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vanilla_gan.py
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# coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Simple Generative Adversarial Model with two linear layers.
Example of how to create a GAN in T2T.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Dependency imports
from tensor2tensor.layers import common_hparams
from tensor2tensor.utils import registry
from tensor2tensor.utils import t2t_model
import tensorflow as tf
def lrelu(input_, leak=0.2, name="lrelu"):
return tf.maximum(input_, leak * input_, name=name)
def deconv2d(
input_, output_shape, k_h, k_w, d_h, d_w, stddev=0.02, name="deconv2d"):
with tf.variable_scope(name):
w = tf.get_variable(
"w", [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
deconv = tf.nn.conv2d_transpose(
input_, w, output_shape=output_shape, strides=[1, d_h, d_w, 1])
biases = tf.get_variable(
"biases", [output_shape[-1]], initializer=tf.constant_initializer(0.0))
return tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
class AbstractGAN(t2t_model.T2TModel):
"""Base class for all GANs."""
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_logit: the output logits (before sigmoid).
"""
hparams = self._hparams
with tf.variable_scope(
"discriminator", reuse=reuse,
initializer=tf.random_normal_initializer(stddev=0.02)):
batch_size = hparams.batch_size
# Mapping x from [bs, h, w, c] to [bs, 1]
net = tf.layers.conv2d(x, 64, (4, 4), strides=(2, 2),
padding="SAME", name="d_conv1")
# [bs, h/2, w/2, 64]
net = lrelu(net)
net = tf.layers.conv2d(net, 128, (4, 4), strides=(2, 2),
padding="SAME", name="d_conv2")
# [bs, h/4, w/4, 128]
if hparams.discriminator_batchnorm:
net = tf.layers.batch_normalization(net, training=is_training,
momentum=0.999, name="d_bn2")
net = lrelu(net)
size = hparams.height * hparams.width
net = tf.reshape(net, [batch_size, size * 8]) # [bs, h * w * 8]
net = tf.layers.dense(net, 1024, name="d_fc3") # [bs, 1024]
if hparams.discriminator_batchnorm:
net = tf.layers.batch_normalization(net, training=is_training,
momentum=0.999, name="d_bn3")
net = lrelu(net)
out_logit = tf.layers.dense(net, 1, name="d_fc4") # [bs, 1]
return out_logit
def generator(self, z, is_training, reuse=False):
"""Generator outputting image in [0, 1]."""
hparams = self._hparams
height = hparams.height
width = hparams.width
batch_size = hparams.batch_size
with tf.variable_scope(
"generator", reuse=reuse,
initializer=tf.random_normal_initializer(stddev=0.02)):
net = tf.layers.dense(z, 1024, name="g_fc1")
net = tf.layers.batch_normalization(net, training=is_training,
momentum=0.999, name="g_bn1")
net = lrelu(net)
net = tf.layers.dense(net, 128 * (height // 4) * (width // 4),
name="g_fc2")
net = tf.layers.batch_normalization(net, training=is_training,
momentum=0.999, name="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 = tf.layers.batch_normalization(net, training=is_training,
momentum=0.999, name="g_bn3")
net = lrelu(net)
net = deconv2d(net, [batch_size, height, width, hparams.c_dim],
4, 4, 2, 2, name="g_dc4")
out = tf.nn.sigmoid(net)
return out
def body(self, features):
"""Body of the model.
Args:
features: a dictionary with the tensors.
Returns:
A pair (predictions, losses) where predictions is the generated image
and losses is a dictionary of losses (that get added for the final loss).
"""
features["targets"] = features["inputs"]
is_training = self.hparams.mode == tf.estimator.ModeKeys.TRAIN
# Input images.
inputs = features["inputs"]
# Noise vector.
z = tf.random_uniform(
shape=[self._hparams.batch_size, self._hparams.z_size],
minval=-1,
maxval=1,
name="z")
# Discriminator output for real images.
d_real_logits = self.discriminator(
inputs, is_training=is_training, reuse=False)
# Discriminator output for fake images.
g = self.generator(z, is_training=is_training, reuse=False)
d_fake_logits_g = self.discriminator(
g, is_training=is_training, reuse=True)
# Discriminator doesn't backprop to generator.
d_fake_logits_d = self.discriminator(
tf.stop_gradient(g), is_training=is_training, reuse=True)
# Loss on real and fake data.
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_real_logits, labels=tf.ones_like(d_real_logits)))
d_loss_fake_g = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_fake_logits_g, labels=tf.zeros_like(d_fake_logits_g)))
d_loss_fake_d = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_fake_logits_d, labels=tf.zeros_like(d_fake_logits_d)))
d_loss = d_loss_real + d_loss_fake_d
losses = {} # All losses get added at the end.
losses["discriminator"] = d_loss
losses["generator"] = - d_loss_fake_g
# Include a dummy training loss to skip self.loss.
losses["training"] = tf.constant(0., dtype=tf.float32)
hparams = self._hparams
summary_g_image = tf.reshape(g[0, :], [1, hparams.height, hparams.width, 1])
tf.summary.image("generated", summary_g_image, max_outputs=1)
if is_training:
# Returns an dummy output and the losses dictionary.
return tf.zeros_like(inputs), losses
return tf.reshape(g, tf.shape(inputs)), losses
def top(self, body_output, features):
"""Override the top function to not do anything."""
return body_output
@registry.register_model
class VanillaGan(AbstractGAN):
"""Simple GAN for demonstration."""
def infer(self,
features=None,
decode_length=50,
beam_size=1,
top_beams=1,
last_position_only=False,
alpha=0.0):
with tf.variable_scope("body/vanilla_gan", reuse=tf.AUTO_REUSE):
z = tf.random_uniform(
shape=[1, self._hparams.random_sample_size],
minval=-1,
maxval=1,
name="z")
g_sample = self.generator(z, self._hparams)
return g_sample
@registry.register_hparams
def vanilla_gan():
"""Basic parameters for a vanilla_gan."""
hparams = common_hparams.basic_params1()
hparams.label_smoothing = 0.0
hparams.hidden_size = 128
hparams.batch_size = 64
hparams.add_hparam("z_size", 64)
hparams.add_hparam("c_dim", 1)
hparams.add_hparam("height", 28)
hparams.add_hparam("width", 28)
hparams.add_hparam("discriminator_batchnorm", int(True))
return hparams