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model.py
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model.py
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from __future__ import division
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import BasicConvLSTMCell
DISP_SCALING_RESNET50 = 10.0
MIN_DISP = 0.01
def resize_like(inputs, ref):
iH, iW = inputs.get_shape()[1], inputs.get_shape()[2]
rH, rW = ref.get_shape()[1], ref.get_shape()[2]
if iH == rH and iW == rW:
return inputs
return tf.image.resize_nearest_neighbor(inputs, [rH.value, rW.value])
def convLSTM(input, hidden, filters, kernel, scope):
with tf.variable_scope(scope, initializer = tf.truncated_normal_initializer(stddev=0.1)):
cell = BasicConvLSTMCell.BasicConvLSTMCell([input.get_shape()[1], input.get_shape()[2]], kernel, filters)
if hidden is None:
hidden = cell.zero_state(input.get_shape()[0], tf.float32)
y_, hidden = cell(input, hidden)
return y_, hidden
def rnn_depth_net_decoderlstm(current_input,hidden_state,is_training=True):
batch_norm_params = {'is_training': is_training,'decay':0.99}
H = current_input.get_shape()[1].value
W = current_input.get_shape()[2].value
with tf.variable_scope('rnn_depth_net', reuse = tf.AUTO_REUSE) as sc:
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.005),
activation_fn=tf.nn.leaky_relu
):
cnv1 = slim.conv2d(current_input, 32, [3, 3], stride=2, scope='cnv1')
#cnv1b, hidden1 = convLSTM(cnv1, hidden_state[0], 32, [3, 3], scope='cnv1_lstm')
cnv1b = slim.conv2d(cnv1, 32, [3, 3], rate=2, stride=1, scope='cnv1b')
cnv2 = slim.conv2d(cnv1b, 64, [3, 3], stride=2, scope='cnv2')
#cnv2b, hidden2 = convLSTM(cnv2, hidden_state[1], 64, [3, 3], scope='cnv2_lstm')
cnv2b = slim.conv2d(cnv2, 64, [3, 3], rate=2, stride=1, scope='cnv2b')
cnv3 = slim.conv2d(cnv2b, 128, [3, 3], stride=2, scope='cnv3')
#cnv3b, hidden3 = convLSTM(cnv3, hidden_state[2], 128, [3, 3], scope='cnv3_lstm')
cnv3b = slim.conv2d(cnv3, 128, [3, 3], rate=2, stride=1, scope='cnv3b')
cnv4 = slim.conv2d(cnv3b, 256, [3, 3], stride=2, scope='cnv4')
#cnv4b, hidden4 = convLSTM(cnv4, hidden_state[3], 256, [3, 3], scope='cnv4_lstm')
cnv4b = slim.conv2d(cnv4, 256, [3, 3], rate=2, stride=1, scope='cnv4b')
cnv5 = slim.conv2d(cnv4b, 256, [3, 3], stride=2, scope='cnv5')
#cnv5b, hidden5 = convLSTM(cnv5, hidden_state[4], 256, [3, 3], scope='cnv5_lstm')
cnv5b = slim.conv2d(cnv5, 256, [3, 3], rate=2, stride=1, scope='cnv5b')
cnv6 = slim.conv2d(cnv5b, 256, [3, 3], stride=2, scope='cnv6')
#cnv6b, hidden6 = convLSTM(cnv6, hidden_state[5], 256, [3, 3], scope='cnv6_lstm')
cnv6b = slim.conv2d(cnv6, 256, [3, 3], rate=2, stride=1, scope='cnv6b')
cnv7 = slim.conv2d(cnv6b, 512, [3, 3], stride=2, scope='cnv7')
#cnv7b, hidden2 = convLSTM(cnv7, hidden_state[1], 512, [3, 3], scope='cnv7_lstm')
cnv7b = slim.conv2d(cnv7, 512, [3, 3], rate=2, stride=1, scope='cnv7b')
upcnv7 = slim.conv2d_transpose(cnv7b, 256, [3, 3], stride=2, scope='upcnv7')
# There might be dimension mismatch due to uneven down/up-sampling
upcnv7 = resize_like(upcnv7, cnv6b)
i7_in = tf.concat([upcnv7, cnv6b], axis=3)
#icnv7 = slim.conv2d(i7_in, 256, [3, 3], stride=1, scope='icnv7')
icnv7, hidden7= convLSTM(i7_in, hidden_state[6], 256, [3, 3], scope='icnv7_lstm')
upcnv6 = slim.conv2d_transpose(icnv7, 128, [3, 3], stride=2, scope='upcnv6')
upcnv6 = resize_like(upcnv6, cnv5b)
i6_in = tf.concat([upcnv6, cnv5b], axis=3)
#icnv6 = slim.conv2d(i6_in, 128, [3, 3], stride=1, scope='icnv6')
icnv6, hidden6= convLSTM(i6_in, hidden_state[5], 128, [3, 3], scope='icnv6_lstm')
upcnv5 = slim.conv2d_transpose(icnv6, 128, [3, 3], stride=2, scope='upcnv5')
upcnv5 = resize_like(upcnv5, cnv4b)
i5_in = tf.concat([upcnv5, cnv4b], axis=3)
#icnv5 = slim.conv2d(i5_in, 128, [3, 3], stride=1, scope='icnv5')
icnv5, hidden5 = convLSTM(i5_in, hidden_state[4], 128, [3, 3], scope='icnv5_lstm')
upcnv4 = slim.conv2d_transpose(icnv5, 128, [3, 3], stride=2, scope='upcnv4')
i4_in = tf.concat([upcnv4, cnv3b], axis=3)
#icnv4 = slim.conv2d(i4_in, 128, [3, 3], stride=1, scope='icnv4')
icnv4, hidden4 = convLSTM(i4_in, hidden_state[3], 128, [3, 3], scope='icnv4_lstm')
upcnv3 = slim.conv2d_transpose(icnv4, 64, [3, 3], stride=2, scope='upcnv3')
i3_in = tf.concat([upcnv3, cnv2b], axis=3)
#icnv3 = slim.conv2d(i3_in, 64, [3, 3], stride=1, scope='icnv3')
icnv3, hidden3 = convLSTM(i3_in, hidden_state[2], 64, [3, 3], scope='icnv3_lstm')
upcnv2 = slim.conv2d_transpose(icnv3, 32, [3, 3], stride=2, scope='upcnv2')
i2_in = tf.concat([upcnv2, cnv1b], axis=3)
#icnv2 = slim.conv2d(i2_in, 32, [3, 3], stride=1, scope='icnv2')
icnv2, hidden2 = convLSTM(i2_in, hidden_state[1], 32, [3, 3], scope='icnv2_lstm')
#import pdb;pdb.set_trace()
upcnv1 = slim.conv2d_transpose(icnv2, 16, [3, 3], stride=2, scope='upcnv1')
icnv1, hidden1 = convLSTM(upcnv1, hidden_state[0], 16, [3, 3], scope='icnv1_lstm')
#icnv1 = slim.conv2d(upcnv1, 16, [3, 3], stride=1, scope='icnv1')
depth = slim.conv2d(icnv1, 1, [3, 3], stride=1,
activation_fn=tf.sigmoid, normalizer_fn=None, scope='disp1')*DISP_SCALING_RESNET50+MIN_DISP
return depth, [hidden1, hidden2, hidden3, hidden4, hidden5, hidden6, hidden7]#, hidden8, hidden9,hidden10, hidden11, hidden12, hidden13, hidden14,hidden15, hidden16]
def pose_net(posenet_inputs, hidden_state, is_training=True):
batch_norm_params = {'is_training': is_training,'decay':0.99}
with tf.variable_scope('pose_net', reuse = tf.AUTO_REUSE) as sc:
with slim.arg_scope([slim.conv2d],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.005),
activation_fn=tf.nn.relu):
conv1 = slim.conv2d(posenet_inputs, 16, 7, 2)
cnv1b, hidden1 = convLSTM(conv1, hidden_state[0], 16, [3, 3], scope='cnv1_lstm')
conv2 = slim.conv2d(cnv1b, 32, 5, 2)
cnv2b, hidden2 = convLSTM(conv2, hidden_state[1], 64, [3, 3], scope='cnv2_lstm')
conv3 = slim.conv2d(cnv2b, 64, 3, 2)
cnv3b, hidden3 = convLSTM(conv3, hidden_state[2], 128, [3, 3], scope='cnv3_lstm')
conv4 = slim.conv2d(cnv3b, 128, 3, 2)
cnv4b, hidden4 = convLSTM(conv4, hidden_state[3], 256, [3, 3], scope='cnv4_lstm')
conv5 = slim.conv2d(cnv4b, 256, 3, 2)
cnv5b, hidden5 = convLSTM(conv5, hidden_state[4], 256, [3, 3], scope='cnv5_lstm')
conv6 = slim.conv2d(cnv5b, 256, 3, 2)
cnv6b, hidden6 = convLSTM(conv6, hidden_state[5], 256, [3, 3], scope='cnv6_lstm')
conv7 = slim.conv2d(cnv6b, 256, 3, 2)
cnv7b, hidden7 = convLSTM(conv7, hidden_state[6], 512, [3, 3], scope='cnv7_lstm')
pose_pred = slim.conv2d(cnv7b, 6, 1, 1,
normalizer_fn=None, activation_fn=None)
pose_avg = tf.reduce_mean(pose_pred, [1, 2])
pose_final = tf.reshape(pose_avg, [-1, 6])*0.01
return pose_final,[hidden1, hidden2, hidden3, hidden4, hidden5, hidden6, hidden7]
def rnn_depth_net_fulllstm(current_input,hidden_state,is_training=True):
batch_norm_params = {'is_training': is_training,'decay':0.99}
H = current_input.get_shape()[1].value
W = current_input.get_shape()[2].value
with tf.variable_scope('rnn_depth_net', reuse = tf.AUTO_REUSE) as sc:
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.005),
activation_fn=tf.nn.leaky_relu
):
cnv1 = slim.conv2d(current_input, 32, [3, 3], stride=2, scope='cnv1')
cnv1b, hidden1 = convLSTM(cnv1, hidden_state[0], 32, [3, 3], scope='cnv1_lstm')
#cnv1b = slim.conv2d(cnv1, 32, [3, 3], rate=2, stride=1, scope='cnv1b')
cnv2 = slim.conv2d(cnv1b, 64, [3, 3], stride=2, scope='cnv2')
cnv2b, hidden2 = convLSTM(cnv2, hidden_state[1], 64, [3, 3], scope='cnv2_lstm')
#cnv2b = slim.conv2d(cnv2, 64, [3, 3], rate=2, stride=1, scope='cnv2b')
cnv3 = slim.conv2d(cnv2b, 128, [3, 3], stride=2, scope='cnv3')
cnv3b, hidden3 = convLSTM(cnv3, hidden_state[2], 128, [3, 3], scope='cnv3_lstm')
#cnv3b = slim.conv2d(cnv3, 128, [3, 3], rate=2, stride=1, scope='cnv3b')
cnv4 = slim.conv2d(cnv3b, 256, [3, 3], stride=2, scope='cnv4')
cnv4b, hidden4 = convLSTM(cnv4, hidden_state[3], 256, [3, 3], scope='cnv4_lstm')
#cnv4b = slim.conv2d(cnv4, 256, [3, 3], rate=2, stride=1, scope='cnv4b')
cnv5 = slim.conv2d(cnv4b, 256, [3, 3], stride=2, scope='cnv5')
cnv5b, hidden5 = convLSTM(cnv5, hidden_state[4], 256, [3, 3], scope='cnv5_lstm')
#cnv5b = slim.conv2d(cnv5, 256, [3, 3], rate=2, stride=1, scope='cnv5b')
cnv6 = slim.conv2d(cnv5b, 256, [3, 3], stride=2, scope='cnv6')
cnv6b, hidden6 = convLSTM(cnv6, hidden_state[5], 256, [3, 3], scope='cnv6_lstm')
#cnv6b = slim.conv2d(cnv6, 256, [3, 3], rate=2, stride=1, scope='cnv6b')
cnv7 = slim.conv2d(cnv6b, 512, [3, 3], stride=2, scope='cnv7')
cnv7b, hidden7 = convLSTM(cnv7, hidden_state[6], 512, [3, 3], scope='cnv7_lstm')
#cnv7b = slim.conv2d(cnv7, 512, [3, 3], rate=2, stride=1, scope='cnv7b')
upcnv7 = slim.conv2d_transpose(cnv7b, 256, [3, 3], stride=2, scope='upcnv7')
# There might be dimension mismatch due to uneven down/up-sampling
upcnv7 = resize_like(upcnv7, cnv6b)
i7_in = tf.concat([upcnv7, cnv6b], axis=3)
#icnv7 = slim.conv2d(i7_in, 256, [3, 3], stride=1, scope='icnv7')
icnv7, hidden8= convLSTM(i7_in, hidden_state[7], 256, [3, 3], scope='icnv7_lstm')
upcnv6 = slim.conv2d_transpose(icnv7, 128, [3, 3], stride=2, scope='upcnv6')
upcnv6 = resize_like(upcnv6, cnv5b)
i6_in = tf.concat([upcnv6, cnv5b], axis=3)
#icnv6 = slim.conv2d(i6_in, 128, [3, 3], stride=1, scope='icnv6')
icnv6, hidden9= convLSTM(i6_in, hidden_state[8], 128, [3, 3], scope='icnv6_lstm')
upcnv5 = slim.conv2d_transpose(icnv6, 128, [3, 3], stride=2, scope='upcnv5')
upcnv5 = resize_like(upcnv5, cnv4b)
i5_in = tf.concat([upcnv5, cnv4b], axis=3)
#icnv5 = slim.conv2d(i5_in, 128, [3, 3], stride=1, scope='icnv5')
icnv5, hidden10 = convLSTM(i5_in, hidden_state[9], 128, [3, 3], scope='icnv5_lstm')
upcnv4 = slim.conv2d_transpose(icnv5, 128, [3, 3], stride=2, scope='upcnv4')
i4_in = tf.concat([upcnv4, cnv3b], axis=3)
#icnv4 = slim.conv2d(i4_in, 128, [3, 3], stride=1, scope='icnv4')
icnv4, hidden11 = convLSTM(i4_in, hidden_state[10], 128, [3, 3], scope='icnv4_lstm')
upcnv3 = slim.conv2d_transpose(icnv4, 64, [3, 3], stride=2, scope='upcnv3')
i3_in = tf.concat([upcnv3, cnv2b], axis=3)
#icnv3 = slim.conv2d(i3_in, 64, [3, 3], stride=1, scope='icnv3')
icnv3, hidden12 = convLSTM(i3_in, hidden_state[11], 64, [3, 3], scope='icnv3_lstm')
upcnv2 = slim.conv2d_transpose(icnv3, 32, [3, 3], stride=2, scope='upcnv2')
i2_in = tf.concat([upcnv2, cnv1b], axis=3)
#icnv2 = slim.conv2d(i2_in, 32, [3, 3], stride=1, scope='icnv2')
icnv2, hidden13 = convLSTM(i2_in, hidden_state[12], 32, [3, 3], scope='icnv2_lstm')
#import pdb;pdb.set_trace()
upcnv1 = slim.conv2d_transpose(icnv2, 16, [3, 3], stride=2, scope='upcnv1')
icnv1, hidden14 = convLSTM(upcnv1, hidden_state[13], 16, [3, 3], scope='icnv1_lstm')
#icnv1 = slim.conv2d(upcnv1, 16, [3, 3], stride=1, scope='icnv1')
depth = slim.conv2d(icnv1, 1, [3, 3], stride=1,
activation_fn=tf.sigmoid, normalizer_fn=None, scope='disp1')*DISP_SCALING_RESNET50+MIN_DISP
return depth, [hidden1, hidden2, hidden3, hidden4, hidden5, hidden6, hidden7, hidden8, hidden9,hidden10, hidden11, hidden12, hidden13, hidden14]
def rnn_depth_net_encoderlstm(current_input,hidden_state,is_training=True):
batch_norm_params = {'is_training': is_training,'decay':0.99}
H = current_input.get_shape()[1].value
W = current_input.get_shape()[2].value
with tf.variable_scope('rnn_depth_net', reuse = tf.AUTO_REUSE) as sc:
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.005),
activation_fn=tf.nn.leaky_relu
):
cnv1 = slim.conv2d(current_input, 32, [3, 3], stride=2, scope='cnv1')
cnv1b, hidden1 = convLSTM(cnv1, hidden_state[0], 32, [3, 3], scope='cnv1_lstm')
#cnv1b = slim.conv2d(cnv1, 32, [3, 3], rate=2, stride=1, scope='cnv1b')
cnv2 = slim.conv2d(cnv1b, 64, [3, 3], stride=2, scope='cnv2')
cnv2b, hidden2 = convLSTM(cnv2, hidden_state[1], 64, [3, 3], scope='cnv2_lstm')
#cnv2b = slim.conv2d(cnv2, 64, [3, 3], rate=2, stride=1, scope='cnv2b')
cnv3 = slim.conv2d(cnv2b, 128, [3, 3], stride=2, scope='cnv3')
cnv3b, hidden3 = convLSTM(cnv3, hidden_state[2], 128, [3, 3], scope='cnv3_lstm')
#cnv3b = slim.conv2d(cnv3, 128, [3, 3], rate=2, stride=1, scope='cnv3b')
cnv4 = slim.conv2d(cnv3b, 256, [3, 3], stride=2, scope='cnv4')
cnv4b, hidden4 = convLSTM(cnv4, hidden_state[3], 256, [3, 3], scope='cnv4_lstm')
#cnv4b = slim.conv2d(cnv4, 256, [3, 3], rate=2, stride=1, scope='cnv4b')
cnv5 = slim.conv2d(cnv4b, 256, [3, 3], stride=2, scope='cnv5')
cnv5b, hidden5 = convLSTM(cnv5, hidden_state[4], 256, [3, 3], scope='cnv5_lstm')
#cnv5b = slim.conv2d(cnv5, 256, [3, 3], rate=2, stride=1, scope='cnv5b')
cnv6 = slim.conv2d(cnv5b, 256, [3, 3], stride=2, scope='cnv6')
cnv6b, hidden6 = convLSTM(cnv6, hidden_state[5], 256, [3, 3], scope='cnv6_lstm')
#cnv6b = slim.conv2d(cnv6, 256, [3, 3], rate=2, stride=1, scope='cnv6b')
cnv7 = slim.conv2d(cnv6b, 512, [3, 3], stride=2, scope='cnv7')
cnv7b, hidden7 = convLSTM(cnv7, hidden_state[6], 512, [3, 3], scope='cnv7_lstm')
#cnv7b = slim.conv2d(cnv7, 512, [3, 3], rate=2, stride=1, scope='cnv7b')
upcnv7 = slim.conv2d_transpose(cnv7b, 256, [3, 3], stride=2, scope='upcnv7')
# There might be dimension mismatch due to uneven down/up-sampling
upcnv7 = resize_like(upcnv7, cnv6b)
i7_in = tf.concat([upcnv7, cnv6b], axis=3)
icnv7 = slim.conv2d(i7_in, 256, [3, 3], stride=1, scope='icnv7')
upcnv6 = slim.conv2d_transpose(icnv7, 128, [3, 3], stride=2, scope='upcnv6')
upcnv6 = resize_like(upcnv6, cnv5b)
i6_in = tf.concat([upcnv6, cnv5b], axis=3)
icnv6 = slim.conv2d(i6_in, 128, [3, 3], stride=1, scope='icnv6')
upcnv5 = slim.conv2d_transpose(icnv6, 128, [3, 3], stride=2, scope='upcnv5')
upcnv5 = resize_like(upcnv5, cnv4b)
i5_in = tf.concat([upcnv5, cnv4b], axis=3)
icnv5 = slim.conv2d(i5_in, 128, [3, 3], stride=1, scope='icnv5')
upcnv4 = slim.conv2d_transpose(icnv5, 128, [3, 3], stride=2, scope='upcnv4')
upcnv4 = resize_like(upcnv4, cnv3b)
i4_in = tf.concat([upcnv4, cnv3b], axis=3)
icnv4 = slim.conv2d(i4_in, 128, [3, 3], stride=1, scope='icnv4')
upcnv3 = slim.conv2d_transpose(icnv4, 64, [3, 3], stride=2, scope='upcnv3')
upcnv3 = resize_like(upcnv3, cnv2b)
i3_in = tf.concat([upcnv3, cnv2b], axis=3)
icnv3 = slim.conv2d(i3_in, 64, [3, 3], stride=1, scope='icnv3')
upcnv2 = slim.conv2d_transpose(icnv3, 32, [3, 3], stride=2, scope='upcnv2')
upcnv2 = resize_like(upcnv2, cnv1b)
i2_in = tf.concat([upcnv2, cnv1b], axis=3)
icnv2 = slim.conv2d(i2_in, 32, [3, 3], stride=1, scope='icnv2')
upcnv1 = slim.conv2d_transpose(icnv2, 16, [3, 3], stride=2, scope='upcnv1')
icnv1 = slim.conv2d(upcnv1, 16, [3, 3], stride=1, scope='icnv1')
depth = slim.conv2d(icnv1, 1, [1, 1], stride=1,
activation_fn=tf.sigmoid, normalizer_fn=None, scope='disp1')*DISP_SCALING_RESNET50+MIN_DISP # was 10.0
return depth, [hidden1, hidden2, hidden3, hidden4, hidden5, hidden6, hidden7]
def rnn_depth_net_encoderlstm_wpose(current_input,hidden_state,is_training=True):
batch_norm_params = {'is_training': is_training,'decay':0.99}
H = current_input.get_shape()[1].value
W = current_input.get_shape()[2].value
with tf.variable_scope('rnn_depth_net', reuse = tf.AUTO_REUSE) as sc:
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
weights_regularizer=slim.l2_regularizer(0.005),
activation_fn=tf.nn.leaky_relu
):
cnv1 = slim.conv2d(current_input, 32, [3, 3], stride=2, scope='cnv1')
cnv1b, hidden1 = convLSTM(cnv1, hidden_state[0], 32, [3, 3], scope='cnv1_lstm')
#cnv1b = slim.conv2d(cnv1, 32, [3, 3], rate=2, stride=1, scope='cnv1b')
cnv2 = slim.conv2d(cnv1b, 64, [3, 3], stride=2, scope='cnv2')
cnv2b, hidden2 = convLSTM(cnv2, hidden_state[1], 64, [3, 3], scope='cnv2_lstm')
#cnv2b = slim.conv2d(cnv2, 64, [3, 3], rate=2, stride=1, scope='cnv2b')
cnv3 = slim.conv2d(cnv2b, 128, [3, 3], stride=2, scope='cnv3')
cnv3b, hidden3 = convLSTM(cnv3, hidden_state[2], 128, [3, 3], scope='cnv3_lstm')
#cnv3b = slim.conv2d(cnv3, 128, [3, 3], rate=2, stride=1, scope='cnv3b')
cnv4 = slim.conv2d(cnv3b, 256, [3, 3], stride=2, scope='cnv4')
cnv4b, hidden4 = convLSTM(cnv4, hidden_state[3], 256, [3, 3], scope='cnv4_lstm')
#cnv4b = slim.conv2d(cnv4, 256, [3, 3], rate=2, stride=1, scope='cnv4b')
cnv5 = slim.conv2d(cnv4b, 256, [3, 3], stride=2, scope='cnv5')
cnv5b, hidden5 = convLSTM(cnv5, hidden_state[4], 256, [3, 3], scope='cnv5_lstm')
#cnv5b = slim.conv2d(cnv5, 256, [3, 3], rate=2, stride=1, scope='cnv5b')
cnv6 = slim.conv2d(cnv5b, 256, [3, 3], stride=2, scope='cnv6')
cnv6b, hidden6 = convLSTM(cnv6, hidden_state[5], 256, [3, 3], scope='cnv6_lstm')
#cnv6b = slim.conv2d(cnv6, 256, [3, 3], rate=2, stride=1, scope='cnv6b')
cnv7 = slim.conv2d(cnv6b, 512, [3, 3], stride=2, scope='cnv7')
cnv7b, hidden7 = convLSTM(cnv7, hidden_state[6], 512, [3, 3], scope='cnv7_lstm')
#cnv7b = slim.conv2d(cnv7, 512, [3, 3], rate=2, stride=1, scope='cnv7b')
with tf.variable_scope('pose'):
pose_pred = slim.conv2d(cnv7b, 6, 1, 1,
normalizer_fn=None, activation_fn=None)
pose_avg = tf.reduce_mean(pose_pred, [1, 2])
pose_final = tf.reshape(pose_avg, [-1, 6])*0.01
upcnv7 = slim.conv2d_transpose(cnv7b, 256, [3, 3], stride=2, scope='upcnv7')
# There might be dimension mismatch due to uneven down/up-sampling
upcnv7 = resize_like(upcnv7, cnv6b)
i7_in = tf.concat([upcnv7, cnv6b], axis=3)
icnv7 = slim.conv2d(i7_in, 256, [3, 3], stride=1, scope='icnv7')
#icnv7, hidden8= convLSTM(i7_in, hidden_state[7], 256, [3, 3], scope='icnv7_lstm')
upcnv6 = slim.conv2d_transpose(icnv7, 128, [3, 3], stride=2, scope='upcnv6')
upcnv6 = resize_like(upcnv6, cnv5b)
i6_in = tf.concat([upcnv6, cnv5b], axis=3)
icnv6 = slim.conv2d(i6_in, 128, [3, 3], stride=1, scope='icnv6')
#icnv6, hidden9= convLSTM(i6_in, hidden_state[8], 128, [3, 3], scope='icnv6_lstm')
upcnv5 = slim.conv2d_transpose(icnv6, 128, [3, 3], stride=2, scope='upcnv5')
upcnv5 = resize_like(upcnv5, cnv4b)
i5_in = tf.concat([upcnv5, cnv4b], axis=3)
icnv5 = slim.conv2d(i5_in, 128, [3, 3], stride=1, scope='icnv5')
#icnv5, hidden10 = convLSTM(i5_in, hidden_state[9], 128, [3, 3], scope='icnv5_lstm')
upcnv4 = slim.conv2d_transpose(icnv5, 128, [3, 3], stride=2, scope='upcnv4')
upcnv4 = resize_like(upcnv4, cnv3b)
i4_in = tf.concat([upcnv4, cnv3b], axis=3)
icnv4 = slim.conv2d(i4_in, 128, [3, 3], stride=1, scope='icnv4')
#icnv4, hidden11 = convLSTM(i4_in, hidden_state[10], 128, [3, 3], scope='icnv4_lstm')
upcnv3 = slim.conv2d_transpose(icnv4, 64, [3, 3], stride=2, scope='upcnv3')
upcnv3 = resize_like(upcnv3, cnv2b)
i3_in = tf.concat([upcnv3, cnv2b], axis=3)
icnv3 = slim.conv2d(i3_in, 64, [3, 3], stride=1, scope='icnv3')
#icnv3, hidden12 = convLSTM(i3_in, hidden_state[11], 64, [3, 3], scope='icnv3_lstm')
upcnv2 = slim.conv2d_transpose(icnv3, 32, [3, 3], stride=2, scope='upcnv2')
upcnv2 = resize_like(upcnv2, cnv1b)
i2_in = tf.concat([upcnv2, cnv1b], axis=3)
icnv2 = slim.conv2d(i2_in, 32, [3, 3], stride=1, scope='icnv2')
#icnv2, hidden13 = convLSTM(i2_in, hidden_state[12], 32, [3, 3], scope='icnv2_lstm')
upcnv1 = slim.conv2d_transpose(icnv2, 16, [3, 3], stride=2, scope='upcnv1')
#icnv1, hidden14 = convLSTM(upcnv1, hidden_state[13], 16, [3, 3], scope='icnv1_lstm')
icnv1 = slim.conv2d(upcnv1, 16, [3, 3], stride=1, scope='icnv1')
depth = slim.conv2d(icnv1, 1, [3, 3], stride=1,
activation_fn=tf.sigmoid, normalizer_fn=None, scope='disp1')*DISP_SCALING_RESNET50+MIN_DISP
return depth,pose_final, [hidden1, hidden2, hidden3, hidden4, hidden5, hidden6, hidden7]