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

from torch.autograd import Variable


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

    


class FPN(nn.Module):
    def __init__(self, block=Bottleneck, num_blocks=[2,4,23,3],n_channels=4,n_classes=1):
        super(FPN, self).__init__()
        self.in_planes = 64
        self.n_channels = n_channels
        self.n_classes = n_classes

        self.conv1 = nn.Conv2d(n_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        # Bottom-up layers
        self.layer1 = self._make_layer(block,  64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)

        # Top layer
        self.toplayer = nn.Conv2d(2048, 256, kernel_size=1, stride=1, padding=0)  # Reduce channels

        # Smooth layers
        self.smooth1 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.smooth3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)

        # Lateral layers
        self.latlayer1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer2 = nn.Conv2d( 512, 256, kernel_size=1, stride=1, padding=0)
        self.latlayer3 = nn.Conv2d( 256, 256, kernel_size=1, stride=1, padding=0)

        self.semantic_branch = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(128, 8, kernel_size=1, stride=1, padding=0)
        self.output_layer = nn.Conv2d(8, 1, kernel_size=1, stride=1, padding=0)
        self.gn1 = nn.GroupNorm(128, 128) 
        self.gn2 = nn.GroupNorm(256, 256)

        # self.output = nn.Conv2d(8, 1, kernel_size=1, stride=1, padding=0)
        
    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def _upsample(self, x, h, w):
        return F.interpolate(x, size=(h, w), mode='bilinear', align_corners=True)

    def _upsample_add(self, x, y):
        '''Upsample and add two feature maps.
        Args:
          x: (Variable) top feature map to be upsampled.
          y: (Variable) lateral feature map.
        Returns:
          (Variable) added feature map.
        Note in PyTorch, when input size is odd, the upsampled feature map
        with `F.upsample(..., scale_factor=2, mode='nearest')`
        maybe not equal to the lateral feature map size.
        e.g.
        original input size: [N,_,15,15] ->
        conv2d feature map size: [N,_,8,8] ->
        upsampled feature map size: [N,_,16,16]
        So we choose bilinear upsample which supports arbitrary output sizes.
        '''
        _,_,H,W = y.size()
        return F.upsample(x, size=(H,W), mode='bilinear') + y

    def forward(self, x):
        # Bottom-up
        c1 = F.relu(self.bn1(self.conv1(x)))
        c1 = F.max_pool2d(c1, kernel_size=3, stride=2, padding=1)
        c2 = self.layer1(c1)
        c3 = self.layer2(c2)
        c4 = self.layer3(c3)
        c5 = self.layer4(c4)
        # print(c1.shape,c2.shape,c3.shape,c4.shape,c5.shape)
        # Top-down
        p5 = self.toplayer(c5)
        p4 = self._upsample_add(p5, self.latlayer1(c4))
        p3 = self._upsample_add(p4, self.latlayer2(c3))
        p2 = self._upsample_add(p3, self.latlayer3(c2))
        # Smooth
        p4 = self.smooth1(p4)
        p3 = self.smooth2(p3)
        p2 = self.smooth3(p2)

        # print(p2.shape,p3.shape,p4.shape,p5.shape)
        # Semantic
        _, _, h, w = p2.size()
        # 256->256
        s5 = self._upsample(F.relu(self.gn2(self.conv2(p5))), h, w)
        # 256->256
        s5 = self._upsample(F.relu(self.gn2(self.conv2(s5))), h, w)
        # 256->128
        s5 = self._upsample(F.relu(self.gn1(self.semantic_branch(s5))), h, w)

        # 256->256
        s4 = self._upsample(F.relu(self.gn2(self.conv2(p4))), h, w)
        # 256->128
        s4 = self._upsample(F.relu(self.gn1(self.semantic_branch(s4))), h, w)

        # 256->128
        s3 = self._upsample(F.relu(self.gn1(self.semantic_branch(p3))), h, w)

        s2 = F.relu(self.gn1(self.semantic_branch(p2)))

        s = self.conv3(s2 + s3 + s4 + s5)

        output = self.output_layer(s)

        _,_,h,w = x.shape
        output = self._upsample(output,h,w)

        return output, s
          
