import torch

# ! amazing!!!! autograd.grad with set_detect_anomaly(True) will cause memory leak
# ! https://github.com/pytorch/pytorch/issues/51349
# torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
from inplace_abn import InPlaceABN


#############################################     MVS Net models        ################################################
class ConvBnReLU(nn.Module):
    def __init__(self, in_channels, out_channels,
                 kernel_size=3, stride=1, pad=1,
                 norm_act=InPlaceABN):
        super(ConvBnReLU, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels,
                              kernel_size, stride=stride, padding=pad, bias=False)
        self.bn = norm_act(out_channels)

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


class ConvBnReLU3D(nn.Module):
    def __init__(self, in_channels, out_channels,
                 kernel_size=3, stride=1, pad=1,
                 norm_act=InPlaceABN):
        super(ConvBnReLU3D, self).__init__()
        self.conv = nn.Conv3d(in_channels, out_channels,
                              kernel_size, stride=stride, padding=pad, bias=False)
        self.bn = norm_act(out_channels)
        # self.bn = nn.ReLU()

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


###################################  feature net  ######################################
class FeatureNet(nn.Module):
    """
    output 3 levels of features using a FPN structure
    """

    def __init__(self, norm_act=InPlaceABN):
        super(FeatureNet, self).__init__()

        self.conv0 = nn.Sequential(
            ConvBnReLU(3, 8, 3, 1, 1, norm_act=norm_act),
            ConvBnReLU(8, 8, 3, 1, 1, norm_act=norm_act))

        self.conv1 = nn.Sequential(
            ConvBnReLU(8, 16, 5, 2, 2, norm_act=norm_act),
            ConvBnReLU(16, 16, 3, 1, 1, norm_act=norm_act),
            ConvBnReLU(16, 16, 3, 1, 1, norm_act=norm_act))

        self.conv2 = nn.Sequential(
            ConvBnReLU(16, 32, 5, 2, 2, norm_act=norm_act),
            ConvBnReLU(32, 32, 3, 1, 1, norm_act=norm_act),
            ConvBnReLU(32, 32, 3, 1, 1, norm_act=norm_act))

        self.toplayer = nn.Conv2d(32, 32, 1)
        self.lat1 = nn.Conv2d(16, 32, 1)
        self.lat0 = nn.Conv2d(8, 32, 1)

        # to reduce channel size of the outputs from FPN
        self.smooth1 = nn.Conv2d(32, 16, 3, padding=1)
        self.smooth0 = nn.Conv2d(32, 8, 3, padding=1)

    def _upsample_add(self, x, y):
        return F.interpolate(x, scale_factor=2,
                             mode="bilinear", align_corners=True) + y

    def forward(self, x):
        # x: (B, 3, H, W)
        conv0 = self.conv0(x)  # (B, 8, H, W)
        conv1 = self.conv1(conv0)  # (B, 16, H//2, W//2)
        conv2 = self.conv2(conv1)  # (B, 32, H//4, W//4)
        feat2 = self.toplayer(conv2)  # (B, 32, H//4, W//4)
        feat1 = self._upsample_add(feat2, self.lat1(conv1))  # (B, 32, H//2, W//2)
        feat0 = self._upsample_add(feat1, self.lat0(conv0))  # (B, 32, H, W)

        # reduce output channels
        feat1 = self.smooth1(feat1)  # (B, 16, H//2, W//2)
        feat0 = self.smooth0(feat0)  # (B, 8, H, W)

        # feats = {"level_0": feat0,
        #          "level_1": feat1,
        #          "level_2": feat2}

        return [feat2, feat1, feat0]  # coarser to finer features