Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

Related tags

Deep Learningdeep-learningtransformersartificial-intelligenceattention-mechanismvideo-classification
Overview

STAM - Pytorch

Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in video classification. This corroborates the finding of TimeSformer. Attention is all we need.

Install

$ pip install stam-pytorch

Usage

import torch
from stam_pytorch import STAM

model = STAM(
    dim = 512,
    image_size = 256,     # size of image
    patch_size = 32,      # patch size
    num_frames = 5,       # number of image frames, selected out of video
    space_depth = 12,     # depth of vision transformer
    space_heads = 8,      # heads of vision transformer
    space_mlp_dim = 2048, # feedforward hidden dimension of vision transformer
    time_depth = 6,       # depth of time transformer (in paper, it was shallower, 6)
    time_heads = 8,       # heads of time transformer
    time_mlp_dim = 2048,  # feedforward hidden dimension of time transformer
    num_classes = 100,    # number of output classes
    space_dim_head = 64,  # space transformer head dimension
    time_dim_head = 64,   # time transformer head dimension
    dropout = 0.,         # dropout
    emb_dropout = 0.      # embedding dropout
)

frames = torch.randn(2, 5, 3, 256, 256) # (batch x frames x channels x height x width)
pred = model(frames) # (2, 100)

Citations

@misc{sharir2021image,
    title   = {An Image is Worth 16x16 Words, What is a Video Worth?}, 
    author  = {Gilad Sharir and Asaf Noy and Lihi Zelnik-Manor},
    year    = {2021},
    eprint  = {2103.13915},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
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Comments
  • train loss curve is realy weird

    train loss curve is realy weird

    Hi Thanks for the great work. I am trying to train your transformer with my video data but the train loss curve looks really weird and its increasing. I tried with normal video data of shape [3,256,256] in range 0-255 and in range 0-1. Could you please tell what I am doing wrong. My data contains a person doing something and I want to recognize the action. loss(4)

    opened by saniazahan 1
  • regression

    regression

    Beautiful work as usual, thanks for this implementation.

    I'm curious if you tried using this for a regression task? I have tried using TimeSFormer without success yet, I know the signal is there because I can learn it with a small 3dcnn trained from scratch so I suspect my understanding of how and where to modify the transformer is the culprit. The output is a 1D vector with len == num_frames. Any suggestions very appreciated!

    opened by raijinspecial 2
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Phil Wang
Working with Attention. It's all we need.
Phil Wang
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