import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

def create_conv_block(in_planes, out_planes, kernel_size=1, stride=1, padding=0,
                      dilation=1, groups=1, bias=False, bn=True, activation=True):
    layers = []
    layers.append(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, 
                            dilation=dilation, groups=groups, bias=bias))
    if bn:
        layers.append(nn.InstanceNorm2d(out_planes))
    if activation:
        layers.append(nn.LeakyReLU(inplace=True))

    return nn.Sequential(*layers)

def create_conv_block_k1(in_planes, out_planes, kernel_size=1, stride=1, padding=0,
                         dilation=1, groups=1, bias=False, bn=True, activation=True):
    return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                             dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)


def create_conv_block_k2(in_planes, out_planes, kernel_size=2, stride=1, padding=0,
                         dilation=1, groups=1, bias=False, bn=True, activation=True):
    return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                             dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)


def create_conv_block_k3(in_planes, out_planes, kernel_size=3, stride=1, padding=1,
                         dilation=1, groups=1, bias=False, bn=True, activation=True):
    return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
                             dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)


class RfbfBlock2d(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, groups=1, droprate=0):
        super(RfbfBlock2d, self).__init__()

        inter_planes = max(int(np.ceil(out_planes / 8)), 1)
        self.groups = groups
        self.group_num = inter_planes // groups
        self.droprate = droprate

        self.branch1 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch2 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch3 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch4 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch5 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch6 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch7 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(9, 9), padding=(4, 4),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )
        self.branch8 = nn.Sequential(
            create_conv_block(in_planes, inter_planes, kernel_size=(9, 9), padding=(4, 4),
                              stride=stride, groups=groups),
            create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
                              dilation=(1, 1), groups=groups)
        )

    def forward(self, x):
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        x4 = self.branch4(x)
        x5 = self.branch5(x)
        x6 = self.branch6(x)
        x7 = self.branch7(x)
        x8 = self.branch8(x)
        if self.groups == 1:
            out = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8), 1)
        else:
            for group in range(self.groups):
                group_out = torch.cat((x1[:, group * self.group_num:(group + 1) * self.group_num],
                                       x2[:, group * self.group_num:(group + 1) * self.group_num],
                                       x3[:, group * self.group_num:(group + 1) * self.group_num],
                                       x4[:, group * self.group_num:(group + 1) * self.group_num],
                                       x5[:, group * self.group_num:(group + 1) * self.group_num],
                                       x6[:, group * self.group_num:(group + 1) * self.group_num],
                                       x7[:, group * self.group_num:(group + 1) * self.group_num],
                                       x8[:, group * self.group_num:(group + 1) * self.group_num]), 1)
                if group == 0:
                    out = group_out
                else:
                    out = torch.cat((out, group_out), 1)

        if self.droprate > 0:
            out = F.dropout2d(out, p=self.droprate, training=self.training)

        return out

class ResBasicBlock2d(nn.Module):

    def __init__(self, in_planes, out_planes, stride=1, groups=1, droprate=0, se=False):
        super(ResBasicBlock2d, self).__init__()

        self.conv1 = create_conv_block_k3(in_planes, out_planes, stride=stride, groups=groups)

        self.conv2 = create_conv_block_k3(out_planes, out_planes, groups=groups, activation=False)

        self.shortcut = None
        if stride != 1 or in_planes != out_planes:
            self.shortcut = create_conv_block_k1(in_planes, out_planes, stride=stride, groups=groups, activation=False)

        self.droprate = droprate
        self.se = se
        if se:
            self.fc1 = nn.Linear(in_features=out_planes, out_features=out_planes // 4)
            self.fc2 = nn.Linear(in_features=out_planes // 4, out_features=out_planes)

    def forward(self, x):
        identity = self.shortcut(x) if self.shortcut is not None else x

        out = self.conv1(x)
        if self.droprate > 0:
            out = F.dropout2d(out, p=self.droprate, training=self.training)
        out = self.conv2(out)

        if self.se:
            original_out = out
            out = F.adaptive_avg_pool2d(out, (1, 1))
            out = torch.flatten(out, 1)
            out = self.fc1(out)
            out = F.leaky_relu(out, inplace=True)
            out = self.fc2(out)
            out = out.sigmoid()
            #这里需要测试一下
            out = out.view(out.size(0), out.size(1), 1, 1)
            out = out * original_out

        out = out + identity
        out = F.leaky_relu(out, inplace=True)

        return out