzhu/SKIG/RGBImages/63122 168 2
314 | /ssd/gmzhu/SKIG/RGBImages/63123 322 3
315 | /ssd/gmzhu/SKIG/RGBImages/63124 184 4
316 | /ssd/gmzhu/SKIG/RGBImages/63125 115 5
317 | /ssd/gmzhu/SKIG/RGBImages/63126 119 6
318 | /ssd/gmzhu/SKIG/RGBImages/63127 109 7
319 | /ssd/gmzhu/SKIG/RGBImages/63128 129 8
320 | /ssd/gmzhu/SKIG/RGBImages/63129 150 9
321 | /ssd/gmzhu/SKIG/RGBImages/63130 297 0
322 | /ssd/gmzhu/SKIG/RGBImages/63131 281 1
323 | /ssd/gmzhu/SKIG/RGBImages/63132 173 2
324 | /ssd/gmzhu/SKIG/RGBImages/63133 348 3
325 | /ssd/gmzhu/SKIG/RGBImages/63134 189 4
326 | /ssd/gmzhu/SKIG/RGBImages/63135 123 5
327 | /ssd/gmzhu/SKIG/RGBImages/63136 116 6
328 | /ssd/gmzhu/SKIG/RGBImages/63137 105 7
329 | /ssd/gmzhu/SKIG/RGBImages/63138 150 8
330 | /ssd/gmzhu/SKIG/RGBImages/63139 146 9
331 | /ssd/gmzhu/SKIG/RGBImages/63210 308 0
332 | /ssd/gmzhu/SKIG/RGBImages/63211 244 1
333 | /ssd/gmzhu/SKIG/RGBImages/63212 158 2
334 | /ssd/gmzhu/SKIG/RGBImages/63213 317 3
335 | /ssd/gmzhu/SKIG/RGBImages/63214 147 4
336 | /ssd/gmzhu/SKIG/RGBImages/63215 120 5
337 | /ssd/gmzhu/SKIG/RGBImages/63216 114 6
338 | /ssd/gmzhu/SKIG/RGBImages/63217 106 7
339 | /ssd/gmzhu/SKIG/RGBImages/63218 117 8
340 | /ssd/gmzhu/SKIG/RGBImages/63219 129 9
341 | /ssd/gmzhu/SKIG/RGBImages/63220 284 0
342 | /ssd/gmzhu/SKIG/RGBImages/63221 236 1
343 | /ssd/gmzhu/SKIG/RGBImages/63222 160 2
344 | /ssd/gmzhu/SKIG/RGBImages/63223 329 3
345 | /ssd/gmzhu/SKIG/RGBImages/63224 165 4
346 | /ssd/gmzhu/SKIG/RGBImages/63225 125 5
347 | /ssd/gmzhu/SKIG/RGBImages/63226 122 6
348 | /ssd/gmzhu/SKIG/RGBImages/63227 116 7
349 | /ssd/gmzhu/SKIG/RGBImages/63228 128 8
350 | /ssd/gmzhu/SKIG/RGBImages/63229 134 9
351 | /ssd/gmzhu/SKIG/RGBImages/63230 270 0
352 | /ssd/gmzhu/SKIG/RGBImages/63231 275 1
353 | /ssd/gmzhu/SKIG/RGBImages/63232 159 2
354 | /ssd/gmzhu/SKIG/RGBImages/63233 330 3
355 | /ssd/gmzhu/SKIG/RGBImages/63234 141 4
356 | /ssd/gmzhu/SKIG/RGBImages/63235 117 5
357 | /ssd/gmzhu/SKIG/RGBImages/63236 107 6
358 | /ssd/gmzhu/SKIG/RGBImages/63237 110 7
359 | /ssd/gmzhu/SKIG/RGBImages/63238 151 8
360 | /ssd/gmzhu/SKIG/RGBImages/63239 130 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_rgb_list2.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/RGBImages/31110 176 0
  2 | /ssd/gmzhu/SKIG/RGBImages/31111 142 1
  3 | /ssd/gmzhu/SKIG/RGBImages/31112 130 2
  4 | /ssd/gmzhu/SKIG/RGBImages/31113 256 3
  5 | /ssd/gmzhu/SKIG/RGBImages/31114 161 4
  6 | /ssd/gmzhu/SKIG/RGBImages/31115 96 5
  7 | /ssd/gmzhu/SKIG/RGBImages/31116 84 6
  8 | /ssd/gmzhu/SKIG/RGBImages/31117 102 7
  9 | /ssd/gmzhu/SKIG/RGBImages/31118 107 8
 10 | /ssd/gmzhu/SKIG/RGBImages/31119 120 9
 11 | /ssd/gmzhu/SKIG/RGBImages/31120 154 0
 12 | /ssd/gmzhu/SKIG/RGBImages/31121 157 1
 13 | /ssd/gmzhu/SKIG/RGBImages/31122 130 2
 14 | /ssd/gmzhu/SKIG/RGBImages/31123 270 3
 15 | /ssd/gmzhu/SKIG/RGBImages/31124 148 4
 16 | /ssd/gmzhu/SKIG/RGBImages/31125 102 5
 17 | /ssd/gmzhu/SKIG/RGBImages/31126 89 6
 18 | /ssd/gmzhu/SKIG/RGBImages/31127 65 7
 19 | /ssd/gmzhu/SKIG/RGBImages/31128 115 8
 20 | /ssd/gmzhu/SKIG/RGBImages/31129 123 9
 21 | /ssd/gmzhu/SKIG/RGBImages/31130 140 0
 22 | /ssd/gmzhu/SKIG/RGBImages/31131 152 1
 23 | /ssd/gmzhu/SKIG/RGBImages/31132 134 2
 24 | /ssd/gmzhu/SKIG/RGBImages/31133 291 3
 25 | /ssd/gmzhu/SKIG/RGBImages/31134 149 4
 26 | /ssd/gmzhu/SKIG/RGBImages/31135 105 5
 27 | /ssd/gmzhu/SKIG/RGBImages/31136 104 6
 28 | /ssd/gmzhu/SKIG/RGBImages/31137 94 7
 29 | /ssd/gmzhu/SKIG/RGBImages/31138 136 8
 30 | /ssd/gmzhu/SKIG/RGBImages/31139 119 9
 31 | /ssd/gmzhu/SKIG/RGBImages/31210 133 0
 32 | /ssd/gmzhu/SKIG/RGBImages/31211 146 1
 33 | /ssd/gmzhu/SKIG/RGBImages/31212 135 2
 34 | /ssd/gmzhu/SKIG/RGBImages/31213 234 3
 35 | /ssd/gmzhu/SKIG/RGBImages/31214 145 4
 36 | /ssd/gmzhu/SKIG/RGBImages/31215 75 5
 37 | /ssd/gmzhu/SKIG/RGBImages/31216 79 6
 38 | /ssd/gmzhu/SKIG/RGBImages/31217 96 7
 39 | /ssd/gmzhu/SKIG/RGBImages/31218 108 8
 40 | /ssd/gmzhu/SKIG/RGBImages/31219 103 9
 41 | /ssd/gmzhu/SKIG/RGBImages/31220 146 0
 42 | /ssd/gmzhu/SKIG/RGBImages/31221 140 1
 43 | /ssd/gmzhu/SKIG/RGBImages/31222 138 2
 44 | /ssd/gmzhu/SKIG/RGBImages/31223 242 3
 45 | /ssd/gmzhu/SKIG/RGBImages/31224 137 4
 46 | /ssd/gmzhu/SKIG/RGBImages/31225 95 5
 47 | /ssd/gmzhu/SKIG/RGBImages/31226 83 6
 48 | /ssd/gmzhu/SKIG/RGBImages/31227 102 7
 49 | /ssd/gmzhu/SKIG/RGBImages/31228 110 8
 50 | /ssd/gmzhu/SKIG/RGBImages/31229 111 9
 51 | /ssd/gmzhu/SKIG/RGBImages/31230 147 0
 52 | /ssd/gmzhu/SKIG/RGBImages/31231 152 1
 53 | /ssd/gmzhu/SKIG/RGBImages/31232 134 2
 54 | /ssd/gmzhu/SKIG/RGBImages/31233 261 3
 55 | /ssd/gmzhu/SKIG/RGBImages/31234 140 4
 56 | /ssd/gmzhu/SKIG/RGBImages/31235 104 5
 57 | /ssd/gmzhu/SKIG/RGBImages/31236 76 6
 58 | /ssd/gmzhu/SKIG/RGBImages/31237 84 7
 59 | /ssd/gmzhu/SKIG/RGBImages/31238 101 8
 60 | /ssd/gmzhu/SKIG/RGBImages/31239 93 9
 61 | /ssd/gmzhu/SKIG/RGBImages/32110 139 0
 62 | /ssd/gmzhu/SKIG/RGBImages/32111 145 1
 63 | /ssd/gmzhu/SKIG/RGBImages/32112 105 2
 64 | /ssd/gmzhu/SKIG/RGBImages/32113 230 3
 65 | /ssd/gmzhu/SKIG/RGBImages/32114 132 4
 66 | /ssd/gmzhu/SKIG/RGBImages/32115 77 5
 67 | /ssd/gmzhu/SKIG/RGBImages/32116 80 6
 68 | /ssd/gmzhu/SKIG/RGBImages/32117 84 7
 69 | /ssd/gmzhu/SKIG/RGBImages/32118 125 8
 70 | /ssd/gmzhu/SKIG/RGBImages/32119 105 9
 71 | /ssd/gmzhu/SKIG/RGBImages/32120 100 0
 72 | /ssd/gmzhu/SKIG/RGBImages/32121 133 1
 73 | /ssd/gmzhu/SKIG/RGBImages/32122 122 2
 74 | /ssd/gmzhu/SKIG/RGBImages/32123 243 3
 75 | /ssd/gmzhu/SKIG/RGBImages/32124 151 4
 76 | /ssd/gmzhu/SKIG/RGBImages/32125 85 5
 77 | /ssd/gmzhu/SKIG/RGBImages/32126 76 6
 78 | /ssd/gmzhu/SKIG/RGBImages/32127 70 7
 79 | /ssd/gmzhu/SKIG/RGBImages/32128 109 8
 80 | /ssd/gmzhu/SKIG/RGBImages/32129 106 9
 81 | /ssd/gmzhu/SKIG/RGBImages/32130 131 0
 82 | /ssd/gmzhu/SKIG/RGBImages/32131 131 1
 83 | /ssd/gmzhu/SKIG/RGBImages/32132 123 2
 84 | /ssd/gmzhu/SKIG/RGBImages/32133 241 3
 85 | /ssd/gmzhu/SKIG/RGBImages/32134 207 4
 86 | /ssd/gmzhu/SKIG/RGBImages/32135 96 5
 87 | /ssd/gmzhu/SKIG/RGBImages/32136 92 6
 88 | /ssd/gmzhu/SKIG/RGBImages/32137 84 7
 89 | /ssd/gmzhu/SKIG/RGBImages/32138 117 8
 90 | /ssd/gmzhu/SKIG/RGBImages/32139 107 9
 91 | /ssd/gmzhu/SKIG/RGBImages/32210 160 0
 92 | /ssd/gmzhu/SKIG/RGBImages/32211 167 1
 93 | /ssd/gmzhu/SKIG/RGBImages/32212 124 2
 94 | /ssd/gmzhu/SKIG/RGBImages/32213 280 3
 95 | /ssd/gmzhu/SKIG/RGBImages/32214 107 4
 96 | /ssd/gmzhu/SKIG/RGBImages/32215 81 5
 97 | /ssd/gmzhu/SKIG/RGBImages/32216 97 6
 98 | /ssd/gmzhu/SKIG/RGBImages/32217 91 7
 99 | /ssd/gmzhu/SKIG/RGBImages/32218 116 8
100 | /ssd/gmzhu/SKIG/RGBImages/32219 120 9
101 | /ssd/gmzhu/SKIG/RGBImages/32220 155 0
102 | /ssd/gmzhu/SKIG/RGBImages/32221 157 1
103 | /ssd/gmzhu/SKIG/RGBImages/32222 135 2
104 | /ssd/gmzhu/SKIG/RGBImages/32223 287 3
105 | /ssd/gmzhu/SKIG/RGBImages/32224 143 4
106 | /ssd/gmzhu/SKIG/RGBImages/32225 88 5
107 | /ssd/gmzhu/SKIG/RGBImages/32226 97 6
108 | /ssd/gmzhu/SKIG/RGBImages/32227 104 7
109 | /ssd/gmzhu/SKIG/RGBImages/32228 118 8
110 | /ssd/gmzhu/SKIG/RGBImages/32229 128 9
111 | /ssd/gmzhu/SKIG/RGBImages/32230 151 0
112 | /ssd/gmzhu/SKIG/RGBImages/32231 169 1
113 | /ssd/gmzhu/SKIG/RGBImages/32232 129 2
114 | /ssd/gmzhu/SKIG/RGBImages/32233 269 3
115 | /ssd/gmzhu/SKIG/RGBImages/32234 149 4
116 | /ssd/gmzhu/SKIG/RGBImages/32235 96 5
117 | /ssd/gmzhu/SKIG/RGBImages/32236 95 6
118 | /ssd/gmzhu/SKIG/RGBImages/32237 101 7
119 | /ssd/gmzhu/SKIG/RGBImages/32238 120 8
120 | /ssd/gmzhu/SKIG/RGBImages/32239 112 9
121 | /ssd/gmzhu/SKIG/RGBImages/33110 208 0
122 | /ssd/gmzhu/SKIG/RGBImages/33111 166 1
123 | /ssd/gmzhu/SKIG/RGBImages/33112 131 2
124 | /ssd/gmzhu/SKIG/RGBImages/33113 332 3
125 | /ssd/gmzhu/SKIG/RGBImages/33114 149 4
126 | /ssd/gmzhu/SKIG/RGBImages/33115 98 5
127 | /ssd/gmzhu/SKIG/RGBImages/33116 83 6
128 | /ssd/gmzhu/SKIG/RGBImages/33117 67 7
129 | /ssd/gmzhu/SKIG/RGBImages/33118 152 8
130 | /ssd/gmzhu/SKIG/RGBImages/33119 108 9
131 | /ssd/gmzhu/SKIG/RGBImages/33120 149 0
132 | /ssd/gmzhu/SKIG/RGBImages/33121 165 1
133 | /ssd/gmzhu/SKIG/RGBImages/33122 142 2
134 | /ssd/gmzhu/SKIG/RGBImages/33123 328 3
135 | /ssd/gmzhu/SKIG/RGBImages/33124 138 4
136 | /ssd/gmzhu/SKIG/RGBImages/33125 71 5
137 | /ssd/gmzhu/SKIG/RGBImages/33126 99 6
138 | /ssd/gmzhu/SKIG/RGBImages/33127 99 7
139 | /ssd/gmzhu/SKIG/RGBImages/33128 170 8
140 | /ssd/gmzhu/SKIG/RGBImages/33129 111 9
141 | /ssd/gmzhu/SKIG/RGBImages/33130 101 0
142 | /ssd/gmzhu/SKIG/RGBImages/33131 156 1
143 | /ssd/gmzhu/SKIG/RGBImages/33132 140 2
144 | /ssd/gmzhu/SKIG/RGBImages/33133 404 3
145 | /ssd/gmzhu/SKIG/RGBImages/33134 117 4
146 | /ssd/gmzhu/SKIG/RGBImages/33135 83 5
147 | /ssd/gmzhu/SKIG/RGBImages/33136 130 6
148 | /ssd/gmzhu/SKIG/RGBImages/33137 95 7
149 | /ssd/gmzhu/SKIG/RGBImages/33138 155 8
150 | /ssd/gmzhu/SKIG/RGBImages/33139 106 9
151 | /ssd/gmzhu/SKIG/RGBImages/33210 181 0
152 | /ssd/gmzhu/SKIG/RGBImages/33211 160 1
153 | /ssd/gmzhu/SKIG/RGBImages/33212 152 2
154 | /ssd/gmzhu/SKIG/RGBImages/33213 262 3
155 | /ssd/gmzhu/SKIG/RGBImages/33214 112 4
156 | /ssd/gmzhu/SKIG/RGBImages/33215 92 5
157 | /ssd/gmzhu/SKIG/RGBImages/33216 102 6
158 | /ssd/gmzhu/SKIG/RGBImages/33217 68 7
159 | /ssd/gmzhu/SKIG/RGBImages/33218 113 8
160 | /ssd/gmzhu/SKIG/RGBImages/33219 113 9
161 | /ssd/gmzhu/SKIG/RGBImages/33220 168 0
162 | /ssd/gmzhu/SKIG/RGBImages/33221 158 1
163 | /ssd/gmzhu/SKIG/RGBImages/33222 143 2
164 | /ssd/gmzhu/SKIG/RGBImages/33223 250 3
165 | /ssd/gmzhu/SKIG/RGBImages/33224 105 4
166 | /ssd/gmzhu/SKIG/RGBImages/33225 96 5
167 | /ssd/gmzhu/SKIG/RGBImages/33226 91 6
168 | /ssd/gmzhu/SKIG/RGBImages/33227 102 7
169 | /ssd/gmzhu/SKIG/RGBImages/33228 116 8
170 | /ssd/gmzhu/SKIG/RGBImages/33229 99 9
171 | /ssd/gmzhu/SKIG/RGBImages/33230 153 0
172 | /ssd/gmzhu/SKIG/RGBImages/33231 160 1
173 | /ssd/gmzhu/SKIG/RGBImages/33232 125 2
174 | /ssd/gmzhu/SKIG/RGBImages/33233 288 3
175 | /ssd/gmzhu/SKIG/RGBImages/33234 147 4
176 | /ssd/gmzhu/SKIG/RGBImages/33235 105 5
177 | /ssd/gmzhu/SKIG/RGBImages/33236 102 6
178 | /ssd/gmzhu/SKIG/RGBImages/33237 88 7
179 | /ssd/gmzhu/SKIG/RGBImages/33238 112 8
180 | /ssd/gmzhu/SKIG/RGBImages/33239 104 9
181 | /ssd/gmzhu/SKIG/RGBImages/41110 193 0
182 | /ssd/gmzhu/SKIG/RGBImages/41111 203 1
183 | /ssd/gmzhu/SKIG/RGBImages/41112 149 2
184 | /ssd/gmzhu/SKIG/RGBImages/41113 291 3
185 | /ssd/gmzhu/SKIG/RGBImages/41114 197 4
186 | /ssd/gmzhu/SKIG/RGBImages/41115 119 5
187 | /ssd/gmzhu/SKIG/RGBImages/41116 131 6
188 | /ssd/gmzhu/SKIG/RGBImages/41117 140 7
189 | /ssd/gmzhu/SKIG/RGBImages/41118 149 8
190 | /ssd/gmzhu/SKIG/RGBImages/41119 117 9
191 | /ssd/gmzhu/SKIG/RGBImages/41120 225 0
192 | /ssd/gmzhu/SKIG/RGBImages/41121 209 1
193 | /ssd/gmzhu/SKIG/RGBImages/41122 144 2
194 | /ssd/gmzhu/SKIG/RGBImages/41123 331 3
195 | /ssd/gmzhu/SKIG/RGBImages/41124 201 4
196 | /ssd/gmzhu/SKIG/RGBImages/41125 120 5
197 | /ssd/gmzhu/SKIG/RGBImages/41126 135 6
198 | /ssd/gmzhu/SKIG/RGBImages/41127 103 7
199 | /ssd/gmzhu/SKIG/RGBImages/41128 151 8
200 | /ssd/gmzhu/SKIG/RGBImages/41129 147 9
201 | /ssd/gmzhu/SKIG/RGBImages/41130 185 0
202 | /ssd/gmzhu/SKIG/RGBImages/41131 207 1
203 | /ssd/gmzhu/SKIG/RGBImages/41132 138 2
204 | /ssd/gmzhu/SKIG/RGBImages/41133 319 3
205 | /ssd/gmzhu/SKIG/RGBImages/41134 192 4
206 | /ssd/gmzhu/SKIG/RGBImages/41135 117 5
207 | /ssd/gmzhu/SKIG/RGBImages/41136 135 6
208 | /ssd/gmzhu/SKIG/RGBImages/41137 125 7
209 | /ssd/gmzhu/SKIG/RGBImages/41138 170 8
210 | /ssd/gmzhu/SKIG/RGBImages/41139 133 9
211 | /ssd/gmzhu/SKIG/RGBImages/41210 153 0
212 | /ssd/gmzhu/SKIG/RGBImages/41211 179 1
213 | /ssd/gmzhu/SKIG/RGBImages/41212 160 2
214 | /ssd/gmzhu/SKIG/RGBImages/41213 290 3
215 | /ssd/gmzhu/SKIG/RGBImages/41214 189 4
216 | /ssd/gmzhu/SKIG/RGBImages/41215 98 5
217 | /ssd/gmzhu/SKIG/RGBImages/41216 108 6
218 | /ssd/gmzhu/SKIG/RGBImages/41217 95 7
219 | /ssd/gmzhu/SKIG/RGBImages/41218 123 8
220 | /ssd/gmzhu/SKIG/RGBImages/41219 139 9
221 | /ssd/gmzhu/SKIG/RGBImages/41220 202 0
222 | /ssd/gmzhu/SKIG/RGBImages/41221 167 1
223 | /ssd/gmzhu/SKIG/RGBImages/41222 122 2
224 | /ssd/gmzhu/SKIG/RGBImages/41223 312 3
225 | /ssd/gmzhu/SKIG/RGBImages/41224 210 4
226 | /ssd/gmzhu/SKIG/RGBImages/41225 125 5
227 | /ssd/gmzhu/SKIG/RGBImages/41226 125 6
228 | /ssd/gmzhu/SKIG/RGBImages/41227 94 7
229 | /ssd/gmzhu/SKIG/RGBImages/41228 153 8
230 | /ssd/gmzhu/SKIG/RGBImages/41229 122 9
231 | /ssd/gmzhu/SKIG/RGBImages/41230 164 0
232 | /ssd/gmzhu/SKIG/RGBImages/41231 168 1
233 | /ssd/gmzhu/SKIG/RGBImages/41232 127 2
234 | /ssd/gmzhu/SKIG/RGBImages/41233 345 3
235 | /ssd/gmzhu/SKIG/RGBImages/41234 199 4
236 | /ssd/gmzhu/SKIG/RGBImages/41235 120 5
237 | /ssd/gmzhu/SKIG/RGBImages/41236 113 6
238 | /ssd/gmzhu/SKIG/RGBImages/41237 115 7
239 | /ssd/gmzhu/SKIG/RGBImages/41238 149 8
240 | /ssd/gmzhu/SKIG/RGBImages/41239 126 9
241 | /ssd/gmzhu/SKIG/RGBImages/42110 137 0
242 | /ssd/gmzhu/SKIG/RGBImages/42111 141 1
243 | /ssd/gmzhu/SKIG/RGBImages/42112 105 2
244 | /ssd/gmzhu/SKIG/RGBImages/42113 265 3
245 | /ssd/gmzhu/SKIG/RGBImages/42114 178 4
246 | /ssd/gmzhu/SKIG/RGBImages/42115 93 5
247 | /ssd/gmzhu/SKIG/RGBImages/42116 113 6
248 | /ssd/gmzhu/SKIG/RGBImages/42117 110 7
249 | /ssd/gmzhu/SKIG/RGBImages/42118 77 8
250 | /ssd/gmzhu/SKIG/RGBImages/42119 106 9
251 | /ssd/gmzhu/SKIG/RGBImages/42120 136 0
252 | /ssd/gmzhu/SKIG/RGBImages/42121 160 1
253 | /ssd/gmzhu/SKIG/RGBImages/42122 133 2
254 | /ssd/gmzhu/SKIG/RGBImages/42123 289 3
255 | /ssd/gmzhu/SKIG/RGBImages/42124 185 4
256 | /ssd/gmzhu/SKIG/RGBImages/42125 109 5
257 | /ssd/gmzhu/SKIG/RGBImages/42126 104 6
258 | /ssd/gmzhu/SKIG/RGBImages/42127 124 7
259 | /ssd/gmzhu/SKIG/RGBImages/42128 98 8
260 | /ssd/gmzhu/SKIG/RGBImages/42129 113 9
261 | /ssd/gmzhu/SKIG/RGBImages/42130 139 0
262 | /ssd/gmzhu/SKIG/RGBImages/42131 168 1
263 | /ssd/gmzhu/SKIG/RGBImages/42132 109 2
264 | /ssd/gmzhu/SKIG/RGBImages/42133 300 3
265 | /ssd/gmzhu/SKIG/RGBImages/42134 192 4
266 | /ssd/gmzhu/SKIG/RGBImages/42135 109 5
267 | /ssd/gmzhu/SKIG/RGBImages/42136 144 6
268 | /ssd/gmzhu/SKIG/RGBImages/42137 119 7
269 | /ssd/gmzhu/SKIG/RGBImages/42138 96 8
270 | /ssd/gmzhu/SKIG/RGBImages/42139 125 9
271 | /ssd/gmzhu/SKIG/RGBImages/42210 131 0
272 | /ssd/gmzhu/SKIG/RGBImages/42211 151 1
273 | /ssd/gmzhu/SKIG/RGBImages/42212 126 2
274 | /ssd/gmzhu/SKIG/RGBImages/42213 302 3
275 | /ssd/gmzhu/SKIG/RGBImages/42214 147 4
276 | /ssd/gmzhu/SKIG/RGBImages/42215 91 5
277 | /ssd/gmzhu/SKIG/RGBImages/42216 94 6
278 | /ssd/gmzhu/SKIG/RGBImages/42217 114 7
279 | /ssd/gmzhu/SKIG/RGBImages/42218 114 8
280 | /ssd/gmzhu/SKIG/RGBImages/42219 114 9
281 | /ssd/gmzhu/SKIG/RGBImages/42220 145 0
282 | /ssd/gmzhu/SKIG/RGBImages/42221 165 1
283 | /ssd/gmzhu/SKIG/RGBImages/42222 114 2
284 | /ssd/gmzhu/SKIG/RGBImages/42223 317 3
285 | /ssd/gmzhu/SKIG/RGBImages/42224 143 4
286 | /ssd/gmzhu/SKIG/RGBImages/42225 78 5
287 | /ssd/gmzhu/SKIG/RGBImages/42226 99 6
288 | /ssd/gmzhu/SKIG/RGBImages/42227 99 7
289 | /ssd/gmzhu/SKIG/RGBImages/42228 101 8
290 | /ssd/gmzhu/SKIG/RGBImages/42229 122 9
291 | /ssd/gmzhu/SKIG/RGBImages/42230 163 0
292 | /ssd/gmzhu/SKIG/RGBImages/42231 161 1
293 | /ssd/gmzhu/SKIG/RGBImages/42232 115 2
294 | /ssd/gmzhu/SKIG/RGBImages/42233 296 3
295 | /ssd/gmzhu/SKIG/RGBImages/42234 135 4
296 | /ssd/gmzhu/SKIG/RGBImages/42235 81 5
297 | /ssd/gmzhu/SKIG/RGBImages/42236 84 6
298 | /ssd/gmzhu/SKIG/RGBImages/42237 102 7
299 | /ssd/gmzhu/SKIG/RGBImages/42238 97 8
300 | /ssd/gmzhu/SKIG/RGBImages/42239 108 9
301 | /ssd/gmzhu/SKIG/RGBImages/43110 165 0
302 | /ssd/gmzhu/SKIG/RGBImages/43111 130 1
303 | /ssd/gmzhu/SKIG/RGBImages/43112 125 2
304 | /ssd/gmzhu/SKIG/RGBImages/43113 196 3
305 | /ssd/gmzhu/SKIG/RGBImages/43114 113 4
306 | /ssd/gmzhu/SKIG/RGBImages/43115 82 5
307 | /ssd/gmzhu/SKIG/RGBImages/43116 102 6
308 | /ssd/gmzhu/SKIG/RGBImages/43117 94 7
309 | /ssd/gmzhu/SKIG/RGBImages/43118 131 8
310 | /ssd/gmzhu/SKIG/RGBImages/43119 136 9
311 | /ssd/gmzhu/SKIG/RGBImages/43120 147 0
312 | /ssd/gmzhu/SKIG/RGBImages/43121 139 1
313 | /ssd/gmzhu/SKIG/RGBImages/43122 146 2
314 | /ssd/gmzhu/SKIG/RGBImages/43123 254 3
315 | /ssd/gmzhu/SKIG/RGBImages/43124 132 4
316 | /ssd/gmzhu/SKIG/RGBImages/43125 73 5
317 | /ssd/gmzhu/SKIG/RGBImages/43126 102 6
318 | /ssd/gmzhu/SKIG/RGBImages/43127 114 7
319 | /ssd/gmzhu/SKIG/RGBImages/43128 133 8
320 | /ssd/gmzhu/SKIG/RGBImages/43129 140 9
321 | /ssd/gmzhu/SKIG/RGBImages/43130 142 0
322 | /ssd/gmzhu/SKIG/RGBImages/43131 136 1
323 | /ssd/gmzhu/SKIG/RGBImages/43132 153 2
324 | /ssd/gmzhu/SKIG/RGBImages/43133 233 3
325 | /ssd/gmzhu/SKIG/RGBImages/43134 136 4
326 | /ssd/gmzhu/SKIG/RGBImages/43135 86 5
327 | /ssd/gmzhu/SKIG/RGBImages/43136 93 6
328 | /ssd/gmzhu/SKIG/RGBImages/43137 110 7
329 | /ssd/gmzhu/SKIG/RGBImages/43138 126 8
330 | /ssd/gmzhu/SKIG/RGBImages/43139 143 9
331 | /ssd/gmzhu/SKIG/RGBImages/43210 157 0
332 | /ssd/gmzhu/SKIG/RGBImages/43211 168 1
333 | /ssd/gmzhu/SKIG/RGBImages/43212 138 2
334 | /ssd/gmzhu/SKIG/RGBImages/43213 169 3
335 | /ssd/gmzhu/SKIG/RGBImages/43214 173 4
336 | /ssd/gmzhu/SKIG/RGBImages/43215 94 5
337 | /ssd/gmzhu/SKIG/RGBImages/43216 99 6
338 | /ssd/gmzhu/SKIG/RGBImages/43217 100 7
339 | /ssd/gmzhu/SKIG/RGBImages/43218 129 8
340 | /ssd/gmzhu/SKIG/RGBImages/43219 100 9
341 | /ssd/gmzhu/SKIG/RGBImages/43220 171 0
342 | /ssd/gmzhu/SKIG/RGBImages/43221 168 1
343 | /ssd/gmzhu/SKIG/RGBImages/43222 171 2
344 | /ssd/gmzhu/SKIG/RGBImages/43223 191 3
345 | /ssd/gmzhu/SKIG/RGBImages/43224 143 4
346 | /ssd/gmzhu/SKIG/RGBImages/43225 96 5
347 | /ssd/gmzhu/SKIG/RGBImages/43226 91 6
348 | /ssd/gmzhu/SKIG/RGBImages/43227 87 7
349 | /ssd/gmzhu/SKIG/RGBImages/43228 112 8
350 | /ssd/gmzhu/SKIG/RGBImages/43229 93 9
351 | /ssd/gmzhu/SKIG/RGBImages/43230 159 0
352 | /ssd/gmzhu/SKIG/RGBImages/43231 174 1
353 | /ssd/gmzhu/SKIG/RGBImages/43232 139 2
354 | /ssd/gmzhu/SKIG/RGBImages/43233 178 3
355 | /ssd/gmzhu/SKIG/RGBImages/43234 148 4
356 | /ssd/gmzhu/SKIG/RGBImages/43235 88 5
357 | /ssd/gmzhu/SKIG/RGBImages/43236 104 6
358 | /ssd/gmzhu/SKIG/RGBImages/43237 88 7
359 | /ssd/gmzhu/SKIG/RGBImages/43238 128 8
360 | /ssd/gmzhu/SKIG/RGBImages/43239 115 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/testing_rgb_list3.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/RGBImages/11110 159 0
  2 | /ssd/gmzhu/SKIG/RGBImages/11111 165 1
  3 | /ssd/gmzhu/SKIG/RGBImages/11112 115 2
  4 | /ssd/gmzhu/SKIG/RGBImages/11113 254 3
  5 | /ssd/gmzhu/SKIG/RGBImages/11114 119 4
  6 | /ssd/gmzhu/SKIG/RGBImages/11115 98 5
  7 | /ssd/gmzhu/SKIG/RGBImages/11116 83 6
  8 | /ssd/gmzhu/SKIG/RGBImages/11117 94 7
  9 | /ssd/gmzhu/SKIG/RGBImages/11118 127 8
 10 | /ssd/gmzhu/SKIG/RGBImages/11119 93 9
 11 | /ssd/gmzhu/SKIG/RGBImages/11120 158 0
 12 | /ssd/gmzhu/SKIG/RGBImages/11121 168 1
 13 | /ssd/gmzhu/SKIG/RGBImages/11122 132 2
 14 | /ssd/gmzhu/SKIG/RGBImages/11123 252 3
 15 | /ssd/gmzhu/SKIG/RGBImages/11124 126 4
 16 | /ssd/gmzhu/SKIG/RGBImages/11125 103 5
 17 | /ssd/gmzhu/SKIG/RGBImages/11126 94 6
 18 | /ssd/gmzhu/SKIG/RGBImages/11127 107 7
 19 | /ssd/gmzhu/SKIG/RGBImages/11128 122 8
 20 | /ssd/gmzhu/SKIG/RGBImages/11129 105 9
 21 | /ssd/gmzhu/SKIG/RGBImages/11130 179 0
 22 | /ssd/gmzhu/SKIG/RGBImages/11131 163 1
 23 | /ssd/gmzhu/SKIG/RGBImages/11132 124 2
 24 | /ssd/gmzhu/SKIG/RGBImages/11133 275 3
 25 | /ssd/gmzhu/SKIG/RGBImages/11134 133 4
 26 | /ssd/gmzhu/SKIG/RGBImages/11135 114 5
 27 | /ssd/gmzhu/SKIG/RGBImages/11136 81 6
 28 | /ssd/gmzhu/SKIG/RGBImages/11137 111 7
 29 | /ssd/gmzhu/SKIG/RGBImages/11138 121 8
 30 | /ssd/gmzhu/SKIG/RGBImages/11139 91 9
 31 | /ssd/gmzhu/SKIG/RGBImages/11210 154 0
 32 | /ssd/gmzhu/SKIG/RGBImages/11211 146 1
 33 | /ssd/gmzhu/SKIG/RGBImages/11212 110 2
 34 | /ssd/gmzhu/SKIG/RGBImages/11213 222 3
 35 | /ssd/gmzhu/SKIG/RGBImages/11214 110 4
 36 | /ssd/gmzhu/SKIG/RGBImages/11215 99 5
 37 | /ssd/gmzhu/SKIG/RGBImages/11216 79 6
 38 | /ssd/gmzhu/SKIG/RGBImages/11217 82 7
 39 | /ssd/gmzhu/SKIG/RGBImages/11218 120 8
 40 | /ssd/gmzhu/SKIG/RGBImages/11219 84 9
 41 | /ssd/gmzhu/SKIG/RGBImages/11220 149 0
 42 | /ssd/gmzhu/SKIG/RGBImages/11221 134 1
 43 | /ssd/gmzhu/SKIG/RGBImages/11222 110 2
 44 | /ssd/gmzhu/SKIG/RGBImages/11223 208 3
 45 | /ssd/gmzhu/SKIG/RGBImages/11224 115 4
 46 | /ssd/gmzhu/SKIG/RGBImages/11225 92 5
 47 | /ssd/gmzhu/SKIG/RGBImages/11226 82 6
 48 | /ssd/gmzhu/SKIG/RGBImages/11227 94 7
 49 | /ssd/gmzhu/SKIG/RGBImages/11228 113 8
 50 | /ssd/gmzhu/SKIG/RGBImages/11229 85 9
 51 | /ssd/gmzhu/SKIG/RGBImages/11230 167 0
 52 | /ssd/gmzhu/SKIG/RGBImages/11231 149 1
 53 | /ssd/gmzhu/SKIG/RGBImages/11232 118 2
 54 | /ssd/gmzhu/SKIG/RGBImages/11233 231 3
 55 | /ssd/gmzhu/SKIG/RGBImages/11234 127 4
 56 | /ssd/gmzhu/SKIG/RGBImages/11235 92 5
 57 | /ssd/gmzhu/SKIG/RGBImages/11236 96 6
 58 | /ssd/gmzhu/SKIG/RGBImages/11237 96 7
 59 | /ssd/gmzhu/SKIG/RGBImages/11238 122 8
 60 | /ssd/gmzhu/SKIG/RGBImages/11239 80 9
 61 | /ssd/gmzhu/SKIG/RGBImages/12110 160 0
 62 | /ssd/gmzhu/SKIG/RGBImages/12111 166 1
 63 | /ssd/gmzhu/SKIG/RGBImages/12112 126 2
 64 | /ssd/gmzhu/SKIG/RGBImages/12113 243 3
 65 | /ssd/gmzhu/SKIG/RGBImages/12114 111 4
 66 | /ssd/gmzhu/SKIG/RGBImages/12115 93 5
 67 | /ssd/gmzhu/SKIG/RGBImages/12116 72 6
 68 | /ssd/gmzhu/SKIG/RGBImages/12117 81 7
 69 | /ssd/gmzhu/SKIG/RGBImages/12118 121 8
 70 | /ssd/gmzhu/SKIG/RGBImages/12119 97 9
 71 | /ssd/gmzhu/SKIG/RGBImages/12120 150 0
 72 | /ssd/gmzhu/SKIG/RGBImages/12121 176 1
 73 | /ssd/gmzhu/SKIG/RGBImages/12122 126 2
 74 | /ssd/gmzhu/SKIG/RGBImages/12123 277 3
 75 | /ssd/gmzhu/SKIG/RGBImages/12124 139 4
 76 | /ssd/gmzhu/SKIG/RGBImages/12125 96 5
 77 | /ssd/gmzhu/SKIG/RGBImages/12126 79 6
 78 | /ssd/gmzhu/SKIG/RGBImages/12127 92 7
 79 | /ssd/gmzhu/SKIG/RGBImages/12128 114 8
 80 | /ssd/gmzhu/SKIG/RGBImages/12129 101 9
 81 | /ssd/gmzhu/SKIG/RGBImages/12130 161 0
 82 | /ssd/gmzhu/SKIG/RGBImages/12131 174 1
 83 | /ssd/gmzhu/SKIG/RGBImages/12132 126 2
 84 | /ssd/gmzhu/SKIG/RGBImages/12133 265 3
 85 | /ssd/gmzhu/SKIG/RGBImages/12134 118 4
 86 | /ssd/gmzhu/SKIG/RGBImages/12135 84 5
 87 | /ssd/gmzhu/SKIG/RGBImages/12136 76 6
 88 | /ssd/gmzhu/SKIG/RGBImages/12137 84 7
 89 | /ssd/gmzhu/SKIG/RGBImages/12138 114 8
 90 | /ssd/gmzhu/SKIG/RGBImages/12139 93 9
 91 | /ssd/gmzhu/SKIG/RGBImages/12210 149 0
 92 | /ssd/gmzhu/SKIG/RGBImages/12211 133 1
 93 | /ssd/gmzhu/SKIG/RGBImages/12212 132 2
 94 | /ssd/gmzhu/SKIG/RGBImages/12213 214 3
 95 | /ssd/gmzhu/SKIG/RGBImages/12214 144 4
 96 | /ssd/gmzhu/SKIG/RGBImages/12215 83 5
 97 | /ssd/gmzhu/SKIG/RGBImages/12216 82 6
 98 | /ssd/gmzhu/SKIG/RGBImages/12217 72 7
 99 | /ssd/gmzhu/SKIG/RGBImages/12218 106 8
100 | /ssd/gmzhu/SKIG/RGBImages/12219 81 9
101 | /ssd/gmzhu/SKIG/RGBImages/12220 156 0
102 | /ssd/gmzhu/SKIG/RGBImages/12221 153 1
103 | /ssd/gmzhu/SKIG/RGBImages/12222 143 2
104 | /ssd/gmzhu/SKIG/RGBImages/12223 250 3
105 | /ssd/gmzhu/SKIG/RGBImages/12224 126 4
106 | /ssd/gmzhu/SKIG/RGBImages/12225 90 5
107 | /ssd/gmzhu/SKIG/RGBImages/12226 89 6
108 | /ssd/gmzhu/SKIG/RGBImages/12227 63 7
109 | /ssd/gmzhu/SKIG/RGBImages/12228 101 8
110 | /ssd/gmzhu/SKIG/RGBImages/12229 82 9
111 | /ssd/gmzhu/SKIG/RGBImages/12230 153 0
112 | /ssd/gmzhu/SKIG/RGBImages/12231 138 1
113 | /ssd/gmzhu/SKIG/RGBImages/12232 142 2
114 | /ssd/gmzhu/SKIG/RGBImages/12233 236 3
115 | /ssd/gmzhu/SKIG/RGBImages/12234 123 4
116 | /ssd/gmzhu/SKIG/RGBImages/12235 91 5
117 | /ssd/gmzhu/SKIG/RGBImages/12236 83 6
118 | /ssd/gmzhu/SKIG/RGBImages/12237 69 7
119 | /ssd/gmzhu/SKIG/RGBImages/12238 104 8
120 | /ssd/gmzhu/SKIG/RGBImages/12239 84 9
121 | /ssd/gmzhu/SKIG/RGBImages/13110 167 0
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124 | /ssd/gmzhu/SKIG/RGBImages/13113 258 3
125 | /ssd/gmzhu/SKIG/RGBImages/13114 119 4
126 | /ssd/gmzhu/SKIG/RGBImages/13115 105 5
127 | /ssd/gmzhu/SKIG/RGBImages/13116 79 6
128 | /ssd/gmzhu/SKIG/RGBImages/13117 82 7
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130 | /ssd/gmzhu/SKIG/RGBImages/13119 91 9
131 | /ssd/gmzhu/SKIG/RGBImages/13120 182 0
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144 | /ssd/gmzhu/SKIG/RGBImages/13133 269 3
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146 | /ssd/gmzhu/SKIG/RGBImages/13135 101 5
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150 | /ssd/gmzhu/SKIG/RGBImages/13139 95 9
151 | /ssd/gmzhu/SKIG/RGBImages/13210 154 0
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155 | /ssd/gmzhu/SKIG/RGBImages/13214 114 4
156 | /ssd/gmzhu/SKIG/RGBImages/13215 85 5
157 | /ssd/gmzhu/SKIG/RGBImages/13216 86 6
158 | /ssd/gmzhu/SKIG/RGBImages/13217 69 7
159 | /ssd/gmzhu/SKIG/RGBImages/13218 113 8
160 | /ssd/gmzhu/SKIG/RGBImages/13219 85 9
161 | /ssd/gmzhu/SKIG/RGBImages/13220 156 0
162 | /ssd/gmzhu/SKIG/RGBImages/13221 156 1
163 | /ssd/gmzhu/SKIG/RGBImages/13222 126 2
164 | /ssd/gmzhu/SKIG/RGBImages/13223 239 3
165 | /ssd/gmzhu/SKIG/RGBImages/13224 124 4
166 | /ssd/gmzhu/SKIG/RGBImages/13225 91 5
167 | /ssd/gmzhu/SKIG/RGBImages/13226 91 6
168 | /ssd/gmzhu/SKIG/RGBImages/13227 71 7
169 | /ssd/gmzhu/SKIG/RGBImages/13228 121 8
170 | /ssd/gmzhu/SKIG/RGBImages/13229 92 9
171 | /ssd/gmzhu/SKIG/RGBImages/13230 170 0
172 | /ssd/gmzhu/SKIG/RGBImages/13231 159 1
173 | /ssd/gmzhu/SKIG/RGBImages/13232 124 2
174 | /ssd/gmzhu/SKIG/RGBImages/13233 250 3
175 | /ssd/gmzhu/SKIG/RGBImages/13234 118 4
176 | /ssd/gmzhu/SKIG/RGBImages/13235 91 5
177 | /ssd/gmzhu/SKIG/RGBImages/13236 85 6
178 | /ssd/gmzhu/SKIG/RGBImages/13237 70 7
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181 | /ssd/gmzhu/SKIG/RGBImages/21110 164 0
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187 | /ssd/gmzhu/SKIG/RGBImages/21116 94 6
188 | /ssd/gmzhu/SKIG/RGBImages/21117 90 7
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190 | /ssd/gmzhu/SKIG/RGBImages/21119 99 9
191 | /ssd/gmzhu/SKIG/RGBImages/21120 190 0
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194 | /ssd/gmzhu/SKIG/RGBImages/21123 242 3
195 | /ssd/gmzhu/SKIG/RGBImages/21124 128 4
196 | /ssd/gmzhu/SKIG/RGBImages/21125 123 5
197 | /ssd/gmzhu/SKIG/RGBImages/21126 86 6
198 | /ssd/gmzhu/SKIG/RGBImages/21127 101 7
199 | /ssd/gmzhu/SKIG/RGBImages/21128 115 8
200 | /ssd/gmzhu/SKIG/RGBImages/21129 112 9
201 | /ssd/gmzhu/SKIG/RGBImages/21130 192 0
202 | /ssd/gmzhu/SKIG/RGBImages/21131 181 1
203 | /ssd/gmzhu/SKIG/RGBImages/21132 138 2
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205 | /ssd/gmzhu/SKIG/RGBImages/21134 128 4
206 | /ssd/gmzhu/SKIG/RGBImages/21135 112 5
207 | /ssd/gmzhu/SKIG/RGBImages/21136 96 6
208 | /ssd/gmzhu/SKIG/RGBImages/21137 96 7
209 | /ssd/gmzhu/SKIG/RGBImages/21138 125 8
210 | /ssd/gmzhu/SKIG/RGBImages/21139 107 9
211 | /ssd/gmzhu/SKIG/RGBImages/21210 184 0
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213 | /ssd/gmzhu/SKIG/RGBImages/21212 124 2
214 | /ssd/gmzhu/SKIG/RGBImages/21213 243 3
215 | /ssd/gmzhu/SKIG/RGBImages/21214 115 4
216 | /ssd/gmzhu/SKIG/RGBImages/21215 100 5
217 | /ssd/gmzhu/SKIG/RGBImages/21216 99 6
218 | /ssd/gmzhu/SKIG/RGBImages/21217 86 7
219 | /ssd/gmzhu/SKIG/RGBImages/21218 121 8
220 | /ssd/gmzhu/SKIG/RGBImages/21219 95 9
221 | /ssd/gmzhu/SKIG/RGBImages/21220 170 0
222 | /ssd/gmzhu/SKIG/RGBImages/21221 169 1
223 | /ssd/gmzhu/SKIG/RGBImages/21222 123 2
224 | /ssd/gmzhu/SKIG/RGBImages/21223 243 3
225 | /ssd/gmzhu/SKIG/RGBImages/21224 122 4
226 | /ssd/gmzhu/SKIG/RGBImages/21225 101 5
227 | /ssd/gmzhu/SKIG/RGBImages/21226 99 6
228 | /ssd/gmzhu/SKIG/RGBImages/21227 93 7
229 | /ssd/gmzhu/SKIG/RGBImages/21228 138 8
230 | /ssd/gmzhu/SKIG/RGBImages/21229 92 9
231 | /ssd/gmzhu/SKIG/RGBImages/21230 170 0
232 | /ssd/gmzhu/SKIG/RGBImages/21231 168 1
233 | /ssd/gmzhu/SKIG/RGBImages/21232 131 2
234 | /ssd/gmzhu/SKIG/RGBImages/21233 242 3
235 | /ssd/gmzhu/SKIG/RGBImages/21234 139 4
236 | /ssd/gmzhu/SKIG/RGBImages/21235 111 5
237 | /ssd/gmzhu/SKIG/RGBImages/21236 100 6
238 | /ssd/gmzhu/SKIG/RGBImages/21237 93 7
239 | /ssd/gmzhu/SKIG/RGBImages/21238 127 8
240 | /ssd/gmzhu/SKIG/RGBImages/21239 102 9
241 | /ssd/gmzhu/SKIG/RGBImages/22110 175 0
242 | /ssd/gmzhu/SKIG/RGBImages/22111 149 1
243 | /ssd/gmzhu/SKIG/RGBImages/22112 127 2
244 | /ssd/gmzhu/SKIG/RGBImages/22113 213 3
245 | /ssd/gmzhu/SKIG/RGBImages/22114 135 4
246 | /ssd/gmzhu/SKIG/RGBImages/22115 112 5
247 | /ssd/gmzhu/SKIG/RGBImages/22116 92 6
248 | /ssd/gmzhu/SKIG/RGBImages/22117 94 7
249 | /ssd/gmzhu/SKIG/RGBImages/22118 111 8
250 | /ssd/gmzhu/SKIG/RGBImages/22119 95 9
251 | /ssd/gmzhu/SKIG/RGBImages/22120 180 0
252 | /ssd/gmzhu/SKIG/RGBImages/22121 163 1
253 | /ssd/gmzhu/SKIG/RGBImages/22122 122 2
254 | /ssd/gmzhu/SKIG/RGBImages/22123 289 3
255 | /ssd/gmzhu/SKIG/RGBImages/22124 138 4
256 | /ssd/gmzhu/SKIG/RGBImages/22125 105 5
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258 | /ssd/gmzhu/SKIG/RGBImages/22127 115 7
259 | /ssd/gmzhu/SKIG/RGBImages/22128 124 8
260 | /ssd/gmzhu/SKIG/RGBImages/22129 101 9
261 | /ssd/gmzhu/SKIG/RGBImages/22130 164 0
262 | /ssd/gmzhu/SKIG/RGBImages/22131 179 1
263 | /ssd/gmzhu/SKIG/RGBImages/22132 117 2
264 | /ssd/gmzhu/SKIG/RGBImages/22133 263 3
265 | /ssd/gmzhu/SKIG/RGBImages/22134 133 4
266 | /ssd/gmzhu/SKIG/RGBImages/22135 114 5
267 | /ssd/gmzhu/SKIG/RGBImages/22136 104 6
268 | /ssd/gmzhu/SKIG/RGBImages/22137 100 7
269 | /ssd/gmzhu/SKIG/RGBImages/22138 125 8
270 | /ssd/gmzhu/SKIG/RGBImages/22139 104 9
271 | /ssd/gmzhu/SKIG/RGBImages/22210 171 0
272 | /ssd/gmzhu/SKIG/RGBImages/22211 179 1
273 | /ssd/gmzhu/SKIG/RGBImages/22212 120 2
274 | /ssd/gmzhu/SKIG/RGBImages/22213 258 3
275 | /ssd/gmzhu/SKIG/RGBImages/22214 143 4
276 | /ssd/gmzhu/SKIG/RGBImages/22215 95 5
277 | /ssd/gmzhu/SKIG/RGBImages/22216 93 6
278 | /ssd/gmzhu/SKIG/RGBImages/22217 89 7
279 | /ssd/gmzhu/SKIG/RGBImages/22218 119 8
280 | /ssd/gmzhu/SKIG/RGBImages/22219 96 9
281 | /ssd/gmzhu/SKIG/RGBImages/22220 173 0
282 | /ssd/gmzhu/SKIG/RGBImages/22221 161 1
283 | /ssd/gmzhu/SKIG/RGBImages/22222 130 2
284 | /ssd/gmzhu/SKIG/RGBImages/22223 258 3
285 | /ssd/gmzhu/SKIG/RGBImages/22224 134 4
286 | /ssd/gmzhu/SKIG/RGBImages/22225 105 5
287 | /ssd/gmzhu/SKIG/RGBImages/22226 99 6
288 | /ssd/gmzhu/SKIG/RGBImages/22227 100 7
289 | /ssd/gmzhu/SKIG/RGBImages/22228 122 8
290 | /ssd/gmzhu/SKIG/RGBImages/22229 97 9
291 | /ssd/gmzhu/SKIG/RGBImages/22230 178 0
292 | /ssd/gmzhu/SKIG/RGBImages/22231 178 1
293 | /ssd/gmzhu/SKIG/RGBImages/22232 136 2
294 | /ssd/gmzhu/SKIG/RGBImages/22233 254 3
295 | /ssd/gmzhu/SKIG/RGBImages/22234 143 4
296 | /ssd/gmzhu/SKIG/RGBImages/22235 104 5
297 | /ssd/gmzhu/SKIG/RGBImages/22236 97 6
298 | /ssd/gmzhu/SKIG/RGBImages/22237 105 7
299 | /ssd/gmzhu/SKIG/RGBImages/22238 124 8
300 | /ssd/gmzhu/SKIG/RGBImages/22239 102 9
301 | /ssd/gmzhu/SKIG/RGBImages/23110 174 0
302 | /ssd/gmzhu/SKIG/RGBImages/23111 168 1
303 | /ssd/gmzhu/SKIG/RGBImages/23112 107 2
304 | /ssd/gmzhu/SKIG/RGBImages/23113 245 3
305 | /ssd/gmzhu/SKIG/RGBImages/23114 122 4
306 | /ssd/gmzhu/SKIG/RGBImages/23115 89 5
307 | /ssd/gmzhu/SKIG/RGBImages/23116 87 6
308 | /ssd/gmzhu/SKIG/RGBImages/23117 81 7
309 | /ssd/gmzhu/SKIG/RGBImages/23118 113 8
310 | /ssd/gmzhu/SKIG/RGBImages/23119 96 9
311 | /ssd/gmzhu/SKIG/RGBImages/23120 187 0
312 | /ssd/gmzhu/SKIG/RGBImages/23121 189 1
313 | /ssd/gmzhu/SKIG/RGBImages/23122 129 2
314 | /ssd/gmzhu/SKIG/RGBImages/23123 244 3
315 | /ssd/gmzhu/SKIG/RGBImages/23124 115 4
316 | /ssd/gmzhu/SKIG/RGBImages/23125 104 5
317 | /ssd/gmzhu/SKIG/RGBImages/23126 95 6
318 | /ssd/gmzhu/SKIG/RGBImages/23127 85 7
319 | /ssd/gmzhu/SKIG/RGBImages/23128 116 8
320 | /ssd/gmzhu/SKIG/RGBImages/23129 98 9
321 | /ssd/gmzhu/SKIG/RGBImages/23130 175 0
322 | /ssd/gmzhu/SKIG/RGBImages/23131 182 1
323 | /ssd/gmzhu/SKIG/RGBImages/23132 104 2
324 | /ssd/gmzhu/SKIG/RGBImages/23133 233 3
325 | /ssd/gmzhu/SKIG/RGBImages/23134 125 4
326 | /ssd/gmzhu/SKIG/RGBImages/23135 104 5
327 | /ssd/gmzhu/SKIG/RGBImages/23136 96 6
328 | /ssd/gmzhu/SKIG/RGBImages/23137 91 7
329 | /ssd/gmzhu/SKIG/RGBImages/23138 111 8
330 | /ssd/gmzhu/SKIG/RGBImages/23139 92 9
331 | /ssd/gmzhu/SKIG/RGBImages/23210 175 0
332 | /ssd/gmzhu/SKIG/RGBImages/23211 157 1
333 | /ssd/gmzhu/SKIG/RGBImages/23212 117 2
334 | /ssd/gmzhu/SKIG/RGBImages/23213 239 3
335 | /ssd/gmzhu/SKIG/RGBImages/23214 116 4
336 | /ssd/gmzhu/SKIG/RGBImages/23215 94 5
337 | /ssd/gmzhu/SKIG/RGBImages/23216 91 6
338 | /ssd/gmzhu/SKIG/RGBImages/23217 78 7
339 | /ssd/gmzhu/SKIG/RGBImages/23218 107 8
340 | /ssd/gmzhu/SKIG/RGBImages/23219 85 9
341 | /ssd/gmzhu/SKIG/RGBImages/23220 162 0
342 | /ssd/gmzhu/SKIG/RGBImages/23221 162 1
343 | /ssd/gmzhu/SKIG/RGBImages/23222 113 2
344 | /ssd/gmzhu/SKIG/RGBImages/23223 219 3
345 | /ssd/gmzhu/SKIG/RGBImages/23224 119 4
346 | /ssd/gmzhu/SKIG/RGBImages/23225 93 5
347 | /ssd/gmzhu/SKIG/RGBImages/23226 87 6
348 | /ssd/gmzhu/SKIG/RGBImages/23227 80 7
349 | /ssd/gmzhu/SKIG/RGBImages/23228 100 8
350 | /ssd/gmzhu/SKIG/RGBImages/23229 87 9
351 | /ssd/gmzhu/SKIG/RGBImages/23230 159 0
352 | /ssd/gmzhu/SKIG/RGBImages/23231 162 1
353 | /ssd/gmzhu/SKIG/RGBImages/23232 120 2
354 | /ssd/gmzhu/SKIG/RGBImages/23233 212 3
355 | /ssd/gmzhu/SKIG/RGBImages/23234 121 4
356 | /ssd/gmzhu/SKIG/RGBImages/23235 108 5
357 | /ssd/gmzhu/SKIG/RGBImages/23236 92 6
358 | /ssd/gmzhu/SKIG/RGBImages/23237 87 7
359 | /ssd/gmzhu/SKIG/RGBImages/23238 113 8
360 | /ssd/gmzhu/SKIG/RGBImages/23239 88 9
361 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/training_rgb_list1.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/RGBImages/11110 159 0
  2 | /ssd/gmzhu/SKIG/RGBImages/11111 165 1
  3 | /ssd/gmzhu/SKIG/RGBImages/11112 115 2
  4 | /ssd/gmzhu/SKIG/RGBImages/11113 254 3
  5 | /ssd/gmzhu/SKIG/RGBImages/11114 119 4
  6 | /ssd/gmzhu/SKIG/RGBImages/11115 98 5
  7 | /ssd/gmzhu/SKIG/RGBImages/11116 83 6
  8 | /ssd/gmzhu/SKIG/RGBImages/11117 94 7
  9 | /ssd/gmzhu/SKIG/RGBImages/11118 127 8
 10 | /ssd/gmzhu/SKIG/RGBImages/11119 93 9
 11 | /ssd/gmzhu/SKIG/RGBImages/11120 158 0
 12 | /ssd/gmzhu/SKIG/RGBImages/11121 168 1
 13 | /ssd/gmzhu/SKIG/RGBImages/11122 132 2
 14 | /ssd/gmzhu/SKIG/RGBImages/11123 252 3
 15 | /ssd/gmzhu/SKIG/RGBImages/11124 126 4
 16 | /ssd/gmzhu/SKIG/RGBImages/11125 103 5
 17 | /ssd/gmzhu/SKIG/RGBImages/11126 94 6
 18 | /ssd/gmzhu/SKIG/RGBImages/11127 107 7
 19 | /ssd/gmzhu/SKIG/RGBImages/11128 122 8
 20 | /ssd/gmzhu/SKIG/RGBImages/11129 105 9
 21 | /ssd/gmzhu/SKIG/RGBImages/11130 179 0
 22 | /ssd/gmzhu/SKIG/RGBImages/11131 163 1
 23 | /ssd/gmzhu/SKIG/RGBImages/11132 124 2
 24 | /ssd/gmzhu/SKIG/RGBImages/11133 275 3
 25 | /ssd/gmzhu/SKIG/RGBImages/11134 133 4
 26 | /ssd/gmzhu/SKIG/RGBImages/11135 114 5
 27 | /ssd/gmzhu/SKIG/RGBImages/11136 81 6
 28 | /ssd/gmzhu/SKIG/RGBImages/11137 111 7
 29 | /ssd/gmzhu/SKIG/RGBImages/11138 121 8
 30 | /ssd/gmzhu/SKIG/RGBImages/11139 91 9
 31 | /ssd/gmzhu/SKIG/RGBImages/11210 154 0
 32 | /ssd/gmzhu/SKIG/RGBImages/11211 146 1
 33 | /ssd/gmzhu/SKIG/RGBImages/11212 110 2
 34 | /ssd/gmzhu/SKIG/RGBImages/11213 222 3
 35 | /ssd/gmzhu/SKIG/RGBImages/11214 110 4
 36 | /ssd/gmzhu/SKIG/RGBImages/11215 99 5
 37 | /ssd/gmzhu/SKIG/RGBImages/11216 79 6
 38 | /ssd/gmzhu/SKIG/RGBImages/11217 82 7
 39 | /ssd/gmzhu/SKIG/RGBImages/11218 120 8
 40 | /ssd/gmzhu/SKIG/RGBImages/11219 84 9
 41 | /ssd/gmzhu/SKIG/RGBImages/11220 149 0
 42 | /ssd/gmzhu/SKIG/RGBImages/11221 134 1
 43 | /ssd/gmzhu/SKIG/RGBImages/11222 110 2
 44 | /ssd/gmzhu/SKIG/RGBImages/11223 208 3
 45 | /ssd/gmzhu/SKIG/RGBImages/11224 115 4
 46 | /ssd/gmzhu/SKIG/RGBImages/11225 92 5
 47 | /ssd/gmzhu/SKIG/RGBImages/11226 82 6
 48 | /ssd/gmzhu/SKIG/RGBImages/11227 94 7
 49 | /ssd/gmzhu/SKIG/RGBImages/11228 113 8
 50 | /ssd/gmzhu/SKIG/RGBImages/11229 85 9
 51 | /ssd/gmzhu/SKIG/RGBImages/11230 167 0
 52 | /ssd/gmzhu/SKIG/RGBImages/11231 149 1
 53 | /ssd/gmzhu/SKIG/RGBImages/11232 118 2
 54 | /ssd/gmzhu/SKIG/RGBImages/11233 231 3
 55 | /ssd/gmzhu/SKIG/RGBImages/11234 127 4
 56 | /ssd/gmzhu/SKIG/RGBImages/11235 92 5
 57 | /ssd/gmzhu/SKIG/RGBImages/11236 96 6
 58 | /ssd/gmzhu/SKIG/RGBImages/11237 96 7
 59 | /ssd/gmzhu/SKIG/RGBImages/11238 122 8
 60 | /ssd/gmzhu/SKIG/RGBImages/11239 80 9
 61 | /ssd/gmzhu/SKIG/RGBImages/12110 160 0
 62 | /ssd/gmzhu/SKIG/RGBImages/12111 166 1
 63 | /ssd/gmzhu/SKIG/RGBImages/12112 126 2
 64 | /ssd/gmzhu/SKIG/RGBImages/12113 243 3
 65 | /ssd/gmzhu/SKIG/RGBImages/12114 111 4
 66 | /ssd/gmzhu/SKIG/RGBImages/12115 93 5
 67 | /ssd/gmzhu/SKIG/RGBImages/12116 72 6
 68 | /ssd/gmzhu/SKIG/RGBImages/12117 81 7
 69 | /ssd/gmzhu/SKIG/RGBImages/12118 121 8
 70 | /ssd/gmzhu/SKIG/RGBImages/12119 97 9
 71 | /ssd/gmzhu/SKIG/RGBImages/12120 150 0
 72 | /ssd/gmzhu/SKIG/RGBImages/12121 176 1
 73 | /ssd/gmzhu/SKIG/RGBImages/12122 126 2
 74 | /ssd/gmzhu/SKIG/RGBImages/12123 277 3
 75 | /ssd/gmzhu/SKIG/RGBImages/12124 139 4
 76 | /ssd/gmzhu/SKIG/RGBImages/12125 96 5
 77 | /ssd/gmzhu/SKIG/RGBImages/12126 79 6
 78 | /ssd/gmzhu/SKIG/RGBImages/12127 92 7
 79 | /ssd/gmzhu/SKIG/RGBImages/12128 114 8
 80 | /ssd/gmzhu/SKIG/RGBImages/12129 101 9
 81 | /ssd/gmzhu/SKIG/RGBImages/12130 161 0
 82 | /ssd/gmzhu/SKIG/RGBImages/12131 174 1
 83 | /ssd/gmzhu/SKIG/RGBImages/12132 126 2
 84 | /ssd/gmzhu/SKIG/RGBImages/12133 265 3
 85 | /ssd/gmzhu/SKIG/RGBImages/12134 118 4
 86 | /ssd/gmzhu/SKIG/RGBImages/12135 84 5
 87 | /ssd/gmzhu/SKIG/RGBImages/12136 76 6
 88 | /ssd/gmzhu/SKIG/RGBImages/12137 84 7
 89 | /ssd/gmzhu/SKIG/RGBImages/12138 114 8
 90 | /ssd/gmzhu/SKIG/RGBImages/12139 93 9
 91 | /ssd/gmzhu/SKIG/RGBImages/12210 149 0
 92 | /ssd/gmzhu/SKIG/RGBImages/12211 133 1
 93 | /ssd/gmzhu/SKIG/RGBImages/12212 132 2
 94 | /ssd/gmzhu/SKIG/RGBImages/12213 214 3
 95 | /ssd/gmzhu/SKIG/RGBImages/12214 144 4
 96 | /ssd/gmzhu/SKIG/RGBImages/12215 83 5
 97 | /ssd/gmzhu/SKIG/RGBImages/12216 82 6
 98 | /ssd/gmzhu/SKIG/RGBImages/12217 72 7
 99 | /ssd/gmzhu/SKIG/RGBImages/12218 106 8
100 | /ssd/gmzhu/SKIG/RGBImages/12219 81 9
101 | /ssd/gmzhu/SKIG/RGBImages/12220 156 0
102 | /ssd/gmzhu/SKIG/RGBImages/12221 153 1
103 | /ssd/gmzhu/SKIG/RGBImages/12222 143 2
104 | /ssd/gmzhu/SKIG/RGBImages/12223 250 3
105 | /ssd/gmzhu/SKIG/RGBImages/12224 126 4
106 | /ssd/gmzhu/SKIG/RGBImages/12225 90 5
107 | /ssd/gmzhu/SKIG/RGBImages/12226 89 6
108 | /ssd/gmzhu/SKIG/RGBImages/12227 63 7
109 | /ssd/gmzhu/SKIG/RGBImages/12228 101 8
110 | /ssd/gmzhu/SKIG/RGBImages/12229 82 9
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524 | /ssd/gmzhu/SKIG/RGBImages/33223 250 3
525 | /ssd/gmzhu/SKIG/RGBImages/33224 105 4
526 | /ssd/gmzhu/SKIG/RGBImages/33225 96 5
527 | /ssd/gmzhu/SKIG/RGBImages/33226 91 6
528 | /ssd/gmzhu/SKIG/RGBImages/33227 102 7
529 | /ssd/gmzhu/SKIG/RGBImages/33228 116 8
530 | /ssd/gmzhu/SKIG/RGBImages/33229 99 9
531 | /ssd/gmzhu/SKIG/RGBImages/33230 153 0
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576 | /ssd/gmzhu/SKIG/RGBImages/41215 98 5
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579 | /ssd/gmzhu/SKIG/RGBImages/41218 123 8
580 | /ssd/gmzhu/SKIG/RGBImages/41219 139 9
581 | /ssd/gmzhu/SKIG/RGBImages/41220 202 0
582 | /ssd/gmzhu/SKIG/RGBImages/41221 167 1
583 | /ssd/gmzhu/SKIG/RGBImages/41222 122 2
584 | /ssd/gmzhu/SKIG/RGBImages/41223 312 3
585 | /ssd/gmzhu/SKIG/RGBImages/41224 210 4
586 | /ssd/gmzhu/SKIG/RGBImages/41225 125 5
587 | /ssd/gmzhu/SKIG/RGBImages/41226 125 6
588 | /ssd/gmzhu/SKIG/RGBImages/41227 94 7
589 | /ssd/gmzhu/SKIG/RGBImages/41228 153 8
590 | /ssd/gmzhu/SKIG/RGBImages/41229 122 9
591 | /ssd/gmzhu/SKIG/RGBImages/41230 164 0
592 | /ssd/gmzhu/SKIG/RGBImages/41231 168 1
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594 | /ssd/gmzhu/SKIG/RGBImages/41233 345 3
595 | /ssd/gmzhu/SKIG/RGBImages/41234 199 4
596 | /ssd/gmzhu/SKIG/RGBImages/41235 120 5
597 | /ssd/gmzhu/SKIG/RGBImages/41236 113 6
598 | /ssd/gmzhu/SKIG/RGBImages/41237 115 7
599 | /ssd/gmzhu/SKIG/RGBImages/41238 149 8
600 | /ssd/gmzhu/SKIG/RGBImages/41239 126 9
601 | /ssd/gmzhu/SKIG/RGBImages/42110 137 0
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605 | /ssd/gmzhu/SKIG/RGBImages/42114 178 4
606 | /ssd/gmzhu/SKIG/RGBImages/42115 93 5
607 | /ssd/gmzhu/SKIG/RGBImages/42116 113 6
608 | /ssd/gmzhu/SKIG/RGBImages/42117 110 7
609 | /ssd/gmzhu/SKIG/RGBImages/42118 77 8
610 | /ssd/gmzhu/SKIG/RGBImages/42119 106 9
611 | /ssd/gmzhu/SKIG/RGBImages/42120 136 0
612 | /ssd/gmzhu/SKIG/RGBImages/42121 160 1
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614 | /ssd/gmzhu/SKIG/RGBImages/42123 289 3
615 | /ssd/gmzhu/SKIG/RGBImages/42124 185 4
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621 | /ssd/gmzhu/SKIG/RGBImages/42130 139 0
622 | /ssd/gmzhu/SKIG/RGBImages/42131 168 1
623 | /ssd/gmzhu/SKIG/RGBImages/42132 109 2
624 | /ssd/gmzhu/SKIG/RGBImages/42133 300 3
625 | /ssd/gmzhu/SKIG/RGBImages/42134 192 4
626 | /ssd/gmzhu/SKIG/RGBImages/42135 109 5
627 | /ssd/gmzhu/SKIG/RGBImages/42136 144 6
628 | /ssd/gmzhu/SKIG/RGBImages/42137 119 7
629 | /ssd/gmzhu/SKIG/RGBImages/42138 96 8
630 | /ssd/gmzhu/SKIG/RGBImages/42139 125 9
631 | /ssd/gmzhu/SKIG/RGBImages/42210 131 0
632 | /ssd/gmzhu/SKIG/RGBImages/42211 151 1
633 | /ssd/gmzhu/SKIG/RGBImages/42212 126 2
634 | /ssd/gmzhu/SKIG/RGBImages/42213 302 3
635 | /ssd/gmzhu/SKIG/RGBImages/42214 147 4
636 | /ssd/gmzhu/SKIG/RGBImages/42215 91 5
637 | /ssd/gmzhu/SKIG/RGBImages/42216 94 6
638 | /ssd/gmzhu/SKIG/RGBImages/42217 114 7
639 | /ssd/gmzhu/SKIG/RGBImages/42218 114 8
640 | /ssd/gmzhu/SKIG/RGBImages/42219 114 9
641 | /ssd/gmzhu/SKIG/RGBImages/42220 145 0
642 | /ssd/gmzhu/SKIG/RGBImages/42221 165 1
643 | /ssd/gmzhu/SKIG/RGBImages/42222 114 2
644 | /ssd/gmzhu/SKIG/RGBImages/42223 317 3
645 | /ssd/gmzhu/SKIG/RGBImages/42224 143 4
646 | /ssd/gmzhu/SKIG/RGBImages/42225 78 5
647 | /ssd/gmzhu/SKIG/RGBImages/42226 99 6
648 | /ssd/gmzhu/SKIG/RGBImages/42227 99 7
649 | /ssd/gmzhu/SKIG/RGBImages/42228 101 8
650 | /ssd/gmzhu/SKIG/RGBImages/42229 122 9
651 | /ssd/gmzhu/SKIG/RGBImages/42230 163 0
652 | /ssd/gmzhu/SKIG/RGBImages/42231 161 1
653 | /ssd/gmzhu/SKIG/RGBImages/42232 115 2
654 | /ssd/gmzhu/SKIG/RGBImages/42233 296 3
655 | /ssd/gmzhu/SKIG/RGBImages/42234 135 4
656 | /ssd/gmzhu/SKIG/RGBImages/42235 81 5
657 | /ssd/gmzhu/SKIG/RGBImages/42236 84 6
658 | /ssd/gmzhu/SKIG/RGBImages/42237 102 7
659 | /ssd/gmzhu/SKIG/RGBImages/42238 97 8
660 | /ssd/gmzhu/SKIG/RGBImages/42239 108 9
661 | /ssd/gmzhu/SKIG/RGBImages/43110 165 0
662 | /ssd/gmzhu/SKIG/RGBImages/43111 130 1
663 | /ssd/gmzhu/SKIG/RGBImages/43112 125 2
664 | /ssd/gmzhu/SKIG/RGBImages/43113 196 3
665 | /ssd/gmzhu/SKIG/RGBImages/43114 113 4
666 | /ssd/gmzhu/SKIG/RGBImages/43115 82 5
667 | /ssd/gmzhu/SKIG/RGBImages/43116 102 6
668 | /ssd/gmzhu/SKIG/RGBImages/43117 94 7
669 | /ssd/gmzhu/SKIG/RGBImages/43118 131 8
670 | /ssd/gmzhu/SKIG/RGBImages/43119 136 9
671 | /ssd/gmzhu/SKIG/RGBImages/43120 147 0
672 | /ssd/gmzhu/SKIG/RGBImages/43121 139 1
673 | /ssd/gmzhu/SKIG/RGBImages/43122 146 2
674 | /ssd/gmzhu/SKIG/RGBImages/43123 254 3
675 | /ssd/gmzhu/SKIG/RGBImages/43124 132 4
676 | /ssd/gmzhu/SKIG/RGBImages/43125 73 5
677 | /ssd/gmzhu/SKIG/RGBImages/43126 102 6
678 | /ssd/gmzhu/SKIG/RGBImages/43127 114 7
679 | /ssd/gmzhu/SKIG/RGBImages/43128 133 8
680 | /ssd/gmzhu/SKIG/RGBImages/43129 140 9
681 | /ssd/gmzhu/SKIG/RGBImages/43130 142 0
682 | /ssd/gmzhu/SKIG/RGBImages/43131 136 1
683 | /ssd/gmzhu/SKIG/RGBImages/43132 153 2
684 | /ssd/gmzhu/SKIG/RGBImages/43133 233 3
685 | /ssd/gmzhu/SKIG/RGBImages/43134 136 4
686 | /ssd/gmzhu/SKIG/RGBImages/43135 86 5
687 | /ssd/gmzhu/SKIG/RGBImages/43136 93 6
688 | /ssd/gmzhu/SKIG/RGBImages/43137 110 7
689 | /ssd/gmzhu/SKIG/RGBImages/43138 126 8
690 | /ssd/gmzhu/SKIG/RGBImages/43139 143 9
691 | /ssd/gmzhu/SKIG/RGBImages/43210 157 0
692 | /ssd/gmzhu/SKIG/RGBImages/43211 168 1
693 | /ssd/gmzhu/SKIG/RGBImages/43212 138 2
694 | /ssd/gmzhu/SKIG/RGBImages/43213 169 3
695 | /ssd/gmzhu/SKIG/RGBImages/43214 173 4
696 | /ssd/gmzhu/SKIG/RGBImages/43215 94 5
697 | /ssd/gmzhu/SKIG/RGBImages/43216 99 6
698 | /ssd/gmzhu/SKIG/RGBImages/43217 100 7
699 | /ssd/gmzhu/SKIG/RGBImages/43218 129 8
700 | /ssd/gmzhu/SKIG/RGBImages/43219 100 9
701 | /ssd/gmzhu/SKIG/RGBImages/43220 171 0
702 | /ssd/gmzhu/SKIG/RGBImages/43221 168 1
703 | /ssd/gmzhu/SKIG/RGBImages/43222 171 2
704 | /ssd/gmzhu/SKIG/RGBImages/43223 191 3
705 | /ssd/gmzhu/SKIG/RGBImages/43224 143 4
706 | /ssd/gmzhu/SKIG/RGBImages/43225 96 5
707 | /ssd/gmzhu/SKIG/RGBImages/43226 91 6
708 | /ssd/gmzhu/SKIG/RGBImages/43227 87 7
709 | /ssd/gmzhu/SKIG/RGBImages/43228 112 8
710 | /ssd/gmzhu/SKIG/RGBImages/43229 93 9
711 | /ssd/gmzhu/SKIG/RGBImages/43230 159 0
712 | /ssd/gmzhu/SKIG/RGBImages/43231 174 1
713 | /ssd/gmzhu/SKIG/RGBImages/43232 139 2
714 | /ssd/gmzhu/SKIG/RGBImages/43233 178 3
715 | /ssd/gmzhu/SKIG/RGBImages/43234 148 4
716 | /ssd/gmzhu/SKIG/RGBImages/43235 88 5
717 | /ssd/gmzhu/SKIG/RGBImages/43236 104 6
718 | /ssd/gmzhu/SKIG/RGBImages/43237 88 7
719 | /ssd/gmzhu/SKIG/RGBImages/43238 128 8
720 | /ssd/gmzhu/SKIG/RGBImages/43239 115 9
721 | 


--------------------------------------------------------------------------------
/trte_splits/SKIG/training_rgb_list2.txt:
--------------------------------------------------------------------------------
  1 | /ssd/gmzhu/SKIG/RGBImages/11110 159 0
  2 | /ssd/gmzhu/SKIG/RGBImages/11111 165 1
  3 | /ssd/gmzhu/SKIG/RGBImages/11112 115 2
  4 | /ssd/gmzhu/SKIG/RGBImages/11113 254 3
  5 | /ssd/gmzhu/SKIG/RGBImages/11114 119 4
  6 | /ssd/gmzhu/SKIG/RGBImages/11115 98 5
  7 | /ssd/gmzhu/SKIG/RGBImages/11116 83 6
  8 | /ssd/gmzhu/SKIG/RGBImages/11117 94 7
  9 | /ssd/gmzhu/SKIG/RGBImages/11118 127 8
 10 | /ssd/gmzhu/SKIG/RGBImages/11119 93 9
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 28 | /ssd/gmzhu/SKIG/RGBImages/11137 111 7
 29 | /ssd/gmzhu/SKIG/RGBImages/11138 121 8
 30 | /ssd/gmzhu/SKIG/RGBImages/11139 91 9
 31 | /ssd/gmzhu/SKIG/RGBImages/11210 154 0
 32 | /ssd/gmzhu/SKIG/RGBImages/11211 146 1
 33 | /ssd/gmzhu/SKIG/RGBImages/11212 110 2
 34 | /ssd/gmzhu/SKIG/RGBImages/11213 222 3
 35 | /ssd/gmzhu/SKIG/RGBImages/11214 110 4
 36 | /ssd/gmzhu/SKIG/RGBImages/11215 99 5
 37 | /ssd/gmzhu/SKIG/RGBImages/11216 79 6
 38 | /ssd/gmzhu/SKIG/RGBImages/11217 82 7
 39 | /ssd/gmzhu/SKIG/RGBImages/11218 120 8
 40 | /ssd/gmzhu/SKIG/RGBImages/11219 84 9
 41 | /ssd/gmzhu/SKIG/RGBImages/11220 149 0
 42 | /ssd/gmzhu/SKIG/RGBImages/11221 134 1
 43 | /ssd/gmzhu/SKIG/RGBImages/11222 110 2
 44 | /ssd/gmzhu/SKIG/RGBImages/11223 208 3
 45 | /ssd/gmzhu/SKIG/RGBImages/11224 115 4
 46 | /ssd/gmzhu/SKIG/RGBImages/11225 92 5
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 50 | /ssd/gmzhu/SKIG/RGBImages/11229 85 9
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 54 | /ssd/gmzhu/SKIG/RGBImages/11233 231 3
 55 | /ssd/gmzhu/SKIG/RGBImages/11234 127 4
 56 | /ssd/gmzhu/SKIG/RGBImages/11235 92 5
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 58 | /ssd/gmzhu/SKIG/RGBImages/11237 96 7
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 60 | /ssd/gmzhu/SKIG/RGBImages/11239 80 9
 61 | /ssd/gmzhu/SKIG/RGBImages/12110 160 0
 62 | /ssd/gmzhu/SKIG/RGBImages/12111 166 1
 63 | /ssd/gmzhu/SKIG/RGBImages/12112 126 2
 64 | /ssd/gmzhu/SKIG/RGBImages/12113 243 3
 65 | /ssd/gmzhu/SKIG/RGBImages/12114 111 4
 66 | /ssd/gmzhu/SKIG/RGBImages/12115 93 5
 67 | /ssd/gmzhu/SKIG/RGBImages/12116 72 6
 68 | /ssd/gmzhu/SKIG/RGBImages/12117 81 7
 69 | /ssd/gmzhu/SKIG/RGBImages/12118 121 8
 70 | /ssd/gmzhu/SKIG/RGBImages/12119 97 9
 71 | /ssd/gmzhu/SKIG/RGBImages/12120 150 0
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 73 | /ssd/gmzhu/SKIG/RGBImages/12122 126 2
 74 | /ssd/gmzhu/SKIG/RGBImages/12123 277 3
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 78 | /ssd/gmzhu/SKIG/RGBImages/12127 92 7
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 80 | /ssd/gmzhu/SKIG/RGBImages/12129 101 9
 81 | /ssd/gmzhu/SKIG/RGBImages/12130 161 0
 82 | /ssd/gmzhu/SKIG/RGBImages/12131 174 1
 83 | /ssd/gmzhu/SKIG/RGBImages/12132 126 2
 84 | /ssd/gmzhu/SKIG/RGBImages/12133 265 3
 85 | /ssd/gmzhu/SKIG/RGBImages/12134 118 4
 86 | /ssd/gmzhu/SKIG/RGBImages/12135 84 5
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 88 | /ssd/gmzhu/SKIG/RGBImages/12137 84 7
 89 | /ssd/gmzhu/SKIG/RGBImages/12138 114 8
 90 | /ssd/gmzhu/SKIG/RGBImages/12139 93 9
 91 | /ssd/gmzhu/SKIG/RGBImages/12210 149 0
 92 | /ssd/gmzhu/SKIG/RGBImages/12211 133 1
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 98 | /ssd/gmzhu/SKIG/RGBImages/12217 72 7
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100 | /ssd/gmzhu/SKIG/RGBImages/12219 81 9
101 | /ssd/gmzhu/SKIG/RGBImages/12220 156 0
102 | /ssd/gmzhu/SKIG/RGBImages/12221 153 1
103 | /ssd/gmzhu/SKIG/RGBImages/12222 143 2
104 | /ssd/gmzhu/SKIG/RGBImages/12223 250 3
105 | /ssd/gmzhu/SKIG/RGBImages/12224 126 4
106 | /ssd/gmzhu/SKIG/RGBImages/12225 90 5
107 | /ssd/gmzhu/SKIG/RGBImages/12226 89 6
108 | /ssd/gmzhu/SKIG/RGBImages/12227 63 7
109 | /ssd/gmzhu/SKIG/RGBImages/12228 101 8
110 | /ssd/gmzhu/SKIG/RGBImages/12229 82 9
111 | /ssd/gmzhu/SKIG/RGBImages/12230 153 0
112 | /ssd/gmzhu/SKIG/RGBImages/12231 138 1
113 | /ssd/gmzhu/SKIG/RGBImages/12232 142 2
114 | /ssd/gmzhu/SKIG/RGBImages/12233 236 3
115 | /ssd/gmzhu/SKIG/RGBImages/12234 123 4
116 | /ssd/gmzhu/SKIG/RGBImages/12235 91 5
117 | /ssd/gmzhu/SKIG/RGBImages/12236 83 6
118 | /ssd/gmzhu/SKIG/RGBImages/12237 69 7
119 | /ssd/gmzhu/SKIG/RGBImages/12238 104 8
120 | /ssd/gmzhu/SKIG/RGBImages/12239 84 9
121 | /ssd/gmzhu/SKIG/RGBImages/13110 167 0
122 | /ssd/gmzhu/SKIG/RGBImages/13111 168 1
123 | /ssd/gmzhu/SKIG/RGBImages/13112 120 2
124 | /ssd/gmzhu/SKIG/RGBImages/13113 258 3
125 | /ssd/gmzhu/SKIG/RGBImages/13114 119 4
126 | /ssd/gmzhu/SKIG/RGBImages/13115 105 5
127 | /ssd/gmzhu/SKIG/RGBImages/13116 79 6
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