Spaces:
Runtime error
Runtime error
File size: 47,631 Bytes
128757a |
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 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 |
import torch
import torch.nn.functional as F
from torch import nn
from collections import defaultdict
from .inference import make_atss_postprocessor
from .loss import make_atss_loss_evaluator
from .anchor_generator import make_anchor_generator_complex
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.layers import Scale, DYReLU, SELayer, ModulatedDeformConv
from maskrcnn_benchmark.layers import NaiveSyncBatchNorm2d, FrozenBatchNorm2d
from maskrcnn_benchmark.modeling.backbone.fbnet import *
from maskrcnn_benchmark.engine.inference import create_positive_map_label_to_token_from_positive_map
from ..utils import cat, concat_box_prediction_layers, permute_and_flatten
from maskrcnn_benchmark.utils.fuse_helper import FeatureResizer, func_attention, _make_mlp, _make_conv, _make_coord, \
BiAttentionBlock, AttentionT2I, BiAttentionBlockForCheckpoint, BertLMPredictionHead
from transformers.models.bert.modeling_bert import BertConfig, BertAttention, BertIntermediate, BertOutput, \
BertPreTrainedModel
from transformers.modeling_utils import apply_chunking_to_forward
import torch.utils.checkpoint as checkpoint
import pdb
from maskrcnn_benchmark.modeling.language_backbone.clip_model import QuickGELU, LayerNorm, DropPath
from timm.models.layers import DropPath, trunc_normal_
class h_sigmoid(nn.Module):
def __init__(self, inplace=True, h_max=1):
super(h_sigmoid, self).__init__()
self.relu = nn.ReLU6(inplace=inplace)
self.h_max = h_max
def forward(self, x):
return self.relu(x + 3) * self.h_max / 6
class BoxCoder(object):
def __init__(self, cfg):
self.cfg = cfg
def encode(self, gt_boxes, anchors):
TO_REMOVE = 1 # TODO remove
ex_widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE
ex_heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE
ex_ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2
ex_ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2
gt_widths = gt_boxes[:, 2] - gt_boxes[:, 0] + TO_REMOVE
gt_heights = gt_boxes[:, 3] - gt_boxes[:, 1] + TO_REMOVE
gt_ctr_x = (gt_boxes[:, 2] + gt_boxes[:, 0]) / 2
gt_ctr_y = (gt_boxes[:, 3] + gt_boxes[:, 1]) / 2
wx, wy, ww, wh = (10., 10., 5., 5.)
targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = ww * torch.log(gt_widths / ex_widths)
targets_dh = wh * torch.log(gt_heights / ex_heights)
targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
return targets
def decode(self, preds, anchors):
anchors = anchors.to(preds.dtype)
TO_REMOVE = 1 # TODO remove
widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE
heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE
ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2
ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2
wx, wy, ww, wh = (10., 10., 5., 5.)
dx = preds[:, 0::4] / wx
dy = preds[:, 1::4] / wy
dw = preds[:, 2::4] / ww
dh = preds[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=math.log(1000. / 16))
dh = torch.clamp(dh, max=math.log(1000. / 16))
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(preds)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1)
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1)
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1)
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1)
return pred_boxes
class Conv3x3Norm(torch.nn.Module):
def __init__(self,
in_channels,
out_channels,
stride,
groups=1,
deformable=False,
bn_type=None):
super(Conv3x3Norm, self).__init__()
if deformable:
self.conv = ModulatedDeformConv(in_channels, out_channels, kernel_size=3, stride=stride, padding=1,
groups=groups)
else:
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, groups=groups)
if isinstance(bn_type, (list, tuple)):
assert len(bn_type) == 2
assert bn_type[0] == "gn"
gn_group = bn_type[1]
bn_type = bn_type[0]
if bn_type == "bn":
bn_op = nn.BatchNorm2d(out_channels)
elif bn_type == "sbn":
bn_op = nn.SyncBatchNorm(out_channels)
elif bn_type == "nsbn":
bn_op = NaiveSyncBatchNorm2d(out_channels)
elif bn_type == "gn":
bn_op = nn.GroupNorm(num_groups=gn_group, num_channels=out_channels)
elif bn_type == "af":
bn_op = FrozenBatchNorm2d(out_channels)
if bn_type is not None:
self.bn = bn_op
else:
self.bn = None
def forward(self, input, **kwargs):
x = self.conv(input, **kwargs)
if self.bn:
x = self.bn(x)
return x
class DyConv(torch.nn.Module):
def __init__(self,
in_channels=256,
out_channels=256,
conv_func=nn.Conv2d,
use_dyfuse=True,
use_dyrelu=False,
use_deform=False
):
super(DyConv, self).__init__()
self.DyConv = nn.ModuleList()
self.DyConv.append(conv_func(in_channels, out_channels, 1))
self.DyConv.append(conv_func(in_channels, out_channels, 1))
self.DyConv.append(conv_func(in_channels, out_channels, 2))
if use_dyfuse:
self.AttnConv = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, 1, kernel_size=1),
nn.ReLU(inplace=True))
self.h_sigmoid = h_sigmoid()
else:
self.AttnConv = None
if use_dyrelu:
self.relu = DYReLU(in_channels, out_channels)
else:
self.relu = nn.ReLU()
if use_deform:
self.offset = nn.Conv2d(in_channels, 27, kernel_size=3, stride=1, padding=1)
else:
self.offset = None
self.init_weights()
def init_weights(self):
for m in self.DyConv.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0, 0.01)
if m.bias is not None:
m.bias.data.zero_()
if self.AttnConv is not None:
for m in self.AttnConv.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight.data, 0, 0.01)
if m.bias is not None:
m.bias.data.zero_()
def forward(self, inputs):
visual_feats = inputs["visual"]
language_dict_features = inputs["lang"]
next_x = []
for level, feature in enumerate(visual_feats):
conv_args = dict()
if self.offset is not None:
offset_mask = self.offset(feature)
offset = offset_mask[:, :18, :, :]
mask = offset_mask[:, 18:, :, :].sigmoid()
conv_args = dict(offset=offset, mask=mask)
temp_fea = [self.DyConv[1](feature, **conv_args)]
if level > 0:
temp_fea.append(self.DyConv[2](visual_feats[level - 1], **conv_args))
if level < len(visual_feats) - 1:
temp_fea.append(F.upsample_bilinear(self.DyConv[0](visual_feats[level + 1], **conv_args),
size=[feature.size(2), feature.size(3)]))
mean_fea = torch.mean(torch.stack(temp_fea), dim=0, keepdim=False)
if self.AttnConv is not None:
attn_fea = []
res_fea = []
for fea in temp_fea:
res_fea.append(fea)
attn_fea.append(self.AttnConv(fea))
res_fea = torch.stack(res_fea)
spa_pyr_attn = self.h_sigmoid(torch.stack(attn_fea))
mean_fea = torch.mean(res_fea * spa_pyr_attn, dim=0, keepdim=False)
next_x.append(mean_fea)
next_x = [self.relu(item) for item in next_x]
features_dict = {"visual": next_x,
"lang": language_dict_features}
return features_dict
class BertEncoderLayer(BertPreTrainedModel):
def __init__(self, config, clamp_min_for_underflow = False, clamp_max_for_overflow = False):
super().__init__(config)
self.config = config
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
from maskrcnn_benchmark.modeling.rpn.modeling_bert import BertAttention, BertIntermediate, BertOutput
self.attention = BertAttention(config, clamp_min_for_underflow, clamp_max_for_overflow)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
def forward(self, inputs):
language_dict_features = inputs["lang"]
hidden_states = language_dict_features["hidden"]
attention_mask = language_dict_features["masks"]
device = hidden_states.device
input_shape = hidden_states.size()[:-1]
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)
self_attention_outputs = self.attention(
hidden_states,
extended_attention_mask,
None,
output_attentions=False,
past_key_value=None,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
hidden_states = outputs[0]
language_dict_features["hidden"] = hidden_states
features_dict = {"visual": inputs["visual"],
"lang": language_dict_features
}
return features_dict
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class CLIPTransformerLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
d_model = self.config.MODEL.CLIP.WIDTH
n_head = self.config.MODEL.CLIP.HEADS
drop_path = self.config.MODEL.CLIP.DROP_PATH
self.context_length = self.config.MODEL.CLIP.CONTEXT_LENGTH
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = None
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) \
if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]
def forward(self, inputs):
language_dict_features = inputs["lang"]
x = language_dict_features["hidden"]
mask = language_dict_features["masks"]
# get extended attention mask for nn.MultiHeadAttention
key_padding_mask = (1.0 - mask).to(torch.bool)
x = x.permute(1, 0, 2)
x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
x = x + self.drop_path(self.mlp(self.ln_2(x)))
x = x.permute(1, 0, 2)
language_dict_features["hidden"] = x
features_dict = {"visual": inputs["visual"],
"lang": language_dict_features
}
return features_dict
class DummyLayer(nn.Module):
def __init__(self):
super().__init__()
def forward(self, inputs):
return inputs
class VLFuse(torch.nn.Module):
"""
Early Fusion Module
"""
def __init__(self, cfg):
super(VLFuse, self).__init__()
self.init_configs(cfg)
self.cfg = cfg
self.use_checkpoint = False
if hasattr(cfg.MODEL.DYHEAD, 'USE_CHECKPOINT'):
self.use_checkpoint = cfg.MODEL.DYHEAD.USE_CHECKPOINT
self.dummy_tensor = torch.ones(1, dtype=torch.float32, requires_grad=True)
# early fusion module
print("EARLY FUSION ON, USING {}".format(cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE))
if cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-S":
# single-direction (text->image)
# text -> image
self.t2i_attn = AttentionT2I(q_dim=self.joint_embedding_size,
k_dim=self.lang_dim,
embed_dim=self.embed_dim,
num_heads=self.n_head,
hidden_dim=self.t2i_hidden_dim,
dropout=0.1,
drop_path=.0,
init_values=1.0 / cfg.MODEL.DYHEAD.NUM_CONVS,
mode="t2i",
use_layer_scale=cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_LAYER_SCALE,
clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MIN_FOR_UNDERFLOW,
clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_MAX_FOR_OVERFLOW
)
elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-B":
# bi-direction (text->image, image->text)
self.b_attn = BiAttentionBlockForCheckpoint(v_dim=self.joint_embedding_size,
l_dim=self.lang_dim,
embed_dim=self.embed_dim,
num_heads=self.n_head,
hidden_dim=self.i2t_hidden_dim,
dropout=0.1,
drop_path=.0,
init_values=1.0 / cfg.MODEL.DYHEAD.NUM_CONVS,
cfg=cfg
)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL and self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT:
self.shrink_lang = FeatureResizer(self.lang_dim * 5,
self.lang_dim, 0.1)
elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "SCAN":
# single-direction (text->image)
self.mapping_lang = _make_mlp(self.lang_dim,
self.joint_embedding_size,
self.joint_embedding_dropout)
self.joint_fusion = nn.ModuleList([_make_conv(self.joint_inp_dim, self.joint_out_dim, 1) \
for _ in range(5)])
elif cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "FILM":
# single-direction (text->image)
self.mapping_lang = _make_mlp(self.lang_dim,
self.joint_embedding_size,
self.joint_embedding_dropout)
self.gamma = nn.ModuleList(nn.Linear(self.joint_embedding_size, self.joint_inp_dim) for _ in range(5))
self.beta = nn.ModuleList(nn.Linear(self.joint_embedding_size, self.joint_inp_dim) for _ in range(5))
self.joint_fusion = nn.ModuleList([_make_conv(self.joint_inp_dim, self.joint_out_dim, 1) \
for _ in range(5)])
else:
print("NO FUSION INVOLVED.")
def init_configs(self, cfg):
# common params
self.lang_model = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE
self.joint_embedding_size = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_SIZE
self.joint_embedding_dropout = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_DROPOUT
self.joint_mlp_layers = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_MLP_LAYERS
self.max_query_len = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN
self.n_layers = cfg.MODEL.LANGUAGE_BACKBONE.N_LAYERS
self.coord_dim = 8
self.joint_inp_dim = self.coord_dim + self.joint_embedding_size
self.joint_out_dim = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_OUT_SIZE
# mha params
self.n_head = 8
self.embed_dim = 2048
self.t2i_hidden_dim = 1024 # 256 * 4
self.i2t_hidden_dim = 3072 # 768 * 4
if self.lang_model in ["bert-base-uncased", "roberta-base", "clip"]:
self.lang_dim = cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM
else:
self.lang_dim = 1024
def forward(self, x):
visual_features = x["visual"]
language_dict_features = x["lang"]
batch_size = visual_features[0].shape[0]
device = visual_features[0].device
fused_visual_features = None
fused_language_dict_features = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-S":
language_feature = language_dict_features['hidden']
mask = language_dict_features['masks']
# text -> image
if self.use_checkpoint:
q0, q1, q2, q3, q4 = checkpoint.checkpoint(
self.t2i_attn,
visual_features[0], visual_features[1],
visual_features[2], visual_features[3],
visual_features[4],
language_feature, language_feature,
mask,
self.dummy_tensor
)
else:
q0, q1, q2, q3, q4 = self.t2i_attn(
visual_features[0], visual_features[1],
visual_features[2], visual_features[3],
visual_features[4],
language_feature, language_feature,
attention_mask=mask
)
fused_visual_features = [q0, q1, q2, q3, q4]
fused_language_dict_features = language_dict_features
elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "MHA-B":
if self.use_checkpoint:
q0, q1, q2, q3, q4, l0, l1, l2, l3, l4 = checkpoint.checkpoint(self.b_attn,
visual_features[0], visual_features[1],
visual_features[2], visual_features[3],
visual_features[4],
language_dict_features['hidden'],
language_dict_features['masks'],
self.dummy_tensor
)
else:
q0, q1, q2, q3, q4, l0, l1, l2, l3, l4 = self.b_attn(
visual_features[0], visual_features[1],
visual_features[2], visual_features[3],
visual_features[4],
language_dict_features['hidden'],
language_dict_features['masks'],
self.dummy_tensor
)
fused_visual_features = [q0, q1, q2, q3, q4]
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SEPARATE_BIDIRECTIONAL and self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DO_LANG_PROJ_OUTSIDE_CHECKPOINT:
language_features = self.shrink_lang(torch.cat([l0, l1, l2, l3, l4], dim = -1))
else:
language_features = l0
language_dict_features['hidden'] = language_features
fused_language_dict_features = language_dict_features
elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "SCAN":
# text -> image
language_feature = language_dict_features['aggregate']
language_feature = self.mapping_lang(language_feature)
visu_feat = []
for ii, feat in enumerate(visual_features):
attn_feat = func_attention(feat, language_feature, smooth=1, raw_feature_norm="softmax")
visu_feat.append(attn_feat)
fused_visual_features = [fusion(feat) for feat, fusion in zip(visu_feat, self.joint_fusion)]
fused_language_dict_features = language_dict_features
elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TYPE == "FILM":
# text -> image
# relative position embedding
coord_feats = [_make_coord(batch_size, x.shape[2], x.shape[3]) for x in visual_features]
# I only use a global representation of language
# you can also use more complex modeling using word-level representations
# Usage: lang_feat = lang_feat['words'] shape [seq_len, dim]
language_feature = language_dict_features['aggregate']
language_feature = self.mapping_lang(language_feature)
# attention mechanism for fusion
gamma = [F.tanh(gamma(language_feature)) for gamma in self.gamma]
beta = [F.tanh(beta(language_feature)) for beta in self.beta]
visu_feat = []
for ii, feat in enumerate(visual_features):
coord_feat = coord_feats[ii].to(device)
feat = torch.cat([feat, coord_feat], dim=1)
b = beta[ii].view(batch_size, -1, 1, 1).expand_as(feat)
g = gamma[ii].view(batch_size, -1, 1, 1).expand_as(feat)
feat = F.relu(g * feat + b)
visu_feat.append(feat)
fused_visual_features = [fusion(feat) for feat, fusion in zip(visu_feat, self.joint_fusion)]
fused_language_dict_features = language_dict_features
else:
fused_visual_features = visual_features
fused_language_dict_features = language_dict_features
features_dict = {"visual": fused_visual_features,
"lang": fused_language_dict_features}
return features_dict
class VLDyHead(torch.nn.Module):
def __init__(self, cfg):
super(VLDyHead, self).__init__()
self.cfg = cfg
# bert_cfg = BertConfig.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE)
if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "bert-base-uncased":
lang_cfg = BertConfig.from_pretrained(cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE)
elif cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip":
lang_cfg = cfg
else:
lang_cfg = None
raise NotImplementedError
num_classes = cfg.MODEL.DYHEAD.NUM_CLASSES - 1
num_tokens = cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN
num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE
in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
channels = cfg.MODEL.DYHEAD.CHANNELS
if cfg.MODEL.DYHEAD.USE_GN:
bn_type = ['gn', cfg.MODEL.GROUP_NORM.NUM_GROUPS]
elif cfg.MODEL.DYHEAD.USE_NSYNCBN:
bn_type = 'nsbn'
elif cfg.MODEL.DYHEAD.USE_SYNCBN:
bn_type = 'sbn'
else:
bn_type = None
use_dyrelu = cfg.MODEL.DYHEAD.USE_DYRELU
use_dyfuse = cfg.MODEL.DYHEAD.USE_DYFUSE
use_deform = cfg.MODEL.DYHEAD.USE_DFCONV
if cfg.MODEL.DYHEAD.CONV_FUNC:
conv_func = lambda i, o, s: eval(cfg.MODEL.DYHEAD.CONV_FUNC)(i, o, s, bn_type=bn_type)
else:
conv_func = lambda i, o, s: Conv3x3Norm(i, o, s, deformable=use_deform, bn_type=bn_type)
dyhead_tower = []
for i in range(cfg.MODEL.DYHEAD.NUM_CONVS):
if cfg.MODEL.DYHEAD.FUSE_CONFIG.EARLY_FUSE_ON:
# cross-modality fusion
dyhead_tower.append(
VLFuse(cfg)
)
# self language path
if i < cfg.MODEL.DYHEAD.NUM_CONVS - 1 or cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_FUSED_FEATURES_DOT_PRODUCT:
# dyhead_tower.append(
# BertEncoderLayer(
# bert_cfg,
# clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MIN_FOR_UNDERFLOW,
# clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MAX_FOR_OVERFLOW)
# )
if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "bert-base-uncased":
dyhead_tower.append(
BertEncoderLayer(
lang_cfg,
clamp_min_for_underflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MIN_FOR_UNDERFLOW,
clamp_max_for_overflow=cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_BERTATTN_MAX_FOR_OVERFLOW)
)
elif cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip":
dyhead_tower.append(
CLIPTransformerLayer(lang_cfg)
)
else:
raise NotImplementedError
else:
dyhead_tower.append(
DummyLayer()
)
# self vision path
dyhead_tower.append(
DyConv(
in_channels if i == 0 else channels,
channels,
conv_func=conv_func,
use_dyrelu=(use_dyrelu and in_channels == channels) if i == 0 else use_dyrelu,
use_dyfuse=(use_dyfuse and in_channels == channels) if i == 0 else use_dyfuse,
use_deform=(use_deform and in_channels == channels) if i == 0 else use_deform,
)
)
self.add_module('dyhead_tower', nn.Sequential(*dyhead_tower))
self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=1)
self.bbox_pred = nn.Conv2d(channels, num_anchors * 4, kernel_size=1)
self.centerness = nn.Conv2d(channels, num_anchors * 1, kernel_size=1)
# initialize the bias for focal loss
prior_prob = cfg.MODEL.DYHEAD.PRIOR_PROB
bias_value = -math.log((1 - prior_prob) / prior_prob)
log_scale = self.cfg.MODEL.DYHEAD.LOG_SCALE
# soft token head
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS:
self.token_logits = nn.Conv2d(channels, num_anchors * num_tokens, kernel_size=1)
# ABLATION
# self.token_logits = nn.Conv2d(channels, num_anchors * num_tokens, kernel_size=1, bias=False)
# self.bias = nn.Parameter(torch.zeros(channels), requires_grad=True)
# self.bias0 = nn.Parameter(torch.Tensor([bias_value]), requires_grad=True)
# contrastive alignment head
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS == False
contrastive_hdim = cfg.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_HIDDEN_DIM
self.contrastive_align_projection_image = nn.Conv2d(channels, num_anchors * contrastive_hdim, kernel_size=1)
self.contrastive_align_projection_text = nn.Linear(channels, contrastive_hdim, bias=True)
self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True)
# dot product soft token head
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
assert self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS == False
self.dot_product_projection_image = nn.Identity()
self.dot_product_projection_text = nn.Linear(self.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM,
num_anchors * channels, bias=True)
self.log_scale = nn.Parameter(torch.Tensor([log_scale]), requires_grad=True)
# DEBUG
# self.bias = nn.Parameter(torch.zeros(channels), requires_grad=True)
self.bias_lang = nn.Parameter(torch.zeros(self.cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM), requires_grad=True)
self.bias0 = nn.Parameter(torch.Tensor([bias_value]), requires_grad=True)
# initialization
for modules in [self.cls_logits, self.bbox_pred,
self.centerness]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, 0)
self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)])
torch.nn.init.constant_(self.cls_logits.bias, bias_value)
# if use soft token loss
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS:
for modules in [self.token_logits]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, 0)
torch.nn.init.constant_(self.token_logits.bias, bias_value)
# print(torch.norm(self.token_logits.weight))
# if use contrastive loss
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
for modules in [self.contrastive_align_projection_image]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, 0)
# if use dot product token loss
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
for modules in [self.dot_product_projection_image]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, bias_value)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS:
if cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE == "clip":
lang_cfg = BertConfig.from_pretrained("bert-base-uncased")
lang_cfg.hidden_size = cfg.MODEL.CLIP.WIDTH
lang_cfg.vocab_size = cfg.MODEL.CLIP.VOCAB_SIZE
self.mlm_head = BertLMPredictionHead(
lang_cfg
) #nn.Linear(hidden_size, config.vocab_size, bias=False)
def forward(self, x, language_dict_features=None, embedding=None, swint_feature_c4=None):
logits = []
bbox_reg = []
centerness = []
feat_inputs = {"visual": x,
"lang": language_dict_features}
dyhead_tower = self.dyhead_tower(feat_inputs)
# soft token
t_logits = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS:
t_logits = []
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_FUSED_FEATURES_DOT_PRODUCT:
embedding = dyhead_tower["lang"]["hidden"]
# MLM loss
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS:
mlm_logits = self.mlm_head(embedding)
else:
mlm_logits = None
# contrastive
contrastive_logits = None
proj_tokens = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
contrastive_logits = []
# follow MDETR's way
proj_tokens = F.normalize(
self.contrastive_align_projection_text(embedding), p=2, dim=-1
)
# dot product soft token
dot_product_logits = None
dot_product_proj_tokens = None
dot_product_proj_tokens_bias = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
dot_product_logits = []
# norm
embedding = F.normalize(embedding, p=2, dim=-1)
dot_product_proj_tokens = self.dot_product_projection_text(embedding / 2.0)
# w/o norm
# dot_product_proj_tokens = self.dot_product_projection_text(embedding / 28.0)
dot_product_proj_tokens_bias = torch.matmul(embedding, self.bias_lang) + self.bias0
# shallow contrastive (original feature from image & text encoder)
shallow_img_emb_feats = None
shallow_text_emb = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS \
or self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS:
shallow_img_emb_feats = []
shallow_text_emb = embedding
# print([v.shape for v in x])
# shallow contrastive: use the feature from swint backbone
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS:
for b, feature in enumerate(swint_feature_c4):
# BF, CF, HF, WF = feat.shape
# shallow_img_emb = permute_and_flatten(feat, BF, -1, CF, HF, WF)
shallow_img_emb_feats.append(feature)
fused_visual_features = None
if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES:
fused_visual_features = []
# use the feature from FPN
for l, feature in enumerate(x):
logits.append(self.cls_logits(dyhead_tower["visual"][l]))
bbox_pred = self.scales[l](self.bbox_pred(dyhead_tower["visual"][l]))
bbox_reg.append(bbox_pred)
centerness.append(self.centerness(dyhead_tower["visual"][l]))
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS:
t_logits.append(self.token_logits(dyhead_tower["visual"][l]))
# ABLATION
# b = self.bias.unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
# x = dyhead_tower["visual"][l]
# B, C, H, W = x.shape
# bias = b.repeat(B, 1, H, W)
# t_logits.append(self.token_logits(dyhead_tower["visual"][l] + bias) + self.bias0)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
x = dyhead_tower["visual"][l]
B, _, H, W = x.shape
C = proj_tokens.shape[2]
proj_queries = self.contrastive_align_projection_image(dyhead_tower["visual"][l])
proj_queries = permute_and_flatten(proj_queries, B, -1, C, H, W)
normalized_img_emb = F.normalize(proj_queries, p=2, dim=-1)
normalized_text_emb = proj_tokens
contrastive_logit = (
torch.matmul(normalized_img_emb, normalized_text_emb.transpose(-1, -2)) / self.log_scale.exp())
contrastive_logits.append(contrastive_logit)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
x = dyhead_tower["visual"][l]
if self.cfg.MODEL.RPN.RETURN_FUSED_FEATURES:
fused_visual_features.append(x)
B, C, H, W = x.shape
# add bias (language)
dot_product_proj_queries = self.dot_product_projection_image(x)
dot_product_proj_queries = permute_and_flatten(dot_product_proj_queries, B, -1, C, H, W)
A = dot_product_proj_queries.shape[1]
bias = dot_product_proj_tokens_bias.unsqueeze(1).repeat(1, A, 1)
dot_product_logit = (torch.matmul(dot_product_proj_queries, dot_product_proj_tokens.transpose(-1, -2)) / self.log_scale.exp()) + bias
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.CLAMP_DOT_PRODUCT:
dot_product_logit = torch.clamp(dot_product_logit, max=50000)
dot_product_logit = torch.clamp(dot_product_logit, min=-50000)
dot_product_logits.append(dot_product_logit)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS:
feat = feature
BF, CF, HF, WF = feat.shape
shallow_img_emb = permute_and_flatten(feat, BF, -1, CF, HF, WF)
shallow_img_emb_feats.append(shallow_img_emb)
# no matter the feature is from backboone or from fpn, we use shallow_img_embs all the time
if shallow_img_emb_feats is not None and shallow_text_emb is not None:
# shallow_img_embs = torch.cat(shallow_img_embs, dim=1)
proj_tokens = shallow_text_emb
return logits, bbox_reg, centerness, t_logits, proj_tokens, contrastive_logits, dot_product_logits, mlm_logits, shallow_img_emb_feats, fused_visual_features
class VLDyHeadModule(torch.nn.Module):
def __init__(self, cfg):
super(VLDyHeadModule, self).__init__()
self.cfg = cfg
self.head = VLDyHead(cfg)
box_coder = BoxCoder(cfg)
self.loss_evaluator = make_atss_loss_evaluator(cfg, box_coder)
self.box_selector_train = make_atss_postprocessor(cfg, box_coder, is_train=True)
self.box_selector_test = make_atss_postprocessor(cfg, box_coder, is_train=False)
self.anchor_generator = make_anchor_generator_complex(cfg)
self.lang_model = cfg.MODEL.LANGUAGE_BACKBONE.MODEL_TYPE
self.joint_embedding_size = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_SIZE
self.joint_embedding_dropout = cfg.MODEL.DYHEAD.FUSE_CONFIG.JOINT_EMB_DROPOUT
if self.lang_model in ["bert-base-uncased", "roberta-base", "clip"]:
self.lang_dim = cfg.MODEL.LANGUAGE_BACKBONE.LANG_DIM
else:
self.lang_dim = 1024
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
self.resizer = FeatureResizer(
input_feat_size=self.lang_dim,
output_feat_size=self.joint_embedding_size,
dropout=self.joint_embedding_dropout
)
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER:
self.tunable_linear = torch.nn.Linear(self.lang_dim, 1000, bias=False)
self.tunable_linear.weight.data.fill_(0.0)
def forward(self, images, features, targets=None,
language_dict_features=None,
positive_map=None,
captions=None,
swint_feature_c4=None
):
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
# resizer needed
embedding = language_dict_features['embedded']
embedding = self.resizer(embedding)
elif self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
# no resizer needed
embedding = language_dict_features['embedded']
else:
embedding = None
if "masks" in language_dict_features:
text_masks = language_dict_features["masks"]
else:
text_masks = None
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.ADD_LINEAR_LAYER:
embedding = self.tunable_linear.weight[:embedding.size(1), :].unsqueeze(0) + embedding
language_dict_features['embedded'] = embedding
language_dict_features['hidden'] = self.tunable_linear.weight[:embedding.size(1), :].unsqueeze(0) + language_dict_features['hidden']
box_cls, box_regression, centerness, token_logits, \
proj_tokens, contrastive_logits, dot_product_logits, mlm_logits, shallow_img_emb_feats, fused_visual_features = self.head(features,
language_dict_features,
embedding,
swint_feature_c4
)
anchors = self.anchor_generator(images, features)
if self.training:
return self._forward_train(box_cls, box_regression, centerness, targets, anchors,
captions,
positive_map,
token_logits,
proj_tokens,
contrastive_logits,
dot_product_logits,
text_masks,
mlm_logits = mlm_logits,
mlm_labels = language_dict_features["mlm_labels"],
shallow_img_emb_feats=shallow_img_emb_feats,
fused_visual_features=fused_visual_features
)
else:
return self._forward_test(box_regression, centerness, anchors,
box_cls,
token_logits,
dot_product_logits,
positive_map,
fused_visual_features=fused_visual_features
)
def _forward_train(self, box_cls, box_regression, centerness, targets, anchors,
captions=None,
positive_map=None,
token_logits=None,
proj_tokens=None,
contrastive_logits=None,
dot_product_logits=None,
text_masks=None,
mlm_logits=None,
mlm_labels=None,
shallow_img_emb_feats=None,
fused_visual_features=None
):
loss_box_cls, loss_box_reg, loss_centerness, loss_token, loss_contrastive_align, loss_dot_product_token, loss_shallow_contrastive = self.loss_evaluator(
box_cls, box_regression, centerness, targets, anchors,
captions,
positive_map,
token_logits,
proj_tokens,
contrastive_logits,
dot_product_logits,
text_masks,
shallow_img_emb_feats
)
losses = {
# "loss_cls": loss_box_cls,
"loss_reg": loss_box_reg,
"loss_centerness": loss_centerness
}
if mlm_labels is not None and mlm_logits is not None:
losses["mlm_loss"] = nn.CrossEntropyLoss(ignore_index = -100)(mlm_logits.view(-1, mlm_logits.size(-1)), mlm_labels.view(-1)) * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.MLM_LOSS_COEF
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CLASSIFICATION_LOSS:
losses["loss_cls"] = loss_box_cls
else:
losses["loss_cls"] = 0.0 * loss_box_cls
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_TOKEN_LOSS:
losses["loss_token"] = loss_token * self.cfg.MODEL.DYHEAD.FUSE_CONFIG.TOKEN_LOSS_WEIGHT
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_CONTRASTIVE_ALIGN_LOSS:
losses["loss_contrastive_align"] = loss_contrastive_align * \
self.cfg.MODEL.DYHEAD.FUSE_CONFIG.CONTRASTIVE_ALIGN_LOSS_WEIGHT
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_DOT_PRODUCT_TOKEN_LOSS:
losses["loss_dot_product_token"] = loss_dot_product_token * \
self.cfg.MODEL.DYHEAD.FUSE_CONFIG.DOT_PRODUCT_TOKEN_LOSS_WEIGHT
if self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_SHALLOW_CONTRASTIVE_LOSS or \
self.cfg.MODEL.DYHEAD.FUSE_CONFIG.USE_BACKBONE_SHALLOW_CONTRASTIVE_LOSS:
losses["loss_shallow_contrastive"] = loss_shallow_contrastive * \
self.cfg.MODEL.DYHEAD.FUSE_CONFIG.SHALLOW_CONTRASTIVE_LOSS_WEIGHT
if self.cfg.MODEL.RPN_ONLY:
return None, losses, None
else:
# Let's just use one image per batch
assert (box_regression[0].shape[0]) == 1
positive_map_label_to_token = create_positive_map_label_to_token_from_positive_map(positive_map, plus=1)
boxes = self.box_selector_train(box_regression, centerness, anchors,
box_cls,
token_logits,
dot_product_logits,
positive_map=positive_map_label_to_token
)
train_boxes = []
for b, t in zip(boxes, targets):
tb = t.copy_with_fields(["labels"])
tb.add_field("scores", torch.ones(tb.bbox.shape[0], dtype=torch.bool, device=tb.bbox.device))
train_boxes.append(cat_boxlist([b, tb]))
return train_boxes, losses, fused_visual_features
def _forward_test(self, box_regression, centerness, anchors,
box_cls=None,
token_logits=None,
dot_product_logits=None,
positive_map=None,
fused_visual_features=None
):
boxes = self.box_selector_test(box_regression, centerness, anchors,
box_cls,
token_logits,
dot_product_logits,
positive_map,
)
return boxes, {}, fused_visual_features
|