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pytorch-image-models/timm/models/layers/attention_pool2d.py

132 lines
4.8 KiB

""" Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Union, Tuple
import torch
import torch.nn as nn
from .helpers import to_2tuple
from .pos_embed import apply_rot_embed, RotaryEmbedding
from .weight_init import trunc_normal_
class RotAttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ rotary (relative) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: While this impl does not require a fixed feature size, performance at differeing resolutions from
train varies widely and falls off dramatically. I'm not sure if there is a way around this... -RW
"""
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
assert embed_dim % num_heads == 0
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
self.pos_embed = RotaryEmbedding(self.head_dim)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
qc, q = q[:, :, :1], q[:, :, 1:]
sin_emb, cos_emb = self.pos_embed.get_embed((H, W))
q = apply_rot_embed(q, sin_emb, cos_emb)
q = torch.cat([qc, q], dim=2)
kc, k = k[:, :, :1], k[:, :, 1:]
k = apply_rot_embed(k, sin_emb, cos_emb)
k = torch.cat([kc, k], dim=2)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]
class AttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ learned (absolute) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
It was based on impl in CLIP by OpenAI
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: This requires feature size upon construction and well prevent adaptive sizing of the network.
"""
def __init__(
self,
in_features: int,
feat_size: Union[int, Tuple[int, int]],
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
assert embed_dim % num_heads == 0
self.feat_size = to_2tuple(feat_size)
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
spatial_dim = self.feat_size[0] * self.feat_size[1]
self.pos_embed = nn.Parameter(torch.zeros(spatial_dim + 1, in_features))
trunc_normal_(self.pos_embed, std=in_features ** -0.5)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
assert self.feat_size[0] == H
assert self.feat_size[1] == W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]