# -------------------------------------------------------- # X-Decoder -- Generalized Decoding for Pixel, Image, and Language # Copyright (c) 2022 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Xueyan Zou (xueyan@cs.wisc.edu) # -------------------------------------------------------- import random from typing import Tuple from unicodedata import name import torch from torch import nn from torch.nn import functional as F import numpy as np from .registry import register_model from ..utils import configurable from ..backbone import build_backbone, Backbone from ..body import build_xdecoder_head from ..modules import sem_seg_postprocess, bbox_postprocess from ..language import build_language_encoder from ..language.loss import vl_similarity from timm.models.layers import trunc_normal_ from nltk.stem.lancaster import LancasterStemmer from detectron2.structures import Boxes, ImageList, Instances, BitMasks, BoxMode from detectron2.utils.memory import retry_if_cuda_oom from detectron2.data import MetadataCatalog from utils.misc import prompt_engineering st = LancasterStemmer() class X_Decoder_Model(nn.Module): @configurable def __init__( self, *, backbone: Backbone, sem_seg_head: nn.Module, criterion: nn.Module, losses: dict, num_queries: int, object_mask_threshold: float, overlap_threshold: float, metadata, task_switch: dict, phrase_prob: float, size_divisibility: int, sem_seg_postprocess_before_inference: bool, pixel_mean: Tuple[float], pixel_std: Tuple[float], # inference semantic_on: bool, panoptic_on: bool, instance_on: bool, test_topk_per_image: int, train_dataset_name: str, retrieval_emsemble: bool, backbone_dim: int, dim_proj: int, ): super().__init__() self.backbone = backbone self.sem_seg_head = sem_seg_head self.criterion = criterion self.losses = losses self.num_queries = num_queries self.overlap_threshold = overlap_threshold self.object_mask_threshold = object_mask_threshold self.metadata = metadata if size_divisibility < 0: # use backbone size_divisibility if not set size_divisibility = self.backbone.size_divisibility self.size_divisibility = size_divisibility self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False) self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False) # additional args self.semantic_on = semantic_on self.instance_on = instance_on self.panoptic_on = panoptic_on # caption argument self.task_switch = task_switch self.phrase_prob = phrase_prob self.test_topk_per_image = test_topk_per_image self.train_class_names = None self.retrieval_emsemble = retrieval_emsemble # backbone itc loss if task_switch['retrieval'] and retrieval_emsemble: self.backbone_proj = nn.Parameter(torch.empty(backbone_dim, dim_proj)) trunc_normal_(self.backbone_proj, std=.02) if not self.semantic_on: assert self.sem_seg_postprocess_before_inference @classmethod def from_config(cls, cfg): enc_cfg = cfg['MODEL']['ENCODER'] dec_cfg = cfg['MODEL']['DECODER'] task_switch = {'bbox': dec_cfg.get('DETECTION', False), 'mask': dec_cfg.get('MASK', True), 'caption': dec_cfg['CAPTION'].get('ENABLED', False), 'captioning': dec_cfg['CAPTIONING'].get('ENABLED', False), 'retrieval': dec_cfg['RETRIEVAL'].get('ENABLED', False), 'grounding': dec_cfg['GROUNDING'].get('ENABLED', False)} # build model extra = {'task_switch': task_switch} backbone = build_backbone(cfg) lang_encoder = build_language_encoder(cfg) sem_seg_head = build_xdecoder_head(cfg, backbone.output_shape(), lang_encoder, extra) # Training Settings. loss_weights = {} matcher = None losses = {} weight_dict = {} grd_weight = {} top_x_layers = {} criterion = None train_dataset_name = None phrase_prob = None # Loss parameters: deep_supervision = None no_object_weight = None return { "backbone": backbone, "sem_seg_head": sem_seg_head, "criterion": criterion, "losses": losses, "num_queries": dec_cfg['NUM_OBJECT_QUERIES'], "object_mask_threshold": dec_cfg['TEST']['OBJECT_MASK_THRESHOLD'], "overlap_threshold": dec_cfg['TEST']['OVERLAP_THRESHOLD'], "metadata": None, "size_divisibility": dec_cfg['SIZE_DIVISIBILITY'], "sem_seg_postprocess_before_inference": ( dec_cfg['TEST']['SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE'] or dec_cfg['TEST']['PANOPTIC_ON'] or dec_cfg['TEST']['INSTANCE_ON'] ), "pixel_mean": cfg['INPUT']['PIXEL_MEAN'], "pixel_std": cfg['INPUT']['PIXEL_STD'], "task_switch": task_switch, "phrase_prob": phrase_prob, # inference "semantic_on": dec_cfg['TEST']['SEMANTIC_ON'], "instance_on": dec_cfg['TEST']['INSTANCE_ON'], "panoptic_on": dec_cfg['TEST']['PANOPTIC_ON'], "test_topk_per_image": cfg['MODEL']['DECODER']['TEST']['DETECTIONS_PER_IMAGE'], "train_dataset_name": train_dataset_name, "retrieval_emsemble": dec_cfg['RETRIEVAL']['ENSEMBLE'], "backbone_dim": cfg['MODEL']['BACKBONE_DIM'], "dim_proj": cfg['MODEL']['DIM_PROJ'], } @property def device(self): return self.pixel_mean.device def forward(self, batched_inputs, mode=None): if self.training: assert False, "Not support trianing mode." else: if mode == 'retrieval': return self.evaluate_retrieval(batched_inputs) elif mode == 'captioning': return self.evaluate_captioning(batched_inputs) elif mode == 'classification': return self.evaluate_classification(batched_inputs) elif mode in ['grounding_phrasecut', 'grounding_refcoco']: return self.evaluate_grounding(batched_inputs, mode) else: return self.evaluate(batched_inputs) def evaluate(self, batched_inputs): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) img_bs = images.tensor.shape[0] targets = targets_grounding = queries_grounding = None features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, target_queries=queries_grounding) mask_cls_results = outputs["pred_logits"] mask_pred_results = outputs["pred_masks"] box_pred_results = outputs["pred_boxes"] if self.task_switch['bbox'] else [None for i in range(len(mask_pred_results))] caption_pred_results = outputs["pred_captions"] if self.task_switch['caption'] else [None for i in range(len(mask_pred_results))] # upsample masks mask_pred_results = F.interpolate( mask_pred_results, size=(images.tensor.shape[-2], images.tensor.shape[-1]), mode="bilinear", align_corners=False, ) input_size = mask_pred_results.shape[-2:] keep_sem_bgd = self.metadata.keep_sem_bgd if hasattr(self.metadata, 'keep_sem_bgd') else False del outputs processed_results = [] for mask_cls_result, mask_pred_result, box_pred_result, caption_pred_result, input_per_image, image_size in zip( mask_cls_results, mask_pred_results, box_pred_results, caption_pred_results, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) processed_results.append({}) if self.sem_seg_postprocess_before_inference: mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( mask_pred_result, image_size, height, width ) mask_cls_result = mask_cls_result.to(mask_pred_result) # semantic segmentation inference if self.semantic_on: r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result, keep_sem_bgd) if not self.sem_seg_postprocess_before_inference: r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width) processed_results[-1]["sem_seg"] = r # panoptic segmentation inference if self.panoptic_on: panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result) processed_results[-1]["panoptic_seg"] = panoptic_r # instance segmentation inference if self.instance_on: if self.task_switch['bbox']: box_pred_result = bbox_postprocess(box_pred_result, input_size, image_size, height, width) instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, box_pred_result) processed_results[-1]["instances"] = instance_r if self.task_switch['caption']: processed_results[-1]["captions"] = caption_pred_result processed_results[-1]["masks"] = mask_pred_result return processed_results def evaluate_retrieval(self, batched_inputs): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) img_bs = images.tensor.shape[0] targets = targets_grounding = queries_grounding = None features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, target_queries=queries_grounding) v_emb_it = outputs['pred_captions'][:,-1] # compute backbone score if self.task_switch['retrieval'] and self.retrieval_emsemble: _v_emb_it = features['res5'] bs,nc,_,_ = _v_emb_it.shape _v_emb_it = _v_emb_it.reshape(bs,nc,-1) _v_emb_it = F.adaptive_avg_pool1d(_v_emb_it, 1).reshape(bs,nc) @ self.backbone_proj processed_results = [] for idx, batch_data in enumerate(batched_inputs): caption_ids = [] t_emb_its = [] processed_results.append({}) for caption in batch_data['captions']: lang_results = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(caption) t_emb_it = lang_results['class_emb'] caption_ids.append(batch_data['image_id']) t_emb_its.append(t_emb_it) t_emb_it = torch.cat(t_emb_its, dim=0) image_embeds = [v_emb_it[idx].unsqueeze(0)] if self.task_switch['retrieval'] and self.retrieval_emsemble: image_embeds += [_v_emb_it[idx].unsqueeze(0)] caption_results = { 'image_embeds': image_embeds, 'text_embeds': t_emb_it, 'caption_ids': caption_ids, 'image_ids': batch_data['image_id'], } processed_results[-1]["caption"] = caption_results return processed_results def evaluate_captioning(self, batched_inputs, extra={}): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) img_bs = images.tensor.shape[0] if not hasattr(self, 'start_token'): self.start_token = torch.tensor([[49406]*77], device=self.device) targets = targets_grounding = queries_grounding = None features = self.backbone(images.tensor) captioning_mask = None if 'captioning_mask' in batched_inputs[-1]: captioning_mask = torch.cat([x['captioning_mask'] for x in batched_inputs]) extra.update({'start_token': self.start_token, 'captioning_mask': captioning_mask}) outputs = self.sem_seg_head(features, target_queries=queries_grounding, task='captioning_infer', extra=extra) processed_results = [] for idx, batch_data in enumerate(batched_inputs): processed_results.append({}) processed_results[-1]["captioning_token"] = outputs['pred_captionings'][idx] processed_results[-1]["captioning_text"] = outputs['pred_texts'][idx].split('.')[0] processed_results[-1]["image_id"] = batched_inputs[idx]['image_id'] return processed_results def evaluate_classification(self, batched_inputs): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) img_bs = images.tensor.shape[0] targets = targets_grounding = queries_grounding = None features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, target_queries=queries_grounding) processed_results = [] for idx, batch_data in enumerate(batched_inputs): processed_results.append({}) processed_results[-1]["pred_class"] = outputs['pred_logits'][idx,-1] return processed_results def evaluate_grounding_baseline(self, batched_inputs, mode): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) img_bs = images.tensor.shape[0] targets = targets_grounding = queries_grounding = None features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, target_queries=queries_grounding) mask_pred_results = outputs["pred_masks"] caption_pred_results = outputs["pred_captions"] if self.task_switch['caption'] else [None for i in range(len(mask_pred_results))] # upsample masks mask_pred_results = F.interpolate( mask_pred_results, size=(images.tensor.shape[-2], images.tensor.shape[-1]), mode="bilinear", align_corners=False, ) processed_results = [] for mask_pred_result, caption_pred_result, input_per_image, image_size in zip( mask_pred_results, caption_pred_results, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) processed_results.append({}) mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( mask_pred_result, image_size, height, width )[:-1] texts_all = input_per_image['groundings']['texts'] grd_masks = [] for texts in texts_all: if mode == 'grounding_refcoco': self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(texts, name='grounding', prompt=False, is_eval=True) elif mode == 'grounding_phrasecut': self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(texts, name='grounding', prompt=True, is_eval=False) t_emb = getattr(self.sem_seg_head.predictor.lang_encoder, "{}_text_embeddings".format('grounding')).t() v_emb = caption_pred_result[:-1] v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7) vt_sim = v_emb @ t_emb max_id = vt_sim.max(0)[1][0] grd_masks += [mask_pred_result[max_id]] processed_results[-1]['grounding_mask'] = torch.stack(grd_masks) return processed_results def evaluate_grounding(self, batched_inputs, mode): images = [x["image"].to(self.device) for x in batched_inputs] images = [(x - self.pixel_mean) / self.pixel_std for x in images] images = ImageList.from_tensors(images, self.size_divisibility) extra = {} # mask_pred_results = [] # for idx, batch_per_image in enumerate(batched_inputs): # grd_texts = batch_per_image['groundings']['texts'] # grd_masks = [] # for anno_text in grd_texts: # gtext = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings([anno_text[0]], name='grounding', token=False, norm=False) # token_emb = gtext['token_emb'] # tokens = gtext['tokens'] # grd_emb = token_emb[0][tokens['attention_mask'].bool()[0]] # extra['grounding_tokens'] = grd_emb[:,None] # assert len(images.tensor) == 1, "grounding evaluation only support single batch size now" # features = self.backbone(images.tensor) # outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval') # pred_gmasks = outputs['pred_masks'][idx,self.num_queries:2*self.num_queries-1] # v_emb = outputs['pred_captions'][idx,self.num_queries:2*self.num_queries-1] # t_emb = grd_emb[-1:] # t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7) # v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7) # temperature = self.sem_seg_head.predictor.lang_encoder.logit_scale # out_prob = vl_similarity(v_emb, t_emb, temperature=temperature) # matched_id = out_prob.max(0)[1] # grd_masks += [pred_gmasks[matched_id,:,:]] # mask_pred_results += [torch.cat(grd_masks)] # comment for multi object inference. mask_pred_results = [] for idx, batch_per_image in enumerate(batched_inputs): grd_texts = batch_per_image['groundings']['texts'] grd_texts = [x[0] for x in grd_texts] gtext = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(grd_texts, name='grounding', token=False, norm=False) token_emb = gtext['token_emb'] tokens = gtext['tokens'] query_emb = token_emb[tokens['attention_mask'].bool()] extra['grounding_tokens'] = query_emb[:,None] features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, extra=extra, task='grounding_eval') pred_gmasks = outputs['pred_masks'][idx,self.num_queries:2*self.num_queries-1] v_emb = outputs['pred_captions'][idx,self.num_queries:2*self.num_queries-1] t_emb = gtext['class_emb'] t_emb = t_emb / (t_emb.norm(dim=-1, keepdim=True) + 1e-7) v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7) temperature = self.sem_seg_head.predictor.lang_encoder.logit_scale out_prob = vl_similarity(v_emb, t_emb, temperature=temperature) matched_id = out_prob.max(0)[1] mask_pred_results += [pred_gmasks[matched_id,:,:]] for i in range(len(mask_pred_results)): # upsample masks mask_pred_results[i] = F.interpolate( mask_pred_results[i][None,], size=(images.tensor.shape[-2], images.tensor.shape[-1]), mode="bilinear", align_corners=False, )[0] processed_results = [] for mask_pred_result, input_per_image, image_size in zip( mask_pred_results, batched_inputs, images.image_sizes ): height = input_per_image.get("height", image_size[0]) width = input_per_image.get("width", image_size[1]) processed_results.append({}) mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)( mask_pred_result, image_size, height, width ) processed_results[-1]['grounding_mask'] = mask_pred_result # compute bbox # bbox = BitMasks(mask_pred_result > 0).get_bounding_boxes() # bbox = BoxMode.convert(bbox.tensor, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) # processed_results[-1]['grounding_box'] = bbox return processed_results def prepare_vlp_targets(self, batched_inputs, device): input_ids = [] attention_mask = [] for cnt, x in enumerate(batched_inputs): captions = x['captions'] randid = random.randint(0, len(captions)-1) input_ids += x['tokens']['input_ids'][randid:randid+1] attention_mask += x['tokens']['attention_mask'][randid:randid+1] input_ids = torch.stack(input_ids) attention_mask = torch.stack(attention_mask) tokens = {"input_ids": input_ids, "attention_mask": attention_mask} lang_results = self.sem_seg_head.predictor.lang_encoder.get_text_token_embeddings(tokens, token=True) target_vlp = [] for cnt, x in enumerate(batched_inputs): target_dict = {} target_dict["caption_tokens"] = lang_results['token_emb'][cnt:cnt+1] target_dict["caption_proj"] = lang_results['class_emb'][cnt:cnt+1] target_dict["caption_tokenids"] = lang_results['tokens']['input_ids'][cnt:cnt+1] target_dict["caption_mask"] = lang_results['tokens']['attention_mask'][cnt:cnt+1] target_vlp.append(target_dict) return target_vlp def semantic_inference(self, mask_cls, mask_pred, keep_sem_bgd=False): if keep_sem_bgd: mask_cls = F.softmax(mask_cls, dim=-1) else: mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1] mask_pred = mask_pred.sigmoid() semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred) return semseg def panoptic_inference(self, mask_cls, mask_pred): scores, labels = F.softmax(mask_cls, dim=-1).max(-1) mask_pred = mask_pred.sigmoid() keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold) cur_scores = scores[keep] cur_classes = labels[keep] cur_masks = mask_pred[keep] cur_mask_cls = mask_cls[keep] cur_mask_cls = cur_mask_cls[:, :-1] cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks h, w = cur_masks.shape[-2:] panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device) segments_info = [] current_segment_id = 0 if cur_masks.shape[0] == 0: # We didn't detect any mask :( return panoptic_seg, segments_info else: # take argmax cur_mask_ids = cur_prob_masks.argmax(0) stuff_memory_list = {} thing_dataset_id_to_contiguous_id = self.metadata.thing_dataset_id_to_contiguous_id if hasattr(self.metadata, 'thing_dataset_id_to_contiguous_id') else {} for k in range(cur_classes.shape[0]): pred_class = cur_classes[k].item() isthing = pred_class in thing_dataset_id_to_contiguous_id.values() mask_area = (cur_mask_ids == k).sum().item() original_area = (cur_masks[k] >= 0.5).sum().item() mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5) if mask_area > 0 and original_area > 0 and mask.sum().item() > 0: if mask_area / original_area < self.overlap_threshold: continue # merge stuff regions if not isthing: if int(pred_class) in stuff_memory_list.keys(): panoptic_seg[mask] = stuff_memory_list[int(pred_class)] continue else: stuff_memory_list[int(pred_class)] = current_segment_id + 1 current_segment_id += 1 panoptic_seg[mask] = current_segment_id segments_info.append( { "id": current_segment_id, "isthing": bool(isthing), "category_id": int(pred_class), } ) return panoptic_seg, segments_info def instance_inference(self, mask_cls, mask_pred, box_pred): # mask_pred is already processed to have the same shape as original input image_size = mask_pred.shape[-2:] # [Q, K] scores = F.softmax(mask_cls, dim=-1)[:, :-1] labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1) # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False) labels_per_image = labels[topk_indices] topk_indices = (topk_indices // self.sem_seg_head.num_classes) # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) mask_pred = mask_pred[topk_indices] if box_pred is not None: box_pred = box_pred[topk_indices] # if this is panoptic segmentation, we only keep the "thing" classes if self.panoptic_on: thing_dataset_id_to_contiguous_id = self.metadata.thing_dataset_id_to_contiguous_id if hasattr(self.metadata, 'thing_dataset_id_to_contiguous_id') else {} keep = torch.zeros_like(scores_per_image).bool() for i, lab in enumerate(labels_per_image): keep[i] = lab in thing_dataset_id_to_contiguous_id.values() scores_per_image = scores_per_image[keep] labels_per_image = labels_per_image[keep] mask_pred = mask_pred[keep] if box_pred is not None: box_pred = box_pred[keep] result = Instances(image_size) # mask (before sigmoid) result.pred_masks = (mask_pred > 0).float() # result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4)) # Uncomment the following to get boxes from masks (this is slow) if box_pred is not None: result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes() else: result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4)) # calculate average mask prob mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6) result.scores = scores_per_image * mask_scores_per_image result.pred_classes = labels_per_image return result @register_model def get_segmentation_model(cfg, **kwargs): return X_Decoder_Model(cfg)