# -------------------------------------------------------- # 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), Ziyi Dou, Jianwei Yang # -------------------------------------------------------- from typing import Tuple import random import torch from torch import nn from torch.nn import functional as F import numpy as np 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 .build import register_model from ..utils import configurable, get_class_names from ..vision.backbone import build_backbone, Backbone from ..body import build_xdecoder_head from ..modules import sem_seg_postprocess, SetCriterion, HungarianMatcher, bbox_postprocess from ..language import build_language_encoder from ..language.loss import vl_similarity, image_text_contrastive_loss_queue from utilities.prompt_engineering import prompt_engineering from utilities.constants import COCO_PANOPTIC_CLASSES st = LancasterStemmer() class GeneralizedXdecoder(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, ): """ Args: backbone: a backbone module, must follow detectron2's backbone interface sem_seg_head: a module that predicts semantic segmentation from backbone features criterion: a module that defines the loss num_queries: int, number of queries object_mask_threshold: float, threshold to filter query based on classification score for panoptic segmentation inference overlap_threshold: overlap threshold used in general inference for panoptic segmentation metadata: dataset meta, get `thing` and `stuff` category names for panoptic segmentation inference size_divisibility: Some backbones require the input height and width to be divisible by a specific integer. We can use this to override such requirement. sem_seg_postprocess_before_inference: whether to resize the prediction back to original input size before semantic segmentation inference or after. For high-resolution dataset like Mapillary, resizing predictions before inference will cause OOM error. pixel_mean, pixel_std: list or tuple with #channels element, representing the per-channel mean and std to be used to normalize the input image semantic_on: bool, whether to output semantic segmentation prediction instance_on: bool, whether to output instance segmentation prediction panoptic_on: bool, whether to output panoptic segmentation prediction test_topk_per_image: int, instance segmentation parameter, keep topk instances per image """ 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 = get_class_names(train_dataset_name) 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'] # Loss parameters: deep_supervision = dec_cfg['DEEP_SUPERVISION'] no_object_weight = dec_cfg['NO_OBJECT_WEIGHT'] # loss weights, switcher for task, and top layers to compute loss loss_weights = {'mask': {'ce': dec_cfg['CLASS_WEIGHT'], 'dice': dec_cfg['DICE_WEIGHT'], 'bce': dec_cfg['MASK_WEIGHT']}, 'bbox': {'l1': dec_cfg['BBOX_WEIGHT'], 'giou': dec_cfg['GIOU_WEIGHT']}, 'caption': dec_cfg['CAPTION_WEIGHT'], 'captioning': dec_cfg['CAPTIONING_WEIGHT'], 'retrieval': {'decoder': dec_cfg['RETRIEVAL_WEIGHT'], 'backbone': dec_cfg['BACKBONER_WEIGHT']}, 'grounding': {'ce': dec_cfg['GCLASS_WEIGHT'], 'dice': dec_cfg['GDICE_WEIGHT'], 'bce': dec_cfg['GMASK_WEIGHT']}} 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)} top_x_layers = {'mask': dec_cfg.get('TOP_MASK_LAYERS', 10), 'caption': dec_cfg.get('TOP_CAPTION_LAYERS', 10), 'captioning': dec_cfg.get('TOP_CAPTIONING_LAYERS', 10), 'retrieval': dec_cfg.get('TOP_RETRIEVAL_LAYERS', 10), 'grounding': dec_cfg.get('TOP_GROUNDING_LAYERS', 10),} # 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) # building criterion matcher = HungarianMatcher( cost_class=loss_weights['mask']['ce'], cost_mask=loss_weights['mask']['bce'], cost_dice=loss_weights['mask']['dice'], num_points=dec_cfg['TRAIN_NUM_POINTS'], ) # init weight dict and criterion loss functions. losses = {'seg': [], 'vlp': []} if task_switch['mask']: losses['seg'] += ["labels", "masks"] if task_switch['caption']: losses['seg'] += ["captions"] if task_switch['grounding']: losses['seg'] += ["groundings"] if task_switch['captioning']: losses['vlp'] += ["captionings"] if task_switch['retrieval']: losses['vlp'] += ["retrievals"] weight_dict = {} for key, turn_on in task_switch.items(): if turn_on: if isinstance(loss_weights[key], dict): # HACK it should support bbox in the future for key_, weight in loss_weights[key].items(): weight_dict["loss_{}_{}_0".format(key, key_)] = weight # NOTE: hard code for segmentation that has multiple loss else: weight_dict["loss_{}_0".format(key)] = loss_weights[key] # generate full weight dict and remove not computed layers. if deep_supervision: dec_layers = dec_cfg['DEC_LAYERS'] aux_weight_dict = {} for i in range(dec_layers - 1): for k, v in weight_dict.items(): if (i+1) > (top_x_layers[k.split('_')[1]] - 1): continue aux_weight_dict.update({k.replace('_0', f"_{i+1}"): v}) weight_dict.update(aux_weight_dict) grd_weight = {'text': dec_cfg['GROUNDING']['TEXT_WEIGHT'], 'class': dec_cfg['GROUNDING']['CLASS_WEIGHT']} # generate critenrion for loss function. criterion = SetCriterion( sem_seg_head.num_classes, matcher=matcher, weight_dict=weight_dict, top_x_layers=top_x_layers, eos_coef=no_object_weight, losses=[], num_points=dec_cfg['TRAIN_NUM_POINTS'], oversample_ratio=dec_cfg['OVERSAMPLE_RATIO'], importance_sample_ratio=dec_cfg['IMPORTANCE_SAMPLE_RATIO'], grounding_weight=grd_weight, ) # extra logistic train_dataset_name = cfg['DATASETS']['TRAIN'][0] # HACK for only one training set. phrase_prob = dec_cfg['CAPTION'].get('PHRASE_PROB', 0.5) 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": MetadataCatalog.get(cfg['DATASETS']['TRAIN'][0]), "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['COCO']['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): """ Args: batched_inputs: a list, batched outputs of :class:`DatasetMapper`. Each item in the list contains the inputs for one image. For now, each item in the list is a dict that contains: * "image": Tensor, image in (C, H, W) format. * "instances": per-region ground truth * Other information that's included in the original dicts, such as: "height", "width" (int): the output resolution of the model (may be different from input resolution), used in inference. Returns: list[dict]: each dict has the results for one image. The dict contains the following keys: * "sem_seg": A Tensor that represents the per-pixel segmentation prediced by the head. The prediction has shape KxHxW that represents the logits of each class for each pixel. * "panoptic_seg": A tuple that represent panoptic output panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. segments_info (list[dict]): Describe each segment in `panoptic_seg`. Each dict contains keys "id", "category_id", "isthing". """ if self.training: losses = {} if self.task_switch['mask']: losses_seg = self.forward_seg(batched_inputs['coco']) losses.update(losses_seg) if self.task_switch['retrieval'] or self.task_switch['captioning']: losses_vlp = self.forward_vlp(batched_inputs['vlp']) losses.update(losses_vlp) for k in list(losses.keys()): if k in self.criterion.weight_dict: losses[k] *= self.criterion.weight_dict[k] else: # remove this loss if not specified in `weight_dict` losses.pop(k) return losses 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 == 'grounding_refcoco': return self.evaluate_grounding(batched_inputs, mode) else: return self.evaluate(batched_inputs) def forward_seg(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) self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(self.train_class_names, is_eval=False) extra = {} # mask classification target if "instances" in batched_inputs[0]: # input bounding box is checked to be correct. targets = self.prepare_targets(batched_inputs, images) if self.task_switch['grounding']: grounding_tokens = [x['grounding_query_embs'] for x in targets] # need to pad for more than one grounding token grounding_tokens = nn.utils.rnn.pad_sequence(grounding_tokens) extra['grounding_tokens'] = grounding_tokens features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, extra=extra) _outputs = {} for key, value in outputs.items(): if key == 'pred_logits': _outputs[key] = value[:,:self.num_queries-1] elif key == 'pred_masks': _outputs[key] = value[:,:self.num_queries-1] if self.task_switch['grounding']: _outputs['pred_gmasks'] = value[:,self.num_queries:2*self.num_queries-1] elif key == 'pred_captions': _outputs[key] = value[:,:self.num_queries-1] if self.task_switch['grounding']: _outputs['pred_gtexts'] = value[:,self.num_queries:2*self.num_queries-1] elif key == 'aux_outputs': _outputs[key] = [] for i in range(len(value)): _outputs[key] += [{}] for _key, _value in value[i].items(): if _key == 'pred_logits': _outputs[key][i][_key] = _value[:,:self.num_queries-1] elif _key == 'pred_masks': _outputs[key][i][_key] = _value[:,:self.num_queries-1] if self.task_switch['grounding']: _outputs[key][i]['pred_gmasks'] = _value[:,self.num_queries:2*self.num_queries-1] elif _key == 'pred_captions': _outputs[key][i][_key] = _value[:,:self.num_queries-1] if self.task_switch['grounding']: _outputs[key][i]['pred_gtexts'] = _value[:,self.num_queries:2*self.num_queries-1] outputs = _outputs extra = {'lang_logit': self.sem_seg_head.predictor.lang_encoder.logit_scale, 'class_embeddings': getattr(self.sem_seg_head.predictor.lang_encoder, '{}_text_embeddings'.format('default'))} # bipartite matching-based loss self.criterion.losses = self.losses['seg'] # seg criterion losses losses = self.criterion(outputs, targets, extra) del outputs del _outputs return losses def forward_vlp(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) targets_vlp = self.prepare_vlp_targets(batched_inputs, images.tensor.device) extra = {"token_embedding": self.sem_seg_head.predictor.lang_encoder.lang_encoder.token_embedding, "lang_encoder": self.sem_seg_head.predictor.lang_encoder, "training": self.training} features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, target_queries=None, target_vlp=targets_vlp, task='vlp', extra=extra) for key, value in outputs.items(): if key == 'pred_captionings': outputs[key] = value elif key == 'pred_captions': # outputs[key] = value[:,-1:] outputs[key] = value elif key == 'aux_outputs': outputs[key] = [] for i in range(len(value)): outputs[key] += [{}] for _key, _value in value[i].items(): if _key == 'pred_captions': # outputs[key][i][_key] = _value[:,-1:] outputs[key][i][_key] = _value elif _key == 'pred_captionings': outputs[key][i][_key] = _value self.criterion.losses = self.losses['vlp'] # seg criterion losses losses = self.criterion.forward_vlp(outputs, targets_vlp, extra) del outputs if self.task_switch['retrieval'] and self.retrieval_emsemble: # compute backbone vlp. v_emb = features['res5'] bs,nc,_,_ = v_emb.shape v_emb = v_emb.reshape(bs,nc,-1) v_emb = F.adaptive_avg_pool1d(v_emb, 1).reshape(bs,nc) @ self.backbone_proj t_emb = torch.cat([x['caption_proj'] for x in targets_vlp], dim=0) loss_contrast = image_text_contrastive_loss_queue(v_emb, t_emb, self.sem_seg_head.predictor.lang_encoder, None) losses['loss_retrieval_backbone_0'] = loss_contrast return losses 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="bicubic", align_corners=False, antialias=True ) 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 del features return processed_results def evaluate_captioning(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] 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]) outputs = self.sem_seg_head(features, target_queries=queries_grounding, task='captioning_infer', extra={'start_token': self.start_token, 'captioning_mask': captioning_mask}) 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="bicubic", align_corners=False, antialias=True ) 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="bicubic", align_corners=False, antialias=True )[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 prepare_targets(self, batched_inputs, images): h_pad, w_pad = images.tensor.shape[-2:] new_targets = [] for idx, batch_per_image in enumerate(batched_inputs): targets_per_image = batch_per_image["instances"].to(self.device) # pad gt gt_masks = targets_per_image.gt_masks padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device) padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks gt_boxes = targets_per_image.gt_boxes.tensor ratio = torch.tensor([w_pad,h_pad,w_pad,h_pad]).to(gt_boxes.device)[None,:] gt_boxes = gt_boxes / ratio xc,yc,w,h = (gt_boxes[:,0] + gt_boxes[:,2])/2, (gt_boxes[:,1] + gt_boxes[:,3])/2, gt_boxes[:,2] - gt_boxes[:,0], gt_boxes[:,3] - gt_boxes[:,1] gt_boxes = torch.stack([xc,yc,w,h]).permute(1,0) target_dict = { "labels": targets_per_image.gt_classes, "is_things": targets_per_image.is_things, "masks": padded_masks, "boxes": gt_boxes } if self.task_switch['caption']: caption = batch_per_image["captions"] caption_noun = batch_per_image["captions_noun"] rand_index = random.randint(0, len(caption)-1) text = caption[rand_index] nouns = caption_noun[rand_index] noun_captions = [prompt_engineering(noun, topk=10000, suffix='.') for noun in nouns] + [text] self.sem_seg_head.predictor.lang_encoder.get_text_embeddings(noun_captions, is_eval=False, name='caption_noun', prompt=False) ctext = getattr(self.sem_seg_head.predictor.lang_encoder, '{}_text_embeddings'.format('caption_noun')) target_dict["captions"] = ctext target_dict["captions_hash"] = [(hash(st.stem(txt)) % 10**16) for txt in (nouns + [text])] target_dict["labels_hash"] = [(hash(st.stem(COCO_PANOPTIC_CLASSES[label_id].replace('-other','').replace('-merged','').replace('-stuff',''))) % 10**16) for label_id in target_dict['labels']] if self.task_switch['grounding']: grd_masks = batch_per_image['groundings']['masks'] grd_texts = batch_per_image['groundings']['texts'] grd_hash = batch_per_image['groundings']['hash'] grd_task = batch_per_image['groundings']['mode'] if len(grd_masks) == 0: padded_masks = None else: padded_masks = torch.zeros((grd_masks.shape[0], h_pad, w_pad), dtype=grd_masks.dtype, device=grd_masks.device) padded_masks[:, : grd_masks.shape[1], : grd_masks.shape[2]] = grd_masks 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'] unique_hash_id = np.unique(grd_hash, return_index=True)[1] selected_mask = np.zeros(len(grd_hash)).astype(np.bool) selected_mask[unique_hash_id] = True selected_token_emb = token_emb[selected_mask] selected_attn_mask = tokens['attention_mask'][selected_mask] query_emb = selected_token_emb[selected_attn_mask.bool()] class_idx = tokens['attention_mask'].sum(dim=-1) - 1 class_idx = torch.stack((torch.arange(len(class_idx), device=class_idx.device), class_idx)).tolist() class_emb = token_emb[class_idx] target_dict['grounding_masks'] = padded_masks target_dict['grounding_query_embs'] = query_emb target_dict['grounding_class_embs'] = class_emb target_dict['grounding_hash'] = grd_hash target_dict['grounding_task'] = grd_task new_targets.append(target_dict) return new_targets 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_xdecoder_model(cfg, **kwargs): return GeneralizedXdecoder(cfg)