def _coefficients(cls, input_tensor, params, is_training):
bs = input_tensor.get_shape().as_list()[0]
gd = params['luma_bins']
cm = params['channel_multiplier']
spatial_bin = params['spatial_bin']
# -----------------------------------------------------------------------
with tf.variable_scope('splat'):
n_ds_layers = int(np.log2(params['net_input_size']/spatial_bin))
current_layer = input_tensor
for i in range(n_ds_layers):
if i > 0: # don't normalize first layer
use_bn = params['batch_norm']
else:
use_bn = False
current_layer = conv(current_layer, cm*(2**i)*gd, 3, stride=2,
batch_norm=use_bn, is_training=is_training,
scope='conv{}'.format(i+1))
splat_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('global'):
n_global_layers = int(np.log2(spatial_bin/4)) # 4x4 at the coarsest lvl
current_layer = splat_features
for i in range(2):
current_layer = conv(current_layer, 8*cm*gd, 3, stride=2,
batch_norm=params['batch_norm'], is_training=is_training,
scope="conv{}".format(i+1))
_, lh, lw, lc = current_layer.get_shape().as_list()
current_layer = tf.reshape(current_layer, [bs, lh*lw*lc])
current_layer = fc(current_layer, 32*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc1")
current_layer = fc(current_layer, 16*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc2")
# don't normalize before fusion
current_layer = fc(current_layer, 8*cm*gd, activation_fn=None, scope="fc3")
global_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('local'):
current_layer = splat_features
current_layer = conv(current_layer, 8*cm*gd, 3,
batch_norm=params['batch_norm'],
is_training=is_training,
scope='conv1')
# don't normalize before fusion
current_layer = conv(current_layer, 8*cm*gd, 3, activation_fn=None,
use_bias=False, scope='conv2')
grid_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.name_scope('fusion'):
fusion_grid = grid_features
fusion_global = tf.reshape(global_features, [bs, 1, 1, 8*cm*gd])
fusion = tf.nn.relu(fusion_grid+fusion_global)
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('prediction'):
current_layer = fusion
current_layer = conv(current_layer, gd*cls.n_out()*cls.n_in(), 1,
activation_fn=None, scope='conv1')
with tf.name_scope('unroll_grid'):
current_layer = tf.stack(
tf.split(current_layer, cls.n_out()*cls.n_in(), axis=3), axis=4)
current_layer = tf.stack(
tf.split(current_layer, cls.n_in(), axis=4), axis=5)
tf.add_to_collection('packed_coefficients', current_layer)
# -----------------------------------------------------------------------
return current_layer
def _coefficients(cls, input_tensor, params, is_training):
bs = input_tensor.get_shape().as_list()[0]
gd = params['luma_bins']
cm = params['channel_multiplier']
spatial_bin = params['spatial_bin']
# -----------------------------------------------------------------------
with tf.variable_scope('splat'):
n_ds_layers = int(np.log2(params['net_input_size']/spatial_bin))
current_layer = input_tensor
for i in range(n_ds_layers):
if i > 0: # don't normalize first layer
use_bn = params['batch_norm']
else:
use_bn = False
current_layer = conv(current_layer, cm*(2**i)*gd, 3, stride=2,
batch_norm=use_bn, is_training=is_training,
scope='conv{}'.format(i+1))
splat_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('global'):
n_global_layers = int(np.log2(spatial_bin/4)) # 4x4 at the coarsest lvl
current_layer = splat_features
for i in range(2):
current_layer = conv(current_layer, 8*cm*gd, 3, stride=2,
batch_norm=params['batch_norm'], is_training=is_training,
scope="conv{}".format(i+1))
_, lh, lw, lc = current_layer.get_shape().as_list()
current_layer = tf.reshape(current_layer, [bs, lh*lw*lc])
current_layer = fc(current_layer, 32*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc1")
current_layer = fc(current_layer, 16*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc2")
# don't normalize before fusion
current_layer = fc(current_layer, 8*cm*gd, activation_fn=None, scope="fc3")
global_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('local'):
current_layer = splat_features
current_layer = conv(current_layer, 8*cm*gd, 3,
batch_norm=params['batch_norm'],
is_training=is_training,
scope='conv1')
# don't normalize before fusion
current_layer = conv(current_layer, 8*cm*gd, 3, activation_fn=None,
use_bias=False, scope='conv2')
grid_features = current_layer
# -----------------------------------------------------------------------
# take the sum of local feature and global feature
# -----------------------------------------------------------------------
with tf.name_scope('fusion'):
fusion_grid = grid_features
fusion_global = tf.reshape(global_features, [bs, 1, 1, 8*cm*gd])
fusion = tf.nn.relu(fusion_grid+fusion_global)
# -----------------------------------------------------------------------
# take the linear prediction, gd*n_out*n_in = 96
# -----------------------------------------------------------------------
with tf.variable_scope('prediction'):
current_layer = fusion
current_layer = conv(current_layer, gd*cls.n_out()*cls.n_in(), 1,
activation_fn=None, scope='conv1')
with tf.name_scope('unroll_grid'):
current_layer = tf.stack(
tf.split(current_layer, cls.n_out()*cls.n_in(), axis=3), axis=4)
current_layer = tf.stack(
tf.split(current_layer, cls.n_in(), axis=4), axis=5)
tf.add_to_collection('packed_coefficients', current_layer)
# -----------------------------------------------------------------------
return current_layer
def _coefficients(cls, input_tensor, params, is_training): # low-res coefficient prediction
bs = input_tensor.get_shape().as_list()[0]
gd = params['luma_bins'] # Number of BGU bins for the luminance.
# # Bilateral grid parameters
cm = params['channel_multiplier'] # Factor to control net throughput (number of intermediate channels).
spatial_bin = params['spatial_bin'] # Size of the spatial BGU bins (pixels).
# -----------------------------------------------------------------------
# low-level features Si
with tf.variable_scope('splat'):
n_ds_layers = int(np.log2(params['net_input_size']/spatial_bin))
current_layer = input_tensor
for i in range(n_ds_layers): # 4个卷积层
if i > 0: # don't normalize first layer
use_bn = params['batch_norm']
else:
use_bn = False
current_layer = conv(current_layer, cm*(2**i)*gd, 3, stride=2, # 可推算出cm*gd=8
batch_norm=use_bn, is_training=is_training,
scope='conv{}'.format(i+1))
splat_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# 3.1.3 global features Gi 经过两层卷积层和三层全连接层得到全局特征
with tf.variable_scope('global'):
n_global_layers = int(np.log2(spatial_bin/4)) # 4x4 at the coarsest lvl
current_layer = splat_features
for i in range(2): # 两层卷积
current_layer = conv(current_layer, 8*cm*gd, 3, stride=2,
batch_norm=params['batch_norm'], is_training=is_training,
scope="conv{}".format(i+1))
_, lh, lw, lc = current_layer.get_shape().as_list()
current_layer = tf.reshape(current_layer, [bs, lh*lw*lc])
# 三层全连接层
current_layer = fc(current_layer, 32*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc1")
current_layer = fc(current_layer, 16*cm*gd,
batch_norm=params['batch_norm'], is_training=is_training,
scope="fc2")
# don't normalize before fusion
current_layer = fc(current_layer, 8*cm*gd, activation_fn=None, scope="fc3")
global_features = current_layer # (1, 64)
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# 3.1.2 local features Li 经过两层卷积层后得到局部特征
with tf.variable_scope('local'):
current_layer = splat_features
#两层卷积层
current_layer = conv(current_layer, 8*cm*gd, 3,
batch_norm=params['batch_norm'],
is_training=is_training,
scope='conv1')
# don't normalize before fusion
current_layer = conv(current_layer, 8*cm*gd, 3, activation_fn=None,
use_bias=False, scope='conv2')
grid_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# 3.1.4 将局部特征与全局特征进行fusion
# “fuse the contributions of the local and global paths with a pointwise affine mixing followed by a ReLU activation”
with tf.name_scope('fusion'):
fusion_grid = grid_features # (1, 16, 16, 64)
fusion_global = tf.reshape(global_features, [bs, 1, 1, 8*cm*gd]) # (1, 1, 1, 64)
fusion = tf.nn.relu(fusion_grid+fusion_global) # (1, 16, 16, 64) 公式(2)，此处获得Fusion F
# fusion is a 16*16*64 array of features
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# 3.1.4 linear prediction, from fusion we make our final 1*1 linear prediction to produce a 16*16 map with 96 channels
with tf.variable_scope('prediction'):
current_layer = fusion # (1,16,16,96)
current_layer = conv(current_layer, gd*cls.n_out()*cls.n_in(), 1,
activation_fn=None, scope='conv1') # 公式(3)， 此处获得feature map A
# 3.2 Image features as a bilateral grid
with tf.name_scope('unroll_grid'): # 公式(4)
current_layer = tf.stack(
tf.split(current_layer, cls.n_out()*cls.n_in(), axis=3), axis=4) # (1,16,16,8,12)
current_layer = tf.stack(
tf.split(current_layer, cls.n_in(), axis=4), axis=5) # (1,16,16,8,3,4)
tf.add_to_collection('packed_coefficients', current_layer)
# -----------------------------------------------------------------------
return current_layer
def _coefficients(cls, input_tensor, params, is_training):
bs = input_tensor.get_shape().as_list()[0] # batch size
gd = params['luma_bins'] # 8- grid for the guidance channel
cm = params['channel_multiplier'] # 1- number of intermediate channels
spatial_bin = params['spatial_bin'] # 16- grid for height&width
# -----------------------------------------------------------------------
with tf.variable_scope('splat'):
n_ds_layers = int(np.log2(params['net_input_size'] / spatial_bin))
current_layer = input_tensor
for i in range(n_ds_layers):
if i > 0: # don't normalize first layer
use_bn = params['batch_norm']
else:
use_bn = False
current_layer = conv(current_layer,
cm * (2**i) * gd,
3,
stride=2,
batch_norm=use_bn,
is_training=is_training,
scope='conv{}'.format(i + 1))
splat_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('global'):
n_global_layers = int(np.log2(spatial_bin /
4)) # 4x4 at the coarsest lvl
current_layer = splat_features
for i in range(2): # shouldn't be n_global_layers???
current_layer = conv(current_layer,
8 * cm * gd,
3,
stride=2,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="conv{}".format(i + 1))
_, lh, lw, lc = current_layer.get_shape().as_list()
current_layer = tf.reshape(current_layer, [bs, lh * lw * lc])
current_layer = fc(current_layer,
32 * cm * gd,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="fc1")
current_layer = fc(current_layer,
16 * cm * gd,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="fc2")
# don't normalize before fusion
current_layer = fc(current_layer,
8 * cm * gd,
activation_fn=None,
scope="fc3")
global_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('local'):
current_layer = splat_features
current_layer = conv(current_layer,
8 * cm * gd,
3,
batch_norm=params['batch_norm'],
is_training=is_training,
scope='conv1')
# don't normalize before fusion
current_layer = conv(current_layer,
8 * cm * gd,
3,
activation_fn=None,
use_bias=False,
scope='conv2')
grid_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.name_scope('fusion'):
fusion_grid = grid_features
fusion_global = tf.reshape(global_features,
[bs, 1, 1, 8 * cm * gd])
fusion = tf.nn.relu(fusion_grid + fusion_global)
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('prediction'):
current_layer = fusion
current_layer = conv(current_layer,
gd * cls.n_out() * cls.n_in(),
1,
activation_fn=None,
scope='conv1')
with tf.name_scope('unroll_grid'):
# splits ths bsx16x16x96 tensor to 12 tensors of bsx16x16x8 and than stacks them, so the result is bsx16x16x8x12
current_layer = tf.stack(tf.split(current_layer,
cls.n_out() * cls.n_in(),
axis=3),
axis=4)
# splits the bsx16x16x8x12 to 4 tensors of bsx16x16x8x3 and then stacks them to bsx16x16x8x3x4
current_layer = tf.stack(tf.split(current_layer,
cls.n_in(),
axis=4),
axis=5)
tf.add_to_collection('packed_coefficients', current_layer)
# -----------------------------------------------------------------------
return current_layer
def _coefficients(cls, input_tensor, params, is_training):
bs = input_tensor.get_shape().as_list()[0]
gd = params['luma_bins']
cm = params['channel_multiplier']
spatial_bin = params['spatial_bin']
# -----------------------------------------------------------------------
with tf.variable_scope('splat'):
# 系数网络层数，n_ds_layers默认是4
n_ds_layers = int(np.log2(params['net_input_size'] / spatial_bin))
current_layer = input_tensor
for i in range(n_ds_layers):
if i > 0: # don't normalize first layer
use_bn = params['batch_norm']
else:
use_bn = False
# 经过4个卷积层，卷积核大小始终是3.由于滑动窗口stride=2，所以卷积后特征图尺寸不断减小
# [-1,256,256,3]->[-1,128,128,8]
# [-1,128,128,8]->[-1,64,64,16]
# [-1,64,64,16]->[-1,32,32,32]
# [-1,32,32,32]->[-1,16,16,64]
current_layer = conv(current_layer,
cm * (2**i) * gd,
3,
stride=2,
batch_norm=use_bn,
is_training=is_training,
scope='conv{}'.format(i + 1))
# [-1,16,16,64]
splat_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('global'):
# 再经过两层卷积得到全局特征图
n_global_layers = int(np.log2(spatial_bin /
4)) # 4x4 at the coarsest lvl
current_layer = splat_features
for i in range(2):
# [-1,16,16,64]->[-1,8,8,64]
# [-1,8,8,64]->[-1,4,4,64]
current_layer = conv(current_layer,
8 * cm * gd,
3,
stride=2,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="conv{}".format(i + 1))
for j in range(0):
current_layer = conv(current_layer,
8 * cm * gd,
3,
stride=1,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="conv{}_{}".format(
i + 1, j + 1))
_, lh, lw, lc = current_layer.get_shape().as_list()
# 将全局特征图扁平化，[-1,4*4*64]
current_layer = tf.reshape(current_layer, [bs, lh * lw * lc])
# 全连接[-1,256]
current_layer = fc(current_layer,
32 * cm * gd,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="fc1")
# [-1, 64]
current_layer = fc(current_layer,
16 * cm * gd,
batch_norm=params['batch_norm'],
is_training=is_training,
scope="fc2")
# don't normalize before fusion
current_layer = fc(current_layer,
8 * cm * gd,
activation_fn=None,
scope="fc3")
# [-1, 64]
global_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.variable_scope('local'):
# 将原图用滑动窗口降采样到16*16的小图，还是保留了一部分空间信息的，只不过比较粗糙
# 用16*16的图记录空间信息，比如那个地方偏亮，哪个地方偏暗。
# 如果不使用，将丢掉任何空间信息，论文有对比图。
current_layer = splat_features
current_layer = conv(current_layer,
8 * cm * gd,
3,
batch_norm=params['batch_norm'],
is_training=is_training,
scope='conv1')
# don't normalize before fusion
for i in range(1):
current_layer = conv(current_layer,
8 * cm * gd,
3,
activation_fn=None,
use_bias=False,
scope='conv%d' % (i + 2))
# [-1, 16, 16, 64]
grid_features = current_layer
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
with tf.name_scope('fusion'):
fusion_grid = grid_features
fusion_global = tf.reshape(global_features,
[bs, 1, 1, 8 * cm * gd])
# 将[-1,16,16,64]的局部特征图和[-1,64]个全局系数相加做融合
fusion = tf.nn.relu(fusion_grid + fusion_global)
# -----------------------------------------------------------------------
# -----------------------------------------------------------------------
# 将64个16*16的特征图片，映射到深度是8的双边网格，即16*16*8。每个格子中，又包含12（3*4）个系数矩阵w_c，predict_color=matmul([r,g,b,1], w_c)
with tf.variable_scope('prediction'):
# [-1,16,16,64]->[-1,16,16,96]
current_layer = fusion
current_layer = conv(current_layer,
gd * cls.n_out() * cls.n_in(),
1,
activation_fn=None,
scope='conv1')
with tf.name_scope('unroll_grid'):
# split后得到tensor数组[[-1,16,16,8] * 12]
# stack后得到[-1,16,16,8,12]
current_layer = tf.stack(tf.split(current_layer,
cls.n_out() * cls.n_in(),
axis=3),
axis=4)
# 将12个像素再分3*4组，得到[-1,16,16,8,3,4]，这就是系数图
current_layer = tf.stack(tf.split(current_layer,
cls.n_in(),
axis=4),
axis=5)
tf.add_to_collection('packed_coefficients', current_layer)
# -----------------------------------------------------------------------
return current_layer
