-
Notifications
You must be signed in to change notification settings - Fork 33
/
Copy pathonn_maskopt.py
executable file
·156 lines (123 loc) · 6.26 KB
/
onn_maskopt.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
import model
import layers.optics as optics
from layers.utils import *
import os
# comment out or change line below based on your machine
os.environ["CUDA_VISIBLE_DEVICES"]= "3"
import numpy as np
import tensorflow as tf
from glob import glob
from datetime import datetime
class PhaseMaskModel(model.Model):
def __init__(self, psf_file,
dim, wave_res,
wavelength,
pixel_size,
n, z_file=None,
ckpt_path=None):
self.dim = dim
self.wave_resolution = wave_res
self.wavelength = wavelength
self.pixel_size = pixel_size
self.n = n
self.r_NA = wave_res[0]/2
self.psf_file = psf_file
self.z_file = z_file
super(PhaseMaskModel, self).__init__(name='PhaseMask_ONN', ckpt_path=ckpt_path)
def _build_graph(self, x_train, hm_reg_scale, hm_init_type='random_normal'):
#with tf.device('/device:GPU:0'):
sensordims = (self.dim,self.dim)
# Start with input image
input_img = x_train/tf.reduce_sum(x_train)
input_img = tf.image.resize_nearest_neighbor(input_img, size=wave_res)
tf.summary.image('input_image', input_img)
doAmplitudeMask=False
doZernike=False
z_modes=512
output_fullres = optical_conv_layer(input_img, hm_reg_scale, self.r_NA, n=self.n, wavelength=self.wavelength,
coherent=False, amplitude_mask=doAmplitudeMask, zernike=doZernike,
n_modes=z_modes, zernike_file=self.z_file, name='maskopt')
# Attach images to summary
tf.summary.image('output_fullres', output_fullres)
# output_img = optics.Sensor(input_is_intensities=False, resolution=sensordims)(output_img)
output_img = tf.image.resize_nearest_neighbor(output_fullres, size=sensordims)
return output_img
def _get_data_loss(self, model_output, ground_truth):
model_output = tf.cast(model_output, tf.float32)
ground_truth = tf.cast(ground_truth, tf.float32)
# model_output /= tf.reduce_max(model_output)
ground_truth /= tf.reduce_sum(ground_truth)
with tf.name_scope('data_loss'):
optics.attach_img('model_output', model_output)
optics.attach_img('ground_truth', ground_truth)
loss = tf.reduce_mean(tf.abs(model_output - ground_truth))
return loss
def _get_training_queue(self, batch_size, num_threads=4):
dim = self.dim
file_list = tf.matching_files('/media/data/onn/quickdraw16_192/im_*.png')
filename_queue = tf.train.string_input_producer(file_list)
image_reader = tf.WholeFileReader()
_, image_file = image_reader.read(filename_queue)
image = tf.image.decode_png(image_file,
channels=1,
dtype=tf.uint8)
image = tf.cast(image, tf.float32) # Shape [height, width, 1]
image = tf.expand_dims(image, 0)
image /= 255.
# Get the ratio of the patch size to the smallest side of the image
img_height_width = tf.cast(tf.shape(image)[1:3], tf.float32)
size_ratio = dim/tf.reduce_min(img_height_width)
# Extract a glimpse from the image
#offset_center = tf.random_uniform([1,2], minval=0.0 + size_ratio/2, maxval=1.0-size_ratio/2, dtype=tf.float32)
offset_center = tf.random_uniform([1,2], minval=0, maxval=0, dtype=tf.float32)
offset_center = offset_center * img_height_width
image = tf.image.extract_glimpse(image, size=[dim,dim], offsets=offset_center, centered=True, normalized=False)
image = tf.squeeze(image, 0)
convolved_image = tf.expand_dims(image, 0)
psf = tf.convert_to_tensor(np.load(self.psf_file), tf.float32)
psf /= tf.reduce_sum(psf)
optics.attach_img('gt_psf', tf.expand_dims(tf.expand_dims(tf.squeeze(psf), 0), -1))
psf = tf.expand_dims(tf.expand_dims(tf.squeeze(psf), -1), -1)
# psf = tf.transpose(psf, [1,2,0,3])
pad = int(dim/2)
convolved_image = tf.abs(optics.fft_conv2d(fftpad(convolved_image, pad), fftpad_psf(psf, pad), adjoint=True))
convolved_image = fftunpad(convolved_image, pad)
convolved_image = tf.squeeze(convolved_image,axis=0)
convolved_image /= tf.reduce_sum(convolved_image)
image_batch, convolved_img_batch = tf.train.batch([image, convolved_image],
shapes=[[dim,dim,1], [dim,dim,1]],
batch_size=batch_size,
num_threads=4,
capacity=4*batch_size)
return image_batch, convolved_img_batch
if __name__=='__main__':
tf.reset_default_graph()
dim = 192 # based on input image size
scale = 4 # upsampling factor for phase mask
wave_res = np.array((scale*dim,scale*dim))
wavelength = 550e-9
pixel_size = 6.5*1e-6
n = 1.5090 # 1.4599
num_steps = 200001
# change this file based on desired PSF
psf_file = 'assets/quickdraw16_tiledpsf_2.npy'
phasemask = PhaseMaskModel(psf_file, dim, wave_res, wavelength, pixel_size, n=n, ckpt_path=None)
now = datetime.now()
run_id = 'quickdraw16_tiledpsf_4x/' + now.strftime('%Y%m%d-%H%M%S') + '/'
log_dir = os.path.join('checkpoints/maskopt/', run_id)
if tf.gfile.Exists(log_dir):
tf.gfile.DeleteRecursively(log_dir)
tf.gfile.MakeDirs(log_dir)
phasemask.fit(model_params = {'hm_reg_scale':1e-1},
opt_type = 'ADAM',
#opt_params = {'beta1':0.8, 'beta2':0.999, 'epsilon':1.},
opt_params = {'momentum':0.5, 'use_nesterov':True},
decay_type = 'polynomial',
decay_params = {'decay_steps':num_steps, 'end_learning_rate':1e-10},
batch_size=1,
adadelta_learning_rate = 1,
starter_learning_rate = 0.0001,
num_steps_until_save=500,
num_steps_until_summary=50,
logdir = log_dir,
num_steps = num_steps)