# Copyright (c) Alibaba, Inc. and its affiliates.
import torch.nn as nn

from .conv_module import build_conv_layer
from .norm import build_norm_layer


class ResLayer(nn.Sequential):
    """ResLayer to build ResNet style backbone.
    Args:
        block (nn.Module): Residual block used to build ResLayer.
        num_blocks (int): Number of blocks.
        in_channels (int): Input channels of this block.
        out_channels (int): Output channels of this block.
        expansion (int, optional): The expansion for BasicBlock/Bottleneck.
            If not specified, it will firstly be obtained via
            ``block.expansion``. If the block has no attribute "expansion",
            the following default values will be used: 1 for BasicBlock and
            4 for Bottleneck. Default: None.
        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, optional): dictionary to construct and config conv
            layer. Default: None
        norm_cfg (dict): dictionary to construct and config norm layer.
            Default: dict(type='BN')
    """

    def __init__(self,
                 block,
                 num_blocks,
                 in_channels,
                 out_channels,
                 expansion=None,
                 stride=1,
                 avg_down=False,
                 conv_cfg=None,
                 norm_cfg=dict(type='BN'),
                 **kwargs):
        self.block = block
        self.expansion = 4

        downsample = None
        if stride != 1 or in_channels != out_channels:
            downsample = []
            conv_stride = stride
            if avg_down and stride != 1:
                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,
                    in_channels,
                    out_channels,
                    kernel_size=1,
                    stride=conv_stride,
                    bias=False),
                build_norm_layer(norm_cfg, out_channels)[1]
            ])
            downsample = nn.Sequential(*downsample)

        layers = []
        layers.append(
            block(
                in_channels=in_channels,
                out_channels=out_channels,
                expansion=self.expansion,
                stride=stride,
                downsample=downsample,
                conv_cfg=conv_cfg,
                norm_cfg=norm_cfg,
                **kwargs))
        in_channels = out_channels
        for i in range(1, num_blocks):
            layers.append(
                block(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    expansion=self.expansion,
                    stride=1,
                    conv_cfg=conv_cfg,
                    norm_cfg=norm_cfg,
                    **kwargs))
        super(ResLayer, self).__init__(*layers)