Wan2GP/preprocessing/sam3/model/multiplex_mask_decoder.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.


from typing import List, Optional, Type

import torch
from ..sam.common import LayerNorm2d
from torch import nn
from torch.nn import functional as F


class MultiplexMaskDecoder(nn.Module):
    def __init__(
        self,
        *,
        transformer_dim: int,
        transformer: nn.Module,
        multiplex_count: int,
        num_multimask_outputs: int = 3,
        activation: Type[nn.Module] = nn.GELU,
        iou_head_depth: int = 3,
        iou_head_hidden_dim: int = 256,
        use_high_res_features: bool = False,
        iou_prediction_use_sigmoid: bool = False,
        dynamic_multimask_via_stability=False,
        dynamic_multimask_stability_delta=0.05,
        dynamic_multimask_stability_thresh=0.98,
        pred_obj_scores: bool = False,
        pred_obj_scores_mlp: bool = False,
        use_multimask_token_for_obj_ptr: bool = False,
        decode_mask_with_shared_tokens: bool = False,
        decode_mask_attribute_with_shared_tokens: bool = False,
        multimask_outputs_only: bool = False,
    ) -> None:
        """
        Predicts masks given an image and prompt embeddings, using a
        transformer architecture with multiplex capabilities.

        Arguments:
          multiplex_count: the number of masks multiplexed into a single feature map
          num_multimask_outputs: the number of masks to predict per multiplex output
            (the total number of masks is (num_multimask_outputs+1) * multiplex_count)
          use_multimask_token_for_obj_ptr: whether to use multimask tokens for object pointers
          decode_mask_with_shared_tokens: use the same mask token for multimasks with different projection layers
          decode_mask_attribute_with_shared_tokens: use the mask tokens (instead of separate tokens)
            to predict iou and object scores
          multimask_outputs_only: predict num_multimask_outputs masks without the single
            mask output token (i.e., without the +1)
        """
        super().__init__()
        self.transformer_dim = transformer_dim
        self.transformer = transformer

        self.multiplex_count = multiplex_count
        self.num_multimask_outputs = num_multimask_outputs
        self.multimask_outputs_only = multimask_outputs_only
        self.decode_mask_with_shared_tokens = decode_mask_with_shared_tokens
        self.decode_mask_attribute_with_shared_tokens = (
            decode_mask_attribute_with_shared_tokens
        )

        if self.decode_mask_with_shared_tokens:
            assert multimask_outputs_only, (
                "multimask_outputs_only must be True if decode_mask_with_shared_tokens"
            )

        if self.multimask_outputs_only:
            self.num_mask_output_per_object = num_multimask_outputs
        else:
            self.num_mask_output_per_object = num_multimask_outputs + 1

        if self.decode_mask_with_shared_tokens:
            self.num_mask_tokens = multiplex_count
        else:
            self.num_mask_tokens = multiplex_count * self.num_mask_output_per_object

        self.pred_obj_scores = pred_obj_scores
        self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr

        if not self.decode_mask_attribute_with_shared_tokens:
            self.iou_token = nn.Embedding(multiplex_count, transformer_dim)
            if self.pred_obj_scores:
                self.obj_score_token = nn.Embedding(multiplex_count, transformer_dim)

        self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)

        self.output_upscaling = nn.Sequential(
            nn.ConvTranspose2d(
                transformer_dim, transformer_dim // 4, kernel_size=2, stride=2
            ),
            LayerNorm2d(transformer_dim // 4),
            activation(),
            nn.ConvTranspose2d(
                transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2
            ),
            activation(),
        )
        self.use_high_res_features = use_high_res_features
        if use_high_res_features:
            self.conv_s0 = nn.Conv2d(
                transformer_dim, transformer_dim // 8, kernel_size=1, stride=1
            )
            self.conv_s1 = nn.Conv2d(
                transformer_dim, transformer_dim // 4, kernel_size=1, stride=1
            )

        if self.num_multimask_outputs == 0:
            self.output_hypernetworks_mlp = MLP(
                transformer_dim, transformer_dim, transformer_dim // 8, 3
            )
        else:
            self.output_hypernetworks_mlps = nn.ModuleList(
                [
                    MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
                    for _ in range(self.num_mask_output_per_object)
                ]
            )

        self.iou_prediction_head = MLP(
            transformer_dim,
            iou_head_hidden_dim,
            (
                1
                if (
                    self.decode_mask_attribute_with_shared_tokens
                    and not self.decode_mask_with_shared_tokens
                )
                else self.num_mask_output_per_object
            ),
            iou_head_depth,
            sigmoid_output=iou_prediction_use_sigmoid,
        )

        if self.pred_obj_scores:
            self.pred_obj_score_head = nn.Linear(transformer_dim, 1)
            if pred_obj_scores_mlp:
                self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3)

        # When outputting a single mask, optionally we can dynamically fall back to the best
        # multimask output token if the single mask output token gives low stability scores.
        self.dynamic_multimask_via_stability = dynamic_multimask_via_stability
        self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta
        self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh

    def forward(
        self,
        image_embeddings: torch.Tensor,
        image_pe: torch.Tensor,
        multimask_output: bool,
        high_res_features: Optional[List[torch.Tensor]] = None,
        extra_per_object_embeddings: Optional[torch.Tensor] = None,
    ) -> dict[str, torch.Tensor]:
        """
        Predict masks given image and prompt embeddings.

        Arguments:
          image_embeddings (torch.Tensor): the embeddings from the image encoder
          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
          extra_per_object_embeddings (torch.Tensor): a tensor with shape b * multiplex_count * C to be added to the mask tokens

        Returns: a dict of Tensors indexed by strings
          masks: batched predicted masks
          iou_pred: batched predictions of mask quality
          object_score_logits: batched predictions of object existence
        """

        if self.num_multimask_outputs <= 0:
            assert not multimask_output, (
                f"multimask_output must be False with {self.num_multimask_outputs=}"
            )

        if self.multimask_outputs_only:
            assert multimask_output, (
                f"multimask_output must be True with {self.multimask_outputs_only=}"
            )

        out = self.predict_masks(
            image_embeddings=image_embeddings,
            image_pe=image_pe,
            high_res_features=high_res_features,
            extra_per_object_embeddings=extra_per_object_embeddings,
        )

        masks = out["masks"]  # [B, M, (self.num_mask_token_per_object), H, W]
        iou_pred = out["iou_pred"]  # [B, M, (self.num_mask_token_per_object)]
        mask_tokens_out = out[
            "mask_tokens_out"
        ]  # [B, M, (self.num_mask_token_per_object), C]

        # Select the correct mask or masks for output
        if multimask_output:
            if not self.multimask_outputs_only:
                masks = masks[:, :, 1:, :, :]
                iou_pred = iou_pred[:, :, 1:]
        elif self.dynamic_multimask_via_stability and not self.training:
            masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred)
        else:
            masks = masks[:, :, 0:1, :, :]
            iou_pred = iou_pred[:, :, 0:1]

        if multimask_output and self.use_multimask_token_for_obj_ptr:
            if self.multimask_outputs_only:
                sam_tokens_out = mask_tokens_out
            else:
                sam_tokens_out = mask_tokens_out[
                    :, :, 1:
                ]  # [B, M, num_multimask_outputs, C] shape
        else:
            # Take the mask output token. Here we *always* use the token for single mask output.
            # At test time, even if we track after 1-click (and using multimask_output=True),
            # we still take the single mask token here. The rationale is that we always track
            # after multiple clicks during training, so the past tokens seen during training
            # are always the single mask token (and we'll let it be the object-memory token).
            sam_tokens_out = mask_tokens_out[:, :, 0:1]  # [B, M, 1, C] shape

        del out["mask_tokens_out"]
        out["masks"] = masks
        out["iou_pred"] = iou_pred
        out["sam_tokens_out"] = sam_tokens_out

        if multimask_output:
            assert masks.shape[2] == self.num_mask_output_per_object, (
                f"{masks.shape=}, {self.num_mask_output_per_object=}"
            )
            assert iou_pred.shape[2] == self.num_mask_output_per_object, (
                f"{iou_pred.shape=}, {self.num_mask_output_per_object=}"
            )
            if self.use_multimask_token_for_obj_ptr:
                if self.decode_mask_with_shared_tokens:
                    assert sam_tokens_out.shape[2] == 1, f"{sam_tokens_out.shape=}"
                else:
                    assert sam_tokens_out.shape[2] == self.num_mask_output_per_object, (
                        f"{sam_tokens_out.shape=}, {self.num_mask_output_per_object=}"
                    )
        else:
            assert masks.shape[2] == 1, f"{masks.shape=}"
            assert iou_pred.shape[2] == 1, f"{iou_pred.shape=}"
            assert sam_tokens_out.shape[2] == 1, f"{sam_tokens_out.shape=}"

        return out

    def predict_masks(
        self,
        image_embeddings: torch.Tensor,
        image_pe: torch.Tensor,
        high_res_features: Optional[List[torch.Tensor]] = None,
        extra_per_object_embeddings: Optional[
            torch.Tensor
        ] = None,  # num_buckets, multiplex_count, C
    ) -> dict[str, torch.Tensor]:
        """Predicts masks. See 'forward' for more details."""
        # Concatenate output tokens
        B = image_embeddings.shape[0]
        token_list = []
        if self.pred_obj_scores and not self.decode_mask_attribute_with_shared_tokens:
            token_list.append(self.obj_score_token.weight)
        if not self.decode_mask_attribute_with_shared_tokens:
            token_list.append(self.iou_token.weight)

        tokens = torch.cat(token_list, dim=0)
        tokens = tokens.unsqueeze(0).expand(B, -1, -1)

        if extra_per_object_embeddings is not None:
            mask_tokens = self.mask_tokens.weight.view(
                1, self.multiplex_count, self.num_mask_output_per_object, -1
            ).expand(B, -1, -1, -1)

            mask_tokens = mask_tokens + extra_per_object_embeddings.unsqueeze(2)
            mask_tokens = mask_tokens.flatten(1, 2)
        else:
            mask_tokens = self.mask_tokens.weight.unsqueeze(0).expand(B, -1, -1)

        tokens = torch.cat([tokens, mask_tokens], dim=1)

        src = image_embeddings

        assert image_pe.size(0) == 1, (
            "image_pe should have size 1 in batch dim (from `get_dense_pe()`)"
        )
        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
        b, c, h, w = src.shape

        # Run the transformer
        hs, src = self.transformer(src, pos_src, tokens)

        # Parse transformer outputs based on token sharing configuration
        if self.decode_mask_attribute_with_shared_tokens:
            assert hs.shape[1] == self.num_mask_tokens, (
                f"{hs.shape=}, {self.num_mask_tokens=}"
            )
            iou_token_out = mask_tokens_out = hs[:, 0 : self.num_mask_tokens]
            if self.pred_obj_scores:
                obj_score_token_out = mask_tokens_out
        else:
            # Separate tokens for each prediction type
            s = 0
            if self.pred_obj_scores:
                obj_score_token_out = hs[:, s : s + self.multiplex_count, :]
                s += self.multiplex_count

            iou_token_out = hs[:, s : s + self.multiplex_count, :]
            s += self.multiplex_count
            mask_tokens_out = hs[:, s : s + self.num_mask_tokens, :]
            assert hs.shape[1] == s + self.num_mask_tokens, (
                f"{hs.shape=}, {s=}, {self.num_mask_tokens=}"
            )

        # Upscale mask embeddings and predict masks using the mask tokens
        src = src.transpose(1, 2).view(b, c, h, w)
        if not self.use_high_res_features:
            upscaled_embedding = self.output_upscaling(src)
        else:
            dc1, ln1, act1, dc2, act2 = self.output_upscaling
            feat_s0, feat_s1 = high_res_features
            upscaled_embedding = act1(ln1(dc1(src) + feat_s1))
            upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0)

        if self.decode_mask_with_shared_tokens:
            mask_tokens_out = mask_tokens_out.view(B, self.multiplex_count, 1, -1)
        else:
            mask_tokens_out = mask_tokens_out.view(
                B, self.multiplex_count, self.num_mask_output_per_object, -1
            )
        if self.num_multimask_outputs == 0:
            hyper_in = self.output_hypernetworks_mlp(
                mask_tokens_out[:, :, 0, :]
            ).unsqueeze(2)  # [B, M, 1, C]
        else:
            hyper_in_list: List[torch.Tensor] = []
            for i in range(self.num_mask_output_per_object):
                if self.decode_mask_with_shared_tokens:
                    hyper_in_list.append(
                        self.output_hypernetworks_mlps[i](mask_tokens_out[:, :, 0, :])
                    )
                else:
                    hyper_in_list.append(
                        self.output_hypernetworks_mlps[i](mask_tokens_out[:, :, i, :])
                    )
            # hyper_in: [B, M, num_multimask_outputs+1, C]
            hyper_in = torch.stack(hyper_in_list, dim=2)

        # generate the masks
        b, c, h, w = upscaled_embedding.shape
        masks = torch.bmm(
            hyper_in.flatten(1, 2), upscaled_embedding.view(b, c, h * w)
        ).view(b, self.multiplex_count, self.num_mask_output_per_object, h, w)

        # Generate mask quality predictions, with shape b * multiplex_count * (num_multimask_outputs+1)
        iou_pred = self.iou_prediction_head(iou_token_out).view(
            b, self.multiplex_count, self.num_mask_output_per_object
        )

        if self.pred_obj_scores:
            # Generate mask quality predictions, with shape b * (num_multimask_outputs+1)
            if (
                self.decode_mask_attribute_with_shared_tokens
                and not self.decode_mask_with_shared_tokens
            ):
                object_score_logits = (
                    self.pred_obj_score_head(obj_score_token_out)
                    .view(b, self.multiplex_count, self.num_mask_output_per_object)
                    .sum(-1, keepdim=True)
                )
            else:
                object_score_logits = self.pred_obj_score_head(obj_score_token_out)
        else:
            # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1
            object_score_logits = 10.0 * iou_pred.new_ones(
                iou_pred.shape[0], iou_pred.shape[1]
            )

        outputs = {
            "masks": masks,
            "iou_pred": iou_pred,
            "mask_tokens_out": mask_tokens_out,
            "object_score_logits": object_score_logits,
        }

        return outputs

    def _get_stability_scores(self, mask_logits):
        """
        Compute stability scores of the mask logits based on the IoU between upper and
        lower thresholds.
        """
        mask_logits = mask_logits.flatten(-2)
        stability_delta = self.dynamic_multimask_stability_delta
        area_i = torch.sum(mask_logits > stability_delta, dim=-1).float()
        area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float()
        stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0)
        return stability_scores

    def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores):
        """
        When outputting a single mask, if the stability score from the current single-mask
        output (based on output token 0) falls below a threshold, we instead select from
        multi-mask outputs (based on output token 1~3) the mask with the highest predicted
        IoU score. This is intended to ensure a valid mask for both clicking and tracking.
        """
        # first, flatten the batch and the multiplex dimensions
        B, M = all_mask_logits.shape[:2]
        all_mask_logits = all_mask_logits.flatten(0, 1)
        all_iou_scores = all_iou_scores.flatten(0, 1)

        # The best mask from multimask output tokens (1~3)
        multimask_logits = all_mask_logits[:, 1:, :, :]
        multimask_iou_scores = all_iou_scores[:, 1:]
        best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1)
        batch_inds = torch.arange(
            multimask_iou_scores.size(0), device=all_iou_scores.device
        )
        best_multimask_logits = multimask_logits[batch_inds, best_scores_inds]
        best_multimask_logits = best_multimask_logits.unsqueeze(1)
        best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds]
        best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1)

        # The mask from singlemask output token 0 and its stability score
        singlemask_logits = all_mask_logits[:, 0:1, :, :]
        singlemask_iou_scores = all_iou_scores[:, 0:1]
        stability_scores = self._get_stability_scores(singlemask_logits)
        is_stable = stability_scores >= self.dynamic_multimask_stability_thresh

        # Dynamically fall back to best multimask output upon low stability scores.
        mask_logits_out = torch.where(
            is_stable[..., None, None].expand_as(singlemask_logits),
            singlemask_logits,
            best_multimask_logits,
        )
        iou_scores_out = torch.where(
            is_stable.expand_as(singlemask_iou_scores),
            singlemask_iou_scores,
            best_multimask_iou_scores,
        )

        # restore the batch and multiplex dimensions
        mask_logits_out = mask_logits_out.unflatten(0, (B, M))
        iou_scores_out = iou_scores_out.unflatten(0, (B, M))

        return mask_logits_out, iou_scores_out


# Lightly adapted from
# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
class MLP(nn.Module):
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_layers: int,
        sigmoid_output: bool = False,
    ) -> None:
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = nn.ModuleList(
            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
        )
        self.sigmoid_output = sigmoid_output

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
        if self.sigmoid_output:
            x = F.sigmoid(x)
        return x