File size: 14,014 Bytes
162943d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
376
377
378
379
380
381
382
383
384
385
386
387
388
'''
Copyright (C) 2019 NVIDIA Corporation. Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu.
BSD License. All rights reserved. 

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL 
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. 
IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL 
DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, 
WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING 
OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
'''
import torch
import torch.nn as nn
import functools
import numpy as np
import pytorch_lightning as pl


###############################################################################
# Functions
###############################################################################
def weights_init(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        m.weight.data.normal_(0.0, 0.02)
    elif classname.find('BatchNorm2d') != -1:
        m.weight.data.normal_(1.0, 0.02)
        m.bias.data.fill_(0)


def get_norm_layer(norm_type='instance'):
    if norm_type == 'batch':
        norm_layer = functools.partial(nn.BatchNorm2d, affine=True)
    elif norm_type == 'instance':
        norm_layer = functools.partial(nn.InstanceNorm2d, affine=False)
    else:
        raise NotImplementedError('normalization layer [%s] is not found' %
                                  norm_type)
    return norm_layer


def define_G(input_nc,
             output_nc,
             ngf,
             netG,
             n_downsample_global=3,
             n_blocks_global=9,
             n_local_enhancers=1,
             n_blocks_local=3,
             norm='instance',
             gpu_ids=[],
             last_op=nn.Tanh()):
    norm_layer = get_norm_layer(norm_type=norm)
    if netG == 'global':
        netG = GlobalGenerator(input_nc,
                               output_nc,
                               ngf,
                               n_downsample_global,
                               n_blocks_global,
                               norm_layer,
                               last_op=last_op)
    elif netG == 'local':
        netG = LocalEnhancer(input_nc, output_nc, ngf, n_downsample_global,
                             n_blocks_global, n_local_enhancers,
                             n_blocks_local, norm_layer)
    elif netG == 'encoder':
        netG = Encoder(input_nc, output_nc, ngf, n_downsample_global,
                       norm_layer)
    else:
        raise ('generator not implemented!')
    # print(netG)
    if len(gpu_ids) > 0:
        assert (torch.cuda.is_available())
        netG.cuda(gpu_ids[0])
    netG.apply(weights_init)
    return netG


def print_network(net):
    if isinstance(net, list):
        net = net[0]
    num_params = 0
    for param in net.parameters():
        num_params += param.numel()
    print(net)
    print('Total number of parameters: %d' % num_params)


##############################################################################
# Generator
##############################################################################
class LocalEnhancer(pl.LightningModule):
    def __init__(self,
                 input_nc,
                 output_nc,
                 ngf=32,
                 n_downsample_global=3,
                 n_blocks_global=9,
                 n_local_enhancers=1,
                 n_blocks_local=3,
                 norm_layer=nn.BatchNorm2d,
                 padding_type='reflect'):
        super(LocalEnhancer, self).__init__()
        self.n_local_enhancers = n_local_enhancers

        ###### global generator model #####
        ngf_global = ngf * (2**n_local_enhancers)
        model_global = GlobalGenerator(input_nc, output_nc, ngf_global,
                                       n_downsample_global, n_blocks_global,
                                       norm_layer).model
        model_global = [model_global[i] for i in range(len(model_global) - 3)
                        ]  # get rid of final convolution layers
        self.model = nn.Sequential(*model_global)

        ###### local enhancer layers #####
        for n in range(1, n_local_enhancers + 1):
            # downsample
            ngf_global = ngf * (2**(n_local_enhancers - n))
            model_downsample = [
                nn.ReflectionPad2d(3),
                nn.Conv2d(input_nc, ngf_global, kernel_size=7, padding=0),
                norm_layer(ngf_global),
                nn.ReLU(True),
                nn.Conv2d(ngf_global,
                          ngf_global * 2,
                          kernel_size=3,
                          stride=2,
                          padding=1),
                norm_layer(ngf_global * 2),
                nn.ReLU(True)
            ]
            # residual blocks
            model_upsample = []
            for i in range(n_blocks_local):
                model_upsample += [
                    ResnetBlock(ngf_global * 2,
                                padding_type=padding_type,
                                norm_layer=norm_layer)
                ]

            # upsample
            model_upsample += [
                nn.ConvTranspose2d(ngf_global * 2,
                                   ngf_global,
                                   kernel_size=3,
                                   stride=2,
                                   padding=1,
                                   output_padding=1),
                norm_layer(ngf_global),
                nn.ReLU(True)
            ]

            # final convolution
            if n == n_local_enhancers:
                model_upsample += [
                    nn.ReflectionPad2d(3),
                    nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
                    nn.Tanh()
                ]

            setattr(self, 'model' + str(n) + '_1',
                    nn.Sequential(*model_downsample))
            setattr(self, 'model' + str(n) + '_2',
                    nn.Sequential(*model_upsample))

        self.downsample = nn.AvgPool2d(3,
                                       stride=2,
                                       padding=[1, 1],
                                       count_include_pad=False)

    def forward(self, input):
        # create input pyramid
        input_downsampled = [input]
        for i in range(self.n_local_enhancers):
            input_downsampled.append(self.downsample(input_downsampled[-1]))

        # output at coarest level
        output_prev = self.model(input_downsampled[-1])
        # build up one layer at a time
        for n_local_enhancers in range(1, self.n_local_enhancers + 1):
            model_downsample = getattr(self,
                                       'model' + str(n_local_enhancers) + '_1')
            model_upsample = getattr(self,
                                     'model' + str(n_local_enhancers) + '_2')
            input_i = input_downsampled[self.n_local_enhancers -
                                        n_local_enhancers]
            output_prev = model_upsample(
                model_downsample(input_i) + output_prev)
        return output_prev


class GlobalGenerator(pl.LightningModule):
    def __init__(self,
                 input_nc,
                 output_nc,
                 ngf=64,
                 n_downsampling=3,
                 n_blocks=9,
                 norm_layer=nn.BatchNorm2d,
                 padding_type='reflect',
                 last_op=nn.Tanh()):
        assert (n_blocks >= 0)
        super(GlobalGenerator, self).__init__()
        activation = nn.ReLU(True)

        model = [
            nn.ReflectionPad2d(3),
            nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
            norm_layer(ngf), activation
        ]
        # downsample
        for i in range(n_downsampling):
            mult = 2**i
            model += [
                nn.Conv2d(ngf * mult,
                          ngf * mult * 2,
                          kernel_size=3,
                          stride=2,
                          padding=1),
                norm_layer(ngf * mult * 2), activation
            ]

        # resnet blocks
        mult = 2**n_downsampling
        for i in range(n_blocks):
            model += [
                ResnetBlock(ngf * mult,
                            padding_type=padding_type,
                            activation=activation,
                            norm_layer=norm_layer)
            ]

        # upsample
        for i in range(n_downsampling):
            mult = 2**(n_downsampling - i)
            model += [
                nn.ConvTranspose2d(ngf * mult,
                                   int(ngf * mult / 2),
                                   kernel_size=3,
                                   stride=2,
                                   padding=1,
                                   output_padding=1),
                norm_layer(int(ngf * mult / 2)), activation
            ]
        model += [
            nn.ReflectionPad2d(3),
            nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)
        ]
        if last_op is not None:
            model += [last_op]
        self.model = nn.Sequential(*model)

    def forward(self, input):
        return self.model(input)


# Define a resnet block
class ResnetBlock(pl.LightningModule):
    def __init__(self,
                 dim,
                 padding_type,
                 norm_layer,
                 activation=nn.ReLU(True),
                 use_dropout=False):
        super(ResnetBlock, self).__init__()
        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer,
                                                activation, use_dropout)

    def build_conv_block(self, dim, padding_type, norm_layer, activation,
                         use_dropout):
        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' %
                                      padding_type)

        conv_block += [
            nn.Conv2d(dim, dim, kernel_size=3, padding=p),
            norm_layer(dim), activation
        ]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(1)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(1)]
        elif padding_type == 'zero':
            p = 1
        else:
            raise NotImplementedError('padding [%s] is not implemented' %
                                      padding_type)
        conv_block += [
            nn.Conv2d(dim, dim, kernel_size=3, padding=p),
            norm_layer(dim)
        ]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        out = x + self.conv_block(x)
        return out


class Encoder(pl.LightningModule):
    def __init__(self,
                 input_nc,
                 output_nc,
                 ngf=32,
                 n_downsampling=4,
                 norm_layer=nn.BatchNorm2d):
        super(Encoder, self).__init__()
        self.output_nc = output_nc

        model = [
            nn.ReflectionPad2d(3),
            nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0),
            norm_layer(ngf),
            nn.ReLU(True)
        ]
        # downsample
        for i in range(n_downsampling):
            mult = 2**i
            model += [
                nn.Conv2d(ngf * mult,
                          ngf * mult * 2,
                          kernel_size=3,
                          stride=2,
                          padding=1),
                norm_layer(ngf * mult * 2),
                nn.ReLU(True)
            ]

        # upsample
        for i in range(n_downsampling):
            mult = 2**(n_downsampling - i)
            model += [
                nn.ConvTranspose2d(ngf * mult,
                                   int(ngf * mult / 2),
                                   kernel_size=3,
                                   stride=2,
                                   padding=1,
                                   output_padding=1),
                norm_layer(int(ngf * mult / 2)),
                nn.ReLU(True)
            ]

        model += [
            nn.ReflectionPad2d(3),
            nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
            nn.Tanh()
        ]
        self.model = nn.Sequential(*model)

    def forward(self, input, inst):
        outputs = self.model(input)

        # instance-wise average pooling
        outputs_mean = outputs.clone()
        inst_list = np.unique(inst.cpu().numpy().astype(int))
        for i in inst_list:
            for b in range(input.size()[0]):
                indices = (inst[b:b + 1] == int(i)).nonzero()  # n x 4
                for j in range(self.output_nc):
                    output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j,
                                         indices[:, 2], indices[:, 3]]
                    mean_feat = torch.mean(output_ins).expand_as(output_ins)
                    outputs_mean[indices[:, 0] + b, indices[:, 1] + j,
                                 indices[:, 2], indices[:, 3]] = mean_feat
        return outputs_mean