SETR-pytorch:SETR模型的实现，原始论文

<img src="./SETR.png" width="800px"></img> ## 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: <a href="https://arxiv.org/abs/2012.15840">Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.</a> ## Vit The Vit model is also implemented, and you can use it for image classification. ## Usage SETR ```python 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 ```python from SETR.transformer_seg import Vit import torch if __name__ == "__main__": model = Vit(patch_size=(7, 7), in_channels=1, out_class=10, hidden_size=1024, num_hidden_layers=1, num_attention_heads=16) print(model) t1 = torch.rand(1, 1, 28, 28) print("input: " + str(t1.shape)) print("output: " + str(model(t1).shape)) ``` The output shape is (1, 10). ## current examples 1. task_mnist: The simplest example, using the Vit model to classify the minst dataset. 2. task_car_seg: The example is sample segmentation task. data download: <a href="https://www.kaggle.com/c/carvana-image-masking-challenge/data">https://www.kaggle.com/c/carvana-image-masking-challenge/data</a> ## more More examples will be updated later.