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