def logit(h, is_training=True, update_batch_stats=True, stochastic=True, seed=1234, dropout_mask=None, return_mask=False, h_before_dropout=None):
rng = np.random.RandomState(seed)
if h_before_dropout is None:
h = L.conv(h, ksize=3, stride=1, f_in=3, f_out=128, seed=rng.randint(123456), name='c1')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b1'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c2')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b2'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c3')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b3'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
if stochastic:
h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden)
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=256, seed=rng.randint(123456), name='c4')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b4'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c5')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b5'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c6')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b6'), FLAGS.lrelu_a)
h_before_dropout = L.max_pool(h, ksize=2, stride=2)
# Making it possible to change or return a dropout mask
if stochastic:
if dropout_mask is None:
dropout_mask = tf.cast(
tf.greater_equal(tf.random_uniform(tf.shape(h_before_dropout), 0, 1, seed=rng.randint(123456)), 1.0 - FLAGS.keep_prob_hidden),
tf.float32)
else:
dropout_mask = tf.reshape(dropout_mask, tf.shape(h_before_dropout))
h = tf.multiply(h_before_dropout, dropout_mask)
h = (1.0 / FLAGS.keep_prob_hidden) * h
else:
h = h_before_dropout
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=512, seed=rng.randint(123456), padding="VALID", name='c7')
h = L.lrelu(L.bn(h, 512, is_training=is_training, update_batch_stats=update_batch_stats, name='b7'), FLAGS.lrelu_a)
h = L.conv(h, ksize=1, stride=1, f_in=512, f_out=256, seed=rng.randint(123456), name='c8')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b8'), FLAGS.lrelu_a)
h = L.conv(h, ksize=1, stride=1, f_in=256, f_out=128, seed=rng.randint(123456), name='c9')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b9'), FLAGS.lrelu_a)
h = tf.reduce_mean(h, reduction_indices=[1, 2]) # Global average pooling
h = L.fc(h, 128, 10, seed=rng.randint(123456), name='fc')
if FLAGS.top_bn:
h = L.bn(h, 10, is_training=is_training,
update_batch_stats=update_batch_stats, name='bfc')
if return_mask:
return h, tf.reshape(dropout_mask, [-1, 8*8*256]), h_before_dropout
else:
return h
def discriminator(x,
y,
is_training=True,
update_batch_stats=True,
act_fn=L.lrelu,
bn=FLAGS.dis_bn,
reuse=True):
with tf.variable_scope('discriminator', reuse=reuse):
if FLAGS.method == 'cgan':
h = L.fc(y,
y_dim,
X_dim * X_dim,
seed=rng.randint(123456),
name='fc_y')
h = tf.reshape(h, [-1, X_dim, X_dim, 1])
h = tf.concat((x, h), axis=3)
h = L.conv(h, 3, 1, num_channels + 1, 32, name="conv1")
else:
h = L.conv(x, 3, 1, num_channels, 32, name="conv1")
h = act_fn(h)
# 64x64 -> 32x32
h = L.conv(
h,
4,
2,
32,
64,
name="conv2",
)
h = L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='bn1') if bn else h
h = act_fn(h)
# 32x32 -> 16x16
h = L.conv(h, 4, 2, 64, 128, name="conv3")
h = L.bn(h,
128,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='bn2') if bn else h
h = act_fn(h)
h = L.conv(h, X_dim / 4, 1, 128, 1, name="conv5", padding="VALID")
logits = tf.reshape(h, [-1, 1])
return logits
def logit(x,
is_training=True,
update_batch_stats=True,
stochastic=True,
seed=1234):
x = tf.reshape(x, [x.get_shape().as_list()[0], -1])
layer_sizes = numpy.asarray(FLAGS.layer_sizes.split('-'), numpy.int32)
num_layers = len(layer_sizes) - 1
rng = numpy.random.RandomState(seed)
h = x
for l, dim in enumerate(layer_sizes):
inp_dim = h.get_shape()[1]
with tf.variable_scope(str(l)):
W = tf.get_variable(
'W',
shape=[inp_dim, dim],
initializer=tf.contrib.layers.xavier_initializer(
uniform=False, seed=rng.randint(123456), dtype=tf.float32))
b = tf.get_variable('b',
shape=[dim],
initializer=tf.constant_initializer(0.0))
h = tf.nn.xw_plus_b(h, W, b)
h = L.bn(h,
dim,
is_training=is_training,
update_batch_stats=update_batch_stats)
if l < num_layers - 1:
h = tf.nn.relu(h)
h = gaussian_noise_layer(
h, stddev=FLAGS.noise_stddev, seed=rng.randint(123456)
) if FLAGS.noise_stddev > 0 and stochastic else h
return h
def logit_small(x,
num_classes,
is_training=True,
update_batch_stats=True,
stochastic=True,
seed=1234):
if is_training:
scope = tf.name_scope("Training")
else:
scope = tf.name_scope("Testing")
with scope:
h = x
rng = np.random.RandomState(seed)
h = L.fc(h,
dim_in=x.shape[1],
dim_out=64,
seed=rng.randint(123456),
name="fc1")
h = L.lrelu(
L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='fc1_normalized'), FLAGS.lrelu_a)
h = L.fc(h,
dim_in=64,
dim_out=64,
seed=rng.randint(123456),
name="fc2")
h = L.lrelu(
L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='fc2_normalized'), FLAGS.lrelu_a)
h = L.fc(h,
dim_in=64,
dim_out=num_classes,
seed=rng.randint(123456),
name="fc3")
return h
def __init__(self, F=None):
from theano.tensor.nnet import sigmoid, relu
from layers import initGain, fcLayer, convUnit, nonlinLayer, reshapeLayer, convLayer
from layers import batchNormLayer2D as bn
l = []
l.append(convLayer(filterShape=(32, 1, 5, 5), stride=2))
l.append(bn(n_out=32))
l.append(nonlinLayer(activation=relu))
l.append(convLayer(filterShape=(128, 32, 5, 5), stride=2))
l.append(bn(n_out=128))
l.append(nonlinLayer(activation=relu))
l.append(convLayer(filterShape=(256, 128, 5, 5), stride=2))
l.append(bn(n_out=256))
l.append(nonlinLayer(activation=relu))
l.append(reshapeLayer((-1, 256 * 14 * 14)))
l.append(fcLayer(n_in=256 * 14 * 14, n_out=1, activation=sigmoid))
self.l = l
self.params = get_params(l)
def logit(x, is_training=True, update_batch_stats=True, stochastic=True, seed=1234):
h = x
rng = numpy.random.RandomState(seed)
h = L.conv(h, ksize=3, stride=1, f_in=3, f_out=128, seed=rng.randint(123456), name='c1')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b1'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c2')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b2'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=128, seed=rng.randint(123456), name='c3')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b3'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h
h = L.conv(h, ksize=3, stride=1, f_in=128, f_out=256, seed=rng.randint(123456), name='c4')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b4'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c5')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b5'), FLAGS.lrelu_a)
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=256, seed=rng.randint(123456), name='c6')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b6'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
h = tf.nn.dropout(h, keep_prob=FLAGS.keep_prob_hidden, seed=rng.randint(123456)) if stochastic else h
h = L.conv(h, ksize=3, stride=1, f_in=256, f_out=512, seed=rng.randint(123456), padding="VALID", name='c7')
h = L.lrelu(L.bn(h, 512, is_training=is_training, update_batch_stats=update_batch_stats, name='b7'), FLAGS.lrelu_a)
h = L.conv(h, ksize=1, stride=1, f_in=512, f_out=256, seed=rng.randint(123456), name='c8')
h = L.lrelu(L.bn(h, 256, is_training=is_training, update_batch_stats=update_batch_stats, name='b8'), FLAGS.lrelu_a)
h = L.conv(h, ksize=1, stride=1, f_in=256, f_out=128, seed=rng.randint(123456), name='c9')
h = L.lrelu(L.bn(h, 128, is_training=is_training, update_batch_stats=update_batch_stats, name='b9'), FLAGS.lrelu_a)
h1 = tf.reduce_mean(h, reduction_indices=[1, 2]) # Features to be aligned
h = L.fc(h1, 128, 10, seed=rng.randint(123456), name='fc')
if FLAGS.top_bn:
h = L.bn(h, 10, is_training=is_training,
update_batch_stats=update_batch_stats, name='bfc')
return h, h1
def __init__(self, nz, F=None):
from theano.tensor.nnet import sigmoid, relu
from layers import initGain, fcLayer, convUnit, nonlinLayer, reshapeLayer, convLayer
from layers import batchNormLayer2D as bn
l = []
l.append(fcLayer(n_in=nz, n_out=256 * 13 * 13))
l.append(bn(n_out=256 * 13 * 13))
l.append(nonlinLayer(activation=relu, a=0.2))
l.append(reshapeLayer((-1, 256, 13, 13)))
l.append(convLayer(filterShape=(128, 256, 5, 5), stride=0.5))
l.append(bn(n_out=128))
l.append(nonlinLayer(activation=relu, a=0.2))
l.append(convLayer(filterShape=(64, 128, 5, 5), stride=0.5))
l.append(bn(n_out=64))
l.append(nonlinLayer(activation=relu, a=0.2))
l.append(
convLayer(filterShape=(1, 64, 4, 4),
stride=0.5,
activation=sigmoid))
self.l = l
self.params = get_params(l)
def __init__(self, G_ch=64, G_depth=2, dim_z=128, bottom_width=4, resolution=128,
G_kernel_size=3, G_attn='64', n_classes=1000,
num_G_SVs=1, num_G_SV_itrs=1,
G_shared=True, shared_dim=0, hier=False,
cross_replica=False, mybn=False,
G_activation=nn.ReLU(inplace=False),
G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8,
BN_eps=1e-5, SN_eps=1e-12, G_mixed_precision=False, G_fp16=False,
G_init='ortho', skip_init=False, no_optim=False,
G_param='SN', norm_style='bn',
**kwargs):
super(Generator, self).__init__()
# Channel width mulitplier
self.ch = G_ch
# Number of resblocks per stage
self.G_depth = G_depth
# Dimensionality of the latent space
self.dim_z = dim_z
# The initial spatial dimensions
self.bottom_width = bottom_width
# Resolution of the output
self.resolution = resolution
# Kernel size?
self.kernel_size = G_kernel_size
# Attention?
self.attention = G_attn
# number of classes, for use in categorical conditional generation
self.n_classes = n_classes
# Use shared embeddings?
self.G_shared = G_shared
# Dimensionality of the shared embedding? Unused if not using G_shared
self.shared_dim = shared_dim if shared_dim > 0 else dim_z
# Hierarchical latent space?
self.hier = hier
# Cross replica batchnorm?
self.cross_replica = cross_replica
# Use my batchnorm?
self.mybn = mybn
# nonlinearity for residual blocks
self.activation = G_activation
# Initialization style
self.init = G_init
# Parameterization style
self.G_param = G_param
# Normalization style
self.norm_style = norm_style
# Epsilon for BatchNorm?
self.BN_eps = BN_eps
# Epsilon for Spectral Norm?
self.SN_eps = SN_eps
# fp16?
self.fp16 = G_fp16
# Architecture dict
self.arch = G_arch(self.ch, self.attention)[resolution]
# Which convs, batchnorms, and linear layers to use
if self.G_param == 'SN':
self.which_conv = functools.partial(layers.SNConv2d,
kernel_size=3, padding=1,
num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
self.which_linear = functools.partial(layers.SNLinear,
num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
else:
self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
self.which_linear = nn.Linear
# We use a non-spectral-normed embedding here regardless;
# For some reason applying SN to G's embedding seems to randomly cripple G
self.which_embedding = nn.Embedding
bn_linear = (functools.partial(self.which_linear, bias=False) if self.G_shared
else self.which_embedding)
self.which_bn = functools.partial(layers.ccbn,
which_linear=bn_linear,
cross_replica=self.cross_replica,
mybn=self.mybn,
input_size=(self.shared_dim + self.dim_z if self.G_shared
else self.n_classes),
norm_style=self.norm_style,
eps=self.BN_eps)
# Prepare model
# If not using shared embeddings, self.shared is just a passthrough
self.shared = (self.which_embedding(n_classes, self.shared_dim) if G_shared
else layers.identity())
# First linear layer
self.linear = self.which_linear(self.dim_z + self.shared_dim, self.arch['in_channels'][0] * (self.bottom_width **2))
# self.blocks is a doubly-nested list of modules, the outer loop intended
# to be over blocks at a given resolution (resblocks and/or self-attention)
# while the inner loop is over a given block
self.blocks = []
for index in range(len(self.arch['out_channels'])):
self.blocks += [[GBlock(in_channels=self.arch['in_channels'][index],
out_channels=self.arch['in_channels'][index] if g_index==0 else self.arch['out_channels'][index],
which_conv=self.which_conv,
which_bn=self.which_bn,
activation=self.activation,
upsample=(functools.partial(F.interpolate, scale_factor=2)
if self.arch['upsample'][index] and g_index == (self.G_depth-1) else None))]
for g_index in range(self.G_depth)]
# If attention on this block, attach it to the end
if self.arch['attention'][self.arch['resolution'][index]]:
print('Adding attention layer in G at resolution %d' % self.arch['resolution'][index])
self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.which_conv)]
# Turn self.blocks into a ModuleList so that it's all properly registered.
self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
# output layer: batchnorm-relu-conv.
# Consider using a non-spectral conv here
self.output_layer = nn.Sequential(layers.bn(self.arch['out_channels'][-1],
cross_replica=self.cross_replica,
mybn=self.mybn),
self.activation,
self.which_conv(self.arch['out_channels'][-1], 3))
# Initialize weights. Optionally skip init for testing.
if not skip_init:
self.init_weights()
# Set up optimizer
# If this is an EMA copy, no need for an optim, so just return now
if no_optim:
return
self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps
if G_mixed_precision:
print('Using fp16 adam in G...')
import utils
self.optim = utils.Adam16(params=self.parameters(), lr=self.lr,
betas=(self.B1, self.B2), weight_decay=0,
eps=self.adam_eps)
else:
self.optim = optim.Adam(params=self.parameters(), lr=self.lr,
betas=(self.B1, self.B2), weight_decay=0,
eps=self.adam_eps)
def __init__(self,
G_ch=64,
dim_z=128,
bottom_width=4,
resolution=128,
G_kernel_size=3,
G_attn='64',
n_classes=1000,
num_G_SVs=1,
num_G_SV_itrs=1,
G_shared=True,
shared_dim=0,
hier=False,
cross_replica=False,
mybn=False,
G_activation=nn.ReLU(inplace=False),
G_lr=5e-5,
G_B1=0.0,
G_B2=0.999,
adam_eps=1e-8,
BN_eps=1e-5,
SN_eps=1e-12,
G_mixed_precision=False,
G_fp16=False,
G_init='ortho',
skip_init=False,
no_optim=False,
G_param='SN',
norm_style='bn',
add_blur=False,
add_noise=False,
add_style=False,
style_mlp=6,
attn_style='nl',
no_conditional=False,
sched_version='default',
num_epochs=500,
arch=None,
skip_z=False,
use_dog_cnt=False,
dim_dog_cnt_z=32,
mix_style=False,
**kwargs):
super(Generator, self).__init__()
# Channel width mulitplier
self.ch = G_ch
# Dimensionality of the latent space
self.dim_z = dim_z
# The initial spatial dimensions
self.bottom_width = bottom_width
# Resolution of the output
self.resolution = resolution
# Kernel size?
self.kernel_size = G_kernel_size
# Attention?
self.attention = G_attn
# number of classes, for use in categorical conditional generation
self.n_classes = n_classes
# Use shared embeddings?
self.G_shared = G_shared
# Dimensionality of the shared embedding? Unused if not using G_shared
self.shared_dim = shared_dim if shared_dim > 0 else dim_z
# Hierarchical latent space?
self.hier = hier
# Cross replica batchnorm?
self.cross_replica = cross_replica
# Use my batchnorm?
self.mybn = mybn
# nonlinearity for residual blocks
self.activation = G_activation
# Initialization style
self.init = G_init
# Parameterization style
self.G_param = G_param
# Normalization style
self.norm_style = norm_style
# Normalization style
self.add_blur = add_blur
self.add_noise = add_noise
self.add_style = add_style
self.skip_z = skip_z
self.use_dog_cnt = use_dog_cnt
self.dim_dog_cnt_z = dim_dog_cnt_z
self.mix_style = mix_style
# Epsilon for BatchNorm?
self.BN_eps = BN_eps
# Epsilon for Spectral Norm?
self.SN_eps = SN_eps
# fp16?
self.fp16 = G_fp16
# Architecture dict
if arch is None:
arch = f'{resolution}'
self.arch = G_arch(self.ch, self.attention)[arch]
# If using hierarchical latents, adjust z
if self.hier:
# Number of places z slots into
self.num_slots = len(self.arch['in_channels']) + 1
self.z_chunk_size = (self.dim_z // self.num_slots)
# Recalculate latent dimensionality for even splitting into chunks
self.dim_z = self.z_chunk_size * self.num_slots
else:
self.num_slots = 1
self.z_chunk_size = 0
# Which convs, batchnorms, and linear layers to use
if self.G_param == 'SN':
self.which_conv = functools.partial(layers.SNConv2d,
kernel_size=3,
padding=1,
num_svs=num_G_SVs,
num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
self.which_linear = functools.partial(layers.SNLinear,
num_svs=num_G_SVs,
num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
else:
self.which_conv = functools.partial(nn.Conv2d,
kernel_size=3,
padding=1)
self.which_linear = nn.Linear
if attn_style == 'cbam':
self.which_attn = layers.CBAM
else:
self.which_attn = layers.Attention
# We use a non-spectral-normed embedding here regardless;
# For some reason applying SN to G's embedding seems to randomly cripple G
self.which_embedding = nn.Embedding
bn_linear = (functools.partial(self.which_linear, bias=False)
if self.G_shared else self.which_embedding)
input_size = self.shared_dim + self.z_chunk_size if self.G_shared else self.n_classes
if self.G_shared and use_dog_cnt:
input_size += dim_dog_cnt_z
self.which_bn = functools.partial(
layers.ccbn,
which_linear=bn_linear,
cross_replica=self.cross_replica,
mybn=self.mybn,
input_size=input_size,
norm_style=self.norm_style,
eps=self.BN_eps,
style_linear=self.which_linear,
dim_z=self.dim_z,
no_conditional=no_conditional,
skip_z=self.skip_z,
use_dog_cnt=use_dog_cnt,
g_shared=G_shared,
)
# Prepare model
# If not using shared embeddings, self.shared is just a passthrough
self.shared = (self.which_embedding(n_classes, self.shared_dim)
if G_shared else layers.identity())
self.dog_cnt_shared = (self.which_embedding(4, self.dim_dog_cnt_z)
if G_shared else layers.identity())
# First linear layer
self.linear = self.which_linear(
self.dim_z // self.num_slots,
self.arch['in_channels'][0] * (self.bottom_width**2))
# self.blocks is a doubly-nested list of modules, the outer loop intended
# to be over blocks at a given resolution (resblocks and/or self-attention)
# while the inner loop is over a given block
self.blocks = []
for index in range(len(self.arch['out_channels'])):
self.blocks += [[
layers.GBlock(
in_channels=self.arch['in_channels'][index],
out_channels=self.arch['out_channels'][index],
which_conv=self.which_conv,
which_bn=self.which_bn,
activation=self.activation,
upsample=(functools.partial(F.interpolate, scale_factor=2)
if self.arch['upsample'][index] else None),
add_blur=add_blur,
add_noise=add_noise,
)
]]
# If attention on this block, attach it to the end
if self.arch['attention'][self.arch['resolution'][index]]:
print('Adding attention layer in G at resolution %d' %
self.arch['resolution'][index])
self.blocks[-1] += [
self.which_attn(self.arch['out_channels'][index],
self.which_conv)
]
# Turn self.blocks into a ModuleList so that it's all properly registered.
self.blocks = nn.ModuleList(
[nn.ModuleList(block) for block in self.blocks])
# output layer: batchnorm-relu-conv.
# Consider using a non-spectral conv here
self.output_layer = nn.Sequential(
layers.bn(self.arch['out_channels'][-1],
cross_replica=self.cross_replica,
mybn=self.mybn), self.activation,
self.which_conv(self.arch['out_channels'][-1], 3))
if self.add_style:
# layers = [PixelNorm()]
style_layers = []
for i in range(style_mlp):
style_layers.append(
layers.StyleLayer(self.dim_z, self.which_linear,
self.activation))
self.style = nn.Sequential(*style_layers)
# Initialize weights. Optionally skip init for testing.
if not skip_init:
self.init_weights()
# Set up optimizer
# If this is an EMA copy, no need for an optim, so just return now
if no_optim:
return
self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps
if G_mixed_precision:
print('Using fp16 adam in G...')
import utils
self.optim = utils.Adam16(params=self.parameters(),
lr=self.lr,
betas=(self.B1, self.B2),
weight_decay=0,
eps=self.adam_eps,
amsgrad=kwargs['amsgrad'])
else:
self.optim = optim.Adam(params=self.parameters(),
lr=self.lr,
betas=(self.B1, self.B2),
weight_decay=0,
eps=self.adam_eps,
amsgrad=kwargs['amsgrad'])
# LR scheduling, left here for forward compatibility
# self.lr_sched = {'itr' : 0}# if self.progressive else {}
# self.j = 0
if sched_version == 'default':
self.lr_sched = None
elif sched_version == 'cal_v0':
self.lr_sched = optim.lr_scheduler.CosineAnnealingLR(
self.optim,
T_max=num_epochs,
eta_min=self.lr / 2,
last_epoch=-1)
elif sched_version == 'cal_v1':
self.lr_sched = optim.lr_scheduler.CosineAnnealingLR(
self.optim,
T_max=num_epochs,
eta_min=self.lr / 4,
last_epoch=-1)
elif sched_version == 'cawr_v0':
self.lr_sched = optim.lr_scheduler.CosineAnnealingWarmRestarts(
self.optim, T_0=10, T_mult=2, eta_min=self.lr / 2)
elif sched_version == 'cawr_v1':
self.lr_sched = optim.lr_scheduler.CosineAnnealingWarmRestarts(
self.optim, T_0=25, T_mult=2, eta_min=self.lr / 4)
else:
self.lr_sched = None
def generator(z,
y,
is_training=True,
update_batch_stats=True,
act_fn=L.lrelu,
bn=FLAGS.gen_bn,
reuse=True,
dropout=FLAGS.gen_dropout):
with tf.variable_scope('generator', reuse=reuse):
if FLAGS.method == "cgan":
inputs = tf.concat(axis=1, values=[z, y])
h = L.fc(inputs,
Z_dim + y_dim, ((X_dim / 4)**2) * 128,
seed=rng.randint(123456),
name='fc1')
else:
h = L.fc(z,
Z_dim, ((X_dim / 4)**2) * 128,
seed=rng.randint(123456),
name='fc1')
h = L.bn(h, ((X_dim / 4)**2) * 128,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='bn1') if bn else h
h = act_fn(h)
h = tf.reshape(h, [-1, X_dim / 4, X_dim / 4, 128])
# 16x16 -> 32x32
h = L.deconv(h, ksize=2, stride=2, f_in=128, f_out=64, name="deconv1")
h = L.conv(h, 5, 1, 64, 64, name="conv1")
h = L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='bn2') if bn else h
h = tf.nn.dropout(h, keep_prob=0.5) if dropout else h
h = act_fn(h)
h = L.conv(h, 3, 1, 64, 64, name="conv2")
h = L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='b3') if bn else h
h = tf.nn.dropout(h, keep_prob=0.5) if dropout else h
h = act_fn(h)
# 32x32 -> 64x64
h = L.deconv(h, ksize=2, stride=2, f_in=64, f_out=32, name="deconv2")
h = L.conv(h, 5, 1, 32, 32, name="conv3")
h = L.bn(h,
32,
is_training=is_training,
update_batch_stats=update_batch_stats,
use_gamma=False,
name='b4')
h = tf.nn.dropout(h, keep_prob=0.5) if dropout else h
h = act_fn(h)
h = L.conv(h, 5, 1, 32, num_channels, name="conv4")
h = tf.nn.tanh(h, name="output")
return h
def logit(x,
num_classes=10,
is_training=True,
update_batch_stats=True,
stochastic=True,
seed=1234):
if is_training:
scope = tf.name_scope("Training")
else:
scope = tf.name_scope("Testing")
with scope:
h = x
rng = np.random.RandomState(seed)
h = L.conv(h,
ksize=3,
stride=1,
f_in=3,
f_out=128,
seed=rng.randint(123456),
name='c1')
h = L.lrelu(
L.bn(h,
128,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b1'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=3,
stride=1,
f_in=128,
f_out=128,
seed=rng.randint(123456),
name='c2')
h = L.lrelu(
L.bn(h,
128,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b2'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=3,
stride=1,
f_in=128,
f_out=128,
seed=rng.randint(123456),
name='c3')
h = L.lrelu(
L.bn(h,
128,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b3'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
h = tf.nn.dropout(h,
keep_prob=FLAGS.keep_prob_hidden,
seed=rng.randint(123456)) if stochastic else h
h = L.conv(h,
ksize=3,
stride=1,
f_in=128,
f_out=256,
seed=rng.randint(123456),
name='c4')
h = L.lrelu(
L.bn(h,
256,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b4'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=3,
stride=1,
f_in=256,
f_out=256,
seed=rng.randint(123456),
name='c5')
h = L.lrelu(
L.bn(h,
256,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b5'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=3,
stride=1,
f_in=256,
f_out=256,
seed=rng.randint(123456),
name='c6')
h = L.lrelu(
L.bn(h,
256,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b6'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
h = tf.nn.dropout(h,
keep_prob=FLAGS.keep_prob_hidden,
seed=rng.randint(123456)) if stochastic else h
h = L.conv(h,
ksize=3,
stride=1,
f_in=256,
f_out=512,
seed=rng.randint(123456),
padding="VALID",
name='c7')
h = L.lrelu(
L.bn(h,
512,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b7'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=1,
stride=1,
f_in=512,
f_out=256,
seed=rng.randint(123456),
name='c8')
h = L.lrelu(
L.bn(h,
256,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b8'), FLAGS.lrelu_a)
h = L.conv(h,
ksize=1,
stride=1,
f_in=256,
f_out=128,
seed=rng.randint(123456),
name='c9')
h = L.lrelu(
L.bn(h,
128,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='b9'), FLAGS.lrelu_a)
h = tf.reduce_mean(h, reduction_indices=[1,
2]) # Global average pooling
h = L.fc(h, 128, num_classes, seed=rng.randint(123456), name='fc')
if FLAGS.top_bn:
h = L.bn(h,
num_classes,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='bfc')
return h
def autoencoder(x,
zca,
is_training=True,
update_batch_stats=True,
stochastic=True,
seed=1234,
use_zca=True):
if is_training:
scope = tf.name_scope("Training")
else:
scope = tf.name_scope("Testing")
with scope:
#Initial shape (-1, 32, 32, 3)
x = x + 0.5 #Recover [0,1] range
if use_zca:
h = zca
else:
h = x
print(h.shape)
rng = np.random.RandomState(seed)
#h = tf.map_fn(lambda x:transform(x),h)
#(1) conv + relu + maxpool (-1, 16, 16, 64)
h = L.conv(h,
ksize=3,
stride=1,
f_in=3,
f_out=64,
seed=rng.randint(123456),
padding="SAME",
name='conv1')
h = L.lrelu(
L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='conv1_bn'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
#(2) conv + relu + maxpool (-1, 8, 8, 32)
h = L.conv(h,
ksize=3,
stride=1,
f_in=64,
f_out=32,
seed=rng.randint(123456),
padding="SAME",
name='conv2')
h = L.lrelu(
L.bn(h,
32,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='conv2_bn'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
#(3) conv + relu + maxpool (-1, 4, 4, 16)
h = L.conv(h,
ksize=3,
stride=1,
f_in=32,
f_out=16,
seed=rng.randint(123456),
padding="SAME",
name='conv3')
h = L.lrelu(
L.bn(h,
16,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='conv3_bn'), FLAGS.lrelu_a)
h = L.max_pool(h, ksize=2, stride=2)
encoded = h
#(4) deconv + relu (-1, 8, 8, 16)
h = L.deconv(encoded,
ksize=5,
stride=1,
f_in=16,
f_out=16,
seed=rng.randint(123456),
padding="SAME",
name="deconv1")
h = L.lrelu(
L.bn(h,
16,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='deconv1_bn'), FLAGS.lrelu_a)
#(5) deconv + relu (-1, 16, 16, 32)
h = L.deconv(h,
ksize=5,
stride=1,
f_in=16,
f_out=32,
padding="SAME",
name="deconv2")
h = L.lrelu(
L.bn(h,
32,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='deconv2_bn'), FLAGS.lrelu_a)
#(5) deconv + relu (-1, 32, 32, 64)
h = L.deconv(h,
ksize=5,
stride=1,
f_in=32,
f_out=64,
padding="SAME",
name="deconv3")
h = L.lrelu(
L.bn(h,
64,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='deconv3_bn'), FLAGS.lrelu_a)
#(7) conv + sigmoid (-1, 32, 32, 3)
h = L.conv(h,
ksize=3,
stride=1,
f_in=64,
f_out=3,
seed=rng.randint(123456),
padding="SAME",
name='convfinal')
if use_zca:
h = L.bn(h,
3,
is_training=is_training,
update_batch_stats=update_batch_stats,
name='deconv4_bn')
else:
h = tf.sigmoid(h)
num_samples = 10
sample_og_zca = tf.reshape(
tf.slice(zca, [0, 0, 0, 0], [num_samples, 32, 32, 3]),
(num_samples * 32, 32, 3))
sample_og_color = tf.reshape(
tf.slice(x, [0, 0, 0, 0], [num_samples, 32, 32, 3]),
(num_samples * 32, 32, 3))
sample_rec = tf.reshape(
tf.slice(h, [0, 0, 0, 0], [num_samples, 32, 32, 3]),
(num_samples * 32, 32, 3))
if use_zca:
sample = tf.concat([sample_og_zca, sample_rec], axis=1)
m = tf.reduce_min(sample)
sample = (sample - m) / (tf.reduce_max(sample) - m)
else:
m = tf.reduce_min(sample_og_zca)
sample_og_zca = (sample_og_zca -
m) / (tf.reduce_max(sample_og_zca) - m)
sample = tf.concat([sample_og_zca, sample_rec], axis=1)
sample = tf.concat([sample_og_color, sample], axis=1)
sample = tf.cast(255.0 * sample, tf.uint8)
if use_zca:
loss = tf.reduce_mean(tf.losses.mean_squared_error(zca, h))
else:
loss = tf.reduce_mean(tf.losses.log_loss(x, h))
return loss, encoded, sample
def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128,
G_kernel_size=3, G_attn='64', n_classes=1000,
num_G_SVs=1, num_G_SV_itrs=1,
G_shared=True, shared_dim=0, hier=False,
cross_replica=False, mybn=False,
G_activation=nn.ReLU(inplace=False),
G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8,
BN_eps=1e-5, SN_eps=1e-12, G_mixed_precision=False, G_fp16=False,
G_init='ortho', skip_init=False, no_optim=False,
G_param='SN', norm_style='bn',
**kwargs):
"""
utils中有这些参数的定义，通过parase和vars方法封装这些参数
看一下模型到底是咋样
G_ch 生成模型的信道 默认64，指的是一种模型机构的总和，64可解析为如下结构
ch = 64
arch[128] = {'in_channels' : [ch * item for item in [16, 16, 8, 4, 2]],
'out_channels' : [ch * item for item in [16, 8, 4, 2, 1]],
'upsample' : [True] * 5,
'resolution' : [8, 16, 32, 64, 128],
'attention' : {2**i: (2**i in [int(item) for item in attention.split('_')])
for i in range(3,8)}}
dim_z 噪声的维度，默认为128
"""
super(Generator, self).__init__()
# Channel width mulitplier
self.ch = G_ch
# Dimensionality of the latent space
self.dim_z = dim_z
# The initial spatial dimensions
## TODO 暂时不理解这个的主要作用
self.bottom_width = bottom_width
# Resolution of the output
## 表示选择的结构
self.resolution = resolution
# Kernel size?
## TODO 这个不是外部参数导入的, 也么有用到
self.kernel_size = G_kernel_size
# Attention?
## 只是做了个中介，转手就到了self.arch中选择，最后会在attention的结构中得到解析
self.attention = G_attn
# number of classes, for use in categorical conditional generation
self.n_classes = n_classes
# Use shared embeddings?
## 默认False
self.G_shared = G_shared
# Dimensionality of the shared embedding? Unused if not using G_shared
self.shared_dim = shared_dim if shared_dim > 0 else dim_z
# Hierarchical latent space?
self.hier = hier
# Cross replica batchnorm?
self.cross_replica = cross_replica
# Use my batchnorm?
self.mybn = mybn
# nonlinearity for residual blocks
self.activation = G_activation
# Initialization style
self.init = G_init
# Parameterization style
self.G_param = G_param
# Normalization style
self.norm_style = norm_style
# Epsilon for BatchNorm?
self.BN_eps = BN_eps
# Epsilon for Spectral Norm?
## https://zhuanlan.zhihu.com/p/68081406
self.SN_eps = SN_eps
# fp16?
self.fp16 = G_fp16
# Architecture dict
self.arch = G_arch(self.ch, self.attention)[resolution]
# If using hierarchical latents, adjust z
if self.hier:
# Number of places z slots into
self.num_slots = len(self.arch['in_channels']) + 1
self.z_chunk_size = (self.dim_z // self.num_slots)
# Recalculate latent dimensionality for even splitting into chunks
self.dim_z = self.z_chunk_size * self.num_slots
else:
self.num_slots = 1
self.z_chunk_size = 0
# Which convs, batchnorms, and linear layers to use
if self.G_param == 'SN':
self.which_conv = functools.partial(layers.SNConv2d,
kernel_size=3, padding=1,
num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
self.which_linear = functools.partial(layers.SNLinear,
num_svs=num_G_SVs, num_itrs=num_G_SV_itrs,
eps=self.SN_eps)
else:
self.which_conv = functools.partial(nn.Conv2d, kernel_size=3, padding=1)
self.which_linear = nn.Linear
# We use a non-spectral-normed embedding here regardless;
# For some reason applying SN to G's embedding seems to randomly cripple G
## *** fluid.dygraph.Embedding == nn.Embedding
self.which_embedding = nn.Embedding
bn_linear = (functools.partial(self.which_linear, bias=False) if self.G_shared
else self.which_embedding)
self.which_bn = functools.partial(layers.ccbn,
which_linear=bn_linear,
cross_replica=self.cross_replica,
mybn=self.mybn,
input_size=(self.shared_dim + self.z_chunk_size if self.G_shared
else self.n_classes),
norm_style=self.norm_style,
eps=self.BN_eps)
# Prepare model
# If not using shared embeddings, self.shared is just a passthrough
self.shared = (self.which_embedding(n_classes, self.shared_dim) if G_shared
else layers.identity())
# First linear layer
self.linear = self.which_linear(self.dim_z // self.num_slots,
self.arch['in_channels'][0] * (self.bottom_width **2))
# self.blocks is a doubly-nested list of modules, the outer loop intended
# to be over blocks at a given resolution (resblocks and/or self-attention)
# while the inner loop is over a given block
self.blocks = []
for index in range(len(self.arch['out_channels'])):
self.blocks += [[layers.GBlock(in_channels=self.arch['in_channels'][index],
out_channels=self.arch['out_channels'][index],
which_conv=self.which_conv,
which_bn=self.which_bn,
activation=self.activation,
upsample=(functools.partial(F.interpolate, scale_factor=2)
if self.arch['upsample'][index] else None))]]
# If attention on this block, attach it to the end
if self.arch['attention'][self.arch['resolution'][index]]:
print('Adding attention layer in G at resolution %d' % self.arch['resolution'][index])
self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.which_conv)]
# Turn self.blocks into a ModuleList so that it's all properly registered.
self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks])
# output layer: batchnorm-relu-conv.
# Consider using a non-spectral conv here
self.output_layer = nn.Sequential(layers.bn(self.arch['out_channels'][-1],
cross_replica=self.cross_replica,
mybn=self.mybn),
self.activation,
self.which_conv(self.arch['out_channels'][-1], 3))
# Initialize weights. Optionally skip init for testing.
if not skip_init:
self.init_weights()
# Set up optimizer
# If this is an EMA copy, no need for an optim, so just return now
if no_optim:
return self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps
if G_mixed_precision:
print('Using fp16 adam in G...')
import utils
self.optim = utils.Adam16(params=self.parameters(), lr=self.lr,
betas=(self.B1, self.B2), weight_decay=0,
eps=self.adam_eps)
else:
self.optim = optim.Adam(params=self.parameters(), lr=self.lr,
betas=(self.B1, self.B2), weight_decay=0,
eps=self.adam_eps)