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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from torch import nn
from .transformer import TwoWayTransformer3D
from typing import Any, Optional, Tuple, Type
class LayerNorm3d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None, None] * x + self.bias[:, None, None, None]
return x
class PromptEncoder3D(nn.Module):
def __init__(
self,
embed_dim: int,
image_embedding_size: Tuple[int, int, int],
input_image_size: Tuple[int, int, int],
mask_in_chans: int,
# transformer_dim: int,
num_multiple_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
multiple_outputs: bool = False,
) -> None:
"""
Encodes prompts for input to SAM's mask decoder.
Arguments:
embed_dim (int): The prompts' embedding dimension
image_embedding_size (tuple(int, int)): The spatial size of the
image embedding, as (H, W).
input_image_size (int): The padded size of the image as input
to the image encoder, as (H, W).
mask_in_chans (int): The number of hidden channels used for
encoding input masks.
activation (nn.Module): The activation to use when encoding
input masks.
"""
super().__init__()
self.embed_dim = embed_dim
self.input_image_size = input_image_size
self.image_embedding_size = image_embedding_size
self.pe_layer = PositionEmbeddingRandom3D(embed_dim // 3)
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
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 = (image_embedding_size[0], image_embedding_size[1], image_embedding_size[2])
self.mask_downscaling = nn.Sequential(
nn.Conv3d(1, mask_in_chans // 4, kernel_size=2, stride=2),
LayerNorm3d(mask_in_chans // 4),
activation(),
# nn.Conv3d(mask_in_chans // 4, mask_in_chans // 2, kernel_size=2, stride=2),
# LayerNorm3d(mask_in_chans // 2),
# activation(),
nn.Conv3d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
LayerNorm3d(mask_in_chans),
activation(),
nn.Conv3d(mask_in_chans, embed_dim, kernel_size=1),
)
self.no_mask_embed = nn.Embedding(1, embed_dim)
self.transformer_dim = embed_dim
self.transformer = TwoWayTransformer3D(
depth=2,
embedding_dim=self.transformer_dim,
mlp_dim=2048,
num_heads=8,
)
self.multiple_outputs = multiple_outputs
self.mask_tokens = nn.Embedding(num_multiple_outputs + 1, embed_dim)
self.iou_token = nn.Embedding(1, embed_dim)
#self.iou_token = nn.Embedding(1, embed_dim) if multiple_outputs else None
def forward(
self,
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
boxes: Optional[torch.Tensor],
masks: Optional[torch.Tensor],
image_embeddings: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Embeds different types of prompts, returning both sparse and dense
embeddings.
Arguments:
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
and labels to embed.
boxes (torch.Tensor or none): boxes to embed
masks (torch.Tensor or none): masks to embed
Returns:
torch.Tensor: sparse embeddings for the points and boxes, with shape
BxNx(embed_dim), where N is determined by the number of input points
and boxes.
torch.Tensor: dense embeddings for the masks, in the shape
Bx(embed_dim)x(embed_H)x(embed_W)
"""
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, 1).expand(
bs, -1, self.image_embedding_size[0], self.image_embedding_size[1], self.image_embedding_size[2]
)
new_prompt_embeddings, new_image_embeddings = self._two_way_transformer(
image_embeddings=image_embeddings,
image_pe=self.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings, # sparse_embeddings --> (B, 2, embed_dim) 2 represents concat of coordination and its label
dense_prompt_embeddings=dense_embeddings, # dense_embeddings --> (B, embed_dim, W, H, D), whd values are customized
)
# new_prompt_embedding --> [b, self.num_mask_tokens, c], new_image_embedding --> [b, c, low_res / 4, low_res / 4, low_res / 4]
# new_image_embedding --> [b, c, low_res / 4, low_res / 4, low_res / 4]
# return sparse_embeddings, dense_embeddings
return new_prompt_embeddings, new_image_embeddings
def _two_way_transformer(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Predicts masks. See 'forward' for more details."""
# Concatenate output tokens
# if self.multiple_outputs:
# output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
# else:
# output_tokens = self.mask_tokens.weight
output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
# Expand per-image data in batch direction to be per-mask
if image_embeddings.shape[0] != tokens.shape[0]:
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
else:
src = image_embeddings
src = src + dense_prompt_embeddings
b, c, x, y, z = src.shape
if image_pe.shape[0] != tokens.shape[0]:
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
else:
pos_src = image_pe
# token size [batch, tokens, embedding dim]
# embedding dim = 384, tokens = number points + box + 3 multiple outputs + 1 iou
new_point_embedding, new_image_embedding = self.transformer(src, pos_src, tokens) # hidden state and src
return new_point_embedding, new_image_embedding.transpose(1, 2).view(b, c, x, y, z)
# else:
# return new_point_embedding[:, 0, :].unsqueeze(1), new_image_embedding.transpose(1, 2).view(b, c, x, y, z)
#
#
# a = new_sparse.shape[1]
# if new_sparse.shape[1] != 1:
# return self.output_sparse_mlps(new_sparse.transpose(1, 2)).transpose(1, 2), new_dense
# else:
# return new_sparse, new_dense
def get_dense_pe(self) -> torch.Tensor:
"""
Returns the positional encoding used to encode point prompts,
applied to a dense set of points the shape of the image encoding.
Returns:
torch.Tensor: Positional encoding with shape
1x(embed_dim)x(embedding_h)x(embedding_w)
"""
return self.pe_layer(self.image_embedding_size).unsqueeze(0) # 1xXxYxZ
def _embed_points(
self,
points: torch.Tensor,
labels: torch.Tensor,
pad: bool,
) -> torch.Tensor:
"""Embeds point prompts."""
points = points + 0.5 # Shift to center of pixel
if pad:
padding_point = torch.zeros((points.shape[0], 1, 3), 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[labels == -1] = 0.0
point_embedding[labels == -1] += self.not_a_point_embed.weight
point_embedding[labels == 0] += self.point_embeddings[0].weight
point_embedding[labels == 1] += self.point_embeddings[1].weight
return point_embedding
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: # XYWH format https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py
"""Embeds box prompts."""
boxes = boxes + 0.5 # Shift to center of pixel
coords = boxes.reshape(-1, 2, 3)
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:
"""Embeds mask inputs."""
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:
"""
Gets the batch size of the output given the batch size of the input prompts.
"""
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
class PositionEmbeddingRandom3D(nn.Module):
"""
Positional encoding using random spatial frequencies.
"""
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
super().__init__()
if scale is None or scale <= 0.0:
scale = 1.0
self.register_buffer(
"positional_encoding_gaussian_matrix",
scale * torch.randn((3, num_pos_feats)),
)
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
"""Positionally encode points that are normalized to [0,1]."""
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
coords = 2 * coords - 1
coords = coords @ self.positional_encoding_gaussian_matrix
coords = 2 * np.pi * coords
# outputs d_1 x ... x d_n x C shape
return torch.cat([torch.sin(coords), torch.cos(coords), torch.sin(coords)], dim=-1)
def forward(self, size: Tuple[int, int, int]) -> torch.Tensor:
"""Generate positional encoding for a grid of the specified size."""
x, y, z = size
device: Any = self.positional_encoding_gaussian_matrix.device
grid = torch.ones((x, y, z), device=device, dtype=torch.float32)
y_embed = grid.cumsum(dim=0) - 0.5
x_embed = grid.cumsum(dim=1) - 0.5
z_embed = grid.cumsum(dim=2) - 0.5
y_embed = y_embed / y
x_embed = x_embed / x
z_embed = z_embed / z
pe = self._pe_encoding(torch.stack([x_embed, y_embed, z_embed], dim=-1))
return pe.permute(3, 0, 1, 2) # C x X x Y x Z
def forward_with_coords(
self, coords_input: torch.Tensor, image_size: Tuple[int, int, int]
) -> torch.Tensor:
"""Positionally encode points that are not normalized to [0,1]."""
coords = coords_input.clone()
coords[:, :, 0] = coords[:, :, 0] / image_size[0]
coords[:, :, 1] = coords[:, :, 1] / image_size[1]
coords[:, :, 2] = coords[:, :, 2] / image_size[2]
return self._pe_encoding(coords.to(torch.float)) # B x N x C
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