def decoder(self, z, is_training, reuse=False):
"""Implements the Bernoulli decoder."""
height = self.input_height
width = self.input_width
with tf.variable_scope("decoder", reuse=reuse):
net = tf.nn.relu(
batch_norm(linear(z, 1024, scope="de_fc1"),
is_training=is_training,
scope="de_bn1"))
net = tf.nn.relu(
batch_norm(linear(net,
128 * (height // 4) * (width // 4),
scope="de_fc2"),
is_training=is_training,
scope="de_bn2"))
net = tf.reshape(net,
[self.batch_size, height // 4, width // 4, 128])
net = tf.nn.relu(
batch_norm(deconv2d(
net, [self.batch_size, height // 2, width // 2, 64],
4,
4,
2,
2,
name="de_dc3"),
is_training=is_training,
scope="de_bn3"))
out = tf.nn.sigmoid(
deconv2d(net, [self.batch_size, height, width, self.c_dim],
4,
4,
2,
2,
name="de_dc4"))
return out
def generator(self, z, is_training, reuse=False):
height = self.input_height
width = self.input_width
batch_size = self.batch_size
with tf.variable_scope("generator", reuse=reuse):
net = linear(z, 1024, scope="g_fc1")
net = batch_norm(net, is_training=is_training, scope="g_bn1")
net = lrelu(net)
net = linear(net,
128 * (height // 4) * (width // 4),
scope="g_fc2")
net = batch_norm(net, is_training=is_training, scope="g_bn2")
net = lrelu(net)
net = tf.reshape(net, [batch_size, height // 4, width // 4, 128])
net = deconv2d(net, [batch_size, height // 2, width // 2, 64],
4,
4,
2,
2,
name="g_dc3")
net = batch_norm(net, is_training=is_training, scope="g_bn3")
net = lrelu(net)
net = deconv2d(net, [batch_size, height, width, self.c_dim],
4,
4,
2,
2,
name="g_dc4")
out = tf.nn.sigmoid(net)
return out
def discriminator(self, x, is_training, reuse=False):
"""Discriminator architecture based on InfoGAN.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out: a float (in [0, 1]) with discriminator prediction
out_logit: the value "out" before sigmoid
net: the architecture
"""
with tf.variable_scope("discriminator", reuse=reuse):
# Mapping x from [bs, h, w, c] to [bs, 1]
net = conv2d(x, 64, 4, 4, 2, 2, name="d_conv1") # [bs, h/2, w/2, 64]
net = lrelu(net)
net = conv2d(net, 128, 4, 4, 2, 2, name="d_conv2") # [bs, h/4, w/4, 128]
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="d_bn2")
net = lrelu(net)
net = tf.reshape(net, [self.batch_size, -1]) # [bs, h * w * 8]
net = linear(net, 1024, scope="d_fc3") # [bs, 1024]
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="d_bn3")
net = lrelu(net)
out_logit = linear(net, 1, scope="d_fc4") # [bs, 1]
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def discriminator(self, x, is_training, reuse=False):
"""Discriminator architecture based on InfoGAN.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out: a float (in [0, 1]) with discriminator prediction
out_logit: the value "out" before sigmoid
net: the architecture
"""
sn = self.discriminator_normalization == consts.SPECTRAL_NORM
with tf.variable_scope("discriminator", reuse=reuse):
# Mapping x from [bs, h, w, c] to [bs, 1]
net = conv2d(x, 64, 4, 4, 2, 2, name="d_conv1",
use_sn=sn) # [bs, h/2, w/2, 64]
net = lrelu(net)
net = conv2d(net, 128, 4, 4, 2, 2, name="d_conv2",
use_sn=sn) # [bs, h/4, w/4, 128]
if self.discriminator_normalization == consts.BATCH_NORM:
net = batch_norm(net, is_training=is_training, scope="d_bn2")
net = lrelu(net)
net = tf.reshape(net, [self.batch_size, -1]) # [bs, h * w * 8]
net = linear(net, 1024, scope="d_fc3", use_sn=sn) # [bs, 1024]
if self.discriminator_normalization == consts.BATCH_NORM:
net = batch_norm(net, is_training=is_training, scope="d_bn3")
net = lrelu(net)
out_logit = linear(net, 1, scope="d_fc4", use_sn=sn) # [bs, 1]
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def discriminator(self, x, is_training, reuse=False):
"""BEGAN discriminator (auto-encoder).
This implementation doesn't match the one from the paper, but is similar
to our "standard" discriminator (same 2 conv layers, using lrelu).
However, it still has less parameters (1.3M vs 8.5M) because of the huge
linear layer in the standard discriminator.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out: a float (in [0, 1]) with discriminator prediction
recon_error: L1 reconstrunction error of the auto-encoder
code: the representation (bottleneck layer of the auto-encoder)
"""
height = self.input_height
width = self.input_width
sn = self.discriminator_normalization == consts.SPECTRAL_NORM
with tf.variable_scope("discriminator", reuse=reuse):
# Encoding step (Mapping from [bs, h, w, c] to [bs, 64])
net = conv2d(
x, 64, 4, 4, 2, 2, name="d_conv1", use_sn=sn) # [bs, h/2, w/2, 64]
net = lrelu(net)
net = conv2d(
net, 128, 4, 4, 2, 2, name="d_conv2",
use_sn=sn) # [bs, h/4, w/4, 128]
net = tf.reshape(net, [self.batch_size, -1]) # [bs, h * w * 8]
code = linear(net, 64, scope="d_fc6", use_sn=sn) # [bs, 64]
if self.discriminator_normalization == consts.BATCH_NORM:
code = batch_norm(code, is_training=is_training, scope="d_bn1")
code = lrelu(code)
# Decoding step (Mapping from [bs, 64] to [bs, h, w, c])
net = linear(
code, 128 * (height // 4) * (width // 4), scope="d_fc1",
use_sn=sn) # [bs, h/4 * w/4 * 128]
if self.discriminator_normalization == consts.BATCH_NORM:
net = batch_norm(net, is_training=is_training, scope="d_bn2")
net = lrelu(net)
net = tf.reshape(net, [
self.batch_size, height // 4, width // 4, 128]) # [bs, h/4, w/4, 128]
net = deconv2d(net, [self.batch_size, height // 2, width // 2, 64],
4, 4, 2, 2, name="d_deconv1") # [bs, h/2, w/2, 64]
if self.discriminator_normalization == consts.BATCH_NORM:
net = batch_norm(net, is_training=is_training, scope="d_bn3")
net = lrelu(net)
net = deconv2d(net, [self.batch_size, height, width, self.c_dim],
4, 4, 2, 2, name="d_deconv2") # [bs, h, w, c]
out = tf.nn.sigmoid(net)
# Reconstruction loss.
recon_error = tf.reduce_mean(tf.abs(out - x))
return out, recon_error, code
def attention_block(self, entities, reuse, name="attention_block"):
"""Performs non-local pairwise relational computations.
Args:
entities: A tensor of shape (B, K, D) where K is the number of entities.
reuse: Whether to reuse the weights.
name: The name of the block.
Returns:
Updated entity representation (B, K, D)
"""
# Estimate local dimensions to support background channel.
k, z_dim = entities.get_shape().as_list()[1:3]
r_entities = tf.reshape(entities, [self.batch_size * k, z_dim])
with tf.variable_scope(name, reuse=reuse):
queries = ops.layer_norm(
tf.nn.relu(
ops.linear(r_entities, self.embedding_dim, scope="q_fc")),
reuse, "q_ln")
queries = tf.reshape(queries,
[self.batch_size, k, self.embedding_dim])
keys = ops.layer_norm(
tf.nn.relu(
ops.linear(r_entities, self.embedding_dim, scope="k_fc")),
reuse, "k_ln")
keys = tf.reshape(keys, [self.batch_size, k, self.embedding_dim])
values = ops.layer_norm(
tf.nn.relu(
ops.linear(r_entities, self.embedding_dim, scope="v_fc")),
reuse, "v_ln")
values = tf.reshape(values,
[self.batch_size, k, self.embedding_dim])
attention_weights = tf.matmul(queries,
tf.transpose(keys, [0, 2, 1]))
norm_attention_weights = tf.nn.softmax(
attention_weights /
tf.sqrt(tf.cast(self.embedding_dim, tf.float32)),
axis=2)
attention = tf.matmul(norm_attention_weights, values)
r_attention = tf.reshape(attention,
[self.batch_size * k, self.embedding_dim])
# Project back to original space.
u_entities = tf.nn.relu(ops.linear(r_attention, z_dim, "e_fc1"))
u_entities = tf.nn.relu(ops.linear(u_entities, z_dim, "e_fc2"))
u_entities = ops.layer_norm(u_entities + r_entities, reuse, "e_ln")
return tf.reshape(u_entities, [self.batch_size, k, z_dim])
def discriminator(self, x, is_training, reuse=False):
"""BEGAN discriminator (auto-encoder).
This implementation doesn't match the one from the paper, but is similar
to our "standard" discriminator (same 2 conv layers, using lrelu).
However, it still has less parameters (1.3M vs 8.5M) because of the huge
linear layer in the standard discriminator.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out: a float (in [0, 1]) with discriminator prediction
recon_error: L1 reconstrunction error of the auto-encoder
code: the representation (bottleneck layer of the auto-encoder)
"""
height = self.input_height
width = self.input_width
with tf.variable_scope("discriminator", reuse=reuse):
# Encoding step (Mapping from [bs, h, w, c] to [bs, 64])
net = conv2d(x, 64, 4, 4, 2, 2, name="d_conv1") # [bs, h/2, w/2, 64]
net = lrelu(net)
net = conv2d(net, 128, 4, 4, 2, 2, name="d_conv2") # [bs, h/4, w/4, 128]
net = tf.reshape(net, [self.batch_size, -1]) # [bs, h * w * 8]
code = linear(net, 64, scope="d_fc6") # [bs, 64]
if self.discriminator_batchnorm:
code = batch_norm(code, is_training=is_training, scope="d_bn1")
code = lrelu(code)
# Decoding step (Mapping from [bs, 64] to [bs, h, w, c])
net = linear(code, 128 * (height // 4) * (width // 4),
scope="d_fc1") # [bs, h/4 * w/4 * 128]
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="d_bn2")
net = lrelu(net)
net = tf.reshape(net, [
self.batch_size, height // 4, width // 4, 128]) # [bs, h/4, w/4, 128]
net = deconv2d(net, [self.batch_size, height // 2, width // 2, 64],
4, 4, 2, 2, name="d_deconv1") # [bs, h/2, w/2, 64]
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="d_bn3")
net = lrelu(net)
net = deconv2d(net, [self.batch_size, height, width, self.c_dim],
4, 4, 2, 2, name="d_deconv2") # [bs, h, w, c]
out = tf.nn.sigmoid(net)
# Reconstruction loss.
recon_error = tf.reduce_mean(tf.abs(out - x))
return out, recon_error, code
def resnet_cifar_generator(noise,
is_training,
reuse=None,
colors=3):
batch_size = noise.get_shape().as_list()[0]
with tf.variable_scope("generator", reuse=reuse):
# Map noise to the actual seed.
output = ops.linear(
noise, 4 * 4 * 256, scope="fc_noise")
# Reshape the seed to be a rank-4 Tensor.
output = tf.reshape(
output, [batch_size, 4, 4, 256], name="fc_reshaped")
for block_idx in range(3):
block_scope = "B%d" % (block_idx + 1)
output = generator_block(output, in_channels=256,
out_channels=256,
scale="up", block_scope=block_scope,
is_training=is_training, reuse=reuse)
# Final processing of the output.
output = batch_norm_resnet(
output, is_training=is_training, scope="final_norm")
output = tf.nn.relu(output)
output = ops.conv2d(
output, output_dim=colors, k_h=3, k_w=3, d_h=1, d_w=1,
name="final_conv")
output = tf.nn.sigmoid(output)
print ("Generator output shape: ", output)
return output
def resnet_cifar_discriminator(inputs,
is_training,
discriminator_normalization,
reuse=None):
_validate_image_inputs(inputs)
colors = inputs.get_shape().as_list()[-1]
assert colors in [1, 3]
with tf.variable_scope("discriminator", values=[inputs], reuse=reuse):
output = inputs
channels = colors
for block_idx in range(4):
block_scope = "B%d" % block_idx
scale = "down" if block_idx <= 1 else "none"
output = discriminator_block(
output, in_channels=channels, out_channels=128,
scale=scale, block_scope=block_scope,
is_training=is_training, reuse=reuse,
discriminator_normalization=discriminator_normalization)
channels = 128
# Final part - ReLU
output = tf.nn.relu(output)
# Global sum pooling (it's actually "mean" here, as that's what they had in
# their implementation for resnet5). There was no implementation for Cifar.
pre_logits = tf.reduce_mean(output, axis=[1, 2])
# dense -> 1
use_sn = discriminator_normalization == consts.SPECTRAL_NORM
out_logit = ops.linear(pre_logits, 1, scope="disc_final_fc", use_sn=use_sn)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, None
def resnet5_discriminator(inputs,
is_training,
discriminator_normalization,
reuse=None):
"""ResNet style discriminator.
Construct discriminator network from inputs to the final endpoint.
Args:
inputs: A tensor of size [batch_size, height, width, channels]. Must be
floating point.
is_training: Is the model currently being trained.
discriminator_normalization: which type of normalization to apply.
reuse: Whether or not the network variables should be reused. `scope`
must be given to be reused.
Returns:
out: The prediction of the discrminator (in [0, 1]). Shape: [bs, 1]
out_logit: The pre-softmax activations for discrimination
real/generated, a tensor of size [batch_size, 1]
Raises:
ValueError: If the input image shape is not 4-dimensional, if the spatial
dimensions aren't defined at graph construction time, if the spatial
dimensions aren't square, or if the spatial dimensions aren"t a power of
two.
"""
_validate_image_inputs(inputs)
colors = inputs.get_shape().as_list()[-1]
assert colors in [1, 3]
ch = 64
with tf.variable_scope("discriminator", values=[inputs], reuse=reuse):
output = discriminator_block(
inputs, in_channels=colors, out_channels=ch,
scale="down", block_scope="B0", is_training=is_training, reuse=reuse,
discriminator_normalization=discriminator_normalization)
# Magic in/out channel numbers copied from SN paper.
magic = [(1, 2), (2, 4), (4, 4), (4, 8), (8, 8)]
for block_idx in range(5):
block_scope = "B%d" % (block_idx + 1)
in_channels = ch * magic[block_idx][0]
out_channels = ch * magic[block_idx][1]
print ("Resnet5 disc, block %d in=%d out=%d" % (
block_idx, in_channels, out_channels))
output = discriminator_block(
output, in_channels=in_channels, out_channels=out_channels,
scale="down", block_scope=block_scope, is_training=is_training,
reuse=reuse, discriminator_normalization=discriminator_normalization)
# Final part
output = tf.nn.relu(output)
pre_logits = tf.reduce_mean(output, axis=[1, 2])
use_sn = discriminator_normalization == consts.SPECTRAL_NORM
out_logit = ops.linear(pre_logits, 1, scope="disc_final_fc", use_sn=use_sn)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, None
def resnet5_generator(noise,
is_training,
reuse=None,
colors=3,
output_shape=128,
unused_ablation_type=""):
# Input is a noise tensor of shape [bs, z_dim]
assert len(noise.get_shape().as_list()) == 2
# Calculate / define a few numbers.
batch_size = noise.get_shape().as_list()[0]
# Each block upscales by a factor of 2:
seed_size = 4
# We want the last block to have 64 channels:
ch = 64
with tf.variable_scope("generator", reuse=reuse):
# Map noise to the actual seed.
output = ops.linear(noise, ch * 8 * seed_size * seed_size, scope="fc_noise")
# Reshape the seed to be a rank-4 Tensor.
output = tf.reshape(
output, [batch_size, seed_size, seed_size, ch * 8], name="fc_reshaped")
# Magic in/out channel numbers copied from SN paper.
magic = [(8, 8), (8, 4), (4, 4), (4, 2), (2, 1)]
up_layers = np.log2(float(output_shape) / seed_size)
assert up_layers.is_integer(), "log2(%d/%d) must be an integer" % (
output_shape, seed_size)
assert up_layers <= 5 and up_layers >= 0, "Invalid output_shape %d" % (
output_shape)
up_layers = int(up_layers)
for block_idx in range(5):
block_scope = "B%d" % (block_idx + 1)
in_channels = ch * magic[block_idx][0]
out_channels = ch * magic[block_idx][1]
print("Resnet5, block %d in=%d out=%d" % (block_idx, in_channels,
out_channels))
if block_idx < up_layers:
scale = "up"
else:
scale = "none"
output = generator_block(output, in_channels=in_channels,
out_channels=out_channels,
scale=scale, block_scope=block_scope,
is_training=is_training, reuse=reuse)
# Final processing of the output.
output = batch_norm_resnet(output, is_training=is_training,
scope="final_norm")
output = tf.nn.relu(output)
output = ops.conv2d(
output, output_dim=colors, k_h=3, k_w=3, d_h=1, d_w=1,
name="final_conv")
output = tf.nn.sigmoid(output)
print("Generator output shape: ", output)
return output
def decoder(self, z, is_training, reuse=False):
"""Implements the Bernoulli decoder."""
height = self.input_height
width = self.input_width
with tf.variable_scope("decoder", reuse=reuse):
net = tf.nn.relu(
batch_norm(linear(z, 1024, scope="de_fc1"), is_training=is_training,
scope="de_bn1"))
net = tf.nn.relu(
batch_norm(linear(net, 128 * (height // 4) * (width // 4), scope="de_fc2"),
is_training=is_training, scope="de_bn2"))
net = tf.reshape(net, [self.batch_size, height // 4, width // 4, 128])
net = tf.nn.relu(batch_norm(deconv2d(
net, [self.batch_size, height // 2, width // 2, 64], 4, 4, 2, 2, name="de_dc3"),
is_training=is_training, scope="de_bn3"))
out = tf.nn.sigmoid(deconv2d(
net, [self.batch_size, height, width, self.c_dim], 4, 4, 2, 2, name="de_dc4"))
return out
def sn_generator(z,
batch_size,
output_height,
output_width,
output_c_dim,
is_training,
reuse=False):
"""Returns the output tensor of the SNDCGAN generator.
Details are available at https://openreview.net/pdf?id=B1QRgziT-.
Args:
z: latent code, shape [batch_size, latent_dimensionality]
batch_size: Batch size.
output_height: Output image height.
output_width: Output image width.
output_c_dim: Number of color channels.
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
net: The generated image Tensor with entries in [0, 1].
"""
s_h, s_w = output_height, output_width
s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2)
s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2)
s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2)
with tf.variable_scope("generator", reuse=reuse):
net = linear(z, s_h8 * s_w8 * 512, scope="g_fc1")
net = batch_norm_dcgan(net, is_training, scope="g_bn1", epsilon=2e-5)
net = tf.nn.relu(net)
net = tf.reshape(net, [batch_size, s_h8, s_w8, 512])
net = deconv2d(net, [batch_size, s_h4, s_w4, 256],
4, 4, 2, 2, name="g_dc2")
net = batch_norm_dcgan(net, is_training, scope="g_bn2", epsilon=2e-5)
net = tf.nn.relu(net)
net = deconv2d(net, [batch_size, s_h2, s_w2, 128],
4, 4, 2, 2, name="g_dc3")
net = batch_norm_dcgan(net, is_training, scope="g_bn3", epsilon=2e-5)
net = tf.nn.relu(net)
net = deconv2d(net, [batch_size, s_h, s_w, 64],
4, 4, 2, 2, name="g_dc4")
net = batch_norm_dcgan(net, is_training, scope="g_bn4", epsilon=2e-5)
net = tf.nn.relu(net)
net = deconv2d(
net, [batch_size, s_h, s_w, output_c_dim], 3, 3, 1, 1, name="g_dc5")
out = tf.tanh(net)
# NOTE: this normalization is introduced to match current image
# preprocessing, which normalize the real image to range [0, 1].
# In author's implementation, they simply use the tanh activation function
# and normalize the image to range [-1, 1].
out = tf.div(out + 1.0, 2.0)
return out
def encoder(self, x, is_training, reuse=False):
"""Implements the Gaussian Encoder."""
with tf.variable_scope("encoder", reuse=reuse):
net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name="en_conv1"))
net = conv2d(net, 128, 4, 4, 2, 2, name="en_conv2")
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="en_bn2")
net = lrelu(net)
net = tf.reshape(net, [self.batch_size, -1])
net = linear(net, 1024, scope="en_fc3")
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="en_bn3")
net = lrelu(net)
gaussian_params = linear(net, 2 * self.z_dim, scope="en_fc4")
mean = gaussian_params[:, :self.z_dim]
stddev = 1e-6 + tf.nn.softplus(gaussian_params[:, self.z_dim:])
return mean, stddev
def encoder(self, x, is_training, reuse=False):
"""Implements the Gaussian Encoder."""
with tf.variable_scope("encoder", reuse=reuse):
net = lrelu(conv2d(x, 64, 4, 4, 2, 2, name="en_conv1"))
net = conv2d(net, 128, 4, 4, 2, 2, name="en_conv2")
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="en_bn2")
net = lrelu(net)
net = tf.reshape(net, [self.batch_size, -1])
net = linear(net, 1024, scope="en_fc3")
if self.discriminator_batchnorm:
net = batch_norm(net, is_training=is_training, scope="en_bn3")
net = lrelu(net)
gaussian_params = linear(net, 2 * self.z_dim, scope="en_fc4")
mean = gaussian_params[:, :self.z_dim]
stddev = 1e-6 + tf.nn.softplus(gaussian_params[:, self.z_dim:])
return mean, stddev
def generator(self, z, is_training, reuse=False):
height = self.input_height
width = self.input_width
batch_size = self.batch_size
with tf.variable_scope("generator", reuse=reuse):
net = linear(z, 1024, scope="g_fc1")
net = batch_norm(net, is_training=is_training, scope="g_bn1")
net = lrelu(net)
net = linear(net, 128 * (height // 4) * (width // 4), scope="g_fc2")
net = batch_norm(net, is_training=is_training, scope="g_bn2")
net = lrelu(net)
net = tf.reshape(net, [batch_size, height // 4, width // 4, 128])
net = deconv2d(net, [batch_size, height // 2, width // 2, 64],
4, 4, 2, 2, name="g_dc3")
net = batch_norm(net, is_training=is_training, scope="g_bn3")
net = lrelu(net)
net = deconv2d(net, [batch_size, height, width, self.c_dim],
4, 4, 2, 2, name="g_dc4")
out = tf.nn.sigmoid(net)
return out
def resnet_stl_discriminator(inputs,
is_training,
discriminator_normalization,
reuse=None):
_validate_image_inputs(inputs, validate_power2=False)
colors = inputs.get_shape().as_list()[-1]
assert colors in [1, 3]
ch = 64
with tf.variable_scope("discriminator", values=[inputs], reuse=reuse):
output = discriminator_block(
inputs,
in_channels=colors,
out_channels=ch,
scale="down",
block_scope="B0",
is_training=is_training,
reuse=reuse,
discriminator_normalization=discriminator_normalization)
# in/out channel numbers copied from SN paper.
magic = [(1, 2), (2, 4), (4, 8), (8, 16)]
for block_idx in range(4):
block_scope = "B%d" % (block_idx + 1)
in_channels = ch * magic[block_idx][0]
out_channels = ch * magic[block_idx][1]
print("Resnet5 disc, block %d in=%d out=%d" %
(block_idx, in_channels, out_channels))
if block_idx < 3:
scale = "down"
else:
scale = "none"
output = discriminator_block(
output,
in_channels=in_channels,
out_channels=out_channels,
scale=scale,
block_scope=block_scope,
is_training=is_training,
reuse=reuse,
discriminator_normalization=discriminator_normalization)
# Final part
output = tf.nn.relu(output)
pre_logits = tf.reduce_mean(output, axis=[1, 2])
use_sn = discriminator_normalization == consts.SPECTRAL_NORM
out_logit = ops.linear(pre_logits,
1,
scope="disc_final_fc",
use_sn=use_sn)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, None
def resnet107_discriminator(inputs,
is_training,
discriminator_normalization,
reuse=None):
_validate_image_inputs(inputs)
colors = inputs.get_shape().as_list()[-1]
assert colors in [1, 3]
ch = 64
with tf.variable_scope("discriminator", values=[inputs], reuse=reuse):
output = ops.conv2d(inputs,
output_dim=ch // 4,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name="color_conv")
in_channels = ch // 4
out_channels = ch // 2
for superblock in range(6):
for i in range(5):
block_scope = "B_%d_%d" % (superblock, i)
output = discriminator_block(
output,
in_channels=in_channels,
out_channels=in_channels,
scale="none",
block_scope=block_scope,
is_training=is_training,
reuse=reuse,
discriminator_normalization=discriminator_normalization)
# We want to downscale 5 times.
if superblock < 5:
output = discriminator_block(
output,
in_channels=in_channels,
out_channels=out_channels,
scale="down",
block_scope="B_%d_up" % superblock,
is_training=is_training,
reuse=reuse,
discriminator_normalization=discriminator_normalization)
in_channels *= 2
out_channels *= 2
# Final part
output = tf.reshape(output, [-1, 4 * 4 * 8 * ch])
use_sn = discriminator_normalization == consts.SPECTRAL_NORM
out_logit = ops.linear(output, 1, scope="disc_final_fc", use_sn=use_sn)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, None
def discriminator(x,
batch_size,
is_training,
discriminator_normalization,
reuse=False):
"""Returns the outputs of the DCGAN discriminator.
Details are available at https://arxiv.org/abs/1511.06434. Notable changes
include BatchNorm in the discriminator and LeakyReLU for all layers.
Args:
x: input images, shape [bs, h, w, channels].
batch_size: integer, number of samples in batch.
is_training: boolean, are we in train or eval model.
discriminator_normalization: which type of normalization to apply.
reuse: boolean, should params be re-used.
Returns:
out: A float (in [0, 1]) with discriminator prediction.
out_logit: Logits (activations of the last linear layer).
net: Logits of the last ReLu layer.
"""
assert discriminator_normalization in [
consts.NO_NORMALIZATION, consts.SPECTRAL_NORM, consts.BATCH_NORM]
bs = batch_size
df_dim = 64 # Dimension of filters in first convolutional layer.
use_sn = discriminator_normalization == consts.SPECTRAL_NORM
with tf.variable_scope("discriminator", reuse=reuse):
net = lrelu(conv2d(x, df_dim, 5, 5, 2, 2,
name="d_conv1", use_sn=use_sn))
net = conv2d(net, df_dim * 2, 5, 5, 2, 2,
name="d_conv2", use_sn=use_sn)
if discriminator_normalization == consts.BATCH_NORM:
net = batch_norm_dcgan(net, is_training, scope="d_bn1")
net = lrelu(net)
net = conv2d(net, df_dim * 4, 5, 5, 2, 2,
name="d_conv3", use_sn=use_sn)
if discriminator_normalization == consts.BATCH_NORM:
net = batch_norm_dcgan(net, is_training, scope="d_bn2")
net = lrelu(net)
net = conv2d(net, df_dim * 8, 5, 5, 2, 2,
name="d_conv4", use_sn=use_sn)
if discriminator_normalization == consts.BATCH_NORM:
net = batch_norm_dcgan(net, is_training, scope="d_bn3")
net = lrelu(net)
out_logit = linear(
tf.reshape(net, [bs, -1]), 1, scope="d_fc4", use_sn=use_sn)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def resnet107_generator(noise, is_training, reuse=None, colors=3):
# Input is a noise tensor of shape [bs, z_dim]
assert len(noise.get_shape().as_list()) == 2
# Calculate / define a few numbers.
batch_size = noise.get_shape().as_list()[0]
ch = 64
with tf.variable_scope("generator", reuse=reuse):
# Map noise to the actual seed.
output = ops.linear(noise, 4 * 4 * 8 * ch, scope="fc_noise")
# Reshape the seed to be a rank-4 Tensor.
output = tf.reshape(output, [batch_size, 4, 4, 8 * ch],
name="fc_reshaped")
in_channels = 8 * ch
out_channels = 4 * ch
for superblock in range(6):
for i in range(5):
block_scope = "B_%d_%d" % (superblock, i)
output = generator_block(output,
in_channels=in_channels,
out_channels=in_channels,
scale="none",
block_scope=block_scope,
is_training=is_training,
reuse=reuse)
# We want to upscale 5 times.
if superblock < 5:
output = generator_block(output,
in_channels=in_channels,
out_channels=out_channels,
scale="up",
block_scope="B_%d_up" % superblock,
is_training=is_training,
reuse=reuse)
in_channels /= 2
out_channels /= 2
output = ops.conv2d(output,
output_dim=colors,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name="final_conv")
output = tf.nn.sigmoid(output)
print("Generator output shape: ", output)
return output
def sn_discriminator(x, batch_size, reuse=False, use_sn=False):
"""Returns the outputs of the SNDCGAN discriminator.
Details are available at https://openreview.net/pdf?id=B1QRgziT-.
Args:
x: input images, shape [bs, h, w, channels].
batch_size: integer, number of samples in batch.
reuse: boolean, should params be re-used.
Returns:
out: A float (in [0, 1]) with discriminator prediction.
out_logit: Logits (activations of the last linear layer).
net: Logits of the last ReLu layer.
"""
# In compare gan framework, the image preprocess normalize image pixel to
# range [0, 1], while author used [-1, 1]. Apply this trick to input image
# instead of changing our preprocessing function.
x = x * 2.0 - 1.0
with tf.variable_scope("discriminator", reuse=reuse):
# Mapping x from [bs, h, w, c] to [bs, 1]
normal = tf.random_normal_initializer
net = conv2d(
x, 64, 3, 3, 1, 1, name="d_conv1", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 128, 4, 4, 2, 2, name="d_conv2", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 128, 3, 3, 1, 1, name="d_conv3", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 256, 4, 4, 2, 2, name="d_conv4", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 256, 3, 3, 1, 1, name="d_conv5", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 512, 4, 4, 2, 2, name="d_conv6", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = conv2d(
net, 512, 3, 3, 1, 1, name="d_conv7", initializer=normal, use_sn=use_sn)
net = lrelu(net, leak=0.1)
net = tf.reshape(net, [batch_size, -1])
out_logit = linear(net, 1, scope="d_fc1", use_sn=use_sn)
out_logit = tf.squeeze(out_logit)
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
def aggregate_heads(self, heads, reuse, name="aggregate_heads"):
"""Returns the aggregated heads."""
# Estimate local dimensions to support background channel.
k, z_dim = heads[0].get_shape().as_list()[1:3]
with tf.variable_scope(name, reuse=reuse):
heads = tf.concat(heads, axis=2)
heads_r = tf.reshape(heads,
[self.batch_size * k, self.n_heads * z_dim])
heads_a = tf.nn.relu(
ops.linear(tf.concat(heads_r, axis=2), z_dim, "a_fc1"))
heads_a = ops.layer_norm(heads_a, reuse, "a_ln")
heads_a = tf.reshape(heads_a, [self.batch_size, k, z_dim])
return heads_a
def generator(z, batch_size, output_height, output_width, output_c_dim,
is_training, reuse=False):
"""Returns the output tensor of the DCGAN generator.
Details are available at https://arxiv.org/abs/1511.06434. Notable changes
include BatchNorm in the generator, ReLu instead of LeakyReLu and ReLu in
generator, except for output which uses TanH.
Args:
z: latent code, shape [batch_size, latent_dimensionality]
batch_size: Batch size.
output_height: Output image height.
output_width: Output image width.
output_c_dim: Number of color channels.
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
net: The generated image Tensor with entries in [0, 1].
"""
gf_dim = 64 # Dimension of filters in first convolutional layer.
bs = batch_size
with tf.variable_scope("generator", reuse=reuse):
s_h, s_w = output_height, output_width
s_h2, s_w2 = conv_out_size_same(s_h, 2), conv_out_size_same(s_w, 2)
s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(s_w2, 2)
s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(s_w4, 2)
s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(s_w8, 2)
net = linear(z, gf_dim * 8 * s_h16 * s_w16, scope="g_fc1")
net = tf.reshape(net, [-1, s_h16, s_w16, gf_dim * 8])
net = tf.nn.relu(batch_norm_dcgan(net, is_training, scope="g_bn1"))
net = deconv2d(net, [bs, s_h8, s_w8, gf_dim*4],
5, 5, 2, 2, name="g_dc1")
net = tf.nn.relu(batch_norm_dcgan(net, is_training, scope="g_bn2"))
net = deconv2d(net, [bs, s_h4, s_w4, gf_dim*2],
5, 5, 2, 2, name="g_dc2")
net = tf.nn.relu(batch_norm_dcgan(net, is_training, scope="g_bn3"))
net = deconv2d(net, [bs, s_h2, s_w2, gf_dim*1],
5, 5, 2, 2, name="g_dc3")
net = tf.nn.relu(batch_norm_dcgan(net, is_training, scope="g_bn4"))
net = deconv2d(net, [bs, s_h, s_w, output_c_dim],
5, 5, 2, 2, name="g_dc4")
net = 0.5 * tf.nn.tanh(net) + 0.5
return net
def resnet_stl_generator(noise, is_training, reuse=None, colors=3):
batch_size = noise.get_shape().as_list()[0]
with tf.variable_scope("generator", reuse=reuse):
# Map noise to the actual seed.
output = ops.linear(noise, 6 * 6 * 512, scope="fc_noise")
# Reshape the seed to be a rank-4 Tensor.
output = tf.reshape(output, [batch_size, 6, 6, 512], name="fc_reshaped")
ch = 64
# in/out channel numbers copied from SN paper.
magic = [(8, 4), (4, 2), (2, 1)]
for block_idx in range(3):
block_scope = "B%d" % (block_idx + 1)
in_channels = ch * magic[block_idx][0]
out_channels = ch * magic[block_idx][1]
output = generator_block(
output,
in_channels=in_channels,
out_channels=out_channels,
scale="up",
block_scope=block_scope,
is_training=is_training,
reuse=reuse)
# Final processing of the output.
output = batch_norm_resnet(
output, is_training=is_training, scope="final_norm")
output = tf.nn.relu(output)
output = ops.conv2d(
output,
output_dim=colors,
k_h=3,
k_w=3,
d_h=1,
d_w=1,
name="final_conv")
output = tf.nn.sigmoid(output)
print("Generator output shape: ", output)
return output
def discriminator(self,
x,
is_training,
reuse=False,
batch_size_multiplier=1):
"""Discriminator architecture based on InfoGAN.
Args:
x: input images, shape [bs, h, w, channels]
is_training: boolean, are we in train or eval model.
reuse: boolean, should params be re-used.
Returns:
out: a float (in [0, 1]) with discriminator prediction
out_logit: the value "out" before sigmoid
net: the architecture
"""
# print("\n"*5)
# print(reuse)
# print("\n"*5)
sn = self.discriminator_normalization == consts.SPECTRAL_NORM
with tf.variable_scope("discriminator", reuse=reuse):
# Mapping x from [bs, h, w, c] to [bs, 1]
# print("X: ", x)
net = conv2d(x, 64, 4, 4, 2, 2, name="d_conv1",
use_sn=sn) # [bs, h/2, w/2, 64]
# net = dropout(net, 0.1)
net = lrelu(net)
net = conv2d(net, 128, 4, 4, 2, 2, name="d_conv2",
use_sn=sn) # [bs, h/4, w/4, 128]
# net = dropout(net, 0.1)
# print("Before: ", net)
if self.discriminator_normalization == consts.BATCH_NORM:
net0, net1 = tf.split(net, 2, 0)
net0 = batch_norm(net0, is_training=is_training, scope="d_bn2")
net1 = batch_norm(net1,
is_training=is_training,
scope="d_bn2",
reuse=True)
net = tf.concat([net0, net1], 0)
# print("After: ", net)
net = lrelu(net)
net = tf.reshape(net,
[self.batch_size * batch_size_multiplier, -1
]) # [bs, h * w * 8]
net = linear(net, 1024, scope="d_fc3", use_sn=sn) # [bs, 1024]
# net = dropout(net, 0.1)
if self.discriminator_normalization == consts.BATCH_NORM:
net0, net1 = tf.split(net, 2, 0)
net0 = batch_norm(net0, is_training=is_training, scope="d_bn3")
net1 = batch_norm(net1,
is_training=is_training,
scope="d_bn3",
reuse=True)
net = tf.concat([net0, net1], 0)
net = lrelu(net)
out_logit = linear(net, 1, scope="d_fc4", use_sn=sn) # [bs, 1]
out = tf.nn.sigmoid(out_logit)
return out, out_logit, net
