Upsample — PyTorch 2.7 documentation (original) (raw)

class torch.nn.Upsample(size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None)[source][source]¶

Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.

The input data is assumed to be of the formminibatch x channels x [optional depth] x [optional height] x width. Hence, for spatial inputs, we expect a 4D Tensor and for volumetric inputs, we expect a 5D Tensor.

The algorithms available for upsampling are nearest neighbor and linear, bilinear, bicubic and trilinear for 3D, 4D and 5D input Tensor, respectively.

One can either give a scale_factor or the target output size to calculate the output size. (You cannot give both, as it is ambiguous)

Parameters

• size (int or Tuple [_int] or Tuple [_int, int] or Tuple [_int,_ int, int] , optional) – output spatial sizes
• scale_factor (float or Tuple [_float] or Tuple [_float, float] or Tuple [_float,_ float, float] , optional) – multiplier for spatial size. Has to match input size if it is a tuple.
• mode (str, optional) – the upsampling algorithm: one of 'nearest','linear', 'bilinear', 'bicubic' and 'trilinear'. Default: 'nearest'
• 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 mode is'linear', 'bilinear', 'bicubic', or 'trilinear'. Default: False
• recompute_scale_factor (bool, optional) – recompute the scale_factor for use in the interpolation calculation. If recompute_scale_factor is True, thenscale_factor must be passed in and scale_factor is used to compute the output size. The computed output size will be used to infer new scales for the interpolation. Note that when scale_factor is floating-point, it may differ from the recomputed scale_factor due to rounding and precision issues. If recompute_scale_factor is False, then size or scale_factor will be used directly for interpolation.

Shape:

• Input: (N,C,Win)(N, C, W_{in}), (N,C,Hin,Win)(N, C, H_{in}, W_{in}) or (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in})
• Output: (N,C,Wout)(N, C, W_{out}), (N,C,Hout,Wout)(N, C, H_{out}, W_{out})or (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}), where

Dout=⌊Din×scale_factor⌋D_{out} = \left\lfloor D_{in} \times \text{scale\_factor} \right\rfloor

Hout=⌊Hin×scale_factor⌋H_{out} = \left\lfloor H_{in} \times \text{scale\_factor} \right\rfloor

Wout=⌊Win×scale_factor⌋W_{out} = \left\lfloor W_{in} \times \text{scale\_factor} \right\rfloor

Warning

With align_corners = True, the linearly interpolating modes (linear, bilinear, bicubic, and trilinear) don’t proportionally align the output and input pixels, and thus the output values can depend on the input size. This was the default behavior for these modes up to version 0.3.1. Since then, the default behavior isalign_corners = False. See below for concrete examples on how this affects the outputs.

Note

If you want downsampling/general resizing, you should use interpolate().

Examples:

input = torch.arange(1, 5, dtype=torch.float32).view(1, 1, 2, 2) input tensor([[[[1., 2.], [3., 4.]]]])

m = nn.Upsample(scale_factor=2, mode='nearest') m(input) tensor([[[[1., 1., 2., 2.], [1., 1., 2., 2.], [3., 3., 4., 4.], [3., 3., 4., 4.]]]])

m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False m(input) tensor([[[[1.0000, 1.2500, 1.7500, 2.0000], [1.5000, 1.7500, 2.2500, 2.5000], [2.5000, 2.7500, 3.2500, 3.5000], [3.0000, 3.2500, 3.7500, 4.0000]]]])

m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) m(input) tensor([[[[1.0000, 1.3333, 1.6667, 2.0000], [1.6667, 2.0000, 2.3333, 2.6667], [2.3333, 2.6667, 3.0000, 3.3333], [3.0000, 3.3333, 3.6667, 4.0000]]]])

Try scaling the same data in a larger tensor

input_3x3 = torch.zeros(3, 3).view(1, 1, 3, 3) input_3x3[:, :, :2, :2].copy_(input) tensor([[[[1., 2.], [3., 4.]]]]) input_3x3 tensor([[[[1., 2., 0.], [3., 4., 0.], [0., 0., 0.]]]])

m = nn.Upsample(scale_factor=2, mode='bilinear') # align_corners=False

Notice that values in top left corner are the same with the small input (except at boundary)

m(input_3x3) tensor([[[[1.0000, 1.2500, 1.7500, 1.5000, 0.5000, 0.0000], [1.5000, 1.7500, 2.2500, 1.8750, 0.6250, 0.0000], [2.5000, 2.7500, 3.2500, 2.6250, 0.8750, 0.0000], [2.2500, 2.4375, 2.8125, 2.2500, 0.7500, 0.0000], [0.7500, 0.8125, 0.9375, 0.7500, 0.2500, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]])

m = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

Notice that values in top left corner are now changed

m(input_3x3) tensor([[[[1.0000, 1.4000, 1.8000, 1.6000, 0.8000, 0.0000], [1.8000, 2.2000, 2.6000, 2.2400, 1.1200, 0.0000], [2.6000, 3.0000, 3.4000, 2.8800, 1.4400, 0.0000], [2.4000, 2.7200, 3.0400, 2.5600, 1.2800, 0.0000], [1.2000, 1.3600, 1.5200, 1.2800, 0.6400, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]])