396 lines
14 KiB
Python
396 lines
14 KiB
Python
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# 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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import torch.nn as nn
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import torch.nn.functional as F
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from typing import Optional, Tuple, Type
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from .common import LayerNorm2d, MLPBlock
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# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
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class ImageEncoderViT(nn.Module):
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def __init__(
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self,
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img_size: int = 1024,
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patch_size: int = 16,
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in_chans: int = 3,
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embed_dim: int = 768,
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depth: int = 12,
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num_heads: int = 12,
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mlp_ratio: float = 4.0,
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out_chans: int = 256,
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qkv_bias: bool = True,
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norm_layer: Type[nn.Module] = nn.LayerNorm,
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act_layer: Type[nn.Module] = nn.GELU,
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use_abs_pos: bool = True,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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window_size: int = 0,
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global_attn_indexes: Tuple[int, ...] = (),
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) -> None:
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"""
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Args:
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img_size (int): Input image size.
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patch_size (int): Patch size.
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in_chans (int): Number of input image channels.
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embed_dim (int): Patch embedding dimension.
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depth (int): Depth of ViT.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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norm_layer (nn.Module): Normalization layer.
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act_layer (nn.Module): Activation layer.
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use_abs_pos (bool): If True, use absolute positional embeddings.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks.
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global_attn_indexes (list): Indexes for blocks using global attention.
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"""
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super().__init__()
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self.img_size = img_size
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self.patch_embed = PatchEmbed(
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kernel_size=(patch_size, patch_size),
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stride=(patch_size, patch_size),
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in_chans=in_chans,
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embed_dim=embed_dim,
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)
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self.pos_embed: Optional[nn.Parameter] = None
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if use_abs_pos:
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# Initialize absolute positional embedding with pretrain image size.
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self.pos_embed = nn.Parameter(
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torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
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)
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self.blocks = nn.ModuleList()
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for i in range(depth):
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block = Block(
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dim=embed_dim,
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num_heads=num_heads,
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mlp_ratio=mlp_ratio,
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qkv_bias=qkv_bias,
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norm_layer=norm_layer,
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act_layer=act_layer,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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window_size=window_size if i not in global_attn_indexes else 0,
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input_size=(img_size // patch_size, img_size // patch_size),
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)
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self.blocks.append(block)
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self.neck = nn.Sequential(
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nn.Conv2d(
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embed_dim,
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out_chans,
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kernel_size=1,
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bias=False,
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),
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LayerNorm2d(out_chans),
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nn.Conv2d(
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out_chans,
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out_chans,
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kernel_size=3,
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padding=1,
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bias=False,
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),
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LayerNorm2d(out_chans),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.patch_embed(x)
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if self.pos_embed is not None:
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x = x + self.pos_embed
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for blk in self.blocks:
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x = blk(x)
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x = self.neck(x.permute(0, 3, 1, 2))
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return x
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class Block(nn.Module):
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"""Transformer blocks with support of window attention and residual propagation blocks"""
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def __init__(
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self,
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dim: int,
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num_heads: int,
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mlp_ratio: float = 4.0,
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qkv_bias: bool = True,
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norm_layer: Type[nn.Module] = nn.LayerNorm,
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act_layer: Type[nn.Module] = nn.GELU,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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window_size: int = 0,
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input_size: Optional[Tuple[int, int]] = None,
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) -> None:
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads in each ViT block.
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mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
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qkv_bias (bool): If True, add a learnable bias to query, key, value.
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norm_layer (nn.Module): Normalization layer.
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act_layer (nn.Module): Activation layer.
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use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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window_size (int): Window size for window attention blocks. If it equals 0, then
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use global attention.
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input_size (int or None): Input resolution for calculating the relative positional
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parameter size.
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"""
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super().__init__()
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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use_rel_pos=use_rel_pos,
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rel_pos_zero_init=rel_pos_zero_init,
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input_size=input_size if window_size == 0 else (window_size, window_size),
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)
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self.norm2 = norm_layer(dim)
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self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
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self.window_size = window_size
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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shortcut = x
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x = self.norm1(x)
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# Window partition
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if self.window_size > 0:
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H, W = x.shape[1], x.shape[2]
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x, pad_hw = window_partition(x, self.window_size)
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x = self.attn(x)
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# Reverse window partition
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if self.window_size > 0:
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x = window_unpartition(x, self.window_size, pad_hw, (H, W))
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x = shortcut + x
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x = x + self.mlp(self.norm2(x))
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return x
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class Attention(nn.Module):
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"""Multi-head Attention block with relative position embeddings."""
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def __init__(
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self,
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dim: int,
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num_heads: int = 8,
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qkv_bias: bool = True,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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input_size: Optional[Tuple[int, int]] = None,
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) -> None:
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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qkv_bias (bool: If True, add a learnable bias to query, key, value.
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rel_pos (bool): If True, add relative positional embeddings to the attention map.
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rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
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input_size (int or None): Input resolution for calculating the relative positional
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parameter size.
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"""
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = head_dim**-0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.proj = nn.Linear(dim, dim)
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self.use_rel_pos = use_rel_pos
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if self.use_rel_pos:
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assert (
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input_size is not None
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), "Input size must be provided if using relative positional encoding."
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# initialize relative positional embeddings
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self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
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self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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B, H, W, _ = x.shape
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# qkv with shape (3, B, nHead, H * W, C)
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qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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# q, k, v with shape (B * nHead, H * W, C)
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q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
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attn = (q * self.scale) @ k.transpose(-2, -1)
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if self.use_rel_pos:
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attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
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attn = attn.softmax(dim=-1)
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x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
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x = self.proj(x)
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return x
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def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
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"""
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Partition into non-overlapping windows with padding if needed.
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Args:
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x (tensor): input tokens with [B, H, W, C].
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window_size (int): window size.
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Returns:
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windows: windows after partition with [B * num_windows, window_size, window_size, C].
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(Hp, Wp): padded height and width before partition
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"""
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B, H, W, C = x.shape
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pad_h = (window_size - H % window_size) % window_size
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pad_w = (window_size - W % window_size) % window_size
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
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Hp, Wp = H + pad_h, W + pad_w
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x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows, (Hp, Wp)
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def window_unpartition(
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windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
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) -> torch.Tensor:
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"""
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Window unpartition into original sequences and removing padding.
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Args:
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x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
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window_size (int): window size.
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pad_hw (Tuple): padded height and width (Hp, Wp).
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hw (Tuple): original height and width (H, W) before padding.
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Returns:
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x: unpartitioned sequences with [B, H, W, C].
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"""
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Hp, Wp = pad_hw
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H, W = hw
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B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
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if Hp > H or Wp > W:
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x = x[:, :H, :W, :].contiguous()
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return x
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def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
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"""
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Get relative positional embeddings according to the relative positions of
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query and key sizes.
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Args:
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q_size (int): size of query q.
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k_size (int): size of key k.
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rel_pos (Tensor): relative position embeddings (L, C).
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Returns:
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Extracted positional embeddings according to relative positions.
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"""
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max_rel_dist = int(2 * max(q_size, k_size) - 1)
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# Interpolate rel pos if needed.
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if rel_pos.shape[0] != max_rel_dist:
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# Interpolate rel pos.
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rel_pos_resized = F.interpolate(
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rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
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size=max_rel_dist,
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mode="linear",
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)
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rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
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else:
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rel_pos_resized = rel_pos
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# Scale the coords with short length if shapes for q and k are different.
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q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
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k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
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relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
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return rel_pos_resized[relative_coords.long()]
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def add_decomposed_rel_pos(
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attn: torch.Tensor,
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q: torch.Tensor,
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rel_pos_h: torch.Tensor,
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rel_pos_w: torch.Tensor,
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q_size: Tuple[int, int],
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k_size: Tuple[int, int],
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) -> torch.Tensor:
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"""
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Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
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https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
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Args:
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attn (Tensor): attention map.
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q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
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rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
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rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
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q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
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k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
attn (Tensor): attention map with added relative positional embeddings.
|
||
|
|
"""
|
||
|
|
q_h, q_w = q_size
|
||
|
|
k_h, k_w = k_size
|
||
|
|
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
|
||
|
|
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
|
||
|
|
|
||
|
|
B, _, dim = q.shape
|
||
|
|
r_q = q.reshape(B, q_h, q_w, dim)
|
||
|
|
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
|
||
|
|
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
|
||
|
|
|
||
|
|
attn = (
|
||
|
|
attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
|
||
|
|
).view(B, q_h * q_w, k_h * k_w)
|
||
|
|
|
||
|
|
return attn
|
||
|
|
|
||
|
|
|
||
|
|
class PatchEmbed(nn.Module):
|
||
|
|
"""
|
||
|
|
Image to Patch Embedding.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
kernel_size: Tuple[int, int] = (16, 16),
|
||
|
|
stride: Tuple[int, int] = (16, 16),
|
||
|
|
padding: Tuple[int, int] = (0, 0),
|
||
|
|
in_chans: int = 3,
|
||
|
|
embed_dim: int = 768,
|
||
|
|
) -> None:
|
||
|
|
"""
|
||
|
|
Args:
|
||
|
|
kernel_size (Tuple): kernel size of the projection layer.
|
||
|
|
stride (Tuple): stride of the projection layer.
|
||
|
|
padding (Tuple): padding size of the projection layer.
|
||
|
|
in_chans (int): Number of input image channels.
|
||
|
|
embed_dim (int): embed_dim (int): Patch embedding dimension.
|
||
|
|
"""
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
self.proj = nn.Conv2d(
|
||
|
|
in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||
|
|
x = self.proj(x)
|
||
|
|
# B C H W -> B H W C
|
||
|
|
x = x.permute(0, 2, 3, 1)
|
||
|
|
return x
|