import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model.warplayer import warp


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
    return nn.Sequential(
        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
                  padding=padding, dilation=dilation, bias=True),
    )


def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
    return nn.Sequential(
        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
                  padding=padding, dilation=dilation, bias=True),
        nn.PReLU(out_planes)
    )

class IFBlock(nn.Module):
    def __init__(self, in_planes, scale=1, c=64):
        super(IFBlock, self).__init__()
        self.scale = scale
        self.conv0 = nn.Sequential(
            conv(in_planes, c, 3, 2, 1),
            conv(c, 2*c, 3, 2, 1),
            )
        self.convblock = nn.Sequential(
            conv(2*c, 2*c),
            conv(2*c, 2*c),
            conv(2*c, 2*c),
            conv(2*c, 2*c),
            conv(2*c, 2*c),
            conv(2*c, 2*c),
        )        
        self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1)
                    
    def forward(self, x):
        if self.scale != 1:
            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
                              align_corners=False)
        x = self.conv0(x)
        x = self.convblock(x)
        x = self.conv1(x)
        flow = x
        if self.scale != 1:
            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
                                 align_corners=False)
        return flow


class IFNet(nn.Module):
    def __init__(self):
        super(IFNet, self).__init__()
        self.block0 = IFBlock(6, scale=8, c=192)
        self.block1 = IFBlock(10, scale=4, c=128)
        self.block2 = IFBlock(10, scale=2, c=96)
        self.block3 = IFBlock(10, scale=1, c=48)

    def forward(self, x, scale=1.0):
        if scale != 1.0:
            x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False)
        flow0 = self.block0(x)
        F1 = flow0
        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
        warped_img0 = warp(x[:, :3], F1_large[:, :2])
        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
        F2 = (flow0 + flow1)
        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
        warped_img0 = warp(x[:, :3], F2_large[:, :2])
        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
        F3 = (flow0 + flow1 + flow2)
        F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
        warped_img0 = warp(x[:, :3], F3_large[:, :2])
        warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
        F4 = (flow0 + flow1 + flow2 + flow3)
        if scale != 1.0:
            F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale
        return F4, [F1, F2, F3, F4]

if __name__ == '__main__':
    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
    img1 = torch.tensor(np.random.normal(
        0, 1, (3, 3, 256, 256))).float().to(device)
    imgs = torch.cat((img0, img1), 1)
    flownet = IFNet()
    flow, _ = flownet(imgs)
    print(flow.shape)