/
inpaint_model.py
310 lines (286 loc) · 12.9 KB
/
inpaint_model.py
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
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
""" common model for DCGAN """
import logging
import cv2
import neuralgym as ng
import tensorflow as tf
from tensorflow.contrib.framework.python.ops import arg_scope
from neuralgym.models import Model
from neuralgym.ops.summary_ops import scalar_summary, images_summary
from neuralgym.ops.summary_ops import gradients_summary
from neuralgym.ops.layers import flatten, resize
from neuralgym.ops.gan_ops import gan_hinge_loss
from neuralgym.ops.gan_ops import random_interpolates
from inpaint_ops import gen_conv, gen_deconv, dis_conv
from inpaint_ops import random_bbox, bbox2mask, local_patch, brush_stroke_mask
from inpaint_ops import resize_mask_like, contextual_attention
logger = logging.getLogger()
class InpaintCAModel(Model):
def __init__(self):
super().__init__('InpaintCAModel')
def build_inpaint_net(self, x, mask, reuse=False,
training=True, padding='SAME', name='inpaint_net'):
"""Inpaint network.
Args:
x: incomplete image, [-1, 1]
mask: mask region {0, 1}
Returns:
[-1, 1] as predicted image
"""
xin = x
offset_flow = None
ones_x = tf.ones_like(x)[:, :, :, 0:1]
x = tf.concat([x, ones_x, ones_x*mask], axis=3)
# two stage network
cnum = 48
with tf.variable_scope(name, reuse=reuse), \
arg_scope([gen_conv, gen_deconv],
training=training, padding=padding):
# stage1
x = gen_conv(x, cnum, 5, 1, name='conv1')
x = gen_conv(x, 2*cnum, 3, 2, name='conv2_downsample')
x = gen_conv(x, 2*cnum, 3, 1, name='conv3')
x = gen_conv(x, 4*cnum, 3, 2, name='conv4_downsample')
x = gen_conv(x, 4*cnum, 3, 1, name='conv5')
x = gen_conv(x, 4*cnum, 3, 1, name='conv6')
mask_s = resize_mask_like(mask, x)
x = gen_conv(x, 4*cnum, 3, rate=2, name='conv7_atrous')
x = gen_conv(x, 4*cnum, 3, rate=4, name='conv8_atrous')
x = gen_conv(x, 4*cnum, 3, rate=8, name='conv9_atrous')
x = gen_conv(x, 4*cnum, 3, rate=16, name='conv10_atrous')
x = gen_conv(x, 4*cnum, 3, 1, name='conv11')
x = gen_conv(x, 4*cnum, 3, 1, name='conv12')
x = gen_deconv(x, 2*cnum, name='conv13_upsample')
x = gen_conv(x, 2*cnum, 3, 1, name='conv14')
x = gen_deconv(x, cnum, name='conv15_upsample')
x = gen_conv(x, cnum//2, 3, 1, name='conv16')
x = gen_conv(x, 3, 3, 1, activation=None, name='conv17')
x = tf.nn.tanh(x)
x_stage1 = x
# stage2, paste result as input
x = x*mask + xin[:, :, :, 0:3]*(1.-mask)
x.set_shape(xin[:, :, :, 0:3].get_shape().as_list())
# conv branch
# xnow = tf.concat([x, ones_x, ones_x*mask], axis=3)
xnow = x
x = gen_conv(xnow, cnum, 5, 1, name='xconv1')
x = gen_conv(x, cnum, 3, 2, name='xconv2_downsample')
x = gen_conv(x, 2*cnum, 3, 1, name='xconv3')
x = gen_conv(x, 2*cnum, 3, 2, name='xconv4_downsample')
x = gen_conv(x, 4*cnum, 3, 1, name='xconv5')
x = gen_conv(x, 4*cnum, 3, 1, name='xconv6')
x = gen_conv(x, 4*cnum, 3, rate=2, name='xconv7_atrous')
x = gen_conv(x, 4*cnum, 3, rate=4, name='xconv8_atrous')
x = gen_conv(x, 4*cnum, 3, rate=8, name='xconv9_atrous')
x = gen_conv(x, 4*cnum, 3, rate=16, name='xconv10_atrous')
x_hallu = x
# attention branch
x = gen_conv(xnow, cnum, 5, 1, name='pmconv1')
x = gen_conv(x, cnum, 3, 2, name='pmconv2_downsample')
x = gen_conv(x, 2*cnum, 3, 1, name='pmconv3')
x = gen_conv(x, 4*cnum, 3, 2, name='pmconv4_downsample')
x = gen_conv(x, 4*cnum, 3, 1, name='pmconv5')
x = gen_conv(x, 4*cnum, 3, 1, name='pmconv6',
activation=tf.nn.relu)
x, offset_flow = contextual_attention(x, x, mask_s, 3, 1, rate=2)
x = gen_conv(x, 4*cnum, 3, 1, name='pmconv9')
x = gen_conv(x, 4*cnum, 3, 1, name='pmconv10')
pm = x
x = tf.concat([x_hallu, pm], axis=3)
x = gen_conv(x, 4*cnum, 3, 1, name='allconv11')
x = gen_conv(x, 4*cnum, 3, 1, name='allconv12')
x = gen_deconv(x, 2*cnum, name='allconv13_upsample')
x = gen_conv(x, 2*cnum, 3, 1, name='allconv14')
x = gen_deconv(x, cnum, name='allconv15_upsample')
x = gen_conv(x, cnum//2, 3, 1, name='allconv16')
x = gen_conv(x, 3, 3, 1, activation=None, name='allconv17')
x = tf.nn.tanh(x)
x_stage2 = x
return x_stage1, x_stage2, offset_flow
def build_sn_patch_gan_discriminator(self, x, reuse=False, training=True):
with tf.variable_scope('sn_patch_gan', reuse=reuse):
cnum = 64
x = dis_conv(x, cnum, name='conv1', training=training)
x = dis_conv(x, cnum*2, name='conv2', training=training)
x = dis_conv(x, cnum*4, name='conv3', training=training)
x = dis_conv(x, cnum*4, name='conv4', training=training)
x = dis_conv(x, cnum*4, name='conv5', training=training)
x = dis_conv(x, cnum*4, name='conv6', training=training)
x = flatten(x, name='flatten')
return x
def build_gan_discriminator(
self, batch, reuse=False, training=True):
with tf.variable_scope('discriminator', reuse=reuse):
d = self.build_sn_patch_gan_discriminator(
batch, reuse=reuse, training=training)
return d
def build_graph_with_losses(
self, FLAGS, batch_data, training=True, summary=False,
reuse=False):
if FLAGS.guided:
batch_data, edge = batch_data
edge = edge[:, :, :, 0:1] / 255.
edge = tf.cast(edge > FLAGS.edge_threshold, tf.float32)
batch_pos = batch_data / 127.5 - 1.
# generate mask, 1 represents masked point
bbox = random_bbox(FLAGS)
# regular_mask = bbox2mask(FLAGS, bbox, name='mask_c')
irregular_mask = brush_stroke_mask(FLAGS, name='mask_c')
mask = tf.cast(
tf.cast(irregular_mask, tf.bool),
tf.float32
)
# mask = tf.cast(
# tf.logical_or(
# tf.cast(irregular_mask, tf.bool),
# tf.cast(regular_mask, tf.bool),
# ),
# tf.float32
# )
batch_incomplete = batch_pos*(1.-mask)
if FLAGS.guided:
edge = edge * mask
xin = tf.concat([batch_incomplete, edge], axis=3)
else:
xin = batch_incomplete
x1, x2, offset_flow = self.build_inpaint_net(
xin, mask, reuse=reuse, training=training,
padding=FLAGS.padding)
batch_predicted = x2
losses = {}
# apply mask and complete image
batch_complete = batch_predicted*mask + batch_incomplete*(1.-mask)
# local patches
losses['ae_loss'] = FLAGS.l1_loss_alpha * tf.reduce_mean(tf.abs(batch_pos - x1))
losses['ae_loss'] += FLAGS.l1_loss_alpha * tf.reduce_mean(tf.abs(batch_pos - x2))
if summary:
scalar_summary('losses/ae_loss', losses['ae_loss'])
if FLAGS.guided:
viz_img = [
batch_pos,
batch_incomplete + edge,
batch_complete]
else:
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow, scale=4,
func=tf.image.resize_bilinear))
images_summary(
tf.concat(viz_img, axis=2),
'raw_incomplete_predicted_complete', FLAGS.viz_max_out)
# gan
batch_pos_neg = tf.concat([batch_pos, batch_complete], axis=0)
if FLAGS.gan_with_mask:
batch_pos_neg = tf.concat([batch_pos_neg, tf.tile(mask, [FLAGS.batch_size*2, 1, 1, 1])], axis=3)
if FLAGS.guided:
# conditional GANs
batch_pos_neg = tf.concat([batch_pos_neg, tf.tile(edge, [2, 1, 1, 1])], axis=3)
# wgan with gradient penalty
if FLAGS.gan == 'sngan':
pos_neg = self.build_gan_discriminator(batch_pos_neg, training=training, reuse=reuse)
pos, neg = tf.split(pos_neg, 2)
g_loss, d_loss = gan_hinge_loss(pos, neg)
losses['g_loss'] = g_loss
losses['d_loss'] = d_loss
else:
raise NotImplementedError('{} not implemented.'.format(FLAGS.gan))
if summary:
# summary the magnitude of gradients from different losses w.r.t. predicted image
gradients_summary(losses['g_loss'], batch_predicted, name='g_loss')
gradients_summary(losses['g_loss'], x2, name='g_loss_to_x2')
# gradients_summary(losses['ae_loss'], x1, name='ae_loss_to_x1')
gradients_summary(losses['ae_loss'], x2, name='ae_loss_to_x2')
losses['g_loss'] = FLAGS.gan_loss_alpha * losses['g_loss']
if FLAGS.ae_loss:
losses['g_loss'] += losses['ae_loss']
g_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'inpaint_net')
d_vars = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, 'discriminator')
return g_vars, d_vars, losses
def build_infer_graph(self, FLAGS, batch_data, bbox=None, name='val'):
"""
"""
if FLAGS.guided:
batch_data, edge = batch_data
edge = edge[:, :, :, 0:1] / 255.
edge = tf.cast(edge > FLAGS.edge_threshold, tf.float32)
# regular_mask = bbox2mask(FLAGS, bbox, name='mask_c')
irregular_mask = brush_stroke_mask(FLAGS, name='mask_c')
mask = tf.cast(
tf.cast(irregular_mask, tf.bool),
tf.float32
)
# mask = tf.cast(
# tf.logical_or(
# tf.cast(irregular_mask, tf.bool),
# tf.cast(regular_mask, tf.bool),
# ),
# tf.float32
# )
batch_pos = batch_data / 127.5 - 1.
batch_incomplete = batch_pos*(1.-mask)
if FLAGS.guided:
edge = edge * mask
xin = tf.concat([batch_incomplete, edge], axis=3)
else:
xin = batch_incomplete
# inpaint
x1, x2, offset_flow = self.build_inpaint_net(
xin, mask, reuse=True,
training=False, padding=FLAGS.padding)
batch_predicted = x2
# apply mask and reconstruct
batch_complete = batch_predicted*mask + batch_incomplete*(1.-mask)
# global image visualization
if FLAGS.guided:
viz_img = [
batch_pos,
batch_incomplete + edge,
batch_complete]
else:
viz_img = [batch_pos, batch_incomplete, batch_complete]
if offset_flow is not None:
viz_img.append(
resize(offset_flow, scale=4,
func=tf.image.resize_bilinear))
images_summary(
tf.concat(viz_img, axis=2),
name+'_raw_incomplete_complete', FLAGS.viz_max_out)
return batch_complete
def build_static_infer_graph(self, FLAGS, batch_data, name):
"""
"""
# generate mask, 1 represents masked point
bbox = (tf.constant(FLAGS.height//2), tf.constant(FLAGS.width//2),
tf.constant(FLAGS.height), tf.constant(FLAGS.width))
return self.build_infer_graph(FLAGS, batch_data, bbox, name)
def build_server_graph(self, FLAGS, batch_data, reuse=False, is_training=False):
"""
"""
# generate mask, 1 represents masked point
if FLAGS.guided:
batch_raw, edge, masks_raw = tf.split(batch_data, 3, axis=2)
edge = edge[:, :, :, 0:1] / 255.
edge = tf.cast(edge > FLAGS.edge_threshold, tf.float32)
else:
batch_raw, masks_raw = tf.split(batch_data, 2, axis=2)
masks = tf.cast(masks_raw[0:1, :, :, 0:1] > 127.5, tf.float32)
# irregular_mask = brush_stroke_mask(FLAGS, name='mask_c')
# masks = tf.cast(
# tf.cast(irregular_mask, tf.bool),
# tf.float32)
batch_pos = batch_raw / 127.5 - 1.
batch_incomplete = batch_pos * (1. - masks)
if FLAGS.guided:
edge = edge * masks[:, :, :, 0:1]
xin = tf.concat([batch_incomplete, edge], axis=3)
else:
xin = batch_incomplete
# inpaint
x1, x2, flow = self.build_inpaint_net(
xin, masks, reuse=reuse, training=is_training)
batch_predict = x2
# apply mask and reconstruct
batch_complete = batch_predict*masks + batch_incomplete*(1-masks)
return batch_complete