# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. 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 import pdb from fvcore.nn import FlopCountAnalysis # a large negative value as a placeholder score for missing objects NO_OBJ_SCORE = -1024.0 class SAM2Base(torch.nn.Module): def __init__( self, image_encoder, memory_attention, memory_encoder, num_maskmem=7, # default 1 input frame + 6 previous frames image_size=512, backbone_stride=16, # stride of the image backbone output sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks binarize_mask_from_pts_for_mem_enc=False, use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit, # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM. max_cond_frames_in_attn=-1, # on the first frame, whether to directly add the no-memory embedding to the image feature # (instead of using the transformer encoder) directly_add_no_mem_embed=False, # whether to use high-resolution feature maps in the SAM mask decoder use_high_res_features_in_sam=False, # whether to output multiple (3) masks for the first click on initial conditioning frames multimask_output_in_sam=False, # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`; # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points) multimask_min_pt_num=1, multimask_max_pt_num=1, # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`) multimask_output_for_tracking=False, # Whether to use multimask tokens for obj ptr; Only relevant when both # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True use_multimask_token_for_obj_ptr: bool = False, # whether to use sigmoid to restrict ious prediction to [0-1] iou_prediction_use_sigmoid=False, # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5). # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame. memory_temporal_stride_for_eval=1, # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks) non_overlap_masks_for_mem_enc=False, # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder use_obj_ptrs_in_encoder=False, # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`) max_obj_ptrs_in_encoder=16, # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`) add_tpos_enc_to_obj_ptrs=True, # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) proj_tpos_enc_in_obj_ptrs=False, # whether to use signed distance (instead of unsigned absolute distance) in the temporal positional encoding in the object pointers # (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) use_signed_tpos_enc_to_obj_ptrs=False, # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking) only_obj_ptrs_in_the_past_for_eval=False, # Whether to predict if there is an object in the frame pred_obj_scores: bool = False, # Whether to use an MLP to predict object scores pred_obj_scores_mlp: bool = False, # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True; # Whether to have a fixed no obj pointer when there is no object present # or to use it as an additive embedding with obj_ptr produced by decoder fixed_no_obj_ptr: bool = False, # Soft no object, i.e. mix in no_obj_ptr softly, # hope to make recovery easier if there is a mistake and mitigate accumulation of errors soft_no_obj_ptr: bool = False, use_mlp_for_obj_ptr_proj: bool = False, # add no obj embedding to spatial frames no_obj_embed_spatial: bool = False, # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class. sam_mask_decoder_extra_args=None, compile_image_encoder: bool = False, ): super().__init__() # Part 1: the image backbone self.image_encoder = image_encoder # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting 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: # A conv layer to downsample the mask prompt to stride 4 (the same stride as # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, # so that it can be fed into the SAM mask decoder to generate a pointer. 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 # these options need to be used together 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 # Part 2: memory attention to condition current frame's visual features # with memories (and obj ptrs) from past frames self.memory_attention = memory_attention self.hidden_dim = image_encoder.neck.d_model # Part 3: memory encoder for the previous frame's outputs 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" ): # if there is compression of memories along channel dim self.mem_dim = self.memory_encoder.out_proj.weight.shape[0] self.num_maskmem = num_maskmem # Number of memories accessible # Temporal encoding of the memories 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) # a single token to indicate no memory embedding from previous frames 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 # Apply sigmoid to the output raw mask logits (to turn them from # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder 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 # On frames with mask input, whether to directly output the input mask without # using a SAM prompt encoder + mask decoder 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 # Part 4: SAM-style prompt encoder (for both mask and point inputs) # and SAM-style mask decoder for the final mask output 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 # Model compilation if compile_image_encoder: # Compile the forward function (not the full module) to allow loading checkpoints. 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): """Build SAM-style prompt encoder and mask decoder.""" self.sam_prompt_embed_dim = self.hidden_dim self.sam_image_embedding_size = self.image_size // self.backbone_stride # build PromptEncoder and MaskDecoder from SAM # (their hyperparameters like `mask_in_chans=16` are from SAM code) 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: # a linear projection on SAM output tokens to turn them into object pointers 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: # a linear projection on temporal positional encoding in object pointers to # avoid potential interference with spatial positional encoding 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, ): """ Forward SAM prompt encoders and mask heads. Inputs: - backbone_features: image features of [B, C, H, W] shape - point_inputs: a dictionary with "point_coords" and "point_labels", where 1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the absolute pixel-unit coordinate in (x, y) format of the P input points 2) "point_labels" has shape [B, P] and int32 dtype, where 1 means positive clicks, 0 means negative clicks, and -1 means padding - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the same spatial size as the image. - high_res_features: either 1) None or 2) or a list of length 2 containing two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively, which will be used as high-resolution feature maps for SAM decoder. - multimask_output: if it's True, we output 3 candidate masks and their 3 corresponding IoU estimates, and if it's False, we output only 1 mask and its corresponding IoU estimate. Outputs: - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM output mask logits (before sigmoid) for the low-resolution masks, with 4x the resolution (1/4 stride) of the input backbone_features. - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3 if `multimask_output=True` and M = 1 if `multimask_output=False`), upsampled from the low-resolution masks, with shape size as the image (stride is 1 pixel). - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1 if `multimask_output=False`), the estimated IoU of each output mask. - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`. If `multimask_output=True`, it's the mask with the highest IoU estimate. If `multimask_output=False`, it's the same as `low_res_multimasks`. - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`. If `multimask_output=True`, it's the mask with the highest IoU estimate. If `multimask_output=False`, it's the same as `high_res_multimasks`. - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted based on the output token from the SAM mask decoder. """ 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 # a) Handle point prompts 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: # If no points are provide, pad with an empty point (with label -1) sam_point_coords = torch.zeros(B, 1, 2, device=device) sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) # b) Handle mask prompts if mask_inputs is not None: # If mask_inputs is provided, downsize it into low-res mask input if needed # and feed it as a dense mask prompt into the SAM mask encoder 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, # use antialias for downsampling ) else: sam_mask_prompt = mask_inputs else: # Otherwise, simply feed None (and SAM's prompt encoder will add # a learned `no_mask_embed` to indicate no mask input in this case). 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, # the image is already batched high_res_features=high_res_features, ) if self.pred_obj_scores: is_obj_appearing = object_score_logits > 0 # Mask used for spatial memories is always a *hard* choice between obj and no obj, # consistent with the actual mask prediction low_res_multimasks = torch.where( is_obj_appearing[:, None, None], low_res_multimasks, NO_OBJ_SCORE, ) # convert masks from possibly bfloat16 (or float16) to float32 # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) 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: # take the best mask prediction (with the highest IoU estimation) 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 # Extract object pointer from the SAM output token (with occlusion handling) obj_ptr = self.obj_ptr_proj(sam_output_token) if self.pred_obj_scores: # Allow *soft* no obj ptr, unlike for masks 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 #######SAM2Long######## obj_ptrs = self.obj_ptr_proj(sam_output_tokens) lambda_is_obj_appearing = is_obj_appearing.float()[:, None] obj_ptrs = lambda_is_obj_appearing * obj_ptrs obj_ptrs = obj_ptrs + (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, obj_ptrs, ) def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): """ Directly turn binary `mask_inputs` into a output mask logits without using SAM. (same input and output shapes as in _forward_sam_heads above). """ # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 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, # use antialias for downsampling ) # a dummy IoU prediction of all 1's under mask input ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float() if not self.use_obj_ptrs_in_encoder: # all zeros as a dummy object pointer (of shape [B, C]) obj_ptr = torch.zeros( mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device ) else: # produce an object pointer using the SAM decoder from the mask input _, _, _, _, _, obj_ptr, _, _ = self._forward_sam_heads( backbone_features=backbone_features, mask_inputs=self.mask_downsample(mask_inputs_float), high_res_features=high_res_features, ) # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying # on the object_scores from the SAM decoder. 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, None, ) def forward_image(self, img_batch: torch.Tensor): """Get the image feature on the input batch.""" backbone_out = self.image_encoder(img_batch) if self.use_high_res_features_in_sam: # precompute projected level 0 and level 1 features in SAM decoder # to avoid running it again on every SAM click 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): """Prepare and flatten visual features.""" 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] # flatten NxCxHxW to HWxNxC 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, # tracking in reverse time order (for demo usage) mem_pick_index=0, start_frame_idx=0, iou_thre=0.1, ): """Fuse the current frame's visual feature map with previous memory.""" B = current_vision_feats[-1].size(1) # batch size on this frame C = self.hidden_dim H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size device = current_vision_feats[-1].device # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images. # In this case, we skip the fusion with any memory. if self.num_maskmem == 0: # Disable memory and skip fusion 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 # Step 1: condition the visual features of the current frame on previous memories if not is_init_cond_frame: # Retrieve the memories encoded with the maskmem backbone to_cat_memory, to_cat_memory_pos_embed = [], [] # Add conditioning frames's output first (all cond frames have t_pos=0 for # when getting temporal positional embedding below) assert len(output_dict["cond_frame_outputs"]) > 0 # Select a maximum number of temporally closest cond frames for cross attention 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()] # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1 # We also allow taking the memory frame non-consecutively (with stride>1), in which case # we take (self.num_maskmem - 2) frames among every stride-th frames plus the last frame. stride = 1 if self.training else self.memory_temporal_stride_for_eval max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) num_object = int(cond_outputs[start_frame_idx]['obj_ptr'].shape[0]) ##always one if frame_idx <= start_frame_idx+1 or mem_pick_index==0: valid_indices = [] else: valid_indices = [] for i in range(frame_idx - 1, start_frame_idx, -1): object_score = output_dict["non_cond_frame_outputs"][i]['object_score_logits'][...,mem_pick_index[i]] iou = output_dict["non_cond_frame_outputs"][i]['ious'][...,mem_pick_index[i]] # print("threshold", iou_thre) if iou.item() > iou_thre and object_score.item() > 0: valid_indices.insert(0, i) if len(valid_indices) >= max_obj_ptrs_in_encoder - 1: break if frame_idx - 1 not in valid_indices: ##pick last frame valid_indices.append(frame_idx-1) prev_idxs = [start_frame_idx] for t_pos in range(1, self.num_maskmem): idx = t_pos - self.num_maskmem if idx < -len(valid_indices): continue out = output_dict["non_cond_frame_outputs"].get(valid_indices[idx], None) if out is None: out = unselected_cond_outputs.get(valid_indices[idx], None) t_pos_and_prevs.append((t_pos, out)) prev_idxs.append(valid_indices[idx]) object_frame_score = [torch.ones(num_object).to(cond_outputs[start_frame_idx]['obj_ptr'].device, torch.bfloat16)*10] for (t_pos, prev), prev_idx in zip(t_pos_and_prevs, prev_idxs): if prev is None: continue # skip padding frames # "maskmem_features" might have been offloaded to CPU in demo use cases, # so we load it back to GPU (it's a no-op if it's already on GPU). if t_pos > 0 and mem_pick_index != 0: object_frame_score.append(prev["object_score_logits"][...,mem_pick_index[prev_idx]].view(-1)) feats = prev["maskmem_features"][...,mem_pick_index[prev_idx]].to(device, non_blocking=True) else: feats = prev["maskmem_features"].to(device, non_blocking=True) to_cat_memory.append(feats.flatten(2).permute(2, 0, 1)) # Spatial positional encoding (it might have been offloaded to CPU in eval) maskmem_enc = prev["maskmem_pos_enc"][-1].to(device) maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1) # Temporal positional encoding maskmem_enc = ( maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1] ) to_cat_memory_pos_embed.append(maskmem_enc) # Construct the list of past object pointers if self.use_obj_ptrs_in_encoder: max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) # First add those object pointers from selected conditioning frames # (optionally, only include object pointers in the past during evaluation) 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 = [ # Temporal pos encoding contains how far away each pointer is from current frame (abs(frame_idx - t), out["obj_ptr"]) for t, out in ptr_cond_outputs.items() ] # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame object_ptr_score = [torch.ones(num_object).to(cond_outputs[start_frame_idx]['obj_ptr'].device, torch.bfloat16)*10] for t_diff in range(1, max_obj_ptrs_in_encoder): if -t_diff <= -len(valid_indices): break out = output_dict["non_cond_frame_outputs"].get( valid_indices[-t_diff], unselected_cond_outputs.get(valid_indices[-t_diff], None)) if out is not None: mem_idx = mem_pick_index[valid_indices[-t_diff]] object_ptr_score.append(out['object_score_logits'][...,mem_idx].view(-1)) pos_and_ptrs.append((t_diff, out["obj_ptr"][...,mem_idx])) # object_ptr_score.append(output_dict["non_cond_frame_outputs"][valid_indices[-t_diff]]['object_score'].item()) # If we have at least one object pointer, add them to the across attention if len(pos_and_ptrs) > 0: pos_list, ptrs_list = zip(*pos_and_ptrs) # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape obj_ptrs = torch.stack(ptrs_list, dim=0) # a temporal positional embedding based on how far each object pointer is from # the current frame (sine embedding normalized by the max pointer num). 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, device=device) 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: # split a pointer into (C // self.mem_dim) tokens for 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: # for initial conditioning frames, encode them without using any previous memory if self.directly_add_no_mem_embed: # directly add no-mem embedding (instead of using the transformer encoder) 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 # Use a dummy token on the first frame (to avoid empty memory input to tranformer encoder) 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)] # Step 2: Concatenate the memories and forward through the transformer encoder 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, object_frame_scores=object_frame_score, object_ptr_scores=object_ptr_score, ) # reshape the output (HW)BC => BCHW 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, ): """Encode the current image and its prediction into a memory feature.""" B = current_vision_feats[-1].size(1) # batch size on this frame C = self.hidden_dim H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size # top-level feature, (HW)BC => BCHW 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: # optionally, apply non-overlapping constraints to the masks (it's applied # in the batch dimension and should only be used during eval, where all # the objects come from the same video under batch size 1). pred_masks_high_res = self._apply_non_overlapping_constraints( pred_masks_high_res ) # scale the raw mask logits with a temperature before applying sigmoid 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: # apply sigmoid on the raw mask logits to turn them into range (0, 1) mask_for_mem = torch.sigmoid(pred_masks_high_res) # apply scale and bias terms to the sigmoid probabilities 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 # sigmoid already applied ) maskmem_features = maskmem_out["vision_features"] maskmem_pos_enc = maskmem_out["vision_pos_enc"] # add a no-object embedding to the spatial memory to indicate that the frame # is predicted to be occluded (i.e. no object is appearing in the frame) 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, mem_pick_index=0, start_frame_idx=0, iou_thre=0.1, ): current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs} # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW 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: # When use_mask_input_as_output_without_sam=True, we directly output the mask input # (see it as a GT mask) without using a SAM prompt encoder + mask decoder. 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: # fused the visual feature with previous memory features in the memory bank 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, mem_pick_index=mem_pick_index, start_frame_idx=start_frame_idx, iou_thre=iou_thre, ) # apply SAM-style segmentation head # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, # e.g. in demo where such logits come from earlier interaction instead of correction sampling # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) 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, # tracking in reverse time order (for demo usage) # Whether to run the memory encoder on the predicted masks. Sometimes we might want # to skip the memory encoder with `run_mem_encoder=False`. For example, # in demo we might call `track_step` multiple times for each user click, # and only encode the memory when the user finalizes their clicks. And in ablation # settings like SAM training on static images, we don't need the memory encoder. run_mem_encoder=True, # The previously predicted SAM mask logits (which can be fed together with new clicks in demo). prev_sam_mask_logits=None, mem_pick_index=0, start_frame_idx=0, iou_thre=0.1, ): 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, mem_pick_index, start_frame_idx, iou_thre, ) ( low_res_multimasks, high_res_multimasks, ious, low_res_masks, high_res_masks, obj_ptr, object_score_logits, obj_ptrs, ) = sam_outputs if mem_pick_index == 0: current_out["pred_masks"] = low_res_masks current_out["ious"] = ious.max(-1)[0] current_out["object_score"] = object_score_logits[:,0] current_out["obj_ptr"] = obj_ptr current_out["pred_masks_high_res"] = high_res_masks else: current_out["pred_masks"] = low_res_multimasks current_out["ious"] = ious current_out["object_score"] = object_score_logits[:,0] current_out["obj_ptr"] = obj_ptrs current_out["pred_masks_high_res"] = high_res_multimasks if not self.training: # Only add this in inference (to avoid unused param in activation checkpointing; # it's mainly used in the demo to encode spatial memories w/ consolidated masks) current_out["object_score_logits"] = object_score_logits return current_out def _use_multimask(self, is_init_cond_frame, point_inputs): """Whether to use multimask output in the SAM head.""" 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): """ Apply non-overlapping constraints to the object scores in pred_masks. Here we keep only the highest scoring object at each spatial location in pred_masks. """ batch_size = pred_masks.size(0) if batch_size == 1: return pred_masks device = pred_masks.device # "max_obj_inds": object index of the object with the highest score at each location max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] keep = max_obj_inds == batch_obj_inds # suppress overlapping regions' scores below -10.0 so that the foreground regions # don't overlap (here sigmoid(-10.0)=4.5398e-05) pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) return pred_masks