#!/usr/bin/env python 
# -*- coding: utf-8 -*-
"""
@project: 
@File    : ceshi
@Author  : qiqq
@create_time    : 2023/6/5 22:29
"""

import torch
import torch.nn as nn

# from  torchvision.models.resnet import resnet18
from compared_experiment.pspnet.model.resnetceshi import resnet18

from torch.nn import BatchNorm2d
from torch.nn import Module, Conv2d, Parameter


def conv3otherRelu(in_planes, out_planes, kernel_size=None, stride=None, padding=None):
    # 3x3 convolution with padding and relu
    if kernel_size is None:
        kernel_size = 3
    assert isinstance(kernel_size, (int, tuple)), 'kernel_size is not in (int, tuple)!'

    if stride is None:
        stride = 1
    assert isinstance(stride, (int, tuple)), 'stride is not in (int, tuple)!'

    if padding is None:
        padding = 1
    assert isinstance(padding, (int, tuple)), 'padding is not in (int, tuple)!'

    return nn.Sequential(
        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=True),
        nn.ReLU(inplace=True)  # inplace=True
    )


def l2_norm(x):
    return torch.einsum("bcn, bn->bcn", x, 1 / torch.norm(x, p=2, dim=-2))


class Attention(Module):
    def __init__(self, in_places, scale=8, eps=1e-6):
        super(Attention, self).__init__()
        self.gamma = Parameter(torch.zeros(1))
        self.in_places = in_places
        self.l2_norm = l2_norm
        self.eps = eps

        self.query_conv = Conv2d(in_channels=in_places, out_channels=in_places // scale, kernel_size=1)
        self.key_conv = Conv2d(in_channels=in_places, out_channels=in_places // scale, kernel_size=1)
        self.value_conv = Conv2d(in_channels=in_places, out_channels=in_places, kernel_size=1)

    def forward(self, x):#x：16*16*128
        # Apply the feature map to the queries and keys
        batch_size, chnnels, width, height = x.shape
        Q = self.query_conv(x).view(batch_size, -1, width * height) #16*16*16 --》16*256
        K = self.key_conv(x).view(batch_size, -1, width * height)  #16*256
        V = self.value_conv(x).view(batch_size, -1, width * height) #16*16*128  --128*256

        Q = self.l2_norm(Q).permute(-3, -1, -2)
        K = self.l2_norm(K)

        tailor_sum = 1 / (width * height + torch.einsum("bnc, bc->bn", Q, torch.sum(K, dim=-1) + self.eps))
        value_sum = torch.einsum("bcn->bc", V).unsqueeze(-1)
        value_sum = value_sum.expand(-1, chnnels, width * height)

        matrix = torch.einsum('bmn, bcn->bmc', K, V)
        matrix_sum = value_sum + torch.einsum("bnm, bmc->bcn", Q, matrix)

        weight_value = torch.einsum("bcn, bn->bcn", matrix_sum, tailor_sum)
        weight_value = weight_value.view(batch_size, chnnels, height, width)

        return (self.gamma * weight_value).contiguous()


class ConvBNReLU(nn.Module):
    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
        super(ConvBNReLU, self).__init__()
        self.conv = nn.Conv2d(in_chan,
                              out_chan,
                              kernel_size=ks,
                              stride=stride,
                              padding=padding,
                              bias=False)
        self.bn = BatchNorm2d(out_chan)
        self.relu = nn.ReLU(inplace=True)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)


class UpSample(nn.Module):

    def __init__(self, n_chan, factor=2):
        super(UpSample, self).__init__()
        out_chan = n_chan * factor * factor
        self.proj = nn.Conv2d(n_chan, out_chan, 1, 1, 0)
        self.up = nn.PixelShuffle(factor)
        self.init_weight()

    def forward(self, x):
        feat = self.proj(x)
        feat = self.up(feat)
        return feat

    def init_weight(self):
        nn.init.xavier_normal_(self.proj.weight, gain=1.)


class Output(nn.Module):
    def __init__(self, in_chan, mid_chan, n_classes, up_factor=32, *args, **kwargs):
        super(Output, self).__init__()
        self.up_factor = up_factor
        out_chan = n_classes * up_factor * up_factor
        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
        self.conv_out = nn.Conv2d(mid_chan, out_chan, kernel_size=1, bias=True)
        self.up = nn.PixelShuffle(up_factor)
        self.init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = self.conv_out(x)
        x = self.up(x)
        return x

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.modules.batchnorm._BatchNorm):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class AttentionEnhancementModule(nn.Module):
    def __init__(self, in_chan, out_chan):
        super(AttentionEnhancementModule, self).__init__()
        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
        self.conv_atten = Attention(out_chan)
        self.bn_atten = BatchNorm2d(out_chan)
        self.init_weight()

    def forward(self, x):
        feat = self.conv(x)
        att = self.conv_atten(feat)
        return self.bn_atten(att)

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None:
                    nn.init.constant_(ly.bias, 0)


class ContextPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(ContextPath, self).__init__()
        self.resnet = resnet18()
        self.arm16 = AttentionEnhancementModule(256, 128)
        self.arm32 = AttentionEnhancementModule(512, 128)
        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
        self.up32 = nn.Upsample(scale_factor=2.)
        self.up16 = nn.Upsample(scale_factor=2.)

        self.init_weight()

    def forward(self, x):
        feat8, feat16, feat32 = self.resnet(x)

        avg = torch.mean(feat32, dim=(2, 3), keepdim=True)
        avg = self.conv_avg(avg)

        feat32_arm = self.arm32(feat32)
        feat32_sum = feat32_arm + avg
        feat32_up = self.up32(feat32_sum)
        feat32_up = self.conv_head32(feat32_up)

        feat16_arm = self.arm16(feat16)
        feat16_sum = feat16_arm + feat32_up
        feat16_up = self.up16(feat16_sum)
        feat16_up = self.conv_head16(feat16_up)

        return feat16_up, self.up16(feat16), self.up32(feat32)  # x8, x16

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None:
                    nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.modules.batchnorm._BatchNorm):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class SpatialPath(nn.Module):
    def __init__(self, *args, **kwargs):
        super(SpatialPath, self).__init__()
        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
        self.init_weight()

    def forward(self, x):
        feat = self.conv1(x)
        feat = self.conv2(feat)
        feat = self.conv3(feat)
        feat = self.conv_out(feat)
        return feat

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.modules.batchnorm._BatchNorm):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class FeatureAggregationModule(nn.Module):
    def __init__(self, in_chan, out_chan):
        super(FeatureAggregationModule, self).__init__()
        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
        self.conv_atten = Attention(out_chan)

        self.init_weight()

    def forward(self, fsp, fcp):
        fcat = torch.cat([fsp, fcp], dim=1)
        feat = self.convblk(fcat)
        atten = self.conv_atten(feat)
        feat_atten = torch.mul(feat, atten)
        feat_out = feat_atten + feat
        return feat_out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params = [], []
        for name, module in self.named_modules():
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                wd_params.append(module.weight)
                if not module.bias is None:
                    nowd_params.append(module.bias)
            elif isinstance(module, nn.modules.batchnorm._BatchNorm):
                nowd_params += list(module.parameters())
        return wd_params, nowd_params


class ABCNet(nn.Module):
    def __init__(self, band, n_classes):
        super(ABCNet, self).__init__()
        self.name = 'ABCNet'
        self.cp = ContextPath()
        self.sp = SpatialPath()
        self.fam = FeatureAggregationModule(256, 256)
        self.conv_out = Output(256, 256, n_classes, up_factor=8)
        if self.training:
            self.conv_out16 = Output(256, 64, n_classes, up_factor=8)
            self.conv_out32 = Output(512, 64, n_classes, up_factor=16)
        self.init_weight()

    def forward(self, x):
        H, W = x.size()[2:]
        feat_cp8, feat_cp16, feat_cp32 = self.cp(x)
        feat_sp = self.sp(x)
        feat_fuse = self.fam(feat_sp, feat_cp8)

        feat_out = self.conv_out(feat_fuse)
        if self.training:
            feat_out16 = self.conv_out16(feat_cp16)
            feat_out32 = self.conv_out32(feat_cp32)
            return feat_out, feat_out16, feat_out32
        # feat_out = feat_out.argmax(dim=1)
        return feat_out

    def init_weight(self):
        for ly in self.children():
            if isinstance(ly, nn.Conv2d):
                nn.init.kaiming_normal_(ly.weight, a=1)
                if not ly.bias is None: nn.init.constant_(ly.bias, 0)

    def get_params(self):
        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
        for name, child in self.named_children():
            child_wd_params, child_nowd_params = child.get_params()
            if isinstance(child, (FeatureAggregationModule, Output)):
                lr_mul_wd_params += child_wd_params
                lr_mul_nowd_params += child_nowd_params
            else:
                wd_params += child_wd_params
                nowd_params += child_nowd_params
        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params


if __name__ == "__main__":
    net = ABCNet(3, 19)

    in_ten = torch.randn(4, 3, 512, 512)
    out = net(in_ten)
    print(out.shape)