Hugging Face's logo

Spaces:
mfrashad
/
ClothingGAN
Runtime error

App Files Community
1
ClothingGAN
File size: 10,416 Bytes 
97069e1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
 
# Copyright (c) 2019, 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.

"""Loss functions."""

import tensorflow as tf
import dnnlib.tflib as tflib
from dnnlib.tflib.autosummary import autosummary

#----------------------------------------------------------------------------
# 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]

#----------------------------------------------------------------------------
# WGAN & WGAN-GP loss functions.

def G_wgan(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    loss = -fake_scores_out
    return loss

def D_wgan(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
    wgan_epsilon = 0.001): # Weight for the epsilon term, \epsilon_{drift}.

    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
    loss = fake_scores_out - real_scores_out

    with tf.name_scope('EpsilonPenalty'):
        epsilon_penalty = autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
    loss += epsilon_penalty * wgan_epsilon
    return loss

def D_wgan_gp(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
    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.

    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', 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 = tflib.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
        mixed_scores_out = fp32(D.get_output_for(mixed_images_out, labels, is_training=True))
        mixed_scores_out = autosummary('Loss/scores/mixed', 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 = 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 = autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
    loss += epsilon_penalty * wgan_epsilon
    return loss

#----------------------------------------------------------------------------
# Hinge loss functions. (Use G_wgan with these)

def D_hinge(G, D, opt, training_set, minibatch_size, reals, labels): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
    loss = tf.maximum(0., 1.+fake_scores_out) + tf.maximum(0., 1.-real_scores_out)
    return loss

def D_hinge_gp(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
    wgan_lambda     = 10.0,     # Weight for the gradient penalty term.
    wgan_target     = 1.0):     # Target value for gradient magnitudes.

    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
    loss = tf.maximum(0., 1.+fake_scores_out) + tf.maximum(0., 1.-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 = tflib.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
        mixed_scores_out = fp32(D.get_output_for(mixed_images_out, labels, is_training=True))
        mixed_scores_out = autosummary('Loss/scores/mixed', 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 = autosummary('Loss/mixed_norms', mixed_norms)
        gradient_penalty = tf.square(mixed_norms - wgan_target)
    loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
    return loss


#----------------------------------------------------------------------------
# Loss functions advocated by the paper
# "Which Training Methods for GANs do actually Converge?"

def G_logistic_saturating(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    loss = -tf.nn.softplus(fake_scores_out)  # log(1 - logistic(fake_scores_out))
    return loss

def G_logistic_nonsaturating(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    loss = tf.nn.softplus(-fake_scores_out)  # -log(logistic(fake_scores_out))
    return loss

def D_logistic(G, D, opt, training_set, minibatch_size, reals, labels): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
    loss = tf.nn.softplus(fake_scores_out)  # -log(1 - logistic(fake_scores_out))
    loss += tf.nn.softplus(-real_scores_out)  # -log(logistic(real_scores_out)) # temporary pylint workaround # pylint: disable=invalid-unary-operand-type
    return loss

def D_logistic_simplegp(G, D, opt, training_set, minibatch_size, reals, labels, r1_gamma=10.0, r2_gamma=0.0): # pylint: disable=unused-argument
    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 = fp32(D.get_output_for(reals, labels, is_training=True))
    fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
    real_scores_out = autosummary('Loss/scores/real', real_scores_out)
    fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
    loss = tf.nn.softplus(fake_scores_out)  # -log(1 - logistic(fake_scores_out))
    loss += tf.nn.softplus(-real_scores_out)  # -log(logistic(real_scores_out)) # temporary pylint workaround # pylint: disable=invalid-unary-operand-type

    if r1_gamma != 0.0:
        with tf.name_scope('R1Penalty'):
            real_loss = opt.apply_loss_scaling(tf.reduce_sum(real_scores_out))
            real_grads = opt.undo_loss_scaling(fp32(tf.gradients(real_loss, [reals])[0]))
            r1_penalty = tf.reduce_sum(tf.square(real_grads), axis=[1,2,3])
            r1_penalty = autosummary('Loss/r1_penalty', r1_penalty)
        loss += r1_penalty * (r1_gamma * 0.5)

    if r2_gamma != 0.0:
        with tf.name_scope('R2Penalty'):
            fake_loss = opt.apply_loss_scaling(tf.reduce_sum(fake_scores_out))
            fake_grads = opt.undo_loss_scaling(fp32(tf.gradients(fake_loss, [fake_images_out])[0]))
            r2_penalty = tf.reduce_sum(tf.square(fake_grads), axis=[1,2,3])
            r2_penalty = autosummary('Loss/r2_penalty', r2_penalty)
        loss += r2_penalty * (r2_gamma * 0.5)
    return loss

#----------------------------------------------------------------------------