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# 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 torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type
from .common import LayerNorm2d
class MaskDecoder(nn.Module):
def __init__(
self,
*,
transformer_dim: int,
transformer: nn.Module,
num_multimask_outputs: int = 3,
activation: Type[nn.Module] = nn.GELU,
iou_head_depth: int = 3,
iou_head_hidden_dim: int = 256,
is_hq = False,
vit_dim: int = 1024,
) -> None:
"""
Predicts masks given an image and prompt embeddings, using a
transformer architecture.
Arguments:
transformer_dim (int): the channel dimension of the transformer
transformer (nn.Module): the transformer used to predict masks
num_multimask_outputs (int): the number of masks to predict
when disambiguating masks
activation (nn.Module): the type of activation to use when
upscaling masks
iou_head_depth (int): the depth of the MLP used to predict
mask quality
iou_head_hidden_dim (int): the hidden dimension of the MLP
used to predict mask quality
"""
super().__init__()
self.transformer_dim = transformer_dim
self.transformer = transformer
self.num_multimask_outputs = num_multimask_outputs
self.iou_token = nn.Embedding(1, transformer_dim)
self.num_mask_tokens = num_multimask_outputs + 1
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.output_hypernetworks_mlps = nn.ModuleList(
[
MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
for i in range(self.num_mask_tokens)
]
)
self.iou_head_depth = iou_head_depth
self.iou_head_hidden_dim = iou_head_hidden_dim
self.iou_prediction_head = MLP(
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
)
self.is_hq = is_hq
if is_hq:
# HQ-SAM parameters
self.hf_token = nn.Embedding(1, transformer_dim) # HQ-Ouptput-Token
self.hf_mlp = MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) # corresponding new MLP layer for HQ-Ouptput-Token
self.num_mask_tokens = self.num_mask_tokens + 1
# three conv fusion layers for obtaining HQ-Feature
self.compress_vit_feat = nn.Sequential(
nn.ConvTranspose2d(vit_dim, transformer_dim, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim),
nn.GELU(),
nn.ConvTranspose2d(transformer_dim, transformer_dim // 8, kernel_size=2, stride=2))
self.embedding_encoder = nn.Sequential(
nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
LayerNorm2d(transformer_dim // 4),
nn.GELU(),
nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
)
self.embedding_maskfeature = nn.Sequential(
nn.Conv2d(transformer_dim // 8, transformer_dim // 4, 3, 1, 1),
LayerNorm2d(transformer_dim // 4),
nn.GELU(),
nn.Conv2d(transformer_dim // 4, transformer_dim // 8, 3, 1, 1))
def forward(
self,
image_embeddings: torch.Tensor,
image_pe: torch.Tensor,
sparse_prompt_embeddings: torch.Tensor,
dense_prompt_embeddings: torch.Tensor,
multimask_output: bool,
hq_token_only: bool = False,
interm_embeddings: torch.Tensor = None,
) -> Tuple[torch.Tensor, 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
sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
multimask_output (bool): Whether to return multiple masks or a single
mask.
Returns:
torch.Tensor: batched predicted masks
torch.Tensor: batched predictions of mask quality
"""
hq_features = None
# token processing
if self.is_hq:
vit_features = interm_embeddings[0].permute(0, 3, 1, 2) # early-layer ViT feature, after 1st global attention block in ViT
hq_features = self.embedding_encoder(image_embeddings) + self.compress_vit_feat(vit_features)
output_tokens = [self.iou_token.weight, self.mask_tokens.weight, self.hf_token.weight]
else:
output_tokens = [self.iou_token.weight, self.mask_tokens.weight]
output_tokens = torch.cat(output_tokens, 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)
# tokens: (batch size, model preserved tokens (iou*1, mask*4, hf token * 1) + user prompts, token dim)
# Expand per-image data in batch direction to be per-mask. multiple user prompts for the same image are divide along batch channel
src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
src = src + dense_prompt_embeddings
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)
iou_token_out = hs[:, 0, :]
mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
# Decode tokens, mask tokens -> iou preds, src tokens (input image tokens) to masks
# Upscale mask embeddings and predict masks using the mask tokens
src = src.transpose(1, 2).view(b, c, h, w)
upscaled_embedding_sam = self.output_upscaling(src)
hyper_in_list: List[torch.Tensor] = []
for i in range(self.num_mask_tokens):
if not self.is_hq or i < self.num_mask_tokens - 1:
hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
else:
hyper_in_list.append(self.hf_mlp(mask_tokens_out[:, i, :]))
hyper_in = torch.stack(hyper_in_list, dim=1)
b, c, h, w = upscaled_embedding_sam.shape
if self.is_hq:
upscaled_embedding_hq = self.embedding_maskfeature(upscaled_embedding_sam) + hq_features.repeat(b,1,1,1)
masks_sam = (hyper_in[:,:self.num_mask_tokens-1] @ upscaled_embedding_sam.view(b, c, h * w)).view(b, -1, h, w)
masks_sam_hq = (hyper_in[:,self.num_mask_tokens-1:] @ upscaled_embedding_hq.view(b, c, h * w)).view(b, -1, h, w)
masks = torch.cat([masks_sam,masks_sam_hq],dim=1)
else:
masks = (hyper_in @ upscaled_embedding_sam.view(b, c, h * w)).view(b, -1, h, w)
# Generate mask quality predictions
iou_pred = self.iou_prediction_head(iou_token_out)
if self.is_hq:
# Select the correct mask or masks for output
if multimask_output:
# mask with highest score
mask_slice = slice(1,self.num_mask_tokens-1)
iou_pred = iou_pred[:, mask_slice]
iou_pred, max_iou_idx = torch.max(iou_pred,dim=1)
iou_pred = iou_pred.unsqueeze(1)
masks_multi = masks[:, mask_slice, :, :]
masks_sam = masks_multi[torch.arange(masks_multi.size(0)),max_iou_idx].unsqueeze(1)
else:
# singale mask output, default
mask_slice = slice(0, 1)
iou_pred = iou_pred[:,mask_slice]
masks_sam = masks[:,mask_slice]
masks_hq = masks[:,slice(self.num_mask_tokens-1, self.num_mask_tokens)]
if hq_token_only:
masks = masks_hq
else:
masks = masks_sam + masks_hq
else:
# Select the correct mask or masks for output
if multimask_output:
mask_slice = slice(1, None)
else:
mask_slice = slice(0, 1)
masks = masks[:, mask_slice, :, :]
iou_pred = iou_pred[:, mask_slice]
# Prepare output
return masks, iou_pred
# 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
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