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loss.py
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loss.py
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import numpy as np
import tensorflow as tf
import tfutil
#----------------------------------------------------------------------------
# Convenience func that casts all of its arguments to tf.float32.
def fp32(*values):
if len(values) == 1 and isinstance(values[0], tuple):
values = values[0]
values = tuple(tf.cast(v, tf.float32) for v in values)
return values if len(values) >= 2 else values[0]
#----------------------------------------------------------------------------
# Generator loss function used in the paper (WGAN + AC-GAN).
def G_wgan_acgan(G, D, opt, training_set, minibatch_size,
cond_weight = 1.0): # Weight of the conditioning term.
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
loss = -fake_scores_out
if D.output_shapes[1][1] > 0:
with tf.name_scope('LabelPenalty'):
label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
loss += label_penalty_fakes * cond_weight
return loss
#----------------------------------------------------------------------------
# Discriminator loss function used in the paper (WGAN-GP + AC-GAN).
def D_wgangp_acgan(G, D, opt, training_set, minibatch_size, reals, labels,
wgan_lambda = 10.0, # Weight for the gradient penalty term.
wgan_epsilon = 0.001, # Weight for the epsilon term, \epsilon_{drift}.
wgan_target = 1.0, # Target value for gradient magnitudes.
cond_weight = 1.0): # Weight of the conditioning terms.
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out, real_labels_out = fp32(D.get_output_for(reals, is_training=True))
fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True))
real_scores_out = tfutil.autosummary('Loss/real_scores', real_scores_out)
fake_scores_out = tfutil.autosummary('Loss/fake_scores', fake_scores_out)
loss = fake_scores_out - real_scores_out
with tf.name_scope('GradientPenalty'):
mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype)
mixed_images_out = tfutil.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
mixed_scores_out, mixed_labels_out = fp32(D.get_output_for(mixed_images_out, is_training=True))
mixed_scores_out = tfutil.autosummary('Loss/mixed_scores', mixed_scores_out)
mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out))
mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0]))
mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3]))
mixed_norms = tfutil.autosummary('Loss/mixed_norms', mixed_norms)
gradient_penalty = tf.square(mixed_norms - wgan_target)
loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
with tf.name_scope('EpsilonPenalty'):
epsilon_penalty = tfutil.autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
loss += epsilon_penalty * wgan_epsilon
if D.output_shapes[1][1] > 0:
with tf.name_scope('LabelPenalty'):
label_penalty_reals = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=real_labels_out)
label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out)
label_penalty_reals = tfutil.autosummary('Loss/label_penalty_reals', label_penalty_reals)
label_penalty_fakes = tfutil.autosummary('Loss/label_penalty_fakes', label_penalty_fakes)
loss += (label_penalty_reals + label_penalty_fakes) * cond_weight
return loss
#----------------------------------------------------------------------------