mirror of
https://github.com/open-mmlab/mmsegmentation.git
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242 lines
8.3 KiB
Python
242 lines
8.3 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 math
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from typing import Tuple, Type
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import torch
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from torch import Tensor, nn
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from mmseg.registry import MODELS
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from .common import MLPBlock
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@MODELS.register_module()
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class TwoWayTransformer(nn.Module):
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def __init__(
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self,
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depth: int,
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embedding_dim: int,
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num_heads: int,
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mlp_dim: int,
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activation: Type[nn.Module] = nn.ReLU,
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attention_downsample_rate: int = 2,
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) -> None:
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"""A transformer decoder that attends to an input image using queries
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whose positional embedding is supplied.
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Args:
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depth (int): number of layers in the transformer
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embedding_dim (int): the channel dimension for the input embeddings
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num_heads (int): the number of heads for multihead attention. Must
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divide embedding_dim
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mlp_dim (int): the channel dimension internal to the MLP block
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activation (nn.Module): the activation to use in the MLP block
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"""
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super().__init__()
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self.depth = depth
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self.embedding_dim = embedding_dim
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self.num_heads = num_heads
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self.mlp_dim = mlp_dim
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self.layers = nn.ModuleList()
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for i in range(depth):
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self.layers.append(
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TwoWayAttentionBlock(
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embedding_dim=embedding_dim,
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num_heads=num_heads,
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mlp_dim=mlp_dim,
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activation=activation,
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attention_downsample_rate=attention_downsample_rate,
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skip_first_layer_pe=(i == 0),
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))
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self.final_attn_token_to_image = Attention(
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embedding_dim,
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num_heads,
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downsample_rate=attention_downsample_rate)
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self.norm_final_attn = nn.LayerNorm(embedding_dim)
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def forward(
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self,
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image_embedding: Tensor,
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image_pe: Tensor,
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point_embedding: Tensor,
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) -> Tuple[Tensor, Tensor]:
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"""
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Args:
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image_embedding (torch.Tensor): image to attend to. Should be shape
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B x embedding_dim x h x w for any h and w.
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image_pe (torch.Tensor): the positional encoding to add to the image. Must
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have the same shape as image_embedding.
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point_embedding (torch.Tensor): the embedding to add to the query points.
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Must have shape B x N_points x embedding_dim for any N_points.
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Returns:
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torch.Tensor: the processed point_embedding
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torch.Tensor: the processed image_embedding
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""" # noqa E501
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# BxCxHxW -> BxHWxC == B x N_image_tokens x C
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bs, c, h, w = image_embedding.shape
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image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
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image_pe = image_pe.flatten(2).permute(0, 2, 1)
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# Prepare queries
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queries = point_embedding
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keys = image_embedding
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# Apply transformer blocks and final layernorm
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for layer in self.layers:
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queries, keys = layer(
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queries=queries,
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keys=keys,
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query_pe=point_embedding,
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key_pe=image_pe,
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)
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# Apply the final attenion layer from the points to the image
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q = queries + point_embedding
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k = keys + image_pe
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attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
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queries = queries + attn_out
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queries = self.norm_final_attn(queries)
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return queries, keys
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class TwoWayAttentionBlock(nn.Module):
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def __init__(
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self,
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embedding_dim: int,
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num_heads: int,
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mlp_dim: int = 2048,
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activation: Type[nn.Module] = nn.ReLU,
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attention_downsample_rate: int = 2,
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skip_first_layer_pe: bool = False,
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) -> None:
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"""A transformer block with four layers: (1) self-attention of sparse
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inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
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block on sparse inputs, and (4) cross attention of dense inputs to
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sparse inputs.
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Arguments:
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embedding_dim (int): the channel dimension of the embeddings
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num_heads (int): the number of heads in the attention layers
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mlp_dim (int): the hidden dimension of the mlp block
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activation (nn.Module): the activation of the mlp block
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skip_first_layer_pe (bool): skip the PE on the first layer
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"""
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super().__init__()
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self.self_attn = Attention(embedding_dim, num_heads)
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self.norm1 = nn.LayerNorm(embedding_dim)
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self.cross_attn_token_to_image = Attention(
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embedding_dim,
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num_heads,
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downsample_rate=attention_downsample_rate)
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self.norm2 = nn.LayerNorm(embedding_dim)
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self.mlp = MLPBlock(embedding_dim, mlp_dim, activation)
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self.norm3 = nn.LayerNorm(embedding_dim)
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self.norm4 = nn.LayerNorm(embedding_dim)
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self.cross_attn_image_to_token = Attention(
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embedding_dim,
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num_heads,
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downsample_rate=attention_downsample_rate)
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self.skip_first_layer_pe = skip_first_layer_pe
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def forward(self, queries: Tensor, keys: Tensor, query_pe: Tensor,
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key_pe: Tensor) -> Tuple[Tensor, Tensor]:
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# Self attention block
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if self.skip_first_layer_pe:
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queries = self.self_attn(q=queries, k=queries, v=queries)
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else:
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q = queries + query_pe
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attn_out = self.self_attn(q=q, k=q, v=queries)
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queries = queries + attn_out
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queries = self.norm1(queries)
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# Cross attention block, tokens attending to image embedding
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q = queries + query_pe
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k = keys + key_pe
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attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
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queries = queries + attn_out
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queries = self.norm2(queries)
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# MLP block
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mlp_out = self.mlp(queries)
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queries = queries + mlp_out
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queries = self.norm3(queries)
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# Cross attention block, image embedding attending to tokens
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q = queries + query_pe
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k = keys + key_pe
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attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
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keys = keys + attn_out
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keys = self.norm4(keys)
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return queries, keys
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class Attention(nn.Module):
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"""An attention layer that allows for downscaling the size of the embedding
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after projection to queries, keys, and values."""
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def __init__(
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self,
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embedding_dim: int,
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num_heads: int,
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downsample_rate: int = 1,
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) -> None:
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super().__init__()
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self.embedding_dim = embedding_dim
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self.internal_dim = embedding_dim // downsample_rate
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self.num_heads = num_heads
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assert self.internal_dim % num_heads == 0, 'num_heads must divide embedding_dim.' # noqa E501
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self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
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self.k_proj = nn.Linear(embedding_dim, self.internal_dim)
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self.v_proj = nn.Linear(embedding_dim, self.internal_dim)
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self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
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def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
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b, n, c = x.shape
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x = x.reshape(b, n, num_heads, c // num_heads)
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return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
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def _recombine_heads(self, x: Tensor) -> Tensor:
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b, n_heads, n_tokens, c_per_head = x.shape
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x = x.transpose(1, 2)
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return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
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# Input projections
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q = self.q_proj(q)
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k = self.k_proj(k)
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v = self.v_proj(v)
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# Separate into heads
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q = self._separate_heads(q, self.num_heads)
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k = self._separate_heads(k, self.num_heads)
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v = self._separate_heads(v, self.num_heads)
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# Attention
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_, _, _, c_per_head = q.shape
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attn = q @ k.permute(0, 1, 3, 2) # B x N_heads x N_tokens x N_tokens
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attn = attn / math.sqrt(c_per_head)
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attn = torch.softmax(attn, dim=-1)
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# Get output
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out = attn @ v
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out = self._recombine_heads(out)
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out = self.out_proj(out)
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return out
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