File size: 39,683 Bytes
749745d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
    - https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
"""
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import logging
import math
from copy import deepcopy
from typing import Optional

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
import numpy as np
from timm.models.layers import DropPath, to_2tuple, trunc_normal_


class Mlp(nn.Module):
    """Multilayer perceptron."""

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size
    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
    return windows


def window_reverse(windows, window_size, H, W):
    """
    Args:
        windows: (num_windows*B, window_size, window_size, C)
        window_size (int): Window size
        H (int): Height of image
        W (int): Width of image
    Returns:
        x: (B, H, W, C)
    """
    B = int(windows.shape[0] / (H * W / window_size / window_size))
    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
    return x


class WindowAttention(nn.Module):
    """Window based multi-head self attention (W-MSA) module with relative position bias.
    It supports both of shifted and non-shifted window.
    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(
        self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, dim_text=None
    ):

        super().__init__()
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim**-0.5

        # define a parameter table of relative position bias
        self.relative_position_bias_table = nn.Parameter(
            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
        )  # 2*Wh-1 * 2*Ww-1, nH

        # get pair-wise relative position index for each token inside the window
        coords_h = torch.arange(self.window_size[0])
        coords_w = torch.arange(self.window_size[1])
        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
        self.register_buffer("relative_position_index", relative_position_index)

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

        trunc_normal_(self.relative_position_bias_table, std=0.02)
        self.softmax = nn.Softmax(dim=-1)

        # dim_text = 768
        if dim_text is not None:
            self.qkv_text_i2t = nn.Linear(dim_text, dim * 2, bias=qkv_bias)
            self.qkv_i2t = nn.Linear(dim, dim, bias=qkv_bias)
            self.attn_drop_i2t = nn.Dropout(attn_drop)
            self.proj_i2t = nn.Linear(dim, dim)
            self.proj_drop_i2t = nn.Dropout(proj_drop)
            # self.proj_i2t = nn.Linear(dim, dim)
            self.alpha_i2t = nn.Parameter(torch.Tensor([0]))
            self.norm_i2t_i = nn.LayerNorm(dim)

            # self.gate_i2t = nn.Linear(2*dim, 1)
            # self.gate_i2t = nn.Linear(2*dim, dim)
            # self.sigmoid_i2t = nn.Sigmoid()

            """self.i2t_relative_position_bias = nn.Parameter(
                torch.zeros(2, num_heads, ntext))  # (2, nH, ntext)
            self.t2t_relative_position_bias = nn.Parameter(
                torch.zeros(num_heads, ntext, ntext))  # (nH, ntext, ntext)
            trunc_normal_(self.i2t_relative_position_bias, std=.02)
            trunc_normal_(self.t2t_relative_position_bias, std=.02)#"""

    def forward(self, x, mask: Optional[torch.Tensor] = None, y=None, y_mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)

        q = q * self.scale
        attn = q @ k.transpose(-2, -1)

        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
        )  # Wh*Ww,Wh*Ww,nH
        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
        attn = attn + relative_position_bias.unsqueeze(0)

        if mask is not None:
            nW = mask.shape[0]
            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
            attn = attn.view(-1, self.num_heads, N, N)

        attn = self.softmax(attn)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)

        if y is not None:
            B_text, N_text, C_text = y.shape
            nW = B_ // B_text  # number of windows
            assert B_text * nW == B_, "B_ is not a multiplier of B_text in window attention"
            # notice that after qkv_text, the hidden dimension is C instead of C_text
            qkv_text = (
                self.qkv_text_i2t(y)
                .reshape(B_text, N_text, 2, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
            k_text, v_text = qkv_text[0], qkv_text[1]

            k_text = torch.repeat_interleave(k_text, nW, dim=0)
            v_text = torch.repeat_interleave(v_text, nW, dim=0)
            # TODO: remove q_text
            q_i2t = (
                self.qkv_i2t(self.norm_i2t_i(x))
                .reshape(B_, N, 1, self.num_heads, C // self.num_heads)
                .permute(2, 0, 3, 1, 4)
            )
            q_i2t = q_i2t[0]

            # image to text attention
            # attn_i2t = (q_i2t @ torch.repeat_interleave(k_text, nW, dim=0).transpose(-2, -1))  # B_, nH, N, N_text
            # print(q_i2t.size())
            # print(k_text.size())
            # torch.Size([4096, 4, 49, 32])
            # torch.Size([4096, 4, 50, 32])
            text_scale = k_text.size(-1) ** -0.5
            q_i2t = q_i2t * text_scale
            attn_i2t = q_i2t @ k_text.transpose(-2, -1)  # B_, nH, N, N_text
            # add image to text bias and text_mask
            if y_mask is not None:
                mask_and_i2t_bias = y_mask.view(
                    B_text, 1, 1, N_text
                )  # + self.i2t_relative_position_bias[:1].expand(B_text, -1, -1).unsqueeze(-2)  # B_text, nH, 1, N_text
                attn_i2t = attn_i2t + torch.repeat_interleave(mask_and_i2t_bias, nW, dim=0)

            attn_i2t = self.softmax(attn_i2t)
            attn_i2t = self.attn_drop_i2t(attn_i2t)
            # torch.Size([4096, 4, 49, 50])
            # torch.Size([64, 4, 50, 32])
            # print(attn_i2t.size())
            # print(v_text.size())
            # 1/0
            y = (attn_i2t @ v_text).transpose(1, 2).reshape(B_, N, C)
            y = self.proj_i2t(y)
            y = self.proj_drop_i2t(y)

            # g = torch.cat([x, y], dim=-1)
            # g = (self.gate_i2t(g))
            # g = self.sigmoid_i2t(self.gate_i2t(g))
            # x = x+g*y

            x = x + self.alpha_i2t * y

        return x


class SwinTransformerBlock(nn.Module):
    """Swin Transformer Block.
    Args:
        dim (int): Number of input channels.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(
        self,
        dim,
        num_heads,
        window_size=7,
        shift_size=0,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=nn.GELU,
        norm_layer=nn.LayerNorm,
        dim_text=None,
    ):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"

        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim,
            window_size=to_2tuple(self.window_size),
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
            dim_text=dim_text,
        )

        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        self.H = None
        self.W = None

    def forward(self, x, mask_matrix, x_text=None, mask_text=None):
        B, L, C = x.shape
        H, W = self.H, self.W
        assert L == H * W, "input feature has wrong size"

        shortcut = x
        x = self.norm1(x)
        x = x.view(B, H, W, C)

        # pad feature maps to multiples of window size
        pad_l = pad_t = 0
        pad_r = (self.window_size - W % self.window_size) % self.window_size
        pad_b = (self.window_size - H % self.window_size) % self.window_size
        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
        _, Hp, Wp, _ = x.shape

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
            attn_mask = mask_matrix
        else:
            shifted_x = x
            attn_mask = None

        # partition windows
        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(
            x_windows, mask=attn_mask, y=x_text, y_mask=mask_text
        )  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
        else:
            x = shifted_x

        if pad_r > 0 or pad_b > 0:
            x = x[:, :H, :W, :].contiguous()

        x = x.view(B, H * W, C)

        # FFN
        x = shortcut + self.drop_path(x)
        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x


class PatchMerging(nn.Module):
    """Patch Merging Layer
    Args:
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x, H, W):
        """Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
        """
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        # TODO: Keep?
        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."

        x = x.view(B, H, W, C)

        # padding
        pad_input = (H % 2 == 1) or (W % 2 == 1)
        if pad_input:
            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x

    # TODO: Keep?
    # def extra_repr(self) -> str:
    #     return f"input_resolution={self.input_resolution}, dim={self.dim}"
    #
    # def flops(self):
    #     H, W = self.input_resolution
    #     flops = H * W * self.dim
    #     flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
    #     return flops


class BasicLayer(nn.Module):
    """A basic Swin Transformer layer for one stage.
    Args:
        dim (int): Number of feature channels
        depth (int): Depths of this stage.
        num_heads (int): Number of attention head.
        window_size (int): Local window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(
        self,
        dim,
        depth,
        num_heads,
        window_size,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
        use_checkpoint=False,
        dim_text=None,
    ):
        super().__init__()
        self.window_size = window_size
        self.shift_size = window_size // 2
        self.depth = depth
        self.use_checkpoint = use_checkpoint

        # build blocks
        self.blocks = nn.ModuleList(
            [
                SwinTransformerBlock(
                    dim=dim,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0 if (i % 2 == 0) else window_size // 2,
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
                    norm_layer=norm_layer,
                    dim_text=(768 if i >= 14 else dim_text),
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
        else:
            self.downsample = None

    def get_attention_mask(self, H, W, device):
        # calculate attention mask for SW-MSA
        Hp = int(np.ceil(H / self.window_size)) * self.window_size
        Wp = int(np.ceil(W / self.window_size)) * self.window_size
        img_mask = torch.zeros((1, Hp, Wp, 1), device=device)  # 1 Hp Wp 1
        h_slices = (
            slice(0, -self.window_size),
            slice(-self.window_size, -self.shift_size),
            slice(-self.shift_size, None),
        )
        w_slices = (
            slice(0, -self.window_size),
            slice(-self.window_size, -self.shift_size),
            slice(-self.shift_size, None),
        )
        cnt = 0
        for h in h_slices:
            for w in w_slices:
                img_mask[:, h, w, :] = cnt
                cnt += 1

        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))

        return attn_mask

    def forward(self, x, H, W, x_text=None, mask_text=None):
        """Forward function.
        Args:
            x: Input feature, tensor size (B, H*W, C).
            H, W: Spatial resolution of the input feature.
            x_text: input text features with shape of (B_text, N_text, C_text)
            mask_text: (0/-inf) mask with shape of (B_text, N_text) or None;
        """
        attn_mask = self.get_attention_mask(H, W, x.device)

        for blk in self.blocks:
            blk.H, blk.W = H, W
            if not torch.jit.is_scripting() and self.use_checkpoint:
                x = checkpoint.checkpoint(blk, x, attn_mask, x_text, mask_text)
            else:
                x = blk(x, mask_matrix=attn_mask, x_text=x_text, mask_text=mask_text)
            # print(x.size())
        if self.downsample is not None:
            x_down = self.downsample(x, H, W)
            Wh, Ww = (H + 1) // 2, (W + 1) // 2
            return x, H, W, x_down, Wh, Ww
        else:
            return x, H, W, x, H, W

    # TODO: Keep?
    # def extra_repr(self) -> str:
    #     return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"


class PatchEmbed(nn.Module):
    """Image to Patch Embedding
    Args:
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
        super().__init__()
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        """Forward function."""
        # padding
        _, _, H, W = x.size()
        if W % self.patch_size[1] != 0:
            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
        if H % self.patch_size[0] != 0:
            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))

        x = self.proj(x)  # B C Wh Ww
        if self.norm is not None:
            Wh, Ww = x.size(2), x.size(3)
            x = x.flatten(2).transpose(1, 2)
            x = self.norm(x)
            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)

        return x


class SwinTransformer(nn.Module):
    """Swin Transformer backbone.
        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030
    Args:
        pretrain_img_size (int): Input image size for training the pretrained model,
            used in absolute postion embedding. Default 224.
        patch_size (int | tuple(int)): Patch size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        depths (tuple[int]): Depths of each Swin Transformer stage.
        num_heads (tuple[int]): Number of attention head of each stage.
        window_size (int): Window size. Default: 7.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
        drop_rate (float): Dropout rate.
        attn_drop_rate (float): Attention dropout rate. Default: 0.
        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
        out_indices (Sequence[int]): Output from which stages.
        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
            -1 means not freezing any parameters.
        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
    """

    def __init__(
        self,
        pretrain_img_size=224,
        patch_size=4,
        in_chans=3,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.2,
        norm_layer=nn.LayerNorm,
        ape=False,
        patch_norm=True,
        frozen_stages=-1,
        use_checkpoint=False,
        out_features=["stage2", "stage3", "stage4", "stage5"],
        backbone_arch="SWINT-FPN-RETINANET",
        max_query_len=None,
        lang_dim=None,
    ):
        super(SwinTransformer, self).__init__()

        print("VISION BACKBONE USE GRADIENT CHECKPOINTING: ", use_checkpoint)

        self.pretrain_img_size = pretrain_img_size
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.frozen_stages = frozen_stages

        self.out_features = out_features

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
        )

        # absolute position embedding
        if self.ape:
            pretrain_img_size = to_2tuple(pretrain_img_size)
            patch_size = to_2tuple(patch_size)
            patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]

            self.absolute_pos_embed = nn.Parameter(
                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
            )
            trunc_normal_(self.absolute_pos_embed, std=0.02)

        self.pos_drop = nn.Dropout(p=drop_rate)

        # stochastic depth
        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule

        self._out_feature_strides = {}
        self._out_feature_channels = {}

        # build layers
        self.layers = nn.ModuleList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2**i_layer),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                use_checkpoint=use_checkpoint and i_layer > self.frozen_stages - 1,
                dim_text=(768 if i_layer == 3 else None),
            )  # TODO: Make this general : lang_dim not 768
            self.layers.append(layer)

            stage = f"stage{i_layer + 2}"
            if stage in self.out_features:
                self._out_feature_channels[stage] = embed_dim * 2**i_layer
                self._out_feature_strides[stage] = 4 * 2**i_layer

        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
        self.num_features = num_features

        # TODO : need this?
        # assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
        # head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
        # if weight_init.startswith('jax'):
        #     for n, m in self.named_modules():
        #         _init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
        # else:
        #     self.apply(_init_vit_weights)

        # add a norm layer for each output
        for i_layer in range(self.num_layers):
            stage = f"stage{i_layer + 2}"
            if stage in self.out_features:
                if i_layer == 0 and backbone_arch.endswith("RETINANET"):
                    layer = nn.Identity()
                else:
                    layer = norm_layer(num_features[i_layer])
                layer_name = f"norm{i_layer}"
                self.add_module(layer_name, layer)

        self._freeze_stages()

    def _freeze_stages(self):
        if self.frozen_stages >= 0:
            self.patch_embed.eval()
            for param in self.patch_embed.parameters():
                param.requires_grad = False

        if self.frozen_stages >= 1 and self.ape:
            self.absolute_pos_embed.requires_grad = False

        if self.frozen_stages >= 2:
            self.pos_drop.eval()
            for i in range(0, self.frozen_stages - 1):
                m = self.layers[i]
                m.eval()
                for param in m.parameters():
                    param.requires_grad = False

    def init_weights(self, pretrained=None):
        """Initialize the weights in backbone.
        Args:
            pretrained (str, optional): Path to pre-trained weights.
                Defaults to None.
        """

        def _init_weights(m):
            if isinstance(m, nn.Linear):
                trunc_normal_(m.weight, std=0.02)
                if isinstance(m, nn.Linear) and m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.LayerNorm):
                nn.init.constant_(m.bias, 0)
                nn.init.constant_(m.weight, 1.0)

        self.apply(_init_weights)

    def forward(self, inputs):
        """Forward function."""
        x = inputs["img"]
        language_dict_features = inputs["lang"]

        x = self.patch_embed(x)

        Wh, Ww = x.size(2), x.size(3)
        if self.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic")
            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
        else:
            x = x.flatten(2).transpose(1, 2)
        x = self.pos_drop(x)

        x_text = language_dict_features["hidden"]
        if "masks" in language_dict_features:
            mask_text = 1.0 - language_dict_features["masks"]  # (B, N_text) 0 means not to be masked out
            mask_text.masked_fill_(mask_text.bool(), -float("inf"))
        else:
            mask_text = None

        outs = []
        for layer_i, layer in enumerate(self.layers):
            # if layer_i > 1:
            # if layer_i > 2:
            if layer_i > -1:
                x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=x_text, mask_text=mask_text)
            else:
                x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww, x_text=None, mask_text=None)
            name = f"stage{layer_i + 2}"
            if name in self.out_features:
                norm_layer = getattr(self, f"norm{layer_i}")
                x_out = norm_layer(x_out)
                out = x_out.view(-1, H, W, self.num_features[layer_i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        # Here the text features are just combined directly with the image features, so language_dict_features is unchanged
        return outs, language_dict_features

    @torch.jit.ignore
    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    @torch.jit.ignore
    def no_weight_decay_keywords(self):
        return {"relative_position_bias_table"}

    def train(self, mode=True):
        """Convert the model into training mode while keep layers freezed."""
        super(SwinTransformer, self).train(mode)
        self._freeze_stages()


class FusionSwinTransformer(nn.Module):
    def __init__(self, vision_backbone, language_backbone, add_linear_layer=False):
        super().__init__()
        self.backbone = vision_backbone
        self.language_backbone = language_backbone
        # self.cross_modal_image_transform2 = nn.Linear(1024, 768)
        # self.cross_modal_image_transform3 = nn.Linear(1024, 768)
        self.add_linear_layer = add_linear_layer
        if self.add_linear_layer:
            self.tunable_linear = torch.nn.Linear(
                self.language_backbone.body.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM, 1000, bias=False
            )
            self.tunable_linear.weight.data.fill_(0.0)

    def forward(
        self,
        tokenizer_input,
        images,
    ):

        # Fusion in the backbone forward - interleaves the passed through the langauge and image backbone.
        x = images.tensors

        # Embed the image
        x = self.backbone.body.patch_embed(x)
        Wh, Ww = x.size(2), x.size(3)

        if self.backbone.body.ape:
            # interpolate the position embedding to the corresponding size
            absolute_pos_embed = F.interpolate(self.backbone.body.absolute_pos_embed, size=(Wh, Ww), mode="bicubic")
            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
        else:
            x = x.flatten(2).transpose(1, 2)
        image_embeds = self.backbone.body.pos_drop(x)

        # Embed the text
        text_embeds = self.language_backbone.body.model.embeddings(input_ids=tokenizer_input["input_ids"])
        input_shape = tokenizer_input["attention_mask"].size()
        extended_text_masks = self.language_backbone.body.model.get_extended_attention_mask(
            tokenizer_input["attention_mask"], input_shape, device=tokenizer_input["attention_mask"].device
        )

        if self.add_linear_layer:
            text_embeds = self.tunable_linear.weight[: text_embeds.size(1), :].unsqueeze(0) + text_embeds

        outs = []
        # Pass the text through the first 10 layers
        num_pre_text = 6
        for layer_i, layer in enumerate(self.language_backbone.body.model.encoder.layer[:num_pre_text]):
            text_embeds = layer(text_embeds, extended_text_masks)[0]

        # Pass through first 2 image backbone layers
        num_pre_vision = 2
        for layer_i, layer in enumerate(self.backbone.body.layers[:num_pre_vision]):
            x_out, H, W, image_embeds, Wh, Ww = layer(image_embeds, Wh, Ww, x_text=None, mask_text=None)
            name = f"stage{layer_i + 2}"
            if name in self.backbone.body.out_features:
                norm_layer = getattr(self.backbone.body, f"norm{layer_i}")
                x_out = norm_layer(x_out)
                out = x_out.view(-1, H, W, self.backbone.body.num_features[layer_i]).permute(0, 3, 1, 2).contiguous()
                outs.append(out)

        num_pre_block = 14
        # Get the attention mask for the third layer:
        attn_mask = self.backbone.body.layers[num_pre_vision].get_attention_mask(Wh, Ww, image_embeds.device)
        for blk_cnt, blk in enumerate(self.backbone.body.layers[num_pre_vision].blocks):
            blk.H, blk.W = Wh, Ww
            if blk_cnt < num_pre_block:
                if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint:
                    image_embeds = checkpoint.checkpoint(blk, image_embeds, attn_mask)
                else:
                    image_embeds = blk(image_embeds, attn_mask)
            else:
                if not torch.jit.is_scripting() and self.backbone.body.layers[num_pre_vision].use_checkpoint:
                    fused_image_embeds = checkpoint.checkpoint(
                        blk, image_embeds, attn_mask, text_embeds, extended_text_masks
                    )
                else:
                    fused_image_embeds = blk(image_embeds, attn_mask, text_embeds, extended_text_masks)
                text_embeds = self.language_backbone.body.model.encoder.layer[blk_cnt - num_pre_block + num_pre_text](
                    text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
                )[0]
                image_embeds = fused_image_embeds

        # Apply layer norm after 3rd layer and take output
        name = f"stage{num_pre_vision + 2}"
        if name in self.backbone.body.out_features:
            norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision}")
            x_out = norm_layer(image_embeds)
            out = (
                x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision]).permute(0, 3, 1, 2).contiguous()
            )
            outs.append(out)

        # Apply downsampling if we need to at the output of third layer for input to next layer
        if self.backbone.body.layers[num_pre_vision].downsample is not None:
            image_embeds = self.backbone.body.layers[num_pre_vision].downsample(image_embeds, Wh, Ww)
            Wh, Ww = (Wh + 1) // 2, (Ww + 1) // 2

        # Final layer

        # Get attention mask for 4th layer
        attn_mask = self.backbone.body.layers[num_pre_vision + 1].get_attention_mask(Wh, Ww, image_embeds.device)
        blk = self.backbone.body.layers[num_pre_vision + 1].blocks[0]
        blk.H, blk.W = Wh, Ww

        fuse_image_embeds = blk(
            x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks
        )

        fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-2](
            text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
        )[0]
        text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds

        blk = self.backbone.body.layers[num_pre_vision + 1].blocks[1]
        blk.H, blk.W = Wh, Ww
        fuse_image_embeds = self.backbone.body.layers[num_pre_vision + 1].blocks[1](
            x=image_embeds, mask_matrix=attn_mask, x_text=text_embeds, mask_text=extended_text_masks
        )

        fuse_text_embeds = self.language_backbone.body.model.encoder.layer[-1](
            text_embeds, extended_text_masks, encoder_hidden_states=(image_embeds)
        )[0]
        text_embeds, image_embeds = fuse_text_embeds, fuse_image_embeds

        # Apply layer norm after 4th layer and take output
        name = f"stage{num_pre_vision + 1 + 2}"
        if name in self.backbone.body.out_features:
            norm_layer = getattr(self.backbone.body, f"norm{num_pre_vision + 1}")
            x_out = norm_layer(image_embeds)
            out = (
                x_out.view(-1, Wh, Ww, self.backbone.body.num_features[num_pre_vision + 1])
                .permute(0, 3, 1, 2)
                .contiguous()
            )
            outs.append(out)

        language_dict_features = self.language_backbone.body.get_aggregated_output(
            text_embeds, tokenizer_input["input_ids"], tokenizer_input["attention_mask"]
        )

        # Apply fpn
        visual_features = self.backbone.fpn(outs)

        # None for now, need to add if we want to add shallow contrastive loss?
        swint_feature_c4 = None

        return visual_features, language_dict_features, swint_feature_c4


def build_swint_backbone(cfg):
    """
    Create a SwinT instance from config.

    Returns:
        VoVNet: a :class:`VoVNet` instance.
    """
    return SwinTransformer(
        patch_size=4,
        in_chans=3,
        embed_dim=cfg.MODEL.SWINT.EMBED_DIM,
        depths=cfg.MODEL.SWINT.DEPTHS,
        num_heads=cfg.MODEL.SWINT.NUM_HEADS,
        window_size=cfg.MODEL.SWINT.WINDOW_SIZE,
        mlp_ratio=cfg.MODEL.SWINT.MLP_RATIO,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=cfg.MODEL.SWINT.DROP_PATH_RATE,
        norm_layer=nn.LayerNorm,
        ape=cfg.MODEL.SWINT.APE,
        patch_norm=True,
        frozen_stages=cfg.MODEL.BACKBONE.FREEZE_CONV_BODY_AT,
        backbone_arch=cfg.MODEL.BACKBONE.CONV_BODY,
        use_checkpoint=cfg.MODEL.BACKBONE.USE_CHECKPOINT,
        out_features=cfg.MODEL.BACKBONE.OUT_FEATURES,
        max_query_len=cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN,
        lang_dim=cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM,
    )


def build_combined_backbone(vision_backbone, language_backbone, add_linear_layer=False):
    return FusionSwinTransformer(vision_backbone, language_backbone, add_linear_layer=add_linear_layer)