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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 | |
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, 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, mask_tokens_out = 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, :, :] | |
mask_tokens_out = mask_tokens_out[:, mask_slice, :] | |
iou_pred = iou_pred[:, mask_slice] | |
# Prepare output | |
return masks, iou_pred, mask_tokens_out | |
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, 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, mask_tokens_out | |
# 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 | |