File size: 28,009 Bytes
18dd6ad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
import collections
from functools import partial
import functools
import logging
from collections import defaultdict

import numpy as np
import torch.nn as nn

from annotator.lama.saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
from annotator.lama.saicinpainting.training.modules.ffc import FFCResnetBlock
from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv

class DotDict(defaultdict):
    # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
    """dot.notation access to dictionary attributes"""
    __getattr__ = defaultdict.get
    __setattr__ = defaultdict.__setitem__
    __delattr__ = defaultdict.__delitem__

class Identity(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return x


class ResnetBlock(nn.Module):
    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
                 dilation=1, in_dim=None, groups=1, second_dilation=None):
        super(ResnetBlock, self).__init__()
        self.in_dim = in_dim
        self.dim = dim
        if second_dilation is None:
            second_dilation = dilation
        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
                                                second_dilation=second_dilation)

        if self.in_dim is not None:
            self.input_conv = nn.Conv2d(in_dim, dim, 1)

        self.out_channnels = dim

    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
                         dilation=1, in_dim=None, groups=1, second_dilation=1):
        conv_layer = get_conv_block_ctor(conv_kind)

        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(dilation)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(dilation)]
        elif padding_type == 'zero':
            p = dilation
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)

        if in_dim is None:
            in_dim = dim

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

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

        return nn.Sequential(*conv_block)

    def forward(self, x):
        x_before = x
        if self.in_dim is not None:
            x = self.input_conv(x)
        out = x + self.conv_block(x_before)
        return out

class ResnetBlock5x5(nn.Module):
    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
                 dilation=1, in_dim=None, groups=1, second_dilation=None):
        super(ResnetBlock5x5, self).__init__()
        self.in_dim = in_dim
        self.dim = dim
        if second_dilation is None:
            second_dilation = dilation
        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
                                                second_dilation=second_dilation)

        if self.in_dim is not None:
            self.input_conv = nn.Conv2d(in_dim, dim, 1)

        self.out_channnels = dim

    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
                         dilation=1, in_dim=None, groups=1, second_dilation=1):
        conv_layer = get_conv_block_ctor(conv_kind)

        conv_block = []
        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(dilation * 2)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(dilation * 2)]
        elif padding_type == 'zero':
            p = dilation * 2
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)

        if in_dim is None:
            in_dim = dim

        conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
                       norm_layer(dim),
                       activation]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        p = 0
        if padding_type == 'reflect':
            conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
        elif padding_type == 'replicate':
            conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
        elif padding_type == 'zero':
            p = second_dilation * 2
        else:
            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
        conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
                       norm_layer(dim)]

        return nn.Sequential(*conv_block)

    def forward(self, x):
        x_before = x
        if self.in_dim is not None:
            x = self.input_conv(x)
        out = x + self.conv_block(x_before)
        return out


class MultidilatedResnetBlock(nn.Module):
    def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
        super().__init__()
        self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)

    def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
        conv_block = []
        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
                       norm_layer(dim),
                       activation]
        if use_dropout:
            conv_block += [nn.Dropout(0.5)]

        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
                       norm_layer(dim)]

        return nn.Sequential(*conv_block)

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


class MultiDilatedGlobalGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
                 n_blocks=3, norm_layer=nn.BatchNorm2d,
                 padding_type='reflect', conv_kind='default',
                 deconv_kind='convtranspose', activation=nn.ReLU(True),
                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
                 add_out_act=True, max_features=1024, multidilation_kwargs={},
                 ffc_positions=None, ffc_kwargs={}):
        assert (n_blocks >= 0)
        super().__init__()

        conv_layer = get_conv_block_ctor(conv_kind)
        resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
        norm_layer = get_norm_layer(norm_layer)
        if affine is not None:
            norm_layer = partial(norm_layer, affine=affine)
        up_norm_layer = get_norm_layer(up_norm_layer)
        if affine is not None:
            up_norm_layer = partial(up_norm_layer, affine=affine)

        model = [nn.ReflectionPad2d(3),
                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
                 norm_layer(ngf),
                 activation]

        identity = Identity()
        ### downsample
        for i in range(n_downsampling):
            mult = 2 ** i

            model += [conv_layer(min(max_features, ngf * mult),
                                    min(max_features, ngf * mult * 2),
                                    kernel_size=3, stride=2, padding=1),
                        norm_layer(min(max_features, ngf * mult * 2)),
                        activation]

        mult = 2 ** n_downsampling
        feats_num_bottleneck = min(max_features, ngf * mult)

        ### resnet blocks
        for i in range(n_blocks):
            if ffc_positions is not None and i in ffc_positions:
                model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
                                         inline=True, **ffc_kwargs)]
            model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
                                              conv_layer=resnet_conv_layer, activation=activation,
                                              norm_layer=norm_layer)]

        ### upsample
        for i in range(n_downsampling):
            mult = 2 ** (n_downsampling - i)
            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
        model += [nn.ReflectionPad2d(3),
                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        if add_out_act:
            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
        self.model = nn.Sequential(*model)

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

class ConfigGlobalGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
                 n_blocks=3, norm_layer=nn.BatchNorm2d,
                 padding_type='reflect', conv_kind='default',
                 deconv_kind='convtranspose', activation=nn.ReLU(True),
                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
                 add_out_act=True, max_features=1024,
                 manual_block_spec=[],
                 resnet_block_kind='multidilatedresnetblock',
                 resnet_conv_kind='multidilated',
                 resnet_dilation=1,
                 multidilation_kwargs={}):
        assert (n_blocks >= 0)
        super().__init__()

        conv_layer = get_conv_block_ctor(conv_kind)
        resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
        norm_layer = get_norm_layer(norm_layer)
        if affine is not None:
            norm_layer = partial(norm_layer, affine=affine)
        up_norm_layer = get_norm_layer(up_norm_layer)
        if affine is not None:
            up_norm_layer = partial(up_norm_layer, affine=affine)

        model = [nn.ReflectionPad2d(3),
                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
                 norm_layer(ngf),
                 activation]

        identity = Identity()

        ### downsample
        for i in range(n_downsampling):
            mult = 2 ** i
            model += [conv_layer(min(max_features, ngf * mult),
                                    min(max_features, ngf * mult * 2),
                                    kernel_size=3, stride=2, padding=1),
                        norm_layer(min(max_features, ngf * mult * 2)),
                        activation]

        mult = 2 ** n_downsampling
        feats_num_bottleneck = min(max_features, ngf * mult)

        if len(manual_block_spec) == 0:
            manual_block_spec = [
                DotDict(lambda : None, {
                    'n_blocks': n_blocks,
                    'use_default': True})
            ]

        ### resnet blocks
        for block_spec in manual_block_spec:
            def make_and_add_blocks(model, block_spec):
                block_spec = DotDict(lambda : None, block_spec)
                if not block_spec.use_default:
                    resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
                    resnet_conv_kind = block_spec.resnet_conv_kind
                    resnet_block_kind = block_spec.resnet_block_kind
                    if block_spec.resnet_dilation is not None:
                        resnet_dilation = block_spec.resnet_dilation
                for i in range(block_spec.n_blocks):
                    if resnet_block_kind == "multidilatedresnetblock":
                        model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
                                                        conv_layer=resnet_conv_layer, activation=activation,
                                                        norm_layer=norm_layer)]
                    if resnet_block_kind == "resnetblock":                                            
                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
                                            conv_kind=resnet_conv_kind)]
                    if resnet_block_kind == "resnetblock5x5":                                            
                        model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
                                            conv_kind=resnet_conv_kind)]
                    if resnet_block_kind == "resnetblockdwdil":
                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
                                            conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
            make_and_add_blocks(model, block_spec)
        
        ### upsample
        for i in range(n_downsampling):
            mult = 2 ** (n_downsampling - i)
            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
        model += [nn.ReflectionPad2d(3),
                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        if add_out_act:
            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
        self.model = nn.Sequential(*model)

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


def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
    blocks = []
    for i in range(dilated_blocks_n):
        if dilation_block_kind == 'simple':
            blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
        elif dilation_block_kind == 'multi':
            blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
        else:
            raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
    return blocks


class GlobalGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
                 up_norm_layer=nn.BatchNorm2d, affine=None,
                 up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
                 dilated_blocks_n_middle=0,
                 add_out_act=True,
                 max_features=1024, is_resblock_depthwise=False,
                 ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
                 dilation_block_kind='simple', multidilation_kwargs={}):
        assert (n_blocks >= 0)
        super().__init__()

        conv_layer = get_conv_block_ctor(conv_kind)
        norm_layer = get_norm_layer(norm_layer)
        if affine is not None:
            norm_layer = partial(norm_layer, affine=affine)
        up_norm_layer = get_norm_layer(up_norm_layer)
        if affine is not None:
            up_norm_layer = partial(up_norm_layer, affine=affine)

        if ffc_positions is not None:
            ffc_positions = collections.Counter(ffc_positions)

        model = [nn.ReflectionPad2d(3),
                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
                 norm_layer(ngf),
                 activation]

        identity = Identity()
        ### downsample
        for i in range(n_downsampling):
            mult = 2 ** i

            model += [conv_layer(min(max_features, ngf * mult),
                                min(max_features, ngf * mult * 2),
                                kernel_size=3, stride=2, padding=1),
                        norm_layer(min(max_features, ngf * mult * 2)),
                        activation]

        mult = 2 ** n_downsampling
        feats_num_bottleneck = min(max_features, ngf * mult)

        dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
                                    activation=activation, norm_layer=norm_layer)
        if dilation_block_kind == 'simple':
            dilated_block_kwargs['conv_kind'] = conv_kind
        elif dilation_block_kind == 'multi':
            dilated_block_kwargs['conv_layer'] = functools.partial(
                get_conv_block_ctor('multidilated'), **multidilation_kwargs)

        # dilated blocks at the start of the bottleneck sausage
        if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
            model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)

        # resnet blocks
        for i in range(n_blocks):
            # dilated blocks at the middle of the bottleneck sausage
            if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
                model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
            
            if ffc_positions is not None and i in ffc_positions:
                for _ in range(ffc_positions[i]):  # same position can occur more than once
                    model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
                                             inline=True, **ffc_kwargs)]

            if is_resblock_depthwise:
                resblock_groups = feats_num_bottleneck
            else:
                resblock_groups = 1

            model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
                                    norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
                                    dilation=dilation, second_dilation=second_dilation)]
            

        # dilated blocks at the end of the bottleneck sausage
        if dilated_blocks_n is not None and dilated_blocks_n > 0:
            model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)

        # upsample
        for i in range(n_downsampling):
            mult = 2 ** (n_downsampling - i)
            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
                                         min(max_features, int(ngf * mult / 2)),
                                         kernel_size=3, stride=2, padding=1, output_padding=1),
                      up_norm_layer(min(max_features, int(ngf * mult / 2))),
                      up_activation]
        model += [nn.ReflectionPad2d(3),
                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
        if add_out_act:
            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
        self.model = nn.Sequential(*model)

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


class GlobalGeneratorGated(GlobalGenerator):
    def __init__(self, *args, **kwargs):
        real_kwargs=dict(
            conv_kind='gated_bn_relu',
            activation=nn.Identity(),
            norm_layer=nn.Identity
        )
        real_kwargs.update(kwargs)
        super().__init__(*args, **real_kwargs)


class GlobalGeneratorFromSuperChannels(nn.Module):
    def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
        super().__init__()
        self.n_downsampling = n_downsampling
        norm_layer = get_norm_layer(norm_layer)
        if type(norm_layer) == functools.partial:
            use_bias = (norm_layer.func == nn.InstanceNorm2d)
        else:
            use_bias = (norm_layer == nn.InstanceNorm2d)

        channels = self.convert_super_channels(super_channels)
        self.channels = channels

        model = [nn.ReflectionPad2d(3),
                 nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
                 norm_layer(channels[0]),
                 nn.ReLU(True)]

        for i in range(n_downsampling):  # add downsampling layers
            mult = 2 ** i
            model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
                      norm_layer(channels[1+i]),
                      nn.ReLU(True)]

        mult = 2 ** n_downsampling

        n_blocks1 = n_blocks // 3
        n_blocks2 = n_blocks1
        n_blocks3 = n_blocks - n_blocks1 - n_blocks2

        for i in range(n_blocks1):
            c = n_downsampling
            dim = channels[c]
            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]

        for i in range(n_blocks2):
            c = n_downsampling+1
            dim = channels[c]
            kwargs = {}
            if i == 0:
                kwargs = {"in_dim": channels[c-1]}
            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]

        for i in range(n_blocks3):
            c = n_downsampling+2
            dim = channels[c]
            kwargs = {}
            if i == 0:
                kwargs = {"in_dim": channels[c-1]}
            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]

        for i in range(n_downsampling):  # add upsampling layers
            mult = 2 ** (n_downsampling - i)
            model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
                                           channels[n_downsampling+3+i+1],
                                           kernel_size=3, stride=2,
                                           padding=1, output_padding=1,
                                           bias=use_bias),
                      norm_layer(channels[n_downsampling+3+i+1]),
                      nn.ReLU(True)]
        model += [nn.ReflectionPad2d(3)]
        model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]

        if add_out_act:
            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
        self.model = nn.Sequential(*model)

    def convert_super_channels(self, super_channels):
        n_downsampling = self.n_downsampling
        result = []
        cnt = 0

        if n_downsampling == 2:
            N1 = 10
        elif n_downsampling == 3:
            N1 = 13
        else:
            raise NotImplementedError

        for i in range(0, N1):
            if i in [1,4,7,10]:
                channel = super_channels[cnt] * (2 ** cnt)
                config = {'channel': channel}
                result.append(channel)
                logging.info(f"Downsample channels {result[-1]}")
                cnt += 1

        for i in range(3):
            for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
                if len(super_channels) == 6:
                    channel = super_channels[3] * 4
                else:
                    channel = super_channels[i + 3] * 4
                config = {'channel': channel}
                if counter == 0:
                    result.append(channel)
                    logging.info(f"Bottleneck channels {result[-1]}")
        cnt = 2

        for i in range(N1+9, N1+21):
            if i in [22, 25,28]:
                cnt -= 1
                if len(super_channels) == 6:
                    channel = super_channels[5 - cnt] * (2 ** cnt)
                else:
                    channel = super_channels[7 - cnt] * (2 ** cnt)
                result.append(int(channel))
                logging.info(f"Upsample channels {result[-1]}")
        return result

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


# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(BaseDiscriminator):
    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
        super().__init__()
        self.n_layers = n_layers

        kw = 4
        padw = int(np.ceil((kw-1.0)/2))
        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
                     nn.LeakyReLU(0.2, True)]]

        nf = ndf
        for n in range(1, n_layers):
            nf_prev = nf
            nf = min(nf * 2, 512)

            cur_model = []
            cur_model += [
                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
                norm_layer(nf),
                nn.LeakyReLU(0.2, True)
            ]
            sequence.append(cur_model)

        nf_prev = nf
        nf = min(nf * 2, 512)

        cur_model = []
        cur_model += [
            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
            norm_layer(nf),
            nn.LeakyReLU(0.2, True)
        ]
        sequence.append(cur_model)

        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]

        for n in range(len(sequence)):
            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))

    def get_all_activations(self, x):
        res = [x]
        for n in range(self.n_layers + 2):
            model = getattr(self, 'model' + str(n))
            res.append(model(res[-1]))
        return res[1:]

    def forward(self, x):
        act = self.get_all_activations(x)
        return act[-1], act[:-1]


class MultidilatedNLayerDiscriminator(BaseDiscriminator):
    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
        super().__init__()
        self.n_layers = n_layers

        kw = 4
        padw = int(np.ceil((kw-1.0)/2))
        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
                     nn.LeakyReLU(0.2, True)]]

        nf = ndf
        for n in range(1, n_layers):
            nf_prev = nf
            nf = min(nf * 2, 512)

            cur_model = []
            cur_model += [
                MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
                norm_layer(nf),
                nn.LeakyReLU(0.2, True)
            ]
            sequence.append(cur_model)

        nf_prev = nf
        nf = min(nf * 2, 512)

        cur_model = []
        cur_model += [
            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
            norm_layer(nf),
            nn.LeakyReLU(0.2, True)
        ]
        sequence.append(cur_model)

        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]

        for n in range(len(sequence)):
            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))

    def get_all_activations(self, x):
        res = [x]
        for n in range(self.n_layers + 2):
            model = getattr(self, 'model' + str(n))
            res.append(model(res[-1]))
        return res[1:]

    def forward(self, x):
        act = self.get_all_activations(x)
        return act[-1], act[:-1]


class NLayerDiscriminatorAsGen(NLayerDiscriminator):
    def forward(self, x):
        return super().forward(x)[0]