def __init__(self,
chz,
out_c,
growth,
actfunc=F.leaky_relu,
norm=nn.BatchNorm2d):
super(DenseNet_decoder, self).__init__()
sizes = getSizes(chz, growth)
skipSize = sizes['dec']['skip']
opSize = sizes['dec']['op']
ipSize = sizes['dec']['ip']
self.up_block4 = DenseNet2D_up_block(skipSize[0], ipSize[0], opSize[0],
2, actfunc)
self.up_block3 = DenseNet2D_up_block(skipSize[1], ipSize[1], opSize[1],
2, actfunc)
self.up_block2 = DenseNet2D_up_block(skipSize[2], ipSize[2], opSize[2],
2, actfunc)
self.up_block1 = DenseNet2D_up_block(skipSize[3], ipSize[3], opSize[3],
2, actfunc)
self.c3_11 = nn.Conv2d(kernel_size=1,
in_channels=opSize[0],
out_channels=3)
self.c2_11 = nn.Conv2d(kernel_size=1,
in_channels=opSize[1],
out_channels=3)
self.c1_11 = nn.Conv2d(kernel_size=1,
in_channels=opSize[2],
out_channels=3)
self.final = convBlock(chz, chz, out_c, actfunc)
def refinement(disp_logits, seg_embedding, is_train, reuse):
with tf.variable_scope('refinement', reuse=reuse):
d = disp_logits.shape.as_list()[-1]
seg_embedding = tf.image.resize_images(seg_embedding,
tf.shape(disp_logits)[1:3])
concated = tf.concat([disp_logits, seg_embedding], axis=-1)
conv1 = conv_norm_lrelu(concated,
64,
kernel_size=5,
strides=(2, 2),
is_train=is_train,
withLrelu=False,
name="conv1")
l2a = convBlock(conv1, [32, 32, 64],
strides=(1, 1),
is_train=is_train,
name='l2a')
l2b = identityBlock(l2a, [32, 32, 64], is_train=is_train, name='l2b')
l2c = identityBlock(l2b, [32, 32, 64], is_train=is_train, name='l2c')
l3a = convBlock(l2c, [64, 64, 128],
strides=(1, 1),
is_train=is_train,
name='l3a')
l3b = identityBlock(l3a, [64, 64, 128], is_train=is_train, name='l3b')
l3c = identityBlock(l3b, [64, 64, 128], is_train=is_train, name='l3c')
l4a = deconv_norm_lrelu(l3c,
64,
3,
strides=(2, 2),
is_train=is_train,
name='deconvl4a')
residul = tf.layers.conv2d(l4a, d, 3, padding='same', name='output')
output = tf.add(disp_logits, residul)
return output
def __init__(self,
in_c=1,
chz=32,
actfunc=F.leaky_relu,
growth=1.5,
norm=nn.BatchNorm2d):
super(DenseNet_encoder, self).__init__()
sizes = getSizes(chz, growth)
interSize = sizes['enc']['inter']
opSize = sizes['enc']['op']
ipSize = sizes['enc']['ip']
self.head = convBlock(in_c=1, inter_c=chz, out_c=chz, actfunc=actfunc)
self.down_block1 = DenseNet2D_down_block(in_c=ipSize[0],
inter_c=interSize[0],
op_c=opSize[0],
down_size=2,
norm=norm,
actfunc=actfunc)
self.down_block2 = DenseNet2D_down_block(in_c=ipSize[1],
inter_c=interSize[1],
op_c=opSize[1],
down_size=2,
norm=norm,
actfunc=actfunc)
self.down_block3 = DenseNet2D_down_block(in_c=ipSize[2],
inter_c=interSize[2],
op_c=opSize[2],
down_size=2,
norm=norm,
actfunc=actfunc)
self.down_block4 = DenseNet2D_down_block(in_c=ipSize[3],
inter_c=interSize[3],
op_c=opSize[3],
down_size=2,
norm=norm,
actfunc=actfunc)
self.bottleneck = DenseNet2D_down_block(in_c=opSize[3],
inter_c=interSize[3],
op_c=opSize[3],
down_size=0,
norm=norm,
actfunc=actfunc)