# ------------------------------------------------------------------------ # 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 from torchvision.ops.boxes import nms from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast from groundingdino.util import box_ops, get_tokenlizer from groundingdino.util.misc import ( NestedTensor, accuracy, get_world_size, interpolate, inverse_sigmoid, is_dist_avail_and_initialized, nested_tensor_from_tensor_list, ) from groundingdino.util.utils import get_phrases_from_posmap from groundingdino.util.visualizer import COCOVisualizer from groundingdino.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 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, ): """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.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 # bert self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type) self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type) 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 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 targets is None: captions = kw["captions"] else: captions = [t["caption"] for t in targets] len(captions) # encoder texts tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to( samples.device ) ( text_self_attention_masks, position_ids, 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 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 } # import ipdb; ipdb.set_trace() if isinstance(samples, (list, torch.Tensor)): samples = nested_tensor_from_tensor_list(samples) 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) 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) ] ) out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]} # # 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): 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, ) return model