Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Related tags

Deep Learningdeep-learningartificial-intelligencesegmentationimage-segmentationattention-mechanismmultilayer-perceptron
Overview

Segformer - Pytorch

Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch.

Install

$ pip install segformer-pytorch

Usage

For example, MiT-B0

import torch
from segformer_pytorch import Segformer

model = Segformer(
    patch_size = 4,                 # patch size
    dims = (32, 64, 160, 256),      # dimensions of each stage
    heads = (1, 2, 5, 8),           # heads of each stage
    ff_expansion = (8, 8, 4, 4),    # feedforward expansion factor of each stage
    reduction_ratio = (8, 4, 2, 1), # reduction ratio of each stage for efficient attention
    num_layers = 2,                 # num layers of each stage
    decoder_dim = 256,              # decoder dimension
    num_classes = 4                 # number of segmentation classes
)

x = torch.randn(1, 3, 256, 256)
pred = model(x) # (1, 4, 64, 64)  # output is (H/4, W/4) map of the number of segmentation classes

Make sure the keywords are at most a tuple of 4, as this repository is hard-coded to give the MiT 4 stages as done in the paper.

Citations

@misc{xie2021segformer,
    title   = {SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers}, 
    author  = {Enze Xie and Wenhai Wang and Zhiding Yu and Anima Anandkumar and Jose M. Alvarez and Ping Luo},
    year    = {2021},
    eprint  = {2105.15203},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
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Comments
  • Something is wrong with your implementation.

    Something is wrong with your implementation.

    Hello!

    First of all, I really like the repo. The implementation is clean and so much easier to understand than the official repo. But after doing some digging, I realized that the number of parameters and layers (especially conv2d) is quite different from the official implementation. This is the case for all variants I have tested (B0 and B5).

    Check out the README in my repo here, and you'll see what I mean. I also included images of the execution graphs of the two different implementations in the 'src' folder, which could help to debug.

    I don't quite have time to dig into the source of the problem, but I just thought I'd share my observations with you.

    opened by camlaedtke 0
  • Models weights + model output HxW

    Models weights + model output HxW

    Hi,

    Could you please add the models weights so we can start training from them?

    Also, why you choose to train models with an output of size (H/4,W/4) and not the original (HxW) size?

    Great job for the paper, very interesting :)

    opened by isega24 2
  • The model configurations for all the SegFormer B0 ~ B5

    The model configurations for all the SegFormer B0 ~ B5

    Hello How are you? Thanks for contributing to this project. Is the model configuration in README MiT-B0 correctly? That's because the total number of params for the model is 36M. Could u provide all the model configurations for SegFormer B0 ~ B5?

    opened by rose-jinyang 5
  • a question about kv reshape in Efficient Self-Attention

    a question about kv reshape in Efficient Self-Attention

    Thanks for sharing your work, your code is so elegant, and inspired me a lot. Here is a question about the implementation of Efficient Self-Attention

    It seems you use a "mean op" to reshape k,v. and the official implementation uses a (learnable) linear mapping to reshape k,v

    may I ask, whether this difference significantly matters in your experiment ?

    in your code:

    k, v = map(lambda t: reduce(t, 'b c (h r1) (w r2) -> b c h w', 'mean', r1 = r, r2 = r), (k, v))

    the original implementation uses:

    self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
self.norm = nn.LayerNorm(dim)

x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
    opened by masszhou 1
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Phil Wang
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Phil Wang
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