# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule

from mmseg.ops import resize
from mmseg.models.decode_heads.decode_head import BaseDecodeHead


class PPM(nn.ModuleList):
    """Pooling Pyramid Module used in PSPNet.

    Args:
        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
            Module.
        in_channels (int): Input channels.
        channels (int): Channels after modules, before conv_seg.
        conv_cfg (dict|None): Config of conv layers.
        norm_cfg (dict|None): Config of norm layers.
        act_cfg (dict): Config of activation layers.
        align_corners (bool): align_corners argument of F.interpolate.
    """

    def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
                 act_cfg, align_corners, **kwargs):
        super(PPM, self).__init__()
        self.pool_scales = pool_scales
        self.align_corners = align_corners
        self.in_channels = in_channels
        self.channels = channels
        self.conv_cfg = conv_cfg
        self.norm_cfg = norm_cfg
        self.act_cfg = act_cfg
        for pool_scale in pool_scales:
            self.append(
                nn.Sequential(
                    nn.AdaptiveAvgPool2d(pool_scale),
                    ConvModule(
                        self.in_channels,
                        self.channels,
                        1,
                        conv_cfg=self.conv_cfg,
                        norm_cfg=self.norm_cfg,
                        act_cfg=self.act_cfg,
                        **kwargs)))

    def forward(self, x):
        """Forward function."""
        ppm_outs = []
        for ppm in self:
            ppm_out = ppm(x)
            ppm_out = ppm_out.to(torch.float32)
            upsampled_ppm_out = resize(
                ppm_out,
                size=x.size()[2:],
                mode='bilinear',
                align_corners=self.align_corners)
            upsampled_ppm_out = upsampled_ppm_out.to(torch.bfloat16)
            ppm_outs.append(upsampled_ppm_out)
        return ppm_outs