Interpolate

Versioned name : Interpolate-4

Category : Image processing

Short description : Interpolate layer performs interpolation of independent slices in input tensor by specified dimensions and attributes.

Attributes

• mode

  • Description : specifies type of interpolation

  • Range of values : one of nearest, linear, linear_onnx, cubic

  • Type : string

  • Required : yes Note : Only 2D, 3D, 4D, 5D tensors with axes = {0, 1}, axes = {0, 1, 2}, axes = {2, 3}, axes = {2, 3, 4} respectively are supported for "mode" == "linear_onnx".

• shape_calculation_mode

  • Description : specifies which input, sizes or scales, is used to calculate an output shape.

  • Range of values : name of a shape calculation mode in string format:

    • sizes - an output shape is calculated as output_shape[axes[i]] = sizes[i] for all i in range(0, len(axes)) and output_shape[j] = input_shape[j] + pads_begin[j] + pads_end[j] for j not in axes, j in range(0, rank(data)).

    • scales - an output shape is calculated as output_shape[axes[i]] = floor(scales[i] \* (input_shape[axes[i]] + pads_begin[axes[i]] + pads_end[axes[i]])) for all i in range(0, len(axes)) and output_shape[j] = input_shape[j] + pads_begin[j] + pads_end[j] for j not in axes, j in range(0, rank(data))

  • Type : string

  • Required : yes

• coordinate_transformation_mode

  • Description : specifies how to transform the coordinate in the resized tensor to the coordinate in the original tensor

  • Range of values : name of the transformation mode in string format (here scale[x] is output_shape[x] / input_shape[x] and x_resized is a coordinate in axis x, for any axis x from the input axes):

    • half_pixel - the coordinate in the original tensor axis x is calculated as ((x_resized + 0.5) / scale[x]) - 0.5.

    • pytorch_half_pixel - the coordinate in the original tensor axis x is calculated by (x_resized + 0.5) / scale[x] - 0.5 if output_shape[x] > 1 else 0.0.

    • asymmetric - the coordinate in the original tensor axis x is calculated according to the formula x_resized / scale[x].

    • tf_half_pixel_for_nn - the coordinate in the original tensor axis x is (x_resized + 0.5) / scale[x].

    • align_corners - the coordinate in the original tensor axis x is calculated as 0 if output_shape[x] == 1 else x_resized \* (input_shape[x] - 1) / (output_shape[x] - 1).

  • Type : string

  • Default value : half_pixel

  • Required : no

• nearest_mode

  • Description : specifies round mode when mode == nearest and is used only when mode == nearest.

  • Range of values : name of the round mode in string format:

    • round_prefer_floor - this mode is known as round half down.

    • round_prefer_ceil - it is round half up mode.

    • floor - this mode computes the largest integer value not greater than rounded value.

    • ceil - this mode computes the smallest integer value not less than rounded value.

    • simple - this mode behaves as ceil mode when Interpolate is downsample, and as dropping the fractional part otherwise.

  • Type : string

  • Default value : round_prefer_floor

  • Required : no

• antialias

  • Description : antialias is a flag that specifies whether to perform anti-aliasing.

  • Range of values :

    • false - do not perform anti-aliasing

    • true - perform anti-aliasing

  • Type : boolean

  • Default value : false

  • Required : no

• pads_begin

  • Description : pads_begin specifies the number of pixels to add to the beginning of the image being interpolated. This addition of pixels is done before interpolation calculation.

  • Range of values : list of non-negative integer numbers

  • Type : int[]

  • Default value : [0]

  • Required : no

• pads_end

  • Description : pads_end specifies the number of pixels to add to the end of the image being interpolated. This addition of pixels is done before interpolation calculation.

  • Range of values : list of non-negative integer numbers

  • Type : int[]

  • Default value : [0]

  • Required : no

• cube_coeff

  • Description : cube_coeff specifies the parameter a for cubic interpolation (see, e.g. article). cube_coeff is used only when mode == cubic.

  • Range of values : floating-point number

  • Type : any of supported floating-point type

  • Default value : -0.75

  • Required : no

Inputs

• 1 : data - tensor of type T with data for interpolation. Required.

• 2 : sizes - 1D tensor of type T_SIZE describing output shape for spatial axes. Number of elements matches the number of indices in axes input, the order matches as well. Required.

• 3 : scales - 1D tensor of type T_SCALES describing scales for spatial axes. Number and order of elements match the number and order of indices in axes input. Required.

• 4 : axes - 1D tensor of type T_AXES specifying dimension indices where interpolation is applied, and axes is any unordered list of indices of different dimensions of input tensor, e.g. [0, 4], [4, 0], [4, 2, 1], [1, 2, 3]. These indices should be non-negative integers from 0 to rank(data) - 1 inclusively. Other dimensions do not change. The order of elements in axes attribute matters, and mapped directly to elements in the 2nd input sizes. Optional with default value [0,...,rank(data) - 1].

Outputs

• 1 : Resulting interpolated tensor with elements of the same type as input data tensor. The shape of the output matches input data shape except spatial dimensions mentioned in axes attribute. For other dimensions shape matches sizes from sizes in order specified in axes.

Types

• T : any supported numeric type.

• T_SIZE : any supported integer type.

• T_SCALES : any supported floating-point type.

• T_AXES : any supported integer type.

Detailed description Calculations are performed according to the following rules.

import math
import numpy as np
from enum import Enum, unique

class GetNearestPixel:
    def __init__(self, mode: str):
        self.func = {
            'round_prefer_floor': GetNearestPixel.prefer_floor_func,
            'round_prefer_ceil': GetNearestPixel.prefer_ceil_func,
            'floor': GetNearestPixel.floor_func,
            'ceil': GetNearestPixel.ceil_func,
            'simple': GetNearestPixel.simple_func
        }[mode]

    def __call__(self, x_original, is_downsample):
        return self.func(x_original, is_downsample)

    @staticmethod
    def prefer_floor_func(x_original, is_downsample):
        if x_original == int(x_original) + 0.5:
            return int(math.floor(x_original))
        else:
            return int(round(x_original))

    @staticmethod
    def prefer_ceil_func(x_original, is_downsample):
        return int(round(x_original))

    @staticmethod
    def floor_func(x_original, is_downsample):
        return int(math.floor(x_original))

    @staticmethod
    def ceil_func(x_original, is_downsample):
        return int(math.ceil(x_original))

    @staticmethod
    def simple_func(x_original, is_downsample):
        if is_downsample:
            return int(math.ceil(x_original))
        else:
            return int(x_original)


class GetOriginalCoordinate:
    def __init__(self, mode: str):
        self.func = {
            'half_pixel': GetOriginalCoordinate.half_pixel_func,
            'pytorch_half_pixel': GetOriginalCoordinate.pytorch_half_pixel_func,
            'asymmetric': GetOriginalCoordinate.asymmetric_func,
            'tf_half_pixel_for_nn': GetOriginalCoordinate.tf_half_pixel_for_nn_func,
            'align_corners': GetOriginalCoordinate.align_corners_func
        }[mode]

    def __call__(self, x_resized, x_scale, length_resized, length_original):
        return self.func(x_resized, x_scale, length_resized, length_original)

    @staticmethod
    def half_pixel_func(x_resized, x_scale, length_resized, length_original):
        return ((x_resized + 0.5) / x_scale) - 0.5

    @staticmethod
    def pytorch_half_pixel_func(x_resized, x_scale, length_resized, length_original):
        return (x_resized + 0.5) / x_scale - 0.5 if length_resized > 1 else 0.0

    @staticmethod
    def asymmetric_func(x_resized, x_scale, length_resized, length_original):
        return x_resized / x_scale

    @staticmethod
    def tf_half_pixel_for_nn_func(x_resized, x_scale, length_resized, length_original):
        return (x_resized + 0.5) / x_scale

    @staticmethod
    def align_corners_func(x_resized, x_scale, length_resized, length_original):
        return  0 if length_resized == 1 else  x_resized \* (length_original - 1) / (length_resized - 1)


def get_cubic_coeff(s, a):
    abs_s = abs(s)
    coeff = np.zeros(4)
    coeff[0] = a \* (abs_s - 1.0) \* (abs_s - 1.0) \* abs_s
    coeff[1] = ((a + 2.0) \* abs_s - (a + 3.0)) \* abs_s \* abs_s + 1.0
    coeff[2] = (((-a -2.0) \* abs_s+ (2.0 \* a + 3.0)) \* abs_s - a) \* abs_s
    coeff[3] = - a \* abs_s \* abs_s \* (abs_s - 1.0)
    return coeff


def triangle_coeffs(dz):
    return np.maximum(0.0, 1.0 - np.abs(dz))


@unique
class ShapeCalculationMode(Enum):
    SIZES = 0
    SCALES = 1


class InterpolateCalculation:
    def __init__(self, attrs: dict):
        self.mode = attrs['mode']
        self.func = {
            'nearest': self.nearest_interpolation,
            'linear': self.linear_interpolation,
            'cubic': self.cubic_interpolation,
            'linear_onnx': self.onnx_linear_interpolation
        }[self.mode]
        self.attrs = attrs

        self.pads_begin = attrs.get('pads_begin', [0])
        self.pads_end = attrs.get('pads_end', [0])
        self.coordinate_transformation_mode = attrs.get('coordinate_transformation_mode', 'half_pixel')
        self.nearest_mode = attrs.get('nearest_mode', 'round_prefer_floor')
        self.cube_coeff = attrs.get('cube_coeff', -0.75)
        self.antialias = attrs.get('antialias', False)

        self.shape_calculation_mode = {
            'sizes': ShapeCalculationMode.SIZES,
            'scales': ShapeCalculationMode.SCALES
        }[attrs['shape_calculation_mode']]

        self.get_original_coordinate = self.get_coordinate_transformation_mode()
        self.get_nearest_pixel = GetNearestPixel(self.nearest_mode)


    def get_coordinate_transformation_mode(self):
        return GetOriginalCoordinate(self.coordinate_transformation_mode)

    def shape_infer(self, input_data, sizes, scales):
        result = input_data.shape + self.pads_begin + self.pads_end

        if self.shape_calculation_mode == ShapeCalculationMode.SIZES:
            for i, axis in enumerate(self.axes):
                result[axis] = sizes[i]
        else:
            for i, axis in enumerate(self.axes):
                result[axis] = math.floor(scales[i] \* result[axis])

        return result

    @staticmethod
    def correct_pad(pad, rank):
        pad_len = len(pad)
        if pad_len < rank:
            return np.pad(pad, (0, rank - pad_len), 'constant').astype(np.int64)
        elif pad_len > rank:
            return np.array(pad[: rank - 1]).astype(np.int64)
        else:
            return np.array(pad, dtype=np.int64)

    def __call__(self, input_data, sizes, scales, axes):
        rank = input_data.ndim
        self.pads_begin = InterpolateCalculation.correct_pad(self.pads_begin, rank)
        self.pads_end = InterpolateCalculation.correct_pad(self.pads_end, rank)
        self.pads = list(zip(self.pads_begin, self.pads_end))
        self.axes = np.array(axes).astype(np.int64)

        self.output_shape = self.shape_infer(input_data, sizes, scales)
        padded_data = np.pad(input_data, self.pads, 'constant')

        if self.shape_calculation_mode == ShapeCalculationMode.SIZES:
            num_of_axes = len(self.axes)
            self.scales = np.zeros(num_of_axes)
            for i, axis in enumerate(axes):
                self.scales[i] = self.output_shape[axis] / padded_data.shape[axis]
        else:
            self.scales = scales

        if self.mode == 'nearest':
            self.all_scales = np.ones(rank).astype(np.float)
            for i, axis in enumerate(self.axes):
                self.all_scales[axis] = self.scales[i]

        self.input_shape = padded_data.shape
        return self.func(padded_data)

    def clip_coord(self, coord, axis):
        return max(0, min(coord, self.input_shape[axis] - 1))

    def cubic_interpolation(self, input_data):
        rank = len(self.input_shape)
        result = np.zeros(self.output_shape)
        num_of_axes = len(self.axes)
        indices = [ind for ind in np.ndindex(tuple(4 for _ in range(num_of_axes)))]
        for coordinates in np.ndindex(tuple(self.output_shape)):
            input_coords = np.array(coordinates, dtype=np.int64)
            cubic_coeffs = np.zeros((rank, 4))
            for i, axis in enumerate(self.axes):
                in_coord = self.get_original_coordinate(coordinates[axis], self.scales[i], self.output_shape[axis], self.input_shape[axis])
                in_coord_int = math.floor(in_coord)
                input_coords[axis] = in_coord_int
                cubic_coeffs[axis] = get_cubic_coeff(in_coord - in_coord_int, self.cube_coeff)
            summa = 0.0
            for index in indices:
                coords_for_sum = input_coords.copy()
                coeffs_prod = 1.0
                for i, axis in enumerate(self.axes):
                    coords_for_sum[axis] = self.clip_coord(input_coords[axis] + index[i] - 1, axis)
                for i, axis in enumerate(self.axes):
                    coeffs_prod = coeffs_prod \* cubic_coeffs[axis][index[i]]
                summa += coeffs_prod \* input_data[tuple(coords_for_sum)]
            result[coordinates] = summa
        return result

    def linear_interpolation(self, input_data):
        result = np.zeros(self.output_shape)
        num_of_axes = len(self.axes)
        is_downsample = False

        for scale in self.scales:
            is_downsample = is_downsample or (scale < 1)

        antialias = is_downsample and self.antialias

        a = np.zeros(num_of_axes)
        for i, _ in enumerate(self.axes):
            a[i] = self.scales[i] if antialias else 1.0

        prod_of_a = np.prod(a)
        r = np.zeros(num_of_axes).astype(np.int64)
        for i, _ in enumerate(self.axes):
            r[i] = 2 if self.scales[i] > 1.0 else int(math.ceil(2.0/a[i]))

        indices = [tuple(np.array(ind).astype(np.int64) - r) for ind in np.ndindex(tuple(2 \* r + 1))]

        for coordinates in np.ndindex(tuple(self.output_shape)):
            icoords = np.array(coordinates).astype(np.float64)
            icoords_r = np.array(coordinates).astype(np.float64)
            for i, axis in enumerate(self.axes):
                in_coord = self.get_original_coordinate(coordinates[axis], self.scales[i], self.output_shape[axis], self.input_shape[axis])
                icoords[axis] = in_coord
                icoords_r[axis] = round(in_coord)

            summa = 0.0
            wsum = 0.0

            for index in indices:
                inner_coords = np.array(coordinates)
                for i, axis in enumerate(self.axes):
                    inner_coords[axis] = index[i] + icoords_r[axis]

                conditions = [inner_coords[axis] >= 0 and inner_coords[axis] < self.input_shape[axis] for axis in self.axes]
                if not all(conditions):
                    continue

                dz = np.zeros(num_of_axes)
                for i, axis in enumerate(self.axes):
                    dz[i] = icoords[axis] - inner_coords[axis]

                w = prod_of_a \* np.prod(triangle_coeffs(a \* dz))
                wsum += w
                summa += w \* input_data[tuple(inner_coords)]

            if wsum == 0:
                result[coordinates] = 0.0
            else:
                result[coordinates] = summa / wsum

        return result

    def onnx_linear_interpolation5D(self, input_data):
        rank = len(self.input_shape)
        assert rank in [3, 5], "mode 'linear_onnx' supports only 3D or 5D tensors"
        assert set(self.axes) == {2, 3, 4} or set(self.axes) == {0, 1, 2}, \
            "mode 'linear_onnx' supports only case when axes = {2, 3, 4} or axes = {0, 1, 2}"

        result = np.zeros(self.output_shape)

        if rank == 3:
            reshaped_data = np.reshape(input_data, (1, 1, self.input_shape[0], self.input_shape[1], self.input_shape[2]))
            result = np.reshape(result,  (1, 1, self.output_shape[0], self.output_shape[1], self.output_shape[2]))
        else:
            reshaped_data = input_data

        input_shape = np.array(reshaped_data.shape).astype(np.int64)
        output_shape = np.array(result.shape).astype(np.int64)

        batch_size = input_shape[0];
        num_channels = input_shape[1];
        input_depth = input_shape[2];
        input_height = input_shape[3];
        input_width = input_shape[4];
        output_depth = output_shape[2];
        output_height = output_shape[3];
        output_width = output_shape[4];

        depth_scale = self.scales[0];
        height_scale = self.scales[1];
        width_scale = self.scales[2];

        z_original = np.zeros(output_depth).astype(np.float)
        y_original = np.zeros(output_height).astype(np.float)
        x_original = np.zeros(output_width).astype(np.float)

        in_z1 = np.zeros(output_depth).astype(np.int64)
        in_z2 = np.zeros(output_depth).astype(np.int64)
        in_y1 = np.zeros(output_height).astype(np.int64)
        in_y2 = np.zeros(output_height).astype(np.int64)
        in_x1 = np.zeros(output_width).astype(np.int64)
        in_x2 = np.zeros(output_width).astype(np.int64)

        dz1 = np.zeros(output_depth).astype(np.float)
        dz2 = np.zeros(output_depth).astype(np.float)

        dy1 = np.zeros(output_height).astype(np.float)
        dy2 = np.zeros(output_height).astype(np.float)

        dx1 = np.zeros(output_width).astype(np.float)
        dx2 = np.zeros(output_width).astype(np.float)

        for z in range(0, output_depth):
            in_z = self.get_original_coordinate(z, depth_scale, output_depth, input_depth)
            z_original[z] = in_z
            in_z = max(0, min(in_z, input_depth - 1))
            in_z1[z] = max(0, min(int(in_z), input_depth - 1))
            in_z2[z] = min(in_z1[z] + 1, input_depth - 1)
            dz1[z] = abs(in_z - in_z1[z])
            dz2[z] = abs(in_z - in_z2[z])

            if in_z1[z] == in_z2[z]:
                dz1[z] = 0.5
                dz2[z] = 0.5

        for y in range(0, output_height):
            in_y = self.get_original_coordinate(y, height_scale, output_height, input_height)
            y_original[y] = in_y
            in_y = max(0, min(in_y, input_height - 1))
            in_y1[y] = max(0, min(int(in_y), input_height - 1))
            in_y2[y] = min(in_y1[y] + 1, input_height - 1)
            dy1[y] = abs(in_y - in_y1[y])
            dy2[y] = abs(in_y - in_y2[y])

            if in_y1[y] == in_y2[y]:
                dy1[y] = 0.5
                dy2[y] = 0.5

        for x in range(0, output_width):
            in_x = self.get_original_coordinate(x, width_scale, output_width, input_width);
            x_original[x] = in_x
            in_x = max(0.0, min(in_x, input_width - 1));

            in_x1[x] = min(in_x, input_width - 1);
            in_x2[x] = min(in_x1[x] + 1, input_width - 1);

            dx1[x] = abs(in_x - in_x1[x]);
            dx2[x] = abs(in_x - in_x2[x]);
            if in_x1[x] == in_x2[x]:
                dx1[x] = 0.5
                dx2[x] = 0.5
        for n in range(0, batch_size):
            for c in range(0, num_channels):
                for z in range(0, output_depth):
                    for y in range(0, output_height):
                        for x in range(0, output_width):
                            x111 = reshaped_data[n, c, in_z1[z], in_y1[y], in_x1[x]]
                            x211 = reshaped_data[n, c, in_z1[z], in_y1[y], in_x2[x]]
                            x121 = reshaped_data[n, c, in_z1[z], in_y2[y], in_x1[x]]
                            x221 = reshaped_data[n, c, in_z1[z], in_y2[y], in_x2[x]]
                            x112 = reshaped_data[n, c, in_z2[z], in_y1[y], in_x1[x]]
                            x212 = reshaped_data[n, c, in_z2[z], in_y1[y], in_x2[x]]
                            x122 = reshaped_data[n, c, in_z2[z], in_y2[y], in_x1[x]]
                            x222 = reshaped_data[n, c, in_z2[z], in_y2[y], in_x2[x]]

                            temp = dx2[x] \* dy2[y] \* dz2[z] \* x111 + dx1[x] \* dy2[y] \* dz2[z] \* x211
                            temp += dx2[x] \* dy1[y] \* dz2[z] \* x121 + dx1[x] \* dy1[y] \* dz2[z] \* x221
                            temp += dx2[x] \* dy2[y] \* dz1[z] \* x112 + dx1[x] \* dy2[y] \* dz1[z] \* x212
                            temp += dx2[x] \* dy1[y] \* dz1[z] \* x122 + dx1[x] \* dy1[y] \* dz1[z] \* x222

                            result[n, c, z, y, x] = temp

        return np.reshape(result, self.output_shape)

    def onnx_linear_interpolation4D(self, input_data):
        rank = len(self.input_shape)
        assert rank in [2, 4], "mode 'linear_onnx' supports only 2D or 4D tensors"
        assert set(self.axes) == {2, 3} or set(self.axes) == {0, 1}, \
            "mode 'linear_onnx' supports only case when axes = {2, 3} or axes = {0, 1}"

        result = np.zeros(self.output_shape)

        if rank == 2:
            reshaped_data = np.reshape(input_data, (1, 1, self.input_shape[0], self.input_shape[1]))
            result = np.reshape(result,  (1, 1, self.output_shape[0], self.output_shape[1]))
        else:
            reshaped_data = input_data

        input_shape = np.array(reshaped_data.shape).astype(np.int64)
        output_shape = np.array(result.shape).astype(np.int64)

        output_height = output_shape[2]
        output_width = output_shape[3]
        input_height = input_shape[2]
        input_width = input_shape[3]
        height_scale = self.scales[0]
        width_scale = self.scales[1]
        batch_size = input_shape[0]
        num_channels = input_shape[1]

        y_original = np.zeros(output_height).astype(np.float)
        x_original = np.zeros(output_width).astype(np.float)

        in_y1 = np.zeros(output_height).astype(np.int64)
        in_y2 = np.zeros(output_height).astype(np.int64)
        in_x1 = np.zeros(output_width).astype(np.int64)
        in_x2 = np.zeros(output_width).astype(np.int64)

        dy1 = np.zeros(output_height).astype(np.float)
        dy2 = np.zeros(output_height).astype(np.float)

        dx1 = np.zeros(output_width).astype(np.float)
        dx2 = np.zeros(output_width).astype(np.float)

        for y in range(0, output_height):
            in_y = self.get_original_coordinate(y, height_scale, output_height, input_height)
            y_original[y] = in_y
            in_y = max(0, min(in_y, input_height - 1))
            in_y1[y] = max(0, min(int(in_y), input_height - 1))
            in_y2[y] = min(in_y1[y] + 1, input_height - 1)
            dy1[y] = abs(in_y - in_y1[y])
            dy2[y] = abs(in_y - in_y2[y])

            if in_y1[y] == in_y2[y]:
                dy1[y] = 0.5
                dy2[y] = 0.5

        for x in range(0, output_width):
            in_x = self.get_original_coordinate(x, width_scale, output_width, input_width);
            x_original[x] = in_x
            in_x = max(0.0, min(in_x, input_width - 1));

            in_x1[x] = min(in_x, input_width - 1);
            in_x2[x] = min(in_x1[x] + 1, input_width - 1);

            dx1[x] = abs(in_x - in_x1[x]);
            dx2[x] = abs(in_x - in_x2[x]);
            if in_x1[x] == in_x2[x]:
                dx1[x] = 0.5
                dx2[x] = 0.5

        for n in range(0, batch_size):
            for c in range(0, num_channels):
                for y in range(0, output_height):
                    for x in range(0, output_width):
                        x11 = reshaped_data[n, c, in_y1[y], in_x1[x]]
                        x21 = reshaped_data[n, c, in_y1[y], in_x2[x]]
                        x12 = reshaped_data[n, c, in_y2[y], in_x1[x]]
                        x22 = reshaped_data[n, c, in_y2[y], in_x2[x]]
                        temp = dx2[x] \* dy2[y] \* x11 + dx1[x] \* dy2[y] \* x21 + dx2[x] \* dy1[y] \* x12 + dx1[x] \* dy1[y] \* x22
                        result[n, c, y, x] = temp

        return np.reshape(result, self.output_shape)

    def onnx_linear_interpolation(self, input_data):
        rank = len(self.input_shape)
        assert rank in [2, 3, 4, 5], "mode 'linear_onnx' supports only 2D, 3D, 4D, or 5D tensors"

        if rank in [2, 4]:
            self.onnx_linear_interpolation4D(input_data)
        else:
            self.onnx_linear_interpolation5D(input_data)

    def nearest_interpolation(self, input_data):
        result = np.zeros(self.output_shape)

        num_of_axes = len(self.axes)
        for coordinates in np.ndindex(tuple(self.output_shape)):
            input_coords = np.array(coordinates, dtype=np.int64)
            for axis, scale in enumerate(self.all_scales):
                in_coord = self.get_original_coordinate(coordinates[axis], scale, self.output_shape[axis], self.input_shape[axis])
                nearest_pixel = self.get_nearest_pixel(in_coord, scale < 1)
                input_coords[axis] = max(0, min(nearest_pixel, self.input_shape[axis] - 1))
            result[coordinates] = input_data[tuple(input_coords)]

        return result

Example

<layer ... type="Interpolate" ...>
    <data shape_calculation_mode="scales" pads_begin="0" pads_end="0" mode="linear"/>
    <input>
        <port id="0">
            <dim>1</dim>
            <dim>2</dim>
            <dim>48</dim>
            <dim>80</dim>
        </port>
        <port id="1">
            <dim>2</dim>  <!--The values in this input are [24, 160] -->
        </port>
        <port id="2">
            <dim>2</dim>  <!--The values in this input are [0.5, 2.0] -->
        </port>
        <port id="3">
            <dim>2</dim>  <!--The values in this input are [2, 3] (axes). -->
        </port>
    </input>
    <output>
        <port id="0"  precision="FP32">
            <dim>1</dim>
            <dim>2</dim>
            <dim>24</dim>
            <dim>160</dim>
        </port>
    </output>
</layer>