Upsampling in Semantic Segmentation

懵懂的女人 提交于 2021-02-09 11:13:47

问题


I am trying to implement a paper on Semantic Segmentation and I am confused about how to Upsample the prediction map produced by my segmentation network to match the input image size.

For example, I am using a variant of Resnet101 as the segmentation network (as used by the paper). With this network structure, an input of size 321x321 (again used in the paper) produces a final prediction map of size 41x41xC (C is the number of classes). Because I have to make pixel-level predictions, I need to upsample it to 321x321xC. Pytorch provides function to Upsample to an output size which is a multiple of the prediction map size. So, I can not directly use that method here.

Because this step is involved in every semantic segmentation network, I am sure there should be a standard way to implement this.

I would appreciate any pointers. Thanks in advance.


回答1:


Maybe the simpliest thing you can try is:

  • upsample 8 times. Then you 41x41 input turns into 328x328
  • perform center cropping to get your desired shape 321x321 (for instance, something like this input[3:,3:,:-4,:-4])


来源:https://stackoverflow.com/questions/47117302/upsampling-in-semantic-segmentation

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