def layer(x, layer_idx, fmaps, kernel, up=False):
x, atts = get_return_v(
build_C_spgroup_layers_with_latents_ready(
x,
'SP_latents',
latent_split_ls_for_std_gen[layer_idx],
layer_idx,
latents_ready_ls[layer_idx],
return_atts=return_atts,
resolution=resolution,
n_subs=n_subs,
**kwargs), 2)
x = conv2d_layer(x, fmaps=fmaps, kernel=kernel, up=up)
if randomize_noise:
noise = tf.random_normal(
[tf.shape(x)[0], 1, x.shape[2], x.shape[3]], dtype=x.dtype)
else:
noise = tf.cast(noise_inputs[layer_idx], x.dtype)
noise_strength = tf.get_variable('noise_strength',
shape=[],
initializer=tf.initializers.zeros())
x += noise * tf.cast(noise_strength, x.dtype)
return apply_bias_act(x, act=act), atts
def build_Cout_spgroup_layers(x,
name,
n_latents,
start_idx,
scope_idx,
atts_in,
act,
fmaps=128,
resolution=128,
**kwargs):
'''
Build continuous latent out layers with learned group spatial attention.
Support square images only.
'''
# atts_in: [b, all_n_latents, 1, resolution, resolution]
with tf.variable_scope(name + '-' + str(scope_idx)):
with tf.variable_scope('Att_spatial'):
x_wh = x.shape[2]
atts = atts_in[:, start_idx:start_idx +
n_latents] # [b, n_latents, 1, resolution, resolution]
atts = tf.reshape(atts, [-1, resolution, resolution, 1])
atts = tf.image.resize(atts, size=(x_wh, x_wh))
atts = tf.reshape(atts, [-1, n_latents, 1, x_wh, x_wh])
x_out_ls = []
for i in range(n_latents):
x_tmp = x * atts[:, i]
x_tmp_2 = tf.reduce_mean(x_tmp, axis=[2, 3]) # [b, in_dim]
with tf.variable_scope('OutDense-' + str(i)):
with tf.variable_scope('Conv0'):
x_tmp_2 = apply_bias_act(dense_layer(x_tmp_2,
fmaps=fmaps),
act=act) # [b, fmaps]
with tf.variable_scope('Conv1'):
x_out_tmp = dense_layer(x_tmp_2, fmaps=1) # [b, 1]
x_out_ls.append(x_out_tmp)
pred_out = tf.concat(x_out_ls, axis=1) # [b, n_latents]
return x, pred_out
def get_s_matrix(s_latents, cond_latent, act='lrelu'):
# s_latents[:, 0]: [-2., 2.] -> [1., 3.]
# s_latents[:, 1]: [-2., 2.] -> [1., 3.]
if s_latents.shape.as_list()[1] == 1:
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
scale = (s_latents + 2.) * cond + 1.
tt_00 = scale
tt_01 = tf.zeros_like(scale)
tt_02 = tf.zeros_like(scale)
tt_10 = tf.zeros_like(scale)
tt_11 = scale
tt_12 = tf.zeros_like(scale)
else:
with tf.variable_scope('Condition0x'):
cond_x = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1x'):
cond_x = apply_bias_act(dense_layer(cond_x, fmaps=1),
act='sigmoid')
with tf.variable_scope('Condition0y'):
cond_y = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1y'):
cond_y = apply_bias_act(dense_layer(cond_y, fmaps=1),
act='sigmoid')
cond = tf.concat([cond_x, cond_y], axis=1)
scale = (s_latents + 2.) * cond + 1.
tt_00 = scale[:, 0:1]
tt_01 = tf.zeros_like(scale[:, 0:1])
tt_02 = tf.zeros_like(scale[:, 0:1])
tt_10 = tf.zeros_like(scale[:, 1:])
tt_11 = scale[:, 1:]
tt_12 = tf.zeros_like(scale[:, 1:])
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
def get_t_matrix(t_latents, cond_latent, act='lrelu'):
# t_latents[:, 0]: [-2., 2.] -> [-0.5, 0.5]
# t_latents[:, 1]: [-2., 2.] -> [-0.5, 0.5]
if t_latents.shape.as_list()[1] == 1:
with tf.variable_scope('Condition0x'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1x'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
xy_shift = t_latents / 4. * cond
tt_00 = tf.ones_like(xy_shift)
tt_01 = tf.zeros_like(xy_shift)
tt_02 = xy_shift
tt_10 = tf.zeros_like(xy_shift)
tt_11 = tf.ones_like(xy_shift)
tt_12 = xy_shift
else:
with tf.variable_scope('Condition0x'):
cond_x = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1x'):
cond_x = apply_bias_act(dense_layer(cond_x, fmaps=1),
act='sigmoid')
with tf.variable_scope('Condition0y'):
cond_y = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1y'):
cond_y = apply_bias_act(dense_layer(cond_y, fmaps=1),
act='sigmoid')
cond = tf.concat([cond_x, cond_y], axis=1)
xy_shift = t_latents / 4. * cond
tt_00 = tf.ones_like(xy_shift[:, 0:1])
tt_01 = tf.zeros_like(xy_shift[:, 0:1])
tt_02 = xy_shift[:, 0:1]
tt_10 = tf.zeros_like(xy_shift[:, 1:])
tt_11 = tf.ones_like(xy_shift[:, 1:])
tt_12 = xy_shift[:, 1:]
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
def vc_head(
fake1, # First input: generated image from z [minibatch, channel, height, width].
fake2, # Second input: hidden features from z + delta(z) [minibatch, channel, height, width].
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
dlatent_size=10,
D_global_size=0,
fmap_base=16 <<
10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
connect_mode='concat', # How fake1 and fake2 connected.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
fake1.set_shape([None, num_channels, resolution, resolution])
fake2.set_shape([None, num_channels, resolution, resolution])
fake1 = tf.cast(fake1, dtype)
fake2 = tf.cast(fake2, dtype)
if connect_mode == 'diff':
images_in = fake1 - fake2
elif connect_mode == 'concat':
images_in = tf.concat([fake1, fake2], axis=1)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
act=act)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(res - 2),
kernel=3,
down=True,
resample_kernel=resample_kernel),
act=act)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t,
fmaps=nf(res - 2),
kernel=1,
down=True,
resample_kernel=resample_kernel)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel)
# Main layers.
x = None
y = images_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
if mbstd_group_size > 1:
with tf.variable_scope('MinibatchStddev'):
x = minibatch_stddev_layer(x, mbstd_group_size,
mbstd_num_features)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
# Output layer with label conditioning from "Which Training Methods for GANs do actually Converge?"
with tf.variable_scope('Output'):
with tf.variable_scope('Dense_VC'):
x = apply_bias_act(
dense_layer(x,
fmaps=(D_global_size +
(dlatent_size - D_global_size))))
# Output.
assert x.dtype == tf.as_dtype(dtype)
return x
def G_synthesis_sb_general_dsp(
dlatents_withl_in, # Input: Disentangled latents (W) [minibatch, label_size+dlatent_size].
dlatent_size=7, # Disentangled latent (W) dimensionality. Including discrete info, rotation, scaling, xy shearing, and xy translation.
label_size=0, # Label dimensionality, 0 if no labels.
D_global_size=3, # Global D_latents.
C_global_size=0, # Global C_latents.
sb_C_global_size=4, # Global spatial-biased C_latents.
C_local_hfeat_size=0, # Local heatmap*features learned C_latents.
C_local_heat_size=0, # Local heatmap learned C_latents.
num_channels=1, # Number of output color channels.
resolution=64, # Output resolution.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
fused_modconv=True, # Implement modulated_conv2d_layer() as a single fused op?
use_noise=False,
randomize_noise=True, # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
**_kwargs): # Ignore unrecognized keyword args.
'''
dlatents_withl_in: dims contain: [label, D_global, C_global, sb_C_global,
C_local_hfeat, C_local_feat]
'''
resolution_log2 = int(np.log2(resolution)) # == 6 for resolution 64
assert resolution == 2**resolution_log2 and resolution >= 4
num_layers = resolution_log2 * 2 - 2 # == 10 for resolution 64
act = nonlinearity
images_out = None
# Primary inputs.
assert dlatent_size == D_global_size + C_global_size + sb_C_global_size + \
C_local_hfeat_size + C_local_heat_size
n_cat = label_size + D_global_size
dlatents_withl_in.set_shape([None, label_size + dlatent_size])
dlatents_withl_in = tf.cast(dlatents_withl_in, dtype)
n_content = label_size + D_global_size + C_global_size
# Noise inputs.
noise_inputs = []
for layer_idx in range(num_layers - 3):
res = (layer_idx + 7) // 2 # [3, 4, 4, 5, 5, 6, 6]
shape = [1, 1, 2**res, 2**res]
noise_inputs.append(
tf.get_variable('noise%d' % layer_idx,
shape=shape,
initializer=tf.initializers.random_normal(),
trainable=False))
# Single convolution layer with all the bells and whistles.
def noised_conv_layer(x, layer_idx, fmaps, kernel, up=False):
x = conv2d_layer(x,
fmaps=fmaps,
up=up,
kernel=kernel,
resample_kernel=resample_kernel)
if use_noise:
if randomize_noise:
noise = tf.random_normal(
[tf.shape(x)[0], 1, x.shape[2], x.shape[3]], dtype=x.dtype)
else:
noise = tf.cast(noise_inputs[layer_idx], x.dtype)
noise_strength = tf.get_variable(
'noise_strength',
shape=[],
initializer=tf.initializers.zeros())
x += noise * tf.cast(noise_strength, x.dtype)
return apply_bias_act(x, act=act)
# Early layers consists of 4x4 constant layer,
# label+global discrete latents,
# and global continuous latents.
y = None
with tf.variable_scope('4x4'):
with tf.variable_scope('Const'):
x = tf.get_variable('const',
shape=[1, 128, 4, 4],
initializer=tf.initializers.random_normal())
x = tf.tile(tf.cast(x, dtype),
[tf.shape(dlatents_withl_in)[0], 1, 1, 1])
with tf.variable_scope('Upconv'):
x = apply_bias_act(conv2d_layer(x,
fmaps=128,
kernel=3,
up=True,
resample_kernel=resample_kernel),
act=act)
with tf.variable_scope('8x8'):
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=128, kernel=3), act=act)
with tf.variable_scope('Label_Dglobal_control'):
x = apply_bias_act(modulated_conv2d_layer(
x,
dlatents_withl_in[:, :n_cat],
fmaps=128,
kernel=3,
up=False,
resample_kernel=resample_kernel,
fused_modconv=fused_modconv),
act=act)
with tf.variable_scope('After_DiscreteGlobal_noised'):
x = noised_conv_layer(x, layer_idx=0, fmaps=128, kernel=3)
with tf.variable_scope('Cglobal_control'):
start_idx = n_cat
x = apply_bias_act(modulated_conv2d_layer(
x,
dlatents_withl_in[:, start_idx:start_idx + C_global_size],
fmaps=128,
kernel=3,
up=False,
resample_kernel=resample_kernel,
fused_modconv=fused_modconv),
act=act)
with tf.variable_scope('After_ContinuousGlobal_noised'):
x = noised_conv_layer(x, layer_idx=1, up=True, fmaps=128, kernel=3)
# Spatial biased layers.
with tf.variable_scope('16x16'):
if C_local_hfeat_size > 0:
with tf.variable_scope('LocalHFeat_C_latents'):
with tf.variable_scope('ConstFeats'):
const_feats = tf.get_variable(
'constfeats',
shape=[1, C_local_hfeat_size, 32, 1, 1],
initializer=tf.initializers.random_normal())
const_feats = tf.tile(
tf.cast(const_feats,
dtype), [tf.shape(const_feats)[0], 1, 1, 1, 1])
with tf.variable_scope('ControlAttHeat'):
hfeat_start_idx = label_size + D_global_size + C_global_size + \
sb_C_global_size
att_heat = get_att_heat(x,
nheat=C_local_hfeat_size,
act=act)
att_heat = tf.reshape(
att_heat,
[tf.shape(att_heat)[0], C_local_hfeat_size, 1] +
att_heat.shape.as_list()[2:4])
# C_local_heat latent [-2, 2] --> [0, 1]
hfeat_modifier = (2 + dlatents_withl_in[:, hfeat_start_idx:hfeat_start_idx + \
C_local_hfeat_size]) / 4.
hfeat_modifier = get_conditional_modifier(
hfeat_modifier,
dlatents_withl_in[:, :n_content],
act=act)
hfeat_modifier = tf.reshape(
hfeat_modifier,
[tf.shape(x)[0], C_local_hfeat_size, 1, 1, 1])
att_heat = att_heat * hfeat_modifier
added_feats = const_feats * att_heat
added_feats = tf.reshape(added_feats, [
tf.shape(att_heat)[0],
C_local_hfeat_size * att_heat.shape.as_list()[2]
] + att_heat.shape.as_list()[3:5])
x = tf.concat([x, added_feats], axis=1)
with tf.variable_scope('SpatialBiased_C_global'):
# Rotation layers.
start_idx = start_idx + C_global_size
with tf.variable_scope('Rotation'):
r_matrix = get_r_matrix(
dlatents_withl_in[:, start_idx:start_idx + 1],
dlatents_withl_in[:, :n_content],
act=act)
x = apply_st(x, r_matrix, up=False, fmaps=128, act=act)
with tf.variable_scope('After_Rotation_noised'):
x = noised_conv_layer(x, layer_idx=2, fmaps=128, kernel=3)
# Scaling layers.
start_idx = start_idx + 1
with tf.variable_scope('Scaling'):
s_matrix = get_s_matrix(
dlatents_withl_in[:, start_idx:start_idx + 1],
dlatents_withl_in[:, :n_content],
act=act)
x = apply_st(x, s_matrix, up=False, fmaps=128, act=act)
with tf.variable_scope('After_Scaling_noised'):
x = noised_conv_layer(x,
layer_idx=3,
up=True,
fmaps=128,
kernel=3)
with tf.variable_scope('32x32'):
with tf.variable_scope('SpatialBiased_C_global'):
# Shearing layers.
with tf.variable_scope('Shearing'):
start_idx = start_idx + 1
sh_matrix = get_sh_matrix(
dlatents_withl_in[:, start_idx:start_idx + 2],
dlatents_withl_in[:, :n_content],
act=act)
x = apply_st(x, sh_matrix, up=False, fmaps=128, act=act)
with tf.variable_scope('After_Shearing_noised'):
x = noised_conv_layer(x, layer_idx=4, fmaps=128, kernel=3)
# Translation layers.
with tf.variable_scope('Translation'):
start_idx = start_idx + 2
t_matrix = get_t_matrix(
dlatents_withl_in[:, start_idx:start_idx + 2],
dlatents_withl_in[:, :n_content],
act=act)
x = apply_st(x, t_matrix, up=False, fmaps=128, act=act)
with tf.variable_scope('After_Translation_noised'):
if resolution_log2 >= 6:
x = noised_conv_layer(x,
layer_idx=5,
up=True,
fmaps=128,
kernel=3)
else:
x = noised_conv_layer(x,
layer_idx=5,
fmaps=128,
kernel=3)
with tf.variable_scope('64x64' if resolution_log2 >= 6 else '32x32'):
with tf.variable_scope('LocalHeat_C_latents'):
with tf.variable_scope('ControlAttHeat'):
heat_start_idx = label_size + D_global_size + C_global_size + \
sb_C_global_size + C_local_hfeat_size
att_heat = get_att_heat(x, nheat=C_local_heat_size, act=act)
# C_local_heat latent [-2, 2] --> [0, 1]
heat_modifier = (2 + dlatents_withl_in[:, heat_start_idx:heat_start_idx + \
C_local_heat_size]) / 4.
heat_modifier = get_conditional_modifier(
heat_modifier, dlatents_withl_in[:, :n_content], act=act)
heat_modifier = tf.reshape(
heat_modifier,
[tf.shape(heat_modifier)[0], C_local_heat_size, 1, 1])
att_heat = att_heat * heat_modifier
x = tf.concat([x, att_heat], axis=1)
with tf.variable_scope('After_LocalHeat_noised'):
x = noised_conv_layer(x, layer_idx=6, fmaps=128, kernel=3)
y = torgb(x, y, num_channels=num_channels)
# # Tail layers.
# for res in range(6, resolution_log2 + 1):
# with tf.variable_scope('%dx%d' % (res * 2, res * 2)):
# x = apply_bias_act(conv2d_layer(x,
# fmaps=128,
# kernel=1,
# up=True,
# resample_kernel=resample_kernel),
# act=act)
# y = torgb(x, y, num_channels=num_channels)
images_out = y
assert images_out.dtype == tf.as_dtype(dtype)
return tf.identity(images_out, name='images_out')
def get_att_heat(x, nheat, act):
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=128, kernel=3), act=act)
with tf.variable_scope('ConvAtt'):
x = apply_bias_act(conv2d_layer(x, fmaps=1, kernel=3), act='sigmoid')
return x
def torgb(x, y, num_channels):
with tf.variable_scope('ToRGB'):
t = apply_bias_act(conv2d_layer(x, fmaps=num_channels, kernel=1))
return t if y is None else y + t
def D_simple_dsp(
images_in, # First input: Images [minibatch, channel, height, width].
labels_in, # Second input: Labels [minibatch, label_size].
num_channels=1, # Number of input color channels. Overridden based on dataset.
label_size=0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
nonlinearity='relu', # Activation function: 'relu', 'lrelu', etc.
dtype='float32', # Data type to use for activations and outputs.
**_kwargs): # Ignore unrecognized keyword args.
act = nonlinearity
images_in.set_shape([None, num_channels, 64, 64])
labels_in.set_shape([None, label_size])
images_in = tf.cast(images_in, dtype)
labels_in = tf.cast(labels_in, dtype)
x = images_in
with tf.variable_scope('32x32'):
w = get_weight([4, 4, x.shape[1].value, 32])
x = tf.nn.conv2d(x,
tf.cast(w, x.dtype),
data_format='NCHW',
strides=[1, 1, 2, 2],
padding='SAME')
x = apply_bias_act(x, act=act)
with tf.variable_scope('16x16'):
w = get_weight([4, 4, x.shape[1].value, 32])
x = tf.nn.conv2d(x,
tf.cast(w, x.dtype),
data_format='NCHW',
strides=[1, 1, 2, 2],
padding='SAME')
x = apply_bias_act(x, act=act)
with tf.variable_scope('8x8'):
w = get_weight([4, 4, x.shape[1].value, 64])
x = tf.nn.conv2d(x,
tf.cast(w, x.dtype),
data_format='NCHW',
strides=[1, 1, 2, 2],
padding='SAME')
x = apply_bias_act(x, act=act)
with tf.variable_scope('4x4'):
w = get_weight([4, 4, x.shape[1].value, 64])
x = tf.nn.conv2d(x,
tf.cast(w, x.dtype),
data_format='NCHW',
strides=[1, 1, 2, 2],
padding='SAME')
x = apply_bias_act(x, act=act)
with tf.variable_scope('output'):
x = tf.reshape(x, [-1, np.prod([d.value for d in x.shape[1:]])])
w = get_weight([x.shape[1].value, 128])
w = tf.cast(w, x.dtype)
x = tf.matmul(x, w)
x = apply_bias_act(x, act=act)
# Output layer with label conditioning from "Which Training Methods for GANs do actually Converge?"
with tf.variable_scope('Score'):
w = get_weight([x.shape[1].value, 1])
w = tf.cast(w, x.dtype)
x = tf.matmul(x, w)
x = apply_bias_act(x)
scores_out = x
# Output.
assert scores_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(scores_out, name='scores_out')
return scores_out
def G_synthesis_spatial_biased_dsp(
dlatents_in, # Input: Disentangled latents (W) [minibatch, dlatent_size].
dlatent_size=7, # Disentangled latent (W) dimensionality. Including discrete info, rotation, scaling, and xy translation.
D_global_size=3, # Discrete latents.
sb_C_global_size=4, # Continuous latents.
label_size=0, # Label dimensionality, 0 if no labels.
num_channels=1, # Number of output color channels.
resolution=64, # Output resolution.
fmap_base=16 <<
10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='skip', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
fused_modconv=True, # Implement modulated_conv2d_layer() as a single fused op?
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
images_out = None
# Primary inputs.
assert dlatent_size == D_global_size + sb_C_global_size
n_cat = label_size + D_global_size
dlatents_in.set_shape([None, label_size + dlatent_size])
dlatents_in = tf.cast(dlatents_in, dtype)
# Return rotation matrix
def get_r_matrix(r_latents, cond_latent):
# r_latents: [-2., 2.] -> [0, 2*pi]
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
rad = (r_latents + 2) / 4. * 2. * np.pi
rad = rad * cond
tt_00 = tf.math.cos(rad)
tt_01 = -tf.math.sin(rad)
tt_02 = tf.zeros_like(rad)
tt_10 = tf.math.sin(rad)
tt_11 = tf.math.cos(rad)
tt_12 = tf.zeros_like(rad)
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Return scaling matrix
def get_s_matrix(s_latents, cond_latent):
# s_latents: [-2., 2.] -> [1, 3]
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
scale = (s_latents / 2. + 2.) * cond
tt_00 = scale
tt_01 = tf.zeros_like(scale)
tt_02 = tf.zeros_like(scale)
tt_10 = tf.zeros_like(scale)
tt_11 = scale
tt_12 = tf.zeros_like(scale)
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Return shear matrix
def get_sh_matrix(sh_latents, cond_latent):
# sh_latents[:, 0]: [-2., 2.] -> [-1., 1.]
# sh_latents[:, 1]: [-2., 2.] -> [-1., 1.]
with tf.variable_scope('Condition0x'):
cond_x = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1x'):
cond_x = apply_bias_act(dense_layer(cond_x, fmaps=1),
act='sigmoid')
with tf.variable_scope('Condition0y'):
cond_y = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1y'):
cond_y = apply_bias_act(dense_layer(cond_y, fmaps=1),
act='sigmoid')
cond = tf.concat([cond_x, cond_y], axis=1)
xy_shear = sh_latents / 2. * cond
tt_00 = tf.ones_like(xy_shear[:, 0:1])
tt_01 = xy_shear[:, 0:1]
tt_02 = tf.zeros_like(xy_shear[:, 0:1])
tt_10 = xy_shear[:, 1:]
tt_11 = tf.ones_like(xy_shear[:, 1:])
tt_12 = tf.zeros_like(xy_shear[:, 1:])
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Return translation matrix
def get_t_matrix(t_latents, cond_latent):
# t_latents[:, 0]: [-2., 2.] -> [-0.5, 0.5]
# t_latents[:, 1]: [-2., 2.] -> [-0.5, 0.5]
with tf.variable_scope('Condition0x'):
cond_x = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1x'):
cond_x = apply_bias_act(dense_layer(cond_x, fmaps=1),
act='sigmoid')
with tf.variable_scope('Condition0y'):
cond_y = apply_bias_act(dense_layer(cond_latent, fmaps=128),
act=act)
with tf.variable_scope('Condition1y'):
cond_y = apply_bias_act(dense_layer(cond_y, fmaps=1),
act='sigmoid')
cond = tf.concat([cond_x, cond_y], axis=1)
xy_shift = t_latents / 4. * cond
tt_00 = tf.ones_like(xy_shift[:, 0:1])
tt_01 = tf.zeros_like(xy_shift[:, 0:1])
tt_02 = xy_shift[:, 0:1]
tt_10 = tf.zeros_like(xy_shift[:, 1:])
tt_11 = tf.ones_like(xy_shift[:, 1:])
tt_12 = xy_shift[:, 1:]
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Apply spatial transform
def apply_st(x, st_matrix, idx, up=True): # idx: 2, 3, 4
with tf.variable_scope('Transform'):
x = tf.transpose(x, [0, 2, 3, 1]) # NCHW -> NHWC
x = transformer(x, st_matrix, out_dims=x.shape.as_list()[1:3])
x = tf.transpose(x, [0, 3, 1, 2]) # NHWC -> NCHW
with tf.variable_scope('Upconv'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(idx),
kernel=3,
up=up,
resample_kernel=resample_kernel),
act=act)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(idx), kernel=3),
act=act)
return x
def upsample(y):
with tf.variable_scope('Upsample'):
return upsample_2d(y, k=resample_kernel)
def torgb(x, y):
with tf.variable_scope('ToRGB'):
t = apply_bias_act(conv2d_layer(x, fmaps=num_channels, kernel=1))
return t if y is None else y + t
# Early layers.
y = None
with tf.variable_scope('4x4'):
with tf.variable_scope('Const'):
x = tf.get_variable('const',
shape=[1, nf(1), 4, 4],
initializer=tf.initializers.random_normal())
x = tf.tile(tf.cast(x, dtype), [tf.shape(dlatents_in)[0], 1, 1, 1])
with tf.variable_scope('Upconv8x8'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(1),
kernel=3,
up=True,
resample_kernel=resample_kernel),
act=act)
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('ModulatedConv'):
x = apply_bias_act(modulated_conv2d_layer(
x,
dlatents_in[:, :n_cat],
fmaps=nf(2),
kernel=3,
up=False,
resample_kernel=resample_kernel,
fused_modconv=fused_modconv),
act=act)
with tf.variable_scope('Conv1'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(2), kernel=3), act=act)
# Rotation layers.
with tf.variable_scope('16x16'):
r_matrix = get_r_matrix(dlatents_in[:, n_cat:n_cat + 1],
dlatents_in[:, :n_cat])
x = apply_st(x, r_matrix, 2)
# Scaling layers.
with tf.variable_scope('32x32'):
s_matrix = get_s_matrix(dlatents_in[:, n_cat + 1:n_cat + 2],
dlatents_in[:, :n_cat])
x = apply_st(x, s_matrix, 3)
# Shearing layers.
with tf.variable_scope('32x32_Shear'):
sh_matrix = get_sh_matrix(dlatents_in[:, n_cat + 2:n_cat + 4],
dlatents_in[:, :n_cat])
x = apply_st(x, sh_matrix, 3, up=False)
# Translation layers.
with tf.variable_scope('64x64'):
t_matrix = get_t_matrix(dlatents_in[:, n_cat + 4:],
dlatents_in[:, :n_cat])
x = apply_st(x, t_matrix, 4)
y = torgb(x, y)
# # Tail layers.
# for res in range(6, resolution_log2 + 1):
# with tf.variable_scope('%dx%d' % (res * 2, res * 2)):
# x = apply_bias_act(conv2d_layer(x,
# fmaps=nf(res),
# kernel=1,
# up=True,
# resample_kernel=resample_kernel),
# act=act)
# if architecture == 'skip':
# y = upsample(y)
# if architecture == 'skip' or res == resolution_log2:
# y = torgb(x, y)
images_out = y
assert images_out.dtype == tf.as_dtype(dtype)
return tf.identity(images_out, name='images_out')
def vpex_net(
fake1, # First input: generated image from z [minibatch, channel, height, width].
fake2, # Second input: hidden features from z + delta(z) [minibatch, channel, height, width].
latents, # Ground-truth latent code for fake1.
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
dlatent_size=10,
D_global_size=0,
fmap_base=16 <<
10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
connect_mode='concat', # How fake1 and fake2 connected.
return_atts=False, # If return I_atts.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
fake1.set_shape([None, num_channels, resolution, resolution])
fake2.set_shape([None, num_channels, resolution, resolution])
latents.set_shape([None, dlatent_size])
fake1 = tf.cast(fake1, dtype)
fake2 = tf.cast(fake2, dtype)
latents = tf.cast(latents, dtype)
if connect_mode == 'diff':
images_in = fake1 - fake2
elif connect_mode == 'concat':
images_in = tf.concat([fake1, fake2], axis=1)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
act=act)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(res - 2),
kernel=3,
down=True,
resample_kernel=resample_kernel),
act=act)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t,
fmaps=nf(res - 2),
kernel=1,
down=True,
resample_kernel=resample_kernel)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel)
# attention features for each latent dimension.
def get_att_map(latents, x=None):
with tf.variable_scope('create_att_feats'):
x_ch, x_h, x_w = x.get_shape().as_list()[1:]
att_feats = tf.get_variable(
'att_feats',
shape=[1, dlatent_size, x_ch, x_h, x_w],
initializer=tf.initializers.random_normal())
att_feats = tf.tile(tf.cast(att_feats, dtype),
[tf.shape(latents)[0], 1, 1, 1, 1])
latents = latents[:, tf.newaxis, :]
latents = tf.tile(latents, [1, dlatent_size, 1])
latents = tf.reshape(latents, [-1, dlatent_size])
# att_map = apply_bias_act(modulated_conv2d_layer(att_feats, latents, fmaps=64, kernel=3,
# demodulate=False, fused_modconv=False),
# act=act) # shape: [b*dlatent_size, 1, 8, 8]
if x is None:
att_map = att_feats
att_map = tf.reshape(att_map, [-1, x_ch, x_h, x_w])
map_ch = x_ch
else:
x = tf.reshape(x, [-1, 1, x_ch, x_h, x_w])
x = tf.tile(x, [1, dlatent_size, 1, 1, 1])
att_map = tf.concat([x, att_feats], axis=2)
att_map = tf.reshape(att_map, [-1, 2 * x_ch, x_h, x_w])
map_ch = 2 * x_ch
with tf.variable_scope('att_conv_3x3'):
att_map = apply_bias_act(conv2d_layer(att_map,
fmaps=map_ch,
kernel=3),
act=act)
with tf.variable_scope('att_conv_1x1'):
att_map = apply_bias_act(
conv2d_layer(att_map, fmaps=1, kernel=1))
att_map = tf.reshape(att_map, [-1, dlatent_size, 1, x_h * x_w])
att_map = tf.nn.softmax(att_map, axis=-1)
# att_map = tf.nn.sigmoid(att_map)
# att_map = tf.reshape(att_map, [-1, dlatent_size, 1, 8, 8])
return att_map
# Main layers.
x = None
y = images_in
for res in range(resolution_log2, 3, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Duplicate for each att.
with tf.variable_scope('apply_att'):
att_map = get_att_map(latents, x)
x_ch, x_h, x_w = x.get_shape().as_list()[1:]
assert x_h == 8
x_ori = tf.reshape(x, [-1, 1, x_ch, x_h * x_w]) # [b, 1, ch, h*w]
x = tf.reshape(x, [-1, 1, x_ch, x_h * x_w])
x = att_map * x
x = tf.reduce_sum(x, axis=-1) # [b, dlatent, ch]
x = tf.reshape(x, [-1, x_ch, 1, 1]) # [b * dlatent, ch, 1, 1]
with tf.variable_scope('after_att_conv_1x1'):
x = apply_bias_act(conv2d_layer(x, fmaps=x_ch, kernel=1))
x = tf.reshape(x, [-1, dlatent_size, x_ch, 1]) # [b, dlatent, ch, 1]
x = tf.tile(x, [1, 1, 1, x_h * x_w])
# x = x + x_ori # [b, dlatent, ch, h * w]
x = tf.reshape(x, [-1, x_ch, x_h, x_w])
y_ch, y_h, y_w = y.get_shape().as_list()[1:]
y = y[:, tf.newaxis, ...]
y = tf.tile(y, [1, dlatent_size, 1, 1, 1])
y = tf.reshape(y, [-1, y_ch, y_h, y_w])
for res in range(3, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
with tf.variable_scope('Output'):
with tf.variable_scope('Dense_VC'):
x = apply_bias_act(dense_layer(x, fmaps=1))
with tf.variable_scope('Final_reshape_x'):
x = tf.reshape(x, [-1, dlatent_size])
# Output.
assert x.dtype == tf.as_dtype(dtype)
if return_atts:
with tf.variable_scope('Reshape_atts'):
att_map = tf.reshape(att_map, [-1, 8, 8, 1])
att_map = tf.image.resize(att_map, size=(resolution, resolution))
att_map = tf.reshape(att_map,
[-1, dlatent_size, 1, resolution, resolution])
return x, att_map
else:
return x
def vid_naive_cluster_head(
fake_in, # First input: generated image from z [minibatch, channel, n_frames, height, width].
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
dlatent_size=10,
D_global_size=0,
fmap_base=16 << 10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
fake_in.set_shape([None, num_channels, None, resolution, resolution])
fake_in = tf.cast(fake_in, dtype)
vid_in = fake_in
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = conv3d_layer(y, fmaps=nf(res - 1), kernel=1)
t = apply_bias_act_3d(t, act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
with tf.variable_scope('Conv3D_0'):
x = conv3d_layer(x, fmaps=nf(res - 1), kernel=3)
x = apply_bias_act_3d(x, act=act)
with tf.variable_scope('Conv1_down'):
x = conv3d_layer(x, fmaps=nf(res - 2), kernel=3, down=True)
x = apply_bias_act_3d(x, act=act)
return x
# Main layers.
x = None
y = vid_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('I_%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample_3d(y)
# Final layers.
with tf.variable_scope('I_4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
with tf.variable_scope('Conv'):
x = conv3d_layer(x, fmaps=nf(1), kernel=3)
x = apply_bias_act_3d(x, act=act)
with tf.variable_scope('Global_temporal_pool'):
x = tf.reduce_mean(x, axis=2)
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
# Output.
with tf.variable_scope('I_Output'):
with tf.variable_scope('Dense_VC'):
x = apply_bias_act(
dense_layer(x, fmaps=dlatent_size - D_global_size))
assert x.dtype == tf.as_dtype(dtype)
return x
def vid_head(
fake_in, # First input: generated image from z [minibatch, channel, n_frames, height, width].
C_delta_idxes, # Second input: the index of the varied latent.
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
dlatent_size=10,
D_global_size=0,
fmap_base=16 << 10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
fake_in.set_shape([None, num_channels, None, resolution, resolution])
fake_in = tf.cast(fake_in, dtype)
C_delta_idxes.set_shape([None, dlatent_size])
C_delta_idxes = tf.cast(C_delta_idxes, dtype)
vid_in = fake_in
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = conv3d_layer(y, fmaps=nf(res - 1), kernel=1)
t = apply_bias_act_3d(t, act=act)
return t if x is None else x + t
# def block(x, res): # res = 2..resolution_log2
# t = x
# with tf.variable_scope('Conv0'):
# x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
# act=act)
# with tf.variable_scope('Conv1_down'):
# x = apply_bias_act(conv2d_layer(x,
# fmaps=nf(res - 2),
# kernel=3,
# down=True,
# resample_kernel=resample_kernel),
# act=act)
# if architecture == 'resnet':
# with tf.variable_scope('Skip'):
# t = conv2d_layer(t,
# fmaps=nf(res - 2),
# kernel=1,
# down=True,
# resample_kernel=resample_kernel)
# x = (x + t) * (1 / np.sqrt(2))
# return x
def block(x, res): # res = 2..resolution_log2
with tf.variable_scope('Conv3D_0'):
x = conv3d_layer(x, fmaps=nf(res - 1), kernel=3)
x = apply_bias_act_3d(x, act=act)
with tf.variable_scope('Conv1_down'):
x = conv3d_layer(x, fmaps=nf(res - 2), kernel=3, down=True)
x = apply_bias_act_3d(x, act=act)
return x
# def downsample(y):
# with tf.variable_scope('Downsample'):
# return downsample_2d(y, k=resample_kernel)
# Main layers.
x = None
y = vid_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('I_%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample_3d(y)
# Final layers.
with tf.variable_scope('I_4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
with tf.variable_scope('Conv'):
x = conv3d_layer(x, fmaps=nf(1), kernel=3)
x = apply_bias_act_3d(x, act=act)
with tf.variable_scope('Global_temporal_pool'):
x = tf.reduce_mean(x, axis=2)
with tf.variable_scope('Dense0'):
x = apply_bias_act(dense_layer(x, fmaps=64), act=act)
print('before from C_delta_idxes, x.get_shape:', x.get_shape().as_list())
print('before from C_delta_idxes, x.shape:', x.shape)
print('before from C_delta_idxes, C_delta_idxes.shape:',
C_delta_idxes.shape)
# From C_delta_idxes
with tf.variable_scope('I_From_C_Delta_Idx'):
x_from_delta = apply_bias_act(dense_layer(C_delta_idxes, fmaps=32),
act=act)
x = tf.concat([x, x_from_delta], axis=1)
# For MINE
with tf.variable_scope('I_Output'):
with tf.variable_scope('Dense_T_0'):
x = apply_bias_act(dense_layer(x, fmaps=128), act=act)
with tf.variable_scope('Dense_T_1'):
x = apply_bias_act(dense_layer(x, fmaps=1))
# Output.
assert x.dtype == tf.as_dtype(dtype)
return x
def G_synthesis_simple_dsp(
dlatents_in, # Input: Disentangled latents (W) [minibatch, dlatent_size].
dlatent_size=7, # Disentangled latent (W) dimensionality. Including discrete info, rotation, scaling, and xy translation.
D_global_size=3, # Discrete latents.
sb_C_global_size=4, # Continuous latents.
label_size=0, # Label dimensionality, 0 if no labels.
num_channels=1, # Number of output color channels.
nonlinearity='relu', # Activation function: 'relu', 'lrelu', etc.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
fused_modconv=True, # Implement modulated_conv2d_layer() as a single fused op?
**_kwargs): # Ignore unrecognized keyword args.
act = nonlinearity
images_out = None
# Primary inputs.
assert dlatent_size == D_global_size + sb_C_global_size
n_cat = label_size + D_global_size
dlatents_in.set_shape([None, label_size + dlatent_size])
dlatents_in = tf.cast(dlatents_in, dtype)
# Return rotation matrix
def get_r_matrix(r_latents, cond_latent):
# r_latents: [-2., 2.] -> [0, 2*pi]
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
rad = (r_latents + 2) / 4. * 2. * np.pi
rad = rad * cond
tt_00 = tf.math.cos(rad)
tt_01 = -tf.math.sin(rad)
tt_02 = tf.zeros_like(rad)
tt_10 = tf.math.sin(rad)
tt_11 = tf.math.cos(rad)
tt_12 = tf.zeros_like(rad)
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Return scaling matrix
def get_s_matrix(s_latents, cond_latent):
# s_latents: [-2., 2.] -> [1, 3]
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
scale = (s_latents / 2. + 2.) * cond
tt_00 = scale
tt_01 = tf.zeros_like(scale)
tt_02 = tf.zeros_like(scale)
tt_10 = tf.zeros_like(scale)
tt_11 = scale
tt_12 = tf.zeros_like(scale)
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Return translation matrix
def get_t_matrix(t_latents, cond_latent):
# t_latents[:, 0]: [-2., 2.] -> [-0.5, 0.5]
# t_latents[:, 1]: [-2., 2.] -> [-0.5, 0.5]
with tf.variable_scope('Condition0'):
cond = apply_bias_act(dense_layer(cond_latent, fmaps=128), act=act)
with tf.variable_scope('Condition1'):
cond = apply_bias_act(dense_layer(cond, fmaps=1), act='sigmoid')
xy_shift = t_latents / 4. * cond
tt_00 = tf.ones_like(xy_shift[:, 0:1])
tt_01 = tf.zeros_like(xy_shift[:, 0:1])
tt_02 = xy_shift[:, 0:1]
tt_10 = tf.zeros_like(xy_shift[:, 1:])
tt_11 = tf.ones_like(xy_shift[:, 1:])
tt_12 = xy_shift[:, 1:]
theta = tf.concat([tt_00, tt_01, tt_02, tt_10, tt_11, tt_12], axis=1)
return theta
# Apply spatial transform
def apply_st(x, st_matrix):
with tf.variable_scope('Transform'):
x = tf.transpose(x, [0, 2, 3, 1]) # NCHW -> NHWC
x = transformer(x, st_matrix, out_dims=x.shape.as_list()[1:3])
x = tf.transpose(x, [0, 3, 1, 2]) # NHWC -> NCHW
return x
def torgb(x, y):
with tf.variable_scope('ToRGB'):
t = apply_bias_act(conv2d_layer(x, fmaps=num_channels, kernel=1))
return t if y is None else y + t
# Early layers.
y = None
with tf.variable_scope('4x4'):
with tf.variable_scope('Const'):
x = tf.get_variable('const',
shape=[1, 64, 4, 4],
initializer=tf.initializers.random_normal())
x = tf.tile(tf.cast(x, dtype), [tf.shape(dlatents_in)[0], 1, 1, 1])
with tf.variable_scope('4x4Conv'):
w = get_weight([3, 3, x.shape[1].value, 64])
x = tf.nn.conv2d(x,
tf.cast(w, x.dtype),
data_format='NCHW',
strides=[1, 1, 1, 1],
padding='SAME')
x = apply_bias_act(x, act=act)
with tf.variable_scope('8x8ModulatedConv'):
x = apply_bias_act(modulated_conv2d_layer(
x,
dlatents_in[:, :n_cat],
fmaps=64,
kernel=3,
up=True,
resample_kernel=resample_kernel,
fused_modconv=fused_modconv),
act=act)
with tf.variable_scope('16x16'):
w = get_weight([4, 4, x.shape[1].value, 32])
# Transpose weights.
w = tf.transpose(w, [0, 1, 3, 2])
x = tf.nn.conv2d_transpose(
x,
w,
output_shape=[tf.shape(dlatents_in)[0], 32, 16, 16],
strides=[1, 1, 2, 2],
padding='SAME',
data_format='NCHW')
x = apply_bias_act(x, act=act)
with tf.variable_scope('rotation'):
r_matrix = get_r_matrix(dlatents_in[:, n_cat:n_cat + 1],
dlatents_in[:, :n_cat])
x = apply_st(x, r_matrix)
with tf.variable_scope('scale'):
s_matrix = get_s_matrix(dlatents_in[:, n_cat + 1:n_cat + 2],
dlatents_in[:, :n_cat])
x = apply_st(x, s_matrix)
with tf.variable_scope('translation'):
t_matrix = get_t_matrix(dlatents_in[:, n_cat + 2:],
dlatents_in[:, :n_cat])
x = apply_st(x, t_matrix)
with tf.variable_scope('32x32'):
w = get_weight([4, 4, x.shape[1].value, 32])
# Transpose weights.
w = tf.transpose(w, [0, 1, 3, 2])
x = tf.nn.conv2d_transpose(
x,
w,
output_shape=[tf.shape(dlatents_in)[0], 32, 32, 32],
strides=[1, 1, 2, 2],
padding='SAME',
data_format='NCHW')
x = apply_bias_act(x, act=act)
with tf.variable_scope('64x64'):
w = get_weight([4, 4, x.shape[1].value, 1])
# Transpose weights.
w = tf.transpose(w, [0, 1, 3, 2])
x = tf.nn.conv2d_transpose(
x,
w,
output_shape=[tf.shape(dlatents_in)[0], 1, 64, 64],
strides=[1, 1, 2, 2],
padding='SAME',
data_format='NCHW')
x = apply_bias_act(x)
images_out = x
assert images_out.dtype == tf.as_dtype(dtype)
return tf.identity(images_out, name='images_out')
def D_info_gan_stylegan2(
images_in, # First input: Images [minibatch, channel, height, width].
labels_in, # Second input: Labels [minibatch, label_size].
num_channels=3, # Number of input color channels. Overridden based on dataset.
resolution=1024, # Input resolution. Overridden based on dataset.
label_size=0, # Dimensionality of the labels, 0 if no labels. Overridden based on dataset.
fmap_base=16 << 10, # Overall multiplier for the number of feature maps.
fmap_decay=1.0, # log2 feature map reduction when doubling the resolution.
fmap_min=1, # Minimum number of feature maps in any layer.
fmap_max=512, # Maximum number of feature maps in any layer.
architecture='resnet', # Architecture: 'orig', 'skip', 'resnet'.
nonlinearity='lrelu', # Activation function: 'relu', 'lrelu', etc.
mbstd_group_size=4, # Group size for the minibatch standard deviation layer, 0 = disable.
mbstd_num_features=1, # Number of features for the minibatch standard deviation layer.
dtype='float32', # Data type to use for activations and outputs.
resample_kernel=[
1, 3, 3, 1
], # Low-pass filter to apply when resampling activations. None = no filtering.
**_kwargs): # Ignore unrecognized keyword args.
resolution_log2 = int(np.log2(resolution))
assert resolution == 2**resolution_log2 and resolution >= 4
def nf(stage):
return np.clip(int(fmap_base / (2.0**(stage * fmap_decay))), fmap_min,
fmap_max)
assert architecture in ['orig', 'skip', 'resnet']
act = nonlinearity
images_in.set_shape([None, num_channels, resolution, resolution])
labels_in.set_shape([None, label_size])
images_in = tf.cast(images_in, dtype)
labels_in = tf.cast(labels_in, dtype)
# Building blocks for main layers.
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def block(x, res): # res = 2..resolution_log2
t = x
with tf.variable_scope('Conv0'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(res - 1), kernel=3),
act=act)
with tf.variable_scope('Conv1_down'):
x = apply_bias_act(conv2d_layer(x,
fmaps=nf(res - 2),
kernel=3,
down=True,
resample_kernel=resample_kernel),
act=act)
if architecture == 'resnet':
with tf.variable_scope('Skip'):
t = conv2d_layer(t,
fmaps=nf(res - 2),
kernel=1,
down=True,
resample_kernel=resample_kernel)
x = (x + t) * (1 / np.sqrt(2))
return x
def downsample(y):
with tf.variable_scope('Downsample'):
return downsample_2d(y, k=resample_kernel)
# Main layers.
x = None
y = images_in
for res in range(resolution_log2, 2, -1):
with tf.variable_scope('%dx%d' % (2**res, 2**res)):
if architecture == 'skip' or res == resolution_log2:
x = fromrgb(x, y, res)
x = block(x, res)
if architecture == 'skip':
y = downsample(y)
# Final layers.
with tf.variable_scope('4x4'):
if architecture == 'skip':
x = fromrgb(x, y, 2)
if mbstd_group_size > 1:
with tf.variable_scope('MinibatchStddev'):
x = minibatch_stddev_layer(x, mbstd_group_size,
mbstd_num_features)
with tf.variable_scope('Conv'):
x = apply_bias_act(conv2d_layer(x, fmaps=nf(1), kernel=3), act=act)
with tf.variable_scope('Dense0'):
hidden = apply_bias_act(dense_layer(x, fmaps=nf(0)), act=act)
# Output layer with label conditioning from "Which Training Methods for GANs do actually Converge?"
with tf.variable_scope('Output'):
x = apply_bias_act(
dense_layer(hidden, fmaps=max(labels_in.shape[1], 1)))
if labels_in.shape[1] > 0:
x = tf.reduce_sum(x * labels_in, axis=1, keepdims=True)
scores_out = x
# Output.
assert scores_out.dtype == tf.as_dtype(dtype)
scores_out = tf.identity(scores_out, name='scores_out')
hidden = tf.identity(hidden, name='hidden')
return scores_out, hidden
def build_C_spgroup_stn_layers(x,
name,
n_latents,
start_idx,
scope_idx,
dlatents_in,
act,
fused_modconv,
fmaps=128,
return_atts=False,
resolution=128,
**kwargs):
'''
Build continuous latent layers with learned group spatial attention with spatial transform.
Support square images only.
'''
with tf.variable_scope(name + '-' + str(scope_idx)):
with tf.variable_scope('Att_spatial'):
x_mean = tf.reduce_mean(x, axis=[2, 3]) # [b, in_dim]
x_wh = x.shape[2]
atts_wh = dense_layer(x_mean, fmaps=n_latents * 4 * x_wh)
atts_wh = tf.reshape(
atts_wh, [-1, n_latents, 4, x_wh]) # [b, n_latents, 4, x_wh]
att_wh_sm = tf.nn.softmax(atts_wh, axis=-1)
att_wh_cs = tf.cumsum(att_wh_sm, axis=-1)
att_h_cs_starts, att_h_cs_ends, att_w_cs_starts, att_w_cs_ends = tf.split(
att_wh_cs, 4, axis=2)
att_h_cs_ends = 1 - att_h_cs_ends # [b, n_latents, 1, x_wh]
att_w_cs_ends = 1 - att_w_cs_ends # [b, n_latents, 1, x_wh]
att_h_cs_starts = tf.reshape(att_h_cs_starts,
[-1, n_latents, 1, x_wh, 1])
att_h_cs_ends = tf.reshape(att_h_cs_ends,
[-1, n_latents, 1, x_wh, 1])
att_h = att_h_cs_starts * att_h_cs_ends # [b, n_latents, 1, x_wh, 1]
att_w_cs_starts = tf.reshape(att_w_cs_starts,
[-1, n_latents, 1, 1, x_wh])
att_w_cs_ends = tf.reshape(att_w_cs_ends,
[-1, n_latents, 1, 1, x_wh])
att_w = att_w_cs_starts * att_w_cs_ends # [b, n_latents, 1, 1, x_wh]
atts = att_h * att_w # [b, n_latents, 1, x_wh, x_wh]
with tf.variable_scope('trans_matrix'):
theta = apply_bias_act(dense_layer(x_mean, fmaps=n_latents * 6))
theta = tf.reshape(theta, [-1, 6]) # [b*n_latents, 6]
atts = tf.reshape(
atts, [-1, x_wh, x_wh, 1]) # [b*n_latents, x_wh, x_wh, 1]
atts = transformer(atts, theta) # [b*n_latents, x_wh, x_wh, 1]
atts = tf.reshape(atts, [-1, n_latents, 1, x_wh, x_wh])
with tf.variable_scope('Att_apply'):
C_global_latents = dlatents_in[:, start_idx:start_idx + n_latents]
x_norm = instance_norm(x)
for i in range(n_latents):
with tf.variable_scope('style_mod-' + str(i)):
x_styled = style_mod(x_norm, C_global_latents[:, i:i + 1])
x = x * (1 - atts[:, i]) + x_styled * atts[:, i]
if return_atts:
with tf.variable_scope('Reshape_output'):
atts = tf.reshape(atts, [-1, x_wh, x_wh, 1])
atts = tf.image.resize(atts, size=(resolution, resolution))
atts = tf.reshape(atts,
[-1, n_latents, 1, resolution, resolution])
return x, atts
else:
return x
def fromrgb(x, y, res): # res = 2..resolution_log2
with tf.variable_scope('FromRGB'):
t = apply_bias_act(conv2d_layer(y, fmaps=nf(res - 1), kernel=1),
act=act)
return t if x is None else x + t
def build_zpos_to_mat_layer(x,
name,
n_layers,
scope_idx,
is_training,
wh,
feat_cnn_dim,
resolution=128,
trans_dim=512,
dff=512,
trans_rate=0.1,
ncut_maxval=5,
post_trans_mat=16,
**kwargs):
'''
Build zpos_to_mat forwarding transformer to extract features per z.
'''
with tf.variable_scope(name + '-' + str(scope_idx)):
with tf.variable_scope('PosConstant'):
n_lat = x.get_shape().as_list()[-1]
pos = tf.get_variable('const',
shape=[1, n_lat, trans_dim],
initializer=tf.initializers.random_normal())
pos = tf.tile(tf.cast(pos, x.dtype), [tf.shape(x)[0], 1, 1])
zpos = pos + x[:, :, np.newaxis]
with tf.variable_scope('MaskEncoding'):
if is_training:
ncut = tf.random.uniform(shape=[],
maxval=ncut_maxval,
dtype=tf.int32)
split_masks_mul, split_idx = create_split_mask(n_lat, ncut)
else:
split_masks_mul = tf.ones(shape=[n_lat, n_lat],
dtype=tf.float32)
split_idx = tf.constant([n_lat])
split_idx = tf.concat([split_idx, [n_lat]], axis=0)
split_idx, _ = tf.unique(split_idx)
mask_logits = get_return_v(
trans_encoder_basic(zpos,
is_training,
split_masks_mul,
n_layers,
trans_dim,
num_heads=8,
dff=dff,
rate=trans_rate), 1) # (b, n_lat, d_model)
mask_groups = dense_layer_last_dim(mask_logits,
post_trans_mat * post_trans_mat)
with tf.variable_scope('GatherSubgroups'):
b = tf.shape(mask_groups)[0]
len_group = tf.shape(split_idx)[0]
gathered_groups = tf.reshape(
tf.gather(mask_groups, split_idx - 1, axis=1),
[b, len_group] + [post_trans_mat, post_trans_mat
]) # (b, len(split_idx), mat * mat)
mat_agg = tf.eye(post_trans_mat, batch_shape=[b])
def cond(i, mats):
return tf.less(i, len_group)
def bod(i, mats):
mats = tf.matmul(gathered_groups[:, i, ...], mats)
i += 1
return (i, mats)
i_mats = (0, mat_agg)
_, mat_agg_final = tf.while_loop(cond, bod,
i_mats) # (b, mat, mat)
mat_agg_final_out = tf.reshape(
mat_agg_final, [b, post_trans_mat * post_trans_mat])
with tf.variable_scope('MaskMapping'):
mat_agg_final = tf.reshape(mat_agg_final,
[b, post_trans_mat * post_trans_mat])
feat = apply_bias_act(
dense_layer(mat_agg_final, feat_cnn_dim * wh * wh))
feat = tf.reshape(feat, [-1, feat_cnn_dim, wh, wh])
with tf.variable_scope('ReshapeAttns'):
split_masks_mul -= 1e-4
atts = tf.reshape(split_masks_mul, [-1, n_lat, n_lat, 1])
atts = tf.image.resize(atts, size=(resolution, resolution))
atts = tf.tile(
tf.reshape(atts, [-1, 1, 1, resolution, resolution]),
[1, n_lat, 1, 1, 1])
return feat, atts, mat_agg_final_out
