"""
code modified from https://github.com/kenshohara/3D-ResNets-PyTorch.git

"""
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from .build import NETWORK_REGISTRY
from dpcv.modeling.module.weight_init_helper import initialize_weights


def get_inplanes():
    return [64, 128, 256, 512]


def conv3x3x3(in_planes, out_planes, stride=1):
    return nn.Conv3d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=False
    )


def conv1x1x1(in_planes, out_planes, stride=1):
    return nn.Conv3d(
        in_planes,
        out_planes,
        kernel_size=1,
        stride=stride,
        bias=False
    )


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super().__init__()

        self.conv1 = conv3x3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm3d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3x3(planes, planes)
        self.bn2 = nn.BatchNorm3d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super().__init__()

        self.conv1 = conv1x1x1(in_planes, planes)
        self.bn1 = nn.BatchNorm3d(planes)
        self.conv2 = conv3x3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm3d(planes)
        self.conv3 = conv1x1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm3d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(
        self,
        block,
        layers,
        block_inplanes,
        n_input_channels=3,
        conv1_t_size=7,
        conv1_t_stride=1,
        no_max_pool=False,
        shortcut_type='B',
        widen_factor=1.0,
        n_classes=5,
        init_weights=True,
    ):
        super().__init__()

        block_inplanes = [int(x * widen_factor) for x in block_inplanes]

        self.in_planes = block_inplanes[0]
        self.no_max_pool = no_max_pool

        self.conv1 = nn.Conv3d(
            n_input_channels,
            self.in_planes,
            kernel_size=(conv1_t_size, 7, 7),
            stride=(conv1_t_stride, 2, 2),
            padding=(conv1_t_size // 2, 3, 3),
            bias=False
        )
        self.bn1 = nn.BatchNorm3d(self.in_planes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0], shortcut_type)
        self.layer2 = self._make_layer(block, block_inplanes[1], layers[1], shortcut_type, stride=2)
        self.layer3 = self._make_layer(block, block_inplanes[2], layers[2], shortcut_type, stride=2)
        self.layer4 = self._make_layer(block, block_inplanes[3], layers[3], shortcut_type, stride=2)

        self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
        self.fc = nn.Linear(block_inplanes[3] * block.expansion, n_classes)

        if init_weights:
            initialize_weights(self)
        # for m in self.modules():
        #     if isinstance(m, nn.Conv3d):
        #         nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        #     elif isinstance(m, nn.BatchNorm3d):
        #         nn.init.constant_(m.weight, 1)
        #         nn.init.constant_(m.bias, 0)

    def _downsample_basic_block(self, x, planes, stride):
        out = F.avg_pool3d(x, kernel_size=1, stride=stride)
        zero_pads = torch.zeros(
            out.size(0), planes - out.size(1), out.size(2), out.size(3), out.size(4)
        )
        if isinstance(out.data, torch.cuda.FloatTensor):
            zero_pads = zero_pads.cuda()

        out = torch.cat([out.data, zero_pads], dim=1)

        return out

    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            if shortcut_type == 'A':
                downsample = partial(
                    self._downsample_basic_block,
                    planes=planes * block.expansion,
                    stride=stride
                )
            else:
                downsample = nn.Sequential(
                    conv1x1x1(self.in_planes, planes * block.expansion, stride),
                    nn.BatchNorm3d(planes * block.expansion)
                )

        layers = []
        layers.append(
            block(
                in_planes=self.in_planes,
                planes=planes,
                stride=stride,
                downsample=downsample
            )
        )
        self.in_planes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.in_planes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        if not self.no_max_pool:
            x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)

        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def get_3d_resnet_model(model_depth, **kwargs):
    assert model_depth in [18, 34, 50, 101, 152]

    if model_depth == 18:
        model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs)
    elif model_depth == 34:
        model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs)
    elif model_depth == 50:
        model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs)
    elif model_depth == 101:
        model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs)
    elif model_depth == 152:
        model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs)

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


@NETWORK_REGISTRY.register()
def resnet50_3d_model(cfg):
    model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes())
    return model.to(device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))


if __name__ == "__main__":
    model = get_3d_resnet_model(50)
    xin = torch.randn(4, 3, 16, 224, 224)
    y = model(xin)
    print(y.shape)
