# Copyright (c) OpenMMLab. All rights reserved.

import warnings

import torch.nn as nn

import torch.nn.functional as F

def resize(input,

           size=None,

           scale_factor=None,

           mode='nearest',

           align_corners=None,

           warning=True):

    if warning:

        if size is not None and align_corners:

            input_h, input_w = tuple(int(x) for x in input.shape[2:])

            output_h, output_w = tuple(int(x) for x in size)

            if output_h > input_h or output_w > output_h:

                if ((output_h > 1 and output_w > 1 and input_h > 1

                     and input_w > 1) and (output_h - 1) % (input_h - 1)

                        and (output_w - 1) % (input_w - 1)):

                    warnings.warn(

                        f'When align_corners={align_corners}, '

                        'the output would more aligned if '

                        f'input size {(input_h, input_w)} is `x+1` and '

                        f'out size {(output_h, output_w)} is `nx+1`')

    return F.interpolate(input, size, scale_factor, mode, align_corners)

class Upsample(nn.Module):

    def __init__(self,

                 size=None,

                 scale_factor=None,

                 mode='nearest',

                 align_corners=None):

        super(Upsample, self).__init__()

        self.size = size

        if isinstance(scale_factor, tuple):

            self.scale_factor = tuple(float(factor) for factor in scale_factor)

        else:

            self.scale_factor = float(scale_factor) if scale_factor else None

        self.mode = mode

        self.align_corners = align_corners

    def forward(self, x):

        if not self.size:

            size = [int(t * self.scale_factor) for t in x.shape[-2:]]

        else:

            size = self.size

        return resize(x, size, None, self.mode, self.align_corners)