torch.nn.functional.InterpolateFunctionalOptions
export interface InterpolateFunctionalOptions {
/** Output spatial size. */
size?: number | number[];
/** Multiplier for spatial size. Has to match input size if it is a list. */
scale_factor?: number | number[];
/** Algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' | 'nearest-exact'. (default: 'nearest') */
mode?: 'nearest' | 'linear' | 'bilinear' | 'trilinear' | 'bicubic' | 'area' | 'nearest-exact';
/** Geometrically, we consider the pixels of the input and output as squares rather than points. (default: false) */
align_corners?: boolean;
/** if True, the input and output tensors are aligned by the center values of their corner pixels, preserving the corner-pixel values. (default: false) */
recompute_scale_factor?: boolean;
/** Whether to use antialiasing. (default: false) */
antialias?: boolean;
}size(number | number[])optional- – Output spatial size.
scale_factor(number | number[])optional- – Multiplier for spatial size. Has to match input size if it is a list.
mode('nearest' | 'linear' | 'bilinear' | 'trilinear' | 'bicubic' | 'area' | 'nearest-exact')optional- – Algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' | 'nearest-exact'. (default: 'nearest')
align_corners(boolean)optional- – Geometrically, we consider the pixels of the input and output as squares rather than points. (default: false)
recompute_scale_factor(boolean)optional- – if True, the input and output tensors are aligned by the center values of their corner pixels, preserving the corner-pixel values. (default: false)
antialias(boolean)optional- – Whether to use antialiasing. (default: false)
Options for interpolate functional operation.