upsample(input, size=None, scale_factor=None, mode='nearest', align_corners=None)¶
Upsamples the input to either the given
sizeor the given
This function is deprecated in favor of
torch.nn.functional.interpolate(). This is equivalent with
This operation may produce nondeterministic gradients when given tensors on a CUDA device. See Reproducibility for more information.
The algorithm used for upsampling is determined by
Currently temporal, spatial and volumetric upsampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape.
The input dimensions are interpreted in the form: mini-batch x channels x [optional depth] x [optional height] x width.
The modes available for upsampling are: nearest, linear (3D-only), bilinear, bicubic (4D-only), trilinear (5D-only)
input (Tensor) – the input tensor
mode (string) – algorithm used for upsampling:
align_corners (bool, optional) – Geometrically, we consider the pixels of the input and output as squares rather than points. If set to
True, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to
False, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation independent of input size when
scale_factoris kept the same. This only has an effect when
mode='bicubic', it’s possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call
result.clamp(min=0, max=255)if you want to reduce the overshoot when displaying the image.
align_corners = True, the linearly interpolating modes (linear, bilinear, 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 is
align_corners = False. See
Upsamplefor concrete examples on how this affects the outputs.