sam2/modeling/sam/prompt_encoder.py

from typing import Optional, Tuple, Type

import torch
from torch import nn

from sam2.modeling.position_encoding import PositionEmbeddingRandom

from sam2.modeling.sam2_utils import LayerNorm2d

class PromptEncoder(nn.Module):
    def __init__(
        self,
        embed_dim: int,
        image_embedding_size: Tuple[int, int],
        input_image_size: Tuple[int, int],
        mask_in_chans: int,
        activation: Type[nn.Module] = nn.GELU,
    ) -> None:
           
        super().__init__()
        self.embed_dim = embed_dim
        self.input_image_size = input_image_size
        self.image_embedding_size = image_embedding_size
        self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)

        self.num_point_embeddings: int = 4  
        point_embeddings = [
            nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)
        ]
        self.point_embeddings = nn.ModuleList(point_embeddings)
        self.not_a_point_embed = nn.Embedding(1, embed_dim)

        self.mask_input_size = (
            4 * image_embedding_size[0],
            4 * image_embedding_size[1],
        )
        self.mask_downscaling = nn.Sequential(
            nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans // 4),
            activation(),
            nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
            LayerNorm2d(mask_in_chans),
            activation(),
            nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
        )
        self.no_mask_embed = nn.Embedding(1, embed_dim)

    def get_dense_pe(self) -> torch.Tensor:
           
        return self.pe_layer(self.image_embedding_size).unsqueeze(0)

    def _embed_points(
        self,
        points: torch.Tensor,
        labels: torch.Tensor,
        pad: bool,
    ) -> torch.Tensor:
                                   
        points = points + 0.5  
        if pad:
            padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
            padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
            points = torch.cat([points, padding_point], dim=1)
            labels = torch.cat([labels, padding_label], dim=1)
        point_embedding = self.pe_layer.forward_with_coords(
            points, self.input_image_size
        )

        point_embedding = torch.where(
            (labels == -1).unsqueeze(-1),
            torch.zeros_like(point_embedding) + self.not_a_point_embed.weight,
            point_embedding,
        )
        point_embedding = torch.where(
            (labels == 0).unsqueeze(-1),
            point_embedding + self.point_embeddings[0].weight,
            point_embedding,
        )
        point_embedding = torch.where(
            (labels == 1).unsqueeze(-1),
            point_embedding + self.point_embeddings[1].weight,
            point_embedding,
        )
        point_embedding = torch.where(
            (labels == 2).unsqueeze(-1),
            point_embedding + self.point_embeddings[2].weight,
            point_embedding,
        )
        point_embedding = torch.where(
            (labels == 3).unsqueeze(-1),
            point_embedding + self.point_embeddings[3].weight,
            point_embedding,
        )
        return point_embedding

    def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
                                 
        boxes = boxes + 0.5  
        coords = boxes.reshape(-1, 2, 2)
        corner_embedding = self.pe_layer.forward_with_coords(
            coords, self.input_image_size
        )
        corner_embedding[:, 0, :] += self.point_embeddings[2].weight
        corner_embedding[:, 1, :] += self.point_embeddings[3].weight
        return corner_embedding

    def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
                                 
        mask_embedding = self.mask_downscaling(masks)
        return mask_embedding

    def _get_batch_size(
        self,
        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
        boxes: Optional[torch.Tensor],
        masks: Optional[torch.Tensor],
    ) -> int:
           
        if points is not None:
            return points[0].shape[0]
        elif boxes is not None:
            return boxes.shape[0]
        elif masks is not None:
            return masks.shape[0]
        else:
            return 1

    def _get_device(self) -> torch.device:
        return self.point_embeddings[0].weight.device

    def forward(
        self,
        points: Optional[Tuple[torch.Tensor, torch.Tensor]],
        boxes: Optional[torch.Tensor],
        masks: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
           
        bs = self._get_batch_size(points, boxes, masks)
        sparse_embeddings = torch.empty(
            (bs, 0, self.embed_dim), device=self._get_device()
        )
        if points is not None:
            coords, labels = points
            point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
            sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
        if boxes is not None:
            box_embeddings = self._embed_boxes(boxes)
            sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)

        if masks is not None:
            dense_embeddings = self._embed_masks(masks)
        else:
            dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
                bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
            )

        return sparse_embeddings, dense_embeddings