833 lines
43 KiB
Python
833 lines
43 KiB
Python
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.distributed
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import torch.nn.functional as F
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from torch.nn.init import trunc_normal_
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from .sam.mask_decoder import MaskDecoder
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from .sam.prompt_encoder import PromptEncoder
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from .sam.transformer import TwoWayTransformer
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from .sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames
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# a large negative value as a placeholder score for missing objects
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NO_OBJ_SCORE = -1024.0
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class SAM2Base(torch.nn.Module):
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def __init__(
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self,
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image_encoder,
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memory_attention,
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memory_encoder,
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num_maskmem=7, # default 1 input frame + 6 previous frames
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image_size=512,
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backbone_stride=16, # stride of the image backbone output
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sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob
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sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob
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# During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks
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binarize_mask_from_pts_for_mem_enc=False,
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use_mask_input_as_output_without_sam=False,
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# on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder
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# 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,
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# 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
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# a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM.
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max_cond_frames_in_attn=-1,
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# on the first frame, whether to directly add the no-memory embedding to the image feature
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# (instead of using the transformer encoder)
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directly_add_no_mem_embed=False,
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# whether to use high-resolution feature maps in the SAM mask decoder
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use_high_res_features_in_sam=False,
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# whether to output multiple (3) masks for the first click on initial conditioning frames
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multimask_output_in_sam=False,
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# the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`;
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# default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points)
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multimask_min_pt_num=1,
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multimask_max_pt_num=1,
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# 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`)
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multimask_output_for_tracking=False,
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# Whether to use multimask tokens for obj ptr; Only relevant when both
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# use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True
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use_multimask_token_for_obj_ptr: bool = False,
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# whether to use sigmoid to restrict ious prediction to [0-1]
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iou_prediction_use_sigmoid=False,
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# The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5).
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# For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of
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# (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame.
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memory_temporal_stride_for_eval=1,
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# if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click
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# if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames
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add_all_frames_to_correct_as_cond=False,
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# whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks)
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non_overlap_masks_for_mem_enc=False,
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# whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder
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use_obj_ptrs_in_encoder=False,
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# the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`)
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max_obj_ptrs_in_encoder=16,
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# whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`)
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add_tpos_enc_to_obj_ptrs=True,
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# whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference
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# with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`)
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proj_tpos_enc_in_obj_ptrs=False,
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# whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation
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# (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking)
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only_obj_ptrs_in_the_past_for_eval=False,
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# Whether to predict if there is an object in the frame
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pred_obj_scores: bool = False,
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# Whether to use an MLP to predict object scores
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pred_obj_scores_mlp: bool = False,
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# Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True;
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# Whether to have a fixed no obj pointer when there is no object present
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# or to use it as an additive embedding with obj_ptr produced by decoder
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fixed_no_obj_ptr: bool = False,
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# Soft no object, i.e. mix in no_obj_ptr softly,
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# hope to make recovery easier if there is a mistake and mitigate accumulation of errors
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soft_no_obj_ptr: bool = False,
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use_mlp_for_obj_ptr_proj: bool = False,
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# 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.
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sam_mask_decoder_extra_args=None,
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compile_image_encoder: bool = False,
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):
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super().__init__()
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# Part 1: the image backbone
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self.image_encoder = image_encoder
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# Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting
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self.use_high_res_features_in_sam = use_high_res_features_in_sam
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self.num_feature_levels = 3 if use_high_res_features_in_sam else 1
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self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder
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self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder
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if use_obj_ptrs_in_encoder:
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# A conv layer to downsample the mask prompt to stride 4 (the same stride as
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# low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale,
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# so that it can be fed into the SAM mask decoder to generate a pointer.
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self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4)
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self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs
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if proj_tpos_enc_in_obj_ptrs:
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assert add_tpos_enc_to_obj_ptrs # these options need to be used together
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self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs
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self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval
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# Part 2: memory attention to condition current frame's visual features
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# with memories (and obj ptrs) from past frames
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self.memory_attention = memory_attention
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self.hidden_dim = memory_attention.d_model
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# Part 3: memory encoder for the previous frame's outputs
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self.memory_encoder = memory_encoder
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self.mem_dim = self.hidden_dim
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if hasattr(self.memory_encoder, "out_proj") and hasattr(
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self.memory_encoder.out_proj, "weight"
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):
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# if there is compression of memories along channel dim
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self.mem_dim = self.memory_encoder.out_proj.weight.shape[0]
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self.num_maskmem = num_maskmem # Number of memories accessible
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# Temporal encoding of the memories
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self.maskmem_tpos_enc = torch.nn.Parameter(
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torch.zeros(num_maskmem, 1, 1, self.mem_dim)
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)
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trunc_normal_(self.maskmem_tpos_enc, std=0.02)
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# a single token to indicate no memory embedding from previous frames
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self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
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self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim))
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trunc_normal_(self.no_mem_embed, std=0.02)
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trunc_normal_(self.no_mem_pos_enc, std=0.02)
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self.directly_add_no_mem_embed = directly_add_no_mem_embed
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# Apply sigmoid to the output raw mask logits (to turn them from
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# range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder
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self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc
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self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc
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self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc
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self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc
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self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval
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# On frames with mask input, whether to directly output the input mask without
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# using a SAM prompt encoder + mask decoder
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self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam
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self.multimask_output_in_sam = multimask_output_in_sam
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self.multimask_min_pt_num = multimask_min_pt_num
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self.multimask_max_pt_num = multimask_max_pt_num
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self.multimask_output_for_tracking = multimask_output_for_tracking
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self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr
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self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid
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# Part 4: SAM-style prompt encoder (for both mask and point inputs)
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# and SAM-style mask decoder for the final mask output
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self.image_size = image_size
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self.backbone_stride = backbone_stride
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self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args
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self.pred_obj_scores = pred_obj_scores
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self.pred_obj_scores_mlp = pred_obj_scores_mlp
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self.fixed_no_obj_ptr = fixed_no_obj_ptr
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self.soft_no_obj_ptr = soft_no_obj_ptr
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if self.fixed_no_obj_ptr:
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assert self.pred_obj_scores
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assert self.use_obj_ptrs_in_encoder
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if self.pred_obj_scores and self.use_obj_ptrs_in_encoder:
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self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim))
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trunc_normal_(self.no_obj_ptr, std=0.02)
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self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj
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self._build_sam_heads()
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self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond
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self.max_cond_frames_in_attn = max_cond_frames_in_attn
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# Model compilation
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if compile_image_encoder:
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# Compile the forward function (not the full module) to allow loading checkpoints.
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print(
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"Image encoder compilation is enabled. First forward pass will be slow."
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)
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self.image_encoder.forward = torch.compile(
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self.image_encoder.forward,
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mode="max-autotune",
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fullgraph=True,
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dynamic=False,
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)
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@property
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def device(self):
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return next(self.parameters()).device
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def forward(self, *args, **kwargs):
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raise NotImplementedError(
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"Please use the corresponding methods in SAM2VideoPredictor for inference."
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"See notebooks/video_predictor_example.ipynb for an example."
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)
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def _build_sam_heads(self):
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"""Build SAM-style prompt encoder and mask decoder."""
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self.sam_prompt_embed_dim = self.hidden_dim
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self.sam_image_embedding_size = self.image_size // self.backbone_stride
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# build PromptEncoder and MaskDecoder from SAM
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# (their hyperparameters like `mask_in_chans=16` are from SAM code)
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self.sam_prompt_encoder = PromptEncoder(
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embed_dim=self.sam_prompt_embed_dim,
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image_embedding_size=(
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self.sam_image_embedding_size,
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self.sam_image_embedding_size,
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),
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input_image_size=(self.image_size, self.image_size),
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mask_in_chans=16,
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)
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self.sam_mask_decoder = MaskDecoder(
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num_multimask_outputs=3,
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transformer=TwoWayTransformer(
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depth=2,
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embedding_dim=self.sam_prompt_embed_dim,
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mlp_dim=2048,
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num_heads=8,
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),
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transformer_dim=self.sam_prompt_embed_dim,
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iou_head_depth=3,
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iou_head_hidden_dim=256,
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use_high_res_features=self.use_high_res_features_in_sam,
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iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid,
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pred_obj_scores=self.pred_obj_scores,
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pred_obj_scores_mlp=self.pred_obj_scores_mlp,
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use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr,
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**(self.sam_mask_decoder_extra_args or {}),
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)
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if self.use_obj_ptrs_in_encoder:
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# a linear projection on SAM output tokens to turn them into object pointers
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self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim)
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if self.use_mlp_for_obj_ptr_proj:
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self.obj_ptr_proj = MLP(
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self.hidden_dim, self.hidden_dim, self.hidden_dim, 3
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)
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else:
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self.obj_ptr_proj = torch.nn.Identity()
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if self.proj_tpos_enc_in_obj_ptrs:
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# a linear projection on temporal positional encoding in object pointers to
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# avoid potential interference with spatial positional encoding
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self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim)
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else:
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self.obj_ptr_tpos_proj = torch.nn.Identity()
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def _forward_sam_heads(
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self,
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backbone_features,
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point_inputs=None,
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mask_inputs=None,
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high_res_features=None,
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multimask_output=False,
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):
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"""
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Forward SAM prompt encoders and mask heads.
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Inputs:
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- backbone_features: image features of [B, C, H, W] shape
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- point_inputs: a dictionary with "point_coords" and "point_labels", where
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1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the
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absolute pixel-unit coordinate in (x, y) format of the P input points
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2) "point_labels" has shape [B, P] and int32 dtype, where 1 means
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positive clicks, 0 means negative clicks, and -1 means padding
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- mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the
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same spatial size as the image.
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- high_res_features: either 1) None or 2) or a list of length 2 containing
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two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively,
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which will be used as high-resolution feature maps for SAM decoder.
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- multimask_output: if it's True, we output 3 candidate masks and their 3
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corresponding IoU estimates, and if it's False, we output only 1 mask and
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its corresponding IoU estimate.
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Outputs:
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- low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if
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`multimask_output=True` and M = 1 if `multimask_output=False`), the SAM
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output mask logits (before sigmoid) for the low-resolution masks, with 4x
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the resolution (1/4 stride) of the input backbone_features.
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- high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3
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if `multimask_output=True` and M = 1 if `multimask_output=False`),
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upsampled from the low-resolution masks, with shape size as the image
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(stride is 1 pixel).
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- 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:
|
||
|
|
# Only hard possible with gt
|
||
|
|
assert not self.teacher_force_obj_scores_for_mem
|
||
|
|
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):
|
||
|
|
"""
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
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)
|
||
|
|
):
|
||
|
|
"""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
|
||
|
|
# 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 r>1), in which case
|
||
|
|
# we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame.
|
||
|
|
r = 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)
|
||
|
|
if not track_in_reverse:
|
||
|
|
# the frame immediately before this frame (i.e. frame_idx - 1)
|
||
|
|
prev_frame_idx = frame_idx - t_rel
|
||
|
|
else:
|
||
|
|
# the frame immediately after this frame (i.e. frame_idx + 1)
|
||
|
|
prev_frame_idx = frame_idx + t_rel
|
||
|
|
else:
|
||
|
|
# for t_rel >= 2, we take the memory frame from every r-th frames
|
||
|
|
if 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 GPU (it's a no-op if it's already on GPU).
|
||
|
|
feats = prev["maskmem_features"].cuda(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].cuda()
|
||
|
|
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
|
||
|
|
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 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 emtpy 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,
|
||
|
|
)
|
||
|
|
# 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,
|
||
|
|
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"]
|
||
|
|
|
||
|
|
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=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,
|
||
|
|
):
|
||
|
|
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_with_mem = 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_with_mem,
|
||
|
|
point_inputs=point_inputs,
|
||
|
|
mask_inputs=mask_inputs,
|
||
|
|
high_res_features=high_res_features,
|
||
|
|
multimask_output=multimask_output,
|
||
|
|
)
|
||
|
|
(
|
||
|
|
_,
|
||
|
|
_,
|
||
|
|
_,
|
||
|
|
low_res_masks,
|
||
|
|
high_res_masks,
|
||
|
|
obj_ptr,
|
||
|
|
_,
|
||
|
|
) = sam_outputs
|
||
|
|
|
||
|
|
current_out["pred_masks"] = low_res_masks
|
||
|
|
current_out["pred_masks_high_res"] = high_res_masks
|
||
|
|
current_out["obj_ptr"] = obj_ptr
|
||
|
|
|
||
|
|
# Finally run the memory encoder on the predicted mask to encode
|
||
|
|
# it into a new memory feature (that can be used in future frames)
|
||
|
|
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,
|
||
|
|
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
|
||
|
|
|
||
|
|
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
|