LogAvgExp - Pytorch Implementation of LogAvgExp

Related tags

Deep Learningpytorchlogsumexp
Overview

LogAvgExp - Pytorch

Implementation of LogAvgExp for Pytorch

Install

$ pip install logavgexp-pytorch

Usage

import torch
from logavgexp_pytorch import logavgexp

# basically it is an improved logsumexp (differentiable max)
# normalized for length

x = torch.arange(1000)
y = logavgexp(x, dim = 0, temp = 0.01) # ~998.8

# more than 1 dimension

x = torch.randn(1, 2048, 5)
y = logavgexp(x, dim = 1, temp = 0.2) # (1, 5)

# keep dimension

x = torch.randn(1, 2048, 5)
y = logavgexp(x, dim = 1, temp = 0.2, keepdim = True) # (1, 1, 5)

# masking (False for mask out with large negative value)

x = torch.randn(1, 2048, 5)
m = torch.randint(0, 2, (1, 2048, 1)).bool()

y = logavgexp(x, mask = m, dim = 1, temp = 0.2, keepdim = True) # (1, 1, 5)

With learned temperature

# learned temperature
import torch
from torch import nn
from logavgexp_pytorch import logavgexp

learned_temp = nn.Parameter(torch.ones(1) * -5).exp().clamp(min = 1e-8) # make sure temperature can't hit 0

x = torch.randn(1, 2048, 5)
y = logavgexp(x, temp = learned_temp, dim = -1) # (1, 5)

Or you can use the LogAvgExp class to handle the learned temperature parameter

import torch
from logavgexp_pytorch import LogAvgExp

logavgexp = LogAvgExp(
    temp = 0.01,
    dim = 1,
    learned_temp = True
)

x = torch.randn(1, 2048, 5)
y = logavgexp(x) # (1, 5)

Citations

@misc{lowe2021logavgexp,
    title   = {LogAvgExp Provides a Principled and Performant Global Pooling Operator}, 
    author  = {Scott C. Lowe and Thomas Trappenberg and Sageev Oore},
    year    = {2021},
    eprint  = {2111.01742},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
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Comments
  • Enhancement: 2d Pooling

    Enhancement: 2d Pooling

    Could put in 2d pooling for an easy to drop in alternative to AvgPool2d by using pixel_unshuffle for cases where there's exact divisions of an input shape, or padding and unfold for overlapping windows / strides that aren't equal to the window size.

    I don't know how fast Unfold is or if there's a better way to do the operation; I tried checking the PyTorch github to see how it does AvgPool2d for strides that aren't equal to the window size behind the scenes but I can never seem to figure out which version of the operation is the one that's used, it's defined in so many places it's beyond me.

    I've once seen an alternative to pixel unshuffle that used grouped conv2d, a kernel that put each position into its own channel output, and stride, but I can't seem to find it again. It was useful because you could adjust the stride and then it'd be like Unfold, but I never got around to testing if it was actually faster.

    opened by torridgristle 6
  • learned temperature stagnates at a low value (a high value is expected)

    learned temperature stagnates at a low value (a high value is expected)

    Hi,

    Big thanks for your pytorch implementation of the logavgexp !

    I noticed that it is easy for logavgexp to reproduce the max operator (temperature goes nicely to 0); but it has trouble reproducing the mean operator, the temperature stagnates in the following example at 0.35; do your have an explanation for that or ways to circumvent this issue? Thanks !

    import torch
torch.manual_seed(12345)
from logavgexp_pytorch import LogAvgExp

B = 10
N = 20
x = torch.randn(B,N)
#y, _ = x.max(dim=-1, keepdim=True)
y    = x.mean(dim=-1, keepdim=True)
logavgexp = LogAvgExp(
    temp = 1,
    dim = 1,
    learned_temp = True,
    keepdim = True)

optimizer = torch.optim.Adam(logavgexp.parameters(), lr=0.01)
loss_func = torch.nn.MSELoss()
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
                                                       factor=0.5,
                                                       patience=100,
                                                       verbose=True)

for i in range(10000):
    prediction = logavgexp(x)
    loss = loss_func(prediction, y) 
    optimizer.zero_grad()
    loss.backward()        
    optimizer.step()
    scheduler.step(loss, epoch=i)
    print(f"ite: {i}, loss: {loss.item():.2e}, temperature: {logavgexp.temp.exp().item():.4f}")
    opened by ldv1 0
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