import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.modules.module_util as mutil


class ResidualDenseBlock_5C(nn.Module):
    def __init__(self, nf=64, gc=32, bias=True):
        '''def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
            norm_type=None, act_type='leakyrelu', mode='CNA'):'''
        super(ResidualDenseBlock_5C, self).__init__()
        # gc: growth channel, i.e. intermediate channels
        '''c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding, \
            dilation=dilation, bias=bias, groups=groups) '''
        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        '''前面提到的对残差信息进行 scaling，在这里可以看出来，系数为 0.2。
        可以看到在 kernel size（3×3）和 stride=1，padding=1 的设置下，
        特征图的大小始终不变，但是通道数由于 concat 的原因，每次都会增加 gc 个通道，
        但是会在最后一层由变回原来的通道数 nc，这里的参数 norm_type=None，表示不要 Batch Norm'''

        # initialization
        mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)

    def forward(self, x):
        x1 = self.lrelu(self.conv1(x))
        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x


class RRDB(nn.Module):
    '''Residual in Residual Dense Block'''

    def __init__(self, nf, gc=32):
        super(RRDB, self).__init__()
        '''self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type,
            norm_type, act_type, mode)'''
        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
        self.RDB3 = ResidualDenseBlock_5C(nf, gc)

    def forward(self, x):
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out * 0.2 + x


class RRDBNet(nn.Module):
    '''注意到这里有个参数 nb，这个参数控制网络中 RDDB 的数量，作者取的是 23'''
    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
        super(RRDBNet, self).__init__()
        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)

        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
        self.RRDB_trunk = mutil.make_layer(RRDB_block_f, nb)
        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        #### upsampling
        #### 因为特征图大小始终不变，所以需要定义上采样模块进行放大，得到最后的结果
        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        '''CR scale2 comment self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True); scale4 remove comment '''
        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        fea = self.conv_first(x)
        trunk = self.trunk_conv(self.RRDB_trunk(fea))
        fea = fea + trunk

        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
        '''CR scale2 fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))); scale4 remove comment '''
        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
        out = self.conv_last(self.lrelu(self.HRconv(fea)))

        return out
