# --------------------------------------------------------
# High Resolution Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Rao Fu, RainbowSecret
# --------------------------------------------------------

import os
import copy
import logging
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp

from mmcv.cnn import build_conv_layer, build_norm_layer


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        with_cp=None,
        norm_cfg=dict(type="BN"),
        conv_cfg=None,
    ):
        super(Bottleneck, self).__init__()
        norm_cfg = copy.deepcopy(norm_cfg)

        self.in_channels = inplanes
        self.out_channels = planes
        self.stride = stride
        self.with_cp = with_cp
        self.downsample = downsample

        self.conv1_stride = 1
        self.conv2_stride = stride

        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
        self.norm3_name, norm3 = build_norm_layer(
            norm_cfg, planes * self.expansion, postfix=3
        )

        self.conv1 = build_conv_layer(
            conv_cfg,
            inplanes,
            planes,
            kernel_size=1,
            stride=self.conv1_stride,
            bias=False,
        )
        self.add_module(self.norm1_name, norm1)

        self.conv2 = build_conv_layer(
            conv_cfg,
            planes,
            planes,
            kernel_size=3,
            stride=self.conv2_stride,
            padding=1,
            bias=False,
        )
        self.add_module(self.norm2_name, norm2)

        self.conv3 = build_conv_layer(
            conv_cfg, planes, planes * self.expansion, kernel_size=1, bias=False
        )
        self.add_module(self.norm3_name, norm3)
        self.relu = nn.ReLU(inplace=True)

    @property
    def norm1(self):
        """nn.Module: normalization layer after the first convolution layer"""
        return getattr(self, self.norm1_name)

    @property
    def norm2(self):
        """nn.Module: normalization layer after the second convolution layer"""
        return getattr(self, self.norm2_name)

    @property
    def norm3(self):
        """nn.Module: normalization layer after the third convolution layer"""
        return getattr(self, self.norm3_name)

    def forward(self, x):
        """Forward function."""

        def _inner_forward(x):
            identity = x

            out = self.conv1(x)
            out = self.norm1(out)
            out = self.relu(out)

            out = self.conv2(out)
            out = self.norm2(out)
            out = self.relu(out)

            out = self.conv3(out)
            out = self.norm3(out)

            if self.downsample is not None:
                identity = self.downsample(x)

            out += identity

            return out

        if self.with_cp and x.requires_grad:
            out = cp.checkpoint(_inner_forward, x)
        else:
            out = _inner_forward(x)

        out = self.relu(out)

        return out