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MQ-Det/groundingdino_new/models/GroundingDINO/groundingdino.py

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# Adapted from https://github.com/IDEA-Research/GroundingDINO. The original liscenses are:
# ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Conditional DETR model and criterion classes.
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
import copy
from typing import List
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torchvision.ops.boxes import nms
from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
from groundingdino_new.util import box_ops, get_tokenlizer
from groundingdino_new.util.misc import (
NestedTensor,
accuracy,
get_world_size,
interpolate,
inverse_sigmoid,
is_dist_avail_and_initialized,
nested_tensor_from_tensor_list,
)
from groundingdino_new.util.utils import get_phrases_from_posmap
from groundingdino_new.util.visualizer import COCOVisualizer
from groundingdino_new.util.vl_utils import create_positive_map_from_span
from ..registry import MODULE_BUILD_FUNCS
from .backbone import build_backbone
from .bertwarper import (
BertModelWarper,
generate_masks_with_special_tokens,
generate_masks_with_special_tokens_and_transfer_map,
)
from .transformer import build_transformer
from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.modeling.rpn.inference import convert_grounding_to_od_logits
from maskrcnn_benchmark.modeling.box_coder import BoxCoder
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.boxlist_ops import remove_small_boxes
from maskrcnn_benchmark.structures.boxlist_ops import boxlist_ml_nms
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
# from groundingdino_new.util.inference import preprocess_caption
from maskrcnn_benchmark.modeling.poolers import CustomPooler, Pooler
from groundingdino_new.models.GroundingDINO.loss import SetCriterion
from groundingdino_new.models.GroundingDINO.matcher import build_matcher
from maskrcnn_benchmark.modeling.language_backbone import build_language_backbone
from maskrcnn_benchmark.modeling.language_backbone.modeling_bert_new import QVBertModel
from transformers import BertConfig, RobertaConfig, RobertaModel
from maskrcnn_benchmark.modeling.query_selector import build_query_selector
import os
def expand_bbox(box_list, expand_ratio=1.5):
new_box_list=[]
for boxes in box_list:
assert boxes.mode == "xyxy"
bbox=boxes.bbox
image_size=boxes.size
box_w, box_h = bbox[:,2] - bbox[:,0], bbox[:,3] - bbox[:,1]
new_box_w, new_box_h = box_w*expand_ratio, box_h*expand_ratio
diff_w=(new_box_w-box_w)/2
diff_h=(new_box_h-box_h)/2
diff=torch.stack([-diff_w, -diff_h, diff_w, diff_h], dim=1)
new_bbox=bbox+diff
new_boxes=BoxList(new_bbox, image_size, mode="xyxy")
labels=boxes.get_field('labels')
new_boxes.add_field('labels', labels)
new_boxes=new_boxes.clip_to_image(remove_empty=True)
new_box_list.append(new_boxes)
return new_box_list
def preprocess_caption(caption: str) -> str:
result = caption.lower().strip()
if result.endswith("."):
return result
return result + "."
class GroundingDINO(nn.Module):
"""This is the Cross-Attention Detector module that performs object detection"""
def __init__(
self,
backbone,
transformer,
num_queries,
aux_loss=False,
iter_update=False,
query_dim=2,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type="no", # ['no', 'standard']
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
text_encoder_type="bert-base-uncased",
sub_sentence_present=True,
max_text_len=256,
cfg = None,
):
"""Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.cfg = cfg
self.box_threshold = cfg.GROUNDINGDINO.box_threshold
self.num_queries = num_queries
self.transformer = transformer
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.max_text_len = 256
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
# loss criterion
self.loss_evaluator = SetCriterion(matcher=build_matcher(cfg.GROUNDINGDINO.matcher), cfg=cfg)
# box pooler for extracting cache
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
if cfg.VISION_QUERY.SELECT_FPN_LEVEL:
self.pooler = Pooler(
output_size= (resolution, resolution) ,
scales=cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES,
sampling_ratio=cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO,
use_v2=True,
)
else:
self.pooler = CustomPooler(
output_size= (resolution, resolution) ,
scales=cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES,
sampling_ratio=cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO,
use_v2=True,
)
self.pool=nn.AvgPool2d(2)
# query selector
if cfg.VISION_QUERY.DISABLE_SELECTOR:
self.query_selector = None
else:
self.query_selector = build_query_selector(cfg)
# bert
self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
if os.path.basename(text_encoder_type) != "bert-base-uncased":
raise NotImplementedError
# self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
config = BertConfig.from_pretrained(text_encoder_type)
self.bert = QVBertModel.from_pretrained(text_encoder_type, dim_t=config.hidden_size, dim_v=self.hidden_dim, share_kv=cfg.VISION_QUERY.SHARE_KV, cfg=cfg, config=config)
self.bert.pooler.dense.weight.requires_grad_(False)
self.bert.pooler.dense.bias.requires_grad_(False)
self.bert = BertModelWarper(bert_model=self.bert)
self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
nn.init.constant_(self.feat_map.bias.data, 0)
nn.init.xavier_uniform_(self.feat_map.weight.data)
# freeze
# special tokens
self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
)
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(
nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
)
)
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)
]
)
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = ContrastiveEmbed()
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [
copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
]
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
# two stage
self.two_stage_type = two_stage_type
assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
two_stage_type
)
if two_stage_type != "no":
if two_stage_bbox_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
self._reset_parameters()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def init_ref_points(self, use_num_queries):
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
def convert_groundingdino_to_glip_output(self, groundingdino_out, positive_map, image_sizes):
dot_product_logits = groundingdino_out['pred_logits']
box_regression = groundingdino_out['pred_boxes']
B, N, _ = dot_product_logits.shape
box_cls = dot_product_logits.new_zeros(B, N, self.cfg.MODEL.DYHEAD.NUM_CLASSES - 1)
# candidate_inds = dot_product_logits.max(dim=-1)[0] > self.box_threshold
scores = convert_grounding_to_od_logits(logits=dot_product_logits, box_cls=box_cls,
positive_map=positive_map,
score_agg="MEAN",
)
box_cls = scores
candidate_inds = box_cls.max(dim=-1)[0] > self.box_threshold
# pre_nms_top_n = candidate_inds.reshape(N, -1).sum(1)
# pre_nms_top_n = pre_nms_top_n.clamp(max=self.pre_nms_top_n)
results = []
for per_box_cls, per_box_regression, per_candidate_inds, image_size \
in zip(box_cls, box_regression, candidate_inds, image_sizes):
per_box_cls = per_box_cls[per_candidate_inds]
per_box_cls, top_k_indices = per_box_cls.topk(1, sorted=False)
per_class = top_k_indices[:, 0] + 1
# print(per_class)
box = per_box_regression[per_candidate_inds, :].view(-1, 4)
H, W = image_size
# from 0..1 to 0..W, 0..H
box = box * torch.Tensor([W, H, W, H]).to(box.device)[None, ...]
# from xywh to xyxy
box[:, :2] = box[:, :2] - box[:, 2:] / 2
box[:, 2:] = box[:, 2:] + box[:, :2]
detections = box
boxlist = BoxList(detections, (W, H), mode="xyxy")
boxlist.add_field("labels", per_class)
boxlist.add_field("scores", per_box_cls[:,0])
boxlist = boxlist.clip_to_image(remove_empty=False)
boxlist = remove_small_boxes(boxlist, min_size=0)
results.append(boxlist)
return results
def load_query_bank(self, query_path):
self.query_selector.load_query_bank(query_path)
@torch.no_grad()
def extract_query(self,
samples=None,
targets=None,
query_images=None, # default_dict(list) ,list[tensors] num_classes: (num_queries, num_scales, num_channels)
visual_features=None,
exclude_similar=False,
device = None,
max_query_number = None,
):
device = device if device else samples.tensors.device
targets = [target.to(device)
for target in targets if target is not None]
targets=expand_bbox(targets, expand_ratio=self.cfg.VISION_QUERY.EXPAND_RATIO)
if visual_features is None:
if isinstance(samples, ImageList):
image_sizes = samples.image_sizes
samples = samples.tensors
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples, image_sizes=image_sizes)
features, poss = self.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
srcs.append(src)
masks.append(mask)
poss.append(pos_l)
visual_features = srcs
else:
visual_features = [v.to(device) for v in visual_features]
if self.cfg.VISION_QUERY.SELECT_FPN_LEVEL:
query_feats=self.pooler(visual_features, targets) # num_boxes, num_channels, pooler_size, pooler_size
query_feats=query_feats[None, ...] # 1, num_boxes, num_channels, pooler_size, pooler_size
else:
query_feats=self.pooler(visual_features, targets) # num_scales, num_boxes, num_channels, pooler_size, pooler_size
# average different fpn levels
if not self.cfg.VISION_QUERY.SELECT_FPN_LEVEL:
assert len(visual_features) == len(query_feats) == 5 # TODO: support flexible level numbers
query_feats = query_feats.mean(dim=[-2,-1]).permute(1, 0, 2) # num_boxes, num_scales, num_channels
labels=torch.cat([t.get_field('labels') for t in targets])
assert len(labels)==len(query_feats)
max_query_number = self.cfg.VISION_QUERY.MAX_QUERY_NUMBER if max_query_number is None else max_query_number
for label, feat in zip(labels, query_feats):
label=label.item()
num_queries=len(query_images[label])
if num_queries >= max_query_number:
continue
if exclude_similar and num_queries > 0:
assert feat.shape[0] == 1 # TODO: enable all-level and spacial features
bank_features = F.normalize(query_images[label], p=2, dim=-1) # N, 1, C
new_features = F.normalize(feat, p=2, dim=-1) # 1, C
similarity = einsum('b n d, n d -> b n', bank_features, new_features)
has_similar_in_bank = (similarity > self.cfg.VISION_QUERY.SIMILARITY_THRESHOLD).sum() > 0
if has_similar_in_bank:
continue
if num_queries==0:
query_images[label] = feat[None, ...]
else:
query_images[label] = torch.cat([query_images[label], feat[None, ...]])
return query_images
def flatten_fpn_features(self, features):
# downsample and flat fpn features for pre-select in language backbone
return torch.cat([self.pool(f).flatten(-2,-1) for i, f in enumerate(features)], dim=2).permute(0,2,1)
@torch.no_grad()
def get_labels_and_maps_from_positive_map(self, positive_map, dtype=torch.float):
# Only for inference
labels_in_caption=[k for k,v in positive_map.items() if len(v) !=0]
num_labels=len(labels_in_caption)
all_map = torch.zeros((num_labels, self.cfg.MODEL.LANGUAGE_BACKBONE.MAX_QUERY_LEN), dtype=dtype, device=self.cfg.MODEL.DEVICE)
for j, label in enumerate(labels_in_caption):
position=positive_map[label]
all_map[j, position] = 1 # inplace
all_map = all_map / (all_map.sum(-1)[:, None] + 1e-6)
return labels_in_caption, all_map
def forward(self, samples: NestedTensor, targets: List = None, **kw):
"""The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
It returns a dict with the following elements:
- "pred_logits": the classification logits (including no-object) for all queries.
Shape= [batch_size x num_queries x num_classes]
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
(center_x, center_y, width, height). These values are normalized in [0, 1],
relative to the size of each individual image (disregarding possible padding).
See PostProcess for information on how to retrieve the unnormalized bounding box.
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
if isinstance(samples, ImageList):
image_sizes = samples.image_sizes
samples = samples.tensors
if targets is None:
captions = kw["captions"]
else:
captions = [t.get_field("caption") for t in targets if "caption" in t.fields()]
len(captions)
captions = [preprocess_caption(c) for c in captions]
positive_map = kw['positive_map']
try:
return_backbone_features = kw['return_backbone_features']
except:
return_backbone_features = False
# import ipdb; ipdb.set_trace()
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples, image_sizes=image_sizes)
features, poss = self.backbone(samples)
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
srcs.append(src)
masks.append(mask)
poss.append(pos_l)
# query embedding
if self.cfg.VISION_QUERY.ENABLED:
if self.training:
batched_labels_in_caption=[t.get_field('labels_in_caption') for t in targets]
batched_all_map=[t.get_field('all_map') for t in targets]
batched_pos_category_map=[t.get_field('positive_category_map') for t in targets]
################ BUG: batched_pos_category_map is not binary ######################
batched_pos_labels = [t.get_field('labels') for t in targets]
else:
assert samples.tensors.shape[0]==1 # TODO: Only support batch size = 1 for test
labels_in_caption, all_map = self.get_labels_and_maps_from_positive_map(positive_map, dtype=srcs[0].dtype)
batched_labels_in_caption = [labels_in_caption]
batched_all_map = [all_map]
batched_pos_category_map = None
batched_pos_labels = None
query_features, query_attetion_masks, batched_has_vision_query=self.query_selector(batched_labels_in_caption, batched_all_map, batched_pos_labels)
vision_inputs_in_language_backbone={'vision': query_features, 'images': self.flatten_fpn_features(srcs), 'vision_attention_mask': query_attetion_masks, 'batched_pos_category_map': batched_pos_category_map}
else:
vision_inputs_in_language_backbone={'vision': None, 'images': None, 'vision_attention_mask': None, 'batched_pos_category_map': None}
# encoder texts
# assume each category is consist of its text tokens and one '.'
# tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
# samples.device
# )
tokenized = self.tokenizer(captions, padding='max_length', return_tensors="pt").to(
samples.device
)
(
text_self_attention_masks, # each category token only attend to its own category tokens and one '.'
position_ids, # [[0, 0, 1, 2, 0, 1, 0]]
cate_to_token_mask_list,
) = generate_masks_with_special_tokens_and_transfer_map(
tokenized, self.specical_tokens, self.tokenizer
)
if text_self_attention_masks.shape[1] > self.max_text_len:
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
position_ids = position_ids[:, : self.max_text_len]
tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
# extract text embeddings
if self.sub_sentence_present:
tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
tokenized_for_encoder["attention_mask"] = text_self_attention_masks
tokenized_for_encoder["position_ids"] = position_ids
else:
# import ipdb; ipdb.set_trace()
tokenized_for_encoder = tokenized
tokenized_for_encoder.update(vision_inputs_in_language_backbone)
bert_output = self.bert(**tokenized_for_encoder) # bs, 195, 768
encoded_text = self.feat_map(bert_output["last_hidden_state"]) # bs, 195, d_model
text_token_mask = tokenized.attention_mask.bool() # bs, 195
# text_token_mask: True for nomask, False for mask
# text_self_attention_masks: True for nomask, False for mask
if encoded_text.shape[1] > self.max_text_len:
encoded_text = encoded_text[:, : self.max_text_len, :]
text_token_mask = text_token_mask[:, : self.max_text_len]
position_ids = position_ids[:, : self.max_text_len]
text_self_attention_masks = text_self_attention_masks[
:, : self.max_text_len, : self.max_text_len
]
text_dict = {
"encoded_text": encoded_text, # bs, 195, d_model
"text_token_mask": text_token_mask, # bs, 195
"position_ids": position_ids, # bs, 195
"text_self_attention_masks": text_self_attention_masks, # bs, 195,195
}
input_query_bbox = input_query_label = attn_mask = dn_meta = None
hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
)
# deformable-detr-like anchor update
outputs_coord_list = []
for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
zip(reference[:-1], self.bbox_embed, hs)
):
layer_delta_unsig = layer_bbox_embed(layer_hs)
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
layer_outputs_unsig = layer_outputs_unsig.sigmoid()
outputs_coord_list.append(layer_outputs_unsig)
outputs_coord_list = torch.stack(outputs_coord_list)
# output
outputs_class = torch.stack(
[
layer_cls_embed(layer_hs, text_dict)
for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
]
)
if self.training:
out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
aux_outputs = [{"pred_logits": outputs_class[k], "pred_boxes": outputs_coord_list[k]} for k in range(len(outputs_class)-1)]
out['aux_outputs'] = aux_outputs
positive_map_ = positive_map.clone().to(outputs_class[-1].device)
positive_map_[positive_map_>0]=1.
# padding to max_text_len
text_mask = torch.full((*text_dict["text_token_mask"].shape[:-1], self.max_text_len), bool(False), device=text_dict["text_token_mask"].device)
text_mask[..., : text_dict["text_token_mask"].shape[-1]] = text_dict["text_token_mask"]
losses = self.loss_evaluator(out, targets, text_mask=text_mask ,positive_map=positive_map_)
if self.cfg.VISION_QUERY.ENABLED:
#### gate loss #####
# concatenate all gates
gates = []
for _ ,g in bert_output['vision_query_gates'].items():
gates = gates + g
num_gates=len(gates)
loss_gate=0
for g in gates:
loss_gate=loss_gate+(1-torch.abs(g[0]))
loss_gate= self.cfg.VISION_QUERY.GATE_REGULARIZATION_SCALE * loss_gate / num_gates
if self.cfg.VISION_QUERY.GATE_REGULARIZATION:
gate_losses = {'loss_gate': loss_gate.sum()}
else:
loss_gate = loss_gate.sum().detach() # Only for analysis
gate_losses = {'loss_gate': loss_gate}
####################
losses.update(gate_losses)
return losses
else:
out = {"pred_logits": outputs_class[-1].sigmoid(), "pred_boxes": outputs_coord_list[-1]}
result = self.convert_groundingdino_to_glip_output(out, positive_map, image_sizes)
if return_backbone_features:
return result, srcs
return result
# # for intermediate outputs
# if self.aux_loss:
# out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
# # for encoder output
# if hs_enc is not None:
# # prepare intermediate outputs
# interm_coord = ref_enc[-1]
# interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
# out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
# out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
# return out
@torch.jit.unused
def _set_aux_loss(self, outputs_class, outputs_coord):
# this is a workaround to make torchscript happy, as torchscript
# doesn't support dictionary with non-homogeneous values, such
# as a dict having both a Tensor and a list.
return [
{"pred_logits": a, "pred_boxes": b}
for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
]
@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
def build_groundingdino(args, cfg):
backbone = build_backbone(args)
transformer = build_transformer(args)
dn_labelbook_size = args.dn_labelbook_size
dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
sub_sentence_present = args.sub_sentence_present
model = GroundingDINO(
backbone,
transformer,
num_queries=args.num_queries,
aux_loss=True,
iter_update=True,
query_dim=4,
num_feature_levels=args.num_feature_levels,
nheads=args.nheads,
dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
two_stage_type=args.two_stage_type,
two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
two_stage_class_embed_share=args.two_stage_class_embed_share,
num_patterns=args.num_patterns,
dn_number=0,
dn_box_noise_scale=args.dn_box_noise_scale,
dn_label_noise_ratio=args.dn_label_noise_ratio,
dn_labelbook_size=dn_labelbook_size,
text_encoder_type=args.text_encoder_type,
sub_sentence_present=sub_sentence_present,
max_text_len=args.max_text_len,
cfg=cfg,
)
return model
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