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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 torch
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Type, Optional
from .common import LayerNorm2d
class MaskDecoder(nn.Module):
def __init__(
self,
*,
image_encoder_type: str,
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,
image_size,
patch_size,
) -> 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)
if image_encoder_type == 'swin_vit':
self.feat_shape = image_size/patch_size
self.output_upscaling = nn.Sequential(
nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
nn.LayerNorm((transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))), # swin
activation(),
nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2), # swin
# nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1), # vit
activation(),
)
else:
self.feat_shape = image_size/patch_size * 2
self.output_upscaling = nn.Sequential(
nn.ConvTranspose3d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
nn.LayerNorm((transformer_dim // 4, int(self.feat_shape[0]), int(self.feat_shape[1]), int(self.feat_shape[2]))), # vit
activation(),
nn.ConvTranspose3d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
# nn.Conv3d(transformer_dim // 4, transformer_dim // 8, kernel_size=3, stride=1, padding=1),
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
)
self.txt_align_upscaled_embedding = nn.Linear(768, 96)
def forward(
self,
image_embeddings: torch.Tensor,
text_embedding: Optional[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
"""
# print('--------------decoder here--------------')
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
text_embedding=text_embedding,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
# 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
def predict_masks(
self,
image_embeddings: torch.Tensor,
text_embedding: 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) # [2, 7=(5+2), 256]
# 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
pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
b, c, h, w, d = 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, d)
# print('src ', src.shape) # vit:[B, 768, 12, 12, 6], swin: [B, 6, 6, 3]
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, d = upscaled_embedding.shape
# print('hyper_in ', hyper_in.shape) # [2, 4, 96]
# print('upscaled_embedding ', upscaled_embedding.shape) # [2, 96, 24, 24, 12]*
masks = (hyper_in @ upscaled_embedding.view(b, c, h * w * d)).view(b, -1, h, w, d)
# print('masks here ', masks.shape) # [2, 4, 24, 24, 12]
if text_embedding is not None:
# text_embedding: B x 768, upscaled_embedding: B x c x h x w x d => B x 1 x h x w x d
text_embedding_down = self.txt_align_upscaled_embedding(text_embedding).unsqueeze(dim=1)
upscaled_embedding = upscaled_embedding.view(b, c, h * w * d)
# print('text_embedding_down ', text_embedding_down.shape) # [2, 1, 96]
# text_embedding_norm = F.normalize(text_embedding_down, dim=-1)
# upscaled_embedding_norm = F.normalize(upscaled_embedding, dim=1)
# sim = (text_embedding_norm @ upscaled_embedding_norm).view(b, -1, h, w, d)
# print(text_embedding_down.shape, upscaled_embedding.shape)
sim = (text_embedding_down @ upscaled_embedding).view(b, -1, h, w, d)
# print('sim ', sim.shape) # [B, 1, 24, 24, 12]
sim = sim.repeat(1, masks.shape[1], 1, 1, 1)
# print('sim after', sim.shape) # [B, 4, 24, 24, 12]
masks = masks + sim
# 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
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