# ------------------------------------------------------------------------------ # Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py # Modified by Jitesh Jain (https://github.com/praeclarumjj3) # ------------------------------------------------------------------------------ from typing import Tuple import torch from torch import nn from torch.nn import functional as F from detectron2.config import configurable from detectron2.data import MetadataCatalog from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head from detectron2.modeling.backbone import Backbone from detectron2.modeling.postprocessing import sem_seg_postprocess from detectron2.structures import Boxes, ImageList, Instances, BitMasks from detectron2.utils.memory import retry_if_cuda_oom from .modeling.criterion import SetCriterion from .modeling.matcher import HungarianMatcher from einops import rearrange from .modeling.transformer_decoder.text_transformer import TextTransformer from .modeling.transformer_decoder.oneformer_transformer_decoder import MLP from oneformer.data.tokenizer import SimpleTokenizer, Tokenize @META_ARCH_REGISTRY.register() class OneFormer(nn.Module): """ Main class for mask classification semantic segmentation architectures. """ @configurable def __init__( self, *, backbone: Backbone, sem_seg_head: nn.Module, task_mlp: nn.Module, text_encoder: nn.Module, text_projector: nn.Module, criterion: nn.Module, prompt_ctx: nn.Embedding, num_queries: int, object_mask_threshold: float, overlap_threshold: float, metadata, 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, detection_on: bool, test_topk_per_image: int, task_seq_len: int, max_seq_len: int, is_demo: bool, ): """ 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.task_mlp = task_mlp self.text_encoder = text_encoder self.text_projector = text_projector self.prompt_ctx = prompt_ctx self.criterion = criterion 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 self.detection_on = detection_on self.test_topk_per_image = test_topk_per_image self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len) self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len) self.is_demo = is_demo self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()] if not self.semantic_on: assert self.sem_seg_postprocess_before_inference @classmethod def from_config(cls, cfg): backbone = build_backbone(cfg) sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape()) if cfg.MODEL.IS_TRAIN: text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH, width=cfg.MODEL.TEXT_ENCODER.WIDTH, layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS, vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE) text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS) if cfg.MODEL.TEXT_ENCODER.N_CTX > 0: prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH) else: prompt_ctx = None else: text_encoder = None text_projector = None prompt_ctx = None task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2) # Loss parameters: deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT # loss weights class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT # building criterion matcher = HungarianMatcher( cost_class=class_weight, cost_mask=mask_weight, cost_dice=dice_weight, num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS, ) weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight, "loss_dice": dice_weight, "loss_contrastive": contrastive_weight} if deep_supervision: dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS aux_weight_dict = {} for i in range(dec_layers - 1): aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) weight_dict.update(aux_weight_dict) losses = ["labels", "masks", "contrastive"] criterion = SetCriterion( sem_seg_head.num_classes, matcher=matcher, weight_dict=weight_dict, eos_coef=no_object_weight, contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE, losses=losses, num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS, oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO, importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO, ) return { "backbone": backbone, "sem_seg_head": sem_seg_head, "task_mlp": task_mlp, "prompt_ctx": prompt_ctx, "text_encoder": text_encoder, "text_projector": text_projector, "criterion": criterion, "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES, "object_mask_threshold": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD, "overlap_threshold": cfg.MODEL.TEST.OVERLAP_THRESHOLD, "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), "size_divisibility": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY, "sem_seg_postprocess_before_inference": ( cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE or cfg.MODEL.TEST.PANOPTIC_ON or cfg.MODEL.TEST.INSTANCE_ON ), "pixel_mean": cfg.MODEL.PIXEL_MEAN, "pixel_std": cfg.MODEL.PIXEL_STD, # inference "semantic_on": cfg.MODEL.TEST.SEMANTIC_ON, "instance_on": cfg.MODEL.TEST.INSTANCE_ON, "panoptic_on": cfg.MODEL.TEST.PANOPTIC_ON, "detection_on": cfg.MODEL.TEST.DETECTION_ON, "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE, "task_seq_len": cfg.INPUT.TASK_SEQ_LEN, "max_seq_len": cfg.INPUT.MAX_SEQ_LEN, "is_demo": cfg.MODEL.IS_DEMO, } @property def device(self): return self.pixel_mean.device def encode_text(self, text): assert text.ndim in [2, 3], text.ndim b = text.shape[0] squeeze_dim = False num_text = 1 if text.ndim == 3: num_text = text.shape[1] text = rearrange(text, 'b n l -> (b n) l', n=num_text) squeeze_dim = True # [B, C] x = self.text_encoder(text) text_x = self.text_projector(x) if squeeze_dim: text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text) if self.prompt_ctx is not None: text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1) text_x = torch.cat([text_x, text_ctx], dim=1) return {"texts": text_x} def forward(self, batched_inputs): """ 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". """ 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) tasks = torch.cat([self.task_tokenizer(x["task"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0) tasks = self.task_mlp(tasks.float()) features = self.backbone(images.tensor) outputs = self.sem_seg_head(features, tasks) if self.training: texts = torch.cat([self.text_tokenizer(x["text"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0) texts_x = self.encode_text(texts) outputs = {**outputs, **texts_x} # mask classification target if "instances" in batched_inputs[0]: gt_instances = [x["instances"].to(self.device) for x in batched_inputs] targets = self.prepare_targets(gt_instances, images) else: targets = None # bipartite matching-based loss losses = self.criterion(outputs, targets) 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: mask_cls_results = outputs["pred_logits"] mask_pred_results = outputs["pred_masks"] # upsample masks mask_pred_results = F.interpolate( mask_pred_results, size=(images.tensor.shape[-2], images.tensor.shape[-1]), mode="bilinear", align_corners=False, ) del outputs processed_results = [] for i, data in enumerate(zip( mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes )): mask_cls_result, mask_pred_result, input_per_image, image_size = data 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) 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: instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) processed_results[-1]["instances"] = instance_r if self.detection_on: bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result) processed_results[-1]["box_instances"] = bbox_r return processed_results def prepare_targets(self, targets, images): h_pad, w_pad = images.tensor.shape[-2:] new_targets = [] for targets_per_image in targets: # 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 new_targets.append( { "labels": targets_per_image.gt_classes, "masks": padded_masks, } ) return new_targets def semantic_inference(self, mask_cls, mask_pred): 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 = {} for k in range(cur_classes.shape[0]): pred_class = cur_classes[k].item() isthing = pred_class in self.metadata.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): # 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] # Only consider scores with confidence over [self.object_mask_threshold] for demo if self.is_demo: keep = scores_per_image > self.object_mask_threshold scores_per_image = scores_per_image[keep] labels_per_image = labels_per_image[keep] mask_pred = mask_pred[keep] # if this is panoptic segmentation, we only keep the "thing" classes if self.panoptic_on: keep = torch.zeros_like(scores_per_image).bool() for i, lab in enumerate(labels_per_image): keep[i] = lab in self.metadata.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 'ade20k' in self.metadata.name: for i in range(labels_per_image.shape[0]): labels_per_image[i] = self.thing_indices.index(labels_per_image[i].item()) result = Instances(image_size) # mask (before sigmoid) result.pred_masks = (mask_pred > 0).float() if self.detection_on: # Uncomment the following to get boxes from masks (this is slow) 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