"""  
Copyright (c) 2019-present NAVER Corp.
MIT License
"""

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F

from model.vgg16_bn import vgg16_bn, init_weights

class double_conv(nn.Module):
    def __init__(self, in_ch, mid_ch, out_ch):
        super(double_conv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_ch + mid_ch, mid_ch, kernel_size=1),
            nn.BatchNorm2d(mid_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )

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


class CRAFT(nn.Module):
    def __init__(self, pretrained=True, freeze=False, amp=False):
        super(CRAFT, self).__init__()

        self.amp = amp

        """ Base network """
        self.basenet = vgg16_bn(pretrained, freeze)

        """ U network """
        self.upconv1 = double_conv(1024, 512, 256)
        self.upconv2 = double_conv(512, 256, 128)
        self.upconv3 = double_conv(256, 128, 64)
        self.upconv4 = double_conv(128, 64, 32)

        num_class = 2
        self.conv_cls = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
            nn.Conv2d(32, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True),
            nn.Conv2d(32, 16, kernel_size=3, padding=1), nn.ReLU(inplace=True),
            nn.Conv2d(16, 16, kernel_size=1), nn.ReLU(inplace=True),
            nn.Conv2d(16, num_class, kernel_size=1),
        )

        init_weights(self.upconv1.modules())
        init_weights(self.upconv2.modules())
        init_weights(self.upconv3.modules())
        init_weights(self.upconv4.modules())
        init_weights(self.conv_cls.modules())
        
    def forward(self, x):
        """ Base network """
        if self.amp:
            with torch.cuda.amp.autocast():
                sources = self.basenet(x)

                """ U network """
                y = torch.cat([sources[0], sources[1]], dim=1)
                y = self.upconv1(y)

                y = F.interpolate(y, size=sources[2].size()[2:], mode='bilinear', align_corners=False)
                y = torch.cat([y, sources[2]], dim=1)
                y = self.upconv2(y)

                y = F.interpolate(y, size=sources[3].size()[2:], mode='bilinear', align_corners=False)
                y = torch.cat([y, sources[3]], dim=1)
                y = self.upconv3(y)

                y = F.interpolate(y, size=sources[4].size()[2:], mode='bilinear', align_corners=False)
                y = torch.cat([y, sources[4]], dim=1)
                feature = self.upconv4(y)

                y = self.conv_cls(feature)

                return y.permute(0,2,3,1), feature
        else:

            sources = self.basenet(x)

            """ U network """
            y = torch.cat([sources[0], sources[1]], dim=1)
            y = self.upconv1(y)

            y = F.interpolate(y, size=sources[2].size()[2:], mode='bilinear', align_corners=False)
            y = torch.cat([y, sources[2]], dim=1)
            y = self.upconv2(y)

            y = F.interpolate(y, size=sources[3].size()[2:], mode='bilinear', align_corners=False)
            y = torch.cat([y, sources[3]], dim=1)
            y = self.upconv3(y)

            y = F.interpolate(y, size=sources[4].size()[2:], mode='bilinear', align_corners=False)
            y = torch.cat([y, sources[4]], dim=1)
            feature = self.upconv4(y)

            y = self.conv_cls(feature)

            return y.permute(0, 2, 3, 1), feature

if __name__ == '__main__':
    model = CRAFT(pretrained=True).cuda()
    output, _ = model(torch.randn(1, 3, 768, 768).cuda())
    print(output.shape)