Reference for ultralytics/models/sam/modules/sam.py

class SAMModel(nn.Module):
    """Segment Anything Model (SAM) for object segmentation tasks.

    This class combines image encoders, prompt encoders, and mask decoders to predict object masks from images and input
    prompts.

    Attributes:
        mask_threshold (float): Threshold value for mask prediction.
        image_encoder (ImageEncoderViT): Backbone for encoding images into embeddings.
        prompt_encoder (PromptEncoder): Encoder for various types of input prompts.
        mask_decoder (MaskDecoder): Predicts object masks from image and prompt embeddings.
        pixel_mean (torch.Tensor): Mean values for normalizing pixels in the input image.
        pixel_std (torch.Tensor): Standard deviation values for normalizing pixels in the input image.

    Methods:
        set_imgsz: Set image size to make model compatible with different image sizes.

    Examples:
        >>> image_encoder = ImageEncoderViT(...)
        >>> prompt_encoder = PromptEncoder(...)
        >>> mask_decoder = MaskDecoder(...)
        >>> sam_model = SAMModel(image_encoder, prompt_encoder, mask_decoder)
        >>> # Further usage depends on SAMPredictor class

    Notes:
        All forward() operations are implemented in the SAMPredictor class.
    """

    mask_threshold: float = 0.0

    def __init__(
        self,
        image_encoder: ImageEncoderViT,
        prompt_encoder: PromptEncoder,
        mask_decoder: MaskDecoder,
        pixel_mean: list[float] = (123.675, 116.28, 103.53),
        pixel_std: list[float] = (58.395, 57.12, 57.375),
    ) -> None:
        """Initialize the SAMModel class to predict object masks from an image and input prompts.

        Args:
            image_encoder (ImageEncoderViT): The backbone used to encode the image into image embeddings.
            prompt_encoder (PromptEncoder): Encodes various types of input prompts.
            mask_decoder (MaskDecoder): Predicts masks from the image embeddings and encoded prompts.
            pixel_mean (list[float]): Mean values for normalizing pixels in the input image.
            pixel_std (list[float]): Standard deviation values for normalizing pixels in the input image.

        Notes:
            All forward() operations moved to SAMPredictor.
        """
        super().__init__()
        self.image_encoder = image_encoder
        self.prompt_encoder = prompt_encoder
        self.mask_decoder = mask_decoder
        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)

def set_imgsz(self, imgsz):
    """Set image size to make model compatible with different image sizes."""
    if hasattr(self.image_encoder, "set_imgsz"):
        self.image_encoder.set_imgsz(imgsz)
    self.prompt_encoder.input_image_size = imgsz
    self.prompt_encoder.image_embedding_size = [x // 16 for x in imgsz]  # 16 is fixed as patch size of ViT model
    self.image_encoder.img_size = imgsz[0]

class SAM2Model(torch.nn.Module):
    """SAM2Model class for Segment Anything Model 2 with memory-based video object segmentation capabilities.

    This class extends the functionality of SAM to handle video sequences, incorporating memory mechanisms for temporal
    consistency and efficient tracking of objects across frames.

    Attributes:
        mask_threshold (float): Threshold value for mask prediction.
        image_encoder (ImageEncoderViT): Visual encoder for extracting image features.
        memory_attention (nn.Module): Module for attending to memory features.
        memory_encoder (nn.Module): Encoder for generating memory representations.
        num_maskmem (int): Number of accessible memory frames.
        image_size (int): Size of input images.
        backbone_stride (int): Stride of the backbone network output.
        sam_prompt_embed_dim (int): Dimension of SAM prompt embeddings.
        sam_image_embedding_size (int): Size of SAM image embeddings.
        sam_prompt_encoder (PromptEncoder): Encoder for processing input prompts.
        sam_mask_decoder (SAM2MaskDecoder): Decoder for generating object masks.
        obj_ptr_proj (nn.Module): Projection layer for object pointers.
        obj_ptr_tpos_proj (nn.Module): Projection for temporal positional encoding in object pointers.
        hidden_dim (int): Hidden dimension of the model.
        mem_dim (int): Memory dimension for encoding features.
        use_high_res_features_in_sam (bool): Whether to use high-resolution feature maps in the SAM mask decoder.
        use_obj_ptrs_in_encoder (bool): Whether to cross-attend to object pointers from other frames in the encoder.
        max_obj_ptrs_in_encoder (int): Maximum number of object pointers from other frames in encoder cross-attention.
        add_tpos_enc_to_obj_ptrs (bool): Whether to add temporal positional encoding to object pointers.
        proj_tpos_enc_in_obj_ptrs (bool): Whether to add an extra linear projection layer for temporal positional
            encoding in object pointers.
        use_signed_tpos_enc_to_obj_ptrs (bool): Whether to use signed distance in temporal positional encoding.
        only_obj_ptrs_in_the_past_for_eval (bool): Whether to only attend to object pointers in the past during
            evaluation.
        pred_obj_scores (bool): Whether to predict if there is an object in the frame.
        pred_obj_scores_mlp (bool): Whether to use an MLP to predict object scores.
        fixed_no_obj_ptr (bool): Whether to have a fixed no-object pointer when there is no object present.
        soft_no_obj_ptr (bool): Whether to mix in no-object pointer softly for easier recovery and error mitigation.
        use_mlp_for_obj_ptr_proj (bool): Whether to use MLP for object pointer projection.
        no_obj_embed_spatial (torch.Tensor | None): No-object embedding for spatial frames.
        max_cond_frames_in_attn (int): Maximum number of conditioning frames to participate in memory attention.
        directly_add_no_mem_embed (bool): Whether to directly add no-memory embedding to image feature on the first
            frame.
        multimask_output_in_sam (bool): Whether to output multiple masks for the first click on initial conditioning
            frames.
        multimask_min_pt_num (int): Minimum number of clicks to use multimask output in SAM.
        multimask_max_pt_num (int): Maximum number of clicks to use multimask output in SAM.
        multimask_output_for_tracking (bool): Whether to use multimask output for tracking.
        use_multimask_token_for_obj_ptr (bool): Whether to use multimask tokens for object pointers.
        iou_prediction_use_sigmoid (bool): Whether to use sigmoid to restrict IoU prediction to [0-1].
        memory_temporal_stride_for_eval (int): Memory bank's temporal stride during evaluation.
        non_overlap_masks_for_mem_enc (bool): Whether to apply non-overlapping constraints on object masks in memory
            encoder during evaluation.
        sigmoid_scale_for_mem_enc (float): Scale factor for mask sigmoid probability.
        sigmoid_bias_for_mem_enc (float): Bias factor for mask sigmoid probability.
        binarize_mask_from_pts_for_mem_enc (bool): Whether to binarize sigmoid mask logits on interacted frames with
            clicks during evaluation.
        use_mask_input_as_output_without_sam (bool): Whether to directly output the input mask without using SAM prompt
            encoder and mask decoder on frames with mask input.

    Methods:
        forward_image: Process image batch through encoder to extract multi-level features.
        track_step: Perform a single tracking step, updating object masks and memory features.
        set_binarize: Set binarize for VideoPredictor.
        set_imgsz: Set image size to make model compatible with different image sizes.

    Examples:
        >>> model = SAM2Model(image_encoder, memory_attention, memory_encoder)
        >>> image_batch = torch.rand(1, 3, 512, 512)
        >>> features = model.forward_image(image_batch)
        >>> track_results = model.track_step(0, True, features, None, None, None, {})
    """

    mask_threshold: float = 0.0

    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,
    ):
        """Initialize the SAM2Model for video object segmentation with memory-based tracking.

        Args:
            image_encoder (nn.Module): Visual encoder for extracting image features.
            memory_attention (nn.Module): Module for attending to memory features.
            memory_encoder (nn.Module): Encoder for generating memory representations.
            num_maskmem (int): Number of accessible memory frames.
            image_size (int): Size of input images.
            backbone_stride (int): Stride of the image backbone output.
            sigmoid_scale_for_mem_enc (float): Scale factor for mask sigmoid probability.
            sigmoid_bias_for_mem_enc (float): Bias factor for mask sigmoid probability.
            binarize_mask_from_pts_for_mem_enc (bool): Whether to binarize sigmoid mask logits on interacted frames with
                clicks during evaluation.
            use_mask_input_as_output_without_sam (bool): Whether to directly output the input mask without using SAM
                prompt encoder and mask decoder on frames with mask input.
            max_cond_frames_in_attn (int): Maximum number of conditioning frames to participate in memory attention.
            directly_add_no_mem_embed (bool): Whether to directly add no-memory embedding to image feature on the first
                frame.
            use_high_res_features_in_sam (bool): Whether to use high-resolution feature maps in the SAM mask decoder.
            multimask_output_in_sam (bool): Whether to output multiple masks for the first click on initial conditioning
                frames.
            multimask_min_pt_num (int): Minimum number of clicks to use multimask output in SAM.
            multimask_max_pt_num (int): Maximum number of clicks to use multimask output in SAM.
            multimask_output_for_tracking (bool): Whether to use multimask output for tracking.
            use_multimask_token_for_obj_ptr (bool): Whether to use multimask tokens for object pointers.
            iou_prediction_use_sigmoid (bool): Whether to use sigmoid to restrict IoU prediction to [0-1].
            memory_temporal_stride_for_eval (int): Memory bank's temporal stride during evaluation.
            non_overlap_masks_for_mem_enc (bool): Whether to apply non-overlapping constraints on object masks in memory
                encoder during evaluation.
            use_obj_ptrs_in_encoder (bool): Whether to cross-attend to object pointers from other frames in the encoder.
            max_obj_ptrs_in_encoder (int): Maximum number of object pointers from other frames in encoder
                cross-attention.
            add_tpos_enc_to_obj_ptrs (bool): Whether to add temporal positional encoding to object pointers in the
                encoder.
            proj_tpos_enc_in_obj_ptrs (bool): Whether to add an extra linear projection layer for temporal positional
                encoding in object pointers.
            use_signed_tpos_enc_to_obj_ptrs (bool): Whether to use signed distance in the temporal positional encoding
                in the object pointers.
            only_obj_ptrs_in_the_past_for_eval (bool): Whether to only attend to object pointers in the past during
                evaluation.
            pred_obj_scores (bool): Whether to predict if there is an object in the frame.
            pred_obj_scores_mlp (bool): Whether to use an MLP to predict object scores.
            fixed_no_obj_ptr (bool): Whether to have a fixed no-object pointer when there is no object present.
            soft_no_obj_ptr (bool): Whether to mix in no-object pointer softly for easier recovery and error mitigation.
            use_mlp_for_obj_ptr_proj (bool): Whether to use MLP for object pointer projection.
            no_obj_embed_spatial (bool): Whether add no obj embedding to spatial frames.
            sam_mask_decoder_extra_args (dict | None): Extra arguments for constructing the SAM mask decoder.
            compile_image_encoder (bool): Whether to compile the image encoder for faster inference.
        """
        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 = memory_attention.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
        self.add_all_frames_to_correct_as_cond = True

        # Model compilation
        if compile_image_encoder:
            # Compile the forward function (not the full module) to allow loading checkpoints.
            LOGGER.info("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 the device on which the model's parameters are stored."""
    return next(self.parameters()).device

@staticmethod
def _apply_non_overlapping_constraints(pred_masks):
    """Apply non-overlapping constraints to masks, keeping the highest scoring object per location."""
    batch_size = pred_masks.shape[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

def _build_sam_heads(self):
    """Build SAM-style prompt encoder and mask decoder for image segmentation tasks."""
    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 (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 = SAM2MaskDecoder(
        num_multimask_outputs=3,
        transformer=SAM2TwoWayTransformer(
            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 _encode_memory_in_output(
    self,
    current_vision_feats,
    feat_sizes,
    point_inputs,
    run_mem_encoder,
    high_res_masks,
    object_score_logits,
    current_out,
):
    """Run memory encoder on predicted mask to encode it into a new memory feature for future frames."""
    if run_mem_encoder and self.num_maskmem > 0:
        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,
            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 _encode_new_memory(
    self,
    current_vision_feats,
    feat_sizes,
    pred_masks_high_res,
    object_score_logits,
    is_mask_from_pts,
):
    """Encode frame features and masks into a new memory representation for video segmentation."""
    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).to(pix_feat.dtype)
    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"]
    # 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_out["vision_pos_enc"]

def _forward_sam_heads(
    self,
    backbone_features,
    point_inputs=None,
    mask_inputs=None,
    high_res_features=None,
    multimask_output=False,
):
    """Forward pass through SAM prompt encoders and mask heads.

    This method processes image features and optional point/mask inputs to generate object masks and scores.

    Args:
        backbone_features (torch.Tensor): Image features with shape (B, C, H, W).
        point_inputs (dict[str, torch.Tensor] | None): Dictionary containing point prompts.
        'point_coords': Tensor of shape (B, P, 2) with float32 dtype, containing absolute pixel-unit coordinates in
            (x, y) format for P input points.
        'point_labels': Tensor of shape (B, P) with int32 dtype, where 1 means positive clicks, 0 means negative
            clicks, and -1 means padding.
        mask_inputs (torch.Tensor | None): Mask of shape (B, 1, H*16, W*16), float or bool, with the same spatial
            size as the image.
        high_res_features (list[torch.Tensor] | None): List of two feature maps with shapes (B, C, 4*H, 4*W) and (B,
            C, 2*H, 2*W) respectively, used as high-resolution feature maps for SAM decoder.
        multimask_output (bool): If True, output 3 candidate masks and their IoU estimates; if False, output only 1
            mask and its IoU estimate.

    Returns:
        low_res_multimasks (torch.Tensor): Tensor of shape (B, M, H*4, W*4) with SAM output mask logits.
        high_res_multimasks (torch.Tensor): Tensor of shape (B, M, H*16, W*16) with upsampled mask logits.
        ious (torch.Tensor): Tensor of shape (B, M) with estimated IoU for each output mask.
        low_res_masks (torch.Tensor): Tensor of shape (B, 1, H*4, W*4) with the best low-resolution mask.
        high_res_masks (torch.Tensor): Tensor of shape (B, 1, H*16, W*16) with the best high-resolution mask.
        obj_ptr (torch.Tensor): Tensor of shape (B, C) with object pointer vector for the output mask.
        object_score_logits (torch.Tensor): Tensor of shape (B) with object score logits.

    Examples:
        >>> backbone_features = torch.rand(1, 256, 32, 32)
        >>> point_inputs = {"point_coords": torch.rand(1, 2, 2), "point_labels": torch.tensor([[1, 0]])}
        >>> mask_inputs = torch.rand(1, 1, 512, 512)
        >>> results = model._forward_sam_heads(backbone_features, point_inputs, mask_inputs)
        >>> (
        ...     low_res_multimasks,
        ...     high_res_multimasks,
        ...     ious,
        ...     low_res_masks,
        ...     high_res_masks,
        ...     obj_ptr,
        ...     object_score_logits,
        ... ) = results
    """
    B = backbone_features.shape[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.shape[0] == B and sam_point_labels.shape[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, dtype=backbone_features.dtype)
        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.to(backbone_features.dtype),
                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

        # Spatial memory mask is a *hard* choice between obj and no obj, consistent with 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)
    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.to(obj_ptr.dtype)

        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 _prepare_backbone_features(self, backbone_out, batch=1):
    """Prepare and flatten visual features from the image backbone output for further processing."""
    if batch > 1:  # expand features if there's more than one prompt
        backbone_out = {
            **backbone_out,
            "backbone_fpn": [feat.expand(batch, -1, -1, -1) for feat in backbone_out["backbone_fpn"]],
            "vision_pos_enc": [pos.expand(batch, -1, -1, -1) for pos in backbone_out["vision_pos_enc"]],
        }
    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)
):
    """Prepare memory-conditioned features by fusing current frame's visual features with previous memories."""
    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
        return current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W)
    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 frame'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 r>1), in which case
        # we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame.
        r = 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  # how many frames before current frame
            if t_rel == 1:
                # for t_rel == 1, we take the last frame (regardless of r)
                prev_frame_idx = frame_idx + t_rel if track_in_reverse else frame_idx - t_rel
            elif not track_in_reverse:
                # first find the nearest frame among every r-th frames before this frame
                # for r=1, this would be (frame_idx - 2)
                prev_frame_idx = ((frame_idx - 2) // r) * r
                # then seek further among every r-th frames
                prev_frame_idx = prev_frame_idx - (t_rel - 2) * r
            else:
                # first find the nearest frame among every r-th frames after this frame
                # for r=1, this would be (frame_idx + 2)
                prev_frame_idx = -(-(frame_idx + 2) // r) * r
                # then seek further among every r-th frames
                prev_frame_idx = prev_frame_idx + (t_rel - 2) * r
            out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None)
            if out is None:
                # If an unselected conditioning frame is among the last (self.num_maskmem - 1)
                # frames, we still attend to it as if it's a non-conditioning frame.
                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  # skip padding frames
            # "maskmem_features" might have been offloaded to CPU in demo use cases,
            # so we load it back to inference device (it's a no-op if it's already on device).
            feats = prev["maskmem_features"].to(device=device, non_blocking=device.type == "cuda")
            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=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
                (
                    (
                        (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()
            ]
            # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame
            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 we have at least one object pointer, add them to the across attention
            if pos_and_ptrs:
                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, dtype=current_vision_feats[-1].dtype)
                    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 transformer 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,
    )
    # Reshape 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 _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,
):
    """Perform a single tracking step, updating object masks and memory features based on current frame 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(mask_inputs, pix_feat, high_res_features)
    else:
        # Fuse visual features 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,
        )
        # 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 sam_outputs, high_res_features, pix_feat

def _use_mask_as_output(self, mask_inputs, backbone_features=None, high_res_features=None):
    """Process mask inputs directly as output, bypassing SAM encoder/decoder."""
    # 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.shape[0], 1).float()
    if not self.use_obj_ptrs_in_encoder or backbone_features is None or high_res_features is None:
        # all zeros as a dummy object pointer (of shape [B, C])
        obj_ptr = torch.zeros(mask_inputs.shape[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.to(backbone_features.dtype)),
            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,
    )

def _use_multimask(self, is_init_cond_frame, point_inputs):
    """Determine whether to use multiple mask outputs in the SAM head based on configuration and inputs."""
    num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1)
    return (
        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)
    )

def forward(self, *args, **kwargs):
    """Process image and prompt inputs to generate object masks and scores in video sequences."""
    raise NotImplementedError(
        "Please use the corresponding methods in SAM2VideoPredictor for inference."
        "See notebooks/video_predictor_example.ipynb for an example."
    )

def forward_image(self, img_batch: torch.Tensor):
    """Process image batch through encoder to extract multi-level features for SAM model."""
    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 set_binarize(self, binarize=False):
    """Set binarize for VideoPredictor."""
    self.binarize_mask_from_pts_for_mem_enc = binarize

def set_imgsz(self, imgsz):
    """Set image size to make model compatible with different image sizes."""
    if hasattr(self.image_encoder, "set_imgsz"):
        self.image_encoder.set_imgsz(imgsz)
    self.image_size = imgsz[0]
    self.sam_prompt_encoder.input_image_size = imgsz
    self.sam_prompt_encoder.image_embedding_size = [
        x // self.backbone_stride for x in imgsz
    ]  # fixed ViT patch size of 16
    self.sam_prompt_encoder.mask_input_size = [
        x // self.backbone_stride * 4 for x in imgsz
    ]  # fixed ViT patch size of 16
    self.sam_image_embedding_size = self.image_size // self.backbone_stride  # update image embedding size

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,
):
    """Perform a single tracking step, updating object masks and memory features based on current frame inputs."""
    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,
        "pred_masks_high_res": high_res_masks,
        "obj_ptr": obj_ptr,
    }
    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

    # Run memory encoder on the predicted mask to encode it into a new memory feature (for use in future frames)
    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