By default pytorch only support padding on both side, but for example, I have a feature of 1x512x37x56(NCHW) and I want to pad on one side to 1x512x38x57, how can I do it?
Hmm, seems they added a lot of padding classes since I looked last time and this is not needed anymore…
This page has all the padding you might want
Padding, whilst copying the values of the tensor is doable with the Functional interface of PyTorch.
You can read more about the different padding modes here.
import torch.nn.functional as F # Pad last 2 dimensions of tensor with (0, 1) -> Adds extra column/row to the right and bottom, whilst copying the values of the current last column/row padded_tensor = F.pad(input_tensor, (0,1,0,1), mode='replicate')
You can wrap this functional interface in a module:
import torch import torch.nn.functional as F class CustomPad(torch.nn.module): def __init__(self, padding): self.padding = padding def forward(self, x): return F.pad(x. self.padding, mode='replicate')
Thank you! I also found nn.ConstantPad2d can do the job
Here is an example how I have used padding! Please check on forward() method.
class Up (nn.Module): def __init__(self, in_ch, out_ch, bilinear= True): super(self, Up).__init__() # bilinear is the upsample algorithm given torch.nn.Up algo. #check more here: https://s0pytorch0org.icopy.site/docs/0.4.0/_modules/torch/nn/modules/upsampling.html if bilinear: #align_corners (bool, optional): if True, the corner pixels of the input #and output tensors are aligned, and thus preserving the values at #those pixels. This only has effect when :attr:`mode` is `linear`, #`bilinear`, or `trilinear`. Default: False self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) # Bilinear vs ConvTrabspose2d #ConvTranspose is a convolution and has trainable kernels # while Upsample is a simple interpolation (bilinear, nearest etc.) else: self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2) def forward(self, x1, x2= None): x1 = self.up(x1) # input in channel height and width #x2 tensor shape = [batch, channel, H, W] diffy = x2.size() - x1.size() diffx = x2.size() -x1.size() # add padding --PADDING ADDED HERE x1 = F.pad(x1, (diffX // 2, diffX - diffX//2, diffY // 2, diffY - diffY//2)) if x2 is not None: x = torch.cat([x2, x1], dim=1) else: x = x1 x = self.conv(x) return x