411 lines
15 KiB
Python
411 lines
15 KiB
Python
# 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 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,
|
|
) -> None:
|
|
"""
|
|
Predicts masks given an image and prompt embeddings, using a
|
|
tranformer 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_prediction_head = MLP(
|
|
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
image_embeddings: torch.Tensor,
|
|
image_pe: torch.Tensor,
|
|
sparse_prompt_embeddings: torch.Tensor,
|
|
dense_prompt_embeddings: torch.Tensor,
|
|
multimask_output: bool,
|
|
) -> 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
|
|
"""
|
|
masks, iou_pred = self.predict_masks(
|
|
image_embeddings=image_embeddings,
|
|
image_pe=image_pe,
|
|
sparse_prompt_embeddings=sparse_prompt_embeddings,
|
|
dense_prompt_embeddings=dense_prompt_embeddings,
|
|
)
|
|
|
|
# Select the correct mask or masks for outptu
|
|
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
|
|
|
|
def predict_masks(
|
|
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
|
|
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
|
|
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), :]
|
|
|
|
# Upscale mask embeddings and predict masks using the mask tokens
|
|
src = src.transpose(1, 2).view(b, c, h, w)
|
|
upscaled_embedding = self.output_upscaling(src)
|
|
hyper_in_list: List[torch.Tensor] = []
|
|
for i in range(self.num_mask_tokens):
|
|
hyper_in_list.append(
|
|
self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :])
|
|
)
|
|
hyper_in = torch.stack(hyper_in_list, dim=1)
|
|
b, c, h, w = upscaled_embedding.shape
|
|
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
|
|
|
|
# Generate mask quality predictions
|
|
iou_pred = self.iou_prediction_head(iou_token_out)
|
|
|
|
return masks, iou_pred
|
|
|
|
# https://github.com/SysCV/sam-hq/blob/main/segment_anything/modeling/mask_decoder_hq.py#L17
|
|
class MaskDecoderHQ(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,
|
|
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_prediction_head = MLP(
|
|
transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
|
|
)
|
|
|
|
# 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,
|
|
interm_embeddings: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Predict masks given image and prompt embeddings.
|
|
|
|
Arguments:
|
|
image_embeddings (torch.Tensor): the embeddings from the ViT 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
|
|
"""
|
|
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
|
|
)
|
|
|
|
masks, iou_pred = self.predict_masks(
|
|
image_embeddings=image_embeddings,
|
|
image_pe=image_pe,
|
|
sparse_prompt_embeddings=sparse_prompt_embeddings,
|
|
dense_prompt_embeddings=dense_prompt_embeddings,
|
|
hq_features=hq_features,
|
|
)
|
|
|
|
# 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
|
|
# Prepare output
|
|
return masks, iou_pred
|
|
|
|
def predict_masks(
|
|
self,
|
|
image_embeddings: torch.Tensor,
|
|
image_pe: torch.Tensor,
|
|
sparse_prompt_embeddings: torch.Tensor,
|
|
dense_prompt_embeddings: torch.Tensor,
|
|
hq_features: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""Predicts masks. See 'forward' for more details."""
|
|
# Concatenate output tokens
|
|
output_tokens = torch.cat(
|
|
[self.iou_token.weight, self.mask_tokens.weight, self.hf_token.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
|
|
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), :]
|
|
|
|
# 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)
|
|
upscaled_embedding_hq = self.embedding_maskfeature(
|
|
upscaled_embedding_sam
|
|
) + hq_features.repeat(b, 1, 1, 1)
|
|
|
|
hyper_in_list: List[torch.Tensor] = []
|
|
for i in range(self.num_mask_tokens):
|
|
if 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
|
|
|
|
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)
|
|
# Generate mask quality predictions
|
|
iou_pred = self.iou_prediction_head(iou_token_out)
|
|
|
|
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
|