You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
85 lines
3.1 KiB
85 lines
3.1 KiB
""" Lambda Layer
|
|
|
|
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
|
|
- https://arxiv.org/abs/2102.08602
|
|
|
|
@misc{2102.08602,
|
|
Author = {Irwan Bello},
|
|
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
|
|
Year = {2021},
|
|
}
|
|
|
|
Status:
|
|
This impl is a WIP. Code snippets in the paper were used as reference but
|
|
good chance some details are missing/wrong.
|
|
|
|
I've only implemented local lambda conv based pos embeddings.
|
|
|
|
For a PyTorch impl that includes other embedding options checkout
|
|
https://github.com/lucidrains/lambda-networks
|
|
|
|
Hacked together by / Copyright 2021 Ross Wightman
|
|
"""
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
|
|
from .weight_init import trunc_normal_
|
|
|
|
|
|
class LambdaLayer(nn.Module):
|
|
"""Lambda Layer w/ lambda conv position embedding
|
|
|
|
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
|
|
- https://arxiv.org/abs/2102.08602
|
|
"""
|
|
def __init__(
|
|
self,
|
|
dim, dim_out=None, stride=1, num_heads=4, dim_head=16, r=7, qkv_bias=False):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.dim_out = dim_out or dim
|
|
self.dim_k = dim_head # query depth 'k'
|
|
self.num_heads = num_heads
|
|
assert self.dim_out % num_heads == 0, ' should be divided by num_heads'
|
|
self.dim_v = self.dim_out // num_heads # value depth 'v'
|
|
self.r = r # relative position neighbourhood (lambda conv kernel size)
|
|
|
|
self.qkv = nn.Conv2d(
|
|
dim,
|
|
num_heads * dim_head + dim_head + self.dim_v,
|
|
kernel_size=1, bias=qkv_bias)
|
|
self.norm_q = nn.BatchNorm2d(num_heads * dim_head)
|
|
self.norm_v = nn.BatchNorm2d(self.dim_v)
|
|
|
|
# NOTE currently only supporting the local lambda convolutions for positional
|
|
self.conv_lambda = nn.Conv3d(1, dim_head, (r, r, 1), padding=(r // 2, r // 2, 0))
|
|
|
|
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
|
|
|
|
def reset_parameters(self):
|
|
trunc_normal_(self.qkv.weight, std=self.dim ** -0.5)
|
|
trunc_normal_(self.conv_lambda.weight, std=self.dim_k ** -0.5)
|
|
|
|
def forward(self, x):
|
|
B, C, H, W = x.shape
|
|
M = H * W
|
|
|
|
qkv = self.qkv(x)
|
|
q, k, v = torch.split(qkv, [
|
|
self.num_heads * self.dim_k, self.dim_k, self.dim_v], dim=1)
|
|
q = self.norm_q(q).reshape(B, self.num_heads, self.dim_k, M).transpose(-1, -2) # B, num_heads, M, K
|
|
v = self.norm_v(v).reshape(B, self.dim_v, M).transpose(-1, -2) # B, M, V
|
|
k = F.softmax(k.reshape(B, self.dim_k, M), dim=-1) # B, K, M
|
|
|
|
content_lam = k @ v # B, K, V
|
|
content_out = q @ content_lam.unsqueeze(1) # B, num_heads, M, V
|
|
|
|
position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v)) # B, H, W, V, K
|
|
position_lam = position_lam.reshape(B, 1, self.dim_k, H * W, self.dim_v).transpose(2, 3) # B, 1, M, K, V
|
|
position_out = (q.unsqueeze(-2) @ position_lam).squeeze(-2) # B, num_heads, M, V
|
|
|
|
out = (content_out + position_out).transpose(3, 1).reshape(B, C, H, W) # B, C (num_heads * V), H, W
|
|
out = self.pool(out)
|
|
return out
|