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

SETR – Pytorch

Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official code,I implemented SETR-Progressive UPsampling(SETR-PUP) using pytorch.

Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

Vit

The Vit model is also implemented, and you can use it for image classification.

Usage SETR

from SETR.transformer_seg import SETRModel
import torch 

if __name__ == "__main__":
    net = SETRModel(patch_size=(32, 32), 
                    in_channels=3, 
                    out_channels=1, 
                    hidden_size=1024, 
                    num_hidden_layers=8, 
                    num_attention_heads=16, 
                    decode_features=[512, 256, 128, 64])
    t1 = torch.rand(1, 3, 256, 256)
    print("input: " + str(t1.shape))
    
    # print(net)
    print("output: " + str(net(t1).shape))

If the output size is (1, 1, 256, 256), the code runs successfully.

Usage Vit

from SETR.transformer_seg import Vit

import torch 

if __name__ == "__main__":

    model = Vit(patch_size=(7, 7),

                    in_channels=1,
 
 

 
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