ofcourse. you can do min-max normalization. I am not sure if there is any direct API available.
import torch
N, C, H, W = 2, 3, 4, 5
feat_maps = torch.randn(N, C, H, W)
vectorized_feat_maps = feat_maps.view(N, C, -1)
feat_maps_0to1 = ((vectorized_feat_maps - vectorized_feat_maps.min(dim=-1, keepdim=True)[0]) / (vectorized_feat_maps.max(dim=-1, keepdim=True)[0]-vectorized_feat_maps.min(dim=-1, keepdim=True)[0])).view_as(feat_maps)

This might be helpful as well.

  
    
    
    Normalize vectors to [-1,1] or [0,1] 
  
  
    I don’t think there currently is an in-build PyTorch function for what you want. 
If you really want a specific function for it, instead of simply doing this yourself, “torchsample”: https://github.com/ncullen93/torchsample offers a “RangeNormalize” function where you can just specify min and max range values (tuples per channel: i.e. ((0,0,0),(1,1,1)) or just floats (0, 1)