import torch import torch.distributed import torch.nn.functional as F from torch.nn.init import trunc_normal_ from sam2.modeling.sam.mask_decoder import MaskDecoder from sam2.modeling.sam.prompt_encoder import PromptEncoder from sam2.modeling.sam.transformer import TwoWayTransformer from sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames NO_OBJ_SCORE = -1024.0 class SAM2Base(torch.nn.Module): def __init__( self, image_encoder, memory_attention, memory_encoder, num_maskmem=7, image_size=512, backbone_stride=16, sigmoid_scale_for_mem_enc=1.0, sigmoid_bias_for_mem_enc=0.0, binarize_mask_from_pts_for_mem_enc=False, use_mask_input_as_output_without_sam=False, max_cond_frames_in_attn=-1, directly_add_no_mem_embed=False, use_high_res_features_in_sam=False, multimask_output_in_sam=False, multimask_min_pt_num=1, multimask_max_pt_num=1, multimask_output_for_tracking=False, use_multimask_token_for_obj_ptr: bool = False, iou_prediction_use_sigmoid=False, memory_temporal_stride_for_eval=1, non_overlap_masks_for_mem_enc=False, use_obj_ptrs_in_encoder=False, max_obj_ptrs_in_encoder=16, add_tpos_enc_to_obj_ptrs=True, proj_tpos_enc_in_obj_ptrs=False, use_signed_tpos_enc_to_obj_ptrs=False, only_obj_ptrs_in_the_past_for_eval=False, pred_obj_scores: bool = False, pred_obj_scores_mlp: bool = False, fixed_no_obj_ptr: bool = False, soft_no_obj_ptr: bool = False, use_mlp_for_obj_ptr_proj: bool = False, no_obj_embed_spatial: bool = False, sam_mask_decoder_extra_args=None, compile_image_encoder: bool = False, ): super().__init__() self.image_encoder = image_encoder self.use_high_res_features_in_sam = use_high_res_features_in_sam self.num_feature_levels = 3 if use_high_res_features_in_sam else 1 self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder if use_obj_ptrs_in_encoder: self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs if proj_tpos_enc_in_obj_ptrs: assert add_tpos_enc_to_obj_ptrs self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs self.use_signed_tpos_enc_to_obj_ptrs = use_signed_tpos_enc_to_obj_ptrs self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval self.memory_attention = memory_attention self.hidden_dim = image_encoder.neck.d_model self.memory_encoder = memory_encoder self.mem_dim = self.hidden_dim if hasattr(self.memory_encoder, "out_proj") and hasattr( self.memory_encoder.out_proj, "weight" ): self.mem_dim = self.memory_encoder.out_proj.weight.shape[0] self.num_maskmem = num_maskmem self.maskmem_tpos_enc = torch.nn.Parameter( torch.zeros(num_maskmem, 1, 1, self.mem_dim) ) trunc_normal_(self.maskmem_tpos_enc, std=0.02) self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) trunc_normal_(self.no_mem_embed, std=0.02) trunc_normal_(self.no_mem_pos_enc, std=0.02) self.directly_add_no_mem_embed = directly_add_no_mem_embed self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam self.multimask_output_in_sam = multimask_output_in_sam self.multimask_min_pt_num = multimask_min_pt_num self.multimask_max_pt_num = multimask_max_pt_num self.multimask_output_for_tracking = multimask_output_for_tracking self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid self.image_size = image_size self.backbone_stride = backbone_stride self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args self.pred_obj_scores = pred_obj_scores self.pred_obj_scores_mlp = pred_obj_scores_mlp self.fixed_no_obj_ptr = fixed_no_obj_ptr self.soft_no_obj_ptr = soft_no_obj_ptr if self.fixed_no_obj_ptr: assert self.pred_obj_scores assert self.use_obj_ptrs_in_encoder if self.pred_obj_scores and self.use_obj_ptrs_in_encoder: self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) trunc_normal_(self.no_obj_ptr, std=0.02) self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj self.no_obj_embed_spatial = None if no_obj_embed_spatial: self.no_obj_embed_spatial = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) trunc_normal_(self.no_obj_embed_spatial, std=0.02) self._build_sam_heads() self.max_cond_frames_in_attn = max_cond_frames_in_attn if compile_image_encoder: print( "Image encoder compilation is enabled. First forward pass will be slow." ) self.image_encoder.forward = torch.compile( self.image_encoder.forward, mode="max-autotune", fullgraph=True, dynamic=False, ) @property def device(self): return next(self.parameters()).device def forward(self, *args, **kwargs): raise NotImplementedError( "Please use the corresponding methods in SAM2VideoPredictor for inference or SAM2Train for training/fine-tuning" "See notebooks/video_predictor_example.ipynb for an inference example." ) def _build_sam_heads(self): self.sam_prompt_embed_dim = self.hidden_dim self.sam_image_embedding_size = self.image_size // self.backbone_stride self.sam_prompt_encoder = PromptEncoder( embed_dim=self.sam_prompt_embed_dim, image_embedding_size=( self.sam_image_embedding_size, self.sam_image_embedding_size, ), input_image_size=(self.image_size, self.image_size), mask_in_chans=16, ) self.sam_mask_decoder = MaskDecoder( num_multimask_outputs=3, transformer=TwoWayTransformer( depth=2, embedding_dim=self.sam_prompt_embed_dim, mlp_dim=2048, num_heads=8, ), transformer_dim=self.sam_prompt_embed_dim, iou_head_depth=3, iou_head_hidden_dim=256, use_high_res_features=self.use_high_res_features_in_sam, iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid, pred_obj_scores=self.pred_obj_scores, pred_obj_scores_mlp=self.pred_obj_scores_mlp, use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr, **(self.sam_mask_decoder_extra_args or {}), ) if self.use_obj_ptrs_in_encoder: self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim) if self.use_mlp_for_obj_ptr_proj: self.obj_ptr_proj = MLP( self.hidden_dim, self.hidden_dim, self.hidden_dim, 3 ) else: self.obj_ptr_proj = torch.nn.Identity() if self.proj_tpos_enc_in_obj_ptrs: self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim) else: self.obj_ptr_tpos_proj = torch.nn.Identity() def _forward_sam_heads( self, backbone_features, point_inputs=None, mask_inputs=None, high_res_features=None, multimask_output=False, ): B = backbone_features.size(0) device = backbone_features.device assert backbone_features.size(1) == self.sam_prompt_embed_dim assert backbone_features.size(2) == self.sam_image_embedding_size assert backbone_features.size(3) == self.sam_image_embedding_size if point_inputs is not None: sam_point_coords = point_inputs["point_coords"] sam_point_labels = point_inputs["point_labels"] assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B else: sam_point_coords = torch.zeros(B, 1, 2, device=device) sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) if mask_inputs is not None: assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1) if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size: sam_mask_prompt = F.interpolate( mask_inputs.float(), size=self.sam_prompt_encoder.mask_input_size, align_corners=False, mode="bilinear", antialias=True, ) else: sam_mask_prompt = mask_inputs else: sam_mask_prompt = None sparse_embeddings, dense_embeddings = self.sam_prompt_encoder( points=(sam_point_coords, sam_point_labels), boxes=None, masks=sam_mask_prompt, ) ( low_res_multimasks, ious, sam_output_tokens, object_score_logits, ) = self.sam_mask_decoder( image_embeddings=backbone_features, image_pe=self.sam_prompt_encoder.get_dense_pe(), sparse_prompt_embeddings=sparse_embeddings, dense_prompt_embeddings=dense_embeddings, multimask_output=multimask_output, repeat_image=False, high_res_features=high_res_features, ) if self.pred_obj_scores: is_obj_appearing = object_score_logits > 0 low_res_multimasks = torch.where( is_obj_appearing[:, None, None], low_res_multimasks, NO_OBJ_SCORE, ) low_res_multimasks = low_res_multimasks.float() high_res_multimasks = F.interpolate( low_res_multimasks, size=(self.image_size, self.image_size), mode="bilinear", align_corners=False, ) sam_output_token = sam_output_tokens[:, 0] if multimask_output: best_iou_inds = torch.argmax(ious, dim=-1) batch_inds = torch.arange(B, device=device) low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) if sam_output_tokens.size(1) > 1: sam_output_token = sam_output_tokens[batch_inds, best_iou_inds] else: low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks obj_ptr = self.obj_ptr_proj(sam_output_token) if self.pred_obj_scores: if self.soft_no_obj_ptr: lambda_is_obj_appearing = object_score_logits.sigmoid() else: lambda_is_obj_appearing = is_obj_appearing.float() if self.fixed_no_obj_ptr: obj_ptr = lambda_is_obj_appearing * obj_ptr obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr return ( low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits, ) def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): out_scale, out_bias = 20.0, -10.0 mask_inputs_float = mask_inputs.float() high_res_masks = mask_inputs_float * out_scale + out_bias low_res_masks = F.interpolate( high_res_masks, size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4), align_corners=False, mode="bilinear", antialias=True, ) ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float() if not self.use_obj_ptrs_in_encoder: obj_ptr = torch.zeros( mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device ) else: _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads( backbone_features=backbone_features, mask_inputs=self.mask_downsample(mask_inputs_float), high_res_features=high_res_features, ) is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) is_obj_appearing = is_obj_appearing[..., None] lambda_is_obj_appearing = is_obj_appearing.float() object_score_logits = out_scale * lambda_is_obj_appearing + out_bias if self.pred_obj_scores: if self.fixed_no_obj_ptr: obj_ptr = lambda_is_obj_appearing * obj_ptr obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr return ( low_res_masks, high_res_masks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits, ) def forward_image(self, img_batch: torch.Tensor): backbone_out = self.image_encoder(img_batch) if self.use_high_res_features_in_sam: backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0( backbone_out["backbone_fpn"][0] ) backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1( backbone_out["backbone_fpn"][1] ) return backbone_out def _prepare_backbone_features(self, backbone_out): backbone_out = backbone_out.copy() assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"]) assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :] vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :] feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds] vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps] vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds] return backbone_out, vision_feats, vision_pos_embeds, feat_sizes def _prepare_memory_conditioned_features( self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, output_dict, num_frames, track_in_reverse=False, ): B = current_vision_feats[-1].size(1) C = self.hidden_dim H, W = feat_sizes[-1] device = current_vision_feats[-1].device if self.num_maskmem == 0: pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) return pix_feat num_obj_ptr_tokens = 0 tpos_sign_mul = -1 if track_in_reverse else 1 if not is_init_cond_frame: to_cat_memory, to_cat_memory_pos_embed = [], [] assert len(output_dict["cond_frame_outputs"]) > 0 cond_outputs = output_dict["cond_frame_outputs"] selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames( frame_idx, cond_outputs, self.max_cond_frames_in_attn ) t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()] stride = 1 if self.training else self.memory_temporal_stride_for_eval for t_pos in range(1, self.num_maskmem): t_rel = self.num_maskmem - t_pos if t_rel == 1: if not track_in_reverse: prev_frame_idx = frame_idx - t_rel else: prev_frame_idx = frame_idx + t_rel else: if not track_in_reverse: prev_frame_idx = ((frame_idx - 2) // stride) * stride prev_frame_idx = prev_frame_idx - (t_rel - 2) * stride else: prev_frame_idx = -(-(frame_idx + 2) // stride) * stride prev_frame_idx = prev_frame_idx + (t_rel - 2) * stride out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None) if out is None: out = unselected_cond_outputs.get(prev_frame_idx, None) t_pos_and_prevs.append((t_pos, out)) for t_pos, prev in t_pos_and_prevs: if prev is None: continue feats = prev["maskmem_features"].to(device, non_blocking=True) to_cat_memory.append(feats.flatten(2).permute(2, 0, 1)) maskmem_enc = prev["maskmem_pos_enc"][-1].to(device) maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1) maskmem_enc = ( maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1] ) to_cat_memory_pos_embed.append(maskmem_enc) if self.use_obj_ptrs_in_encoder: max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) if not self.training and self.only_obj_ptrs_in_the_past_for_eval: ptr_cond_outputs = { t: out for t, out in selected_cond_outputs.items() if (t >= frame_idx if track_in_reverse else t <= frame_idx) } else: ptr_cond_outputs = selected_cond_outputs pos_and_ptrs = [ ( ( (frame_idx - t) * tpos_sign_mul if self.use_signed_tpos_enc_to_obj_ptrs else abs(frame_idx - t) ), out["obj_ptr"] ) for t, out in ptr_cond_outputs.items() ] for t_diff in range(1, max_obj_ptrs_in_encoder): t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff if t < 0 or (num_frames is not None and t >= num_frames): break out = output_dict["non_cond_frame_outputs"].get( t, unselected_cond_outputs.get(t, None) ) if out is not None: pos_and_ptrs.append((t_diff, out["obj_ptr"])) if len(pos_and_ptrs) > 0: pos_list, ptrs_list = zip(*pos_and_ptrs) obj_ptrs = torch.stack(ptrs_list, dim=0) if self.add_tpos_enc_to_obj_ptrs: t_diff_max = max_obj_ptrs_in_encoder - 1 tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim obj_pos = torch.tensor(pos_list).to(device=device, non_blocking=True) obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim) obj_pos = self.obj_ptr_tpos_proj(obj_pos) obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim) else: obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim) if self.mem_dim < C: obj_ptrs = obj_ptrs.reshape( -1, B, C // self.mem_dim, self.mem_dim ) obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1) obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0) to_cat_memory.append(obj_ptrs) to_cat_memory_pos_embed.append(obj_pos) num_obj_ptr_tokens = obj_ptrs.shape[0] else: num_obj_ptr_tokens = 0 else: if self.directly_add_no_mem_embed: pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) return pix_feat_with_mem to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)] to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)] memory = torch.cat(to_cat_memory, dim=0) memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0) pix_feat_with_mem = self.memory_attention( curr=current_vision_feats, curr_pos=current_vision_pos_embeds, memory=memory, memory_pos=memory_pos_embed, num_obj_ptr_tokens=num_obj_ptr_tokens, ) pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) return pix_feat_with_mem def _encode_new_memory( self, current_vision_feats, feat_sizes, pred_masks_high_res, object_score_logits, is_mask_from_pts, ): B = current_vision_feats[-1].size(1) C = self.hidden_dim H, W = feat_sizes[-1] pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) if self.non_overlap_masks_for_mem_enc and not self.training: pred_masks_high_res = self._apply_non_overlapping_constraints( pred_masks_high_res ) binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts if binarize and not self.training: mask_for_mem = (pred_masks_high_res > 0).float() else: mask_for_mem = torch.sigmoid(pred_masks_high_res) if self.sigmoid_scale_for_mem_enc != 1.0: mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc if self.sigmoid_bias_for_mem_enc != 0.0: mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc maskmem_out = self.memory_encoder( pix_feat, mask_for_mem, skip_mask_sigmoid=True ) maskmem_features = maskmem_out["vision_features"] maskmem_pos_enc = maskmem_out["vision_pos_enc"] if self.no_obj_embed_spatial is not None: is_obj_appearing = (object_score_logits > 0).float() maskmem_features += ( 1 - is_obj_appearing[..., None, None] ) * self.no_obj_embed_spatial[..., None, None].expand( *maskmem_features.shape ) return maskmem_features, maskmem_pos_enc def _track_step( self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, point_inputs, mask_inputs, output_dict, num_frames, track_in_reverse, prev_sam_mask_logits, ): current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs} if len(current_vision_feats) > 1: high_res_features = [ x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1]) ] else: high_res_features = None if mask_inputs is not None and self.use_mask_input_as_output_without_sam: pix_feat = current_vision_feats[-1].permute(1, 2, 0) pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1]) sam_outputs = self._use_mask_as_output( pix_feat, high_res_features, mask_inputs ) else: pix_feat = self._prepare_memory_conditioned_features( frame_idx=frame_idx, is_init_cond_frame=is_init_cond_frame, current_vision_feats=current_vision_feats[-1:], current_vision_pos_embeds=current_vision_pos_embeds[-1:], feat_sizes=feat_sizes[-1:], output_dict=output_dict, num_frames=num_frames, track_in_reverse=track_in_reverse, ) if prev_sam_mask_logits is not None: assert point_inputs is not None and mask_inputs is None mask_inputs = prev_sam_mask_logits multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) sam_outputs = self._forward_sam_heads( backbone_features=pix_feat, point_inputs=point_inputs, mask_inputs=mask_inputs, high_res_features=high_res_features, multimask_output=multimask_output, ) return current_out, sam_outputs, high_res_features, pix_feat def _encode_memory_in_output( self, current_vision_feats, feat_sizes, point_inputs, run_mem_encoder, high_res_masks, object_score_logits, current_out, ): if run_mem_encoder and self.num_maskmem > 0: high_res_masks_for_mem_enc = high_res_masks maskmem_features, maskmem_pos_enc = self._encode_new_memory( current_vision_feats=current_vision_feats, feat_sizes=feat_sizes, pred_masks_high_res=high_res_masks_for_mem_enc, object_score_logits=object_score_logits, is_mask_from_pts=(point_inputs is not None), ) current_out["maskmem_features"] = maskmem_features current_out["maskmem_pos_enc"] = maskmem_pos_enc else: current_out["maskmem_features"] = None current_out["maskmem_pos_enc"] = None def track_step( self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, point_inputs, mask_inputs, output_dict, num_frames, track_in_reverse=False, run_mem_encoder=True, prev_sam_mask_logits=None, ): current_out, sam_outputs, _, _ = self._track_step( frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, point_inputs, mask_inputs, output_dict, num_frames, track_in_reverse, prev_sam_mask_logits, ) ( _, _, _, low_res_masks, high_res_masks, obj_ptr, object_score_logits, ) = sam_outputs current_out["pred_masks"] = low_res_masks current_out["pred_masks_high_res"] = high_res_masks current_out["obj_ptr"] = obj_ptr if not self.training: current_out["object_score_logits"] = object_score_logits self._encode_memory_in_output( current_vision_feats, feat_sizes, point_inputs, run_mem_encoder, high_res_masks, object_score_logits, current_out, ) return current_out def _use_multimask(self, is_init_cond_frame, point_inputs): num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) multimask_output = ( self.multimask_output_in_sam and (is_init_cond_frame or self.multimask_output_for_tracking) and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num) ) return multimask_output def _apply_non_overlapping_constraints(self, pred_masks): batch_size = pred_masks.size(0) if batch_size == 1: return pred_masks device = pred_masks.device max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] keep = max_obj_inds == batch_obj_inds pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) return pred_masks