-
Notifications
You must be signed in to change notification settings - Fork 2
/
pix2pix.py
375 lines (305 loc) · 18.1 KB
/
pix2pix.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
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
import os
import cv2
import logging
import collections
import numpy as np
import matplotlib as mpl
mpl.use('TkAgg') # or whatever other backend that you want to solve Segmentation fault (core dumped)
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import tensorflow as tf
# noinspection PyPep8Naming
import tensorflow_utils as tf_utils
import utils as utils
from reader import Reader
logger = logging.getLogger(__name__) # logger
logger.setLevel(logging.INFO)
# noinspection PyPep8Naming
class Pix2Pix(object):
def __init__(self, sess, flags, img_size, data_path, log_path=None):
self.sess = sess
self.flags = flags
self.img_size = img_size
self.data_path = data_path
self.log_path = log_path
self.L1_lamba = 100.
self._gen_train_ops, self._dis_train_ops = [], []
self.gen_c = [64, 128, 256, 512, 512, 512, 512, 512,
512, 512, 512, 512, 256, 128, 64, self.img_size[2]]
self.dis_c = [64, 128, 256, 512, 1]
self.start_decay_step = int(self.flags.iters / 2)
self.decay_steps = self.flags.iters - self.start_decay_step
self.eps = 1e-12
self._init_logger() # init logger
self._build_net() # init graph
self._tensorboard() # init tensorboard
def _init_logger(self):
if self.flags.is_train:
tf_utils._init_logger(self.log_path)
def _build_net(self):
# tfph: TensorFlow PlaceHolder
self.x_test_tfph = tf.placeholder(tf.float32, shape=[None, *self.img_size], name='x_test_tfph')
self.generator = Generator(name='gen', gen_c=self.gen_c, image_size=self.img_size, _ops=self._gen_train_ops)
self.discriminator = Discriminator(name='dis', dis_c=self.dis_c, _ops=self._dis_train_ops)
data_reader = Reader(self.data_path, name='data', image_size=self.img_size, batch_size=self.flags.batch_size,
is_train=self.flags.is_train)
# self.x_imgs_ori and self.y_imgs_ori are the images before data augmentation
self.x_imgs, self.y_imgs, self.x_imgs_ori, self.y_imgs_ori, self.img_name = data_reader.feed()
self.g_samples = self.generator(self.x_imgs)
self.real_pair = tf.concat([self.x_imgs, self.y_imgs], axis=3)
self.fake_pair = tf.concat([self.x_imgs, self.g_samples], axis=3)
d_logit_real = self.discriminator(self.real_pair)
d_logit_fake = self.discriminator(self.fake_pair)
# discriminator loss
self.d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logit_real, labels=tf.ones_like(d_logit_real)))
self.d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logit_fake, labels=tf.zeros_like(d_logit_fake)))
self.d_loss = self.d_loss_real + self.d_loss_fake
# generator loss
self.gan_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logit_fake, labels=tf.ones_like(d_logit_fake)))
self.cond_loss = self.L1_lamba * tf.reduce_mean(tf.abs(self.y_imgs - self.g_samples))
self.g_loss = self.gan_loss + self.cond_loss
gen_op = self.optimizer(loss=self.g_loss, variables=self.generator.variables, name='Adam_gen')
gen_ops = [gen_op] + self._gen_train_ops
self.gen_optim = tf.group(*gen_ops)
dis_op = self.optimizer(loss=self.d_loss, variables=self.discriminator.variables, name='Adam_dis')
dis_ops = [dis_op] + self._dis_train_ops
self.dis_optim = tf.group(*dis_ops)
# for sampling function
self.fake_y_sample = self.generator(self.x_test_tfph)
def optimizer(self, loss, variables, name='Adam'):
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = self.flags.learning_rate
end_learning_rate = 0.
start_decay_step = self.start_decay_step
decay_steps = self.decay_steps
learning_rate = (tf.where(tf.greater_equal(global_step, start_decay_step),
tf.train.polynomial_decay(starter_learning_rate,
global_step - start_decay_step,
decay_steps, end_learning_rate, power=1.0),
starter_learning_rate))
tf.summary.scalar('learning_rate/{}'.format(name), learning_rate)
learn_step = tf.train.AdamOptimizer(learning_rate, beta1=self.flags.beta1, name=name).\
minimize(loss, global_step=global_step, var_list=variables)
return learn_step
def _tensorboard(self):
tf.summary.scalar('loss/gen_total_loss', self.g_loss)
tf.summary.scalar('loss/gen_gan_loss', self.gan_loss)
tf.summary.scalar('loss/gen_cond_loss', self.cond_loss)
tf.summary.scalar('loss/dis_total_loss', self.d_loss)
self.summary_op = tf.summary.merge_all()
def train_step(self):
dis_ops = [self.dis_optim, self.d_loss]
gen_ops = [self.gen_optim, self.gan_loss, self.cond_loss, self.g_loss, self.summary_op]
_, d_loss = self.sess.run(dis_ops)
_, gan_loss, cond_loss, g_loss, summary = self.sess.run(gen_ops)
# Run g_optim twice to make sure that d_loss does not go to zero (different from paper)
_, gan_loss, cond_loss, g_loss, summary = self.sess.run(gen_ops)
return [gan_loss, cond_loss, g_loss, d_loss], summary
def test_step(self):
x_vals, y_vals, img_name = self.sess.run([self.x_imgs, self.y_imgs, self.img_name])
fakes_y = self.sess.run(self.fake_y_sample, feed_dict={self.x_test_tfph: x_vals})
return [x_vals, fakes_y, y_vals], img_name
def sample_imgs(self):
x_vals, y_vals = self.sess.run([self.x_imgs, self.y_imgs])
fakes_y = self.sess.run(self.fake_y_sample, feed_dict={self.x_test_tfph: x_vals})
return [x_vals, fakes_y, y_vals]
def print_info(self, loss, iter_time):
if np.mod(iter_time, self.flags.print_freq) == 0:
ord_output = collections.OrderedDict([('cur_iter', iter_time), ('tar_iters', self.flags.iters),
('batch_size', self.flags.batch_size),
('gen_gan_loss', loss[0]), ('gen_cond_loss', loss[1]),
('gen_total_loss', loss[2]), ('dis_total_loss', loss[3]),
('dataset', self.flags.dataset),
('gpu_index', self.flags.gpu_index)])
utils.print_metrics(iter_time, ord_output)
@staticmethod
def plots(imgs, iter_time, image_size, save_file):
# parameters for plot size
scale, margin = 0.02, 0.02
n_cols, n_rows = len(imgs), imgs[0].shape[0]
cell_size_h, cell_size_w = imgs[0].shape[1] * scale, imgs[0].shape[2] * scale
fig = plt.figure(figsize=(cell_size_w * n_cols, cell_size_h * n_rows)) # (column, row)
gs = gridspec.GridSpec(n_rows, n_cols) # (row, column)
gs.update(wspace=margin, hspace=margin)
imgs = [utils.inverse_transform(imgs[idx]) for idx in range(len(imgs))]
# save more bigger image
for col_index in range(n_cols):
for row_index in range(n_rows):
ax = plt.subplot(gs[row_index * n_cols + col_index])
plt.axis('off')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_aspect('equal')
plt.imshow((imgs[col_index][row_index]).reshape(image_size[0], image_size[1]), cmap='Greys_r')
plt.savefig(save_file + '/sample_{}.png'.format(str(iter_time).zfill(5)), bbox_inches='tight')
plt.close(fig)
def plots_test(self, imgs, img_name, save_file, eval_file, gt_file, ct_file):
num_imgs = len(imgs)
canvas = np.zeros((self.img_size[0], num_imgs * self.img_size[1]), np.uint8)
for idx in range(num_imgs):
canvas[:, idx * self.img_size[1]: (idx+1) * self.img_size[1]] = \
np.squeeze(255. * utils.inverse_transform(imgs[idx]))
img_name_ = img_name.astype('U26')[0]
# img_name_ = img_name_ + ".jpg"
img_name_ = img_name_
# save imgs on test folder
cv2.imwrite(os.path.join(save_file, img_name_), canvas)
# save imgs on eval folder
cv2.imwrite(os.path.join(eval_file, img_name_), canvas[:,self.img_size[1]:2*self.img_size[1]])
# save imgs on gt folder
cv2.imwrite(os.path.join(gt_file, img_name_), canvas[:, 2*self.img_size[1]:3*self.img_size[1]])
cv2.imwrite(os.path.join(ct_file, img_name_), canvas[:, :self.img_size[1]])
class Generator(object):
def __init__(self, name=None, gen_c=None, image_size=(256, 256, 1), _ops=None):
self.name = name
self.gen_c = gen_c
self.image_size = image_size
self._ops = _ops
self.reuse = False
def __call__(self, x):
with tf.variable_scope(self.name, reuse=self.reuse):
tf_utils.print_activations(x)
# (300, 200) -> (150, 100)
e0_conv2d = tf_utils.conv2d(x, self.gen_c[0], name='e0_conv2d')
e0_lrelu = tf_utils.lrelu(e0_conv2d, name='e0_lrelu')
# (150, 100) -> (75, 50)
e1_conv2d = tf_utils.conv2d(e0_lrelu, self.gen_c[1], name='e1_conv2d')
e1_batchnorm = tf_utils.batch_norm(e1_conv2d, name='e1_batchnorm', _ops=self._ops)
e1_lrelu = tf_utils.lrelu(e1_batchnorm, name='e1_lrelu')
# (75, 50) -> (38, 25)
e2_conv2d = tf_utils.conv2d(e1_lrelu, self.gen_c[2], name='e2_conv2d')
e2_batchnorm = tf_utils.batch_norm(e2_conv2d, name='e2_batchnorm', _ops=self._ops)
e2_lrelu = tf_utils.lrelu(e2_batchnorm, name='e2_lrelu')
# (38, 25) -> (19, 13)
e3_conv2d = tf_utils.conv2d(e2_lrelu, self.gen_c[3], name='e3_conv2d')
e3_batchnorm = tf_utils.batch_norm(e3_conv2d, name='e3_batchnorm', _ops=self._ops)
e3_lrelu = tf_utils.lrelu(e3_batchnorm, name='e3_lrelu')
# (19, 13) -> (10, 7)
e4_conv2d = tf_utils.conv2d(e3_lrelu, self.gen_c[4], name='e4_conv2d')
e4_batchnorm = tf_utils.batch_norm(e4_conv2d, name='e4_batchnorm', _ops=self._ops)
e4_lrelu = tf_utils.lrelu(e4_batchnorm, name='e4_lrelu')
# (10, 7) -> (5, 4)
e5_conv2d = tf_utils.conv2d(e4_lrelu, self.gen_c[5], name='e5_conv2d')
e5_batchnorm = tf_utils.batch_norm(e5_conv2d, name='e5_batchnorm', _ops=self._ops)
e5_lrelu = tf_utils.lrelu(e5_batchnorm, name='e5_lrelu')
# (5, 4) -> (3, 2)
e6_conv2d = tf_utils.conv2d(e5_lrelu, self.gen_c[6], name='e6_conv2d')
e6_batchnorm = tf_utils.batch_norm(e6_conv2d, name='e6_batchnorm', _ops=self._ops)
e6_lrelu = tf_utils.lrelu(e6_batchnorm, name='e6_lrelu')
# (3, 2) -> (2, 1)
e7_conv2d = tf_utils.conv2d(e6_lrelu, self.gen_c[7], name='e7_conv2d')
e7_batchnorm = tf_utils.batch_norm(e7_conv2d, name='e7_batchnorm', _ops=self._ops)
e7_relu = tf_utils.relu(e7_batchnorm, name='e7_relu')
# (2, 1) -> (4, 2)
d0_deconv = tf_utils.deconv2d(e7_relu, self.gen_c[8], name='d0_deconv2d')
shapeA = e6_conv2d.get_shape().as_list()[1]
shapeB = d0_deconv.get_shape().as_list()[1] - e6_conv2d.get_shape().as_list()[1]
# (4, 2) -> (3, 2)
d0_split, _ = tf.split(d0_deconv, [shapeA, shapeB], axis=1, name='d0_split')
tf_utils.print_activations(d0_split)
d0_batchnorm = tf_utils.batch_norm(d0_split, name='d0_batchnorm', _ops=self._ops)
d0_drop = tf.nn.dropout(d0_batchnorm, keep_prob=0.5, name='d0_dropout')
d0_concat = tf.concat([d0_drop, e6_batchnorm], axis=3, name='d0_concat')
d0_relu = tf_utils.relu(d0_concat, name='d0_relu')
# (3, 2) -> (6, 4)
d1_deconv = tf_utils.deconv2d(d0_relu, self.gen_c[9], name='d1_deconv2d')
# (6, 4) -> (5, 4)
shapeA = e5_batchnorm.get_shape().as_list()[1]
shapeB = d1_deconv.get_shape().as_list()[1] - e5_batchnorm.get_shape().as_list()[1]
d1_split, _ = tf.split(d1_deconv, [shapeA, shapeB], axis=1, name='d1_split')
tf_utils.print_activations(d1_split)
d1_batchnorm = tf_utils.batch_norm(d1_split, name='d1_batchnorm', _ops=self._ops)
d1_drop = tf.nn.dropout(d1_batchnorm, keep_prob=0.5, name='d1_dropout')
d1_concat = tf.concat([d1_drop, e5_batchnorm], axis=3, name='d1_concat')
d1_relu = tf_utils.relu(d1_concat, name='d1_relu')
# (5, 4) -> (10, 8)
d2_deconv = tf_utils.deconv2d(d1_relu, self.gen_c[10], name='d2_deconv2d')
# (10, 8) -> (10, 7)
shapeA = e4_batchnorm.get_shape().as_list()[2]
shapeB = d2_deconv.get_shape().as_list()[2] - e4_batchnorm.get_shape().as_list()[2]
d2_split, _ = tf.split(d2_deconv, [shapeA, shapeB], axis=2, name='d2_split')
tf_utils.print_activations(d2_split)
d2_batchnorm = tf_utils.batch_norm(d2_split, name='d2_batchnorm', _ops=self._ops)
d2_drop = tf.nn.dropout(d2_batchnorm, keep_prob=0.5, name='d2_dropout')
d2_concat = tf.concat([d2_drop, e4_batchnorm], axis=3, name='d2_concat')
d2_relu = tf_utils.relu(d2_concat, name='d2_relu')
# (10, 7) -> (20, 14)
d3_deconv = tf_utils.deconv2d(d2_relu, self.gen_c[11], name='d3_deconv2d')
# (20, 14) -> (19, 14)
shapeA = e3_batchnorm.get_shape().as_list()[1]
shapeB = d3_deconv.get_shape().as_list()[1] - e3_batchnorm.get_shape().as_list()[1]
d3_split_1, _ = tf.split(d3_deconv, [shapeA, shapeB], axis=1, name='d3_split_1')
tf_utils.print_activations(d3_split_1)
# (19, 14) -> (19, 13)
shapeA = e3_batchnorm.get_shape().as_list()[2]
shapeB = d3_split_1.get_shape().as_list()[2] - e3_batchnorm.get_shape().as_list()[2]
d3_split_2, _ = tf.split(d3_split_1, [shapeA, shapeB], axis=2, name='d3_split_2')
tf_utils.print_activations(d3_split_2)
d3_batchnorm = tf_utils.batch_norm(d3_split_2, name='d3_batchnorm', _ops=self._ops)
d3_concat = tf.concat([d3_batchnorm, e3_batchnorm], axis=3, name='d3_concat')
d3_relu = tf_utils.relu(d3_concat, name='d3_relu')
# (19, 13) -> (38, 26)
d4_deconv = tf_utils.deconv2d(d3_relu, self.gen_c[12], name='d4_deconv2d')
# (38, 26) -> (38, 25)
shapeA = e2_batchnorm.get_shape().as_list()[2]
shapeB = d4_deconv.get_shape().as_list()[2] - e2_batchnorm.get_shape().as_list()[2]
d4_split, _ = tf.split(d4_deconv, [shapeA, shapeB], axis=2, name='d4_split')
tf_utils.print_activations(d4_split)
d4_batchnorm = tf_utils.batch_norm(d4_split, name='d4_batchnorm', _ops=self._ops)
d4_concat = tf.concat([d4_batchnorm, e2_batchnorm], axis=3, name='d4_concat')
d4_relu = tf_utils.relu(d4_concat, name='d4_relu')
# (38, 25) -> (76, 50)
d5_deconv = tf_utils.deconv2d(d4_relu, self.gen_c[13], name='d5_deconv2d')
# (76, 50) -> (75, 50)
shapeA = e1_batchnorm.get_shape().as_list()[1]
shapeB = d5_deconv.get_shape().as_list()[1] - e1_batchnorm.get_shape().as_list()[1]
d5_split, _ = tf.split(d5_deconv, [shapeA, shapeB], axis=1, name='d5_split')
tf_utils.print_activations(d5_split)
d5_batchnorm = tf_utils.batch_norm(d5_split, name='d5_batchnorm', _ops=self._ops)
d5_concat = tf.concat([d5_batchnorm, e1_batchnorm], axis=3, name='d5_concat')
d5_relu = tf_utils.relu(d5_concat, name='d5_relu')
# (75, 50) -> (150, 100)
d6_deconv = tf_utils.deconv2d(d5_relu, self.gen_c[14], name='d6_deconv2d')
d6_batchnorm = tf_utils.batch_norm(d6_deconv, name='d6_batchnorm', _ops=self._ops)
d6_concat = tf.concat([d6_batchnorm, e0_conv2d], axis=3, name='d6_concat')
d6_relu = tf_utils.relu(d6_concat, name='d6_relu')
# (150, 100) -> (300, 200)
d7_deconv = tf_utils.deconv2d(d6_relu, self.gen_c[15], name='d7_deconv2d')
output = tf_utils.tanh(d7_deconv, name='output_tanh')
# set reuse=True for next call
self.reuse = True
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output
class Discriminator(object):
def __init__(self, name=None, dis_c=None, _ops=None):
self.name = name
self.dis_c = dis_c
self._ops = _ops
self.reuse = False
def __call__(self, x):
with tf.variable_scope(self.name, reuse=self.reuse):
tf_utils.print_activations(x)
# 200 -> 100
h0_conv2d = tf_utils.conv2d(x, self.dis_c[0], name='h0_conv2d')
h0_lrelu = tf_utils.lrelu(h0_conv2d, name='h0_lrelu')
# 100 -> 50
h1_conv2d = tf_utils.conv2d(h0_lrelu, self.dis_c[1], name='h1_conv2d')
h1_batchnorm = tf_utils.batch_norm(h1_conv2d, name='h1_batchnorm', _ops=self._ops)
h1_lrelu = tf_utils.lrelu(h1_batchnorm, name='h1_lrelu')
# 50 -> 25
h2_conv2d = tf_utils.conv2d(h1_lrelu, self.dis_c[2], name='h2_conv2d')
h2_batchnorm = tf_utils.batch_norm(h2_conv2d, name='h2_batchnorm', _ops=self._ops)
h2_lrelu = tf_utils.lrelu(h2_batchnorm, name='h2_lrelu')
# 25 -> 13
h3_conv2d = tf_utils.conv2d(h2_lrelu, self.dis_c[3], name='h3_conv2d')
h3_batchnorm = tf_utils.batch_norm(h3_conv2d, name='h3_batchnorm', _ops=self._ops)
h3_lrelu = tf_utils.lrelu(h3_batchnorm, name='h3_lrelu')
# Patch GAN: 13 -> 13
output = tf_utils.conv2d(h3_lrelu, self.dis_c[4], k_h=3, k_w=3, d_h=1, d_w=1, name='output_conv2d')
# set reuse=True for next call
self.reuse = True
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.name)
return output