Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Related tags

Deep Learning transformer segmentation semantic-segmentation segmentation-transformer
Overview

Segmentation Transformer

Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using transformer style encoders.

SETR

Features

  • SETR
    • SETR-Naive
    • SETR-PUP
    • SETR-MLA
  • SETR-Hybrid

To Do:

  • Training Scripts

Installation

Create the environment:

conda env create -f environment.yml
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Comments
  • redundant dropout at the same place

    redundant dropout at the same place

    Line 103 in Transformer.py: PreNormDrop( dim, dropout_rate, SelfAttention( dim, heads=heads, dropout_rate=attn_dropout_rate ), ) In the PreNormDrop module, a dropout is implemented after SelfAttention. However, in the SelfAttention module, there already exists a dropout layer at the end. I believe PreNorm should be used instead of PreNormDrop.

    opened by zaocan666 3
  • model building problem

    model building problem

    Hi~ I think there exists some problem in model building. I notice that in the decode function of SETR_Naive/SETR_PUP/SETR_MLA, some layers are initialized, like nn.Conv2d. However, the decode function is in the forward function of the model, so these layers will be initialized every time the model is feed data. Therefore, these layers' weight are not trained at all. Initialization of these layers should be placed in the init function of the model.

    opened by zaocan666 2

  • "pass the intermediate layers for MLA"

    Thanks for the implementation. Can you please give an example of how to specify the intermediate layers for MLA?

    assert intmd_layers is not None, "pass the intermediate layers for MLA"

    opened by rawmean 4
Owner
Abhay Gupta
Engineer with an AI background.
Abhay Gupta
GitHub https://arxiv.org/abs/2012.15840
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