def inference_refine(images,
coarse7_output,
keep_conv,
reuse=False,
trainable=True):
fine1_conv = conv2d('fine1',
images, [9, 9, 3, 63], [63], [1, 2, 2, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
fine1 = tf.nn.max_pool(fine1_conv,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='fine_pool1')
fine1_dropout = tf.nn.dropout(fine1, keep_conv)
fine2 = tf.concat([fine1_dropout, coarse7_output], 3)
fine3 = conv2d('fine3',
fine2, [5, 5, 64, 64], [64], [1, 1, 1, 1],
padding='SAME',
reuse=reuse,
trainable=trainable)
fine3_dropout = tf.nn.dropout(fine3, keep_conv)
fine4 = conv2d('fine4',
fine3_dropout, [5, 5, 64, 1], [1], [1, 1, 1, 1],
padding='SAME',
reuse=reuse,
trainable=trainable)
return fine4
def inference(self, x, reuse=False):
print(x.get_shape())
# conv2d arguments = (scope_name, inputs, shape, bias_shape, stride, padding='VALID', wd=0.0, reuse=False, trainable=True, with_w=False)
conv_h0, conv_h0_w, conv_h0_b = mp.conv2d(
'd_conv_h0',
x, [5, 5, x.get_shape()[-1], self.first_conv_dim],
[self.first_conv_dim], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True)
h0 = mp.lrelu(conv_h0)
# conv2d arguments = (scope_name, inputs, shape, bias_shape, stride, padding='VALID', wd=0.0, reuse=False, trainable=True, with_w=False)
conv_h1, conv_h1_w, conv_h1_b = mp.conv2d(
'd_conv_h1',
h0, [5, 5, h0.get_shape()[-1], self.first_conv_dim],
[self.first_conv_dim], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True)
h1 = mp.lrelu(conv_h1)
# linear projection
h2 = mp.linear_project('d_lin_project_h1',
tf.reshape(h1, [self.batch_size, 7 * 7 * 64]),
1,
reuse=reuse)
return tf.nn.sigmoid(h2), h2
def globalDepthMap(images, reuse=False, trainable=True):
with tf.name_scope("Global_Depth"):
coarse1_conv = conv2d('coarse1',
images, [11, 11, 3, 96], [96], [1, 4, 4, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
coarse1 = tf.nn.max_pool(coarse1_conv,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool1')
coarse2_conv = conv2d('coarse2',
coarse1, [5, 5, 96, 256], [256], [1, 1, 1, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
coarse2 = tf.nn.max_pool(coarse2_conv,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
coarse3 = conv2d('coarse3',
coarse2, [3, 3, 256, 384], [384], [1, 1, 1, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
coarse4 = conv2d('coarse4',
coarse3, [3, 3, 384, 384], [384], [1, 1, 1, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
coarse5 = conv2d('coarse5',
coarse4, [3, 3, 384, 256], [256], [1, 1, 1, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
coarse6 = fullyConnectedLayer('coarse6',
coarse5, [6 * 10 * 256, 4096], [4096],
reuse=reuse,
trainable=trainable)
coarse6_dropout = tf.nn.dropout(coarse6, 0.8)
coarse7 = fullyConnectedLayer('coarse7',
coarse6_dropout, [4096, 4070], [4070],
reuse=reuse,
trainable=trainable)
coarse7_output = tf.reshape(coarse7, [-1, 55, 74, 1])
#print("Coarse1_Conv: ", coarse1_conv._shape)
#print("Coarse1: ", coarse1._shape)
#print("Coarse2_Conv: ", coarse2_conv._shape)
#print("Coarse2: ", coarse2._shape)
#print("Coarse3: ", coarse3._shape)
#print("Coarse4: ", coarse4._shape)
#print("Coarse5: ", coarse5._shape)
#print("Coarse6: ", coarse6._shape)
#print("Coarse7: ", coarse7._shape)
#print("Coarse7_output: ", coarse7_output._shape)
#return coarse7_output, coarse6, coarse5, coarse3
return coarse7_output
def inference(images, reuse=False, trainable=True):
coarse1_conv = conv2d('coarse1', images, [11, 11, 3, 96], [96], [1, 4, 4, 1], padding='VALID', reuse=reuse, trainable=trainable)
coarse1 = tf.nn.max_pool(coarse1_conv, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='VALID', name='pool1')
coarse2_conv = conv2d('coarse2', coarse1, [5, 5, 96, 256], [256], [1, 1, 1, 1], padding='VALID', reuse=reuse, trainable=trainable)
coarse2 = tf.nn.max_pool(coarse2_conv, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1')
coarse3 = conv2d('coarse3', coarse2, [3, 3, 256, 384], [384], [1, 1, 1, 1], padding='VALID', reuse=reuse, trainable=trainable)
coarse4 = conv2d('coarse4', coarse3, [3, 3, 384, 384], [384], [1, 1, 1, 1], padding='VALID', reuse=reuse, trainable=trainable)
coarse5 = conv2d('coarse5', coarse4, [3, 3, 384, 256], [256], [1, 1, 1, 1], padding='VALID', reuse=reuse, trainable=trainable)
coarse6 = fc('coarse6', coarse5, [6*10*256, 4096], [4096], reuse=reuse, trainable=trainable)
coarse7 = fc('coarse7', coarse6, [4096, 4070], [4070], reuse=reuse, trainable=trainable)
coarse7_output = tf.reshape(coarse7, [-1, 55, 74, 1])
return coarse7_output
def localDepthMap(images,
coarse7_output,
keep_conv,
reuse=False,
trainable=True):
with tf.name_scope("Local_Depth"):
fine1_conv = conv2d('fine1',
images, [9, 9, 3, 63], [63], [1, 2, 2, 1],
padding='VALID',
reuse=reuse,
trainable=trainable)
fine1 = tf.nn.max_pool(fine1_conv,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='fine_pool1')
fine1_dropout = tf.nn.dropout(fine1, keep_conv)
fine2 = tf.concat(axis=3,
values=[fine1_dropout, coarse7_output],
name="fine2_concat")
fine3 = conv2d('fine3',
fine2, [5, 5, 64, 64], [64], [1, 1, 1, 1],
padding='SAME',
reuse=reuse,
trainable=trainable)
fine3_dropout = tf.nn.dropout(fine3, keep_conv)
print("fine3_dropout ", fine3_dropout._shape)
fine4_conv = conv2d('fine4_conv',
fine3_dropout, [5, 5, 64, 1], [1], [1, 1, 1, 1],
padding='SAME',
reuse=reuse,
trainable=trainable)
print("fine4_conv ", fine4_conv._shape)
fine4_full = fullyConnectedLayer('fine4_full',
fine4_conv, [55 * 74 * 1, 4070],
[4070],
reuse=reuse,
trainable=trainable)
print("fine4_full ", fine4_full._shape)
fine4 = tf.reshape(fine4_full, [-1, 55, 74, 1])
#print("fine1_conv ", fine1_conv._shape)
#print("fine1 ", fine1._shape)
#print("fine1_dropout ", fine1_dropout._shape)
#print("fine2 ", fine2._shape)
#print("fine3 ", fine3._shape)
#print("fine3_dropout ", fine3_dropout._shape)
#print("fine4 ", fine4._shape)
#return fine4, fine3_dropout, fine3, fine2, fine1_dropout, fine1, fine1_conv
return fine4
def inference(self, x, reuse=False, trainable=True):
with tf.variable_scope("discriminator") as scope:
if reuse:
scope.reuse_variables()
print("===D")
print(x.get_shape())
# conv2d arguments = (scope_name, inputs, shape, bias_shape, stride, padding='VALID', wd=0.0, reuse=False, trainable=True, with_w=False)
conv_h0, conv_h0_w, conv_h0_b = mp.conv2d(
'd_conv_h0',
x, [5, 5, x.get_shape()[-1], self.first_conv_dim],
[self.first_conv_dim], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
h0 = mp.lrelu(conv_h0)
print(h0.get_shape())
# conv2d arguments = (scope_name, inputs, shape, bias_shape, stride, padding='VALID', wd=0.0, reuse=False, trainable=True, with_w=False)
conv_h1, conv_h1_w, conv_h1_b = mp.conv2d(
'd_conv_h1',
h0, [5, 5, h0.get_shape()[-1], self.first_conv_dim * 2],
[self.first_conv_dim * 2], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
#h1 = mp.lrelu(conv_h1)
h1 = mp.lrelu(self.d_bn1(conv_h1, trainable=trainable))
# h1 = mp.lrelu(mp.batch_norm(conv_h1, scope_name='d_bn_h1', reuse=reuse, trainable=trainable))
# 3rd
conv_h2, conv_h2_w, conv_h2_b = mp.conv2d(
'd_conv_h2',
h1, [5, 5, h1.get_shape()[-1], self.first_conv_dim * 4],
[self.first_conv_dim * 4], [1, 2, 2, 1],
padding='SAME',
reuse=reuse,
with_w=True,
trainable=trainable)
#h2 = mp.lrelu(conv_h2)
h2 = mp.lrelu(self.d_bn2(conv_h2, trainable=trainable))
# h2 = mp.lrelu(mp.batch_norm(conv_h2, scope_name='d_bn_h2', reuse=reuse, trainable=trainable))
print(h2.get_shape())
# linear projection (skip h3)
h3 = mp.linear_project('d_lin_project_h3',
tf.reshape(h2, [self.batch_size, -1]),
1,
reuse=reuse)
return tf.nn.sigmoid(h3), h3
