-
Notifications
You must be signed in to change notification settings - Fork 12
/
utils.py
165 lines (118 loc) · 5.01 KB
/
utils.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
'''
symmetrical-synthesis
Copyright (c) 2020-present NAVER Corp.
MIT license
'''
import numpy as np
import tensorflow as tf
from tensorflow.contrib import slim
import os
import glob
from PIL import Image, ImageDraw, ImageFont
FLAGS = tf.app.flags.FLAGS
def prepare_checkpoint_path(save_path, restore):
if not tf.gfile.Exists(save_path):
tf.gfile.MkDir(save_path)
else:
if not restore:
tf.gfile.DeleteRecursively(save_path)
tf.gfile.MkDir(save_path)
def configure_learning_rate(learning_rate_init_value, global_step):
learning_rate = tf.train.exponential_decay(learning_rate_init_value, global_step, decay_steps=FLAGS.decay_steps, decay_rate=FLAGS.decay_ratio, staircase=True) # 10000
return learning_rate
def configure_optimizer(learning_rate):
print('\nAdam used!!!\n')
return tf.train.AdamOptimizer(learning_rate)
def get_restore_op(vgg_path, train_vars, check=False):
vgg_19_vars = [var for var in train_vars if var.name.startswith('vgg')]
if check:
print_vars(vgg_19_vars)
variable_restore_op = slim.assign_from_checkpoint_fn(
vgg_path,
vgg_19_vars,
ignore_missing_vars=True)
return variable_restore_op
def print_vars(var_list):
print('')
for var in var_list:
print(var)
print('')
def loss_parser(str_loss):
'''
NOTE!!! str_loss should be like 'mse,perceptual,texture,adv'...
'''
selected_loss_array = str_loss.split(',')
return selected_loss_array
def rollback_parser(str_rollback):
rollback_array = str_rollback.split(',')
return [int(element) for element in rollback_array]
def get_last_ckpt_path(folder_path):
'''
folder_path = .../where/your/saved/model/folder
'''
meta_paths = sorted(glob.glob(os.path.join(folder_path, '*.meta')))
numbers = []
for meta_path in meta_paths:
numbers.append(int(meta_path.split('-')[-1].split('.')[0]))
numbers = np.asarray(numbers)
sorted_idx = np.argsort(numbers)
latest_meta_path = meta_paths[sorted_idx[-1]]
ckpt_path = latest_meta_path.replace('.meta', '')
return ckpt_path
def get_ckpt_list(folder_path):
'''
folder_path = .../where/your/saved/model/folder
'''
meta_paths = sorted(glob.glob(os.path.join(folder_path, '*.meta')))
numbers = []
for meta_path in meta_paths:
numbers.append(int(meta_path.split('-')[-1].split('.')[0]))
numbers = np.asarray(numbers)
sorted_idx = np.argsort(numbers)
ckpt_list = []
for meta_idx in sorted_idx:
meta_path = meta_paths[meta_idx]
ckpt_list.append(meta_path.replace('.meta', ''))
return ckpt_list
def get_image_paths(image_folder):
possible_image_type = ['jpg', 'png', 'JPEG', 'jpeg']
image_list = [image_path for image_paths in [glob.glob(os.path.join(image_folder, '*.%s' % ext)) for ext in possible_image_type] for image_path in image_paths]
return image_list
class painter:
def __init__(self, font_path='./dataset/Arial-Unicode-Bold.ttf'):
self.font_path = font_path
self.font = ImageFont.truetype(self.font_path, 20)
self.label_dict = ['others', 'exterior']
def write_results(self, images, gt_list, pred_list, how_many=5):
'''
images: (batch_size, 224, 224, 3)
gt_list: (batch_size, 1)
pred_list: (batch_size, 1), not gone through tf.nn.sigmoid
'''
batch_size, height, width, c_ch = images.shape
images = images.astype('uint8')
gt_list = gt_list.astype('int32')
pred_list = (pred_list >= 0.5).astype('int32')
for idx in range(5):
img_pil = Image.fromarray(images[idx])
d_pil = ImageDraw.Draw(img_pil)
# write gt
d_pil.text((10, 3), str(self.label_dict[gt_list[idx][0]]), font=self.font, fill=(0,0,255))
# write pred
d_pil.text((10, 3+20), str(self.label_dict[pred_list[idx][0]]), font=self.font, fill=(255, 0, 0))
images[idx] = np.asarray(img_pil)
return images.astype('float32')
##########################################
## rank results along to cosine similarity
def cond(do_idx, input_how_many, input_query, input_features, return_outputs):
return tf.less(do_idx, input_how_many)
def body(do_idx, input_how_many, input_query, input_features, return_outputs):
tmp_output = tf.reduce_sum(tf.multiply(input_query[do_idx:do_idx+1], input_features), axis=1)
return_outputs = return_outputs.write(do_idx, tmp_output)
do_idx = do_idx + 1
return do_idx, input_how_many, input_query, input_features, return_outputs
def get_coss_list(input_how_many, input_query, input_features):
return_outputs = tf.TensorArray(dtype=tf.float32, size=input_how_many, infer_shape=False)
_, _, _, _, return_outputs = tf.while_loop(cond, body, [0, input_how_many, input_query, input_features, return_outputs])
return_outputs = return_outputs.stack()
return return_outputs