Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Last update: Dec 22, 2022

Overview

Cross Transformers - Pytorch (wip)

Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch

Install

$ pip install cross-transformers-pytorch

Usage

import torch
from torch import nn
import torch.nn.functional as F
from torchvision import models
from cross_transformers_pytorch import CrossTransformer

resnet = models.resnet34(pretrained = True)
model = nn.Sequential(*[*resnet.children()][:-2])

cross_transformer = CrossTransformer(
    dim = 512,
    dim_key = 128,
    dim_value = 128
)

# (batch, channels, height, width)
img_query = torch.randn(1, 3, 224, 224)

# (batch, classes, num supports, channels, height, width)
img_supports = torch.randn(1, 2, 4, 3, 224, 224)

labels = torch.randint(0, 2, (1,))

dists = cross_transformer(model, img_query, img_supports) # (1, 2)

loss = F.cross_entropy(dists, labels)
loss.backward()

Citations

@misc{doersch2020crosstransformers,
    title={CrossTransformers: spatially-aware few-shot transfer}, 
    author={Carl Doersch and Ankush Gupta and Andrew Zisserman},
    year={2020},
    eprint={2007.11498},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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Related tags

Overview

Cross Transformers - Pytorch (wip)

Install

Usage

Citations

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Releases(0.0.2)

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0.0.1(Dec 16, 2020)

Owner

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