177 lines
6.5 KiB
Python
177 lines
6.5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from torch import nn
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from torch.nn import functional as F
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from typing import List, Tuple, Type
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from .common import LayerNorm2d
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class MaskDecoder(nn.Module):
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def __init__(
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self,
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*,
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transformer_dim: int,
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transformer: nn.Module,
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num_multimask_outputs: int = 3,
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activation: Type[nn.Module] = nn.GELU,
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iou_head_depth: int = 3,
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iou_head_hidden_dim: int = 256,
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) -> None:
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"""
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Predicts masks given an image and prompt embeddings, using a
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tranformer architecture.
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Arguments:
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transformer_dim (int): the channel dimension of the transformer
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transformer (nn.Module): the transformer used to predict masks
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num_multimask_outputs (int): the number of masks to predict
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when disambiguating masks
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activation (nn.Module): the type of activation to use when
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upscaling masks
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iou_head_depth (int): the depth of the MLP used to predict
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mask quality
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iou_head_hidden_dim (int): the hidden dimension of the MLP
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used to predict mask quality
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"""
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super().__init__()
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self.transformer_dim = transformer_dim
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self.transformer = transformer
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self.num_multimask_outputs = num_multimask_outputs
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self.iou_token = nn.Embedding(1, transformer_dim)
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self.num_mask_tokens = num_multimask_outputs + 1
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self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim)
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self.output_upscaling = nn.Sequential(
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nn.ConvTranspose2d(transformer_dim, transformer_dim // 4, kernel_size=2, stride=2),
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LayerNorm2d(transformer_dim // 4),
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activation(),
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nn.ConvTranspose2d(transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2),
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activation(),
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)
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self.output_hypernetworks_mlps = nn.ModuleList(
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[
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MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3)
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for i in range(self.num_mask_tokens)
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]
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)
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self.iou_prediction_head = MLP(
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transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth
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)
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def forward(
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self,
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image_embeddings: torch.Tensor,
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image_pe: torch.Tensor,
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sparse_prompt_embeddings: torch.Tensor,
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dense_prompt_embeddings: torch.Tensor,
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multimask_output: bool,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Predict masks given image and prompt embeddings.
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Arguments:
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image_embeddings (torch.Tensor): the embeddings from the image encoder
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image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
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sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
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dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
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multimask_output (bool): Whether to return multiple masks or a single
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mask.
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Returns:
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torch.Tensor: batched predicted masks
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torch.Tensor: batched predictions of mask quality
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"""
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masks, iou_pred = self.predict_masks(
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image_embeddings=image_embeddings,
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image_pe=image_pe,
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sparse_prompt_embeddings=sparse_prompt_embeddings,
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dense_prompt_embeddings=dense_prompt_embeddings,
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)
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# Select the correct mask or masks for outptu
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if multimask_output:
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mask_slice = slice(1, None)
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else:
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mask_slice = slice(0, 1)
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masks = masks[:, mask_slice, :, :]
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iou_pred = iou_pred[:, mask_slice]
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# Prepare output
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return masks, iou_pred
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def predict_masks(
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self,
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image_embeddings: torch.Tensor,
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image_pe: torch.Tensor,
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sparse_prompt_embeddings: torch.Tensor,
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dense_prompt_embeddings: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Predicts masks. See 'forward' for more details."""
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# Concatenate output tokens
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output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0)
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output_tokens = output_tokens.unsqueeze(0).expand(sparse_prompt_embeddings.size(0), -1, -1)
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tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
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# Expand per-image data in batch direction to be per-mask
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src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
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src = src + dense_prompt_embeddings
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pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
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b, c, h, w = src.shape
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# Run the transformer
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hs, src = self.transformer(src, pos_src, tokens)
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iou_token_out = hs[:, 0, :]
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mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
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# Upscale mask embeddings and predict masks using the mask tokens
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src = src.transpose(1, 2).view(b, c, h, w)
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upscaled_embedding = self.output_upscaling(src)
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hyper_in_list: List[torch.Tensor] = []
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for i in range(self.num_mask_tokens):
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hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]))
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hyper_in = torch.stack(hyper_in_list, dim=1)
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b, c, h, w = upscaled_embedding.shape
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masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w)
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# Generate mask quality predictions
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iou_pred = self.iou_prediction_head(iou_token_out)
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return masks, iou_pred
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# Lightly adapted from
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# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa
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class MLP(nn.Module):
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def __init__(
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self,
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input_dim: int,
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hidden_dim: int,
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output_dim: int,
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num_layers: int,
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sigmoid_output: bool = False,
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) -> None:
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super().__init__()
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self.num_layers = num_layers
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h = [hidden_dim] * (num_layers - 1)
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self.layers = nn.ModuleList(
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nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
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)
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self.sigmoid_output = sigmoid_output
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def forward(self, x):
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for i, layer in enumerate(self.layers):
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x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
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if self.sigmoid_output:
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x = F.sigmoid(x)
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return x
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