# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import Sequential
from torch import nn as nn


class ResLayer(Sequential):
    """ResLayer to build ResNet style backbone.

    Args:
        block (nn.Module): block used to build ResLayer.
        inplanes (int): inplanes of block.
        planes (int): planes of block.
        num_blocks (int): number of blocks.
        stride (int): stride of the first block. Default: 1
        avg_down (bool): Use AvgPool instead of stride conv when
            downsampling in the bottleneck. Default: False
        conv_cfg (dict): dictionary to construct and config conv layer.
            Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
        multi_grid (int | None): Multi grid dilation rates of last
            stage. Default: None
        contract_dilation (bool): Whether contract first dilation of each layer
            Default: False
    """

    def __init__(self,
                 block,
                 inplanes,
                 planes,
                 num_blocks,
                 stride=1,
                 dilation=1,
                 avg_down=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 multi_grid=None,
                 contract_dilation=False,
                 **kwargs):
        self.block = block

        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = []
            conv_stride = stride
            if avg_down:
                conv_stride = 1
                downsample.append(
                    nn.AvgPool2d(
                        kernel_size=stride,
                        stride=stride,
                        ceil_mode=True,
                        count_include_pad=False))
            downsample.extend([
                build_conv_layer(
                    conv_cfg,
                    inplanes,
                    planes * block.expansion,
                    kernel_size=1,
                    stride=conv_stride,
                    bias=False),
                build_norm_layer(norm_cfg, planes * block.expansion)[1]
            ])
            downsample = nn.Sequential(*downsample)

        layers = []
        if multi_grid is None:
            if dilation > 1 and contract_dilation:
                first_dilation = dilation // 2
            else:
                first_dilation = dilation
        else:
            first_dilation = multi_grid[0]
        layers.append(
            block(
                inplanes=inplanes,
                planes=planes,
                stride=stride,
                dilation=first_dilation,
                downsample=downsample,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                **kwargs))
        inplanes = planes * block.expansion
        for i in range(1, num_blocks):
            layers.append(
                block(
                    inplanes=inplanes,
                    planes=planes,
                    stride=1,
                    dilation=dilation if multi_grid is None else multi_grid[i],
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    **kwargs))
        super(ResLayer, self).__init__(*layers)
