-
Notifications
You must be signed in to change notification settings - Fork 31
/
measurements.py
290 lines (221 loc) · 8.59 KB
/
measurements.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
'''This module handles task-dependent operations (A) and noises (n) to simulate a measurement y=Ax+n.'''
from abc import ABC, abstractmethod
from functools import partial
import yaml
from torch.nn import functional as F
from torchvision import torch
from motionblur.motionblur import Kernel
from util.resizer import Resizer
from util.img_utils import Blurkernel, fft2_m
# =================
# Operation classes
# =================
__OPERATOR__ = {}
def register_operator(name: str):
def wrapper(cls):
if __OPERATOR__.get(name, None):
raise NameError(f"Name {name} is already registered!")
__OPERATOR__[name] = cls
return cls
return wrapper
def get_operator(name: str, **kwargs):
if __OPERATOR__.get(name, None) is None:
raise NameError(f"Name {name} is not defined.")
return __OPERATOR__[name](**kwargs)
class LinearOperator(ABC):
@abstractmethod
def forward(self, data, **kwargs):
# calculate A * X
pass
@abstractmethod
def transpose(self, data, **kwargs):
# calculate A^T * X
pass
def ortho_project(self, data, **kwargs):
# calculate (I - A^T * A)X
return data - self.transpose(self.forward(data, **kwargs), **kwargs)
def project(self, data, measurement, **kwargs):
# calculate (I - A^T * A)Y - AX
return self.ortho_project(measurement, **kwargs) - self.forward(data, **kwargs)
@register_operator(name='noise')
class DenoiseOperator(LinearOperator):
def __init__(self, device):
self.device = device
def forward(self, data):
return data
def transpose(self, data):
return data
def ortho_project(self, data):
return data
def project(self, data):
return data
@register_operator(name='super_resolution')
class SuperResolutionOperator(LinearOperator):
def __init__(self, in_shape, scale_factor, device):
self.device = device
self.up_sample = partial(F.interpolate, scale_factor=scale_factor)
self.down_sample = Resizer(in_shape, 1/scale_factor).to(device)
def forward(self, data, **kwargs):
return self.down_sample(data)
def transpose(self, data, **kwargs):
return self.up_sample(data)
def project(self, data, measurement, **kwargs):
return data - self.transpose(self.forward(data)) + self.transpose(measurement)
@register_operator(name='motion_blur')
class MotionBlurOperator(LinearOperator):
def __init__(self, kernel_size, intensity, device):
self.device = device
self.kernel_size = kernel_size
self.conv = Blurkernel(blur_type='motion',
kernel_size=kernel_size,
std=intensity,
device=device).to(device) # should we keep this device term?
self.kernel = Kernel(size=(kernel_size, kernel_size), intensity=intensity)
kernel = torch.tensor(self.kernel.kernelMatrix, dtype=torch.float32)
self.conv.update_weights(kernel)
def forward(self, data, **kwargs):
# A^T * A
return self.conv(data)
def transpose(self, data, **kwargs):
return data
def get_kernel(self):
kernel = self.kernel.kernelMatrix.type(torch.float32).to(self.device)
return kernel.view(1, 1, self.kernel_size, self.kernel_size)
@register_operator(name='gaussian_blur')
class GaussialBlurOperator(LinearOperator):
def __init__(self, kernel_size, intensity, device):
self.device = device
self.kernel_size = kernel_size
self.conv = Blurkernel(blur_type='gaussian',
kernel_size=kernel_size,
std=intensity,
device=device).to(device)
self.kernel = self.conv.get_kernel()
self.conv.update_weights(self.kernel.type(torch.float32))
def forward(self, data, **kwargs):
return self.conv(data)
def transpose(self, data, **kwargs):
return data
def get_kernel(self):
return self.kernel.view(1, 1, self.kernel_size, self.kernel_size)
@register_operator(name='inpainting')
class InpaintingOperator(LinearOperator):
'''This operator get pre-defined mask and return masked image.'''
def __init__(self, device):
self.device = device
def forward(self, data, **kwargs):
try:
return data * kwargs.get('mask', None).to(self.device)
except:
raise ValueError("Require mask")
def transpose(self, data, **kwargs):
return data
def ortho_project(self, data, **kwargs):
return data - self.forward(data, **kwargs)
class NonLinearOperator(ABC):
@abstractmethod
def forward(self, data, **kwargs):
pass
def project(self, data, measurement, **kwargs):
return data + measurement - self.forward(data)
@register_operator(name='phase_retrieval')
class PhaseRetrievalOperator(NonLinearOperator):
def __init__(self, oversample, device):
self.pad = int((oversample / 8.0) * 256)
self.device = device
def forward(self, data, **kwargs):
padded = F.pad(data, (self.pad, self.pad, self.pad, self.pad))
amplitude = fft2_m(padded).abs()
return amplitude
@register_operator(name='nonlinear_blur')
class NonlinearBlurOperator(NonLinearOperator):
def __init__(self, opt_yml_path, device):
self.device = device
self.blur_model = self.prepare_nonlinear_blur_model(opt_yml_path)
def prepare_nonlinear_blur_model(self, opt_yml_path):
'''
Nonlinear deblur requires external codes (bkse).
'''
from bkse.models.kernel_encoding.kernel_wizard import KernelWizard
with open(opt_yml_path, "r") as f:
opt = yaml.safe_load(f)["KernelWizard"]
model_path = opt["pretrained"]
blur_model = KernelWizard(opt)
blur_model.eval()
blur_model.load_state_dict(torch.load(model_path))
blur_model = blur_model.to(self.device)
return blur_model
def forward(self, data, **kwargs):
random_kernel = torch.randn(1, 512, 2, 2).to(self.device) * 1.2
data = (data + 1.0) / 2.0 #[-1, 1] -> [0, 1]
blurred = self.blur_model.adaptKernel(data, kernel=random_kernel)
blurred = (blurred * 2.0 - 1.0).clamp(-1, 1) #[0, 1] -> [-1, 1]
return blurred
# =============
# Noise classes
# =============
__NOISE__ = {}
def register_noise(name: str):
def wrapper(cls):
if __NOISE__.get(name, None):
raise NameError(f"Name {name} is already defined!")
__NOISE__[name] = cls
return cls
return wrapper
def get_noise(name: str, **kwargs):
if __NOISE__.get(name, None) is None:
raise NameError(f"Name {name} is not defined.")
noiser = __NOISE__[name](**kwargs)
noiser.__name__ = name
return noiser
class Noise(ABC):
def __call__(self, data):
return self.forward(data)
@abstractmethod
def forward(self, data):
pass
@register_noise(name='clean')
class Clean(Noise):
def forward(self, data):
return data
@register_noise(name='gaussian')
class GaussianNoise(Noise):
def __init__(self, sigma):
self.sigma = sigma
def forward(self, data):
return data + torch.randn_like(data, device=data.device) * self.sigma
@register_noise(name='poisson')
class PoissonNoise(Noise):
def __init__(self, rate):
self.rate = rate
def forward(self, data):
'''
Follow skimage.util.random_noise.
'''
# TODO: set one version of poisson
# version 3 (stack-overflow)
import numpy as np
data = (data + 1.0) / 2.0
data = data.clamp(0, 1)
device = data.device
data = data.detach().cpu()
data = torch.from_numpy(np.random.poisson(data * 255.0 * self.rate) / 255.0 / self.rate)
data = data * 2.0 - 1.0
data = data.clamp(-1, 1)
return data.to(device)
# version 2 (skimage)
# if data.min() < 0:
# low_clip = -1
# else:
# low_clip = 0
# # Determine unique values in iamge & calculate the next power of two
# vals = torch.Tensor([len(torch.unique(data))])
# vals = 2 ** torch.ceil(torch.log2(vals))
# vals = vals.to(data.device)
# if low_clip == -1:
# old_max = data.max()
# data = (data + 1.0) / (old_max + 1.0)
# data = torch.poisson(data * vals) / float(vals)
# if low_clip == -1:
# data = data * (old_max + 1.0) - 1.0
# return data.clamp(low_clip, 1.0)