-
Notifications
You must be signed in to change notification settings - Fork 26.4k
/
modeling_tf_groupvit.py
1862 lines (1518 loc) · 76 KB
/
modeling_tf_groupvit.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
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
994
995
996
997
998
999
1000
# coding=utf-8
# Copyright 2022 NVIDIA and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 GroupViT model."""
from __future__ import annotations
import collections.abc
import math
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
TFModelInputType,
TFPreTrainedModel,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_tensorflow_probability_available,
logging,
replace_return_docstrings,
)
from .configuration_groupvit import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
logger = logging.get_logger(__name__)
# soft dependency
if is_tensorflow_probability_available():
try:
import tensorflow_probability as tfp
# On the first call, check whether a compatible version of TensorFlow is installed
# TensorFlow Probability depends on a recent stable release of TensorFlow
_ = tfp.distributions.Normal(loc=0.0, scale=1.0)
except ImportError:
logger.error(
"GroupViT models are not usable since `tensorflow_probability` can't be loaded."
"It seems you have `tensorflow_probability` installed with the wrong tensorflow version."
"Please try to reinstall it following the instructions here: https://github.com/tensorflow/probability."
)
_CHECKPOINT_FOR_DOC = "nvidia/groupvit-gcc-yfcc"
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"nvidia/groupvit-gcc-yfcc",
# See all GroupViT models at https://huggingface.co/models?filter=groupvit
]
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
return tf.math.reduce_mean(
tf.keras.metrics.sparse_categorical_crossentropy(
y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
)
)
# Copied from transformers.models.clip.modeling_tf_clip.clip_loss with clip->groupvit
def groupvit_loss(similarity: tf.Tensor) -> tf.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(tf.transpose(similarity))
return (caption_loss + image_loss) / 2.0
def hard_softmax(logits: tf.Tensor, dim: int) -> tf.Tensor:
y_soft = stable_softmax(logits, dim)
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
return ret
def gumbel_softmax(logits: tf.Tensor, tau: float = 1, hard: bool = False, dim: int = -1) -> tf.Tensor:
gumbel_dist = tfp.distributions.Gumbel(0.0, 1.0)
gumbels = gumbel_dist.sample(tf.shape(logits), dtype=logits.dtype)
gumbels = (logits + gumbels) / tau # ~Gumbel(logits,tau)
y_soft = stable_softmax(gumbels, dim)
if hard:
# Straight through.
index = tf.argmax(y_soft, dim)
y_hard = tf.one_hot(
index,
depth=shape_list(logits)[dim],
# TensorFlow expects axis to be -1 or between [0, 3). But received: -2
# This is why the following code snippet is used.
axis=range(len(shape_list(logits)))[dim],
dtype=y_soft.dtype,
)
ret = y_hard - tf.stop_gradient(y_soft) + y_soft
else:
# Reparametrization trick.
ret = y_soft
return ret
def resize_attention_map(attentions: tf.Tensor, height: int, width: int, align_corners: bool = False) -> tf.Tensor:
"""
Args:
attentions (`tf.Tensor`): attention map of shape [batch_size, groups, feat_height*feat_width]
height (`int`): height of the output attention map
width (`int`): width of the output attention map
align_corners (`bool`, *optional*): the `align_corner` argument for `nn.functional.interpolate`.
Returns:
`tf.Tensor`: resized attention map of shape [batch_size, groups, height, width]
"""
scale = (height * width // attentions.shape[2]) ** 0.5
if height > width:
feat_width = int(np.round(width / scale))
feat_height = shape_list(attentions)[2] // feat_width
else:
feat_height = int(np.round(height / scale))
feat_width = shape_list(attentions)[2] // feat_height
batch_size = shape_list(attentions)[0]
groups = shape_list(attentions)[1] # number of group token
# [batch_size, groups, height x width, groups] -> [batch_size, groups, height, width]
attentions = tf.reshape(attentions, (batch_size, groups, feat_height, feat_width))
attentions = tf.transpose(attentions, perm=(0, 2, 3, 1))
if align_corners:
attentions = tf.compat.v1.image.resize(
attentions,
size=(height, width),
method="bilinear",
align_corners=align_corners,
)
else:
attentions = tf.image.resize(attentions, size=(height, width), method="bilinear")
attentions = tf.transpose(attentions, perm=(0, 3, 1, 2))
return attentions
def get_grouping_from_attentions(attentions: Tuple[tf.Tensor], hw_shape: Tuple[int]) -> tf.Tensor:
"""
Args:
attentions (`tuple(tf.Tensor)`: tuple of attention maps returned by `TFGroupViTVisionTransformer`
hw_shape (`tuple(int)`): height and width of the output attention map
Returns:
`tf.Tensor`: the attention map of shape [batch_size, groups, height, width]
"""
attn_maps = []
prev_attn_masks = None
for attn_masks in attentions:
# [batch_size, num_groups, height x width] -> [batch_size, height x width, num_groups]
attn_masks = tf.transpose(attn_masks, perm=(0, 2, 1))
if prev_attn_masks is None:
prev_attn_masks = attn_masks
else:
prev_attn_masks = tf.matmul(prev_attn_masks, attn_masks)
# [batch_size, height x width, num_groups] -> [batch_size, num_groups, height x width] -> [batch_size, num_groups, height, width]
cur_attn_map = resize_attention_map(tf.transpose(prev_attn_masks, perm=(0, 2, 1)), *hw_shape)
attn_maps.append(cur_attn_map)
# [batch_size, num_groups, height, width]
final_grouping = attn_maps[-1]
return tf.stop_gradient(final_grouping)
@dataclass
class TFGroupViTModelOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text (`tf.Tensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
segmentation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`):
Classification scores for each pixel.
<Tip warning={true}>
The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.
</Tip>
text_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTTextModel`].
image_embeds (`tf.Tensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`TFGroupViTVisionModel`].
text_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTTextModel`].
vision_model_output (`TFBaseModelOutputWithPooling`):
The output of the [`TFGroupViTVisionModel`].
"""
loss: tf.Tensor | None = None
logits_per_image: tf.Tensor = None
logits_per_text: tf.Tensor = None
segmentation_logits: tf.Tensor = None
text_embeds: tf.Tensor = None
image_embeds: tf.Tensor = None
text_model_output: TFBaseModelOutputWithPooling = None
vision_model_output: TFBaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class TFGroupViTCrossAttentionLayer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.attn = TFGroupViTAttention(config, name="attn")
self.norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.norm_post = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post")
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.Tensor:
x = query
x = x + self.attn(query, encoder_hidden_states=key)[0]
x = x + self.mlp(self.norm2(x))
x = self.norm_post(x)
return x
class TFGroupViTAssignAttention(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.scale = config.hidden_size**-0.5
self.q_proj = tf.keras.layers.Dense(config.hidden_size, name="q_proj")
self.k_proj = tf.keras.layers.Dense(config.hidden_size, name="k_proj")
self.v_proj = tf.keras.layers.Dense(config.hidden_size, name="v_proj")
self.proj = tf.keras.layers.Dense(config.hidden_size, name="proj")
self.assign_eps = config.assign_eps
def get_attn(self, attn: tf.Tensor, gumbel: bool = True, hard: bool = True, training: bool = False) -> tf.Tensor:
if gumbel and training:
attn = gumbel_softmax(attn, dim=-2, hard=hard)
else:
if hard:
attn = hard_softmax(attn, dim=-2)
else:
attn = stable_softmax(attn, axis=-2)
return attn
def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False):
value = key
# [batch_size, query_length, channels]
query = self.q_proj(query)
# [batch_size, key_length, channels]
key = self.k_proj(key)
# [batch_size, key_length, channels]
value = self.v_proj(value)
# [batch_size, query_length, key_length]
raw_attn = tf.matmul(query, key, transpose_b=True) * self.scale
attn = self.get_attn(raw_attn, training=training)
soft_attn = self.get_attn(raw_attn, training=training, gumbel=False, hard=False)
attn = attn / (tf.math.reduce_sum(attn, axis=-1, keepdims=True) + self.assign_eps)
out = tf.matmul(attn, value)
out = self.proj(out)
return out, soft_attn
class TFGroupViTTokenAssign(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
super().__init__(**kwargs)
self.num_output_group = num_output_group
# norm on group_tokens
self.norm_tokens = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_tokens")
assign_mlp_ratio = (
config.assign_mlp_ratio
if isinstance(config.assign_mlp_ratio, collections.abc.Iterable)
else (config.assign_mlp_ratio, config.assign_mlp_ratio)
)
tokens_dim, channels_dim = [int(x * config.hidden_size) for x in assign_mlp_ratio]
self.mlp_inter = TFGroupViTMixerMLP(config, num_group_token, tokens_dim, num_output_group, name="mlp_inter")
self.norm_post_tokens = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="norm_post_tokens"
)
# norm on x
self.norm_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_x")
self.pre_assign_attn = TFGroupViTCrossAttentionLayer(config, name="pre_assign_attn")
self.assign = TFGroupViTAssignAttention(config, name="assign")
self.norm_new_x = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_new_x")
self.mlp_channels = TFGroupViTMLP(
config, config.hidden_size, channels_dim, config.hidden_size, name="mlp_channels"
)
def project_group_token(self, group_tokens: tf.Tensor) -> tf.Tensor:
"""
Args:
group_tokens (tf.Tensor): group tokens, [batch_size, num_group_tokens, channels]
Returns:
projected_group_tokens (tf.Tensor): [batch_size, num_output_groups, channels]
"""
# [B, num_output_groups, C] <- [B, num_group_tokens, C]
projected_group_tokens = self.mlp_inter(group_tokens)
projected_group_tokens = self.norm_post_tokens(projected_group_tokens)
return projected_group_tokens
def call(self, image_tokens: tf.Tensor, group_tokens: tf.Tensor, training: bool = False):
"""
Args:
image_tokens (`tf.Tensor`): image tokens, of shape [batch_size, input_length, channels]
group_tokens (`tf.Tensor`): group tokens, [batch_size, num_group_tokens, channels]
"""
group_tokens = self.norm_tokens(group_tokens)
image_tokens = self.norm_x(image_tokens)
# [batch_size, num_output_groups, channels]
projected_group_tokens = self.project_group_token(group_tokens)
projected_group_tokens = self.pre_assign_attn(projected_group_tokens, image_tokens)
new_image_tokens, attention = self.assign(projected_group_tokens, image_tokens)
new_image_tokens += projected_group_tokens
new_image_tokens = new_image_tokens + self.mlp_channels(self.norm_new_x(new_image_tokens))
return new_image_tokens, attention
# Adapted from transformers.models.vit.modeling_tf_vit.TFViTPatchEmbeddings with ViT->GroupViT
class TFGroupViTPatchEmbeddings(tf.keras.layers.Layer):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels = config.num_channels
# hidden_size is a member as it will be required in the call method
self.hidden_size = config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.image_size = image_size
self.patch_size = patch_size
self.num_patches = num_patches
self.num_channels = num_channels
self.config = config
self.projection = tf.keras.layers.Conv2D(
filters=self.hidden_size,
kernel_size=patch_size,
strides=patch_size,
padding="valid",
data_format="channels_last",
use_bias=True,
kernel_initializer=get_initializer(self.config.initializer_range),
bias_initializer="zeros",
name="projection",
)
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
batch_size, num_channels, height, width = shape_list(pixel_values)
if tf.executing_eagerly() and num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
if (
not interpolate_pos_encoding
and tf.executing_eagerly()
and (height != self.image_size[0] or width != self.image_size[1])
):
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
)
# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
# So change the input format from `NCHW` to `NHWC`.
# shape = (batch_size, in_height, in_width, in_channels=num_channels)
pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
projection = self.projection(pixel_values)
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0])
# In the TFGroupViTVisionEmbeddings the embeddings from this layer will be layer normalized
# LayerNormalization layer needs to have static last dimension (otherwise the test_keras_save_load fails with symbolic tensors)
# This is why we have used the hidden_size in the reshape method
embeddings = tf.reshape(tensor=projection, shape=(batch_size, num_patches, self.hidden_size))
return embeddings
# Adapted from transformers.vit.modeling_tf_vit.TFViTEmbeddings
class TFGroupViTVisionEmbeddings(tf.keras.layers.Layer):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: GroupViTVisionConfig, **kwargs):
super().__init__(**kwargs)
self.patch_embeddings = TFGroupViTPatchEmbeddings(config, name="patch_embeddings")
self.dropout = tf.keras.layers.Dropout(rate=config.dropout, name="dropout")
self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
self.config = config
def build(self, input_shape: tf.TensorShape):
num_patches = self.patch_embeddings.num_patches
self.position_embeddings = self.add_weight(
shape=(1, num_patches, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="position_embeddings",
)
super().build(input_shape)
def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
resolution images.
Source:
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
"""
batch_size, num_patches, dim = shape_list(embeddings)
num_positions = shape_list(self.position_embeddings)[1]
if num_patches == num_positions and height == width:
return self.position_embeddings
patch_pos_embed = self.position_embeddings
h0 = height // self.config.patch_size
w0 = width // self.config.patch_size
patch_pos_embed = tf.image.resize(
images=tf.reshape(
patch_pos_embed, shape=(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
),
size=(h0, w0),
method="bicubic",
)
patch_pos_embed = tf.reshape(tensor=patch_pos_embed, shape=(1, -1, dim))
return patch_pos_embed
def call(
self, pixel_values: tf.Tensor, interpolate_pos_encoding: bool = False, training: bool = False
) -> tf.Tensor:
_, _, height, width = shape_list(pixel_values)
embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
embeddings = self.layernorm(embeddings)
# add positional encoding to each token
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embeddings
embeddings = self.dropout(embeddings)
return embeddings
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextEmbeddings with CLIP->GroupViT
class TFGroupViTTextEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.config = config
def build(self, input_shape: tf.TensorShape = None):
with tf.name_scope("token_embedding"):
self.weight = self.add_weight(
shape=(self.config.vocab_size, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="weight",
)
with tf.name_scope("position_embedding"):
self.position_embedding = self.add_weight(
shape=(self.config.max_position_embeddings, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="embeddings",
)
super().build(input_shape)
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
if input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
check_embeddings_within_bounds(input_ids, self.config.vocab_size)
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embedding, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
final_embeddings = inputs_embeds + position_embeds
return final_embeddings
class TFGroupViTStage(tf.keras.layers.Layer):
"""This corresponds to the `GroupingLayer` class in the GroupViT implementation."""
def __init__(
self,
config: GroupViTVisionConfig,
depth: int,
num_prev_group_token: int,
num_group_token: int,
num_output_group: int,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.depth = depth
self.num_group_token = num_group_token
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(depth)]
if num_group_token > 0:
self.downsample = TFGroupViTTokenAssign(
config=config,
num_group_token=num_group_token,
num_output_group=num_output_group,
name="downsample",
)
else:
self.downsample = None
if num_prev_group_token > 0 and num_group_token > 0:
self.group_projector = [
tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="group_projector.0"),
TFGroupViTMixerMLP(
config, num_prev_group_token, config.hidden_size // 2, num_group_token, name="group_projector.1"
),
]
else:
self.group_projector = None
def build(self, input_shape: tf.TensorShape):
if self.num_group_token > 0:
self.group_token = self.add_weight(
shape=(1, self.num_group_token, self.config.hidden_size),
initializer="zeros",
trainable=True,
name="group_token",
)
else:
self.group_token = None
super().build(input_shape)
@property
def with_group_token(self):
return self.group_token is not None
def split_x(self, x: tf.Tensor) -> tf.Tensor:
if self.with_group_token:
return x[:, : -self.num_group_token], x[:, -self.num_group_token :]
else:
return x, None
def concat_x(self, x: tf.Tensor, group_token: tf.Tensor | None = None) -> tf.Tensor:
if group_token is None:
return x
return tf.concat([x, group_token], axis=1)
def call(
self,
hidden_states: tf.Tensor,
prev_group_token: tf.Tensor | None = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bool`, *optional*):
Whether or not to return the grouping tensors of Grouping block.
"""
if self.with_group_token:
group_token = tf.tile(self.group_token, multiples=(shape_list(hidden_states)[0], 1, 1))
if self.group_projector is not None:
for layer in self.group_projector:
prev_group_token = layer(prev_group_token)
group_token = group_token + prev_group_token
else:
group_token = None
x = hidden_states
cat_x = self.concat_x(x, group_token)
for layer in self.layers:
layer_out = layer(
cat_x,
attention_mask=None,
causal_attention_mask=None,
output_attentions=None,
)
cat_x = layer_out[0]
x, group_token = self.split_x(cat_x)
attention = None
if self.downsample is not None:
x, attention = self.downsample(x, group_token)
outputs = (x, group_token)
if output_attentions:
outputs = outputs + (attention,)
return outputs
class TFGroupViTMLP(tf.keras.layers.Layer):
def __init__(
self,
config: GroupViTVisionConfig,
hidden_size: Optional[int] = None,
intermediate_size: Optional[int] = None,
output_size: Optional[int] = None,
**kwargs,
):
super().__init__(**kwargs)
self.config = config
self.activation_fn = get_tf_activation(config.hidden_act)
hidden_size = hidden_size if hidden_size is not None else config.hidden_size
intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
output_size = output_size if output_size is not None else hidden_size
self.fc1 = tf.keras.layers.Dense(intermediate_size, name="fc1")
self.fc2 = tf.keras.layers.Dense(output_size, name="fc2")
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
hidden_states = self.fc1(hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(hidden_states)
return hidden_states
class TFGroupViTMixerMLP(TFGroupViTMLP):
def call(self, x, training: bool = False):
x = super().call(hidden_states=tf.transpose(x, perm=(0, 2, 1)))
return tf.transpose(x, perm=(0, 2, 1))
# Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPAttention
class TFGroupViTAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = self.embed_dim // self.num_attention_heads
if self.attention_head_size * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_attention_heads})."
)
factor = config.initializer_factor
in_proj_std = (self.embed_dim**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (self.embed_dim**-0.5) * factor
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.q_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="q_proj"
)
self.k_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="k_proj"
)
self.v_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="v_proj"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_dropout)
self.out_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(out_proj_std), name="out_proj"
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention.transpose_for_scores
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor = None,
causal_attention_mask: tf.Tensor = None,
output_attentions: bool = None,
encoder_hidden_states: tf.Tensor = None,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""Input shape: Batch x Time x Channel"""
batch_size = shape_list(hidden_states)[0]
is_cross_attention = encoder_hidden_states is not None
mixed_query_layer = self.q_proj(inputs=hidden_states)
if is_cross_attention:
mixed_key_layer = self.k_proj(inputs=encoder_hidden_states)
mixed_value_layer = self.v_proj(inputs=encoder_hidden_states)
else:
mixed_key_layer = self.k_proj(inputs=hidden_states)
mixed_value_layer = self.v_proj(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
# Apply the causal attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, causal_attention_mask)
if attention_mask is not None:
# Apply the attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
_attention_probs = stable_softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=_attention_probs)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, embed_dim)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.embed_dim))
attention_output = self.out_proj(attention_output)
# In TFBert, attention weights are returned after dropout.
# However, in CLIP, they are returned before dropout.
outputs = (attention_output, _attention_probs) if output_attentions else (attention_output,)
return outputs
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPEncoderLayer with CLIP->GroupViT
class TFGroupViTEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFGroupViTAttention(config, name="self_attn")
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
self.mlp = TFGroupViTMLP(config, name="mlp")
self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
causal_attention_mask (`tf.Tensor`): causal attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`):
Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(inputs=hidden_states)
attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = attention_outputs[0]
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(inputs=hidden_states)
hidden_states = self.mlp(hidden_states=hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,) + attention_outputs[1:] # add attentions if we output them
return outputs
# Adapted from transformers.models.clip.modeling_tf_clip.TFGroupViTTextEncoder
class TFGroupViTTextEncoder(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.layers = [TFGroupViTEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[Tuple, TFBaseModelOutput]:
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
causal_attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
class TFGroupViTVisionEncoder(tf.keras.layers.Layer):
def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.stages = [
TFGroupViTStage(
config=config,
depth=config.depths[i],
num_group_token=config.num_group_tokens[i],
num_output_group=config.num_output_groups[i],
num_prev_group_token=config.num_output_groups[i - 1] if i > 0 else 0,
name=f"stages_._{i}",
)
for i in range(len(config.depths))
]
def call(
self,
hidden_states: tf.Tensor,
output_hidden_states: bool,
output_attentions: bool,
return_dict: bool,
training: bool = False,
) -> Union[tuple, TFBaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_groupings = () if output_attentions else None
group_tokens = None
for stage in self.stages:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = stage(hidden_states, group_tokens, output_attentions)
hidden_states = layer_outputs[0]
group_tokens = layer_outputs[1]
if output_attentions and layer_outputs[2] is not None:
all_groupings = all_groupings + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_groupings] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
)
# Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder
class TFGroupViTTextTransformer(tf.keras.layers.Layer):
def __init__(self, config: GroupViTTextConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFGroupViTTextEmbeddings(config, name="embeddings")
self.encoder = TFGroupViTTextEncoder(config, name="encoder")